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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 65,351 bioRxiv papers from 289,484 authors.

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in category cancer biology

2,129 results found. For more information, click each entry to expand.

1: Report of Partial findings from the National Toxicology Program Carcinogenesis Studies of Cell Phone Radiofrequency Radiation in Hsd: Sprague Dawley® SD rats (Whole Body Exposure)
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Posted to bioRxiv 26 May 2016

Report of Partial findings from the National Toxicology Program Carcinogenesis Studies of Cell Phone Radiofrequency Radiation in Hsd: Sprague Dawley® SD rats (Whole Body Exposure)
239,332 downloads cancer biology

Michael Wyde, Mark Cesta, Chad Blystone, Susan Elmore, Paul Foster, Michelle Hooth, Grace Kissling, David Malarkey, Robert Sills, Matthew Stout, Nigel Walker, Kristine Witt, Mary Wolfe, John Bucher

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.

2: The Repertoire of Mutational Signatures in Human Cancer
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Posted to bioRxiv 15 May 2018

The Repertoire of Mutational Signatures in Human Cancer
13,027 downloads cancer biology

Ludmil Alexandrov, Jaegil Kim, Nicholas J Haradhvala, Mi Ni Huang, Alvin W T 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.

3: Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body
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Posted to bioRxiv 05 Feb 2019

Deep learning reveals cancer metastasis and therapeutic antibody targeting in whole body
10,778 downloads 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 Erturk

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.

4: Integrating single-cell RNA-Seq with spatial transcriptomics in pancreatic ductal adenocarcinoma using multimodal intersection analysis
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Posted to bioRxiv 26 Jan 2018

Integrating single-cell RNA-Seq with spatial transcriptomics in pancreatic ductal adenocarcinoma using multimodal intersection analysis
10,438 downloads cancer biology

Reuben Moncada, Florian Wagner, Marta Chiodin, Joseph C. Devlin, Maayan Baron, Cristina H. Hajdu, Diane M. Simeone, Itai Yanai

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.

5: Why cancer cells have a more hyperpolarised mitochondrial membrane potential and emergent prospects for therapy
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Posted to bioRxiv 21 Aug 2015

Why cancer cells have a more hyperpolarised mitochondrial membrane potential and emergent prospects for therapy
8,671 downloads cancer biology

Michael D Forrest

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.

6: The evolutionary history of 2,658 cancers
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Posted to bioRxiv 11 Jul 2017

The evolutionary history of 2,658 cancers
7,801 downloads cancer biology

Moritz Gerstung, Clemency Jolly, Ignaty Leshchiner, Stefan C. Dentro, Santiago Gonzalez Rosado, Daniel Rosebrock, Thomas J. Mitchell, Yulia Rubanova, Pavana Anur, Kaixian Yu, Maxime Tarabichi, Amit Deshwar, Jeff Wintersinger, Kortine Kleinheinz, Ignacio Vázquez-García, Kerstin Haase, Lara Jerman, Subhajit Sengupta, Geoff Macintyre, Salem Malikic, 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 D 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.

7: Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning
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Posted to bioRxiv 03 Oct 2017

Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning
7,130 downloads cancer biology

Nicolas Coudray, Andre L Moreira, Theodore Sakellaropoulos, David Fenyö, Narges Razavian, Aristotelis Tsirigos

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

8: Pan-cancer analysis of whole genomes
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Posted to bioRxiv 12 Jul 2017

Pan-cancer analysis of whole genomes
6,205 downloads cancer biology

Peter J. Campbell, Gaddy Getz, Joshua M Stuart, Jan O Korbel, Lincoln D. Stein

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.

9: Endogenous insulin contributes to pancreatic cancer development
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Posted to bioRxiv 24 Jan 2019

Endogenous insulin contributes to pancreatic cancer development
5,921 downloads cancer biology

Anni MY Zhang, Jamie Magrill, Twan J.J. de Winter, Xiaoke Hu, Sos Skovso, David F Schaeffer, Janel L Kopp, James D Johnson

Obesity and early-stage type 2 diabetes (T2D) increase the risk for many cancers, including pancreatic ductal adenocarcinoma (PDAC). The mechanisms linking obesity and T2D to cancer have not been established, preventing targeted interventions. Arguments have been made that hyperinsulinemia, hyperglycemia, or inflammation could drive cancer initiation and/or progression. Hyperinsulinemia is a cardinal feature of obesity and T2D, and is independently associated with PDAC incidence and mortality, even in non-obese people. Despite ample human epidemiological evidence linking hyperinsulinemia to PDAC, there is no direct in vivo evidence of a causal role for endogenous insulin in cancer in any system. Using mice with reduced insulin gene dosage, we show here that a modest reduction in endogenous insulin production leads to a ~50% reduction in pancreatic intraepithelial neoplasia (PanIN) pre-cancerous lesions in high fat diet-fed mice expressing the KrasG12D oncogene. The significant reduction in PanIN lesions occurred in the absence of changes in fasting glucose. Reduced insulin also led to a ~50% reduction in pancreatic fibrosis, suggesting that endogenous insulin drives PanIN development, in part, via its pro-fibrotic effects on the stroma surrounding acinar cells and PanIN. Collectively, our data indicate that endogenous insulin hypersecretion contributes causally to pancreatic cancer development. This suggests a modest reduction in fasting insulin via lifestyle interventions or therapeutics may be useful in cancer prevention.

10: Comparing cancer cell lines and tumor samples by genomic profiles
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Posted to bioRxiv 02 Oct 2015

Comparing cancer cell lines and tumor samples by genomic profiles
5,485 downloads cancer biology

Rileen Sinha, Nikolaus Schultz, Chris Sander

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.

11: Gene expression profiling reveals U1 snRNA regulates cancer gene expression
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Posted to bioRxiv 12 Jan 2017

Gene expression profiling reveals U1 snRNA regulates cancer gene expression
5,484 downloads cancer biology

Zhi Cheng, Yu Sun, Xiaoran Niu, YingChun Shang, Zhenfeng Wu, Jinsong Shi, Shan Gao, Tao Zhang

U1 small nuclear RNA (U1 snRNA), as one of the most abundant noncoding RNA in eukaryotic cells plays an important role in splicing of pre-mRNAs. Compared to other studies which have focused on the primary function of U1 snRNA and the neurodegenerative diseases caused by the abnormalities of U1 snRNA, this study is to investigate how the U1 snRNA over-expression affects the expression of genes on a genome-wide scale. In this study, we built a model of U1 snRNA over-expression in a rat cell line. By comparing the gene expression profiles of U1 snRNA over-expressed cells with those of their controls using the microarray experiments, 916 genes or loci were identified significantly differentially expressed. These 595 up-regulated genes and 321 down-regulated genes were further analyzed using the annotations from the GO terms and the KEGG database. As a result, three of 12 enriched pathways are well-known cancer pathways, while nine of them were associated to cancers in previous studies. The further analysis of 73 genes involved in 12 pathways suggests that U1 snRNA regulates cancer gene expression. The microarray data with ID GSE84304 is available in the NCBI GEO database.

12: The whole-genome panorama of cancer drivers
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Posted to bioRxiv 20 Sep 2017

The whole-genome panorama of cancer drivers
5,295 downloads cancer biology

Radhakrishnan Sabarinathan, Oriol Pich, Inigo Martincorena, Carlota Rubio-Perez, Malene Juul, Jeremiah Wala, Steven E Schumacher, Ofer Shapira, Nikos Sidiropoulos, Sebastian M Waszak, david tamborero, Loris Mularoni, Esther Rheinbay, Henrik Hornshoj, Jordi Deu-Pons, Ferran Muiños, Johanna Bertl, Qianyun Guo, Chad J Creighton, Joachim Weischenfeldt, Jan O Korbel, Gad Getz, Peter J. Campbell, Jakob Skou Pedersen, Rameen Beroukhim, Abel Perez-Gonzalez, 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.

13: Challenges in Using ctDNA to Achieve Early Detection of Cancer
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Posted to bioRxiv 21 Dec 2017

Challenges in Using ctDNA to Achieve Early Detection of Cancer
4,913 downloads cancer biology

Imran S. Haque, Olivier Elemento

Early detection of cancer is a significant unmet clinical need. Improved technical ability to detect circulating tumor-derived DNA (ctDNA) in the cell-free DNA (cfDNA) component of blood plasma via next-generation sequencing and established correlations between ctDNA load and tumor burden in cancer patients have spurred excitement about the possibilities of detecting cancer early by performing ctDNA mutation detection. We reanalyze published data on the expected ctDNA allele fraction in early-stage cancer and the population statistics of cfDNA concentration to show that under conservative technical assumptions, high-sensitivity cancer detection by ctDNA mutation detection will require either more blood volume (150-300mL) than practical for a routine screen or variant filtering that may be impossible given our knowledge of cancer evolution, and will likely remain out of economic reach for routine population screening without multiple-order-of-magnitude decreases in sequencing cost. Instead, new approaches that integrate ctDNA mutations with multiple other blood-based analytes (such as exosomes, circulating tumor cells, ctDNA epigenetics, metabolites) as well as integration of these signals over time for each individual may be needed.

14: NADH as a cancer medicine
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Posted to bioRxiv 13 May 2015

NADH as a cancer medicine
4,906 downloads cancer biology

Michael D Forrest

We propose that NADH will exert a specific kill action against some cancers. NADH is a natural metabolite. We envisage a low side effect profile and that NADH therapy will, additionally, combat the wastage and weakness of cancer patients, which can be the cause of death in some cases. Significantly, NADH can be administered orally and has already cleared clinical trials, all be it for other pathologies.

15: A simple high-throughput approach identifies actionable drug responses in patient-derived tumor organoids
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Posted to bioRxiv 28 Jun 2017

A simple high-throughput approach identifies actionable drug responses in patient-derived tumor organoids
4,757 downloads cancer biology

Nhan Phan, Jenny J Hong, Bobby Tofig, Matthew Mapua, David Elashoff, Neda A Moatamed, Jin Huang, Sanaz Memarzadeh, Robert Damoiseaux, Alice Soragni

There is increasing interest in developing 3D tumor organoid models for drug development and personalized medicine applications. While tumor organoids are in principle amenable to high-throughput drug screenings, progress has been hampered by technical constraints and extensive manipulations required by current methodologies. Here, we introduce a miniaturized, fully automatable, flexible high-throughput method using a simplified geometry to rapidly establish 3D organoids from cell lines and primary tissue and robustly assay drug responses. As proof of principle, we use our mini-ring approach to establish organoids of high-grade serous tumors and one carcinosarcoma of the ovaries and screen hundreds of protein kinase compounds currently FDA-approved or in clinical development. In all cases we could identify drugs causing significant reduction in cell viability, number and size of organoids within a week from surgery, a timeline compatible with therapeutic decision making.

16: Pan-cancer whole genome analyses of metastatic solid tumors
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Posted to bioRxiv 20 Sep 2018

Pan-cancer whole genome analyses of metastatic solid tumors
4,680 downloads cancer biology

Peter Priestley, Jonathan Baber, Martijn P. Lolkema, Neeltje Steeghs, Ewart de Bruijn, Charles Shale, Korneel Duyvesteyn, Susan Haidari, Arne van Hoeck, Wendy Onstenk, Paul Roepman, Mircea Voda, Haiko J. Bloemendal, Vivianne C.G. Tjan-Heijnen, Carla M.L. van Herpen, Mariette Labots, Petronella O. Witteveen, Egbert F. Smit, Stefan Sleijfer, Emile E. Voest, Edwin Cuppen

Metastatic cancer is one of the major causes of death and is associated with poor treatment efficiency. A better understanding of the characteristics of late stage cancer is required to help tailor personalised treatment, reduce overtreatment and improve outcomes. Here we describe the largest pan-cancer study of metastatic solid tumor genomes, including 2,520 whole genome-sequenced tumor-normal pairs, analyzed at a median depth of 106x and 38x respectively, and surveying over 70 million somatic variants. Metastatic lesions were found to be very diverse, with mutation characteristics reflecting those of the primary tumor types, although with high rates of whole genome duplication events (56%). Metastatic lesions are relatively homogeneous with the vast majority (96%) of driver mutations being clonal and up to 80% of tumor suppressor genes bi-allelically inactivated through different mutational mechanisms. For 62% of all patients, genetic variants that may be associated with outcome of approved or experimental therapies were detected. These actionable events were distributed across various mutation types underlining the importance of comprehensive genomic tumor profiling for cancer precision medicine.

17: Patterns of structural variation in human cancer
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Posted to bioRxiv 27 Aug 2017

Patterns of structural variation in human cancer
4,525 downloads cancer biology

Yilong Li, Nicola D. Roberts, Joachim Weischenfeldt, Jeremiah A. Wala, Ofer Shapira, Steven E Schumacher, Ekta Khurana, Jan Korbel, Marcin Imielinski, Rameen Beroukhim, Peter J. Campbell, on behalf of the PCAWG-Structural Variation Working Group, and the PCAWG Network

A key mutational process in cancer is structural variation, in which rearrangements delete, amplify or reorder genomic segments ranging in size from kilobases to whole chromosomes. We developed methods to group, classify and describe structural variants, applied to >2,500 cancer genomes. Nine signatures of structural variation emerged. Deletions have trimodal size distribution; assort unevenly across tumour types and patients; enrich in late-replicating regions; and correlate with inversions. Tandem duplications also have trimodal size distribution, but enrich in early-replicating regions, as do unbalanced translocations. Replication-based mechanisms of rearrangement generate varied chromosomal structures with low-level copy number gains and frequent inverted rearrangements. One prominent structure consists of 1-7 templates copied from distinct regions of the genome strung together within one locus. Such cycles of templated insertions correlate with tandem duplications, frequently activating the telomerase gene, TERT, in liver cancer. Cancers access many rearrangement processes, flexibly sculpting the genome to maximise oncogenic potential.

18: The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression
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Posted to bioRxiv 01 Sep 2015

The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression
4,293 downloads cancer biology

Yasin Şenbabaoğlu

Infiltrating T cells in the tumor microenvironment have crucial roles in the competing processes of pro-tumor and anti-tumor immune response. However, the infiltration level of distinct T cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery (APM) genes, remain poorly characterized across human cancers. Here, we define a novel mRNA-based T cell infiltration score (TIS) and profile infiltration levels in 19 tumor types. We find that clear cell renal cell carcinoma (ccRCC) is the highest for TIS and among the highest for the correlation between TIS and APM expression, despite a modest mutation burden. This finding is contrary to the expectation that immune infiltration and mutation burden are linked. To further characterize the immune infiltration in ccRCC, we use RNA-seq data to computationally infer the infiltration levels of 24 immune cell types in a discovery cohort of 415 ccRCC patients and validate our findings in an independent cohort of 101 ccRCC patients. We find three clusters of tumors that are primarily separated by levels of T cell infiltration and APM gene expression. In ccRCC, the levels of Th17 cells and the ratio of CD8+ T/Treg levels are associated with improved survival whereas the levels of Th2 cells and Tregs are associated with negative clinical outcome. Our analysis illustrates the utility of computational immune cell decomposition for solid tumors, and the potential of this method to guide clinical decision-making.

19: Using Ordinary Differential Equations to Explore Cancer-Immune Dynamics and Tumor Dormancy
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Posted to bioRxiv 22 Apr 2016

Using Ordinary Differential Equations to Explore Cancer-Immune Dynamics and Tumor Dormancy
4,119 downloads cancer biology

Kathleen P. Wilkie, Philip Hahnfeldt, Lynn Hlatky

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.

20: Tumor evolution of glioma intrinsic gene expression subtype associates with immunological changes in the microenvironment
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Posted to bioRxiv 08 May 2016

Tumor evolution of glioma intrinsic gene expression subtype associates with immunological changes in the microenvironment
3,961 downloads cancer biology

Qianghu Wang, Xin Hu, Baoli Hu, Florian Muller, Hoon Kim, Massimo Squatrito, Tom Millelsen, Lisa Scarpace, Floris Barthel, Yu-Hsi Lin, Nikunj Satani, Emmanuel Martinez-Ledesma, Edward Chang, Adriana Olar, Guocan Wang, Ana C. deCarvalho, Eskil Eskilsson, Siyuan Zheng, Amy B. Heimberger, Erik P. Sulman, Do-Hyun Nam, Roel GW Verhaak

We leveraged IDH wild type glioblastomas and derivative neurospheres to define tumor-intrinsic transcription phenotypes. Transcriptomic multiplicity correlated with increased intratumoral heterogeneity and tumor microenvironment presence. In silico cell sorting demonstrated that M2 macrophages/microglia are the most frequent type of immune cells in the glioma microenvironment, followed by CD4 T lymphocytes and neutrophils. Hypermutation associated with CD8+ T cell enrichment. Longitudinal transcriptome analysis of 124 pairs of primary and recurrent gliomas showed expression subtype is retained in 53% of cases with no proneural to mesenchymal transition being apparent. Inference of the tumor microenvironment through gene signatures revealed a decrease in invading monocytes but a subtype dependent increase in M2 macrophages/microglia cells after disease recurrence. All expression datasets are accessible through http://recur.bioinfo.cnio.es/.

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