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	<title>bioRxiv Channel: IMO Workshop</title>
	<link>https://biorxiv.org</link>
	<description>
	This feed contains articles for bioRxiv Channel "IMO Workshop"
	</description>

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	<link>https://biorxiv.org</link>
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	<item rdf:about="https://biorxiv.org/cgi/content/short/049072v1?rss=1">
<title>
<![CDATA[
Exploiting Homeostatic Repopulation to Increase DC Vaccine Efficacy in Multiple Myeloma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/049072v1?rss=1"
</link>
<description><![CDATA[
Standard of care for multiple myeloma involves autologous hematopoietic cell transplant (AHCT), which can extend life by a year; however the disease remains incurable. A dendritic cell vaccine developed at Moffitt will be used both immediately before and after AHCT, with the aim of achieving complete response. Data will be collected during the trial to test the biological activity of the vaccine. This data will parameterize a model that facilitates the exploration of outcomes when varying the timing of vaccination. This calibrated model will also inform the design of a follow-up trial, which will include vaccination in conjunction with other immunotherapies.
]]></description>
<dc:creator>Chandler Gatenbee</dc:creator>
<dc:creator>Nuria Folguera-Blasco</dc:creator>
<dc:creator>Charlie Daneils</dc:creator>
<dc:creator>Jill Gallaher</dc:creator>
<dc:creator>Russ Rockne</dc:creator>
<dc:creator>Casey Adams</dc:creator>
<dc:creator>Michael Nicholson</dc:creator>
<dc:creator>Eleni Maniati</dc:creator>
<dc:creator>John Kennedy</dc:creator>
<dc:creator>Kimberly Luddy</dc:creator>
<dc:creator>Frederick L. Locke</dc:creator>
<dc:creator>Mark Robertson-Tessi</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-04-18</dc:date>
<dc:identifier>doi:10.1101/049072</dc:identifier>
<dc:title><![CDATA[Exploiting Homeostatic Repopulation to Increase DC Vaccine Efficacy in Multiple Myeloma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-04-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/055988v1?rss=1">
<title>
<![CDATA[
Enhancing synergy of CAR T cell therapy and oncolytic virus therapy for pancreatic cancer. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/055988v1?rss=1"
</link>
<description><![CDATA[
The poor immunogenicity of pancreatic tumors makes them particularly difficult to treat. Standard chemotherapies and single agent immunotherapies have had notoriously little success in this arena. Oncolytic virus therapy has the potential to enhance the penetration of immunotherapeutically-delivered CAR T cells into the tumor and improve treatment outcomes. We evaluate this potential by combining two different mathematical approaches: an ordinary differential equation model to simulate population level tumor response to cytotoxic activity of T cells, coupled with an agent-based model to simulate the enhancement of CAR T cell penetration by oncolytic virus therapy.
]]></description>
<dc:creator>Rachel Walker</dc:creator>
<dc:creator>Pedro E Navas</dc:creator>
<dc:creator>Samuel H Friedman</dc:creator>
<dc:creator>Simona Galliani</dc:creator>
<dc:creator>Aleksandra Karolak</dc:creator>
<dc:creator>Fiona MacFarlane</dc:creator>
<dc:creator>Robert Noble</dc:creator>
<dc:creator>Jan Poleszczuk</dc:creator>
<dc:creator>Shonagh Russell</dc:creator>
<dc:creator>Katarzyna A Rejniak</dc:creator>
<dc:creator>Amir Shahmoradi</dc:creator>
<dc:creator>Frederik Ziebell</dc:creator>
<dc:creator>Jason Brayer</dc:creator>
<dc:creator>Daniel Abate-Daga</dc:creator>
<dc:creator>Heiko Enderling</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-05-30</dc:date>
<dc:identifier>doi:10.1101/055988</dc:identifier>
<dc:title><![CDATA[Enhancing synergy of CAR T cell therapy and oncolytic virus therapy for pancreatic cancer.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-05-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/049908v1?rss=1">
<title>
<![CDATA[
Toxicity Management in CAR T cell therapy for B-ALL: Mathematical modelling as a new avenue for improvement. 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/049908v1?rss=1"
</link>
<description><![CDATA[
Advances in genetic engineering have made it possible to reprogram individual immune cells to express receptors that recognise markers on tumour cell surfaces. The process of re-engineering T cell lymphocytes to express Chimeric Antigen Receptors (CARs), and then re-infusing the CAR-modified T cells into patients to treat various cancers is referred to as CAR T cell therapy. This therapy is being explored in clinical trials - most prominently for B Cell Acute Lymphoblastic Leukaemia (B-ALL), a common B cell malignancy, for which CAR T cell therapy has led to remission in up to 90% of patients. Despite this extraordinary response rate, however, potentially fatal inflammatory side effects occur in up to 10% of patients who have positive responses. Further, approximately 50% of patients who initially respond to the therapy relapse. Significant improvement is thus necessary before the therapy can be made widely available for use in the clinic.nnTo inform future development, we develop a mathematical model to explore interactions between CAR T cells, inflammatory toxicity, and individual patients tumour burdens in silico. This paper outlines the underlying system of coupled ordinary differential equations designed based on well-known immunological principles and widely accepted views on the mechanism of toxicity development in CAR T cell therapy for B-ALL - and reports in silico outcomes in relationship to standard and recently conjectured predictors of toxicity in a heterogeneous, randomly generated patient population. Our initial results and analyses are consistent with and connect immunological mechanisms to the clinically observed, counterintuitive hypothesis that initial tumour burden is a stronger predictor of toxicity than is the dose of CAR T cells administered to patients.nnWe outline how the mechanism of action in CAR T cell therapy can give rise to such non-standard trends in toxicity development, and demonstrate the utility of mathematical modelling in understanding the relationship between predictors of toxicity, mechanism of action, and patient outcomes.
]]></description>
<dc:creator>Shalla Hanson</dc:creator>
<dc:creator>David Robert Grimes</dc:creator>
<dc:creator>Jake P. Taylor-King</dc:creator>
<dc:creator>Benedikt Bauer</dc:creator>
<dc:creator>Pravnam I. Warman</dc:creator>
<dc:creator>Ziv Frankenstein</dc:creator>
<dc:creator>Artem Kaznatcheev</dc:creator>
<dc:creator>Michael J. Bonassar</dc:creator>
<dc:creator>Vincent L. Cannataro</dc:creator>
<dc:creator>Zeinab Y. Motawe</dc:creator>
<dc:creator>Ernesto A. B. F. Lima</dc:creator>
<dc:creator>Sungjune Kim</dc:creator>
<dc:creator>Marco L. Davila</dc:creator>
<dc:creator>Arturo Araujo</dc:creator>
<dc:creator></dc:creator>
<dc:date>2016-04-22</dc:date>
<dc:identifier>doi:10.1101/049908</dc:identifier>
<dc:title><![CDATA[Toxicity Management in CAR T cell therapy for B-ALL: Mathematical modelling as a new avenue for improvement.]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-04-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/085910v1?rss=1">
<title>
<![CDATA[
Harnessing the lymphocyte meta-phenotype to optimize adoptive cell therapy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/085910v1?rss=1"
</link>
<description><![CDATA[
There is an urgent need for reliable effective therapy for patients with metastatic sarcoma. Approaches that manipulate the immune system have shown promise for patients with advanced, widely disseminated malignancies. One of these approaches is adoptive cell therapy (ACT), where tumor-infiltrating lymphocytes (TIL) are isolated from the tumor, expanded ex vivo, and then transferred back to the patient. This approach has shown great promise in melanoma, leading to an objective response in approximately half of treated patients [14]. Standard protocols involve characterization of TIL populations with respect to adaptive CD4+ and CD8+ T-lymphocytes, but neglect the possible role of the innate lymphoid repertoire. Due to toxicity and the high cost associated with ACT, the IFN-{gamma} release assay is currently used as a proxy to identify suitable TIL isolates for ACT. Efforts in TIL-ACT for sarcoma, which are pre-clinical and pioneered at Moffitt Cancer Center, have shown that only a minority of the TIL cultures show tumor specific activity in ex vivo IFN-{gamma} assays. Surprisingly, internal melanoma trial data reveal a lack of correlation between IFN-{gamma} assay and clinical outcomes, highlighting the need for a more reliable proxy. We hypothesize the existence of a predictable TIL meta-phenotype that leads to optimal tumor response. Here, we describe preliminary efforts to integrate prospective and existing patient data with mathematical models to optimize the TIL meta-phenotype prior to re-injection.
]]></description>
<dc:creator>Buttenschoen, A.</dc:creator>
<dc:creator>Elkenawi, A.</dc:creator>
<dc:creator>El Moustaid, F.</dc:creator>
<dc:creator>Fletcher, A.</dc:creator>
<dc:creator>Grassberger, C.</dc:creator>
<dc:creator>Kim, E.</dc:creator>
<dc:creator>Marusyk, A.</dc:creator>
<dc:creator>McClelland, H.-L.</dc:creator>
<dc:creator>Miroshnychenko, D.</dc:creator>
<dc:creator>Nichol, D.</dc:creator>
<dc:creator>Mullinax, J.</dc:creator>
<dc:creator>O'Farrely, C.</dc:creator>
<dc:creator>Scott, J.</dc:creator>
<dc:date>2016-11-05</dc:date>
<dc:identifier>doi:10.1101/085910</dc:identifier>
<dc:title><![CDATA[Harnessing the lymphocyte meta-phenotype to optimize adoptive cell therapy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2016-11-05</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/190561v1?rss=1">
<title>
<![CDATA[
Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/190561v1?rss=1"
</link>
<description><![CDATA[
Immune therapies have shown promise in a number of cancers, and clinical trials using the anti-PD-L1/PD-1 checkpoint inhibitor in lung cancer have been successful for a number of patients. However, some patients either do not respond to the treatment or have cancer recurrence after an initial response. It is not clear which patients might fall into these categories or what mechanisms are responsible for treatment failure. To explore the different underlying biological mechanisms of resistance, we created a spatially explicit mathematical model with a modular framework. This construction enables different potential mechanisms to be turned on and off in order to adjust specific tumor and tissue interactions to match a specific patient's disease. In parallel, we developed a software suite to identify significant computed tomography (CT) imaging features correlated with outcome using data from an anti-PDL-1 checkpoint inhibitor clinical trial for lung cancer and a tool that extracts these features from both patient CT images and "virtual CT" images created from the cellular density profile of the model. The combination of our two toolkits provides a framework that feeds patient data through an iterative pipeline to identify predictive imaging features associated with outcome, whilst at the same time proposing hypotheses about the underlying resistance mechanisms.
]]></description>
<dc:creator>Saeed-Vafa, D.</dc:creator>
<dc:creator>Bravo, R.</dc:creator>
<dc:creator>Dean, J. A.</dc:creator>
<dc:creator>El-Kenawi, A.</dc:creator>
<dc:creator>Mon Pere, N.</dc:creator>
<dc:creator>Strobl, M.</dc:creator>
<dc:creator>Daniels, C.</dc:creator>
<dc:creator>Stringfield, O.</dc:creator>
<dc:creator>Damaghi, M.</dc:creator>
<dc:creator>Tunali, I.</dc:creator>
<dc:creator>Brown, L.</dc:creator>
<dc:creator>Curtin, L.</dc:creator>
<dc:creator>Nichol, D.</dc:creator>
<dc:creator>Peck, H.</dc:creator>
<dc:creator>Gillies, R. J.</dc:creator>
<dc:creator>Gallaher, J.</dc:creator>
<dc:date>2017-09-22</dc:date>
<dc:identifier>doi:10.1101/190561</dc:identifier>
<dc:title><![CDATA[Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-09-22</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/211151v1?rss=1">
<title>
<![CDATA[
Dark selection for JAK/STAT-inhibitor resistance in chronic myelomonocytic leukemia 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/211151v1?rss=1"
</link>
<description><![CDATA[
Acquired therapy resistance to cancer treatment is a common and serious clinical problem. The classic U-shape model for the emergence of resistance supposes that: (1) treatment changes the selective pressure on the treatment-naive tumour; (2) this shifting pressure creates a proliferative or survival difference between sensitive cancer cells and either an existing or de novo mutant; (3) the resistant cells then out-compete the sensitive cells and - if further interventions (like drug holidays or new drugs or dosage changes) are not pursued - take over the tumour: returning it to a state dangerous to the patient. The emergence of ruxolitinib resistance in chronic myelomonocytic leukemia (CMML) seems to challenge the classic model: we see the global properties of resistance, but not the drastic change in clonal architecture expected with the selection bottleneck. To study this, we explore three population-level models as alternatives to the classic model of resistance. These three effective models are designed in such a way that they are distinguishable based on limited experimental data on the time-progression of resistance in CMML. We also propose a candidate reductive implementation of the proximal cause of resistance to ground these effective theories. With these reductive implementations in mind, we also explore the impact of oxygen diffusion and spatial structure more generally on the dynamics of CMML in the bone marrow concluding that, even small fluctuations in oxygen availability can seriously impact the efficacy of ruxolitinib. Finally, we look at the ability of spatially distributed cytokine signaling feedback loops to produce a relapse in symptoms similar to what we observe in the clinic.
]]></description>
<dc:creator>Kaznatcheev, A.</dc:creator>
<dc:creator>Grimes, D. R.</dc:creator>
<dc:creator>Vander Velde, R.</dc:creator>
<dc:creator>Cannataro, V. L.</dc:creator>
<dc:creator>Baratchart, E.</dc:creator>
<dc:creator>Dhawan, A.</dc:creator>
<dc:creator>Liu, L.</dc:creator>
<dc:creator>Myroshnychenko, D.</dc:creator>
<dc:creator>Taylor-King, J. P.</dc:creator>
<dc:creator>Yoon, N.</dc:creator>
<dc:creator>Padron, E.</dc:creator>
<dc:creator>Marusyk, A.</dc:creator>
<dc:creator>Basanta, D.</dc:creator>
<dc:date>2017-10-30</dc:date>
<dc:identifier>doi:10.1101/211151</dc:identifier>
<dc:title><![CDATA[Dark selection for JAK/STAT-inhibitor resistance in chronic myelomonocytic leukemia]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/211383v1?rss=1">
<title>
<![CDATA[
Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/211383v1?rss=1"
</link>
<description><![CDATA[
De novo resistance is a major issue for the use of targeted anticancer drugs in the clinic. By integrating experimental data we have created a hybrid neural network/agent-based model to simulate the evolution and spread of resistance to the drug Pramlintide in cutaneous squamous cell carcinoma. Our model can eventually be used to predict patient responses to the drug and thus enable clinicians to make decisions regarding personalized, precision treatment regimes for patients.
]]></description>
<dc:creator>Kim, E.</dc:creator>
<dc:creator>Schenck, R.</dc:creator>
<dc:creator>West, J.</dc:creator>
<dc:creator>Cross, W.</dc:creator>
<dc:creator>Harris, V.</dc:creator>
<dc:creator>McKenna, J.</dc:creator>
<dc:creator>Cho, H.</dc:creator>
<dc:creator>Coker, E.</dc:creator>
<dc:creator>Lee-Kramer, S.</dc:creator>
<dc:creator>Tsai, K.</dc:creator>
<dc:creator>Flores, E.</dc:creator>
<dc:creator>Gatenbee, C. D.</dc:creator>
<dc:date>2017-10-30</dc:date>
<dc:identifier>doi:10.1101/211383</dc:identifier>
<dc:title><![CDATA[Targeting the Untargetable: Predicting Pramlintide Resistance Using a Neural Network Based Cellular Automata]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2017-10-30</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/451047v1?rss=1">
<title>
<![CDATA[
The impact of tumor stromal architecture on therapy response and clinical progression 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/451047v1?rss=1"
</link>
<description><![CDATA[
-- Advances in molecular oncology research culminated in the development of targeted therapies that act on defined molecular targets either on tumor cells directly (such as inhibitors of oncogenic kinases), or indirectly by targeting the tumor microenvironment (such as anti-angiogenesis drugs). These therapies can induce strong clinical responses, when properly matched to patients. Unfortunately, most targeted therapies ultimately fail as tumors evolve resistance. Tumors consist not only of neoplastic cells, but also of stroma, whereby "stroma" is the umbrella term for non-tumor cells and extracellular matrix (ECM) within the tumor microenvironment, possibly excluding immune cells1. We know that tumor stroma is an important player in the development of resistance. We also know that stromal architecture is spatially complex, differs from patient to patient and changes with therapy. However, to this date we do not understand the link between spatial and temporal changes in stromal architecture and response of tumors to therapy, in space and time. In this project we sought to address this gap of knowledge using a combination of mathematical and statistical modeling, experimental in vivo studies, and analysis of clinical samples in therapies that target tumor cells directly (in lung and breast cancers) and indirectly (in kidney cancer). This knowledge will inform therapy choices and offer new angles for therapeutic interventions. Our main question is: how does spatial architecture of stroma impact the emergence or evolution of resistance to targeted therapies, and how can we use this knowledge clinically?
]]></description>
<dc:creator>Altrock, P.</dc:creator>
<dc:creator>Yoon, N.</dc:creator>
<dc:creator>Bull, J. A.</dc:creator>
<dc:creator>Wu, H.</dc:creator>
<dc:creator>Ruiz-Ramirez, J.</dc:creator>
<dc:creator>Miroshnychenko, D.</dc:creator>
<dc:creator>Kimmel, G. J.</dc:creator>
<dc:creator>Kim, E.</dc:creator>
<dc:creator>Vander Velde, R. J.</dc:creator>
<dc:creator>Rejniak, K.</dc:creator>
<dc:creator>Manley, B. J.</dc:creator>
<dc:creator>Spill, F.</dc:creator>
<dc:creator>Marusyk, A.</dc:creator>
<dc:date>2018-10-24</dc:date>
<dc:identifier>doi:10.1101/451047</dc:identifier>
<dc:title><![CDATA[The impact of tumor stromal architecture on therapy response and clinical progression]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-10-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/452052v1?rss=1">
<title>
<![CDATA[
Connecting the Microenvironmental Niche to Treatment Response in Ovarian Cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/452052v1?rss=1"
</link>
<description><![CDATA[
Ovarian cancer has the highest mortality rate of all gynecologic cancers, which may be attributed to an often late stage diagnosis, when the cancer is already metastatic, and rapid development of treatment resistance. We propose that the metastatic disease could be better characterized by observing interactions within the microenvironmental niche of the primary site that shapes the tumors early phenotypic progression. We present a mechanistic mathematical model of ovarian cancer that considers spatial interactions between tumor cells and several key stromal components. We demonstrate how spatial biomarker imaging data from the primary tumor can be analyzed to define a patient-specific microenvironment in the mathematical model. We then show preliminary results, using this model, that demonstrate how differences in the niche composition of a tumor affects phenotypic evolution and treatment response.
]]></description>
<dc:creator>Strobl, M.</dc:creator>
<dc:creator>Wicker, M.</dc:creator>
<dc:creator>Adhikarla, V.</dc:creator>
<dc:creator>Shockey, A.</dc:creator>
<dc:creator>Lakatos, E.</dc:creator>
<dc:creator>Pooladvand, P.</dc:creator>
<dc:creator>Schenck, R.</dc:creator>
<dc:creator>Saputro, L.</dc:creator>
<dc:creator>Gatenby, C.</dc:creator>
<dc:creator>Koppens, M.</dc:creator>
<dc:creator>Cruz Garcia, S.</dc:creator>
<dc:creator>Wenham, R.</dc:creator>
<dc:creator>Damaghi, M.</dc:creator>
<dc:creator>Gallaher, J. A.</dc:creator>
<dc:date>2018-10-24</dc:date>
<dc:identifier>doi:10.1101/452052</dc:identifier>
<dc:title><![CDATA[Connecting the Microenvironmental Niche to Treatment Response in Ovarian Cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-10-24</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/454447v1?rss=1">
<title>
<![CDATA[
Evolutionary exploitation of PD-L1 expression in hormone receptor positive breast cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/454447v1?rss=1"
</link>
<description><![CDATA[
Based on clinical data from hormone positive breast cancer patients, we determined that there is a potential tradeoff between reducing tumor burden and altering metastatic potential when administering combination therapy of aromatase inhibitors and immune checkpoint inhibitors. While hormone-deprivation therapies serve to reduce tumor size in the neoadjuvant setting pre-surgery, they may induce tumors to change expression patterns towards a metastatic phenotype. We used mathematical modeling to explore how the timing of the therapies affects tumor burden and metastatic potential with an eye toward developing a dynamic prognostic score and reducing both tumor size and risk of metastasis.
]]></description>
<dc:creator>West, J.</dc:creator>
<dc:creator>Park, D.</dc:creator>
<dc:creator>Harmon, C.</dc:creator>
<dc:creator>Williamson, D.</dc:creator>
<dc:creator>Ashcroft, P.</dc:creator>
<dc:creator>Maestrini, D.</dc:creator>
<dc:creator>Ardaseva, A.</dc:creator>
<dc:creator>Bravo, R.</dc:creator>
<dc:creator>Sahoo, P.</dc:creator>
<dc:creator>Khong, H.</dc:creator>
<dc:creator>Luddy, K.</dc:creator>
<dc:creator>Robertson-Tessi, M.</dc:creator>
<dc:date>2018-10-26</dc:date>
<dc:identifier>doi:10.1101/454447</dc:identifier>
<dc:title><![CDATA[Evolutionary exploitation of PD-L1 expression in hormone receptor positive breast cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-10-26</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/458372v1?rss=1">
<title>
<![CDATA[
Overcoming non-small cell lung cancer radiation resistance by modulating the tumor-immune ecosystem 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/458372v1?rss=1"
</link>
<description><![CDATA[
Non-small-cell lung cancer is the leading cause of cancer death worldwide. Although radiotherapy is an effective treatment choice for early-stage cases, the 5-year survival rate of patients diagnosed in late-stages remains poor. Increasing evidence suggests that the local and systemic effects of radiotherapy dependent on the induced anti-tumor immune responses. We believe that an educated adaptation of radiotherapy plans based not only on the induced immune responses, but also on the tumor-immune ecosystem composition at the beginning of treatment might increase local tumor control. We propose two different mathematical models to evaluate the potential of the tumor-immune context to inform adaptation of treatment plans with the aim of improving outcomes.
]]></description>
<dc:creator>Nizzero, S.</dc:creator>
<dc:creator>Alfonso, J. C. L.</dc:creator>
<dc:creator>Alvarez-Arenas, A.</dc:creator>
<dc:creator>Mirzaev, I.</dc:creator>
<dc:creator>Zervantonakis, I. K.</dc:creator>
<dc:creator>Lewin, T.</dc:creator>
<dc:creator>Rishi, A.</dc:creator>
<dc:creator>Piretto, E.</dc:creator>
<dc:creator>Joshi, T. V.</dc:creator>
<dc:creator>Santiago, D. N.</dc:creator>
<dc:creator>Karolak, A. M.</dc:creator>
<dc:creator>Howard, R.</dc:creator>
<dc:creator>Enderling, H.</dc:creator>
<dc:creator>Karreth, F. A.</dc:creator>
<dc:creator>Torres-Roca, J.</dc:creator>
<dc:date>2018-11-08</dc:date>
<dc:identifier>doi:10.1101/458372</dc:identifier>
<dc:title><![CDATA[Overcoming non-small cell lung cancer radiation resistance by modulating the tumor-immune ecosystem]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2018-11-08</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/563130v1?rss=1">
<title>
<![CDATA[
Harnessing patient-specific response dynamics to optimize evolutionary therapies for metastatic clear cell renal cell carcinoma - Learning to adapt 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/563130v1?rss=1"
</link>
<description><![CDATA[
Renal cell carcinoma (RCC) is one of the ten most common and lethal cancers in the United States. Tumor heterogeneity and development of resistance to treatment suggest that patient-specific evolutionary therapies may hold the key to better patients prognosis. Mathematical models are a powerful tool to help develop such strategies; however, they depend on reliable biomarker information. In this paper, we present a dynamic model of tumor-immune interactions, as well as the treatment effect on tumor cells and the tumor-immune environment. We hypothesize that the neutrophil-to-lymphocyte ratio (NLR) is a powerful biomarker that can be used to predict an individual patients response to treatment. Using randomly sampled virtual patients, we show that the model recapitulates patient outcomes from clinical trials in RCC. Finally, we use in silico patient data to recreate realistic tumor behaviors and simulate various treatment strategies to find optimal treatments for each virtual patient.
]]></description>
<dc:creator>Sorribes, I. C.</dc:creator>
<dc:creator>Basu, A.</dc:creator>
<dc:creator>Brady, R.</dc:creator>
<dc:creator>Enriquez-Navas, P. M.</dc:creator>
<dc:creator>Feng, X.</dc:creator>
<dc:creator>Kather, J. N.</dc:creator>
<dc:creator>Nerlakanti, N.</dc:creator>
<dc:creator>Stephens, R.</dc:creator>
<dc:creator>Strobl, M.</dc:creator>
<dc:creator>Tavassoly, I.</dc:creator>
<dc:creator>Vitos, N.</dc:creator>
<dc:creator>Lemanne, D.</dc:creator>
<dc:creator>Manley, B.</dc:creator>
<dc:creator>O'Farrelly, C.</dc:creator>
<dc:creator>Enderling, H.</dc:creator>
<dc:date>2019-02-28</dc:date>
<dc:identifier>doi:10.1101/563130</dc:identifier>
<dc:title><![CDATA[Harnessing patient-specific response dynamics to optimize evolutionary therapies for metastatic clear cell renal cell carcinoma - Learning to adapt]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-02-28</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/826438v1?rss=1">
<title>
<![CDATA[
Modeling adaptive therapy in non-muscle invasive bladder cancer 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/826438v1?rss=1"
</link>
<description><![CDATA[
Bladder cancer is the 9th most commonly diagnosed cancer. Nearly half of patients with early stage bladder cancer treated with the immune-stimulating agent BCG have disease recurrence, while 13% progress to invasive bladder cancer. Here we explored the potential of tumor mutational heterogeneity and the role of pro- and anti-inflammatory cytokines to identify different subtypes of bladder cancer that may predict therapeutic response to BCG. Further, we used mathematical modeling of dosing strategies to infer tumor response to varying doses and time schedules f BCG administration. As a proof-of-concept, present adaptive therapy scheduling of BCG as a viable strategy to control tumor size and minimize recurrence.
]]></description>
<dc:creator>Ferrall-Fairbanks, M. C.</dc:creator>
<dc:creator>Kimmel, G. J.</dc:creator>
<dc:creator>Black, M.</dc:creator>
<dc:creator>Bravo, R.</dc:creator>
<dc:creator>Deac, O.</dc:creator>
<dc:creator>Martinez, P.</dc:creator>
<dc:creator>Myers, M.</dc:creator>
<dc:creator>Nazari, F.</dc:creator>
<dc:creator>Osojnik, A.</dc:creator>
<dc:creator>Subramanian, H.</dc:creator>
<dc:creator>Viossat, Y.</dc:creator>
<dc:creator>Whiting, F.</dc:creator>
<dc:creator>Li, R.</dc:creator>
<dc:creator>Mann, K. M.</dc:creator>
<dc:creator>Altrock, P. M.</dc:creator>
<dc:date>2019-11-06</dc:date>
<dc:identifier>doi:10.1101/826438</dc:identifier>
<dc:title><![CDATA[Modeling adaptive therapy in non-muscle invasive bladder cancer]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2019-11-06</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2023.10.17.562670v1?rss=1">
<title>
<![CDATA[
Interactions between ploidy and resource availability shape clonal interference at initiation and recurrence of glioblastoma 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2023.10.17.562670v1?rss=1"
</link>
<description><![CDATA[
Glioblastoma (GBM) is the most aggressive form of primary brain tumor. Complete surgical resection of GBM is almost impossible due to the infiltrative nature of the cancer. While no evidence for recent selection events have been found after diagnosis, the selective forces that govern gliomagenesis are strong, shaping the tumors cell composition during the initial progression to malignancy with late consequences for invasiveness and therapy response. We present a mathematical model that simulates the growth and invasion of a glioma, given its ploidy level and the nature of its brain tissue micro-environment (TME), and use it to make inferences about GBM initiation and response to standard-of-care treatment. We approximate the spatial distribution of resource access in the TME through integration of in-silico modelling, multi-omics data and image analysis of primary and recurrent GBM. In the pre-malignant setting, our in-silico results suggest that low ploidy cancer cells are more resistant to starvation-induced cell death. In the malignant setting, between first and second surgery, simulated tumors with different ploidy compositions progressed at different rates. Whether higher ploidy predicted fast recurrence, however, depended on the TME. Historical data supports this dependence on TME resources, as shown by a significant correlation between the median glucose uptake rates in human tissues and the median ploidy of cancer types that arise in the respective tissues (Spearman r = -0.70; P = 0.026). Taken together our findings suggest that availability of metabolic substrates in the TME drives different cell fate decisions for cancer cells with different ploidy and shapes GBM disease initiation and relapse characteristics.
]]></description>
<dc:creator>Nowicka, Z.</dc:creator>
<dc:creator>Rentzeperis, F.</dc:creator>
<dc:creator>Beck, R.</dc:creator>
<dc:creator>Tagal, V.</dc:creator>
<dc:creator>Forero Pinto, A. M.</dc:creator>
<dc:creator>Scanu, E.</dc:creator>
<dc:creator>Veith, T.</dc:creator>
<dc:creator>Cole, J.</dc:creator>
<dc:creator>Ilter, D.</dc:creator>
<dc:creator>Dominguez Viqueira, W.</dc:creator>
<dc:creator>Teer, J. K.</dc:creator>
<dc:creator>Maksin, K.</dc:creator>
<dc:creator>Pasetto, S.</dc:creator>
<dc:creator>Abdalah, M. A.</dc:creator>
<dc:creator>Fiandaca, G.</dc:creator>
<dc:creator>Prabhakaran, S.</dc:creator>
<dc:creator>Schultz, A.</dc:creator>
<dc:creator>Ojwang, M.</dc:creator>
<dc:creator>Barnholtz-Sloan, J. S.</dc:creator>
<dc:creator>Joaquim, F. M.</dc:creator>
<dc:creator>Gomes, A. P.</dc:creator>
<dc:creator>Katira, P.</dc:creator>
<dc:creator>Andor, N.</dc:creator>
<dc:date>2023-10-20</dc:date>
<dc:identifier>doi:10.1101/2023.10.17.562670</dc:identifier>
<dc:title><![CDATA[Interactions between ploidy and resource availability shape clonal interference at initiation and recurrence of glioblastoma]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2023-10-20</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2025.07.10.664235v1?rss=1">
<title>
<![CDATA[
Controlling treatment toxicity in ovarian cancer to prime the patient for tumor extinction therapy 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2025.07.10.664235v1?rss=1"
</link>
<description><![CDATA[
High-grade serous ovarian cancer (HGSOC) remains a major clinical challenge. In particular among those patients with homologous recombination (HR)-proficient tumors (>50%), most eventually succumb to their disease due to high recurrence rates, acquired resistance, and cumulative toxicity. This report summarizes work from the 12th IMO Workshop in which we explored an alternative "extinction therapy" strategy for frontline treatment of HGSOC. Inspired by ecological principles, this multi-strike approach aims to eradicate tumors not through a singular "magic bullet" but through a series of therapies after standard frontline treatment when the tumor is still, and perhaps most, vulnerable. We present a framework leveraging mathematical modeling (MM) to develop personalized multi-strike protocols for HGSOC. Key contributions include: 1) An "IMOme" score using liquid biopsy data to assess patient-specific hematopoietic toxicity risk, guiding the timing and selection of subsequent therapies, 2) MM strategies to design effective lowdose combinations of targeted agents to achieve synthetic lethality while managing toxicity, and 3) A MM framework to analyze the interplay between chemotherapy, gut microbiome toxicity, and immunotherapy, demonstrating how mitigating microbiome damage could enhance immune response. Overall, the computational approaches presented herein aim to support the design of personalized, multi-strike regimens in the frontline setting that proactively target tumor extinction while managing toxicity, ultimately seeking to deliver cures for patients with HGSOC.
]]></description>
<dc:creator>Gallagher, K.</dc:creator>
<dc:creator>Sousa, R. S.</dc:creator>
<dc:creator>Gatenbee, C. D.</dc:creator>
<dc:creator>Schenck, R.</dc:creator>
<dc:creator>Chen, P.</dc:creator>
<dc:creator>Citak, T.</dc:creator>
<dc:creator>Leither, S.</dc:creator>
<dc:creator>Mazzacurati, L.</dc:creator>
<dc:creator>Xella, A.</dc:creator>
<dc:creator>Zhou, Z.</dc:creator>
<dc:creator>Lemanne, D.</dc:creator>
<dc:creator>Rodriguez, P.</dc:creator>
<dc:creator>George, E.</dc:creator>
<dc:creator>Strobl, M. A. R.</dc:creator>
<dc:date>2025-07-16</dc:date>
<dc:identifier>doi:10.1101/2025.07.10.664235</dc:identifier>
<dc:title><![CDATA[Controlling treatment toxicity in ovarian cancer to prime the patient for tumor extinction therapy]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2025-07-16</prism:publicationDate>
<prism:section></prism:section>
</item>
</rdf:RDF>
