Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 62,747 bioRxiv papers from 278,434 authors.
Most downloaded bioRxiv papers, since beginning of last month
in category cancer biology
2,027 results found. For more information, click each entry to expand.
2,302 downloads 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.
987 downloads cancer biology
Chloe Chong, Markus Muller, HuiSong Pak, Dermot Harnett, Florian Huber, Delphine Grun, Marion Leleu, Aymeric Auger, Marion Arnaud, Brian J Stevenson, Justine Michaux, Ilija Bilic, Antje Hirsekorn, Lorenzo Calviello, Laia Simo-Riudalbas, Evarist Planet, Jan Lubinski, Marta Bryskiewicz, Maciej Wiznerowicz, Ioannis Xenarios, Lin Zhang, Didier Trono, Alexandre Harari, Uwe Ohler, George Coukos, Michal Bassani-Sternberg
Efforts to precisely identify tumor human leukocyte antigen (HLA) bound peptides capable of mediating T cell-based tumor rejection still face important challenges. Recent studies suggest that non-canonical tumor-specific HLA peptides that derive from annotated non-coding regions could elicit anti-tumor immune responses. However, sensitive and accurate mass-spectrometry (MS)-based proteogenomics approaches are required to robustly identify these non-canonical peptides. We present an MS-based analytical approach that characterizes the non-canonical tumor HLA peptide repertoire, by incorporating whole exome sequencing, bulk and single cell transcriptomics, ribosome profiling, and a combination of two MS/MS search tools. This approach results in the accurate identification of hundreds of shared and tumor-specific non-canonical HLA peptides and of an immunogenic peptide from a downstream reading frame in the melanoma stem cell marker gene ABCB5. It holds great promise for the discovery of novel cancer antigens for cancer immunotherapy.
813 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.
587 downloads 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.
462 downloads cancer biology
Steven M. Corsello, Rohith T Nagari, Ryan D Spangler, Jordan Rossen, Mustafa Kocak, Jordan G Bryan, Ranad Humeidi, David Peck, Xiaoyun Wu, Andrew A Tang, Vickie M Wang, Samantha A Bender, Evan Lemire, Rajiv Narayan, Philip Montgomery, Uri Ben-David, Yejia Chen, Matthew G Rees, Nicholas J. Lyons, James M McFarland, Bang T Wong, Li Wang, Nancy Dumont, Patrick J. O’Hearn, Eric Stefan, John G. Doench, Heidi Greulich, Matthew Meyerson, Francisca Vazquez, Aravind Subramanian, Jennifer A Roth, Joshua A. Bittker, Jesse S Boehm, Christopher C Mader, Aviad Tsherniak, Todd R. Golub
Anti-cancer uses of non-oncology drugs have been found on occasion, but such discoveries have been serendipitous and rare. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. To accomplish this, we used PRISM, which involves drug treatment of molecularly barcoded cell lines in pools. Relative barcode abundance following treatment thus reflects viability of each cell line. We found that an unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines. Moreover, the killing activity of the majority of these drugs was predictable based on the molecular features of the cell lines. Follow-up of several of these compounds revealed novel mechanisms. For example, compounds that kill by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing is dependent on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which kills cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, whose killing is dependent on high expression of the multi-drug resistance gene ABCB1. These results illustrate the potential of the PRISM drug repurposing resource as a starting point for new oncology therapeutic development. The resource is available at https://depmap.org.
386 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.
372 downloads cancer biology
Richard C.A. Sainson, Anil K. Thotakura, Miha Kosmac, Gwenoline Borhis, Nahida Parveen, Rachael Kimber, Joana Carvalho, Simon Henderson, Kerstin Pryke, Tracey Okell, Siobhan O'Leary, Stuart Ball, Lauriane Gamand, Emma Taggart, Eleanor Pring, Hanif Ali, Hannah Craig, Vivian W.Y. Wong, Qi Liang, Robert J. Rowlands, Morgane Lecointre, Jamie Campbell, Ian Kirby, David Melvin, Volker Germaschewski, Elisabeth Oelmann, Sonia Quaratino, Matthew McCourt
The immunosuppressive tumour microenvironment constitutes a significant hurdle to the response to immune checkpoint inhibitors. Both soluble factors and specialised immune cells such as regulatory T cells (TReg) are key components of active intratumoural immunosuppression. Previous studies have shown that Inducible Co-Stimulatory receptor (ICOS) is highly expressed in the tumour microenvironment, especially on TReg, suggesting that it represents a relevant target for preferential depletion of these cells. Here, we used immune profiling of samples from tumour bearing mice and cancer patients to characterise the expression of ICOS in different tissues and solid tumours. By immunizing an Icos knockout transgenic mouse line expressing antibodies with human variable domains, we selected a fully human IgG1 antibody called KY1044 that binds ICOS from different species. Using KY1044, we demonstrated that we can exploit the differential expression of ICOS on T cell subtypes to modify the tumour microenvironment and thereby improve the anti-tumour immune response. We showed that KY1044 induces sustained depletion of ICOShigh TReg cells in mouse tumours and depletion of ICOShigh T cells in the blood of non-human primates, but was also associated with secretion of pro-inflammatory cytokines from ICOSlow TEFF cells. Altogether, KY1044 improved the intratumoural TEFF:TReg ratio and increased activation of TEFF cells, resulting in monotherapy efficacy or in synergistic combinatorial efficacy when administered with the immune checkpoint blocker anti-PD-L1. In summary, our data demonstrate that targeting ICOS with KY1044 can favourably alter the intratumoural immune contexture, promoting an anti-tumour response.
366 downloads 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
347 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.
344 downloads cancer biology
The remarkable evolutionary capacity of cancer is a major challenge to current therapeutic efforts. Fueling this evolution is its vast clonal heterogeneity and ability to adapt to diverse selective pressures. Although the genetic and transcriptional mechanisms underlying these responses have been independently evaluated, the ability to couple genetic alterations present within individual clones to their respective transcriptional or functional outputs has been lacking in the field. To this end, we developed a high-complexity expressed barcode library that integrates DNA barcoding with single-cell RNA sequencing through use of the CROP-seq sgRNA expression/capture system, and which is compatible with the COLBERT clonal isolation workflow for subsequent genomic and epigenomic characterization of specific clones of interest. We applied this approach to study chronic lymphocytic leukemia (CLL), a mature B cell malignancy notable for its genetic and transcriptomic heterogeneity and variable disease course. Here, we demonstrate the clonal composition and gene expression states of HG3, a CLL cell line harboring the common alteration del(13q), in response to front-line cytotoxic therapy of fludarabine and mafosfamide (an analog of the clinically used cyclophosphamide). Analysis of clonal abundance and clonally-resolved single-cell RNA sequencing revealed that only a small fraction of clones consistently survived therapy. These rare highly drug tolerant clones comprise 94% of the post-treatment population and share a stable, pre-existing gene expression state characterized by upregulation of CXCR4 and WNT signaling and a number of DNA damage and cell survival genes. Taken together, these data demonstrate at unprecedented resolution the diverse clonal characteristics and therapeutic responses of a heterogeneous cancer cell population. Further, this approach provides a template for the high-resolution study of thousands of clones and the respective gene expression states underlying their response to therapy.
332 downloads cancer biology
Stefan Prekovic, Karianne Schuurman, Anna Gonzalez Manjon, Mark Buijs, Isabel Mayayo Peralta, Max D Wellenstein, Selcuk Yavuz, Alejandro Barrera, Kim Monkhorst, Anne Huber, Ben Morris, Cor Lieftink, Joana Silva, Balázs Györffy, Liesbeth Hoekman, Bram van den Broek, Hans Teunissen, Timothy E. Reddy, William Faller, Roderick Beijersbergen, Jos Jonkers, A. F. Maarten Altelaar, Karin E de Visser, Elzo de Wit, Rene H Medema, Wilbert Zwart
The glucocorticoid receptor directly regulates thousands of genes across the human genome in a cell-type specific manner, governing various aspects of homeostasis. The influence of the glucocorticoid receptor is also seen in various pathologies, including cancer, where it has been linked to tumorigenesis, metastasis, apoptosis resistance, and therapy bypass. Nonetheless, the direct genetic and molecular underpinnings of glucocorticoid action in cancer remain elusive. Here, we dissected the glucocorticoid receptor signalling axis and uncovered the mechanism of glucocorticoid-mediated cancer cell dormancy. Upon glucocorticoid receptor activation cancer cells undergo quiescence, subserved by cell cycle arrest through CDKN1C and reprogramming of signalling orchestrated via FOXO1/IRS2. Strikingly, co-expression of these three genes, directly regulated by glucocorticoid-induced chromatin looping, correlates with a benign molecular phenotype across human cancers, whereas triple loss is associated with increased expression of proliferation/aggressiveness markers. Finally, we show that the glucocorticoid receptor signalling axis is inactivated by alterations of either the chromatin remodelling complex or TP53 in vitro and in vivo. Our results indicate that the activation of the glucocorticoid receptor leads to cancer cell dormancy, which has several implications in terms of glucocorticoid use in cancer therapy.
327 downloads cancer biology
Luca Gerosa, Christopher Chidley, Fabian Fröhlich, Gabriela Sanchez, Sang Kyun Lim, Jeremy L Muhlich, Jia-Yun Chen, Gregory J Baker, Denis Schapiro, Tujin Shi, Lian Yi, Carrie D. Nicora, Allison Claas, Douglas A Lauffenburger, Wei-Jun Qian, Steven Wiley, Peter Sorger
Anti-cancer drugs commonly target signal transduction proteins activated by mutation. In patients with BRAFV600E melanoma, small molecule RAF and MEK kinase inhibitors cause dramatic but often transient tumor regression. Emerging evidence suggests that cancer cells adapting by non-genetic mechanisms constitute a reservoir for the development of drug-resistant tumors. Here, we show that few hours after exposure to RAF/MEK inhibitors, BRAFV600E melanomas undergo adaptive changes involving disruption of negative feedback and sporadic pulsatile reactivation of the MAPK pathway, so that MAPK activity is transiently high enough in some cells to drive proliferation. Quantitative proteomics and computational modeling show that pulsatile MAPK reactivation is possible due to the co-existence in cells of two MAPK cascades: one driven by BRAFV600E that is drug-sensitive and a second driven by receptors that is drug-resistant. Paradoxically, this may account both for the frequent emergence of drug resistance and for the tolerability of RAF/MEK therapy in patients.
315 downloads cancer biology
Chronic lymphocytic leukaemia (CLL) is characterised by considerable clinical and biological heterogeneity, with specific recurrent genomic alterations, including TP53 mutations, deletions of chromosome 17p, and IgHV mutational status, impacting on response to chemo-immunotherapy and targeted agents. Consequently, diagnostic screening for these predictive biomarkers is recommended in both national and international clinical guidelines. Current conventional methods, including fluorescent in-situ hybridisation and Sanger sequencing, exhibit shortcomings in terms of cost, speed and sensitivity, and even second-generation sequencing methods encounter technical limitations imposed by short-read lengths and bio-informatics analysis. The MinION platform from Oxford Nanopore Technologies generates exceptionally long (1-100kbp) read lengths in a short period of time and at low cost, making it a good candidate for diagnostic testing. In this paper, we present a nanopore-based CLL-specific screening assay, to simultaneously screen for both TP53 mutations and del17p13.1, as well as determining the IgHV mutation status for a single patient in one sequencing run. We sequenced 11 CLL patients and were able to generate a full diagnostic dataset for all. We identified somatic SNVs and indels in the coding region of TP53, and demonstrate that, following error correction of the data, we could accurately define the somatically hypermutated IgHV region in all patients. We also demonstrated the ability of the MinION platform to detect large-scale genomic deletions through low-coverage whole-genome sequencing. We conclude that nanopore sequencing has the potential to provide accurate, low-cost and rapid diagnostic information, which could be applied to other cancer types.
306 downloads cancer biology
Naiara Santana-Codina, Amrita Singh Chandhoke, Qijia Yu, Beata Małachowska, Miljan Kuljanin, Ajami Gikandi, Marcin Stańczak, Sebastian Gableske, Mark P. Jedrychowski, David A Scott, Andrew J. Aguirre, Wojciech Fendler, Nathanael S. Gray, Joseph D. Mancias
Covalent inhibitors of the KRASG12C oncoprotein have recently been developed and are being evaluated in clinical trials. Resistance to targeted therapies is common and likely to limit long-term efficacy of KRAS inhibitors (KRASi). To identify pathways of adaptation to KRASi and to predict drug combinations that circumvent resistance, we used a mass spectrometry-based quantitative temporal proteomics and bioinformatics workflow to profile the temporal proteomic response to KRASG12C inhibition in pancreatic and lung cancer 2D and 3D cellular models. We quantified 10,805 proteins across our datasets, representing the most comprehensive KRASi proteomics effort to date. Our data reveal common mechanisms of acute and long-term response between KRASG12C-driven tumors. To facilitate discovery in the cancer biology community, we generated an interactive KRASi proteome website (https://manciaslab.shinyapps.io/KRASi/). Based on these proteomic data, we identified potent combinations of KRASi with PI3K, HSP90, CDK4/6, and SHP2 inhibitors, in some instances converting a cytostatic response to KRASi monotherapy to a cytotoxic response to combination treatment. Overall, using our quantitative temporal proteomics-bioinformatics platform, we have comprehensively characterized the proteomic adaptations to KRASi and identified combinatorial regimens to induce cytotoxicity with potential therapeutic utility.
299 downloads cancer biology
Peter Ulz, Samantha Perakis, Qing Zhou, Tina Moser, Jelena Belic, Isaac Lazzeri, Albert Wölfler, Armin Zebisch, Armin Gerger, Gunda Pristauz, Edgar Petru, Brandon White, Charles E.S. Roberts, John St. John, Michael G Schimek, Jochen B Geigl, Thomas Bauernhofer, Heinz Sill, Christoph Bock, Ellen Heitzer, Michael R Speicher
Deregulation of transcription factors (TFs) is an important driver of tumorigenesis. We developed and validated a minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyzed whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a newly developed bioinformatics pipeline that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observed patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of early detection of colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.
291 downloads 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.
277 downloads cancer biology
Brain resident and infiltrating innate immune cells adapt a tumor-supportive phenotype in the glioma microenvironment. Flow cytometry analysis supported by a single-cell RNA sequencing study of human gliomas indicate considerable cell type heterogeneity. It remains disputable whether microglia and infiltrating macrophages have the same or distinct roles in supporting glioma progression. Here, we performed single-cell transcriptomics analyses of CD11b+ cells sorted from murine syngeneic gliomas, indicating distinct activity of microglia, infiltrating monocytes/macrophages and CNS border-associated macrophages. Our results demonstrate a previously immeasurable scale of molecular heterogeneity in the innate immune response in gliomas. We identified genes differentially expressed in activated microglia from glioma-bearing mice of different sex, and profound overexpression of the MHCII genes by male microglial cells, which we also observed in bulk human glioma samples. Sex-specific gene expression in microglia in the glioma microenvironment may be relevant to sex differences in incidence and outcomes of glioblastoma patients.
265 downloads 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.
259 downloads 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.
258 downloads cancer biology
Aurelie Kamoun, Aurélien de Reyniès, Yves Allory, Gottfrid Sjödahl, A. Gordon Robertson, Roland Seiler, Katherine A Hoadley, Hikmat Al-Ahmadie, Woonyoung Choi, Clarice S. Groeneveld, Mauro A. A. Castro, Jacqueline Fontugne, Pontus Eriksson, Qianxing Mo, Jordan Kardos, Alexandre Zlotta, Arndt Hartmann, Colin P. Dinney, Joaquim Bellmunt, Thomas Powles, Núria Malats, Keith S. Chan, William Y. Kim, David J McConkey, Peter C. Black, Lars Dyrskjot, Mattias Höglund, Seth P. Lerner, Francisco X Real, François Radvanyi, The Bladder Cancer Molecular Taxonomy Group
Muscle-Invasive Bladder Cancer (MIBC) is a molecularly diverse disease with heterogeneous clinical outcomes. Several molecular classifications have been proposed, yielding diverse sets of subtypes. This diversity hampers the clinical application of such knowledge. Here, we report the results of a large international effort to reach a consensus on MIBC molecular subtypes. Using 1750 MIBC transcriptomes and a network-based analysis of six independent MIBC classification systems, we identified a consensus set of six molecular classes: Luminal Papillary (24%), Luminal Non-Specified (8%), Luminal Unstable (15%), Stroma-rich (15%), Basal/Squamous (35%), and Neuroendocrine-like (3%). These consensus classes differ regarding underlying oncogenic mechanisms, infiltration by immune and stromal cells, and histological and clinical characteristics. This consensus system offers a robust framework that will enable testing and validating predictive biomarkers in future clinical trials.
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