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	<title>bioRxiv Channel: NCI Cancer Systems Biology Consortium</title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "NCI Cancer Systems Biology Consortium"
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	<item rdf:about="https://biorxiv.org/cgi/content/short/175661v1?rss=1">
<title>
<![CDATA[
Hybrid approach for parameter estimation in agent-based models 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/175661v1?rss=1"
</link>
<description><![CDATA[
Agent-based models are valuable in cancer research to show how different behaviors emerge from individual interactions between cells and their environment. However, calibrating such models can be difficult, especially if the parameters that govern the underlying interactions are hard to measure experimentally. Herein, we detail a new method to converge on parameter sets that fit an agent-based model to multiscale data using a model of glioblastoma as an example.
]]></description>
<dc:creator>Gallaher, J.</dc:creator>
<dc:creator>Hawkins-Daarud, A.</dc:creator>
<dc:creator>Massey, S. C.</dc:creator>
<dc:creator>Swanson, K.</dc:creator>
<dc:creator>Anderson, A.</dc:creator>
<dc:date>2017-08-13</dc:date>
<dc:identifier>doi:10.1101/175661</dc:identifier>
<dc:title><![CDATA[Hybrid approach for parameter estimation in agent-based models]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-08-13</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/270900v1?rss=1">
<title>
<![CDATA[
The dynamic tumor ecosystem: how cell turnover and trade-offs affect cancer evolution 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/270900v1?rss=1"
</link>
<description><![CDATA[
AO_SCPCAPBSTRACTC_SCPCAPTumors are not static masses of cells but rather dynamic ecosystems where cancer cells experience constant turnover and evolve fitness-enhancing phenotypes. Selection for different phenotypes may vary with 1) the tumor niche (edge or core), 2) cell turnover rates, 3) the nature of the tradeoff between traits (proliferation vs migration), and 4) whether deaths occur in response to demographic or environmental stochasticity. In an agent based, spatially-explicit model, we observe how two traits (proliferation rate and migration speed) evolve under different trade-off conditions with different turnover rates. Migration rate is favored over proliferation at the tumors edge and vice-versa for the interior. Increasing cell turnover rates only slightly slows the growth of the tumor, but accelerates the rate of evolution for both proliferation and migration. The absence of a tradeoff favors ever higher values for proliferation and migration. A convex tradeoff tends to favor proliferation over migration while often promoting the coexistence of a generalist and specialist phenotype. A concave tradeoff slows the rate of evolution, and favors migration at low death rates and proliferation at higher death rates. Mortality via demographic stochasticity favors proliferation at the expense of migration; and vice-versa for environmental stochasticity. All of these factors and their interactions contribute to the ecology of the tumor, tumor heterogeneity, trait evolution, and phenotypic variation. While diverse, these effects may be predictable and empirically accessible.
]]></description>
<dc:creator>Gallaher, J.</dc:creator>
<dc:creator>Brown, J.</dc:creator>
<dc:creator>Anderson, A. R. A.</dc:creator>
<dc:date>2018-02-26</dc:date>
<dc:identifier>doi:10.1101/270900</dc:identifier>
<dc:title><![CDATA[The dynamic tumor ecosystem: how cell turnover and trade-offs affect cancer evolution]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-02-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/128959v1?rss=1">
<title>
<![CDATA[
Adaptive Therapy For Heterogeneous Cancer: Exploiting Space And Trade-Offs In Drug Scheduling 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/128959v1?rss=1"
</link>
<description><![CDATA[
Treatment of advanced cancers has benefited from new agents that supplement or bypass conventional therapies. However, even effective therapies fail as cancer cells deploy a wide range of resistance strategies. We propose that evolutionary dynamics ultimately determine survival and proliferation of resistant cells, therefore evolutionary strategies should be used with conventional therapies to delay or prevent resistance. Using an agent-based framework to model spatial competition among sensitive and resistant populations, we apply anti-proliferative drug treatments to varying ratios of sensitive and resistant cells. We compare a continuous maximum tolerated dose schedule with an adaptive schedule aimed at tumor control through competition between sensitive and resistant cells. We find that continuous treatment cures mostly sensitive tumors, but with any resistant cells, recurrence is inevitable. We identify two adaptive strategies that control heterogeneous tumors: dose modulation controls most tumors with less drug, while a more vacation-oriented schedule can control more invasive tumors.
]]></description>
<dc:creator>Gallaher, J. A.</dc:creator>
<dc:creator>Enriquez-Navas, P. M.</dc:creator>
<dc:creator>Luddy, K. A.</dc:creator>
<dc:creator>Gatenby, R. A.</dc:creator>
<dc:creator>Anderson, A. R. A.</dc:creator>
<dc:date>2017-04-20</dc:date>
<dc:identifier>doi:10.1101/128959</dc:identifier>
<dc:title><![CDATA[Adaptive Therapy For Heterogeneous Cancer: Exploiting Space And Trade-Offs In Drug Scheduling]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-04-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/743815v1?rss=1">
<title>
<![CDATA[
EvoFreq: Visualization of the Evolutionary Frequencies of Sequence and Model Data 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/743815v1?rss=1"
</link>
<description><![CDATA[
High throughput sequence data has provided in depth means of molecular characterization of populations. When recorded at numerous time steps, such data can reveal the evolutionary dynamics of the population under study by tracking the changes in genotype frequencies over time. This necessitates a simple and flexible means of visualizing an increasingly complex set of data. Here we offer EvoFreq as a comprehensive tool set to visualize the evolutionary and population frequency dynamics of clones at a single point in time or as population frequencies over time using a variety of informative methods. EvoFreq expands substantially on previous means of visualizing the clonal, temporal dynamics and offers users a range of options for displaying their sequence or model data. EvoFreq, implemented in R with robust user options and few dependencies, offers a high-throughput means of quickly building, and interrogating the temporal dynamics of hereditary information across many systems. EvoFreq is freely available via https://github.com/MathOnco/EvoFreq.
]]></description>
<dc:creator>Gatenbee, C. D.</dc:creator>
<dc:creator>Schenck, R. O.</dc:creator>
<dc:creator>Bravo, R. R.</dc:creator>
<dc:creator>Anderson, A. R. A.</dc:creator>
<dc:date>2019-08-22</dc:date>
<dc:identifier>doi:10.1101/743815</dc:identifier>
<dc:title><![CDATA[EvoFreq: Visualization of the Evolutionary Frequencies of Sequence and Model Data]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-08-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.10.08.331678v1?rss=1">
<title>
<![CDATA[
Antifragile therapy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.10.08.331678v1?rss=1"
</link>
<description><![CDATA[
Antifragility is a recently coined word used to describe the opposite of fragility. Systems or organisms can be described as antifragile if they derive a benefit from systemic variability, volatility, randomness, or disorder. Herein, we introduce a mathematical framework to quantify the fragility or antifragility of cancer cell lines in response to treatment variability. This framework enables straightforward prediction of the optimal dose treatment schedule for a range of treatment schedules with identical cumulative dose. We apply this framework to non-small-cell lung cancer cell lines with evolved resistance to ten anti-cancer drugs. We show the utility of this antifragile framework when applied to 1) treatment resistance, and 2) collateral sensitivity of sequential monotherapies.
]]></description>
<dc:creator>West, J.</dc:creator>
<dc:creator>Strobl, M.</dc:creator>
<dc:creator>Armagost, C.</dc:creator>
<dc:creator>Miles, R.</dc:creator>
<dc:creator>Marusyk, A.</dc:creator>
<dc:creator>Anderson, A. R. A.</dc:creator>
<dc:date>2020-10-09</dc:date>
<dc:identifier>doi:10.1101/2020.10.08.331678</dc:identifier>
<dc:title><![CDATA[Antifragile therapy]]></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/2021.04.10.439301v1?rss=1">
<title>
<![CDATA[
DDN2.0: R and Python packages for differential dependency network analysis of biological systems 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.04.10.439301v1?rss=1"
</link>
<description><![CDATA[
Data-driven differential dependency network analysis identifies in a complex and often unknown overall molecular circuitry a network of differentially connected molecular entities (pairwise selective coupling or uncoupling depending on the specific phenotypes or experimental conditions) (Herrington, et al. 2018; Zhang, et al., 2009; Zhang and Wang, 2010; Zhang, et al., 2016). Such differential dependency networks are typically used to assist in the inference of potential key pathways. Based on our previously developed Differential Dependency Network (DDN) method, we report here the fully implemented R and Python software tool packages for public use. The DDN2.0 algorithm uses a fused Lasso model and block-wise coordinate descent to estimate both the common and differential edges of dependency networks. The identified DDN can help to provide plausible interpretation of data, gain new insight of disease biology, and generate novel hypotheses for further validation and investigations.

To address the imbalanced sample group problem, we propose a sample-size normalized formulation to correct systematic bias. To address high computational complexity, we propose four strategies to accelerate DDN2.0 learning. The experimental results show that new DDN2.0+ learning speed with combined four accelerating strategies is hundreds of times faster than that of DDN2.0 algorithm on medium-sized data (Fu, 2019). To detect intra-omics and inter-omics network rewiring, we propose multiDDN using a multi-layer signaling model to integrate multi-omics data. The simulation study shows that the multiDDN method can achieve higher accuracy of detecting network rewiring (Fu, 2019).
]]></description>
<dc:creator>Zhang, B.</dc:creator>
<dc:creator>Fu, Y.</dc:creator>
<dc:creator>Lu, Y.</dc:creator>
<dc:creator>Zhang, Z.</dc:creator>
<dc:creator>Clarke, R.</dc:creator>
<dc:creator>Van Eyk, J. E.</dc:creator>
<dc:creator>Herrington, D. M.</dc:creator>
<dc:creator>Wang, Y.</dc:creator>
<dc:date>2021-04-11</dc:date>
<dc:identifier>doi:10.1101/2021.04.10.439301</dc:identifier>
<dc:title><![CDATA[DDN2.0: R and Python packages for differential dependency network analysis of biological systems]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-04-11</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.07.22.451363v1?rss=1">
<title>
<![CDATA[
A community-based approach to image analysis of cells, tissues and tumors 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.07.22.451363v1?rss=1"
</link>
<description><![CDATA[
Emerging multiplexed imaging platforms provide an unprecedented view of an increasing number of molecular markers at subcellular resolution and the dynamic evolution of tumor cellular composition. As such, they are capable of elucidating cell-to-cell interactions within the tumor microenvironment that impact clinical outcome and therapeutic response. However, the rapid development of these platforms has far outpaced the computational methods for processing and analyzing the data they generate. While being technologically disparate, all imaging assays share many computational requirements for post-collection data processing. We convened a workshop to characterize these shared computational challenges and a follow-up hackathon to implement solutions for a selected subset of them. Here, we delineate these areas that reflect major axes of research within the field, including image registration, segmentation of cells and subcellular structures, and identification of cell types from their morphology. We further describe the logistical organization of these events, believing our lessons learned can aid others in uniting the imaging community around self-identified topics of mutual interest, in designing and implementing operational procedures to address those topics and in mitigating issues inherent in image analysis (e.g., sharing exemplar images of large datasets and disseminating baseline solutions to hackathon challenges through open-source code repositories).
]]></description>
<dc:creator>CSBC/PS-ON Image Analysis Working Group,</dc:creator>
<dc:creator>Vizcarra, J. C.</dc:creator>
<dc:creator>Burlingame, E. A.</dc:creator>
<dc:creator>Hug, C. B.</dc:creator>
<dc:creator>Goltsev, Y.</dc:creator>
<dc:creator>White, B. S.</dc:creator>
<dc:creator>Tyson, D. R.</dc:creator>
<dc:creator>Sokolov, A.</dc:creator>
<dc:date>2021-07-25</dc:date>
<dc:identifier>doi:10.1101/2021.07.22.451363</dc:identifier>
<dc:title><![CDATA[A community-based approach to image analysis of cells, tissues and tumors]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-07-25</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.03.15.435426v1?rss=1">
<title>
<![CDATA[
Reconstruction of Contemporary Human Stem Cell Dynamics with Oscillatory Molecular Clocks 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.03.15.435426v1?rss=1"
</link>
<description><![CDATA[
Molecular clocks record cellular ancestry. However, currently used clocks  tick too slowly to measure the short-timescale dynamics of cellular renewal in adult tissues. Here we develop  rapidly oscillating DNA methylation clocks where ongoing (de)methylation causes the clock to  tick-tock back-and-forth between methylated and unmethylated states like a pendulum. We identify oscillators using standard methylation arrays and develop a mathematical modelling framework to quantitatively measure human adult stem cell dynamics from these data. Small intestinal crypts were inferred to contain slightly more stem cells than colon (6.5 {+/-} 1.0 vs 5.8 {+/-} 1.7 stem cells/crypt) with slower stem cell replacement in small intestine (0.79 {+/-} 0.5 vs 1.1 {+/-} 0.8 replacements/stem cell/year). Germline APC mutation increased the number of replacements per crypt (13.0 {+/-} 2.4 replacements/crypt/year vs 6.9 {+/-} 4.6 for healthy colon). In blood, we measure rapid expansion of acute leukaemia and slower growth of chronic disease. Rapidly oscillating molecular clocks are a new methodology to quantitatively measure human somatic cell dynamics.
]]></description>
<dc:creator>Gabbutt, C.</dc:creator>
<dc:creator>Schenck, R. O.</dc:creator>
<dc:creator>Weisenberger, D.</dc:creator>
<dc:creator>Kimberley, C.</dc:creator>
<dc:creator>Berner, A.</dc:creator>
<dc:creator>Househam, J.</dc:creator>
<dc:creator>Lakatos, E.</dc:creator>
<dc:creator>Robertson-Tessi, M.</dc:creator>
<dc:creator>Martin, I.</dc:creator>
<dc:creator>Patel, R.</dc:creator>
<dc:creator>Clark, S.</dc:creator>
<dc:creator>Latchford, A.</dc:creator>
<dc:creator>Barnes, C. P.</dc:creator>
<dc:creator>Leedham, S. J.</dc:creator>
<dc:creator>Anderson, A. R.</dc:creator>
<dc:creator>Graham, T. A.</dc:creator>
<dc:creator>Shibata, D.</dc:creator>
<dc:date>2021-03-16</dc:date>
<dc:identifier>doi:10.1101/2021.03.15.435426</dc:identifier>
<dc:title><![CDATA[Reconstruction of Contemporary Human Stem Cell Dynamics with Oscillatory Molecular Clocks]]></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/2021.01.22.427865v1?rss=1">
<title>
<![CDATA[
Cancer Hallmarks Define a Continuum of Plastic Cell States between Small Cell Lung Cancer Archetypes 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.01.22.427865v1?rss=1"
</link>
<description><![CDATA[
Small Cell Lung Cancer (SCLC) tumors are heterogeneous mixtures of transcriptional subtypes. Understanding subtype dynamics could be key to explaining the aggressive properties that make SCLC a recalcitrant cancer. Applying archetype analysis and evolutionary theory to bulk and single-cell transcriptomics, we show that SCLC cells reside within a cell-state continuum rather than in discrete subtype clusters. Gene expression signatures and ontologies indicate each vertex of the continuum corresponds to a functional phenotype optimized for a cancer hallmark task: three neuroendocrine archetypes specialize in proliferation/survival, inflammation and immune evasion, and two non-neuroendocrine archetypes in angiogenesis and metabolic dysregulation. Single cells can trade-off between these defined tasks to increase fitness and survival. SCLC cells can easily transition from specialists that optimize a single task to generalists that fall within the continuum, suggesting that phenotypic plasticity may be a mechanism by which SCLC cells become recalcitrant to treatment and adaptable to diverse microenvironments. We show that plasticity is uncoupled from the phenotype of single cells using a novel RNA-velocity-based metric, suggesting both specialist and generalist cells have the capability of becoming destabilized and transitioning to other phenotypes. We use network simulations to identify transcription factors such as MYC that promote plasticity and resistance to treatment. Our analysis pipeline is suitable to elucidate the role of phenotypic plasticity in any cancer type, and positions SCLC as a prime candidate for treatments that target plasticity.
]]></description>
<dc:creator>Groves, S. M.</dc:creator>
<dc:creator>Ireland, A.</dc:creator>
<dc:creator>Liu, Q.</dc:creator>
<dc:creator>Simmons, A. J.</dc:creator>
<dc:creator>Lau, K.</dc:creator>
<dc:creator>Iams, W. T.</dc:creator>
<dc:creator>Tyson, D. R.</dc:creator>
<dc:creator>Lovly, C.</dc:creator>
<dc:creator>Oliver, T. G.</dc:creator>
<dc:creator>Quaranta, V.</dc:creator>
<dc:date>2021-01-24</dc:date>
<dc:identifier>doi:10.1101/2021.01.22.427865</dc:identifier>
<dc:title><![CDATA[Cancer Hallmarks Define a Continuum of Plastic Cell States between Small Cell Lung Cancer Archetypes]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-01-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2020.07.25.220673v1?rss=1">
<title>
<![CDATA[
A unified atlas of CD8 T cell dysfunctional states in cancer and infection 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2020.07.25.220673v1?rss=1"
</link>
<description><![CDATA[
CD8 T cells play an essential role in defense against viral and bacterial infections and in tumor immunity. Deciphering T cell loss of functionality is complicated by the conspicuous heterogeneity of CD8 T cell states described across different experimental and clinical settings. By carrying out a unified analysis of over 300 ATAC-seq and RNA-seq experiments from twelve independent studies of CD8 T cell dysfunction in cancer and infection we defined a shared differentiation trajectory towards terminal dysfunction and its underlying transcriptional drivers and revealed a universal early bifurcation of functional and dysfunctional T cell activation states across models. Experimental dissection of acute and chronic viral infection using scATAC-seq and allele-specific scRNA-seq identified state-specific transcription factors and captured the early emergence of highly similar TCF1+ progenitor-like populations at an early branch point, at which epigenetic features of functional and dysfunctional T cells diverge. Our atlas of CD8 T cell states will facilitate mechanistic studies of T cell immunity and translational efforts.
]]></description>
<dc:creator>Pritykin, Y.</dc:creator>
<dc:creator>van der Veeken, J.</dc:creator>
<dc:creator>Pine, A.</dc:creator>
<dc:creator>Zhong, Y.</dc:creator>
<dc:creator>Sahin, M.</dc:creator>
<dc:creator>Mazutis, L.</dc:creator>
<dc:creator>Pe'er, D.</dc:creator>
<dc:creator>Rudensky, A.</dc:creator>
<dc:creator>Leslie, C. S.</dc:creator>
<dc:date>2020-07-26</dc:date>
<dc:identifier>doi:10.1101/2020.07.25.220673</dc:identifier>
<dc:title><![CDATA[A unified atlas of CD8 T cell dysfunctional states in cancer and infection]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2020-07-26</prism:publicationDate>
<prism:section></prism:section>
</item>
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