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	<title>bioRxiv Channel: NCI Human Tumor Atlas Network</title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "NCI Human Tumor Atlas Network"
	</description>

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	<title>bioRxiv</title>
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	<item rdf:about="https://biorxiv.org/cgi/content/short/656587v1?rss=1">
<title>
<![CDATA[
Pediatric High Grade Glioma Resources From the Children’s Brain Tumor Tissue Consortium (CBTTC) and Pediatric Brain Tumor Atlas (PBTA) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/656587v1?rss=1"
</link>
<description><![CDATA[
BackgroundPediatric high grade glioma (pHGG) remains a fatal disease. Increased access to richly annotated biospecimens and patient derived tumor models will accelerate pHGG research and support translation of research discoveries. This work describes the pediatric high grade glioma set of the Childrens Brain Tumor Tissue Consortium (CBTTC) from the first release (October 2018) of the Pediatric Brain Tumor Atlas (PBTA).nnMethodspHGG tumors with associated clinical data and imaging were prospectively collected through the CBTTC and analyzed as the Pediatric Brain Tumor Atlas (PBTA) with processed genomic data deposited into PedcBioPortal for broad access and visualization. Matched tumor was cultured to create high grade glioma cell lines analyzed by targeted and WGS and RNA-seq. A tissue microarray (TMA) of primary pHGG tumors was also created.nnResultsThe pHGG set included 87 collection events (73 patients, 60% at diagnosis, median age of 9 yrs, 55% female, 46% hemispheric). Analysis of somatic mutations and copy number alterations of known glioma genes were of expected distribution (36% H3.3, 47% TP53, 24% ATRX and 7% BRAF V600E variants). A pHGG TMA (n=77), includes 36 (53%) patient tumors with matched sequencing. At least one established glioma cell line was generated from 23 patients (32%). Unique reagents include those derived from a H3.3 G34R glioma and from tumors with mismatch repair deficiency.nnConclusionThe CBTTC and PBTA have created an openly available integrated resource of over 2,000 tumors, including a rich set of pHGG primary tumors, corresponding cell lines and archival fixed tissue to advance translational research for pHGG.nnIMPORTANCE OF STUDYHigh-grade gliomas (HGG) remain the leading cause of cancer death in children. Since molecularly heterogeneous, preclinical studies of pediatric HGG will be most informative if able to compare across groups. Given their relatively rarity, there are few readily available biospecimens and cellular models to inform preclinical laboratory and genomic translational research. Therefore, the aim of this CBTTC study was to highlight the panel of pediatric HGG cases whose primary tumors have undergone extensive genomic analysis, have clinical data, available imaging and additional biospecimens, including tumor, nucleic acids, cell lines and FFPE tissue on a tissue microarray (TMA).
]]></description>
<dc:creator>Ijaz, H.</dc:creator>
<dc:creator>Koptyra, M. P.</dc:creator>
<dc:creator>Gaonkar, K. S.</dc:creator>
<dc:creator>Rokita, J. L.</dc:creator>
<dc:creator>Baubet, V. P.</dc:creator>
<dc:creator>Tauhid, L.</dc:creator>
<dc:creator>Zhu, Y.</dc:creator>
<dc:creator>Brown, M. A.</dc:creator>
<dc:creator>Lopez Garcia, G.</dc:creator>
<dc:creator>Zhang, B.</dc:creator>
<dc:creator>Diskin, S. J.</dc:creator>
<dc:creator>Vaksman, Z.</dc:creator>
<dc:creator>Mason, J. L.</dc:creator>
<dc:creator>Appert, E. M.</dc:creator>
<dc:creator>Lilly, J. V.</dc:creator>
<dc:creator>Lulla, R. R.</dc:creator>
<dc:creator>De Raedt, T.</dc:creator>
<dc:creator>Heath, A. P.</dc:creator>
<dc:creator>Felmeister, A.</dc:creator>
<dc:creator>Raman, P.</dc:creator>
<dc:creator>Nazarian, J.</dc:creator>
<dc:creator>Santi-Vicini, M.</dc:creator>
<dc:creator>Storm, P. B.</dc:creator>
<dc:creator>Resnick, A. C.</dc:creator>
<dc:creator>Waanders, A. J.</dc:creator>
<dc:creator>Cole, K. A.</dc:creator>
<dc:date>2019-05-31</dc:date>
<dc:identifier>doi:10.1101/656587</dc:identifier>
<dc:title><![CDATA[Pediatric High Grade Glioma Resources From the Children’s Brain Tumor Tissue Consortium (CBTTC) and Pediatric Brain Tumor Atlas (PBTA)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-05-31</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/704114v1?rss=1">
<title>
<![CDATA[
Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/704114v1?rss=1"
</link>
<description><![CDATA[
In this data descriptor, we document a dataset of multiplexed immunofluorescence images and derived single-cell measurements of immune lineage and other markers in formaldehyde-fixed and paraffin-embedded (FFPE) human tonsil and lung cancer tissue. We used tissue cyclic immunofluorescence (t-CyCIF) to generate fluorescence images which we artifact corrected using the BaSiC tool, stitched and registered using the ASHLAR algorithm, and segmented using ilastik software and MATLAB. We extracted single-cell features from these images using HistoCAT software. The resulting dataset can be visualized using image browsers and analyzed using high-dimensional, single-cell methods. This dataset is a valuable resource for biological discovery of the immune system in normal and diseased states as well as for the development of multiplexed image analysis and viewing tools.nnMETADATA SUMMARYnnO_TBL View this table:norg.highwire.dtl.DTLVardef@6edf3eorg.highwire.dtl.DTLVardef@1026e07org.highwire.dtl.DTLVardef@85a7baorg.highwire.dtl.DTLVardef@c6d7e5org.highwire.dtl.DTLVardef@87fccf_HPS_FORMAT_FIGEXP  M_TBL C_TBL
]]></description>
<dc:creator>Rashid, R.</dc:creator>
<dc:creator>Gaglia, G.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Du, Z.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Schapiro, D.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Muhlich, J.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:date>2019-07-17</dc:date>
<dc:identifier>doi:10.1101/704114</dc:identifier>
<dc:title><![CDATA[Highly multiplexed immunofluorescence images and single-cell data of immune markers in tonsil and lung cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-07-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/684340v1?rss=1">
<title>
<![CDATA[
A quantitative framework for evaluating single-cell data structure preservation by dimensionality reduction techniques 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/684340v1?rss=1"
</link>
<description><![CDATA[
High-dimensional data, such as those generated using single-cell RNA sequencing, present challenges in interpretation and visualization. Numerical and computational methods for dimensionality reduction allow for low-dimensional representation of genome-scale expression data for downstream clustering, trajectory reconstruction, and biological interpretation. However, a comprehensive and quantitative evaluation of the performance of these techniques has not been established. We present an unbiased framework that defines metrics of global and local structure preservation in dimensionality reduction transformations. Using discrete and continuous scRNA-seq datasets, we find that input cell distribution and method parameters are largely determinant of global, local, and organizational data structure preservation by eleven published dimensionality reduction methods. Code available at github.com/KenLauLab/DR-structure-preservation allows for rapid evaluation of further datasets and methods.
]]></description>
<dc:creator>Heiser, C. N.</dc:creator>
<dc:creator>Lau, K. S.</dc:creator>
<dc:date>2019-06-27</dc:date>
<dc:identifier>doi:10.1101/684340</dc:identifier>
<dc:title><![CDATA[A quantitative framework for evaluating single-cell data structure preservation by dimensionality reduction techniques]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-06-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/671180v1?rss=1">
<title>
<![CDATA[
Surface protein imputation from single cell transcriptomes by deep neural networks 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/671180v1?rss=1"
</link>
<description><![CDATA[
While single cell RNA sequencing (scRNA-seq) is invaluable for studying cell populations, cell-surface proteins are often integral markers of cellular function and serve as primary targets for therapeutic intervention. Here we propose a transfer learning framework, single cell Transcriptome to Protein prediction with deep neural network (cTP-net), to impute surface protein abundances from scRNA-seq data by learning from existing single-cell multi-omic resources.
]]></description>
<dc:creator>Zhou, Z.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Ye, C.</dc:creator>
<dc:creator>Zhang, N.</dc:creator>
<dc:date>2019-06-14</dc:date>
<dc:identifier>doi:10.1101/671180</dc:identifier>
<dc:title><![CDATA[Surface protein imputation from single cell transcriptomes by deep neural networks]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-06-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/457622v1?rss=1">
<title>
<![CDATA[
Genetic Heterogeneity Profiling by Single Cell RNA Sequencing 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/457622v1?rss=1"
</link>
<description><![CDATA[
Detection of genetically distinct subclones and profiling the transcriptomic differences between them is important for studying the evolutionary dynamics of tumors, as well as for accurate prognosis and effective treatment of cancer in the clinic. For the profiling of intra-tumor transcriptional heterogeneity, single cell RNA-sequencing (scRNA-seq) is now ubiquitously adopted in ongoing and planned cancer studies. Detection of somatic DNA mutations and inference of clonal membership from scRNA-seq, however, is currently unreliable. We propose DENDRO, an analysis method for scRNA-seq data that detects genetically distinct subclones, assigns each single cell to a subclone, and reconstructs the phylogenetic tree describing the tumors evolutionary history. DENDRO utilizes information from single nucleotide mutations in transcribed regions and accounts for technical noise and expression stochasticity at the single cell level. The accuracy of DENDRO was benchmarked on spike-in datasets and on scRNA-seq data with known subpopulation structure. We applied DENDRO to delineate subclonal expansion in a mouse melanoma model in response to immunotherapy, highlighting the role of neoantigens in treatment response. We also applied DENDRO to primary and lymph-node metastasis samples in breast cancer, where the new approach allowed us to better understand the relationship between genetic and transcriptomic intratumor variation.
]]></description>
<dc:creator>Zhou, Z.</dc:creator>
<dc:creator>Zhang, N. R.</dc:creator>
<dc:date>2018-10-30</dc:date>
<dc:identifier>doi:10.1101/457622</dc:identifier>
<dc:title><![CDATA[Genetic Heterogeneity Profiling by Single Cell RNA Sequencing]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-10-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/824326v1?rss=1">
<title>
<![CDATA[
scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/824326v1?rss=1"
</link>
<description><![CDATA[
Single cell chromatin accessibility sequencing (scCAS) has become a powerful technology for understanding epigenetic heterogeneity of complex tissues. The development of several experimental protocols has led to a rapid accumulation of scCAS data. In contrast, there is a lack of open-source software tools for comprehensive processing, analysis and visualization of scCAS data generated using all existing experimental protocols. Here we present scATAC-pro for quality assessment, analysis and visualization of scCAS data. scATAC-pro provides flexible choice of methods for different data processing and analytical tasks, with carefully curated default parameters. A range of quality control metrics are computed for several key steps of the experimental protocol. scATAC-pro generates summary reports for both quality assessment and downstream analysis. It also provides additional utility functions for generating input files for various types of downstream analyses and data visualization. With the rapid accumulation of scCAS data, scATAC-pro will facilitate studies of epigenomic heterogeneity in healthy and diseased tissues.
]]></description>
<dc:creator>Yu, W.</dc:creator>
<dc:creator>Uzun, Y.</dc:creator>
<dc:creator>Zhu, Q.</dc:creator>
<dc:creator>Chen, C.</dc:creator>
<dc:creator>Tan, K.</dc:creator>
<dc:date>2019-10-30</dc:date>
<dc:identifier>doi:10.1101/824326</dc:identifier>
<dc:title><![CDATA[scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-10-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.06.05.137000v1?rss=1">
<title>
<![CDATA[
Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.06.05.137000v1?rss=1"
</link>
<description><![CDATA[
Despite rapid advances in single-cell DNA methylation profiling methods, computational tools for data analysis are lagging far behind. A number of tasks, including cell type calling and integration with transcriptome data, requires the construction of a robust gene activity matrix as the prerequisite but challenging task. The advent of multi-omics data enables measurement of both DNA methylation and gene expression for the same single cells. Although such data is rather sparse, they are sufficient to train supervised models that capture the complex relationship between DNA methylation and gene expression and predict gene activities at single-cell level. Here, we present MAPLE (Methylome Association by Predictive Linkage to Expression), a computational framework that learns the association between DNA methylation and expression using both gene- and cell-dependent statistical features. Using multiple datasets generated with different experimental protocols, we show that using predicted gene activity values significantly improves several analysis tasks, including clustering, cell type identification and integration with transcriptome data. With the rapid accumulation of single-cell epigenomics data, MAPLE provides a general framework for integrating such data with transcriptome data.
]]></description>
<dc:creator>Uzun, Y.</dc:creator>
<dc:creator>Wu, H.</dc:creator>
<dc:creator>Tan, K.</dc:creator>
<dc:date>2020-06-06</dc:date>
<dc:identifier>doi:10.1101/2020.06.05.137000</dc:identifier>
<dc:title><![CDATA[Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-06-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.03.27.001834v1?rss=1">
<title>
<![CDATA[
Interpretative guides for interacting with tissue atlas and digital pathology data using the Minerva browser 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.03.27.001834v1?rss=1"
</link>
<description><![CDATA[
The recent development of highly multiplexed tissue imaging promises to substantially accelerate research into basic biology and human disease. Concurrently, histopathology in a clinical setting is undergoing a rapid transition to digital methods. Online tissue atlases involving highly multiplexed images of research and clinical specimens will soon join genomics as a systematic source of information on the molecular basis of disease and therapeutic response. However, even with recent advances in machine learning, experience with anatomic pathology shows that there is no immediate substitute for expert visual review, annotation, and description of tissue images. In this perspective we review the ecosystem of software available for analysis of tissue images and identify a need for interactive guides or "digital docents" that allow experts to help make complex images intelligible. We illustrate this idea using Minerva software and discuss how interactive image guides are being integrated into multi-omic browsers for effective dissemination of atlas data.
]]></description>
<dc:creator>Rashid, R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Hoffer, J.</dc:creator>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Krueger, R.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:creator>Mitchell, R.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P.</dc:creator>
<dc:date>2020-03-30</dc:date>
<dc:identifier>doi:10.1101/2020.03.27.001834</dc:identifier>
<dc:title><![CDATA[Interpretative guides for interacting with tissue atlas and digital pathology data using the Minerva browser]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-03-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.02.25.963272v1?rss=1">
<title>
<![CDATA[
Accelerated single cell seeding in relapsed multiple myeloma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.02.25.963272v1?rss=1"
</link>
<description><![CDATA[
The malignant progression of multiple myeloma is characterized by the seeding of cancer cells in different anatomic sites followed by their clonal expansion. It has been demonstrated that this spatial evolution at varying anatomic sites is characterized by genomic heterogeneity. However, it is unclear whether each anatomic site at relapse reflects the expansion of pre-existing but previously undetected disease or secondary seeding from other sites. Furthermore, genomic evolution over time at spatially distinct sites of disease has not been investigated in a systematic manner.

To address this, we interrogated 25 samples, by whole genome sequencing, collected at autopsy from 4 patients with relapsed multiple myeloma and demonstrated that each site had a unique evolutionary trajectory characterized by distinct single and complex structural variants and copy number changes. By analyzing the landscape of mutational signatures at these sites and for an additional set of 125 published whole exomes collected from 51 patients, we demonstrate the profound mutagenic effect of melphalan and platinum in relapsed multiple myeloma. Chemotherapy-related mutagenic processes are known to introduce hundreds of unique mutations in each surviving cancer cell. These mutations can be detectable by bulk sequencing only in cases of clonal expansion of a single cancer cell bearing the mutational signature linked to chemotherapy exposure thus representing a unique single-cell genomic barcode linked to a discrete time window in each patients life. We leveraged this concept to show that multiple myeloma systemic seeding is accelerated at clinical relapse and appears to be driven by the survival and subsequent expansion of a single myeloma cell following treatment with high dose melphalan therapy and autologous stem cell transplant.
]]></description>
<dc:creator>Landau, H. J.</dc:creator>
<dc:creator>Yellapantula, V.</dc:creator>
<dc:creator>Diamond, B. J.</dc:creator>
<dc:creator>Rustad, E. H.</dc:creator>
<dc:creator>Maclachlan, K.</dc:creator>
<dc:creator>Gundem, G.</dc:creator>
<dc:creator>Medina-Martinez, J.</dc:creator>
<dc:creator>Arango Ossa, J. E.</dc:creator>
<dc:creator>Levine, M.</dc:creator>
<dc:creator>Zhou, Y.</dc:creator>
<dc:creator>Rajya, K. L.</dc:creator>
<dc:creator>Baez, P.</dc:creator>
<dc:creator>Attiyeh, M.</dc:creator>
<dc:creator>Makohon-Moore, A.</dc:creator>
<dc:creator>Zhang, L.</dc:creator>
<dc:creator>Boyle, E. E.</dc:creator>
<dc:creator>Ashby, C.</dc:creator>
<dc:creator>Blaney, P.</dc:creator>
<dc:creator>Patel, M.</dc:creator>
<dc:creator>Zhang, Y.</dc:creator>
<dc:creator>Dogan, A.</dc:creator>
<dc:creator>Chung, D.</dc:creator>
<dc:creator>Giralt, S.</dc:creator>
<dc:creator>Lahoud, O. B.</dc:creator>
<dc:creator>Peled, J.</dc:creator>
<dc:creator>Scordo, M.</dc:creator>
<dc:creator>Shah, G.</dc:creator>
<dc:creator>Hassoun, H.</dc:creator>
<dc:creator>Korde, N. S.</dc:creator>
<dc:creator>Lesokhin, A. M.</dc:creator>
<dc:creator>Lu, S.</dc:creator>
<dc:creator>Mailankody, S.</dc:creator>
<dc:creator>Shah, U. A.</dc:creator>
<dc:creator>Smith, E.</dc:creator>
<dc:creator>Hultcrantz, M. L.</dc:creator>
<dc:creator>Ulaner, G.</dc:creator>
<dc:creator>van Rhee, F.</dc:creator>
<dc:creator>Morgan, G.</dc:creator>
<dc:creator>Landgren, O.</dc:creator>
<dc:creator>Papaemmanuil, E.</dc:creator>
<dc:creator>Iacobuzio-Donahue, C. A.</dc:creator>
<dc:creator>Maura, F.</dc:creator>
<dc:date>2020-02-26</dc:date>
<dc:identifier>doi:10.1101/2020.02.25.963272</dc:identifier>
<dc:title><![CDATA[Accelerated single cell seeding in relapsed multiple myeloma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-02-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/842203v1?rss=1">
<title>
<![CDATA[
Minimal Barriers to Invasion During Human Colorectal Tumor Growth 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/842203v1?rss=1"
</link>
<description><![CDATA[
Intra-tumoral heterogeneity (ITH) could represent clonal evolution where subclones with greater fitness confer more malignant phenotypes and invasion constitutes an evolutionary bottleneck. Alternatively, ITH could represent branching evolution with invasion of multiple subclones. The two models respectively predict a hierarchy of subclones arranged by phenotype, or multiple subclones with shared phenotypes. We delineated these modes of invasion by merging ancestral, topographic, and phenotypic information from 12 human colorectal tumors (11 carcinomas, 1 adenoma) obtained through saturation microdissection of 325 small tumor regions. The majority of subclones (29/46, 60%) shared superficial and invasive phenotypes. Of 11 carcinomas, 9 showed evidence of multiclonal invasion, and invasive and metastatic subclones arose early along the ancestral trees. Early multiclonal invasion in the majority of these tumors indicates the expansion of co-evolving subclones with similar malignant potential in absence of late bottlenecks, and suggests that barriers to invasion are minimal during colorectal cancer growth.
]]></description>
<dc:creator>Ryser, M. D.</dc:creator>
<dc:creator>Mallo, D.</dc:creator>
<dc:creator>Hall, A.</dc:creator>
<dc:creator>Hardman, T.</dc:creator>
<dc:creator>King, L. M.</dc:creator>
<dc:creator>Sorribes, I. C.</dc:creator>
<dc:creator>Maley, C. C.</dc:creator>
<dc:creator>Marks, J. R.</dc:creator>
<dc:creator>Hwang, E. S.</dc:creator>
<dc:creator>Shibata, D.</dc:creator>
<dc:date>2019-11-14</dc:date>
<dc:identifier>doi:10.1101/842203</dc:identifier>
<dc:title><![CDATA[Minimal Barriers to Invasion During Human Colorectal Tumor Growth]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-11-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.03.05.978965v1?rss=1">
<title>
<![CDATA[
The Evolution of Human Cancer Gene Duplications across Mammals 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.03.05.978965v1?rss=1"
</link>
<description><![CDATA[
Cancer is caused by genetic alterations that affect cellular fitness, and multicellular organisms have evolved mechanisms to suppress cancer such as cell cycle checkpoints and apoptosis. These pathways may be enhanced by the addition of tumor suppressor gene paralogs or deletion of oncogenes. To provide insights to the evolution of cancer suppression across the mammalian radiation, we estimated copy numbers for 548 human tumor suppressor gene and oncogene homologs in 63 mammalian genome assemblies. The naked mole rat contained the most cancer gene copies, consistent with the extremely low rates of cancer found in this species. We found a positive correlation between a species cancer gene copy number and its longevity, but not body size, contrary to predictions from Petos Paradox. Extremely long-lived mammals also contained more copies of caretaker genes in their genomes, suggesting that the maintenance of genome integrity is an essential form of cancer prevention in long-lived species. We found the strongest association between longevity and copy numbers of genes that are both germline and somatic tumor suppressor genes, suggesting selection has acted to suppress both hereditary and sporadic cancers. We also found a strong relationship between the number of tumor suppressor genes and the number of oncogenes in mammalian genomes, suggesting complex regulatory networks mediate the balance between cell proliferation and checks on tumor progression. This study is the first to investigate cancer gene expansions across the mammalian radiation and provides a springboard for potential human therapies based on evolutionary medicine.
]]></description>
<dc:creator>Tollis, M.</dc:creator>
<dc:creator>Schneider-Utaka, A. K.</dc:creator>
<dc:creator>Maley, C. C.</dc:creator>
<dc:date>2020-03-05</dc:date>
<dc:identifier>doi:10.1101/2020.03.05.978965</dc:identifier>
<dc:title><![CDATA[The Evolution of Human Cancer Gene Duplications across Mammals]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-03-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/812735v1?rss=1">
<title>
<![CDATA[
Unmasking the tissue microecology of ductal carcinoma in situ with deep learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/812735v1?rss=1"
</link>
<description><![CDATA[
Despite increasing evidence supporting the clinical relevance of tumour infiltrating lymphocytes (TILs) in invasive breast cancer, TIL spatial distribution pattern surrounding ductal carcinoma in situ (DCIS) and its association with progression is not well understood.

To characterize the tissue microecology of DCIS, we designed and tested a new deep learning pipeline, UNMaSk (UNet-IM-Net-SCCNN), for the automated detection and simultaneous segmentation of DCIS ducts. This new method achieved the highest sensitivity and recall over cutting-edge deep learning networks in three patient cohorts, as well as the highest concordance with DCIS identification based on CK5 staining.

Following automated DCIS detection, spatial tessellation centred at each DCIS duct created the boundary in which local ecology can be studied. Single cell identification and classification was performed with an existing deep learning method to map the distribution of TILs. In a dataset comprising grade 2-3 pure DCIS and DCIS adjacent to invasive cancer (adjacent DCIS), we found that pure DCIS cases had more TILs compared to adjacent DCIS. However, TILs co-localise significantly less with DCIS ducts in pure DCIS compared with adjacent DCIS, suggesting a more inflamed tissue ecology local to adjacent DCIS cases.

Our experiments demonstrate that technological developments in deep convolutional neural networks and digital pathology can enable us to automate the identification of DCIS as well as to quantify the spatial relationship with TILs, providing a new way to study immune response and identify new markers of progression, thereby improving clinical management.
]]></description>
<dc:creator>Narayanan, P. L.</dc:creator>
<dc:creator>Raza, S. E. A.</dc:creator>
<dc:creator>Hall, A.</dc:creator>
<dc:creator>Marks, J. R.</dc:creator>
<dc:creator>King, L.</dc:creator>
<dc:creator>West, R. B.</dc:creator>
<dc:creator>Hernandez, L.</dc:creator>
<dc:creator>Dowsett, M.</dc:creator>
<dc:creator>Gusterson, B.</dc:creator>
<dc:creator>Maley, C.</dc:creator>
<dc:creator>Hwang, S. E.</dc:creator>
<dc:creator>Yuan, Y.</dc:creator>
<dc:date>2019-10-28</dc:date>
<dc:identifier>doi:10.1101/812735</dc:identifier>
<dc:title><![CDATA[Unmasking the tissue microecology of ductal carcinoma in situ with deep learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-10-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/843102v1?rss=1">
<title>
<![CDATA[
Diverse noncoding mutations contribute to deregulation of cis-regulatory landscape in pediatric cancers 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/843102v1?rss=1"
</link>
<description><![CDATA[
Interpreting the function of noncoding mutations in cancer genomes remains a major challenge. Here we developed a computational framework to identify risk noncoding mutations of all classes by joint analysis of mutation and gene expression data. We identified thousands of SNVs/small indels and structural variants as candidate risk mutations in five major pediatric cancers. We experimentally validated the oncogenic role of CHD4 overexpression via enhancer hijacking in B-ALL. We observed a general exclusivity of coding and noncoding mutations affecting the same genes and pathways. We showed that integrated mutation signatures can help define novel patient subtypes with different clinical outcomes. Our study introduces a general strategy to systematically identify and characterize the full spectrum of noncoding mutations in cancers.
]]></description>
<dc:creator>He, B.</dc:creator>
<dc:creator>Gao, P.</dc:creator>
<dc:creator>Ding, Y.-y.</dc:creator>
<dc:creator>Chen, C.-h.</dc:creator>
<dc:creator>Chen, G.</dc:creator>
<dc:creator>Chen, C.</dc:creator>
<dc:creator>Kim, H.</dc:creator>
<dc:creator>Tasian, S. K.</dc:creator>
<dc:creator>Hunger, S. P.</dc:creator>
<dc:creator>Tan, K.</dc:creator>
<dc:date>2019-11-15</dc:date>
<dc:identifier>doi:10.1101/843102</dc:identifier>
<dc:title><![CDATA[Diverse noncoding mutations contribute to deregulation of cis-regulatory landscape in pediatric cancers]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-11-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2019.12.19.882050v1?rss=1">
<title>
<![CDATA[
Massively parallel, time-resolved single-cell RNA sequencing with scNT-Seq 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2019.12.19.882050v1?rss=1"
</link>
<description><![CDATA[
Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell new transcript tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking metabolically labeled newly-transcribed RNAs with T-to-C substitutions. By simultaneously measuring new and old transcriptomes, scNT-Seq reveals neuronal subtype-specific gene regulatory networks and time-resolved RNA trajectories in response to brief (minutes) versus sustained (hours) neuronal activation. Integrating scNT-Seq with genetic perturbation reveals that DNA methylcytosine dioxygenases may inhibit stepwise transition from pluripotent embryonic stem cell state to intermediate and totipotent two-cell-embryo-like (2C-like) states by promoting global RNA biogenesis. Furthermore, pulse-chase scNT-Seq enables transcriptome-wide measurements of RNA stability in rare 2C-like cells. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.
]]></description>
<dc:creator>Qiu, Q.</dc:creator>
<dc:creator>Hu, P.</dc:creator>
<dc:creator>Govek, K. W.</dc:creator>
<dc:creator>Camara, P. G.</dc:creator>
<dc:creator>Wu, H.</dc:creator>
<dc:date>2019-12-20</dc:date>
<dc:identifier>doi:10.1101/2019.12.19.882050</dc:identifier>
<dc:title><![CDATA[Massively parallel, time-resolved single-cell RNA sequencing with scNT-Seq]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-12-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/761429v1?rss=1">
<title>
<![CDATA[
A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/761429v1?rss=1"
</link>
<description><![CDATA[
Single cell genomics is essential to chart the complex tumor ecosystem. While single cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumor tissues, single nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each strategy requires modifications to fit the unique characteristics of different tissue and tumor types, posing a barrier to adoption. Here, we developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We tested eight tumor types of varying tissue and sample characteristics (resection, biopsy, ascites, and orthotopic patient-derived xenograft): lung cancer, metastatic breast cancer, ovarian cancer, melanoma, neuroblastoma, pediatric sarcoma, glioblastoma, pediatric high-grade glioma, and chronic lymphocytic leukemia. Analyzing 212,498 cells and nuclei from 39 clinical samples, we evaluated protocols by cell quality, recovery rate, and cellular composition. We optimized protocols for fresh tissue dissociation for different tumor types using a decision tree to account for the technical and biological variation between clinical samples. We established methods for nucleus isolation from OCT embedded and fresh-frozen tissues, with an optimization matrix varying mechanical force, buffer, and detergent. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types and intrinsic expression profiles, but at different proportions. Our work provides direct guidance across a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.
]]></description>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Porter, C. B. M.</dc:creator>
<dc:creator>Ashenberg, O.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Drokhlyansky, E.</dc:creator>
<dc:creator>Wakiro, I.</dc:creator>
<dc:creator>Smillie, C.</dc:creator>
<dc:creator>Smith-Rosario, G.</dc:creator>
<dc:creator>Wu, J.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Vigneau, S.</dc:creator>
<dc:creator>Jane-Valbuena, J.</dc:creator>
<dc:creator>Napolitano, S.</dc:creator>
<dc:creator>Su, M.-J.</dc:creator>
<dc:creator>Patel, A. G.</dc:creator>
<dc:creator>Karlstrom, A.</dc:creator>
<dc:creator>Gritsch, S.</dc:creator>
<dc:creator>Nomura, M.</dc:creator>
<dc:creator>Waghray, A.</dc:creator>
<dc:creator>Gohil, S. H.</dc:creator>
<dc:creator>Tsankov, A. M.</dc:creator>
<dc:creator>Jerby-Arnon, L.</dc:creator>
<dc:creator>Cohen, O.</dc:creator>
<dc:creator>Klughammer, J.</dc:creator>
<dc:creator>Rosen, Y.</dc:creator>
<dc:creator>Gould, J.</dc:creator>
<dc:creator>Li, B.</dc:creator>
<dc:creator>Nguyen, L.</dc:creator>
<dc:creator>Wu, C. J.</dc:creator>
<dc:creator>Izar, B.</dc:creator>
<dc:creator>Haq, R.</dc:creator>
<dc:creator>Hodi, F. S.</dc:creator>
<dc:creator>Yoon, C. H.</dc:creator>
<dc:creator>Hata, A. N.</dc:creator>
<dc:creator>Baker, S. J.</dc:creator>
<dc:creator>Suva, M. L.</dc:creator>
<dc:creator>Bueno, R.</dc:creator>
<dc:creator>Stover, E. H.</dc:creator>
<dc:creator>Matulonis, U. A.</dc:creator>
<dc:creator>Clay, M. R.</dc:creator>
<dc:creator>Dyer, M. A.</dc:creator>
<dc:creator>Collins, N. B.</dc:creator>
<dc:creator>Wagle, N.</dc:creator>
<dc:creator>Rotem, A.</dc:creator>
<dc:creator>Johnson, B. E.</dc:creator>
<dc:date>2019-09-12</dc:date>
<dc:identifier>doi:10.1101/761429</dc:identifier>
<dc:title><![CDATA[A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-09-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.05.13.094268v1?rss=1">
<title>
<![CDATA[
Expansion Sequencing: Spatially Precise In Situ Transcriptomics in Intact Biological Systems 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.05.13.094268v1?rss=1"
</link>
<description><![CDATA[
AbstractMethods for highly multiplexed RNA imaging are limited in spatial resolution, and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to mouse brain, yielding readout of thousands of genes, including splice variants and novel transcripts. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in neurons of the mouse hippocampus, revealing patterns across multiple cell types; layer-specific cell types across mouse visual cortex; and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus ExSeq enables highly multiplexed mapping of RNAs, from nanoscale to system scale.

One Sentence SummaryIn situ sequencing of physically expanded specimens enables multiplexed mapping of RNAs at nanoscale, subcellular resolution.
]]></description>
<dc:creator>Alon, S.</dc:creator>
<dc:creator>Goodwin, D.</dc:creator>
<dc:creator>Sinha, A.</dc:creator>
<dc:creator>Wassie, A.</dc:creator>
<dc:creator>Chen, F.</dc:creator>
<dc:creator>Daugharthy, E.</dc:creator>
<dc:creator>Bando, Y.</dc:creator>
<dc:creator>Kajita, A.</dc:creator>
<dc:creator>Xue, A.</dc:creator>
<dc:creator>Marrett, K.</dc:creator>
<dc:creator>Prior, R.</dc:creator>
<dc:creator>Cui, Y.</dc:creator>
<dc:creator>Payne, A.</dc:creator>
<dc:creator>Yao, C.-C.</dc:creator>
<dc:creator>Suk, H.-J.</dc:creator>
<dc:creator>Wang, R.</dc:creator>
<dc:creator>Yu, C.-C.</dc:creator>
<dc:creator>Tillberg, P.</dc:creator>
<dc:creator>Reginato, P.</dc:creator>
<dc:creator>Pak, N.</dc:creator>
<dc:creator>Liu, S.</dc:creator>
<dc:creator>Punthambaker, S.</dc:creator>
<dc:creator>Iyer, E.</dc:creator>
<dc:creator>Kohman, R.</dc:creator>
<dc:creator>Miller, J.</dc:creator>
<dc:creator>Lein, E.</dc:creator>
<dc:creator>Lako, A.</dc:creator>
<dc:creator>Cullen, N.</dc:creator>
<dc:creator>Rodig, S.</dc:creator>
<dc:creator>Helvie, K.</dc:creator>
<dc:creator>Abravanel, D.</dc:creator>
<dc:creator>Wagle, N.</dc:creator>
<dc:creator>Johnson, B.</dc:creator>
<dc:creator>Klughammer, J.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Jane-Valbuena, J.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Church, G.</dc:creator>
<dc:creator>Marblestone, A.</dc:creator>
<dc:creator>Boyden, E. S.</dc:creator>
<dc:date>2020-05-15</dc:date>
<dc:identifier>doi:10.1101/2020.05.13.094268</dc:identifier>
<dc:title><![CDATA[Expansion Sequencing: Spatially Precise In Situ Transcriptomics in Intact Biological Systems]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-05-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.01.020842v1?rss=1">
<title>
<![CDATA[
VISTA: Virtual ImmunoSTAining for pancreatic disease quantification in murine cohorts 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.01.020842v1?rss=1"
</link>
<description><![CDATA[
Mechanistic studies of pancreatic disease progression using animal models require objective and quantifiable assessment of tissue changes among animal cohorts. Disease state quantification, however, relies heavily on tissue immunostaining, which can be expensive, labor- and time-intensive, and all too often produces uneven staining that is prone to variable interpretation between experts and inaccurate quantification. Here we develop a fully automated semantic segmentation tool using deep learning for the rapid and objective quantification of histologic features using hematoxylin and eosin (H&E) stained pancreatic tissue sections acquired from murine pancreatic cancer models. The tool was successfully trained to segment and quantify multiple histopathologic features of pancreatic pre-cancer evolution, including normal acinar structures, the ductal phenotype of acinar-to ductal metaplasia (ADM), dysplasia, and the expanding stromal compartment. Disease quantifications produced by our computational tool were highly correlated to the results obtained by immunostaining markers of normal and diseased tissue (DAPI, amylase, and cytokeratins; correlation score= 0.9, 0.95, and 0.91, respectively) and were able to accurately reproduce immunostain patterns. Moreover, our tool was able to distinguish ADM from dysplasia, which are not reliably distinguished by immunostaining, and avoid the pitfalls of uneven or poor-quality staining. Using this tool, we quantified the changes in histologic feature abundance for murine cohorts with oncogenic Kras-driven disease at 2 months and 5 months of age (n=12, n=13). The calculated changes in histologic feature abundance were consistent with biological expectations, showing an expansion of the stromal compartment, a reduction of normal acinar tissue, and an increase in both ADM and dysplasia as disease progresses (p= 2e-6, 6e-7, 4e-4, and 3e-5, respectively). These results demonstrate the tools efficacy for accurate and rapid quantification of multiple histologic features using an objective and automated platform. Our tool promises to rapidly accelerate and improve the quantification of altered pancreatic disease progression in animal studies.
]]></description>
<dc:creator>Ternes, L.</dc:creator>
<dc:creator>Huang, G.</dc:creator>
<dc:creator>Lanciault, C.</dc:creator>
<dc:creator>Thibault, G.</dc:creator>
<dc:creator>Riggers, R.</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:creator>Muschler, J.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:date>2020-04-02</dc:date>
<dc:identifier>doi:10.1101/2020.04.01.020842</dc:identifier>
<dc:title><![CDATA[VISTA: Virtual ImmunoSTAining for pancreatic disease quantification in murine cohorts]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/792770v1?rss=1">
<title>
<![CDATA[
RESTORE: Robust intEnSiTy nORmalization mEthod for Multiplexed Imaging 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/792770v1?rss=1"
</link>
<description><![CDATA[
Recent advances in multiplexed imaging technologies promise to improve the understanding of the functional states of individual cells and the interactions between the cells in tissues. This often requires compilation of results from multiple samples. However, quantitative integration of information between samples is complicated by variations in staining intensity and background fluorescence that obscure biological variations. Failure to remove these unwanted artefacts will complicate downstream analysis and diminish the value of multiplexed imaging for clinical applications. Here, to compensate for unwanted variations, we automatically identify negative control cells for each marker within the same tissue and use their expression levels to infer background signal level. The intensity profile is normalized by the inferred level of the negative control cells to remove between-sample variation. Using a tissue microarray data and a pair of longitudinal biopsies sample, we demonstrated that the proposed approach can remove unwanted variations effectively and shows robust performance.
]]></description>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:creator>Chin, K.</dc:creator>
<dc:creator>Thibault, G.</dc:creator>
<dc:creator>Eng, J.</dc:creator>
<dc:creator>Grace, L.</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:date>2019-10-03</dc:date>
<dc:identifier>doi:10.1101/792770</dc:identifier>
<dc:title><![CDATA[RESTORE: Robust intEnSiTy nORmalization mEthod for Multiplexed Imaging]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-10-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/689828v1?rss=1">
<title>
<![CDATA[
Predicting Primary Site of Secondary Liver Cancer with a Neural Estimator of Metastatic Origin (NEMO) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/689828v1?rss=1"
</link>
<description><![CDATA[
Pathologists rely on clinical information, tissue morphology, and sophisticated molecular diagnostics to accurately infer the metastatic origin of secondary liver cancer. In this paper, we introduce a deep learning approach to identify spatially localized regions of cancerous tumor within hematoxylin and eosin stained tissue sections of liver cancer and to generate predictions of the cancers metastatic origin. Our approach achieves an accuracy of 90.2% when classifying metastatic origin of whole slide images into three distinct classes, which compares favorably to an established clinical benchmark by three board-certified pathologists whose accuracies ranged from 90.2% to 94.1% on the same prediction task. This approach illustrates the potential impact of deep learning systems to leverage morphological and structural features of H&E stained tissue sections to guide pathological and clinical determination of the metastatic origin of secondary liver cancers.
]]></description>
<dc:creator>Schau, G. F.</dc:creator>
<dc:creator>Burlingame, E. A.</dc:creator>
<dc:creator>Thibault, G.</dc:creator>
<dc:creator>Anekpuritanang, T.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:creator>Corless, C.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:date>2019-07-02</dc:date>
<dc:identifier>doi:10.1101/689828</dc:identifier>
<dc:title><![CDATA[Predicting Primary Site of Secondary Liver Cancer with a Neural Estimator of Metastatic Origin (NEMO)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-07-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/730309v1?rss=1">
<title>
<![CDATA[
SHIFT: speedy histological-to-immunofluorescent translation of whole slide images enabled by deep learning 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/730309v1?rss=1"
</link>
<description><![CDATA[
Spatially-resolved molecular profiling by immunostaining tissue sections is a key feature in cancer diagnosis, subtyping, and treatment, where it complements routine histopathological evaluation by clarifying tumor phenotypes. In this work, we present a deep learning-based method called speedy histological-to-immunofluorescent translation (SHIFT) which takes histologic images of hematoxylin and eosin-stained tissue as input, then in near-real time returns inferred virtual immunofluorescence (IF) images that accurately depict the underlying distribution of phenotypes without requiring immunostaining of the tissue being tested. We show that deep learning-extracted feature representations of histological images can guide representative sample selection, which improves SHIFT generalizability. SHIFT could serve as an efficient preliminary, auxiliary, or substitute for IF by delivering multiplexed virtual IF images for a fraction of the cost and in a fraction of the time required by nascent multiplexed imaging technologies.nnKEY POINTSO_LISpatially-resolved molecular profiling is an essential complement to histopathological evaluation of cancer tissues.nC_LIO_LIInformation obtained by immunofluorescence imaging is encoded by features in histological images.nC_LIO_LISHIFT leverages previously unappreciated features in histological images to facilitate virtual immunofluorescence staining.nC_LIO_LIFeature representations of images guide sample selection, improving model generalizability.nC_LI
]]></description>
<dc:creator>Burlingame, E. A.</dc:creator>
<dc:creator>McDonnell, M.</dc:creator>
<dc:creator>Schau, G. F.</dc:creator>
<dc:creator>Thibault, G.</dc:creator>
<dc:creator>Lanciault, C.</dc:creator>
<dc:creator>Morgan, T.</dc:creator>
<dc:creator>Johnson, B. E.</dc:creator>
<dc:creator>Corless, C.</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:date>2019-08-15</dc:date>
<dc:identifier>doi:10.1101/730309</dc:identifier>
<dc:title><![CDATA[SHIFT: speedy histological-to-immunofluorescent translation of whole slide images enabled by deep learning]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/675371v1?rss=1">
<title>
<![CDATA[
A workflow for visualizing human cancer biopsies using large-format electron microscopy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/675371v1?rss=1"
</link>
<description><![CDATA[
Recent developments in large format electron microscopy have enabled generation of images that provide detailed ultrastructural information on normal and diseased cells and tissues. Analyses of these images increase our understanding of cellular organization and interactions and disease-related changes therein. In this manuscript, we describe a workflow for two-dimensional (2D) and three-dimensional (3D) imaging, including both optical and scanning electron microscopy (SEM) methods, that allow pathologists and cancer biology researchers to identify areas of interest from human cancer biopsies. The protocols and mounting strategies described in this workflow are compatible with 2D large format EM mapping, 3D focused ion beam-SEM and serial block face-SEM. The flexibility to use diverse imaging technologies available at most academic institutions makes this workflow useful and applicable for most life science samples. Volumetric analysis of the biopsies studied here revealed morphological, organizational and ultrastructural aspects of the tumor cells and surrounding environment that cannot be revealed by conventional 2D EM imaging. Our results indicate that although 2D EM is still an important tool in many areas of diagnostic pathology, 3D images of ultrastructural relationships between both normal and cancerous cells, in combination with their extracellular matrix, enables cancer researchers and pathologists to better understand the progression of the disease and identify potential therapeutic targets.
]]></description>
<dc:creator>Riesterer, J. L.</dc:creator>
<dc:creator>Lopez, C. S.</dc:creator>
<dc:creator>Stempinski, E. S.</dc:creator>
<dc:creator>Williams, M.</dc:creator>
<dc:creator>Loftis, K.</dc:creator>
<dc:creator>Stoltz, K.</dc:creator>
<dc:creator>Thibault, G.</dc:creator>
<dc:creator>Lanicault, C.</dc:creator>
<dc:creator>Williams, T.</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:date>2019-06-19</dc:date>
<dc:identifier>doi:10.1101/675371</dc:identifier>
<dc:title><![CDATA[A workflow for visualizing human cancer biopsies using large-format electron microscopy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-06-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.16.045617v1?rss=1">
<title>
<![CDATA[
Single-cell analysis of human lung epithelia reveals concomitant expression of the SARS-CoV-2 receptor ACE2 with multiple virus receptors and scavengers in alveolar type II cells 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.16.045617v1?rss=1"
</link>
<description><![CDATA[
The novel coronavirus SARS-CoV-2 was identified as the causative agent of the ongoing pandemic COVID 19. COVID-19-associated deaths are mainly attributed to severe pneumonia and respiratory failure. Recent work demonstrated that SARS-CoV-2 binds to angiotensin converting enzyme 2 (ACE2) in the lung. To better understand ACE2 abundance and expression patterns in the lung we interrogated our in-house single-cell RNA-sequencing dataset containing 70,085 EPCAM+ lung epithelial cells from paired normal and lung adenocarcinoma tissues. Transcriptomic analysis revealed a diverse repertoire of airway lineages that included alveolar type I and II, bronchioalveolar, club/secretory, quiescent and proliferating basal, ciliated and malignant cells as well as rare populations such as ionocytes. While the fraction of lung epithelial cells expressing ACE2 was low (1.7% overall), alveolar type II (AT2, 2.2% ACE2+) cells exhibited highest levels of ACE2 expression among all cell subsets. Further analysis of the AT2 compartment (n = 27,235 cells) revealed a number of genes co-expressed with ACE2 that are important for lung pathobiology including those associated with chronic obstructive pulmonary disease (COPD; HHIP), pneumonia and infection (FGG and C4BPA) as well as malarial/bacterial (CD36) and viral (DMBT1) scavenging which, for the most part, were increased in smoker versus light or non-smoker cells. Notably, DMBT1 was highly expressed in AT2 cells relative to other lung epithelial subsets and its expression positively correlated with ACE2. We describe a population of ACE2-positive AT2 cells that co-express pathogen (including viral) receptors (e.g. DMBT1) with crucial roles in host defense thus comprising plausible phenotypic targets for treatment of COVID-19.
]]></description>
<dc:creator>Han, G.</dc:creator>
<dc:creator>Sinjab, A.</dc:creator>
<dc:creator>Treekitkarnmongkol, W.</dc:creator>
<dc:creator>Brennan, P.</dc:creator>
<dc:creator>Hara, K.</dc:creator>
<dc:creator>Chang, K.</dc:creator>
<dc:creator>Bogatenkova, E.</dc:creator>
<dc:creator>Sanchez-Espiridion, B.</dc:creator>
<dc:creator>Behrens, C.</dc:creator>
<dc:creator>Gao, B.</dc:creator>
<dc:creator>Girard, L.</dc:creator>
<dc:creator>Zhang, J.</dc:creator>
<dc:creator>Sepesi, B.</dc:creator>
<dc:creator>Cascone, T.</dc:creator>
<dc:creator>Byers, L.</dc:creator>
<dc:creator>Gibbons, D. L.</dc:creator>
<dc:creator>Chen, J.</dc:creator>
<dc:creator>Moghaddam, S. J.</dc:creator>
<dc:creator>Ostrin, E. J.</dc:creator>
<dc:creator>Fujimoto, J.</dc:creator>
<dc:creator>Shay, J.</dc:creator>
<dc:creator>Heymach, J. V.</dc:creator>
<dc:creator>Minna, J. D.</dc:creator>
<dc:creator>Dubinett, S.</dc:creator>
<dc:creator>Scheet, P. A.</dc:creator>
<dc:creator>Wistuba, I. I.</dc:creator>
<dc:creator>Hill, E.</dc:creator>
<dc:creator>Telesco, S.</dc:creator>
<dc:creator>Stevenson, C.</dc:creator>
<dc:creator>Spira, A. E.</dc:creator>
<dc:creator>Wang, L.</dc:creator>
<dc:creator>Kadara, H.</dc:creator>
<dc:date>2020-04-17</dc:date>
<dc:identifier>doi:10.1101/2020.04.16.045617</dc:identifier>
<dc:title><![CDATA[Single-cell analysis of human lung epithelia reveals concomitant expression of the SARS-CoV-2 receptor ACE2 with multiple virus receptors and scavengers in alveolar type II cells]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/743989v1?rss=1">
<title>
<![CDATA[
Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/743989v1?rss=1"
</link>
<description><![CDATA[
Antitumoral immunity requires organized, spatially nuanced interactions between components of the immune tumor microenvironment (iTME). Understanding this coordinated behavior in effective versus ineffective tumor control will advance immunotherapies. We optimized CO-Detection by indEXing (CODEX) for para ffin-em bedded tissue microarrays, enabling profiling of 140 tissue regions from 35 advanced-stage colorectal cancer (CRC) patients with 56 protein markers simultaneously. We identified nine conserved, distinct cellular neighborhoods (CNs)-a collection of components characteristic of the CRC iTME. Enrichment of PD-1+CD4+ T cells only within a granulocyte CN positively correlated with survival in a high-risk patient subset. Coupling of tumor and immune CNs, fragmentation of T cell and macrophage CNs, and disruption of inter-CN communication was associated with inferior outcomes. This study provides a framework for interrogating complex biological processes, such as antitumoral immunity, demonstrating an example of how tumors can disrupt imm une functionality through interference in the concerted action of cells and spatial domains.
]]></description>
<dc:creator>Schürch, C. M.</dc:creator>
<dc:creator>Bhate, S. S.</dc:creator>
<dc:creator>Barlow, G. L.</dc:creator>
<dc:creator>Phillips, D. J.</dc:creator>
<dc:creator>Noti, L.</dc:creator>
<dc:creator>Zlobec, I.</dc:creator>
<dc:creator>Chu, P.</dc:creator>
<dc:creator>Black, S.</dc:creator>
<dc:creator>Demeter, J.</dc:creator>
<dc:creator>McIlwain, D. R.</dc:creator>
<dc:creator>Samusik, N.</dc:creator>
<dc:creator>Goltsev, Y.</dc:creator>
<dc:creator>Nolan, G. P.</dc:creator>
<dc:date>2019-08-24</dc:date>
<dc:identifier>doi:10.1101/743989</dc:identifier>
<dc:title><![CDATA[Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/790162v1?rss=1">
<title>
<![CDATA[
Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/790162v1?rss=1"
</link>
<description><![CDATA[
Increasingly, highly multiplexed in situ tissue imaging methods are used to profile protein expression at the single-cell level. However, a critical limitation is a lack of robust cell segmentation tools applicable for sections of tissues with a complex architecture and multiple cell types. Using human colorectal adenomas, we present a pipeline for cell segmentation and quantification that utilizes machine learning-based pixel classification to define cellular compartments, a novel method for extending incomplete cell membranes, quantification of antibody staining, and a deep learning-based cell shape descriptor. We envision that this method can be broadly applied to different imaging platforms and tissue types.
]]></description>
<dc:creator>McKinley, E. T.</dc:creator>
<dc:creator>Roland, J. T.</dc:creator>
<dc:creator>Franklin, J. L.</dc:creator>
<dc:creator>Macedonia, M. C.</dc:creator>
<dc:creator>Vega, P. N.</dc:creator>
<dc:creator>Shin, S.</dc:creator>
<dc:creator>Coffey, R. J.</dc:creator>
<dc:creator>Lau, K.</dc:creator>
<dc:date>2019-10-02</dc:date>
<dc:identifier>doi:10.1101/790162</dc:identifier>
<dc:title><![CDATA[Machine and deep learning single-cell segmentation and quantification of multi-dimensional tissue images]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-10-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.04.15.030874v1?rss=1">
<title>
<![CDATA[
Clinically adaptable polymer enables simultaneous spatial analysis of colonic tissues and biofilms 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.04.15.030874v1?rss=1"
</link>
<description><![CDATA[
Microbial influences on host cells depend upon the identities of the microbes, their spatial localization, and the responses they invoke on specific host cell populations. Multi-modal analyses of both microbes and host cells in a spatially-resolved fashion would enable studies into these complex interactions in native tissue environments, potentially in clinical specimens. While techniques to preserve each of the microbial and host cell compartments have been used to examine tissues and microbes separately, we endeavored to develop approaches to simultaneously analyze both compartments. Herein, we established an original method for mucus preservation using Poloxamer 407 (also known as Pluronic F-127), a thermoreversible polymer with mucus-adhesive characteristics. We demonstrate that this approach can preserve spatially-defined compartments of the mucus bi-layer in the colon and the bacterial communities within, compared with their marked absence when tissues were processed with traditional formalin-fixed paraffin-embedded (FFPE) pipelines. Additionally, antigens for antibody staining of host cells were preserved and signal intensity for 16S rRNA fluorescence in situ hybridization (FISH) was enhanced in Poloxamer-fixed samples. This in turn enabled us to integrate multi-modal analysis using a modified multiplex immunofluorescence (MxIF) protocol. Importantly, we have formulated Poloxamer 407 to polymerize and crosslink at room temperature for use in clinical workflows. These results suggest that the fixative formulation of Poloxamer 407 can be integrated into biospecimen collection pipelines for simultaneous analysis of microbes and host cells.
]]></description>
<dc:creator>Macedonia, M. C.</dc:creator>
<dc:creator>Drewes, J. L.</dc:creator>
<dc:creator>Markham, N. O.</dc:creator>
<dc:creator>Simmons, A. J.</dc:creator>
<dc:creator>Roland, J. T.</dc:creator>
<dc:creator>Vega, P. N.</dc:creator>
<dc:creator>Scurrah, C. R.</dc:creator>
<dc:creator>Coffey, R. J.</dc:creator>
<dc:creator>Shrubsole, M. J.</dc:creator>
<dc:creator>Sears, C. L.</dc:creator>
<dc:creator>Lau, K. S.</dc:creator>
<dc:date>2020-04-17</dc:date>
<dc:identifier>doi:10.1101/2020.04.15.030874</dc:identifier>
<dc:title><![CDATA[Clinically adaptable polymer enables simultaneous spatial analysis of colonic tissues and biofilms]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-04-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/835488v1?rss=1">
<title>
<![CDATA[
Dual indexed design of in-Drop single-cell RNA-seq libraries improves sequencing quality and throughput 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/835488v1?rss=1"
</link>
<description><![CDATA[
The increasing demand of single-cell RNA-sequencing (scRNA-seq) experiments, such as the number of experiments and cells queried per experiment, necessitates higher sequencing depth coupled to high data quality. New high-throughput sequencers, such as the Illumina NovaSeq 6000, enables this demand to be filled in a cost-effective manner. However, current scRNA-seq library designs present compatibility challenges with newer sequencing technologies, such as index-hopping, and their ability to generate high quality data has yet to be systematically evaluated. Here, we engineered a new dual-indexed library structure, called TruDrop, on top of the inDrop scRNA-seq platform to solve these compatibility challenges, such that TruDrop libraries and standard Illumina libraries can be sequenced alongside each other on the NovaSeq. We overcame the index-hopping issue, demonstrated significant improvements in base-calling accuracy, and provided an example of multiplexing twenty-four scRNA-seq libraries simultaneously. We showed favorable comparisons in transcriptional diversity of TruDrop compared with prior library structures. Our approach enables cost-effective, high throughput generation of sequencing data with high quality, which should enable more routine use of scRNA-seq technologies.
]]></description>
<dc:creator>Southard-Smith, A. N.</dc:creator>
<dc:creator>Simmons, A. J.</dc:creator>
<dc:creator>Chen, B.</dc:creator>
<dc:creator>Jones, A. L.</dc:creator>
<dc:creator>Ramirez-Solano, M. A.</dc:creator>
<dc:creator>Vega, P. N.</dc:creator>
<dc:creator>Scurrah, C. R.</dc:creator>
<dc:creator>Zhao, Y.</dc:creator>
<dc:creator>Brenan, M. J.</dc:creator>
<dc:creator>Xuan, J.</dc:creator>
<dc:creator>Porter, E. B.</dc:creator>
<dc:creator>Chen, X.</dc:creator>
<dc:creator>Brenan, C. J. H.</dc:creator>
<dc:creator>Liu, Q.</dc:creator>
<dc:creator>Quigley, L. N. M.</dc:creator>
<dc:creator>Lau, K. N.</dc:creator>
<dc:date>2019-11-08</dc:date>
<dc:identifier>doi:10.1101/835488</dc:identifier>
<dc:title><![CDATA[Dual indexed design of in-Drop single-cell RNA-seq libraries improves sequencing quality and throughput]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-11-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/776724v1?rss=1">
<title>
<![CDATA[
Commensal-derived succinate enhances tuft cell specification and suppresses ileal inflammation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/776724v1?rss=1"
</link>
<description><![CDATA[
Longitudinal analysis of Crohn's disease (CD) incidence has identified an inverse correlation with helminth infestation and recent studies have revealed that intestinal tuft cell hyperplasia is critical for helminth response. Tuft cell frequency was decreased in the inflamed ilea of CD patients and a mouse model of TNF-induced Crohn's-like ileitis (TNF{Delta}ARE). Single-cell RNA sequencing paired with unbiased differential trajectory analysis of the tuft cell lineage in a genetic model of tuft cell hyperplasia (AtohKO) demonstrated that the tuft cell lineage had increased tricarboxylic acid (TCA) cycle gene signatures. Commensal microbiome-derived succinate was detected in the ileal lumen of these animals while microbiome depletion suppressed tuft cell hyperplasia. Therapeutic succinate treatment in TNF{Delta}ARE animals reduced pathology in correlation with induced tuft cell specification. We provide evidence implicating the modulatory role of intestinal tuft cells in chronic intestinal inflammation, which could facilitate leveraging this rare and elusive cell type for CD treatment.
]]></description>
<dc:creator>Banerjee, A.</dc:creator>
<dc:creator>Herring, C. A.</dc:creator>
<dc:creator>Kim, H.</dc:creator>
<dc:creator>Chen, B.</dc:creator>
<dc:creator>Simmons, A. J.</dc:creator>
<dc:creator>Southard-Smith, A. N.</dc:creator>
<dc:creator>White, J. R.</dc:creator>
<dc:creator>Ramirez Solano, M. A.</dc:creator>
<dc:creator>Scoville, E. A.</dc:creator>
<dc:creator>Liu, Q.</dc:creator>
<dc:creator>Washington, M. K.</dc:creator>
<dc:creator>Lau, K. S.</dc:creator>
<dc:date>2019-09-20</dc:date>
<dc:identifier>doi:10.1101/776724</dc:identifier>
<dc:title><![CDATA[Commensal-derived succinate enhances tuft cell specification and suppresses ileal inflammation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-09-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.01.406363v1?rss=1">
<title>
<![CDATA[
Single cell profiling reveals novel tumor and myeloid subpopulations in small cell lung cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.01.406363v1?rss=1"
</link>
<description><![CDATA[
Small cell lung cancer (SCLC) is an aggressive malignancy that includes subtypes defined by differential expression of ASCL1, NEUROD1, and POU2F3 (SCLC-A, -N, and -P, respectively), which are associated with distinct therapeutic vulnerabilities. To define the heterogeneity of tumors and their associated microenvironments across subtypes, we sequenced 54,523 cellular transcriptomes from 21 human biospecimens. Our single-cell SCLC atlas reveals tumor diversity exceeding lung adenocarcinoma, driven by canonical, intermediate, and admixed subtypes. We discovered a PLCG2-high tumor cell population with stem-like, pro-metastatic features that recurs across subtypes and predicts worse overall survival, and manipulation of PLCG2 expression in cells confirms correlation with key metastatic markers. Treatment and subtype are associated with substantial phenotypic changes in the SCLC immune microenvironment, with greater T-cell dysfunction in SCLC-N than SCLC-A. Moreover, the recurrent, PLCG2-high subclone is associated with exhausted CD8+ T-cells and a pro-fibrotic, immunosuppressive monocyte/macrophage population, suggesting possible tumor-immune coordination to promote metastasis.
]]></description>
<dc:creator>Chan, J. M.</dc:creator>
<dc:creator>Quintanal-Villalonga, A.</dc:creator>
<dc:creator>Gao, V.</dc:creator>
<dc:creator>Xie, Y.</dc:creator>
<dc:creator>Allaj, V.</dc:creator>
<dc:creator>Chaudhary, O.</dc:creator>
<dc:creator>Masilionis, I.</dc:creator>
<dc:creator>Egger, J.</dc:creator>
<dc:creator>Chow, A.</dc:creator>
<dc:creator>Walle, T.</dc:creator>
<dc:creator>Mattar, M.</dc:creator>
<dc:creator>Yarlagadda, D. V.</dc:creator>
<dc:creator>Wang, J. L.</dc:creator>
<dc:creator>Offin, M.</dc:creator>
<dc:creator>Ciampricotti, M.</dc:creator>
<dc:creator>Bhanot, U. K.</dc:creator>
<dc:creator>Lai, W. V.</dc:creator>
<dc:creator>Bott, M. J.</dc:creator>
<dc:creator>Jones, D. R.</dc:creator>
<dc:creator>Ruiz, A.</dc:creator>
<dc:creator>Hollmann, T.</dc:creator>
<dc:creator>Poirier, J. T.</dc:creator>
<dc:creator>Nawy, T.</dc:creator>
<dc:creator>Mazutis, L.</dc:creator>
<dc:creator>Sen, T.</dc:creator>
<dc:creator>Pe'er, D.</dc:creator>
<dc:creator>Rudin, C. M.</dc:creator>
<dc:date>2020-12-01</dc:date>
<dc:identifier>doi:10.1101/2020.12.01.406363</dc:identifier>
<dc:title><![CDATA[Single cell profiling reveals novel tumor and myeloid subpopulations in small cell lung cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.06.413930v1?rss=1">
<title>
<![CDATA[
Single-cell multi-omics reveals elevated plasticity and stem-cell-like blasts relevant to the poor prognosis of KMT2A-rearranged leukemia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.06.413930v1?rss=1"
</link>
<description><![CDATA[
Infant ALL is a devastating malignancy caused by rearrangements of the KMT2A gene (KMT2A-r) in approximately 70% of patients. The outcome is dismal and younger age at diagnosis is associated with increased risk of relapse. To discover age-specific differences and critical drivers that mediate the poor outcome in KMT2A-r ALL, we subjected KMT2A-r leukemias and normal hematopoietic cells from patients of different ages to multi-omic single cell analysis using scRNA-Seq, scATAC-Seq and snmC-Seq2. We uncovered the following critical new insights: Leukemia cells from infants younger than 6 months have a greatly increased lineage plasticity and contain a hematopoietic stem and progenitor-like (HSPC-like) population compared to older infants. We identified an immunosuppressive signaling circuit between the HSPC-like blasts and cytotoxic lymphocytes in younger patients. Both observations offer a compelling explanation for the ability of leukemias in young infants to evade chemotherapy and immune mediated control. Our analysis also revealed pre-existing lymphomyeloid primed progenitor and myeloid blasts at initial diagnosis of B-ALL. Tracking of leukemic clones in two patients whose leukemia underwent a lineage switch documented the evolution of such clones into frank AML. These findings provide critical insights into KMT2A-r ALL and have potential clinical implications for targeted inhibitors or multi-target immunotherapy approaches. Beyond infant ALL, our study demonstrates the power of single cell multi-omics to detect tumor intrinsic and extrinsic factors affecting rare but critical subpopulations within a malignant population that ultimately determines patient outcome.
]]></description>
<dc:creator>Chen, C.</dc:creator>
<dc:creator>Yu, W.</dc:creator>
<dc:creator>Alikarami, F.</dc:creator>
<dc:creator>Qiu, Q.</dc:creator>
<dc:creator>Chen, C.-h.</dc:creator>
<dc:creator>Flournoy, J.</dc:creator>
<dc:creator>Gao, P.</dc:creator>
<dc:creator>Uzun, Y.</dc:creator>
<dc:creator>Fang, L.</dc:creator>
<dc:creator>Yu, Y.</dc:creator>
<dc:creator>Zhu, Q.</dc:creator>
<dc:creator>Wang, K.</dc:creator>
<dc:creator>Libbrecht, C.</dc:creator>
<dc:creator>Felmeister, A.</dc:creator>
<dc:creator>Rozich, I.</dc:creator>
<dc:creator>Ding, Y.-y.</dc:creator>
<dc:creator>Hunger, S. P.</dc:creator>
<dc:creator>Wu, H.</dc:creator>
<dc:creator>Brown, P. A.</dc:creator>
<dc:creator>Guest, E. M.</dc:creator>
<dc:creator>Barrett, D. M.</dc:creator>
<dc:creator>Bernt, K. M.</dc:creator>
<dc:creator>Tan, K.</dc:creator>
<dc:date>2020-12-07</dc:date>
<dc:identifier>doi:10.1101/2020.12.06.413930</dc:identifier>
<dc:title><![CDATA[Single-cell multi-omics reveals elevated plasticity and stem-cell-like blasts relevant to the poor prognosis of KMT2A-rearranged leukemia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.03.408500v1?rss=1">
<title>
<![CDATA[
An Integrated Clinical, Omic, and Image Atlas of an Evolving Metastatic Breast Cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.03.408500v1?rss=1"
</link>
<description><![CDATA[
Mechanisms of therapeutic resistance manifest in metastatic cancers as tumor cell intrinsic alterations and extrinsic microenvironmental influences that can change during treatment. To support the development of methods for the identification of these mechanisms in individual patients, we present here an Omic and Multidimensional Spatial (OMS) Atlas generated from four serial biopsies of a metastatic breast cancer patient during 3.5 years of therapy. This resource links detailed, longitudinal clinical metadata including treatment times and doses, anatomic imaging, and blood-based response measurements to exploratory analytics including comprehensive DNA, RNA, and protein profiles, images of multiplexed immunostaining, and 2- and 3-dimensional scanning electron micrographs. These data reveal aspects of therapy-associated heterogeneity and evolution of the cancers genome, signaling pathways, immune microenvironment, cellular composition and organization, and ultrastructure. We present illustrative examples showing how integrative analyses of these data provide insights into potential mechanisms of response and resistance, and suggest novel therapeutic vulnerabilities.
]]></description>
<dc:creator>Johnson, B. E.</dc:creator>
<dc:creator>Creason, A. L.</dc:creator>
<dc:creator>Stommel, J. M.</dc:creator>
<dc:creator>Keck, J.</dc:creator>
<dc:creator>Parmar, S.</dc:creator>
<dc:creator>Betts, C. B.</dc:creator>
<dc:creator>Blucher, A.</dc:creator>
<dc:creator>Boniface, C.</dc:creator>
<dc:creator>Bucher, E.</dc:creator>
<dc:creator>Burlingame, E. A.</dc:creator>
<dc:creator>Chin, K.</dc:creator>
<dc:creator>Eng, J.</dc:creator>
<dc:creator>Feiler, H. S.</dc:creator>
<dc:creator>Kolodzie, A.</dc:creator>
<dc:creator>Kong, B.</dc:creator>
<dc:creator>Labrie, M.</dc:creator>
<dc:creator>Leyshock, P.</dc:creator>
<dc:creator>Mitri, S.</dc:creator>
<dc:creator>Patterson, J.</dc:creator>
<dc:creator>Riesterer, J. L.</dc:creator>
<dc:creator>Sivagnanam, S.</dc:creator>
<dc:creator>Sudar, D.</dc:creator>
<dc:creator>Thibault, G.</dc:creator>
<dc:creator>Zheng, C.</dc:creator>
<dc:creator>Nan, X.</dc:creator>
<dc:creator>Heiser, L. M.</dc:creator>
<dc:creator>Spellman, P. T.</dc:creator>
<dc:creator>Thomas, G. V.</dc:creator>
<dc:creator>Demir, E.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:creator>Coussens, L. M.</dc:creator>
<dc:creator>Guimaraes, A. R.</dc:creator>
<dc:creator>Corless, C.</dc:creator>
<dc:creator>Goecks, J.</dc:creator>
<dc:creator>Bergan, R.</dc:creator>
<dc:creator>Mitri, Z.</dc:creator>
<dc:creator>Mills, G. B.</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:date>2020-12-03</dc:date>
<dc:identifier>doi:10.1101/2020.12.03.408500</dc:identifier>
<dc:title><![CDATA[An Integrated Clinical, Omic, and Image Atlas of an Evolving Metastatic Breast Cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-03</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.12.25.424416v1?rss=1">
<title>
<![CDATA[
Single-cell characterization of subsolid and solid lesions in the lung adenocarcinoma spectrum 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.12.25.424416v1?rss=1"
</link>
<description><![CDATA[
Determining the clinical significance of CT scan-detected subsolid pulmonary nodules requires an understanding of the molecular and cellular features that may foreshadow disease progression. We studied the alterations at the transcriptome level in both immune and non-immune cells, utilizing single-cell RNA sequencing, to compare the microenvironment of subsolid, solid, and non-involved lung tissues from surgical resection specimens. This evaluation of early spectrum lung adenocarcinoma reveals a significant decrease in the cytolytic activities of natural killer and natural killer T cells, accompanied by a reduction of effector T cells as well as an increase of CD4+ regulatory T cells in subsolid lesions. Characterization of non-immune cells revealed that both cancer-associated alveolar type 2 cells and fibroblasts contribute to the deregulation of the extracellular matrix, potentially affecting immune infiltration in subsolid lesions through ligand-receptor interactions. These findings suggest a decrement of immune surveillance in subsolid lesions.
]]></description>
<dc:creator>Yanagawa, J.</dc:creator>
<dc:creator>Tran, L. M.</dc:creator>
<dc:creator>Fung, E.</dc:creator>
<dc:creator>Wallace, W. D.</dc:creator>
<dc:creator>Prosper, A. E.</dc:creator>
<dc:creator>Fishbein, G. A.</dc:creator>
<dc:creator>Shea, C.</dc:creator>
<dc:creator>Hong, R.</dc:creator>
<dc:creator>Liu, B.</dc:creator>
<dc:creator>Salehi-Rad, R.</dc:creator>
<dc:creator>Deng, J. Z.</dc:creator>
<dc:creator>Gower, A.</dc:creator>
<dc:creator>Campbell, J. D.</dc:creator>
<dc:creator>Mazzilli, S. A.</dc:creator>
<dc:creator>Beane, J.</dc:creator>
<dc:creator>Kadara, H.</dc:creator>
<dc:creator>Lenburg, M. E.</dc:creator>
<dc:creator>Spira, A.</dc:creator>
<dc:creator>Aberle, D. R.</dc:creator>
<dc:creator>Krysan, K.</dc:creator>
<dc:creator>Dubinett, S. M.</dc:creator>
<dc:date>2020-12-26</dc:date>
<dc:identifier>doi:10.1101/2020.12.25.424416</dc:identifier>
<dc:title><![CDATA[Single-cell characterization of subsolid and solid lesions in the lung adenocarcinoma spectrum]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-12-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.01.11.426044v1?rss=1">
<title>
<![CDATA[
Human colorectal pre-cancer atlas identifies distinct molecular programs underlying two major subclasses of pre-malignant tumors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.01.11.426044v1?rss=1"
</link>
<description><![CDATA[
Most colorectal cancers (CRCs) develop from either adenomas (ADs) or sessile serrated lesions (SSLs). The origins and molecular landscapes of these histologically distinct pre-cancerous polyps remain incompletely understood. Here, we present an atlas at single-cell resolution of sporadic conventional tubular/tubulovillous ADs, SSLs, hyperplastic polyps (HPs), microsatellite stable (MSS) and unstable (MSI-H) CRC, and normal colonic mucosa. Using single-cell transcriptomics and multiplex imaging, we studied 69 datasets from 33 participants. We also examined separate sets of 66 and 274 polyps for RNA and targeted gene sequencing, respectively. We performed multiplex imaging on a tissue microarray of 14 ADs and 15 CRCs, and we integrated pre-cancer polyp data with published single-cell and The Cancer Genome Atlas (TCGA) bulk CRC data to establish potential polyp-cancer relationships. Striking differences were observed between ADs and SSLs that extended to MSS and MSI-H CRCs, respectively, reflecting their distinct origins and trajectories. ADs arose from WNT pathway dysregulation in stem cells, which aberrantly expanded and expressed a Hippo and ASCL2 regenerative program. In marked contrast, SSLs were depleted of stem cell-like populations and instead exhibited a program of gastric metaplasia in the setting of elevated cytotoxic inflammation. Using subtype-specific gene regulatory networks and shared genetic variant analysis, we implicated serrated polyps, including some HPs conventionally considered benign, as arising from a metaplastic program in committed absorptive cells. ADs and SSLs displayed distinct patterns of immune cell infiltration that may influence their natural history. Our multi-omic atlas provides novel insights into the malignant potential of colorectal polyps and serves as a framework for precision surveillance and prevention of sporadic CRC.
]]></description>
<dc:creator>Chen, B.</dc:creator>
<dc:creator>McKinley, E. T.</dc:creator>
<dc:creator>Simmons, A. J.</dc:creator>
<dc:creator>Ramirez, M. A.</dc:creator>
<dc:creator>Zhu, X.</dc:creator>
<dc:creator>Southard-Smith, A. N.</dc:creator>
<dc:creator>Markham, N. O.</dc:creator>
<dc:creator>Sheng, Q.</dc:creator>
<dc:creator>Drewes, J.</dc:creator>
<dc:creator>Xu, Y.</dc:creator>
<dc:creator>Heiser, C. N.</dc:creator>
<dc:creator>Zhou, Y.</dc:creator>
<dc:creator>Revetta, F.</dc:creator>
<dc:creator>Berry, L. D.</dc:creator>
<dc:creator>Zheng, W.</dc:creator>
<dc:creator>Washington, M. K.</dc:creator>
<dc:creator>Cai, Q.</dc:creator>
<dc:creator>Sears, C. L.</dc:creator>
<dc:creator>Goldenring, J. R.</dc:creator>
<dc:creator>Franklin, J. L.</dc:creator>
<dc:creator>Vandekar, S.</dc:creator>
<dc:creator>Roland, J. T.</dc:creator>
<dc:creator>Su, T.</dc:creator>
<dc:creator>Huh, W. J.</dc:creator>
<dc:creator>Liu, Q.</dc:creator>
<dc:creator>Coffey, R. J.</dc:creator>
<dc:creator>Shrubsole, M. J.</dc:creator>
<dc:creator>Lau, K.</dc:creator>
<dc:date>2021-01-13</dc:date>
<dc:identifier>doi:10.1101/2021.01.11.426044</dc:identifier>
<dc:title><![CDATA[Human colorectal pre-cancer atlas identifies distinct molecular programs underlying two major subclasses of pre-malignant tumors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-01-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.01.25.427845v1?rss=1">
<title>
<![CDATA[
GLUER: integrative analysis of single-cell omics and imaging data by deep neural network 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.01.25.427845v1?rss=1"
</link>
<description><![CDATA[
Single-cell omics assays have become essential tools for identifying and characterizing cell types and states of complex tissues. While each single-modality assay reveals distinctive features about the sequenced cells, true multi-omics assays are still in early stage of development. This notion signifies the importance of computationally integrating single-cell omics data that are conducted on various samples across various modalities. In addition, the advent of multiplexed molecular imaging assays has given rise to a need for computational methods for integrative analysis of single-cell imaging and omics data. Here, we present GLUER (inteGrative anaLysis of mUlti-omics at single-cEll Resolution), a flexible tool for integration of single-cell multi-omics data and imaging data. Using multiple true multi-omics data sets as the ground truth, we demonstrate that GLUER achieved significant improvement over existing methods in terms of the accuracy of matching cells across different data modalities resulting in ameliorating downstream analyses such as clustering and trajectory inference. We further demonstrate the broad utility of GLUER for integrating single-cell transcriptomics data with imaging-based spatial proteomics and transcriptomics data. Finally, we extend GLUER to leverage true cell-pair labels when available in true multi-omics data, and show that this approach improves co-embedding and clustering results. With the rapid accumulation of single-cell multi-omics and imaging data, integrated data holds the promise of furthering our understanding of the role of heterogeneity in development and disease.
]]></description>
<dc:creator>Peng, T.</dc:creator>
<dc:creator>Chen, G.</dc:creator>
<dc:creator>Tan, K.</dc:creator>
<dc:date>2021-01-26</dc:date>
<dc:identifier>doi:10.1101/2021.01.25.427845</dc:identifier>
<dc:title><![CDATA[GLUER: integrative analysis of single-cell omics and imaging data by deep neural network]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-01-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.15.435473v1?rss=1">
<title>
<![CDATA[
MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.15.435473v1?rss=1"
</link>
<description><![CDATA[
Highly multiplexed tissue imaging makes molecular analysis of single cells possible in a preserved spatial context. However, reproducible analysis of the underlying data poses a substantial computational challenge. Here we describe a modular and open-source computational pipeline (MCMICRO) for performing the sequential steps needed to transform large, multi-channel whole slide images into single-cell data. We demonstrate use of MCMICRO on images of different tissues and tumors acquired using multiple imaging platforms, thereby providing a solid foundation for the continued development of tissue imaging software.
]]></description>
<dc:creator>Schapiro, D.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Hess, J.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Nariya, M. K.</dc:creator>
<dc:creator>Baker, G. J.</dc:creator>
<dc:creator>Ruokonen, J.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Jacobson, C. A.</dc:creator>
<dc:creator>Farhi, S. L.</dc:creator>
<dc:creator>Abbondanza, D.</dc:creator>
<dc:creator>McKinley, E. T.</dc:creator>
<dc:creator>Betts, C.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Coffey, R. J.</dc:creator>
<dc:creator>Coussens, L. M.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-03-16</dc:date>
<dc:identifier>doi:10.1101/2021.03.15.435473</dc:identifier>
<dc:title><![CDATA[MCMICRO: A scalable, modular image-processing pipeline for multiplexed tissue imaging]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.11.16.385328v1?rss=1">
<title>
<![CDATA[
Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.11.16.385328v1?rss=1"
</link>
<description><![CDATA[
Single-cell RNA sequencing (scRNA-seq) can be used to gain insights into cellular heterogeneity within complex tissues. However, a variety of technical artifacts can be present in scRNA-seq data and need to be assessed before downstream analyses can be performed. While several algorithms and tools have been developed to perform individual quality control (QC) tasks, they are scattered in different packages across several programming environments. Comprehensive pipelines to streamline the process of generating and visualizing QC metrics are lacking. To address this need, we built the SCTK-QC pipeline within the singleCellTK R package (https://github.com/compbiomed/singleCellTK). Features in this pipeline include the ability to import data from 11 different preprocessing tools or file formats, perform empty droplet detection with 2 different algorithms, generate standard quality control metrics such as number of UMIs per cell or the percentage of mitochondrial counts, predict doublets using 6 different algorithms, and estimate ambient RNA. QC data can be exported to R and/or Python objects used in popular down-stream workflows. Results are visualized in an easy-to-read HTML report. This pipeline can also be used by non-computational users with an interactive graphical user interface developed with R/Shiny. Overall, the SCTK-QC pipeline will streamline and standardize QC analysis for scRNA-seq data across a variety of different single-cell transcriptomic platforms and preprocessing tools.
]]></description>
<dc:creator>Hong, R.</dc:creator>
<dc:creator>Koga, Y.</dc:creator>
<dc:creator>Bandyadka, S.</dc:creator>
<dc:creator>Leshchyk, A.</dc:creator>
<dc:creator>Wang, Z.</dc:creator>
<dc:creator>Alabdullatif, S.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Akavoor, V.</dc:creator>
<dc:creator>Cao, X.</dc:creator>
<dc:creator>Sarfraz, I.</dc:creator>
<dc:creator>Jansen, F.</dc:creator>
<dc:creator>Johnson, W. E.</dc:creator>
<dc:creator>Yajima, M.</dc:creator>
<dc:creator>Campbell, J. D.</dc:creator>
<dc:date>2020-11-17</dc:date>
<dc:identifier>doi:10.1101/2020.11.16.385328</dc:identifier>
<dc:title><![CDATA[Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-11-17</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.01.13.426413v1?rss=1">
<title>
<![CDATA[
Spatial drivers and pre-cancer populations collaborate with the microenvironment in untreated and chemo-resistant pancreatic cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.01.13.426413v1?rss=1"
</link>
<description><![CDATA[
Pancreatic Ductal Adenocarcinoma (PDAC) is a lethal disease with limited treatment options and poor survival. We studied 73 samples from 21 patients (7 treatment-naive and 14 treated with neoadjuvant regimens), analyzing distinct spatial units and performing bulk proteogenomics, single cell sequencing, and cellular imaging. Spatial drivers, including mutant KRAS, SMAD4, and GNAQ, were associated with differential phosphosignaling and metabolic responses compared to wild type. Single cell subtyping discovered 12 of 21 tumors with mixed basal and classical features. Trefoil factor family members were upregulated in classical populations, while the basal populations showed enhanced expression of mesenchymal genes, including VIM and IGTB1. Acinar-ductal metaplasia (ADM) populations, present in 95% of patients, with 46% reduction of driver mutation fractions compared to tumor populations, exhibited suppressive and oncogenic features linked to morphologic states. We identified coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin receptor expression in tumor cells. Higher expression of angiogenic and stress response genes in dendritic cells compared to tumor cells suggests they have a pro-tumorigenic role in remodeling the microenvironment. Treated samples contain a three-fold enrichment of inflammatory CAFs when compared to untreated samples, while other CAF subtypes remain similar. A subset of tumor and/or ADM-specific biomarkers showed differential expression between treatment groups, and several known drug targets displayed potential cross-cell type reactivities. This resolution that spatially defined single cell omics provides reveals the diversity of tumor and microenvironment populations in PDAC. Such understanding may lead to more optimal treatment regimens for patients with this devastating disease.

HIGHLIGHTSO_LIAcinar-ductal metaplasia (ADM) cells represent a genetic and morphologic transition state between acinar and tumor cells.
C_LIO_LIInflammatory cancer associated fibroblasts (iCAFs) are a major component of the PDAC TME and are significantly higher in treated samples
C_LIO_LIReceptor-ligand analysis reveals tumor cell-TME interactions through NECTIN4-TIGIT
C_LIO_LITumor and ADM cell proteogenomics differ between treated and untreated samples, with unique and shared potential drug targets
C_LI
]]></description>
<dc:creator>Cui Zhou, D.</dc:creator>
<dc:creator>Jayasinghe, R. G.</dc:creator>
<dc:creator>Herndon, J. M.</dc:creator>
<dc:creator>Storrs, E.</dc:creator>
<dc:creator>Mo, C.-K.</dc:creator>
<dc:creator>Wu, Y.</dc:creator>
<dc:creator>Fulton, R. S.</dc:creator>
<dc:creator>Wyczalkowski, M. A.</dc:creator>
<dc:creator>Fronick, C. C.</dc:creator>
<dc:creator>Fulton, L. A.</dc:creator>
<dc:creator>Thammavong, L.</dc:creator>
<dc:creator>Sato, K.</dc:creator>
<dc:creator>Zhu, H.</dc:creator>
<dc:creator>Sun, H.</dc:creator>
<dc:creator>Wang, L.-B.</dc:creator>
<dc:creator>Li, Y.</dc:creator>
<dc:creator>Zuo, C.</dc:creator>
<dc:creator>McMichael, J. F.</dc:creator>
<dc:creator>Davies, S. R.</dc:creator>
<dc:creator>Appelbaum, E. L.</dc:creator>
<dc:creator>Robbins, K. J.</dc:creator>
<dc:creator>Chasnoff, S. E.</dc:creator>
<dc:creator>Yang, X.</dc:creator>
<dc:creator>Liu, R.</dc:creator>
<dc:creator>Reeb, A. N.</dc:creator>
<dc:creator>Wendl, M. C.</dc:creator>
<dc:creator>Oh, C.</dc:creator>
<dc:creator>Serasanambati, M.</dc:creator>
<dc:creator>Lal, P.</dc:creator>
<dc:creator>Varghese, R.</dc:creator>
<dc:creator>Mashl, J. R.</dc:creator>
<dc:creator>Ponce, J.</dc:creator>
<dc:creator>Terekhanova, N. V.</dc:creator>
<dc:creator>Naser Al Deen, N.</dc:creator>
<dc:creator>Yao, L.</dc:creator>
<dc:creator>Wang, F.</dc:creator>
<dc:creator>Chen, L.</dc:creator>
<dc:creator>Schnaubelt, M.</dc:creator>
<dc:creator>Puram, S. V.</dc:creator>
<dc:creator>Kim, A. H.</dc:creator>
<dc:creator>Song, S.-K.</dc:creator>
<dc:creator>Shoghi, K. I.</dc:creator>
<dc:creator>Ju, T.</dc:creator>
<dc:creator>Hawkins, W. G.</dc:creator>
<dc:creator>Chen, K.</dc:creator>
<dc:creator>Ch</dc:creator>
<dc:date>2021-01-14</dc:date>
<dc:identifier>doi:10.1101/2021.01.13.426413</dc:identifier>
<dc:title><![CDATA[Spatial drivers and pre-cancer populations collaborate with the microenvironment in untreated and chemo-resistant pancreatic cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-01-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.01.05.425362v1?rss=1">
<title>
<![CDATA[
Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.01.05.425362v1?rss=1"
</link>
<description><![CDATA[
Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand how the tumor microenvironment (TME) changes with transition to IBC, we used Multiplexed Ion Beam Imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to analyze 140 clinically annotated surgical resections covering the full spectrum of breast cancer progression. We compared normal, DCIS, and IBC tissues using machine learning tools for multiplexed cell segmentation, pixel-based clustering, and object morphometrics. Transition from DCIS to IBC was found to occur along a trajectory marked by coordinated shifts in location and function of myoepithelium, fibroblasts, and infiltrating immune cells in the surrounding stroma. Taken together, this comprehensive study within the HTAN Breast PreCancer Atlas offers insight into the etiologies of DCIS, its transition to IBC, and emphasizes the importance of the TME stroma in promoting these processes.
]]></description>
<dc:creator>Risom, T.</dc:creator>
<dc:creator>Glass, D. R.</dc:creator>
<dc:creator>Liu, C. C.</dc:creator>
<dc:creator>Rivero-Gutierrez, B.</dc:creator>
<dc:creator>Baranski, A.</dc:creator>
<dc:creator>McCaffrey, E. F.</dc:creator>
<dc:creator>Greenwald, N. F.</dc:creator>
<dc:creator>Kagel, A. C.</dc:creator>
<dc:creator>Strand, S. H.</dc:creator>
<dc:creator>Varma, S.</dc:creator>
<dc:creator>Kong, A.</dc:creator>
<dc:creator>Keren, L.</dc:creator>
<dc:creator>Srivastava, S.</dc:creator>
<dc:creator>Zhu, C.</dc:creator>
<dc:creator>Khair, Z.</dc:creator>
<dc:creator>Veis, D. J.</dc:creator>
<dc:creator>Deschryver, K.</dc:creator>
<dc:creator>Vennam, S.</dc:creator>
<dc:creator>Maley, C.</dc:creator>
<dc:creator>Hwang, E. S.</dc:creator>
<dc:creator>Marks, J. R.</dc:creator>
<dc:creator>Bendall, S. C.</dc:creator>
<dc:creator>Colditz, G. A.</dc:creator>
<dc:creator>West, R. B.</dc:creator>
<dc:creator>Angelo, M.</dc:creator>
<dc:date>2021-01-06</dc:date>
<dc:identifier>doi:10.1101/2021.01.05.425362</dc:identifier>
<dc:title><![CDATA[Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-01-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.01.30.426796v1?rss=1">
<title>
<![CDATA[
Multicellular immune hubs and their organization in MMRd and MMRp colorectal cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.01.30.426796v1?rss=1"
</link>
<description><![CDATA[
Immune responses to cancer are highly variable, with mismatch repair-deficient (MMRd) tumors exhibiting more anti-tumor immunity than mismatch repair-proficient (MMRp) tumors. To understand the rules governing these varied responses, we transcriptionally profiled 371,223 cells from colorectal tumors and adjacent normal tissues of 28 MMRp and 34 MMRd patients. Analysis of 88 cell subsets and their 204 associated gene expression programs revealed extensive transcriptional and spatial remodeling across tumors. To discover hubs of interacting malignant and immune cells, we identified expression programs in different cell types that co-varied across patient tumors and used spatial profiling to localize coordinated programs. We discovered a myeloid cell-attracting hub at the tumor-luminal interface associated with tissue damage, and an MMRd-enriched immune hub within the tumor, with activated T cells together with malignant and myeloid cells expressing T-cell-attracting chemokines. By identifying interacting cellular programs, we thus reveal the logic underlying spatially organized immune-malignant cell networks.
]]></description>
<dc:creator>Pelka, K.</dc:creator>
<dc:creator>Hofree, M.</dc:creator>
<dc:creator>Chen, J.</dc:creator>
<dc:creator>Sarkizova, S.</dc:creator>
<dc:creator>Pirl, J. D.</dc:creator>
<dc:creator>Jorgji, V.</dc:creator>
<dc:creator>Bejnood, A.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Ge, W. H.</dc:creator>
<dc:creator>Xu, K. H.</dc:creator>
<dc:creator>Chao, S. X.</dc:creator>
<dc:creator>Zollinger, D. R.</dc:creator>
<dc:creator>Lieb, D. J.</dc:creator>
<dc:creator>Reeves, J. W.</dc:creator>
<dc:creator>Fuhrman, C. A.</dc:creator>
<dc:creator>Hoang, M. L.</dc:creator>
<dc:creator>Delorey, T.</dc:creator>
<dc:creator>Nguyen, L. T.</dc:creator>
<dc:creator>Waldmann, J. A.</dc:creator>
<dc:creator>Klapholz, M.</dc:creator>
<dc:creator>Wakiro, I.</dc:creator>
<dc:creator>Cohen, O.</dc:creator>
<dc:creator>Smillie, C. S.</dc:creator>
<dc:creator>Cuoco, M. S.</dc:creator>
<dc:creator>Wu, J.</dc:creator>
<dc:creator>Su, M.-j.</dc:creator>
<dc:creator>Yeung, J.</dc:creator>
<dc:creator>Vijaykumar, B.</dc:creator>
<dc:creator>Magnuson, A. M.</dc:creator>
<dc:creator>Asinovski, N.</dc:creator>
<dc:creator>Moll, T.</dc:creator>
<dc:creator>Goder-Reiser, M. N.</dc:creator>
<dc:creator>Applebaum, A. S.</dc:creator>
<dc:creator>Brais, L. K.</dc:creator>
<dc:creator>DelloStritto, L. K.</dc:creator>
<dc:creator>Denning, S. L.</dc:creator>
<dc:creator>Phillips, S. T.</dc:creator>
<dc:creator>Hill, E. K.</dc:creator>
<dc:creator>Meehan, J. K.</dc:creator>
<dc:creator>Frederick, D. T.</dc:creator>
<dc:creator>Sharova, T.</dc:creator>
<dc:creator>Kanodia, A.</dc:creator>
<dc:creator>Todres, E. Z</dc:creator>
<dc:date>2021-02-01</dc:date>
<dc:identifier>doi:10.1101/2021.01.30.426796</dc:identifier>
<dc:title><![CDATA[Multicellular immune hubs and their organization in MMRd and MMRp colorectal cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-02-01</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.31.437984v1?rss=1">
<title>
<![CDATA[
Multiplexed 3D atlas of state transitions and immune interactions in colorectal cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.31.437984v1?rss=1"
</link>
<description><![CDATA[
Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T-cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.
]]></description>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Wang, S.</dc:creator>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Tyler, M. A.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Heiser, C. N.</dc:creator>
<dc:creator>Lau, K.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-04-02</dc:date>
<dc:identifier>doi:10.1101/2021.03.31.437984</dc:identifier>
<dc:title><![CDATA[Multiplexed 3D atlas of state transitions and immune interactions in colorectal cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.08.332288v1?rss=1">
<title>
<![CDATA[
Automated quality control and cell identification of droplet-based single-cell data using dropkick 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.08.332288v1?rss=1"
</link>
<description><![CDATA[
A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in datasets with disparate library sizes confounded by high technical noise (i.e. batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining dataset-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against a conventional thresholding approach and EmptyDrops, a popular computational method, demonstrating greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low and high-background datasets that dropkicks weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to dataset-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell analysis Python packages.
]]></description>
<dc:creator>Heiser, C. N.</dc:creator>
<dc:creator>Wang, V. M.</dc:creator>
<dc:creator>Chen, B.</dc:creator>
<dc:creator>Hughey, J. J.</dc:creator>
<dc:creator>Lau, K. S.</dc:creator>
<dc:date>2020-10-09</dc:date>
<dc:identifier>doi:10.1101/2020.10.08.332288</dc:identifier>
<dc:title><![CDATA[Automated quality control and cell identification of droplet-based single-cell data using dropkick]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-10-09</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.24.436532v1?rss=1">
<title>
<![CDATA[
Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.24.436532v1?rss=1"
</link>
<description><![CDATA[
To chart cell composition and cell state changes that occur during the transformation of healthy colon to precancerous adenomas to colorectal cancer (CRC), we generated 451,886 single-cell chromatin accessibility profiles and 208,557 single-cell transcriptomes from 48 polyps, 27 normal tissues, and 6 CRCs collected from patients with and without germline APC mutations. A large fraction of polyp and CRC cells exhibit a stem-like phenotype, and we define a continuum of epigenetic and transcriptional changes occurring in these stem-like cells as they progress from normal to CRC. Advanced polyps contain increasing numbers of stem-like cells, regulatory T-cells, and a subtype of FOX-regulated pre-cancer associated fibroblasts. In the cancerous state, we observe T-cell exhaustion, RUNX1-regulated cancer associated fibroblasts, and increasing accessibility associated with HNF4A motifs in epithelia. Methylation changes in sporadic CRC are strongly anti-correlated with accessibility changes along this continuum, further identifying regulatory markers for molecular staging of polyps.
]]></description>
<dc:creator>Becker, W. R.</dc:creator>
<dc:creator>Nevins, S. A.</dc:creator>
<dc:creator>Chen, D. C.</dc:creator>
<dc:creator>Chiu, R.</dc:creator>
<dc:creator>Horning, A.</dc:creator>
<dc:creator>Laquindanum, R.</dc:creator>
<dc:creator>Mills, M.</dc:creator>
<dc:creator>Chaib, H.</dc:creator>
<dc:creator>Ladabaum, U.</dc:creator>
<dc:creator>Longacre, T.</dc:creator>
<dc:creator>Shen, J.</dc:creator>
<dc:creator>Esplin, E. D.</dc:creator>
<dc:creator>Kundaje, A.</dc:creator>
<dc:creator>Ford, J. M.</dc:creator>
<dc:creator>Curtis, C.</dc:creator>
<dc:creator>Snyder, M. P.</dc:creator>
<dc:creator>Greenleaf, W. J.</dc:creator>
<dc:date>2021-03-25</dc:date>
<dc:identifier>doi:10.1101/2021.03.24.436532</dc:identifier>
<dc:title><![CDATA[Single-cell analyses reveal a continuum of cell state and composition changes in the malignant transformation of polyps to colorectal cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-03-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.11.443641v1?rss=1">
<title>
<![CDATA[
The breast pre-cancer atlas illustrates the molecular and micro-environmental diversity of ductal carcinoma in situ 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.11.443641v1?rss=1"
</link>
<description><![CDATA[
Micro-environmental and molecular factors mediating the progression of Breast Ductal Carcinoma In Situ (DCIS) are not well understood, impeding the development of prevention strategies and the safe testing of treatment de-escalation. We addressed methodological barriers and characterized the mutational, transcriptional, histological and microenvironmental landscape across 85 multiple micro-dissected regions from 39 cases. Most somatic alterations, including whole genome duplications, were clonal, but genetic divergence increased with physical distance. Phenotypic and subtype heterogeneity frequently associated with underlying genetic heterogeneity and regions with low-risk features preceded those with high-risk features according to the inferred phylogeny. B- and T-lymphocytes spatial analysis identified 3 immune states, including an epithelial excluded state located preferentially at DCIS regions, and characterized by histological and molecular features of immune escape, independently from molecular subtypes. Such breast pre-cancer atlas with uniquely integrated observations will help scope future expansion studies and build finer models of outcomes and progression risk.
]]></description>
<dc:creator>Nachmanson, D.</dc:creator>
<dc:creator>Officer, A.</dc:creator>
<dc:creator>Mori, H.</dc:creator>
<dc:creator>Gordon, J.</dc:creator>
<dc:creator>Evans, M. F.</dc:creator>
<dc:creator>Steward, J.</dc:creator>
<dc:creator>Yao, H.</dc:creator>
<dc:creator>O'Keefe, T.</dc:creator>
<dc:creator>Hasteh, F.</dc:creator>
<dc:creator>Stein, G. S.</dc:creator>
<dc:creator>Jepsen, K.</dc:creator>
<dc:creator>Weaver, D. L.</dc:creator>
<dc:creator>Hirst, G.</dc:creator>
<dc:creator>Sprague, B. L.</dc:creator>
<dc:creator>Esserman, L. J.</dc:creator>
<dc:creator>Borowsky, A. D.</dc:creator>
<dc:creator>Stein, J. L.</dc:creator>
<dc:creator>Harismendy, O.</dc:creator>
<dc:date>2021-05-12</dc:date>
<dc:identifier>doi:10.1101/2021.05.11.443641</dc:identifier>
<dc:title><![CDATA[The breast pre-cancer atlas illustrates the molecular and micro-environmental diversity of ductal carcinoma in situ]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.05.23.445310v1?rss=1">
<title>
<![CDATA[
The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.05.23.445310v1?rss=1"
</link>
<description><![CDATA[
Cutaneous melanoma is a highly immunogenic malignancy, surgically curable at early stages, but life- threatening when metastatic. Here we integrate high-plex imaging, 3D high-resolution microscopy, and spatially-resolved micro-region transcriptomics to study immune evasion and immunoediting in primary melanoma. We find that recurrent cellular neighborhoods involving tumor, immune, and stromal cells change significantly along a progression axis involving precursor states, melanoma in situ, and invasive tumor. Hallmarks of immunosuppression are already detectable in precursor regions. When tumors become locally invasive, a consolidated and spatially restricted suppressive environment forms along the tumor-stromal boundary. This environment is established by cytokine gradients that promote expression of MHC-II and IDO1, and by PD1-PDL1 mediated cell contacts involving macrophages, dendritic cells, and T cells. A few millimeters away, cytotoxic T cells synapse with melanoma cells in fields of tumor regression. Thus, invasion and immunoediting can co-exist within a few millimeters of each other in a single specimen.

STATEMENT OF SIGNIFICANCEThe reorganization of the tumor ecosystem in primary melanoma is an excellent setting in which to study immunoediting and immune evasion. Guided by classical histopathology, spatial profiling of proteins and mRNA reveals recurrent morphological and molecular features of tumor evolution that involve localized paracrine cytokine signaling and direct cell-cell contact.
]]></description>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Vallius, T.</dc:creator>
<dc:creator>Quattrochi, B.</dc:creator>
<dc:creator>Chen, A. A.</dc:creator>
<dc:creator>Jacobson, C. A.</dc:creator>
<dc:creator>Pelletier, R. J.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Arias-Camison, R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Lian, C. G.</dc:creator>
<dc:creator>Murphy, G. F.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-05-23</dc:date>
<dc:identifier>doi:10.1101/2021.05.23.445310</dc:identifier>
<dc:title><![CDATA[The spatial landscape of progression and immunoediting in primary melanoma at single cell resolution]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-05-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.20.440625v1?rss=1">
<title>
<![CDATA[
Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR software 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.20.440625v1?rss=1"
</link>
<description><![CDATA[
MotivationStitching microscope images into a mosaic is an essential step in the analysis and visualization of large biological specimens, particularly human and animal tissues. Recent approaches to highly-multiplexed imaging generate high-plex data from sequential rounds of lower-plex imaging. These multiplexed imaging methods promise to yield precise molecular single-cell data and information on cellular neighborhoods and tissue architecture. However, attaining mosaic images with single-cell accuracy requires robust image stitching and image registration capabilities that are not met by existing methods.

ResultsWe describe the development and testing of ASHLAR, a Python tool for coordinated stitching and registration of 103 or more individual multiplexed images to generate accurate whole-slide mosaics. ASHLAR reads image formats from most commercial microscopes and slide scanners, and we show that it performs better than existing open source and commercial software. ASHLAR outputs standard OME-TIFF images that are ready for analysis by other open-source tools and recently developed image analysis pipelines.

Availability and implementationASHLAR is written in Python and available under an MIT license at https://github.com/labsyspharm/ashlar. An informational website with user guides and test data is available at https://labsyspharm.github.io/ashlar/.
]]></description>
<dc:creator>Muhlich, J. L.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Russell, D. P. W.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-04-21</dc:date>
<dc:identifier>doi:10.1101/2021.04.20.440625</dc:identifier>
<dc:title><![CDATA[Stitching and registering highly multiplexed whole slide images of tissues and tumors using ASHLAR software]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.04.02.438285v1?rss=1">
<title>
<![CDATA[
UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.02.438285v1?rss=1"
</link>
<description><![CDATA[
Newly developed technologies have made it feasible to routinely collect highly multiplexed (20-60 channel) images at subcellular resolution from human tissues for research and diagnostic purposes. Extracting single cell data from such images requires efficient and accurate image segmentation, a challenging problem that has recently benefited from the use of deep learning. In this paper, we demonstrate two approaches to improving tissue segmentation that are applicable to multiple deep learning frameworks. The first uses "real augmentations" that comprise defocused and saturated image data collected on the same instruments as the actual data; using real augmentation improves model accuracy to a significantly greater degree than computational augmentation (Gaussian blurring). The second involves imaging the nuclear envelope to better identify nuclear outlines. The two approaches cumulatively and substantially improve segmentation on a wide range of tissue types and provide a set of improved segmentation models. We speculate that the use of real augmentations may have applications in image processing outside of microscopy.
]]></description>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Novikov, E.</dc:creator>
<dc:creator>Jang, W.-D.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Cicconet, M.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Jacobson, C. A.</dc:creator>
<dc:creator>Wei, D.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Pfister, H.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2021-04-04</dc:date>
<dc:identifier>doi:10.1101/2021.04.02.438285</dc:identifier>
<dc:title><![CDATA[UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.06.16.448585v1?rss=1">
<title>
<![CDATA[
The Human Tumor Atlas Network (HTAN) Breast PreCancer Atlas: A multi-omic integrative analysis of ductal carcinoma in situ with clinical outcomes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.06.16.448585v1?rss=1"
</link>
<description><![CDATA[
Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We identified 812 genes associated with ipsilateral recurrence within 5 years from treatment and developed a classifier that was predictive of DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions were identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome.

HIGHLIGHTS Development of a new classifier for DCIS recurrence or progression
 Outcome associated pathways identified across multiple data types and compartments
 Four stroma-specific signatures identified
 CNAs characterize DCIS subgroups associated with high risk invasive cancers
]]></description>
<dc:creator>Strand, S. H.</dc:creator>
<dc:creator>Rivero-Gutierrez, B.</dc:creator>
<dc:creator>Houlahan, K. E.</dc:creator>
<dc:creator>Seoane, J. A.</dc:creator>
<dc:creator>King, L.</dc:creator>
<dc:creator>Risom, T.</dc:creator>
<dc:creator>Simpson, L. A.</dc:creator>
<dc:creator>Vennam, S.</dc:creator>
<dc:creator>Khan, A.</dc:creator>
<dc:creator>Cisneros, L.</dc:creator>
<dc:creator>Hardman, T.</dc:creator>
<dc:creator>Harmon, B.</dc:creator>
<dc:creator>Couch, F.</dc:creator>
<dc:creator>Gallagher, K.</dc:creator>
<dc:creator>Kilgore, M.</dc:creator>
<dc:creator>Rocque, G. B.</dc:creator>
<dc:creator>DeMichele, A.</dc:creator>
<dc:creator>King, T.</dc:creator>
<dc:creator>McAuliffe, P.</dc:creator>
<dc:creator>Nangia, J.</dc:creator>
<dc:creator>Lee, J.</dc:creator>
<dc:creator>Tseng, J.</dc:creator>
<dc:creator>Storniolo, A. M.</dc:creator>
<dc:creator>Thompson, A.</dc:creator>
<dc:creator>Gupta, G.</dc:creator>
<dc:creator>Burns, R.</dc:creator>
<dc:creator>Veis, D. J.</dc:creator>
<dc:creator>DeSchryver, K.</dc:creator>
<dc:creator>Zhu, C.</dc:creator>
<dc:creator>Matusiak, M.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Zhu, S. X.</dc:creator>
<dc:creator>Tappenden, J.</dc:creator>
<dc:creator>Ding, D. Y.</dc:creator>
<dc:creator>Zhang, D.</dc:creator>
<dc:creator>Luo, J.</dc:creator>
<dc:creator>Jiang, S.</dc:creator>
<dc:creator>Varma, S.</dc:creator>
<dc:creator>Anderson, L.</dc:creator>
<dc:creator>Straub, C.</dc:creator>
<dc:creator>Srivastava, S.</dc:creator>
<dc:creator>Curtis, C.</dc:creator>
<dc:creator>Tibshirani, R.</dc:creator>
<dc:creator>Angelo, R. M.</dc:creator>
<dc:creator>Hall, A.</dc:creator>
<dc:creator>Owzar, K.</dc:creator>
<dc:creator>Pol</dc:creator>
<dc:date>2021-06-16</dc:date>
<dc:identifier>doi:10.1101/2021.06.16.448585</dc:identifier>
<dc:title><![CDATA[The Human Tumor Atlas Network (HTAN) Breast PreCancer Atlas: A multi-omic integrative analysis of ductal carcinoma in situ with clinical outcomes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-06-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.08.25.505257v1?rss=1">
<title>
<![CDATA[
SnFFPE-Seq: towards scalable single nucleus RNA-Seq of formalin-fixed paraffin-embedded (FFPE) tissue 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.08.25.505257v1?rss=1"
</link>
<description><![CDATA[
Profiling cellular heterogeneity in formalin-fixed paraffin-embedded (FFPE) tissues is key to characterizing clinical specimens for biomarkers, therapeutic targets, and drug responses. Here, we optimize methods for isolating intact nuclei and single nucleus RNA-Seq from FFPE tissues in the mouse brain, and demonstrate a pilot application to a human clinical specimen of lung adenocarcinoma. Our method opens the way to broad applications of snRNA-Seq to archival tissues, including clinical samples.
]]></description>
<dc:creator>Chung, H.</dc:creator>
<dc:creator>Melnikov, A.</dc:creator>
<dc:creator>McCabe, C.</dc:creator>
<dc:creator>Drokhlyansky, E.</dc:creator>
<dc:creator>Van Wittenberghe, N.</dc:creator>
<dc:creator>Magee, E. M.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Spira, A.</dc:creator>
<dc:creator>Chen, F.</dc:creator>
<dc:creator>Mazzilli, S.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2022-08-25</dc:date>
<dc:identifier>doi:10.1101/2022.08.25.505257</dc:identifier>
<dc:title><![CDATA[SnFFPE-Seq: towards scalable single nucleus RNA-Seq of formalin-fixed paraffin-embedded (FFPE) tissue]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-08-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.10.14.512148v1?rss=1">
<title>
<![CDATA[
Integrated Molecular Characterization of Intraductal Papillary Mucinous Neoplasms: An NCI Cancer Moonshot Precancer Atlas Pilot Project 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.10.14.512148v1?rss=1"
</link>
<description><![CDATA[
Intraductal papillary mucinous neoplasms (IPMNs) are cystic precursor lesions to pancreatic ductal adenocarcinoma (PDAC). IPMNs undergo multistep progression from low grade (LG) to high grade (HG) dysplasia, culminating in invasive neoplasia. While patterns of IPMN progression have been analyzed using multi-region sequencing for somatic mutations, there is no integrated assessment of molecular events, including copy number alterations (CNAs) and transcriptomics changes, that accompany IPMN progression. We performed laser capture microdissection on surgically resected IPMNs of varying grades of histological dysplasia obtained from 24 patients (total of 74 independent histological lesions), followed by whole exome and whole transcriptome sequencing. Overall, HG IPMNs displayed a significantly greater aneuploidy score than LG lesions, with chromosome 1q amplification, in particular, being associated with HG progression and with cases that harbored cooccurring PDAC. Furthermore, the combined assessment of single nucleotide variants (SNVs) and CNAs identified both linear and branched evolutionary trajectories, underscoring the heterogeneity in the progression of LG lesions to HG and PDAC. At the transcriptome level, upregulation of MYC-regulated targets and downregulation of transcripts associated with the MHC class I antigen presentation machinery was a common feature of progression to HG. Taken together, this work emphasizes the role of 1q copy number amplification as a putative biomarker of high-risk IPMNs, underscores the importance of immune evasion even in non-invasive precursor lesions, and supports a previously underappreciated role of CNA-driven branching evolution as an avenue for IPMN progression. Our study provides important molecular context for risk stratification and cancer interception opportunities in IPMNs.

SignificanceIntegrated molecular analysis of genomic and transcriptomic alterations in the multistep progression of intraductal papillary mucinous neoplasms (IPMNs), which are bona fide precursors of pancreatic cancer, identifies features associated with progression of low-risk lesions to high-risk lesions and cancer, which might enable patient stratification and cancer interception strategies.
]]></description>
<dc:creator>Semaan, A.</dc:creator>
<dc:creator>Bernard, V.</dc:creator>
<dc:creator>Wong, J.</dc:creator>
<dc:creator>Makino, Y.</dc:creator>
<dc:creator>Swartzlander, D.</dc:creator>
<dc:creator>Rajapakshe, K. I.</dc:creator>
<dc:creator>Lee, J.</dc:creator>
<dc:creator>Officer, A.</dc:creator>
<dc:creator>Schmidt, C. M.</dc:creator>
<dc:creator>Wu, H. H.</dc:creator>
<dc:creator>Scaife, C. L.</dc:creator>
<dc:creator>Affolter, K. E.</dc:creator>
<dc:creator>Nachmanson, D.</dc:creator>
<dc:creator>Firpo, M. A.</dc:creator>
<dc:creator>Yip-Schneider, M.</dc:creator>
<dc:creator>Lowy, A. M.</dc:creator>
<dc:creator>Harismendy, O.</dc:creator>
<dc:creator>Sen, S.</dc:creator>
<dc:creator>Maitra, A. A.</dc:creator>
<dc:creator>Jakubek, Y. A.</dc:creator>
<dc:creator>Guerrero, P. A.</dc:creator>
<dc:date>2022-10-18</dc:date>
<dc:identifier>doi:10.1101/2022.10.14.512148</dc:identifier>
<dc:title><![CDATA[Integrated Molecular Characterization of Intraductal Papillary Mucinous Neoplasms: An NCI Cancer Moonshot Precancer Atlas Pilot Project]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-10-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2022.11.25.517982v1?rss=1">
<title>
<![CDATA[
SlideCNA: Spatial copy number alteration detection from Slide-seq-like spatial transcriptomics data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2022.11.25.517982v1?rss=1"
</link>
<description><![CDATA[
Solid tumors are spatially heterogeneous in their genetic, molecular and cellular composition, and this variation can be meaningful for diagnosis, prognosis and therapy. Recent spatial profiling studies have mostly charted genetic and RNA variation in tumors separately. To leverage the potential of RNA to identify copy number alterations (CNAs), we developed SlideCNA, a computational tool to extract sparse spatial CNA signals from spatial transcriptomics data, using expression-aware spatial binning. We test SlideCNA on simulated and real Slide-seq data of metastatic breast cancer (MBC) and demonstrate its potential for spatial sub-clone detection.
]]></description>
<dc:creator>Zhang, D. K.</dc:creator>
<dc:creator>Segerstolpe, A.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Murray, E.</dc:creator>
<dc:creator>Cohen, O.</dc:creator>
<dc:creator>Ashenberg, O.</dc:creator>
<dc:creator>Abravanel, D.</dc:creator>
<dc:creator>Jane-Valbuena, J.</dc:creator>
<dc:creator>Mages, S.</dc:creator>
<dc:creator>Lako, A.</dc:creator>
<dc:creator>Helvie, K.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Rodig, S.</dc:creator>
<dc:creator>Chen, F.</dc:creator>
<dc:creator>Wagle, N.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Klughammer, J.</dc:creator>
<dc:date>2022-11-27</dc:date>
<dc:identifier>doi:10.1101/2022.11.25.517982</dc:identifier>
<dc:title><![CDATA[SlideCNA: Spatial copy number alteration detection from Slide-seq-like spatial transcriptomics data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2022-11-27</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.02.23.529809v1?rss=1">
<title>
<![CDATA[
SEG: Segmentation Evaluation in absence of Ground truth labels 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.02.23.529809v1?rss=1"
</link>
<description><![CDATA[
Identifying individual cells or nuclei is often the first step in the analysis of multiplex tissue imaging (MTI) data. Recent efforts to produce plug-and-play, end-to-end MTI analysis tools such as MCMICRO1- though groundbreaking in their usability and extensibility - are often unable to provide users guidance regarding the most appropriate models for their segmentation task among an endless proliferation of novel segmentation methods. Unfortunately, evaluating segmentation results on a users dataset without ground truth labels is either purely subjective or eventually amounts to the task of performing the original, time-intensive annotation. As a consequence, researchers rely on models pre-trained on other large datasets for their unique tasks. Here, we propose a methodological approach for evaluating MTI nuclei segmentation methods in absence of ground truth labels by scoring relatively to a larger ensemble of segmentations. To avoid potential sensitivity to collective bias from the ensemble approach, we refine the ensemble via weighted average across segmentation methods, which we derive from a systematic model ablation study. First, we demonstrate a proof-of-concept and the feasibility of the proposed approach to evaluate segmentation performance in a small dataset with ground truth annotation. To validate the ensemble and demonstrate the importance of our method-specific weighting, we compare the ensembles detection and pixel-level predictions - derived without supervision - with the datas ground truth labels. Second, we apply the methodology to an unlabeled larger tissue microarray (TMA) dataset, which includes a diverse set of breast cancer phenotypes, and provides decision guidelines for the general user to more easily choose the most suitable segmentation methods for their own dataset by systematically evaluating the performance of individual segmentation approaches in the entire dataset.
]]></description>
<dc:creator>Sims, Z.</dc:creator>
<dc:creator>Strgar, L.</dc:creator>
<dc:creator>Thirumalaisamy, D.</dc:creator>
<dc:creator>Heussner, R.</dc:creator>
<dc:creator>Thibault, G.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:date>2023-02-24</dc:date>
<dc:identifier>doi:10.1101/2023.02.23.529809</dc:identifier>
<dc:title><![CDATA[SEG: Segmentation Evaluation in absence of Ground truth labels]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-02-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.09.530832v1?rss=1">
<title>
<![CDATA[
Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.09.530832v1?rss=1"
</link>
<description><![CDATA[
Colorectal cancer exhibits dynamic cellular and genetic heterogeneity during progression from precursor lesions toward malignancy. Leveraging spatial molecular information to construct a phylogeographic map of tumor evolution can reveal individualized growth trajectories with diagnostic and therapeutic potential. Integrative analysis of spatial multi-omic data from 31 colorectal specimens revealed simultaneous microenvironmental and clonal alterations as a function of progression. Copy number variation served to re-stratify microsatellite stable and unstable tumors into chromosomally unstable (CIN+) and hypermutated (HM) classes. Phylogeographical maps classified tumors by their evolutionary dynamics, and clonal regions were placed along a global pseudotemporal progression trajectory. Cell-state discovery from a single-cell cohort revealed recurring epithelial gene signatures and infiltrating immune states in spatial transcriptomics. Charting these states along progression pseudotime, we observed a transition to immune exclusion in CIN+ tumors as characterized by a novel gene expression signature comprised of DDR1, TGFBI, PAK4, and DPEP1. We demonstrated how these genes and their protein products are key regulators of extracellular matrix components, are associated with lower cytotoxic immune infiltration, and show prognostic value in external cohorts. Through high-dimensional data integration, this atlas provides insights into co-evolution of tumors and their microenvironments, serving as a resource for stratification and targeted treatment of CRC.
]]></description>
<dc:creator>Heiser, C. N.</dc:creator>
<dc:creator>Simmons, A. J.</dc:creator>
<dc:creator>Revetta, F.</dc:creator>
<dc:creator>McKinley, E. T.</dc:creator>
<dc:creator>Ramirez-Solano, M. A.</dc:creator>
<dc:creator>Wang, J.</dc:creator>
<dc:creator>Shao, J.</dc:creator>
<dc:creator>Ayers, G. D.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Glass, S. E.</dc:creator>
<dc:creator>Kaur, H.</dc:creator>
<dc:creator>Rolong, A.</dc:creator>
<dc:creator>Chen, B.</dc:creator>
<dc:creator>Vega, P. N.</dc:creator>
<dc:creator>Drewes, J. L.</dc:creator>
<dc:creator>Saleh, N.</dc:creator>
<dc:creator>Vandekar, S.</dc:creator>
<dc:creator>Jones, A. L.</dc:creator>
<dc:creator>Washington, M. K.</dc:creator>
<dc:creator>Roland, J. T.</dc:creator>
<dc:creator>Sears, C. L.</dc:creator>
<dc:creator>Liu, Q.</dc:creator>
<dc:creator>Shrubsole, M. J.</dc:creator>
<dc:creator>Coffey, R. J.</dc:creator>
<dc:creator>Lau, K. S.</dc:creator>
<dc:date>2023-03-12</dc:date>
<dc:identifier>doi:10.1101/2023.03.09.530832</dc:identifier>
<dc:title><![CDATA[Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-12</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.15.532870v1?rss=1">
<title>
<![CDATA[
Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.15.532870v1?rss=1"
</link>
<description><![CDATA[
Cancer represents a broad spectrum of molecularly and morphologically diverse diseases. Individuals with the same clinical diagnosis can have tumors with drastically different molecular profiles and clinical response to treatment. It remains unclear when these differences arise during disease course and why some tumors are addicted to one oncogenic pathway over another. Somatic genomic aberrations occur within the context of an individuals germline genome, which can vary across millions of polymorphic sites. An open question is whether germline differences influence somatic tumor evolution. Interrogating 3,855 breast cancer lesions, spanning pre-invasive to metastatic disease, we demonstrate that germline variants in highly expressed and amplified genes influence somatic evolution by modulating immunoediting at early stages of tumor development. Specifically, we show that the burden of germline-derived epitopes in recurrently amplified genes selects against somatic gene amplification in breast cancer. For example, individuals with a high burden of germline-derived epitopes in ERBB2, encoding human epidermal growth factor receptor 2 (HER2), are significantly less likely to develop HER2-positive breast cancer compared to other subtypes. The same holds true for recurrent amplicons that define four subgroups of ER-positive breast cancers at high risk of distant relapse. High epitope burden in these recurrently amplified regions is associated with decreased likelihood of developing high risk ER-positive cancer. Tumors that overcome such immune-mediated negative selection are more aggressive and demonstrate an "immune cold" phenotype. These data show the germline genome plays a previously unappreciated role in dictating somatic evolution. Exploiting germline-mediated immunoediting may inform the development of biomarkers that refine risk stratification within breast cancer subtypes.
]]></description>
<dc:creator>Houlahan, K. E.</dc:creator>
<dc:creator>Khan, A.</dc:creator>
<dc:creator>Greenwald, N. F.</dc:creator>
<dc:creator>West, R. B.</dc:creator>
<dc:creator>Angelo, M.</dc:creator>
<dc:creator>Curtis, C.</dc:creator>
<dc:date>2023-03-16</dc:date>
<dc:identifier>doi:10.1101/2023.03.15.532870</dc:identifier>
<dc:title><![CDATA[Germline-mediated immunoediting sculpts breast cancer subtypes and metastatic proclivity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-16</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.17.533041v1?rss=1">
<title>
<![CDATA[
Leptomeningeal anti-tumor immunity follows unique signaling principles 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.17.533041v1?rss=1"
</link>
<description><![CDATA[
Metastasis to the cerebrospinal fluid (CSF)-filled leptomeninges, or leptomeningeal metastasis (LM), represents a fatal complication of cancer. Proteomic and transcriptomic analyses of human CSF reveal a substantial inflammatory infiltrate in LM. We find the solute and immune composition of CSF in the setting of LM changes dramatically, with notable enrichment in IFN-{gamma} signaling. To investigate the mechanistic relationships between immune cell signaling and cancer cells within the leptomeninges, we developed syngeneic lung, breast, and melanoma LM mouse models. Here we show that transgenic host mice, lacking IFN-{gamma} or its receptor, fail to control LM growth. Overexpression of Ifng through a targeted AAV system controls cancer cell growth independent of adaptive immunity. Instead, leptomeningeal IFN-{gamma} actively recruits and activates peripheral myeloid cells, generating a diverse spectrum of dendritic cell subsets. These migratory, CCR7+ dendritic cells orchestrate the influx, proliferation, and cytotoxic action of natural killer cells to control cancer cell growth in the leptomeninges. This work uncovers leptomeningeal-specific IFN-{gamma} signaling and suggests a novel immune-therapeutic approach against tumors within this space.
]]></description>
<dc:creator>Remsik, J.</dc:creator>
<dc:creator>Tong, X.</dc:creator>
<dc:creator>Kunes, R. Z.</dc:creator>
<dc:creator>Li, M. J.</dc:creator>
<dc:creator>Osman, A.</dc:creator>
<dc:creator>Chabot, K.</dc:creator>
<dc:creator>Sener, U.</dc:creator>
<dc:creator>Wilcox, J. A.</dc:creator>
<dc:creator>Isakov, D.</dc:creator>
<dc:creator>Snyder, J.</dc:creator>
<dc:creator>Bale, T.</dc:creator>
<dc:creator>Chaligne, R.</dc:creator>
<dc:creator>Pe'er, D. D.</dc:creator>
<dc:creator>Boire, A.</dc:creator>
<dc:date>2023-03-20</dc:date>
<dc:identifier>doi:10.1101/2023.03.17.533041</dc:identifier>
<dc:title><![CDATA[Leptomeningeal anti-tumor immunity follows unique signaling principles]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.21.533680v1?rss=1">
<title>
<![CDATA[
Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF) 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.21.533680v1?rss=1"
</link>
<description><![CDATA[
Tissue biology involves an intricate balance between cell-intrinsic processes and interactions between cells organized in specific spatial patterns, which can be respectively captured by single cell profiling methods, such as single cell RNA-seq (scRNA-seq) and spatial transcriptomics, and histology imaging data, such as Hematoxylin-and-Eosin (H&E) stains. While single cell profiles provide rich molecular information, they can be challenging to collect routinely in the clinic and either lack spatial resolution or high gene throughput. Conversely, histological H&E assays have been a cornerstone of tissue pathology for decades, but do not directly report on molecular details, although the observed structure they capture arises from molecules and cells. Here, we leverage vision transformers and adversarial deep learning to develop the Single Cell omics from Histology Analysis Framework (SCHAF), which generates a tissue samples spatially-resolved whole transcriptome single cell omics dataset from its H&E histology image. We demonstrate SCHAF on a variety of tissues-- including lung cancer, metastatic breast cancer, placentae, and whole mouse pups--training with matched samples analyzed by sc/snRNA-seq, H&E staining, and, when available, spatial transcriptomics. SCHAF generated appropriate single cell profiles from histology images in test data, related them spatially, and compared well to ground-truth scRNA-Seq, expert pathologist annotations, or direct spatial transcriptomic measurements, with some limitations. SCHAF opens the way to next-generation H&E analyses and an integrated understanding of cell and tissue biology in health and disease.
]]></description>
<dc:creator>Comiter, C.</dc:creator>
<dc:creator>Vaishnav, E. D.</dc:creator>
<dc:creator>Ciapmricotti, M.</dc:creator>
<dc:creator>Li, B.</dc:creator>
<dc:creator>Yang, Y.</dc:creator>
<dc:creator>Rodig, S. J.</dc:creator>
<dc:creator>Turner, M.</dc:creator>
<dc:creator>Pfaff, K. L.</dc:creator>
<dc:creator>Jane-Valbuena, J.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Vigneau, S.</dc:creator>
<dc:creator>Wu, J.</dc:creator>
<dc:creator>Blosser, T. R.</dc:creator>
<dc:creator>Segerstolpe, A.</dc:creator>
<dc:creator>Abravanel, D.</dc:creator>
<dc:creator>Wagle, N.</dc:creator>
<dc:creator>Zhuang, X.</dc:creator>
<dc:creator>Rudin, C. M.</dc:creator>
<dc:creator>Klughammer, J.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Kobayash-Kirschvink, K. J.</dc:creator>
<dc:creator>Shu, J.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:date>2023-03-23</dc:date>
<dc:identifier>doi:10.1101/2023.03.21.533680</dc:identifier>
<dc:title><![CDATA[Inference of single cell profiles from histology stains with the Single-Cell omics from Histology Analysis Framework (SCHAF)]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-23</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.02.15.528365v1?rss=1">
<title>
<![CDATA[
CRACD, a gatekeeper restricting proliferation, heterogeneity, and immune evasion of small cell lung cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.02.15.528365v1?rss=1"
</link>
<description><![CDATA[
Small cell lung cancer (SCLC) is aggressive with limited therapeutic options. Despite recent advances in targeted therapies and immunotherapies, therapy resistance is a recurring issue, which might be partly due to tumor cell plasticity, a change in cell fate. Nonetheless, the mechanisms underlying tumor cell plasticity and immune evasion in SCLC remain elusive. CRACD, a capping protein inhibitor that promotes actin polymerization, is frequently inactivated in SCLC. Cracd knockout (KO) transforms preneoplastic cells into SCLC tumor-like cells and promotes in vivo SCLC development driven by Rb1, Trp53, and Rbl2 triple KO. Cracd KO induces neuroendocrine (NE) plasticity and increases tumor cell heterogeneity of SCLC tumor cells via dysregulated NOTCH1 signaling by actin cytoskeleton disruption. CRACD depletion also reduces nuclear actin and induces EZH2-mediated H3K27 methylation. This nuclear event suppresses the MHC-I genes and thereby depletes intratumoral CD8+ T cells for accelerated SCLC tumorigenesis. Pharmacological blockade of EZH2 inhibits CRACD-negative SCLC tumorigenesis by restoring MHC-I expression and immune surveillance. Unsupervised single-cell transcriptomics identifies SCLC patient tumors with concomitant inactivation of CRACD and downregulated MHC-I pathway. This study defines CRACD, an actin regulator, as a tumor suppressor that limits cell plasticity and immune evasion and proposes EZH2 blockade as a viable therapeutic option for CRACD-negative SCLC.
]]></description>
<dc:creator>Zhang, S.</dc:creator>
<dc:creator>Kim, K.-B.</dc:creator>
<dc:creator>Huang, Y.</dc:creator>
<dc:creator>Kim, D.-W.</dc:creator>
<dc:creator>Kim, B.</dc:creator>
<dc:creator>Ko, K.-P.</dc:creator>
<dc:creator>Zou, G.</dc:creator>
<dc:creator>Zhang, J.</dc:creator>
<dc:creator>Jun, S.</dc:creator>
<dc:creator>Kirk, N. A.</dc:creator>
<dc:creator>Hwang, Y. E.</dc:creator>
<dc:creator>Ban, Y. H.</dc:creator>
<dc:creator>Chan, J. M.</dc:creator>
<dc:creator>Rudin, C. M.</dc:creator>
<dc:creator>PARK, K.-S.</dc:creator>
<dc:creator>Park, J.-I.</dc:creator>
<dc:date>2023-02-15</dc:date>
<dc:identifier>doi:10.1101/2023.02.15.528365</dc:identifier>
<dc:title><![CDATA[CRACD, a gatekeeper restricting proliferation, heterogeneity, and immune evasion of small cell lung cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-02-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.07.21.548450v1?rss=1">
<title>
<![CDATA[
Addressing persistent challenges in digital image analysis of cancerous tissues 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.07.21.548450v1?rss=1"
</link>
<description><![CDATA[
The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.
]]></description>
<dc:creator>Prabhakaran, S.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Baker, G. J.</dc:creator>
<dc:creator>Beyer, J.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:creator>Creason, A. L.</dc:creator>
<dc:creator>Krueger, R.</dc:creator>
<dc:creator>Muhlich, J.</dc:creator>
<dc:creator>Patterson, N. H.</dc:creator>
<dc:creator>Sidak, K.</dc:creator>
<dc:creator>Sudar, D.</dc:creator>
<dc:creator>Taylor, A. J.</dc:creator>
<dc:creator>Ternes, L.</dc:creator>
<dc:creator>Troidl, J.</dc:creator>
<dc:creator>Yubin, X.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Tyson, D. R.</dc:creator>
<dc:creator>Participants of the Cell Imaging Hackathon 2022,</dc:creator>
<dc:date>2023-07-24</dc:date>
<dc:identifier>doi:10.1101/2023.07.21.548450</dc:identifier>
<dc:title><![CDATA[Addressing persistent challenges in digital image analysis of cancerous tissues]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-07-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.07.31.551378v1?rss=1">
<title>
<![CDATA[
The relationship between diet, plasma glucose, and cancer prevalence across vertebrates 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.07.31.551378v1?rss=1"
</link>
<description><![CDATA[
Could diet and mean plasma glucose concentration (MPGluC) explain the variation in cancer prevalence across species? We collected diet, MPGluC, and neoplasia data for 160 vertebrate species from existing databases. We found that MPGluC negatively correlates with cancer and neoplasia prevalence, mostly of gastrointestinal organs. Trophic level positively correlates with cancer and neoplasia prevalence even after controlling for species MPGluC. Most species with high MPGluC (50/78 species = 64.1%) were birds. Most species in high trophic levels (42/53 species = 79.2%) were reptiles and mammals. Our results may be explained by the evolution of insulin resistance in birds which selected for loss or downregulation of genes related to insulin-mediated glucose import in cells. This led to higher MPGluC, intracellular caloric restriction, production of fewer reactive oxygen species and inflammatory cytokines, and longer telomeres contributing to longer longevity and lower neoplasia prevalence in extant birds relative to other vertebrates.
]]></description>
<dc:creator>Kapsetaki, S. E.</dc:creator>
<dc:creator>Basile, A. J.</dc:creator>
<dc:creator>Compton, Z. T.</dc:creator>
<dc:creator>Rupp, S. M.</dc:creator>
<dc:creator>Duke, E. G.</dc:creator>
<dc:creator>Boddy, A. M.</dc:creator>
<dc:creator>Harrison, T. M.</dc:creator>
<dc:creator>Sweazea, K. L.</dc:creator>
<dc:creator>Maley, C. C.</dc:creator>
<dc:date>2023-08-02</dc:date>
<dc:identifier>doi:10.1101/2023.07.31.551378</dc:identifier>
<dc:title><![CDATA[The relationship between diet, plasma glucose, and cancer prevalence across vertebrates]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.05.10.540265v1?rss=1">
<title>
<![CDATA[
A Masked Image Modeling Approach to Cyclic Immunofluorescence (CyCIF) Panel Reduction and Marker Imputation 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.05.10.540265v1?rss=1"
</link>
<description><![CDATA[
CyCIF quantifies multiple biomarkers, but panel capacity is compromised by technical challenges including tissue loss. We propose a computational panel reduction, inferring surrogate CyCIF data from a subset of biomarkers. Our model reconstructs the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer tissue microarrays, illustrating broader applicability to diverse tissue types.
]]></description>
<dc:creator>Sims, Z.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:date>2023-05-13</dc:date>
<dc:identifier>doi:10.1101/2023.05.10.540265</dc:identifier>
<dc:title><![CDATA[A Masked Image Modeling Approach to Cyclic Immunofluorescence (CyCIF) Panel Reduction and Marker Imputation]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-05-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.05.05.539614v1?rss=1">
<title>
<![CDATA[
Signal recovery in single cell batch integration 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.05.05.539614v1?rss=1"
</link>
<description><![CDATA[
Data integration to align cells across batches has become a cornerstone of single cell data analysis, critically affecting downstream results. Yet, how much biological signal is erased during integration? Currently, there are no guidelines for when the biological differences between samples are separable from batch effects, and thus, data integration usually involve a lot of guesswork: Cells across batches should be aligned to be "appropriately" mixed, while preserving "main cell type clusters". We show evidence that current paradigms for single cell data integration are unnecessarily aggressive, removing biologically meaningful variation. To remedy this, we present a novel statistical model and computationally scalable algorithm, CellANOVA, to recover biological signal that is lost during single cell data integration. CellANOVA utilizes a "pool-of-controls" design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest. When applied with existing integration methods, CellANOVA allows the recovery of subtle biological signals and corrects, to a large extent, the data distortion introduced by integration. Further, CellANOVA explicitly estimates cell- and gene-specific batch effect terms which can be used to identify the cell types and pathways exhibiting the largest batch variations, providing clarity as to which biological signals can be recovered. These concepts are illustrated on studies of diverse designs, where the biological signals that are recovered by CellANOVA are shown to be validated by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nuclei data integration, where the recovered biological signals are replicated in an independent study.
]]></description>
<dc:creator>Zhang, Z.</dc:creator>
<dc:creator>Mathew, D.</dc:creator>
<dc:creator>Lim, T.</dc:creator>
<dc:creator>Huang, S.</dc:creator>
<dc:creator>Wherry, E. J.</dc:creator>
<dc:creator>Minn, A. J.</dc:creator>
<dc:creator>Ma, Z.</dc:creator>
<dc:creator>Zhang, N. R.</dc:creator>
<dc:date>2023-05-08</dc:date>
<dc:identifier>doi:10.1101/2023.05.05.539614</dc:identifier>
<dc:title><![CDATA[Signal recovery in single cell batch integration]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-05-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.18.553854v1?rss=1">
<title>
<![CDATA[
Multiplex imaging of localized prostate tumors reveals changes in mast cell type composition and spatial organization of AR-positive cells in the tumor microenvironment 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.18.553854v1?rss=1"
</link>
<description><![CDATA[
Mapping spatial interactions of cancer, immune and stromal cells present novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate tumors, there is currently no understanding of how immune cell heterogeneity impacts spatial coordination between tumor and stromal cells in localized tumors. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of 699,461 single-cells that show epigenetic and molecular differences in distinct clinical grades. We report unique populations of mast cells that differentially express CD44, CD90 and Granzyme B (GZMB) and demonstrate GZMB+ mast cells are spatially associated with M2 macrophages in prostate tumors. Finally, we uncover recurrent neighborhoods that are primarily driven by androgen receptor positive (AR+) stromal cells and identify transcriptional networks active in AR+ prostate stroma.
]]></description>
<dc:creator>Eksi, S. E.</dc:creator>
<dc:creator>Ak, C.</dc:creator>
<dc:creator>Sayar, Z.</dc:creator>
<dc:creator>Thibault, G.</dc:creator>
<dc:creator>Burlingame, E.</dc:creator>
<dc:creator>Eng, J.</dc:creator>
<dc:creator>Chitsazan, A.</dc:creator>
<dc:creator>Adey, A.</dc:creator>
<dc:creator>Boniface, C.</dc:creator>
<dc:creator>Spellman, P. T.</dc:creator>
<dc:creator>Thomas, G. V.</dc:creator>
<dc:creator>Kopp, R.</dc:creator>
<dc:creator>Demir, E.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:creator>Stavrinides, V.</dc:creator>
<dc:date>2023-08-21</dc:date>
<dc:identifier>doi:10.1101/2023.08.18.553854</dc:identifier>
<dc:title><![CDATA[Multiplex imaging of localized prostate tumors reveals changes in mast cell type composition and spatial organization of AR-positive cells in the tumor microenvironment]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-21</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.08.24.554733v1?rss=1">
<title>
<![CDATA[
Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.08.24.554733v1?rss=1"
</link>
<description><![CDATA[
Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population identified in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of patient peripheral blood mononuclear cells (PBMCs) is a time-consuming and subjective process that currently relies on manual annotation by laboratory technicians. Additionally, while IF is relatively easy to apply to tissue sections, its application on PBMC smears presents challenges due to the presence of biological and technical artifacts. To address these challenges, we present a robust image analysis pipeline to automate the detection and analyses of CHCs in IF images. The pipeline incorporates quality control to optimize specimen preparation protocols and remove unwanted artifacts, leverages a {beta}-variational autoencoder (VAE) to learn meaningful latent representations of single-cell images and employs a support vector machine (SVM) classifier to achieve human-level CHC detection. We created a rigorously labeled IF CHC dataset including 9 patients and 2 disease sites with the assistance of 10 annotators to evaluate the pipeline. We examined annotator variation and bias in CHC detection and then provided guidelines to optimize the accuracy of CHC annotation. We found that all annotators agreed on CHC identification for only 65% of the cells in the dataset and had a tendency to underestimate CHC counts for regions of interest (ROI) containing relatively large amounts of cells (>50,000) when using conventional enumeration methods. On the other hand, our proposed approach is unbiased to ROI size. The SVM classifier trained on the {beta}-VAE encodings achieved an F1 score of 0.80, matching the average performance of annotators. Our pipeline enables researchers to explore the role of CHCs in cancer progression and assess their potential as a clinical biomarker for metastasis. Further, we demonstrate that the pipeline can identify discrete cellular phenotypes among PBMCs, highlighting its utility beyond CHCs.
]]></description>
<dc:creator>Anderson, A.</dc:creator>
<dc:creator>Theison, H.</dc:creator>
<dc:creator>Baik, J.</dc:creator>
<dc:creator>Gibbs, S.</dc:creator>
<dc:creator>Wong, M. H.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:date>2023-08-26</dc:date>
<dc:identifier>doi:10.1101/2023.08.24.554733</dc:identifier>
<dc:title><![CDATA[Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-08-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.27.557464v1?rss=1">
<title>
<![CDATA[
Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.27.557464v1?rss=1"
</link>
<description><![CDATA[
CDK4/6 inhibitors (CDK4/6i) have transformed the treatment of hormone receptor-positive (HR+), HER2-negative (HR+) breast cancers as they are effective across all clinicopathological, age, and ethnicity subgroups for metastatic HR+ breast cancer. In metastatic ER+ breast cancer, CDK4/6i lead to strong and consistent improvement in survival across different lines of therapy. To improve understanding of how metastatic HR+ breast cancers become refractory to CDK4/6i, we have created a multimodal and longitudinal tumor atlas to investigate therapeutic adaptations in malignant cells and in the tumor immune microenvironment. This atlas is part of the NCI Cancer Moonshot Human Tumor Atlas Network and includes seven pairs of pre- and on-progression biopsies from five metastatic HR+ breast cancer patients treated with CDK4/6i. Biopsies were profiled with bulk genomics, transcriptomics, and proteomics as well as single-cell ATAC-seq and multiplex tissue imaging for spatial, single-cell resolution. These molecular datasets were then linked with detailed clinical metadata to create an atlas for understanding tumor adaptations during therapy. Analysis of our atlas datasets suggests a diverse set of tumor adaptations to CDK4/6i therapy. Malignant cells may adapt to therapy via mTORC1 activation, cell cycle bypass, and increased replication stress. The tumor immune microenvironment displayed evidence of both immune activation and immune suppression during therapy. Together, our metastatic ER+ breast cancer atlas represents a rich multimodal resource to better understand HR+ breast cancer tumor therapeutic adaptations to CDK4/6i therapy.
]]></description>
<dc:creator>Creason, A. L.</dc:creator>
<dc:creator>Egger, J.</dc:creator>
<dc:creator>Watson, C.</dc:creator>
<dc:creator>Sivagnanam, S.</dc:creator>
<dc:creator>Chin, K.</dc:creator>
<dc:creator>MacPherson, K.</dc:creator>
<dc:creator>Lin, J.-R.</dc:creator>
<dc:creator>Chen, Y.-A.</dc:creator>
<dc:creator>Johnson, B. E.</dc:creator>
<dc:creator>Feiler, H. S.</dc:creator>
<dc:creator>Galipeau, D.</dc:creator>
<dc:creator>Navin, N. E.</dc:creator>
<dc:creator>Demir, E.</dc:creator>
<dc:creator>Chang, Y. H.</dc:creator>
<dc:creator>Corless, C. L.</dc:creator>
<dc:creator>Mitri, Z. I.</dc:creator>
<dc:creator>Thomas, G.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Adey, A. C.</dc:creator>
<dc:creator>Coussens, L. M.</dc:creator>
<dc:creator>Gray, J. W.</dc:creator>
<dc:creator>Mills, G. B.</dc:creator>
<dc:creator>Goecks, J.</dc:creator>
<dc:date>2023-09-29</dc:date>
<dc:identifier>doi:10.1101/2023.09.27.557464</dc:identifier>
<dc:title><![CDATA[Longitudinal and multimodal auditing of tumor adaptation to CDK4/6 inhibitors in HR+ metastatic breast cancers]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-09-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.09.28.560000v1?rss=1">
<title>
<![CDATA[
Analysis of ductal carcinoma in situ by self-reported race reveals molecular differences related to outcome. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.09.28.560000v1?rss=1"
</link>
<description><![CDATA[
Ductal carcinoma in situ (DCIS) is a non-obligate precursor to invasive breast cancer (IBC). Studies have indicated differences in DCIS outcome based on race or ethnicity, but molecular differences have not been investigated. We examined the molecular profile of DCIS by self-reported race (SRR) and outcome groups in Black (n=99) and White (n=191) women with DCIS in a large DCIS case-control cohort with longitudinal follow up. Gene expression and pathway analyses indicated that different genes and pathways are involved in ipsilateral breast outcome (DCIS or IBC) after DCIS treatment in White versus Black women. We identified differences in ER and HER2 expression, tumor microenvironment composition, and copy number variations by SRR and outcome groups. Our results suggest that different molecular mechanisms drive subsequent ipsilateral breast events in Black versus White women.
]]></description>
<dc:creator>Strand, S. H.</dc:creator>
<dc:creator>Houlahan, K. E.</dc:creator>
<dc:creator>Branch, V.</dc:creator>
<dc:creator>Lynch, T.</dc:creator>
<dc:creator>Harmon, B.</dc:creator>
<dc:creator>Couch, F.</dc:creator>
<dc:creator>Gallagher, K.</dc:creator>
<dc:creator>Kilgore, M.</dc:creator>
<dc:creator>Wei, S.</dc:creator>
<dc:creator>DeMichele, A.</dc:creator>
<dc:creator>King, T.</dc:creator>
<dc:creator>McAuliffe, P.</dc:creator>
<dc:creator>Curtis, C.</dc:creator>
<dc:creator>Owzar, K.</dc:creator>
<dc:creator>Marks, J. R.</dc:creator>
<dc:creator>Colditz, G. A.</dc:creator>
<dc:creator>Hwang, E. S.</dc:creator>
<dc:creator>West, R. B.</dc:creator>
<dc:date>2023-10-02</dc:date>
<dc:identifier>doi:10.1101/2023.09.28.560000</dc:identifier>
<dc:title><![CDATA[Analysis of ductal carcinoma in situ by self-reported race reveals molecular differences related to outcome.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.03.06.531314v1?rss=1">
<title>
<![CDATA[
Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.03.06.531314v1?rss=1"
</link>
<description><![CDATA[
A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of featureobservation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple listlike schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.
]]></description>
<dc:creator>Wang, Y.</dc:creator>
<dc:creator>Sarfraz, I.</dc:creator>
<dc:creator>Teh, W. K.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:creator>Herb, B. R.</dc:creator>
<dc:creator>Creasy, H. H.</dc:creator>
<dc:creator>Virshup, I.</dc:creator>
<dc:creator>Dries, R.</dc:creator>
<dc:creator>Degatano, K.</dc:creator>
<dc:creator>Mahurkar, A.</dc:creator>
<dc:creator>Schnell, D. J.</dc:creator>
<dc:creator>Madrigal, P.</dc:creator>
<dc:creator>Hilton, J.</dc:creator>
<dc:creator>Gehlenborg, N.</dc:creator>
<dc:creator>Tickle, T.</dc:creator>
<dc:creator>Campbell, J. D.</dc:creator>
<dc:date>2023-03-07</dc:date>
<dc:identifier>doi:10.1101/2023.03.06.531314</dc:identifier>
<dc:title><![CDATA[Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-03-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.12.18.572260v1?rss=1">
<title>
<![CDATA[
Temporal recording of mammalian development and precancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.12.18.572260v1?rss=1"
</link>
<description><![CDATA[
Key to understanding many biological phenomena is knowing the temporal ordering of cellular events, which often require continuous direct observations [1, 2]. An alternative solution involves the utilization of irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8]. Using NSC-seq, a newly designed and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo, while incorporating assigned cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development and identified new intestinal epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas and single-cell multi-omic profiling of human precancers as part of the Human Tumor Atlas Network (HTAN), including 116 scRNA-seq datasets and clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Thus, our multimodal framework augments existing single-cell analyses and lays the foundation for in vivo multimodal recording, enabling the tracking of lineage and temporal events during development and tumorigenesis.
]]></description>
<dc:creator>Islam, M.</dc:creator>
<dc:creator>Yang, Y.</dc:creator>
<dc:creator>Simmons, A. J.</dc:creator>
<dc:creator>Shah, V. M.</dc:creator>
<dc:creator>Pavan, M. K.</dc:creator>
<dc:creator>Xu, Y.</dc:creator>
<dc:creator>Tasneem, N.</dc:creator>
<dc:creator>Chen, Z.</dc:creator>
<dc:creator>Trinh, L. T.</dc:creator>
<dc:creator>Molina, P.</dc:creator>
<dc:creator>Ramirez-Solano, M. A.</dc:creator>
<dc:creator>Sadien, I.</dc:creator>
<dc:creator>Dou, J.</dc:creator>
<dc:creator>Chen, K.</dc:creator>
<dc:creator>Magnuson, M. A.</dc:creator>
<dc:creator>Rathmell, J.</dc:creator>
<dc:creator>Macara, I. G.</dc:creator>
<dc:creator>Winton, D. J.</dc:creator>
<dc:creator>Liu, Q.</dc:creator>
<dc:creator>Zafar, H.</dc:creator>
<dc:creator>Kalhor, R.</dc:creator>
<dc:creator>Church, G. M.</dc:creator>
<dc:creator>Shrubsole, M. J.</dc:creator>
<dc:creator>Coffey, R. J.</dc:creator>
<dc:creator>Lau, K.</dc:creator>
<dc:date>2023-12-19</dc:date>
<dc:identifier>doi:10.1101/2023.12.18.572260</dc:identifier>
<dc:title><![CDATA[Temporal recording of mammalian development and precancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-12-19</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.10.566670v1?rss=1">
<title>
<![CDATA[
Multiplexed 3D Analysis of Cell Plasticity and Immune Niches in Melanoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.10.566670v1?rss=1"
</link>
<description><![CDATA[
Diseases like cancer involve alterations in in cell proportions, states, and local interactions as well as complex changes in 3D tissue architecture. However, disease diagnosis and most multiplexed spatial profiling studies rely on inspecting thin (4-5 micron) tissue specimens. Here, we use confocal microscopy and cyclic immunofluorescence (3D CyCIF) to show that few if any cells are intact in these thin sections; this reduces the accuracy of cell phenotyping and interaction analysis. In contrast, high-plex 3D CyCIF imaging of intact cells in thick tissue sections enables accurate quantification of marker proteins and detailed analysis of intracellular structures and organelles. Precise imaging of cell membranes also makes it possible to detect juxtacrine signalling among interacting tumour and immune cells and reveals the formation of spatially-restricted cytokine niches. Thus, 3D CyCIF provides insights into cell states and morphologies in preserved human tissues at a level of detail previously limited to cultured cells.
]]></description>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Nirmal, A. J.</dc:creator>
<dc:creator>Zhou, F. Y.</dc:creator>
<dc:creator>Maliga, Z.</dc:creator>
<dc:creator>Montero Llopis, P.</dc:creator>
<dc:creator>Murphy, G. F.</dc:creator>
<dc:creator>Lian, C.</dc:creator>
<dc:creator>Danuser, G.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:date>2023-11-15</dc:date>
<dc:identifier>doi:10.1101/2023.11.10.566670</dc:identifier>
<dc:title><![CDATA[Multiplexed 3D Analysis of Cell Plasticity and Immune Niches in Melanoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-15</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.11.07.566063v1?rss=1">
<title>
<![CDATA[
2D and 3D multiplexed subcellular profiling of nuclear instability in human cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.11.07.566063v1?rss=1"
</link>
<description><![CDATA[
Nuclear atypia, including altered nuclear size, contour, and chromatin organization, is ubiquitous in cancer cells. Atypical primary nuclei and micronuclei can rupture during interphase; however, the frequency, causes, and consequences of nuclear rupture are unknown in most cancers. We demonstrate that nuclear envelope rupture is surprisingly common in many human cancers, particularly glioblastoma. Using highly-multiplexed 2D and super-resolution 3D-imaging of glioblastoma tissues and patient-derived xenografts and cells, we link primary nuclear rupture with reduced lamin A/C and micronuclear rupture with reduced lamin B1. Moreover, ruptured glioblastoma cells activate cGAS-STING-signaling involved in innate immunity. We observe that local patterning of cell states influences tumor spatial organization and is linked to both lamin expression and rupture frequency, with neural-progenitor-cell-like states exhibiting the lowest lamin A/C levels and greatest susceptibility to primary nuclear rupture. Our study reveals that nuclear instability is a core feature of cancer, and links nuclear integrity, cell state, and immune signaling.
]]></description>
<dc:creator>Coy, S.</dc:creator>
<dc:creator>Cheng, B.</dc:creator>
<dc:creator>Lee, J. S.</dc:creator>
<dc:creator>Rashid, R.</dc:creator>
<dc:creator>Browning, L.</dc:creator>
<dc:creator>Xu, Y.</dc:creator>
<dc:creator>Chakrabarty, S. S.</dc:creator>
<dc:creator>Yapp, C.</dc:creator>
<dc:creator>Chan, S.</dc:creator>
<dc:creator>Tefft, J. B.</dc:creator>
<dc:creator>Spektor, A.</dc:creator>
<dc:creator>Ligon, K. L.</dc:creator>
<dc:creator>Baker, G. J.</dc:creator>
<dc:creator>Pellman, D.</dc:creator>
<dc:creator>Sorger, P. K.</dc:creator>
<dc:creator>Santagata, S.</dc:creator>
<dc:date>2023-11-11</dc:date>
<dc:identifier>doi:10.1101/2023.11.07.566063</dc:identifier>
<dc:title><![CDATA[2D and 3D multiplexed subcellular profiling of nuclear instability in human cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.31.565031v1?rss=1">
<title>
<![CDATA[
Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.31.565031v1?rss=1"
</link>
<description><![CDATA[
Breast cancer is a heterogeneous disease, and treatment is guided by biomarker profiles representing distinct molecular subtypes. Breast cancer arises from the breast ductal epithelium, and experimental data suggests breast cancer subtypes have different cells of origin within that lineage. The precise cells of origin for each subtype and the transcriptional networks that characterize these tumor-normal lineages are not established. In this work, we applied bulk, single-cell (sc), and single-nucleus (sn) multi-omic techniques as well as spatial transcriptomics and multiplex imaging on 61 samples from 37 breast cancer patients to show characteristic links in gene expression and chromatin accessibility between breast cancer subtypes and their putative cells of origin. We applied the PAM50 subtyping algorithm in tandem with bulk RNA-seq and snRNA-seq to reliably subtype even low-purity tumor samples and confirm promoter accessibility using snATAC. Trajectory analysis of chromatin accessibility and differentially accessible motifs clearly connected progenitor populations with breast cancer subtypes supporting the cell of origin for basal-like and luminal A and B tumors. Regulatory network analysis of transcription factors underscored the importance of BHLHE40 in luminal breast cancer and luminal mature cells, and KLF5 in basal-like tumors and luminal progenitor cells. Furthermore, we identify key genes defining the basal-like (PRKCA, SOX6, RGS6, KCNQ3) and luminal A/B (FAM155A, LRP1B) lineages, with expression in both precursor and cancer cells and further upregulation in tumors. Exhausted CTLA4-expressing CD8+ T cells were enriched in basal-like breast cancer, suggesting altered means of immune dysfunction among breast cancer subtypes. We used spatial transcriptomics and multiplex imaging to provide spatial detail for key markers of benign and malignant cell types and immune cell colocation. These findings demonstrate analysis of paired transcription and chromatin accessibility at the single cell level is a powerful tool for investigating breast cancer lineage development and highlight transcriptional networks that define basal and luminal breast cancer lineages.
]]></description>
<dc:creator>Iglesia, M. D.</dc:creator>
<dc:creator>Jayasinghe, R. G.</dc:creator>
<dc:creator>Chen, S.</dc:creator>
<dc:creator>Terekhanova, N. V.</dc:creator>
<dc:creator>Herndon, J. M.</dc:creator>
<dc:creator>Storrs, E.</dc:creator>
<dc:creator>Karpova, A.</dc:creator>
<dc:creator>Zhou, D. C.</dc:creator>
<dc:creator>Naser Al Deen, N.</dc:creator>
<dc:creator>Shinkle, A. T.</dc:creator>
<dc:creator>Lu, R. J.-H.</dc:creator>
<dc:creator>Caravan, W.</dc:creator>
<dc:creator>Houston, A.</dc:creator>
<dc:creator>Zhao, Y.</dc:creator>
<dc:creator>Sato, K.</dc:creator>
<dc:creator>Lal, P.</dc:creator>
<dc:creator>Street, C.</dc:creator>
<dc:creator>Rodrigues, F. M.</dc:creator>
<dc:creator>Southard-Smith, A. N.</dc:creator>
<dc:creator>Targino da Costa, A. L. N.</dc:creator>
<dc:creator>Zhu, H.</dc:creator>
<dc:creator>Mo, C.-K.</dc:creator>
<dc:creator>Crowson, L.</dc:creator>
<dc:creator>Fulton, R. S.</dc:creator>
<dc:creator>Wyczalkowski, M. A.</dc:creator>
<dc:creator>Fronick, C. C.</dc:creator>
<dc:creator>Fulton, L. A.</dc:creator>
<dc:creator>Sun, H.</dc:creator>
<dc:creator>Davies, S. R.</dc:creator>
<dc:creator>Appelbaum, E. L.</dc:creator>
<dc:creator>Chasnoff, S. E.</dc:creator>
<dc:creator>Carmody, M.</dc:creator>
<dc:creator>Brooks, C.</dc:creator>
<dc:creator>Liu, R.</dc:creator>
<dc:creator>Wendl, M. C.</dc:creator>
<dc:creator>Oh, C.</dc:creator>
<dc:creator>Bender, D.</dc:creator>
<dc:creator>Cruchaga, C.</dc:creator>
<dc:creator>Harari, O.</dc:creator>
<dc:creator>Bredemeyer, A.</dc:creator>
<dc:creator>Lavine, K.</dc:creator>
<dc:creator>Bose, R.</dc:creator>
<dc:creator>Marge</dc:creator>
<dc:date>2023-11-02</dc:date>
<dc:identifier>doi:10.1101/2023.10.31.565031</dc:identifier>
<dc:title><![CDATA[Differential chromatin accessibility and transcriptional dynamics define breast cancer subtypes and their lineages]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-11-02</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.12.01.569676v1?rss=1">
<title>
<![CDATA[
Tumor microenvironmental determinants of high-risk DCIS progression 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.12.01.569676v1?rss=1"
</link>
<description><![CDATA[
ABSTRACT/SUMMARYDuctal carcinoma in situ (DCIS) constitutes an array of morphologically recognized intraductal neoplasms in the mammary ductal tree defined by an increased risk for subsequent invasive carcinomas at or near the site of biopsy detection. However, only 15-45% of untreated DCIS cases progress to invasive cancer, so understanding mechanisms that prevent progression is key to avoid overtreatment and provides a basis for alternative therapies and prevention. This study was designed to characterize the tumor microenvironment and molecular profile of high-risk DCIS that grew to a large size but remained as DCIS. All patients had DCIS lesions >5cm in size with at least one additional high-risk feature: young age (<45 years), high nuclear grade, hormone receptor negativity, HER2 positivity, the presence of comedonecrosis, or a palpable mass. The tumor immune microenvironment was characterized using multiplex immunofluorescence to identify immune cells and their spatial relationships within the ducts and stroma. Gene copy number analysis and whole exome DNA sequencing identified the mutational burden and driver mutations, and quantitative whole-transcriptome/gene expression analyses were performed. There was no association between the percent of the DCIS genome characterized by copy number variants (CNAs) and recurrence events (DCIS or invasive). Mutations, especially missense mutations, in the breast cancer driver genes PIK3CA and TP53 were common in this high-risk DCIS cohort (47% of evaluated lesions). Tumor infiltrating lymphocyte (TIL) density was higher in DCIS lesions with TP53 mutations (p=0.0079) compared to wildtype lesions, but not in lesions with PIK3CA mutations (p=0.44). Immune infiltrates were negatively associated with hormone receptor status and positively associated with HER2 expression. High levels of CD3+CD8-T cells were associated with good outcomes with respect to any subsequent recurrence (DCIS or invasive cancer), whereas high levels of CD3+Foxp3+ Treg cells were associated with poor outcomes. Spatial proximity analyses of immune cells and tumor cells demonstrated that close proximity of T cells with tumor cells was associated with good outcomes with respect to any recurrence as well as invasive recurrences. Interestingly, we found that myoepithelial continuity (distance between myoepithelial cells surrounding the involved ducts) was significantly lower in DCIS lesions compared to normal tissue (p=0.0002) or to atypical ductal hyperplasia (p=0.011). Gene set enrichment analysis identified several immune pathways associated with low myoepithelial continuity and a low myoepithelial continuity score was associated with better outcomes, suggesting that gaps in the myoepithelial layer may allow access/interactions between immune infiltrates and tumor cells. Our study demonstrates the immune microenvironment of DCIS, in particular the spatial proximity of tumor cells and T cells, and myoepithelial continuity are important determinants for progression of disease.
]]></description>
<dc:creator>Glencer, A.</dc:creator>
<dc:creator>Ramalingam, K.</dc:creator>
<dc:creator>Schindler, N.</dc:creator>
<dc:creator>Mori, H.</dc:creator>
<dc:creator>Ghule, P.</dc:creator>
<dc:creator>Lee, K. C.</dc:creator>
<dc:creator>Nachmanson, D.</dc:creator>
<dc:creator>Officer, A.</dc:creator>
<dc:creator>Harismendy, O.</dc:creator>
<dc:creator>Stein, J. L.</dc:creator>
<dc:creator>Stein, G.</dc:creator>
<dc:creator>Weaver, D.</dc:creator>
<dc:creator>Yau, C.</dc:creator>
<dc:creator>Hirst, G. L.</dc:creator>
<dc:creator>Campbell, M. J.</dc:creator>
<dc:creator>Esserman, L. J.</dc:creator>
<dc:creator>Borowsky, A. D.</dc:creator>
<dc:date>2023-12-04</dc:date>
<dc:identifier>doi:10.1101/2023.12.01.569676</dc:identifier>
<dc:title><![CDATA[Tumor microenvironmental determinants of high-risk DCIS progression]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-12-04</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.03.06.583588v1?rss=1">
<title>
<![CDATA[
A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.03.06.583588v1?rss=1"
</link>
<description><![CDATA[
Pediatric high-grade glioma (pHGG) is an incurable central nervous system malignancy that is a leading cause of pediatric cancer death. While pHGG shares many similarities to adult glioma, it is increasingly recognized as a molecularly distinct, yet highly heterogeneous disease. In this study, we longitudinally profiled a molecularly diverse cohort of 16 pHGG patients before and after standard therapy through single-nucleus RNA and ATAC sequencing, whole-genome sequencing, and CODEX spatial proteomics to capture the evolution of the tumor microenvironment during progression following treatment. We found that the canonical neoplastic cell phenotypes of adult glioblastoma are insufficient to capture the range of tumor cell states in a pediatric cohort and observed differential tumor-myeloid interactions between malignant cell states. We identified key transcriptional regulators of pHGG cell states and did not observe the marked proneural to mesenchymal shift characteristic of adult glioblastoma. We showed that essential neuromodulators and the interferon response are upregulated post-therapy along with an increase in non-neoplastic oligodendrocytes. Through in vitro pharmacological perturbation, we demonstrated novel malignant cell-intrinsic targets. This multiomic atlas of longitudinal pHGG captures the key features of therapy response that support distinction from its adult counterpart and suggests therapeutic strategies which are targeted to pediatric gliomas.
]]></description>
<dc:creator>Sussman, J. H.</dc:creator>
<dc:creator>Oldridge, D. A.</dc:creator>
<dc:creator>Yu, W.</dc:creator>
<dc:creator>Chen, C.-H.</dc:creator>
<dc:creator>Zellmer, A. M.</dc:creator>
<dc:creator>Rong, J.</dc:creator>
<dc:creator>Parvaresh-Rizi, A.</dc:creator>
<dc:creator>Thadi, A.</dc:creator>
<dc:creator>Xu, J.</dc:creator>
<dc:creator>Bandyopadhyay, S.</dc:creator>
<dc:creator>Sun, Y.</dc:creator>
<dc:creator>Wu, D.</dc:creator>
<dc:creator>Hunter, C. E.</dc:creator>
<dc:creator>Brosius, S.</dc:creator>
<dc:creator>Ahn, K. J.</dc:creator>
<dc:creator>Baxter, A. E.</dc:creator>
<dc:creator>Koptyra, M. P.</dc:creator>
<dc:creator>Vanguri, R.</dc:creator>
<dc:creator>McGrory, S.</dc:creator>
<dc:creator>Resnick, A. C.</dc:creator>
<dc:creator>Storm, P. B.</dc:creator>
<dc:creator>Amankulor, N. M.</dc:creator>
<dc:creator>Santi, M.</dc:creator>
<dc:creator>Viaene, A. N.</dc:creator>
<dc:creator>Zhang, N.</dc:creator>
<dc:creator>De Raedt, T.</dc:creator>
<dc:creator>Cole, K.</dc:creator>
<dc:creator>Tan, K.</dc:creator>
<dc:date>2024-03-08</dc:date>
<dc:identifier>doi:10.1101/2024.03.06.583588</dc:identifier>
<dc:title><![CDATA[A longitudinal single-cell and spatial multiomic atlas of pediatric high-grade glioma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-03-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.06.28.546977v1?rss=1">
<title>
<![CDATA[
A cellular and spatial atlas of TP53-associated tissue remodeling in lung adenocarcinoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.06.28.546977v1?rss=1"
</link>
<description><![CDATA[
TP53 is the most frequently mutated gene across many cancers and is associated with shorter survival in lung adenocarcinoma (LUAD). To define how TP53 mutations affect the LUAD tumor microenvironment (TME), we constructed a multi-omic cellular and spatial tumor atlas of 23 treatment-naive human lung tumors. We found that TP53-mutant (TP53mut) malignant cells lose alveolar identity and upregulate highly proliferative and entropic gene expression programs consistently across resectable LUAD patient tumors, genetically engineered mouse models, and cell lines harboring a wide spectrum of TP53 mutations. We further identified a multicellular tumor niche composed of SPP1+ macrophages and collagen-expressing fibroblasts that coincides with hypoxic, pro-metastatic expression programs in TP53mut tumors. Spatially correlated angiostatic and immune checkpoint interactions, including CD274-PDCD1 and PVR-TIGIT, are also enriched in TP53mut LUAD tumors, which may influence response to checkpoint blockade therapy. Our methodology can be further applied to investigate mutation-specific TME changes in other cancers.
]]></description>
<dc:creator>Zhao, W.</dc:creator>
<dc:creator>Kepecs, B.</dc:creator>
<dc:creator>Mahadevan, N. R.</dc:creator>
<dc:creator>Segerstolpe, A.</dc:creator>
<dc:creator>Weirather, J. L.</dc:creator>
<dc:creator>Besson, N. R.</dc:creator>
<dc:creator>Giotti, B.</dc:creator>
<dc:creator>Soong, B. Y.</dc:creator>
<dc:creator>Li, C.</dc:creator>
<dc:creator>Vigneau, S.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Wakiro, I.</dc:creator>
<dc:creator>Jane-Valbuena, J.</dc:creator>
<dc:creator>Ashenberg, O.</dc:creator>
<dc:creator>Rotem, A.</dc:creator>
<dc:creator>Bueno, R.</dc:creator>
<dc:creator>Rozenblatt-Rosen, O.</dc:creator>
<dc:creator>Pfaff, K.</dc:creator>
<dc:creator>Rodig, S.</dc:creator>
<dc:creator>Hata, A. N.</dc:creator>
<dc:creator>Regev, A.</dc:creator>
<dc:creator>Johnson, B. E.</dc:creator>
<dc:creator>Tsankov, A. M.</dc:creator>
<dc:date>2023-06-29</dc:date>
<dc:identifier>doi:10.1101/2023.06.28.546977</dc:identifier>
<dc:title><![CDATA[A cellular and spatial atlas of TP53-associated tissue remodeling in lung adenocarcinoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-06-29</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.01.07.574538v1?rss=1">
<title>
<![CDATA[
A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.01.07.574538v1?rss=1"
</link>
<description><![CDATA[
Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.
]]></description>
<dc:creator>Patel, A. G.</dc:creator>
<dc:creator>Ashenberg, O.</dc:creator>
<dc:creator>Collins, N. B.</dc:creator>
<dc:creator>Segerstolpe, A.</dc:creator>
<dc:creator>Jiang, S.</dc:creator>
<dc:creator>Slyper, M.</dc:creator>
<dc:creator>Huang, X.</dc:creator>
<dc:creator>Caraccio, C.</dc:creator>
<dc:creator>Jin, H.</dc:creator>
<dc:creator>Sheppard, H.</dc:creator>
<dc:creator>Xu, K.</dc:creator>
<dc:creator>Chang, T.-C.</dc:creator>
<dc:creator>Orr, B. A.</dc:creator>
<dc:creator>Shirinifard, A.</dc:creator>
<dc:creator>Chapple, R. H.</dc:creator>
<dc:creator>Shen, A.</dc:creator>
<dc:creator>Clay, M. R.</dc:creator>
<dc:creator>Tatevossian, R. G.</dc:creator>
<dc:creator>Reilly, C.</dc:creator>
<dc:creator>Patel, J.</dc:creator>
<dc:creator>Lupo, M.</dc:creator>
<dc:creator>Cline, C.</dc:creator>
<dc:creator>Dionne, D.</dc:creator>
<dc:creator>Porter, C. B. M.</dc:creator>
<dc:creator>Waldman, J.</dc:creator>
<dc:creator>Bai, Y.</dc:creator>
<dc:creator>Zhu, B.</dc:creator>
<dc:creator>Barrera, I.</dc:creator>
<dc:creator>Murray, E.</dc:creator>
<dc:creator>Vigneau, S.</dc:creator>
<dc:creator>Napolitano, S.</dc:creator>
<dc:creator>Wakiro, I.</dc:creator>
<dc:creator>Wu, J.</dc:creator>
<dc:creator>Grimaldi, G.</dc:creator>
<dc:creator>Dellostritto, L.</dc:creator>
<dc:creator>Helvie, K.</dc:creator>
<dc:creator>Rotem, A.</dc:creator>
<dc:creator>Lako, A.</dc:creator>
<dc:creator>Cullen, N.</dc:creator>
<dc:creator>Pfaff, K. L.</dc:creator>
<dc:creator>Karlstrom, A.</dc:creator>
<dc:creator>Jane-Valbuena, J.</dc:creator>
<dc:creator>Todres, E.</dc:creator>
<dc:creator>Thorner, A.</dc:creator>
<dc:creator>Geeleher, P.</dc:creator>
<dc:creator>Rodig, S. J.</dc:creator>
<dc:creator></dc:creator>
<dc:date>2024-01-07</dc:date>
<dc:identifier>doi:10.1101/2024.01.07.574538</dc:identifier>
<dc:title><![CDATA[A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-01-07</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2024.07.23.604673v1?rss=1">
<title>
<![CDATA[
Genetic evolution of keratinocytes to cutaneous squamous cell carcinoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2024.07.23.604673v1?rss=1"
</link>
<description><![CDATA[
We performed multi-omic profiling of epidermal keratinocytes, precancerous actinic keratoses, and squamous cell carcinomas to understand the molecular transitions during skin carcinogenesis. Single-cell mutational analyses of normal skin cells showed that most keratinocytes have remarkably low mutation burdens, despite decades of sun exposure, however keratinocytes with TP53 or NOTCH1 mutations had substantially higher mutation burdens. These observations suggest that wild-type keratinocytes (i.e. without pathogenic mutations) are able to withstand high dosages of cumulative UV radiation, but certain pathogenic mutations break these adaptive mechanisms, priming keratinocytes for transformation by increasing their mutation rate. Mutational profiling of squamous cell carcinomas adjacent to actinic keratoses revealed TERT promoter and CDKN2A mutations emerging in actinic keratoses, whereas additional mutations inactivating ARID2 and activating the MAPK-pathway delineated the transition to squamous cell carcinomas. Surprisingly, actinic keratoses were often not related to their neighboring squamous cell carcinoma, indicating that collisions of unrelated neoplasms are common in the skin. Spatial variation in gene expression patterns was common in both tumor and immune cells, with high expression of checkpoint molecules at the invasive front of tumors. In conclusion, this study catalogues the key events during the evolution of cutaneous squamous cell carcinoma.
]]></description>
<dc:creator>Tandukar, B.</dc:creator>
<dc:creator>Deivendran, D.</dc:creator>
<dc:creator>Chen, L.</dc:creator>
<dc:creator>Cruz-Pacheco, N.</dc:creator>
<dc:creator>Sharma, H.</dc:creator>
<dc:creator>Xu, A.</dc:creator>
<dc:creator>Bandari, A. K.</dc:creator>
<dc:creator>Chen, D.</dc:creator>
<dc:creator>George, C.</dc:creator>
<dc:creator>Marty, A.</dc:creator>
<dc:creator>Cho, R. J.</dc:creator>
<dc:creator>Cheng, J.</dc:creator>
<dc:creator>Saylor, D.</dc:creator>
<dc:creator>Gerami, P.</dc:creator>
<dc:creator>Arron, S. T.</dc:creator>
<dc:creator>Bastian, B. C.</dc:creator>
<dc:creator>Shain, A. H.</dc:creator>
<dc:date>2024-07-24</dc:date>
<dc:identifier>doi:10.1101/2024.07.23.604673</dc:identifier>
<dc:title><![CDATA[Genetic evolution of keratinocytes to cutaneous squamous cell carcinoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2024-07-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.02.07.637114v1?rss=1">
<title>
<![CDATA[
Somatic mutations distinguish melanocyte subpopulations in human skin 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.02.07.637114v1?rss=1"
</link>
<description><![CDATA[
To better understand the homeostatic mechanisms governing melanocytes, we performed deep phenotyping of clonal expansions of single melanocytes from human skin. In total, we interrogated the mutational landscapes, gene expression profiles, and morphological features of 297 melanocytes from 31 donors. To our surprise, a population of melanocytes with low mutation burden was maintained in sun damaged skin. These melanocytes were more stem-like, smaller, less dendritic and displayed distinct gene expression profiles compared to their counterparts with high mutation burdens. We used single-cell spatial transcriptomics (10X Xenium) to reveal the spatial distribution of melanocytes inferred to have low and high mutation burdens (LowMut and HighMut cells), based on their gene expression profiles. LowMut melanocytes were found in hair follicles as well as in the interfollicular epidermis, whereas HighMut melanocytes resided almost exclusively in the interfollicular epidermis. We propose that melanocytes in the hair follicle occupy a privileged niche, protected from UV radiation, but periodically migrate out of the hair follicle to replenish the interfollicular epidermis after waves of photodamage. More broadly, our study illustrates the advantages of a cell atlas that includes mutational information, as cells can change their cellular states and positional coordinates over time, but mutations are like scars, providing a historical record of the homeostatic processes that were operative on each cell.
]]></description>
<dc:creator>Tandukar, B.</dc:creator>
<dc:creator>Deivendran, D.</dc:creator>
<dc:creator>Chen, L.</dc:creator>
<dc:creator>Bahrani, N.</dc:creator>
<dc:creator>Weier, B.</dc:creator>
<dc:creator>Sharma, H.</dc:creator>
<dc:creator>Cruz-Pacheco, N.</dc:creator>
<dc:creator>Hu, M.</dc:creator>
<dc:creator>Marks, K.</dc:creator>
<dc:creator>Zitnay, R. G.</dc:creator>
<dc:creator>Bandari, A. K.</dc:creator>
<dc:creator>Nekoonam, R.</dc:creator>
<dc:creator>Yeh, I.</dc:creator>
<dc:creator>Judson-Torres, R.</dc:creator>
<dc:creator>Shain, A. H.</dc:creator>
<dc:date>2025-02-08</dc:date>
<dc:identifier>doi:10.1101/2025.02.07.637114</dc:identifier>
<dc:title><![CDATA[Somatic mutations distinguish melanocyte subpopulations in human skin]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-02-08</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
