Most downloaded biology preprints, all time
in category cancer biology
6,779 results found. For more information, click each entry to expand.
296,537 downloads bioRxiv cancer biology
The U.S. National Toxicology Program (NTP) has carried out extensive rodent toxicology and carcinogenesis studies of radiofrequency radiation (RFR) at frequencies and modulations used in the U.S. telecommunications industry. This report presents partial findings from these studies. The occurrences of two tumor types in male Harlan Sprague Dawley rats exposed to RFR, malignant gliomas in the brain and schwannomas of the heart, were considered of particular interest and are the subject of this report. The findings in this report were reviewed by expert peer reviewers selected by the NTP and National Institutes of Health (NIH). These reviews and responses to comments are included as appendices to this report, and revisions to the current document have incorporated and addressed these comments. When the studies are completed, they will undergo additional peer review before publication in full as part of the NTP's Toxicology and Carcinogenesis Technical Reports Series. No portion of this work has been submitted for publication in a scientific journal. Supplemental information in the form of four additional manuscripts has or will soon be submitted for publication. These manuscripts describe in detail the designs and performance of the RFR exposure system, the dosimetry of RFR exposures in rats and mice, the results to a series of pilot studies establishing the ability of the animals to thermoregulate during RFR exposures, and studies of DNA damage. (1) Capstick M, Kuster N, Kuhn S, Berdinas-Torres V, Wilson P, Ladbury J, Koepke G, McCormick D, Gauger J, and Melnick R. A radio frequency radiation reverberation chamber exposure system for rodents; (2) Yijian G, Capstick M, McCormick D, Gauger J, Horn T, Wilson P, Melnick RL, and Kuster N. Life time dosimetric assessment for mice and rats exposed to cell phone radiation; (3) Wyde ME, Horn TL, Capstick M, Ladbury J, Koepke G, Wilson P, Stout MD, Kuster N, Melnick R, Bucher JR, and McCormick D. Pilot studies of the National Toxicology Program's cell phone radiofrequency radiation reverberation chamber exposure system; (4) Smith-Roe SL, Wyde ME, Stout MD, Winters J, Hobbs CA, Shepard KG, Green A, Kissling GE, Tice RR, Bucher JR, and Witt KL. Evaluation of the genotoxicity of cell phone radiofrequency radiation in male and female rats and mice following subchronic exposure.
20,779 downloads bioRxiv cancer biology
Ludmil B Alexandrov, Jaegil Kim, Nicholas J. Haradhvala, Mi Ni Huang, Alvin WT Ng, Yang Wu, Arnoud Boot, Kyle R Covington, Dmitry A Gordenin, Erik N. Bergstrom, S M Ashiqul Islam, Nuria Lopez-Bigas, Leszek J. Klimczak, John R McPherson, Sandro Morganella, Radhakrishnan Sabarinathan, David A. Wheeler, Ville Mustonen, the PCAWG Mutational Signatures Working Group, Gad Getz, Steven G. Rozen, Michael R Stratton
Somatic mutations in cancer genomes are caused by multiple mutational processes each of which generates a characteristic mutational signature. Using 84,729,690 somatic mutations from 4,645 whole cancer genome and 19,184 exome sequences encompassing most cancer types we characterised 49 single base substitution, 11 doublet base substitution, four clustered base substitution, and 17 small insertion and deletion mutational signatures. The substantial dataset size compared to previous analyses enabled discovery of new signatures, separation of overlapping signatures and decomposition of signatures into components that may represent associated, but distinct, DNA damage, repair and/or replication mechanisms. Estimation of the contribution of each signature to the mutational catalogues of individual cancer genomes revealed associations with exogenous and endogenous exposures and defective DNA maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes contributing to the development of human cancer including a comprehensive reference set of mutational signatures in human cancer.
18,271 downloads bioRxiv cancer biology
Visual analysis of histopathology slides of lung cell tissues is one of the main methods used by pathologists to assess the stage, types and sub-types of lung cancers. Adenocarcinoma and squamous cell carcinoma are two most prevalent sub-types of lung cancer, but their distinction can be challenging and time-consuming even for the expert eye. In this study, we trained a deep learning convolutional neural network (CNN) model (inception v3) on histopathology images obtained from The Cancer Genome Atlas (TCGA) to accurately classify whole-slide pathology images into adenocarcinoma, squamous cell carcinoma or normal lung tissue. Our method slightly outperforms a human pathologist, achieving better sensitivity and specificity, with ~0.97 average Area Under the Curve (AUC) on a held-out population of whole-slide scans. Furthermore, we trained the neural network to predict the ten most commonly mutated genes in lung adenocarcinoma. We found that six of these genes - STK11, EGFR, FAT1, SETBP1, KRAS and TP53 - can be predicted from pathology images with an accuracy ranging from 0.733 to 0.856, as measured by the AUC on the held-out population. These findings suggest that deep learning models can offer both specialists and patients a fast, accurate and inexpensive detection
15,497 downloads bioRxiv cancer biology
To understand the architecture of a tissue it is necessary to know both the cell populations and their physical relationships to one another. Single-cell RNA-Seq (scRNA-Seq) has made significant progress towards the unbiased and systematic characterization of the cell populations within a tissue, as well as their cellular states, by studying hundreds and thousands of cells in a single experiment. However, the characterization of the spatial organization of individual cells within a tissue has been more elusive. The recently introduced "spatial transcriptomics" method (ST) reveals the spatial pattern of gene expression within a tissue section at a resolution of one thousand 100 µm spots, each capturing the transcriptomes of ~10-20 cells. Here, we present an approach for the integration of scRNA-Seq and ST data generated from the same sample of pancreatic cancer tissue. Using markers for cell-types identified by scRNA-Seq, we robustly deconvolved the cell-type composition of each ST spot, to generate a spatial atlas of cell proportions across the tissue. Studying this atlas, we found that distinct spatial localizations accompany each of the three cancer cell populations that we identified. Strikingly, we find that subpopulations defined in the scRNA-Seq data also exhibit spatial segregation in the atlas, suggesting such an atlas may be used to study the functional attributes of subpopulations. Our results provide a framework for creating a tumor atlas by mapping single-cell populations to their spatial region, as well as the inference of cell architecture in any tissue.
15,372 downloads bioRxiv cancer biology
Chenchen Pan, Oliver Schoppe, Arnaldo Parra-Damas, Ruiyao Cai, Mihail Ivilinov Todorov, Gabor Gondi, Bettina von Neubeck, Alireza Ghasemi, Madita Alice Reimer, Javier Coronel, Boyan K. Garvalov, Bjoern Menze, Reinhard Zeidler, Ali Ertuerk
Reliable detection of disseminated tumor cells and of the biodistribution of tumor-targeting therapeutic antibodies within the entire body has long been needed to better understand and treat cancer metastasis. Here, we developed an integrated pipeline for automated quantification of cancer metastases and therapeutic antibody targeting, named DeepMACT. First, we enhanced the fluorescent signal of tumor cells more than 100-fold by applying the vDISCO method to image single cancer cells in intact transparent mice. Second, we developed deep learning algorithms for automated quantification of metastases with an accuracy matching human expert manual annotation. Deep learning-based quantifications in a model of spontaneous metastasis using human breast cancer cells allowed us to systematically analyze clinically relevant features such as size, shape, spatial distribution, and the degree to which metastases are targeted by a therapeutic monoclonal antibody in whole mice. DeepMACT can thus considerably improve the discovery of effective therapeutic strategies for metastatic cancer.
13,783 downloads bioRxiv cancer biology
Karen Levy, Suchitra Natarajan, Jinghui Wang, Stephanie Chow, Joshua T. Eggold, Phoebe Loo, Rakesh Manjappa, Frederick M. Lartey, Emil Schüler, Lawrie Skinner, Marjan Rafat, Ryan Ko, Anna A Kim, Duaa Al Rawi, Rie von Eyben, Oliver Dorigo, Kerriann M Casey, Edward E Graves, Karl Bush, Amy S. Yu, Albert C Koong, Peter G. Maxim, Billy W Loo, Erinn B Rankin
Peritoneal metastases are the leading cause of morbidity and mortality in ovarian cancer. Despite current surgery, chemotherapy and targeted therapies, the majority of patients diagnosed with advanced epithelial ovarian cancer develop recurrent disease and overall survival rates remain poor. It is known that ovarian cancer is a radiosensitive tumor. Historically, total abdominal irradiation (TAI) was used as an effective postsurgical adjuvant therapy in the management of chemotherapy sensitive and resistant ovarian cancer. However, TAI fell out of favor due to high toxicity, particularly of the gastrointestinal tract. We have developed a preclinical irradiation platform that allows for total abdominal ultrahigh dose rate FLASH irradiation. We demonstrate that TAI-FLASH reduces radiation-induced intestinal injury in both healthy and tumor-bearing mice compared to conventional dose rate (CONV) irradiation. Single high dose TAI-FLASH reduced mortality from gastrointestinal syndrome, spared gut function and epithelial integrity, and decreased cell death in crypt base columnar cells. Importantly, FLASH and CONV irradiation had similar efficacy in the reduction of ovarian cancer peritoneal metastases. These findings suggest that FLASH irradiation may be an effective strategy to enhance the therapeutic index of radiotherapy for the treatment of metastatic ovarian cancer in women.
13,031 downloads bioRxiv cancer biology
Cancer cells have a more hyperpolarised mitochondrial membrane potential (Ψ) than normal cells. Ψ = ~-220 mV in cancer cells as compared to ~-140 mV in normal cells. Until now it has not been known why. This paper explains this disparity, in a mathematical framework, and identifies molecular targets and operations unique to cancer cells. These are thence prospective cancer drug targets. BMS-199264 is proposed as an anti-cancer drug. It inhibits the reverse, proton-pumping mode of ATP synthase, which this paper identifies as crucial to cancer cells but not to healthy, normal adult cells. In the cancer cell model, the adenine nucleotide exchanger (ANT) is inversely orientated in the mitochondrial inner membrane as compared to normal cells. This predicts it to have a different drug interaction profile, which can be leveraged for cancer therapy. Uncouplers, which dissipate the proton motive force, are proposed as anti-cancer medicines e.g. 2,4-dinitrophenol.
10,496 downloads bioRxiv cancer biology
Intratumoral immune cells are crucial for tumor control and anti-tumor responses during immunotherapy. Immune cell trafficking into tumors is mediated by chemokines, which are expressed and secreted upon various stimuli and interact with specific receptors. To broadly characterize chemokine expression and function in tumors, we have used multiplex mass cytometry-based imaging of protein markers and RNA transcripts to analyze the chemokine landscape and immune infiltration in metastatic melanoma samples. Tumors that lacked immune infiltration were devoid of most chemokines and exhibited particularly low levels of antigen presentation and inflammation. Infiltrated tumors were characterized by expression of multiple chemokines. CXCL9 and CXCL10 were often localized in patches associated with dysfunctional T cells expressing CXCL13 which was strongly associated with B cell patches and follicles. TCF7+ naive-like T cells, which predict response to immunotherapy, were enriched in the vicinity of B cell patches and follicles. Our data highlight the strength of RNA and protein co-detection which was critical to deconvolve specialized immune microenvironments in inflamed tumors based on chemokine expression. Our findings further suggest that the formation of tertiary lymphoid structures is accompanied by naive and naive-like T cell recruitment, which ultimately boosts anti-tumor activity.
9,832 downloads bioRxiv cancer biology
Moritz Gerstung, Clemency Jolly, Ignaty Leshchiner, Stefan Dentro, Santiago Gonzalez, Daniel Rosebrock, Thomas J Mitchell, Yulia Rubanova, Pavana Anur, Kaixian Yu, Maxime Tarabichi, Amit G. Deshwar, Jeff A. Wintersinger, Kortine Kleinheinz, Ignacio Vázquez-García, Kerstin Haase, Lara Jerman, Subhajit Sengupta, Geoff Macintyre, Salem Malikić, Nilgun Donmez, Dimitri G Livitz, Marek Cmero, Jonas Demeulemeester, Steven Schumacher, Yu Fan, Xiaotong Yao, Juhee Lee, Matthias Schlesner, Paul C Boutros, David D. Bowtell, Hongtu Zhu, Gad Getz, Marcin Imielinski, Rameen Beroukhim, S. Cenk Sahinalp, Yuan Ji, Martin Peifer, Florian Markowetz, Ville Mustonen, Ke Yuan, Wenyi Wang, Quaid Morris, Paul T Spellman, David C Wedge, Peter Van Loo, on behalf of the PCAWG Evolution and Heterogeneity Working Group, the PCAWG network
Cancer develops through a process of somatic evolution. Here, we reconstruct the evolutionary history of 2,778 tumour samples from 2,658 donors spanning 39 cancer types. Characteristic copy number gains, such as trisomy 7 in glioblastoma or isochromosome 17q in medulloblastoma, are found amongst the earliest events in tumour evolution. The early phases of oncogenesis are driven by point mutations in a restricted set of cancer genes, often including biallelic inactivation of tumour suppressors. By contrast, increased genomic instability, a more than three-fold diversification of driver genes, and an acceleration of mutational processes are features of later stages. Clock-like mutations yield estimates for whole genome duplications and subclonal diversification in chronological time. Our results suggest that driver mutations often precede diagnosis by many years, and in some cases decades. Taken together, these data reveal common and divergent trajectories of cancer evolution, pivotal for understanding tumour biology and guiding early cancer detection.
9,738 downloads bioRxiv cancer biology
Yiwu Yan, Bo Zhou, Chen Qian, Alex Vasquez, Avradip Chatterjee, Xiaopu Yuan, Edwin Posadas, Natasha Kyprianou, Beatrice S. Knudsen, Ramachandran Murali, Arkadiusz Gertych, Sungyong You, Michael R Freeman, Wei Yang
Despite advances in diagnosis and treatment, metastatic prostate cancer remains incurable and is associated with high mortality rates. Thus, novel actionable drug targets are urgently needed for therapeutic interventions in advanced prostate cancer. Here we report receptor-interacting protein kinase 2 (RIPK2) as an actionable drug target for suppressing prostate cancer metastasis. RIPK2 is frequently amplified in lethal prostate cancers and its overexpression is associated with disease progression and aggressiveness. Genetic and pharmacological inhibition of RIPK2 significantly suppressed prostate cancer progression in vitro and metastasis in vivo. Multi-level proteomic analysis revealed that RIPK2 strongly regulates c-Myc protein stability and activity, largely by activating the MKK7/JNK/c-Myc phosphorylation pathway - a novel, non-canonical RIPK2 signaling pathway. Targeting RIPK2 inhibits this phosphorylation pathway, and thus promotes the degradation of c-Myc - a potent oncoprotein for which no drugs have been approved for clinical use yet. These results support targeting RIPK2 for personalized therapy in prostate cancer patients towards improving survival. ### Competing Interest Statement The authors have declared no competing interest.
9,194 downloads bioRxiv cancer biology
Crosstalk between tumor cells and other cells within the tumor microenvironment (TME) plays a crucial role in tumor progression, metastases, and therapy resistance. We present iTALK, a computational approach to characterize and illustrate intercellular communication signals in the multicellular tumor ecosystem using single-cell RNA sequencing data. iTALK can in principle be used to dissect the complexity, diversity, and dynamics of cell-cell communication from a wide range of cellular processes.
9,070 downloads bioRxiv cancer biology
Paula Nieto, Marc Marc Elosua-Bayes, Juan Luis Trincado, Domenica Marchese, Ramon Massoni-Badosa, Maria Salvany, Ana Henriques, Elisabetta Mereu, Catia Moutinho, Sara Ruiz, Patricia Lorden, Vanessa Chin, Dominik Conrad Kaczorowski, Chia-Ling Chan, Richard Gallagher, Angela Chou, Ester Planas-Rigol, Carlota Rubio-Perez, Ivo Glynne Gut, Joseph Maria Piulats, Joan Seoane, Joseph Powell, Eduard Batlle, Holger Heyn
The tumor immune microenvironment is a main contributor to cancer progression and a promising therapeutic target for oncology. However, immune microenvironments vary profoundly between patients and biomarkers for prognosis and treatment response lack precision. A comprehensive compendium of tumor immune cells is required to pinpoint predictive cellular states and their spatial localization. We generated a single-cell tumor immune atlas, jointly analyzing >500,000 cells from 217 patients and 13 cancer types, providing the basis for a patient stratification based on immune cell compositions. Projecting immune cells from external tumors onto the atlas facilitated an automated cell annotation system for a harmonized interpretation. To enable in situ mapping of immune populations for digital pathology, we developed, benchmarked and applied SPOTlight, a computational tool that identified striking spatial immune cell patterns in tumor sections. We expect the atlas, together with our versatile toolbox for precision oncology, to advance currently applied stratification approaches for prognosis and immuno-therapy. ### Competing Interest Statement JS is co-founder of Mosaic Biomedicals, Board member of Northern-Mosaic partnership and has ownership interests from Mosaic Biomedicals and Northern Biologics. JS received grant/research support from Mosaic Biomedicals, Northern Biologics, Roche/Glycart and Hoffmann la Roche. JS reports scientific consultancy role for Merck Serono, GSK, Eli Lilly. All authors declare no conflicts of interest associated with this manuscript. All other authors declare no conflicts of interest associated with this manuscript.
9,008 downloads bioRxiv cancer biology
One of the main goals of the Cancer Dependency Map project is to systematically identify cancer vulnerabilities across cancer types to accelerate therapeutic discovery. Project Achilles serves this goal through the in vitro study of genetic dependencies in cancer cell lines using CRISPR/Cas9 (and, previously, RNAi) loss-of-function screens. The project is committed to the public release of its experimental results quarterly on the DepMap Portal (https://depmap.org), on a pre-publication basis. As the experiment has evolved, data processing procedures have changed. Here we present the current and projected Achilles processing pipeline, including recent improvements and the analyses that led us to adopt them, spanning data releases from early 2018 to the first quarter of 2020. Notable changes include quality control metrics, calculation of probabilities of dependency, and correction for screen quality and other biases. Developing and improving methods for extracting biologically-meaningful scores from Achilles experiments is an ongoing process, and we will continue to evaluate and revise data processing procedures to produce the best results.
8,781 downloads bioRxiv cancer biology
Michele Olivieri, Tiffany Cho, Alejandro Álvarez-Quilón, Kejiao Li, Matthew J. Schellenberg, Michal Zimmermann, Nicole Hustedt, Silvia Emma Rossi, Salomé Adam, Henrique Melo, Anne Margriet Heijink, Guillermo Sastre-Moreno, Nathalie Moatti, Rachel Szilard, Andrea McEwan, Alexanda K. Ling, Almudena Serrano-Benitez, Tajinder Ubhi, Irene Delgado-Sainz, Michael W. Ferguson, Grant W Brown, Felipe Cortés-Ledesma, R. Scott Williams, Alberto Martin, Dongyi Xu, Daniel Durocher
The response to DNA damage is critical for cellular homeostasis, tumor suppression, immunity and gametogenesis. In order to provide an unbiased and global view of the DNA damage response in human cells, we undertook 28 CRISPR/Cas9 screens against 25 genotoxic agents in the retinal pigment epithelium-1 (RPE1) cell line. These screens identified 840 genes whose loss causes either sensitivity or resistance to DNA damaging agents. Mining this dataset, we uncovered that ERCC6L2, which is mutated in a bone-marrow failure syndrome, codes for a canonical non-homologous end-joining pathway factor; that the RNA polymerase II component ELOF1 modulates the response to transcription-blocking agents and that the cytotoxicity of the G-quadruplex ligand pyridostatin involves trapping topoisomerase II on DNA. This map of the DNA damage response provides a rich resource to study this fundamental cellular system and has implications for the development and use of genotoxic agents in cancer therapy.
8,589 downloads bioRxiv cancer biology
UCSC Xena is a visual exploration resource for both public and private omics data, supported through the web-based Xena Browser and multiple turn-key Xena Hubs. This unique archecture allows researchers to view their own data securely, using private Xena Hubs, simultaneously visualizing large public cancer genomics datasets, including TCGA and the GDC. Data integration occurs only within the Xena Browser, keeping private data private. Xena supports virtually any functional genomics data, including SNVs, INDELs, large structural variants, CNV, expression, DNA methylation, ATAC-seq signals, and phenotypic annotations. Browser features include the Visual Spreadsheet, survival analyses, powerful filtering and subgrouping, statistical analyses, genomic signatures, and bookmarks. Xena differentiates itself from other genomics tools, including its predecessor, the UCSC Cancer Genomics Browser, by its ability to easily and securely view public and private data, its high performance, its broad data type support, and many unique features.
8,278 downloads bioRxiv cancer biology
We report the integrative analysis of more than 2,600 whole cancer genomes and their matching normal tissues across 39 distinct tumour types. By studying whole genomes we have been able to catalogue non-coding cancer driver events, study patterns of structural variation, infer tumour evolution, probe the interactions among variants in the germline genome, the tumour genome and the transcriptome, and derive an understanding of how coding and non-coding variations together contribute to driving individual patient's tumours. This work represents the most comprehensive look at cancer whole genomes to date. NOTE TO READERS: This is an incomplete draft of the marker paper for the Pan-Cancer Analysis of Whole Genomes Project, and is intended to provide the background information for a series of in-depth papers that will be posted to BioRixv during the summer of 2017.
8,041 downloads bioRxiv cancer biology
Cancer is not solely a disease of the genome, but is a systemic disease that affects the host on many functional levels, including, and perhaps most notably, the function of the immune response, resulting in both tumor-promoting inflammation and tumor-inhibiting cytotoxic action. The dichotomous actions of the immune response induce significant variations in tumor growth dynamics that mathematical modeling can help to understand. Here we present a general method using ordinary differential equations (ODEs) to model and analyze cancer-immune interactions, and in particular, immune-induced tumor dormancy.
7,698 downloads bioRxiv cancer biology
Amanda Janesick, Robert Shelansky, Andrew Gottscho, Florian Wagner, Morgane Rouault, Ghezal Beliakoff, Michelli Faria de Oliveira, Andrew Kohlway, Jawad Abousoud, Carolyn Morrison, Tingsheng Yu Drennon, Syrus Mohabbat, Stephen Williams, 10x Development Teams, Sarah Taylor
Single cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical tissue samples. Commercially available methods that characterize either single cell or spatial gene expression are currently limited by low sample throughput and/or gene plexy, lack of on-instrument analysis, and the destruction of histological features and epitopes during the workflow. Here, we analyzed large, serial formalin-fixed, paraffin-embedded (FFPE) human breast cancer sections using a novel FFPE-compatible single cell gene expression workflow (Chromium Fixed RNA Profiling; scFFPE-seq), spatial transcriptomics (Visium CytAssist), and automated microscopy-based in situ technology using a 313-plex gene panel (Xenium In Situ). Whole transcriptome profiling of the FFPE tissue using scFFPE-seq and Visium facilitated the identification of 17 different cell types. Xenium allowed us to spatially resolve these cell types and their gene expression profiles with single cell resolution. Due to the non-destructive nature of the Xenium workflow, we were able to perform H&E staining and immunofluorescence on the same section post-processing which allowed us to spatially register protein, histological, and RNA data together into a single image. Integration of data from Chromium scFFPE-seq, Visium, and Xenium across serial sections allowed us to do extensive benchmarking of sensitivity and specificity between the technologies. Furthermore, data integration inspired the interrogation of three molecularly distinct tumor subtypes (low-grade and high-grade ductal carcinoma in situ (DCIS), and invasive carcinoma). We used Xenium to characterize cellular composition and differentially expressed genes within these subtypes. This analysis allowed us to draw biological insights about DCIS progression to infiltrating carcinoma, as the myoepithelial layer degrades and tumor cells invade the surrounding stroma. Xenium also allowed us to further predict the hormone receptor status of tumor subtypes, including a small 0.1 mm2 DCIS region that was triple positive for ESR1 (estrogen receptor), PGR (progesterone receptor), and ERBB2 (human epidermal growth factor receptor 2, a.k.a. HER2) RNA. In order to derive whole transcriptome information from these cells, we used Xenium data to interpolate the cell composition of Visium spots, and used Visium whole transcriptome information to discover new biomarkers of breast tumor subtypes. We demonstrate that scFFPE-seq, Visium, and Xenium independently provide information about molecular signatures relevant to understanding cancer heterogeneity. However, it is the integration of these technologies that leads to even deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.
7,539 downloads bioRxiv cancer biology
Radhakrishnan Sabarinathan, Oriol Pich, Inigo Martincorena, Carlota Rubio-Perez, Malene Juul, Jeremiah Wala, Steven Schumacher, Ofer Shapira, Nikos Sidiropoulos, Sebastian M Waszak, David Tamborero, Loris Mularoni, Esther Rheinbay, Henrik Hornshøj, Jordi Deu-Pons, Ferran Muinos, Johanna Bertl, Qianyun Guo, Chad J. Creighton, Joachim Weischenfeldt, Jan Korbel, Gad Getz, Peter J. Campbell, Jakob Skou Pedersen, Rameen Beroukhim, Abel Gonzalez-Perez, Nuria Lopez-Bigas, on behalf of the PCAWG Drivers and Functional Interpretation Group and the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Network
The advance of personalized cancer medicine requires the accurate identification of the mutations driving each patient's tumor. However, to date, we have only been able to obtain partial insights into the contribution of genomic events to tumor development. Here, we design a comprehensive approach to identify the driver mutations in each patient's tumor and obtain a whole-genome panorama of driver events across more than 2,500 tumors from 37 types of cancer. This panorama includes coding and non-coding point mutations, copy number alterations and other genomic rearrangements of somatic origin, and potentially predisposing germline variants. We demonstrate that genomic events are at the root of virtually all tumors, with each carrying on average 4.6 driver events. Most individual tumors harbor a unique combination of drivers, and we uncover the most frequent co-occurring driver events. Half of all cancer genes are affected by several types of driver mutations. In summary, the panorama described here provides answers to fundamental questions in cancer genomics and bridges the gap between cancer genomics and personalized cancer medicine.
7,286 downloads bioRxiv cancer biology
Cancer cell lines are often used in laboratory experiments as models of tumors, although they can have substantially different genetic and epigenetic profiles compared to tumors. We have developed a general computational method, TumorComparer, to systematically quantify similarities and differences between tumor material when detailed genetic and molecular profiles are available. The comparisons can be flexibly tailored to a particular biological question by placing a higher weight on functional alterations of interest (weighted similarity). In a first pan-cancer application, we have compared 260 cell lines from the Cancer Cell Line Encyclopaedia (CCLE) and 1914 tumors of six different cancer types from The Cancer Genome Atlas (TCGA), using weights to emphasize genomic alterations that frequently recur in tumors. We report the potential suitability of particular cell lines as tumor models and identify apparently unsuitable outlier cell lines, some of which are in wide use, for each of the six cancer types. In future, this weighted similarity method may be generalized for use in a clinical setting to compare patient profiles consisting of genomic patterns combined with clinical attributes, such as diagnosis, treatment and response to therapy.
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