Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 66,827 bioRxiv papers from 294,255 authors.
Most downloaded bioRxiv papers, all time
in category cancer biology
2,193 results found. For more information, click each entry to expand.
1,684 downloads cancer biology
Anand Vasudevan, Prasamit S. Baruah, Joan C. Smith, Zihua Wang, Nicole M. Sayles, Peter Andrews, Jude Kendall, Justin E. Leu, Narendra Kumar Chunduri, Dan Levy, Michael Wigler, Zuzana Storchová, Jason M. Sheltzer
Most human tumors display chromosome-scale copy number alterations, and high levels of aneuploidy are frequently associated with advanced disease and poor patient prognosis. To examine the relationship between aneuploidy and cancer progression, we generated and analyzed a series of congenic human cell lines that harbor single extra chromosomes. We find that different aneuploidies can have distinct effects on invasive behavior: across 13 different cell lines, 12 trisomies suppressed invasiveness or were largely neutral, while a single trisomy increased metastatic behavior by triggering a partial epithelial-mesenchymal transition. In contrast, chromosomal instability, which can lead to the development of aneuploidy, uniformly suppressed cellular invasion. By analyzing genomic copy number and survival data from 10,133 cancer patients, we demonstrate that specific aneuploidies are associated with distinct clinical outcomes, and the acquisition of certain aneuploidies is in fact linked with a favorable prognosis. Thus, aneuploidy is not a uniform driver of malignancy, and different chromosome copy number changes can uniquely influence tumor progression. At the same time, the gain of a single chromosome is capable of inducing a profound cell state transition, underscoring how genomic plasticity can engender phenotypic plasticity and lead to the acquisition of enhanced metastatic properties.
1,683 downloads cancer biology
Nathan P. Coussens, Stephen C. Kales, Mark J. Henderson, Olivia W Lee, Kurumi Y. Horiuchi, Yuren Wang, Qing Chen, Ekaterina Kuznetsova, Jianghong Wu, Dorian M Cheff, Ken Chih-Chien Cheng, Paul Shinn, Kyle R Brimacombe, Min Shen, Anton Simeonov, Haiching Ma, Ajit Jadhav, Matthew D Hall
The activity of the histone lysine methyltransferase NSD2 is thought to play a driving role in oncogenesis. Both overexpression of NSD2 and point mutations that increase its catalytic activity are associated with a variety of human cancers. While NSD2 is an attractive therapeutic target, no potent, selective and cell-active inhibitors have been reported to date, possibly due to the challenging nature of developing high-throughput assays for NSD2. To establish a platform for the discovery and development of selective NSD2 inhibitors, multiple assays were optimized and implemented. Quantitative high-throughput screening was performed with full-length wild-type NSD2 and a nucleosome substrate against a diverse collection of known bioactives comprising 16,251 compounds. Actives from the primary screen were further interrogated with orthogonal and counter assays, as well as activity assays with the clinically relevant NSD2 mutants E1099K and T1150A. Five confirmed inhibitors were selected for follow-up, which included a radiolabeled validation assay, surface plasmon resonance studies, methyltransferase profiling, and histone methylation in cells. The identification of NSD2 inhibitors that bind the catalytic SET domain and demonstrate activity in cells validates the workflow, providing a template for identifying selective NSD2 inhibitors.
1,672 downloads cancer biology
Jun Xia, Li-Ya Chiu, Ralf B Nehring, María Angélica Bravo Núñez, Qian Mei, Mercedes Perez, Yin Zhai, Devon M Fitzgerald, John P Pribis, Yumeng Wang, Chenyue W Hu, Reid T Powell, Sandra A LaBonte, Ali Jalali, Meztli L. Matadamas Guzmán, Alfred M Lentzsch, Adam T Szafran, Mohan C Joshi, Megan Richters, Janet L Gibson, Ryan L Frisch, P.J. Hastings, David Bates, Christine Queitsch, Susan G Hilsenbeck, Cristian Coarfa, James C Hu, Deborah A Siegele, Kenneth L Scott, Han Liang, Michael A Mancini, Christophe Herman, Kyle M Miller, Susan M Rosenberg
DNA damage provokes mutations and cancer, and results from external carcinogens or endogenous cellular processes. Yet, the intrinsic instigators of DNA damage are poorly understood. Here we identify proteins that promote endogenous DNA damage when overproduced: the DNA-damaging proteins (DDPs). We discover a large network of DDPs in Escherichia coli and deconvolute them into six DNA-damage-causing function clusters, demonstrating DDP mechanisms in three: reactive-oxygen increase by transmembrane transporters, chromosome loss by replisome binding, and replication stalling by transcription factors. Their 284 human homologs are over-represented among known cancer drivers, and their expression in tumors predicts heavy mutagenesis and poor prognosis. Half of tested human homologs, when overproduced in human cells, promote DNA damage and mutation, with DNA-damaging mechanisms like those in E. coli. Together, our work reveals DDP networks that provoke endogenous DNA damage and may indicate functions of many human known and newly implicated cancer-promoting proteins.
1,659 downloads cancer biology
Cancer arises from accumulation of somatic mutations and accompanying evolutionary selection for growth advantage. During the evolutionary process, an ancestor clone branches into multiple clones, yielding intratumor heterogeneity. However, principles underlying intratumor heterogeneity have been poorly understood. Here, to explore the principles, we built a cellular automaton model, termed the BEP model, which can reproduce the branching cancer evolution in silico. We then extensively searched for conditions leading to high intratumor heterogeneity by performing simulations with various parameter settings on a supercomputer. Our result suggests that multiple driver genes of moderate strength can shape subclonal structures by positive natural selection. Moreover, we found that high mutation rate and a stem cell hierarchy can contribute to extremely high intratumor heterogeneity, which is characterized by fractal patterns, through neutral evolution. Collectively, This study identified the possible principles underlying intratumor heterogeneity, which provide novel insights into the origin of cancer robustness and evolvability.
1,643 downloads cancer biology
Alternative splicing changes are frequently observed in cancer and are starting to be recognized as important signatures for tumor progression and therapy. However, their functional impact and relevance to tumorigenesis remains mostly unknown. We carried out a systematic analysis to characterize the potential functional consequences of alternative splicing changes in thousands of tumor samples. This analysis revealed that a subset of alternative splicing changes affect protein domain families that are frequently mutated in tumors and potentially disrupt protein protein interactions in cancer-related pathways. Moreover, there was a negative correlation between the number of these alternative splicing changes in a sample and the number of somatic mutations in drivers. We propose that a subset of the alternative splicing changes observed in tumors may represent independent oncogenic processes that could be relevant to explain the functional transformations in cancer and some of them could potentially be considered alternative splicing drivers (AS drivers).
1,620 downloads cancer biology
Cell growth and division are stochastic processes that exhibit significant amount of cell-to-cell variation and randomness. In order to connect single cell division dynamics with overall cell population, stochastic population models are needed. We summarize the basic concepts, computational approaches and discuss simple applications of this modeling approach to understanding cancer cell population growth as well as population fluctuations in experiments.
1,616 downloads cancer biology
Tumor-propagating glioblastoma (GBM) stem-like cells (GSCs) of the proneural and mesenchymal molecular subtypes have been described. However, it is unknown if these two GSC populations are sufficient to generate the spectrum of cellular heterogeneity observed in GBM. The lineage relationships and niche interactions of GSCs have not been fully elucidated. We perform single-cell RNA-sequencing (scRNA-seq) and matched exome sequencing of human GBMs (12 patients; >37,000 cells) to identify recurrent hierarchies of GSCs and their progeny. We map sequenced cells to tumor-anatomical structures and identify microenvironment interactions using reference atlases and quantitative immunohistochemistry. We find that all GSCs can be described by a single axis of variation, ranging from proneural to mesenchymal. Increasing mesenchymal GSC (mGSC) content, but not proneural GSC (pGSC) content, correlates with significantly inferior survival. All clonal expressed mutations are found in the GSC populations, with a greater representation of mutations found in mGSCs. While pGSCs upregulate markers of cell-cycle progression, mGSCs are largely quiescent and overexpress cytokines mediating the chemotaxis of myeloid-derived suppressor cells. We find mGSCs enriched in hypoxic regions while pGSCs are enriched in the tumor's invasive edge. We show that varying proportions of mGSCs, pGSCs, their progeny and stromal/immune cells are sufficient to explain the genetic and phenotypic heterogeneity observed in GBM. This study sheds light on a long-standing debate regarding the lineage relationships between GSCs and other glioma cell types.
1,614 downloads cancer biology
Joshua M Dempster, Clare Pacini, Sasha Pantel, Fiona M Behan, Thomas Green, John Krill-Burger, Charlotte M Beaver, Scott T Younger, Victor Zhivich, Hanna Najgebauer, Felicity Allen, Emanuel Gonçalves, Rebecca Shepherd, John G. Doench, Kosuke Yusa, Francisca Vazquez, Leopold Parts, Jesse S Boehm, Todd R. Golub, William C Hahn, David E Root, Mathew J Garnett, Francesco Iorio, Aviad Tsherniak
Genome-scale CRISPR-Cas9 viability screens performed in cancer cell lines provide a systematic approach to identify cancer dependencies and new therapeutic targets. As multiple large-scale screens become available, a formal assessment of the reproducibility of these experiments becomes necessary. We analyzed data from recently published pan-cancer CRISPR-Cas9 screens performed at the Broad and Sanger institutes. Despite significant differences in experimental protocols and reagents, we found that the screen results are highly concordant across multiple metrics with both common and specific dependencies jointly identified across the two studies. Furthermore, robust biomarkers of gene dependency found in one dataset are recovered in the other. Through further analysis and replication experiments at each institute, we found that batch effects are driven principally by two key experimental parameters: the reagent library and the assay length. These results indicate that the Broad and Sanger CRISPR-Cas9 viability screens yield robust and reproducible findings.
1,608 downloads cancer biology
Entropy rising within normal hematopoiesis is the core idea of the proposed thermodynamical model of malignancy in leukemia. Mathematically its description is supposed to be similar to the Lorenz system of ordinary differential equations for simplified processes of heat flow in fluids. The model provides description of remission and relapse in leukemia as two hierarchical and qualitatively different states of normal hematopoiesis with their own phase spaces. Phase space transition is possible through pitchfork bifurcation, which is considered as the common symmetrical scenario for relapse, induced remission and spontaneous remission of leukemia. Cytopenia is regarded as an adaptive reaction of hematopoiesis to entropy increase caused by leukemia clone. The following hypotheses are formulated: a) Percentage of leukemia cells in marrow as criterion of remission or relapse is not necessarily constant but a variable value; b) Probability of getting remission depends upon normal hematopoiesis reaching bifurcation; c) Duration of remission depends upon eradication of leukemia cells in induction or consolidation therapies; d) Excessively high doses of chemotherapy in consolidation might induce relapse.
1,576 downloads cancer biology
Korsuk Sirinukunwattana, Enric Domingo, Susan Richman, Keara L Redmond, Andrew Blake, Clare Verrill, Simon J Leedham, Aikaterini Chatzipli, Claire Hardy, Celina Whalley, Chieh-Hsi Wu, Andrew D Beggs, Ultan McDermott, Philip Dunne, Angela A Meade, Steven M Walker, Graeme I Murray, Leslie M Samuel, Matthew Seymour, Ian Tomlinson, Philip Quirke, Tim Maughan, Jens Rittscher, Viktor H Koelzer, on behalf of S:CORT consortium
Image analysis is a cost-effective tool to associate complex features of tissue organisation with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of colorectal cancer (CRC) from standard H&E sections using deep learning. Domain adversarial training of a neural classification network was performed using 1,553 tissue sections with comprehensive multi-omic data from three independent datasets. Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies, spatially resolved intratumoural heterogeneity and provided accurate secondary calls with higher discriminatory power than bioinformatic prediction. In all three cohorts imCMS established sensible classification in CMS unclassified samples, reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation.
1,568 downloads cancer biology
Extracellular vesicles (EVs) are recognized cancer biomarkers, however, clinical analysis has been difficult due to a lack of simple and sensitive assays. Here, we describe a bead-enhanced flow cytometry method, BEAD flow, using biotinylated EVs captured on streptavidin particles. With this method, we show analysis of patient-derived EVs using a panel of pancreatic cancer biomarkers. BEAD flow is easily translatable to any biomarker or cancer type and can be run with conventional flow cytometers, making it highly flexible and adaptable to diverse research and clinical needs.
1,559 downloads cancer biology
Pancreatic cancers are typically diagnosed at late stage where disease prognosis is poor as exemplified by a 5-year survival rate of 8.2%. Earlier diagnosis would be beneficial by enabling surgical resection or earlier application of therapeutic regimens. We investigated the detection of pancreatic ductal adenocarcinoma (PDAC) in a non-invasive manner by interrogating changes in 5-hydroxymethylation cytosine status (5hmC) of circulating cell free DNA in the plasma of a PDAC cohort (n=51) in comparison with a non-cancer cohort (n=41). We found that 5hmC sites are enriched in a disease and stage specific manner in eons, 3'UTRs, transcription termination sites. Our data show that the 5hmC density in H3K4me3 sites is reduced in progressive disease suggesting increased transcriptional activity. 5hmC density is differentially represented in thousands of genes, and a stringently filtered set of the most significant genes exhibited biology related to pancreas (GATA4, GATA6, PROX1, ONECUT1) and/or cancer development (YAP1, TEAD1, PROX1, ONECUT1, ONECUT2, IGF1 and IGF2). Regularized regression models were built using 5hmC densities in statistically filtered genes or a comprehensive set of highly variable gene counts and performed with an AUC = 0.94-0.96 on training data. We were able to test the ability to classify PDAC and non-cancer samples with the elastic net and lasso models on two external pancreatic cancer 5hmC data sets and found validation performance to be AUC = 0.74-0.97. The findings suggest that 5hmC changes enable classification of PDAC patients with a high fidelity and are worthy of further investigation on larger cohorts of patient samples.
1,543 downloads cancer biology
Ana C. deCarvalho, Hoon Kim, Laila M. Poisson, Mary E. Winn, Claudius Mueller, David Cherba, Julie Koeman, Sahil Seth, Alexei Protopopov, Michelle Felicella, Siyuan Zheng, Asha Multani, Yongying Jiang, Jianhua Zhang, Do-Hyun Nam, Emanuel F. Petricoin, Lynda Chin, Tom Mikkelsen, Roel GW Verhaak
To understand how genomic heterogeneity of glioblastoma (GBM) contributes to the poor response to therapy, which is characteristic of this disease, we performed DNA and RNA sequencing on GBM tumor samples and the neurospheres and orthotopic xenograft models derived from them. We used the resulting data set to show that somatic driver alterations including single nucleotide variants, focal DNA alterations, and oncogene amplification in extrachromosomal DNA (ecDNA) elements were in majority propagated from tumor to model systems. In several instances, ecDNAs and chromosomal alterations demonstrated divergent inheritance patterns and clonal selection dynamics during cell culture and xenografting. Longitudinal patient tumor profiling showed that oncogenic ecDNAs are frequently retained after disease recurrence. Our analysis shows that extrachromosomal elements increase the genomic heterogeneity during tumor evolution of glioblastoma, independent of chromosomal DNA alterations.
1,535 downloads cancer biology
The analysis of cell-free DNA (cfDNA) in plasma represents a rapidly advancing field in medicine. cfDNA consists predominantly of nucleosome-protected DNA shed into the bloodstream by cells undergoing apoptosis. We performed whole-genome sequencing (WGS) of plasma DNA and identified two discrete regions at transcription start sites (TSS) where the nucleosome occupancy results in different read-depth coverage patterns in expressed and silent genes. By employing machine learning for gene classification, we found that the plasma DNA read depth patterns from healthy donors reflected the expression signature of hematopoietic cells. In cancer patients with metastatic disease, we were able to classify expressed cancer driver genes in regions with somatic copy number gains with high accuracy. We could even determine the expressed isoform of genes with several TSSs as confirmed by RNA-Seq of the matching primary tumor. Our analyses provide functional information about the cells releasing their DNA into the circulation.
1,527 downloads cancer biology
Most cancers in humans are large, measuring centimeters in diameter, composed of many billions of cells. An equivalent mass of normal cells would be highly heterogeneous as a result of the mutations that occur during each cell division. What is remarkable about cancers is their homogeneity - virtually every neoplastic cell within a large cancer contains the same core set of genetic alterations, with heterogeneity confined to mutations that have emerged after the last clonal expansions. How such clones expand within the spatially-constrained three dimensional architecture of a tumor, and come to dominate a large, pre-existing lesion, has never been explained. We here describe a model for tumor evolution that shows how short-range migration and cell turnover can account for rapid cell mixing inside the tumor. With it, we show that even a small selective advantage of a single cell within a large tumor allows the descendants of that cell to replace the precursor mass in a clinically relevant time frame. We also demonstrate that the same mechanisms can be responsible for the rapid onset of resistance to chemotherapy. Our model not only provides novel insights into spatial and temporal aspects of tumor growth but also suggests that targeting short range cellular migratory activity could have dramatic effects on tumor growth rates.
1,517 downloads cancer biology
Breast cancer has long been classified into a number of molecular subtypes that predict prognosis and therefore influence clinical treatment decisions. Cellular heterogeneity is also evident in breast cancers and plays a key role in the development, evolution and metastatic progression of many cancers. How clinical heterogeneity relates to cellular heterogeneity is poorly understood, so we approached this question using single cell gene expression analysis of well established in vitro and in vivo models of disease. To explore the cellular heterogeneity in breast cancer we first examined a panel of genes that define the PAM50 classifier of molecular subtype. Five breast cancer cell line models (MCF7, BT474, SKBR3, MDA-MB-231, and MDA-MB-468) were selected as representatives of the intrinsic molecular subtypes (luminal A and B, basal-like, and Her2-enriched). Single cell multiplex RT-PCR was used to isolate and quantify the gene expression of single cells from each of these models, and the PAM50 classifier applied. Using this approach, we identified heterogeneity of intrinsic subtypes at single-cell level, indicating that cells with different subtypes exist within a cell line. Using the Chromium 10X system, this study was extended into thousands of cells from the MCF7 cell-line and an ER+ patient derived xenograft (PDX) model and again identified significant intra-tumour heterogeneity of molecular subtype. Estrogen Receptor (ER) is an important driver and therapeutic target in many breast cancers. It is heterogeneously expressed in a proportion of clinical cases but the significance of this to ER activity is unknown. Significant heterogeneity in the transcriptional activation of ER regulated genes was observed within tumours. This differential activation of the ER cistrome aligned with expression of two known transcriptional co-regulatory factors of ER (FOXA1 and PGR). To examine the degree of heterogeneity for other important phenotypic traits, we used an unsupervised clustering approach to identify cellular sub-populations with diverse cancer associated transcriptional properties, such as: proliferation; hypoxia; and treatment resistance. In particular, we show that we can identify two distinct sub-populations of cells that may have de-novo resistance to endocrine therapies in a treatment naive PDX model of ER+ breast cancer. One of these consists of cells with a non-proliferative transcriptional phenotype that is enriched for transcriptional properties of ERBB2 tumours. The other is heavily enriched for components of the primary cilia. Gene regulatory networks were used to identify transcription factor regulons that are active in each cell, leading us to identify potential transcriptional drivers (such as E2F7, MYB and RFX3) of the cilia associated endocrine resistant cells. This rare subpopulation of cells also has a highly heterogenous mix of intrinsic subtypes highlighting a potential role of intra-tumour subtype heterogeneity in endocrine resistance and metastatic potential. Overall, These results suggest a high degree of cellular heterogeneity within breast cancer models, even cell lines, that can be functionally dissected into sub-populations of cells with transcriptional phenotypes of potential clinical relevance.
1,509 downloads cancer biology
Johanna Klughammer, Barbara Kiesel, Thomas Roetzer, Nikolaus Fortelny, Amelie Kuchler, Nathan C. Sheffield, Paul Datlinger, Nadine Peter, Karl-Heinz Nenning, Julia Furtner, Martha Nowosielski, Marco Augustin, Mario Mischkulnig, Thomas Ströbel, Patrizia Moser, Christian F. Freyschlag, Johannes Kerschbaumer, Claudius Thomé, Astrid E. Grams, Günther Stockhammer, Melitta Kitzwoegerer, Stefan Oberndorfer, Franz Marhold, Serge Weis, Johannes Trenkler, Johanna Buchroithner, Josef Pichler, Johannes Haybaeck, Stefanie Krassnig, Kariem Madhy Ali, Gord von Campe, Franz Payer, Camillo Sherif, Julius Preiser, Thomas Hauser, Peter A. Winkler, Waltraud Kleindienst, Franz Würtz, Tanisa Brandner-Kokalj, Martin Stultschnig, Stefan Schweiger, Karin Dieckmann, Matthias Preusser, Georg Langs, Bernhard Baumann, Engelbert Knosp, Georg Widhalm, Christine Marosi, Johannes A. Hainfellner, Adelheid Woehrer, Christoph Bock
Glioblastoma is characterized by widespread genetic and transcriptional heterogeneity, yet little is known about the role of the epigenome in glioblastoma disease progression. Here, we present genome-scale maps of the DNA methylation dynamics in matched primary and recurring glioblastoma tumors, based on a national population registry and a comprehensively annotated clinical cohort. We demonstrate the feasibility of DNA methylation mapping in a large set of routinely collected formalin-fixed paraffin-embedded (FFPE) samples, and we validate bisulfite sequencing as a multi-purpose assay that allowed us to infer a range of different genetic, epigenetic, and transcriptional tumor characteristics. Based on these data, we identified characteristic differences between primary and recurring tumors, links between DNA methylation and the tumor microenvironment, and an association of epigenetic tumor heterogeneity with patient survival. In summary, this study provides a resource for dissecting DNA methylation heterogeneity in genetically diverse and heterogeneous tumors, and it demonstrates the feasibility of integrating epigenomics, radiology, and digital pathology in a representative national cohort, leveraging samples and data collected as part of routine clinical practice.
1,507 downloads cancer biology
Variation in the gut microbiome has been linked to colorectal cancer (CRC), as well as to host genetic variation. However, we do not know whether, in addition to baseline host genetics, somatic mutational profiles in CRC tumors interact with the surrounding tumor microbiome, and if so, whether these changes can be used to understand microbe-host interactions with potential functional biological relevance. Here, we characterized the association between CRC microbial communities and tumor mutations using microbiome profiling and whole-exome sequencing in 44 pairs of tumors and matched normal tissues. We found statistically significant associations between loss-of-function mutations in tumor genes and shifts in the abundances of specific sets of bacterial taxa, suggestive of potential functional interaction. This correlation allows us to statistically predict interactions between loss-of-function tumor mutations in cancer-related genes and pathways, including MAPK and Wnt signaling, solely based on the composition of the microbiome. These results can serve as a starting point for fine-grained exploration of the functional interactions between discrete alterations in tumor DNA and proximal microbial communities in CRC. In addition, these findings can lead to the development of improved microbiome-based CRC screening methods, as well as individualized microbiota-targeting therapies.
1,505 downloads cancer biology
Whole-chromosome aneuploidy is a hallmark of human malignancies. The prevalence of chromosome segregation errors in cancer - first noted more than 100 years ago - has led to the widespread belief that aneuploidy plays a crucial role in tumor development. Here, we set out to test this hypothesis. We transduced congenic euploid and trisomic fibroblasts with 14 different oncogenes or oncogene combinations, thereby creating genetically-matched cancer cell lines that differ only in karyotype. Surprisingly, nearly all aneuploid cell lines divided slowly in vitro, formed few colonies in soft agar, and grew poorly as xenografts, relative to matched euploid lines. Similar results were obtained when comparing a near-diploid human colorectal cancer cell line with derivatives of that line that harbored extra chromosomes. Only a few aneuploid lines grew at close to wild-type levels, and no aneuploid line exhibited greater tumorigenic capabilities than its euploid counterpart. These results demonstrate that rather than promoting tumorigenesis, aneuploidy, particularly single chromosome gains, can very often function as a tumor suppressor. Moreover, our results suggest one potential way that cancers can overcome the tumor suppressive effects of aneuploidy: rapidly-growing aneuploid cell lines that had evolved in vitro or in vivo demonstrated recurrent karyotype changes that were absent from their euploid counterparts. Thus, the genome-destabilizing effects of single-chromosome aneuploidy may facilitate the development of balanced, high-complexity karyotypes that are frequently found in advanced malignancies.
1,503 downloads cancer biology
Marc Zapatka, Ivan Borozan, Daniel S Brewer, Murat Iskar, Adam Grundhoff, Malik Alawi, Nikita Desai, Holger Sueltmann, Holger Moch, PCAWG Pathogens Working Group, ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Network, Colin S Cooper, Roland Eils, Vincent Ferretti, Peter Lichter
Potential viral pathogens were systematically investigated in the whole-genome and transcriptome sequencing of 2,656 donors as part of the Pan-Cancer Analysis of Whole Genomes using a consensus approach integrating three independent pathogen detection pipelines. Viruses were detected in 382 genomic and 68 transcriptome data sets. We extensively searched and characterized numerous features of virus-positive cancers integrating various PCAWG datasets. We show the high prevalence of known tumor associated viruses such as EBV, HBV and several HPV types. Our systematic analysis revealed that HPV presence was significantly exclusive with well-known driver mutations in head/neck cancer. A strong association was observed between HPV infection and the APOBEC mutational signatures, suggesting the role of impaired mechanism of antiviral cellular defense as a driving force in the development of cervical, bladder and head neck carcinoma. Viral integration into the host genome was observed for HBV, HPV16, HPV18 and AAV2 and associated with a local increase in copy number variations. The recurrent viral integrations at the TERT promoter were coupled to high telomerase expression uncovering a further mechanism to activate this tumor driving process. High levels of endogenous retrovirus ERV1 expression is linked to worse survival outcome in kidney cancer.
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