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Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 83,819 bioRxiv papers from 361,076 authors.

Most downloaded bioRxiv papers, all time

in category cancer biology

2,877 results found. For more information, click each entry to expand.

141: Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia
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Posted to bioRxiv 29 May 2014

Higher gene expression variability in the more aggressive subtype of chronic lymphocytic leukemia
1,476 downloads cancer biology

Simone Ecker, Vera Pancaldi, Daniel Rico, Alfonso Valencia

Background: Chronic Lymphocytic Leukemia (CLL) presents two subtypes which have drastically different clinical outcomes. So far, these two subtypes are not associated to clear differences in gene expression profiles. Interestingly, recent results have highlighted important roles for heterogeneity, both at the genetic and at the epigenetic level in CLL progression. Results: We propose to use gene expression variability across patients to investigate differences between the two CLL subtypes. We find that the most aggressive type of this disease shows higher variability of gene expression across patients and we elaborate on this observation to produce a method that classifies patients into clinical subtypes. Finally, we find that, overall, genes that show higher variability in the aggressive subtype are related to cell cycle, development and inter-cellular communication, probably related to faster progression of this disease subtype. Conclusions: There are strong relations between disease subtype and gene expression variability linking significantly increased expression variability to phenotypes such as aggressiveness and resistance to therapy in CLL.

142: Direct genome editing of patient-derived xenografts using CRISPR-Cas9 enables rapid in vivo functional genomics
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Posted to bioRxiv 26 Mar 2019

Direct genome editing of patient-derived xenografts using CRISPR-Cas9 enables rapid in vivo functional genomics
1,474 downloads cancer biology

Christopher H Hulton, Emily A Costa, Nisargbhai S Shah, Alvaro Quintanal-Villalonga, Glenn Heller, Elisa de Stanchina, Charles M Rudin, John T Poirier

Patient-derived xenografts (PDXs) constitute a powerful set of preclinical models for in vivo cancer research, reflecting the spectrum of genomic alterations and therapeutic liabilities of human cancers. In contrast to either cancer cell lines or genetically engineered mouse models, the utility of PDXs has been limited by the inability to perform targeted genome editing of these tumors. To address this limitation, we have generated a lentiviral platform for CRISPR-Cas9 editing of PDXs using a tightly regulated, inducible Cas9 vector that does not require in vitro culture for selection of transduced cells. We demonstrate the utility of this platform in PDXs (1) to analyze genetic dependencies by targeted gene disruption and (2) to analyze mechanisms of acquired drug resistance by site-specific gene editing using templated homology-directed repair. This flexible system has broad application to other explant models and substantially augments the utility of PDXs as genetically programmable models of human cancer.

143: Unraveling Tumor-Immune Heterogeneity in Advanced Ovarian Cancer Uncovers Immunogenic Effect of Chemotherapy
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Posted to bioRxiv 12 Oct 2018

Unraveling Tumor-Immune Heterogeneity in Advanced Ovarian Cancer Uncovers Immunogenic Effect of Chemotherapy
1,466 downloads cancer biology

Alejandro Jiménez-Sánchez, Paulina Cybulska, Katherine Lavigne, Tyler Walther, Ines Nikolovski, Yousef Mazaheri, Britta Weigelt, Dennis S. Chi, Kay J. Park, Travis Hollmann, Dominique-Laurent Couturier, Alberto Vargas, James D. Brenton, Evis Sala, Alexandra Snyder, Martin L. Miller

In metastatic cancer, the role of heterogeneity at the tumor-immune microenvironment, its molecular underpinnings and clinical relevance remain largely unexplored. To understand tumor-immune dynamics at baseline and upon chemotherapy treatment, we performed unbiased pathway and cell type-specific immunogenomics analysis of treatment-naive (38 samples from 8 patients) and paired chemotherapy treated (80 paired samples from 40 patients) high-grade serous ovarian cancer (HGSOC) samples. Whole transcriptome analysis and image- based quantification of T cells from treatment-naive tumors revealed ubiquitous variability in immune signaling and distinct immune microenvironments co-existing within the same individuals and within tumor deposits at diagnosis. To systematically explore cell type composition of the tumor microenvironment using bulk mRNA, we derived consensus immune and stromal cell gene signatures by intersecting state-of-the-art deconvolution methods, providing improved accuracy and sensitivity when compared to HGSOC immunostaining and leukocyte methylation data sets. Cell-type deconvolution and pathway analyses revealed that Myc and Wnt signaling associate with immune cell exclusion in untreated HGSOC. To evaluate the effect of chemotherapy on the intrinsic tumor-immune heterogeneity, we compared site- matched and site-unmatched tumors before and after neoadjuvant chemotherapy. Transcriptomic and T-cell receptor sequencing analyses showed that site-matched samples had increased cytotoxic immune activation and oligoclonal expansion of T cells after chemotherapy, which was not seen in site-unmatched samples where heterogeneity could not be accounted for. These results demonstrate that the tumor-immune interface in advanced HGSOC is intrinsically heterogeneous, and thus requires site-specific analysis to reliably unmask the impact of therapy on the tumor-immune microenvironment.

144: The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic
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Posted to bioRxiv 28 Apr 2018

The landscape of selection in 551 Esophageal Adenocarcinomas defines genomic biomarkers for the clinic
1,462 downloads cancer biology

AM Frankell, S Jammula, X Li, G Contino, Sarah Killcoyne, S Abbas, J Perner, L Bower, G Devonshire, E Ococks, N Grehan, J Mok, M O’Donovan, S MacRae, M Eldridge, S Tavare, RC Fitzgerald, the Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) Consortium

Esophageal Adenocarcinoma (EAC) is a poor prognosis cancer type with rapidly rising incidence. Our understanding of genetic events which drive EAC development is limited and there are few molecular biomarkers for prognostication or therapeutics. We have accumulated a cohort of 551 genomically characterised EACs (73% WGS and 27% WES) with clinical annotation and matched RNA-seq. Using a variety of driver gene detection methods, we discover 77 EAC driver genes (73% novel) and 21 non-coding driver elements (95% novel), and describe mutation and CNV types with specific functional impact. We identify a mean of 4.4 driver events per case derived from both copy number events and mutations. We compare driver mutation rates to the exome-wide mutational excess calculated using Non-synonymous vs Synonymous mutation rates (dNdS). We observe mutual exclusivity or co-occurrence of events within and between a number of EAC pathways (GATA factors, Core Cell cycle genes, TP53 regulators and the SWI/SNF complex) suggestive of important functional relationships. These driver variants correlate with tumour differentiation, sex and prognosis. Poor prognostic indicators (SMAD4, GATA4) are verified in independent cohorts with significant predictive value. Over 50% of EACs contain sensitising events for CDK4/6 inhibitors which are highly correlated with clinically relevant sensitivity in a panel EAC cell lines and organoids.

145: Continuous lineage recording reveals rapid, multidirectional metastasis in a lung cancer xenograft model in mouse
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Posted to bioRxiv 17 Apr 2020

Continuous lineage recording reveals rapid, multidirectional metastasis in a lung cancer xenograft model in mouse
1,460 downloads cancer biology

Jeffrey J Quinn, Matthew G Jones, Ross A. Okimoto, Michelle M. Chan, Nir Yosef, Trever G. Bivona, Jonathan S. Weissman

Consequential events in cancer progression are typically rare and occur in the unobserved past. Detailed cell phylogenies can capture the history and chronology of such transient events - including metastasis. Here, we applied our Cas9-based lineage tracer to study metastatic progression in a lung cancer xenograft mouse model, revealing the underlying rates, routes, and patterns of metastasis. We report deeply resolved phylogenies for tens of thousands of metastatically disseminated cancer cells. We observe surprisingly diverse metastatic phenotypes, ranging from metastasis-incompetent to highly aggressive populations, and these differences are associated with characteristic changes in transcriptional state, including differential expression of metastasis-related genes like IFI27 and ID3. We further show that metastases transit via tissue routes that are diverse, complex, and multidirectional, and identify examples of reseeding, seeding cascades, and parallel seeding topologies. More broadly, we demonstrate the power of next-generation lineage tracers to record cancer evolution at high resolution and vast scale. ### Competing Interest Statement J.S.W. is an advisor and/or has equity in KSQ Therapeutics, Maze Therapeutics, Amgen, Tenaya, and 5 AM Ventures. T.G.B. is an advisor to Novartis, Astrazeneca, Revolution Medicines, Array, Springworks, Strategia, Relay, Jazz, Rain and receives research funding from Novartis and Revolution Medicines.

146: Glioblastoma recurrence and the role of MGMT promoter methylation
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Posted to bioRxiv 09 May 2018

Glioblastoma recurrence and the role of MGMT promoter methylation
1,455 downloads cancer biology

Katie Storey, Kevin Leder, Andrea Hawkins-Daarud, Kristin Swanson, Atique U. Ahmed, Russell C. Rockne, Jasmine Foo

Tumor recurrence in glioblastoma multiforme (GBM) is often attributed to acquired resistance to the standard chemotherapeutic agent temozolomide (TMZ). Promoter methylation of the DNA repair gene MGMT has been associated with sensitivity to TMZ, while increased expression of MGMT has been associated with TMZ resistance. Clinical studies have observed a downward shift in MGMT methylation percentage from primary to recurrent stage tumors. However, the evolutionary processes driving this shift, and more generally the emergence and growth of TMZ-resistant tumor subpopulations, are still poorly understood. Here we develop a mathematical model, parameterized using clinical and experimental data, to investigate the role of MGMT methylation in TMZ resistance during the standard treatment regimen for GBM (surgery, chemotherapy and radiation). We first find that the observed downward shift in MGMT promoter methylation status between detection and recurrence cannot be explained solely by evolutionary selection. Next, our model suggests that TMZ has an inhibitory effect on maintenance methylation of MGMT after cell division. Finally, incorporating this inhibitory effect, we study the optimal number of TMZ doses per adjuvant cycle for GBM patients with high and low levels of MGMT methylation at diagnosis.

147: Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy
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Posted to bioRxiv 18 Mar 2018

Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy
1,447 downloads cancer biology

Avinash Das, Joo Sang Lee, Gao Zhang, Zhiyong Wang, Ramiro Iglesias-Bartolome, Tian Tian, Zhi Wei, Benchun Miao, Nishanth Ulhas Nair, Olga Ponomarova, Adam A. Friedman, Arnaud Amzallag, Tabea Moll, Gyulnara Kasumova, Patricia Greninger, Regina K. Egan, Leah J. Damon, Dennie T. Frederick, Allon Wagner, Kuoyuan Cheng, Seung Gu Park, Welles Robinson, Kevin Gardner, Genevieve Boland, Sridhar Hannenhalli, Meenhard Herlyn, Cyril Benes, J Silvio Gutkind, Keith Flaherty, Eytan Ruppin

Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here we perform a genome-wide prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients' response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.

148: Spatial structure governs the mode of tumour evolution
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Posted to bioRxiv 23 Mar 2019

Spatial structure governs the mode of tumour evolution
1,443 downloads cancer biology

Robert Noble, Dominik Burri, Jakob Nikolas Kather, Niko Beerenwinkel

Characterizing the mode – the way, manner, or pattern – of evolution in tumours is important for clinical forecasting and optimizing cancer treatment. DNA sequencing studies have inferred various modes, including branching, punctuated and neutral evolution, but it is unclear why a particular pattern predominates in any given tumour.[1][1], [2][2] Here we propose that differences in tumour architecture alone can explain the variety of observed patterns. We examine this hypothesis using spatially explicit population genetic models and demonstrate that, within biologically relevant parameter ranges, human tumours are expected to exhibit four distinct onco-evolutionary modes (oncoevotypes): rapid clonal expansion (predicted in leukaemia); progressive diversification (in colorectal adenomas and early-stage colorectal carcinomas); branching evolution (in invasive glandular tumours); and effectively almost neutral evolution (in certain non-glandular and poorly differentiated solid tumours). We thus provide a simple, mechanistic explanation for a wide range of empirical observations. Oncoevotypes are governed by the mode of cell dispersal and the range of cell-cell interaction, which we show are essential factors in accurately characterizing, forecasting and controlling tumour evolution. [1]: #ref-1 [2]: #ref-2

149: High risk glioblastoma cells revealed by machine learning and single cell signaling profiles
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Posted to bioRxiv 10 May 2019

High risk glioblastoma cells revealed by machine learning and single cell signaling profiles
1,440 downloads cancer biology

Nalin Leelatian, Justine Sinnaeve, Akshitkumar M Mistry, Sierra M Barone, Kirsten E Diggins, Allison R Greenplate, Kyle D Weaver, Reid C Thompson, Lola B Chambless, Bret C Mobley, Rebecca A. Ihrie, Jonathan M. Irish

Recent developments in machine learning implemented dimensionality reduction and clustering tools to classify the cellular composition of patient-derived tissue in multi-dimensional, single cell studies. Current approaches, however, require prior knowledge of either categorical clinical outcomes or cell type identities. These algorithms are not well suited for application in tumor biology, where clinical outcomes can be continuous and censored and cell identities may be novel and plastic. Risk Assessment Population IDentification (RAPID) is an unsupervised, machine learning algorithm that identifies single cell phenotypes and assesses clinical risk stratification as a continuous variable. Single cell mass cytometry evaluated 34 different phospho-proteins, transcription factors, and cell identity proteins in tumor tissue resected from patients bearing IDH wild-type glioblastomas. RAPID identified and characterized multiple biologically distinct tumor cell subsets that independently and continuously stratified patient outcome. RAPID is broadly applicable for single cell studies where atypical cancer and immune cells may drive disease biology and treatment responses.

150: Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in continuous and adaptive cancer therapies
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Posted to bioRxiv 20 Apr 2017

Spatial heterogeneity and evolutionary dynamics modulate time to recurrence in continuous and adaptive cancer therapies
1,437 downloads cancer biology

Jill A. Gallaher, Pedro M. Enriquez-Navas, Kimberly A. Luddy, Robert Gatenby, Alexander R.A. Anderson

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.

151: Pathway and network analysis of more than 2,500 whole cancer genomes
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Posted to bioRxiv 07 Aug 2018

Pathway and network analysis of more than 2,500 whole cancer genomes
1,436 downloads cancer biology

Matthew A. Reyna, David Haan, Marta Paczkowska, Lieven P.C. Verbeke, Miguel Vazquez, Abdullah Kahraman, Sergio Pulido-Tamayo, Jonathan Barenboim, Lina Wadi, Priyanka Dhingra, Raunak Shrestha, Gad Getz, Michael S Lawrence, Jakob Skou Pedersen, Mark A. Rubin, David A. Wheeler, Søren Brunak, Jose MG Izarzugaza, Ekta Khurana, Kathleen Marchal, Christian von Mering, S. Cenk Sahinalp, Alfonso Valencia, Jüri Reimand, Joshua M. Stuart, Benjamin J. Raphael, on behalf of the PCAWG Drivers and Functional Interpretation Group and the ICGC/TCGA Pan-Cancer Analysis of Whole Genome Network

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

152: Heterogeneity and targeted therapy-induced adaptations in lung cancer revealed by longitudinal single-cell RNA sequencing
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Posted to bioRxiv 13 Dec 2019

Heterogeneity and targeted therapy-induced adaptations in lung cancer revealed by longitudinal single-cell RNA sequencing
1,433 downloads cancer biology

Ashley Maynard, Caroline E McCoach, Julia K Rotow, Lincoln Harris, Franziska Haderk, Lucas Kerr, Elizabeth A Yu, Erin L Schenk, Weilun Tan, Alexander Zee, Michelle Tan, Philippe Gui, Tasha Lea, Wei Wu, Anatoly Urisman, Kirk Jones, Rene Sit, Pallav K Kolli, Eric Seeley, Yaron Gesthalter, Daniel D Le, Kevin A Yamauchi, David Naeger, Nicholas J Thomas, Anshal Gupta, Mayra Gonzalez, Hien Do, Lisa Tan, Rafael Gomez-Sjoberg, Matthew Gubens, Thierry Jahan, Johannes R Kratz, David Jablons, Norma Neff, Robert C. Doebele, Jonathan Weissman, Collin M. Blakely, Spyros Darmanis, Trever G. Bivona

Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNAseq) of metastatic lung cancer was performed using 44 tumor biopsies obtained longitudinally from 27 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNAseq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNAseq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.

153: Rapid identification of optimal drug combinations for personalized cancer therapy using microfluidics
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Posted to bioRxiv 14 Dec 2016

Rapid identification of optimal drug combinations for personalized cancer therapy using microfluidics
1,433 downloads cancer biology

Federica Eduati, Ramesh Utharala, Dharanija Madhavan, Ulf Peter Neumann, Thorsten Cramer, Julio Saez-Rodriguez, Christoph A. Merten

Functional screening of live patient cancer cells holds great potential for personalized medicine and allows to overcome the limited translatability of results from existing in-vitro and ex-vivo screening models. Here we present a plug-based microfluidics approach enabling the testing of drug combinations directly on cancer cells from patient biopsies. The entire procedure takes less than 48 hours after surgery and does not require ex vivo cultivation. We screened more than 1100 samples for different primary human tumors (each with 56 conditions and at least 20 replicates), and obtained highly specific sensitivity profiles. This approach allowed us to derive optimal treatment options which we further validated in two different pancreatic cancer cell lines. This workflow should pave the way for rapid determination of optimal personalized cancer therapies at assay costs of less than US$ 150 per patient.

154: Reconstructing phylogenies of metastatic cancers
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Posted to bioRxiv 11 Apr 2016

Reconstructing phylogenies of metastatic cancers
1,431 downloads cancer biology

Johannes G. Reiter, Alvin P. Makohon-Moore, Jeffrey M. Gerold, Ivana Bozic, Krishnendu Chatterjee, Christine A. Iacobuzio-Donahue, Bert Vogelstein, Martin A. Nowak

Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumor samples at unprecedented depth. However, existing methods are not designed to infer metastatic seeding patterns. We have developed a tool, called Treeomics, that utilizes Bayesian inference and Integer Linear Programming to reconstruct the phylogeny of metastases. Treeomics allowed us to infer comprehensive seeding patterns for pancreatic, ovarian, and prostate cancers. Moreover, Treeomics correctly disambiguated true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumor heterogeneity among distinct samples. Last, we performed in silico benchmarking on simulated tumor phylogenies across a wide range of sample purities (30-90%) and sequencing depths (50-800x) to demonstrate the high accuracy of Treeomics compared to existing methods.

155: Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance.
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Posted to bioRxiv 10 May 2017

Integrated time course omics analysis distinguishes immediate therapeutic response from acquired resistance.
1,429 downloads cancer biology

Genevieve L. Stein-O’Brien, Luciane T Kagohara, Sijia Li, Manjusha Thakar, Ruchira Ranaweera, Hiroyuki Ozawa, Haixia Cheng, Michael Considine, Sandra Schmitz, Alexander V Favorov, Ludmila Danilova, Joseph A Califano, Evgeny Izumchenko, Daria A Gaykalova, Christine H Chung, Elana J. Fertig

BACKGROUND: Targeted therapies specifically act by blocking the activity of proteins that are encoded by genes critical for tumorigenesis. However, most cancers acquire resistance and long-term disease remission is rarely observed. Understanding the time course of molecular changes responsible for the development of acquired resistance could enable optimization of patients treatment options. Clinically, acquired therapeutic resistance can only be studied at a single time point in resistant tumors. To determine the dynamics of these molecular changes, we obtained high throughput omics data weekly during the development of cetuximab resistance in a head and neck cancer in vitro model. RESULTS: An unsupervised algorithm, CoGAPS, was used to quantify the evolving transcriptional and epigenetic changes. Applying a PatternMarker statistic to the results from CoGAPS enabled novel heatmap-based visualization of the dynamics in these time course omics data. We demonstrate that transcriptional changes result from immediate therapeutic response or resistance, whereas epigenetic alterations only occur with resistance. Integrated analysis demonstrates delayed onset of changes in DNA methylation relative to transcription, suggesting that resistance is stabilized epigenetically. CONCLUSIONS: Genes with epigenetic alterations associated with resistance that have concordant expression changes are hypothesized to stabilize resistance. These genes include FGFR1, which was associated with EGFR inhibitor resistance previously. Thus, integrated omics analysis distinguishes the timing of molecular drivers of resistance. Our findings provide a relevant towards better understanding of the time course progression of changes resulting in acquired resistance to targeted therapies. This is an important contribution to the development of alternative treatment strategies that would introduce new drugs before the resistant phenotype develops.

156: Bifunctional Small Molecule Ligands of K-Ras Induce Its Association with Immunophilin Proteins
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Posted to bioRxiv 26 Apr 2019

Bifunctional Small Molecule Ligands of K-Ras Induce Its Association with Immunophilin Proteins
1,428 downloads cancer biology

Ziyang Zhang, Kevan M. Shokat

Here we report the design, synthesis and characterization of bifunctional chemical ligands that induce the association of Ras with ubiquitously expressed immunophilin proteins such as FKBP12 and cyclophilin A. We show this approach is applicable to two distinct Ras ligand scaffolds, and that both the identity of the immunophilin ligand and the linker chemistry affect compound efficacy in biochemical and cellular contexts. These ligands bind to Ras in an immunophilin-dependent fashion and mediate the formation of tripartite complexes of Ras, immunophilin and the ligand. The recruitment of cyclophilin A to GTP-bound Ras blocks its interaction with B-Raf in biochemical assays. Our study demonstrates the feasibility of ligand-induced association of Ras with intracellular proteins and suggests it as a promising therapeutic strategy for Ras-driven cancers.

157: Exploiting evolutionary herding to control drug resistance in cancer
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Posted to bioRxiv 04 Mar 2019

Exploiting evolutionary herding to control drug resistance in cancer
1,426 downloads cancer biology

Ahmet Acar, Daniel Nichol, Javier Fernandez-Mateos, George D. Cresswell, Iros Barozzi, Sung Pil Hong, Inmaculada Spiteri, Mark Stubbs, Rosemary Burke, Adam Stewart, Georgios Vlachogiannis, Carlo C. Maley, Luca Magnani, Nicola Valeri, Udai Banerji, Andrea Sottoriva

Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased growth rate or increased sensitivity to another drug due to evolutionary trade offs. This weakness can be exploited in the clinic using an approach called evolutionary herding that aims at controlling the tumour cell population to delay or prevent resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here we present a novel approach for evolutionary herding based on a combination of single-cell barcoding, very large populations of 10^8-10^9 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary herding in non-small cell lung cancer, showing that herding allows shifting the clonal composition of a tumour in our favour, leading to collateral drug sensitivity and proliferative fitness costs. Through genomic analysis and single-cell sequencing, we were also able to determine the mechanisms that drive such evolved sensitivity. Our approach allows modelling evolutionary trade-offs experimentally to test patient-specific evolutionary herding strategies that can potentially be translated into the clinic to control treatment resistance.

158: Widespread impact of DNA replication on mutational mechanisms in cancer
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Posted to bioRxiv 24 Feb 2017

Widespread impact of DNA replication on mutational mechanisms in cancer
1,418 downloads cancer biology

Marketa Tomkova, Jakub Tomek, Skirmantas Kriaucionis, B Schuster-Böckler

DNA replication plays an important role in mutagenesis, yet little is known about how it interacts with other mutagenic processes. Here, we use somatic mutation signatures - each representing a mutagenic process - derived from 3056 patients spanning 19 cancer types to quantify the asymmetry of mutational signatures around replication origins and between early and late replicating regions. We observe that 22 out of 29 mutational signatures are significantly impacted by DNA replication. The distinct associations of different signatures with replication timing and direction around origins shed new light on several mutagenic processes, for example suggesting that oxidative damage to the nucleotide pool substantially contributes to the mutational landscape of esophageal adenocarcinoma. Together, our results indicate an involvement of DNA replication and associated damage repair in most mutagenic processes.

159: Identification of an immune gene expression signature associated with favorable clinical features in Treg-enriched patient tumor samples
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Posted to bioRxiv 11 Jan 2018

Identification of an immune gene expression signature associated with favorable clinical features in Treg-enriched patient tumor samples
1,416 downloads cancer biology

Kevin B. Givechian, Kamil Wnuk, Chad Garner, Stephen Benz, Hermes Garban, Shahrooz Rabizadeh, Kayvan Niazi, Patrick Soon-Shiong

Immune heterogeneity within the tumor microenvironment undoubtedly adds several layers of complexity to our understanding of drug sensitivity and patient prognosis across various cancer types. Within the tumor microenvironment, immunogenicity is a favorable clinical feature in part driven by the antitumor activity of CD8+ T cells. However, tumors often inhibit this antitumor activity by exploiting the suppressive function of Regulatory T cells (Tregs), thus suppressing the adaptive immune response. Despite the seemingly intuitive immunosuppressive biology of Tregs, prognostic studies have produced contradictory results regarding the relationship between Treg enrichment and survival. We therefore analyzed RNA-seq data of Treg-enriched tumor samples to derive a pan-cancer gene signature able to help reconcile the inconsistent results of Treg studies, by better understanding the variable clinical association of Tregs across alternative tumor contexts. We show that increased expression of a 32-gene signature in Treg-enriched tumor samples (n=135) is able to distinguish a cohort of patients associated with chemosensitivity and overall survival This cohort is also enriched for CD8+ T cell abundance, as well as the antitumor M1 macrophage subtype. With a subsequent validation in a larger TCGA pool of Treg-enriched patients (n = 626), our results reveal a gene signature able to produce unsupervised clusters of Treg-enriched patients, with one cluster of patients uniquely representative of an immunogenic tumor microenvironment. Ultimately, these results support the proposed gene signature as a putative biomarker to identify certain Treg-enriched patients with immunogenic tumors that are more likely to be associated with features of favorable clinical outcome.

160: A population of stress-like cancer cells in melanoma promotes tumorigenesis and confers drug resistance
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Posted to bioRxiv 22 Aug 2018

A population of stress-like cancer cells in melanoma promotes tumorigenesis and confers drug resistance
1,416 downloads cancer biology

Maayan Baron, Mohita Tagore, Miranda V Hunter, Isabella S Kim, Reuben Moncada, Yun Yan, Nathaniel R. Campbell, Richard M White, Itai Yanai

Transcriptional profiling has revealed a diverse range of cancer cell states, however an understanding of their function has remained elusive. Using a combination of zebrafish melanoma modeling and human validation, we have identified a conserved stress-like state that confers intrinsic drug resistance. The stress-like state expresses genes such as fos , hsp70 and ubb , all required for adaptation to diverse cellular stresses, and we confirmed its existence using immunofluorescence and spatial transcriptomics. We provide evidence that this state has a higher tumor seeding capabilities compared to non-stressed cells, and confers intrinsic resistance to MEK inhibitors, a commonly used melanoma therapeutic. Furthermore, the stress-like program can be induced by extrinsic processes such as heat shock, and confers resistance to both MEK and BRAF inhibitors in both zebrafish and human melanomas. Collectively, our study suggests that the transcriptional states associated with therapeutic failure are established during the earliest steps of tumorigenesis.

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