Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 93,037 bioRxiv papers from 397,248 authors.
Most downloaded bioRxiv papers, all time
in category cancer biology
3,229 results found. For more information, click each entry to expand.
2,010 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.
2,003 downloads cancer biology
Maxime Tarabichi, Iñigo Martincorena, Moritz Gerstung, Armand M Leroi, Florian Markowetz, Paul T. Spellman, Quaid Morris, Ole Christian Lingjærde, David C Wedge, Peter Van Loo, on behalf of the PCAWG Evolution and Heterogeneity Working Group
Williams et al. (Nat. Genet. 48:238-224, 2016) recently reported neutral tumor evolution in one third of 904 samples from The Cancer Genome Atlas. Here, we assess the reproducibility and validity of their method and the extent of positive selection in subclonal mutations across cancer types. Our results do not support observable neutral tumor evolution and uncover strong positive selection within subclonal mutations across cancers.
1,998 downloads cancer biology
Constance H. Li, Stephenie D. Prokopec, Ren X. Sun, Fouad Yousif, Nathaniel Schmitz, Paul C. Boutros, for the PCAWG Molecular Subtypes and Clinical Correlates Working Group, ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Network
Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here, we describe the first pan-cancer analysis of sex differences in whole genomes of 1,983 tumours of 28 subtypes from the ICGC Pan-Cancer Analysis of Whole Genomes project. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in cancer research.
1,987 downloads cancer biology
Alternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1. We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in non-tumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression.
1,985 downloads cancer biology
The identification of molecular pathways driving cancer progression is a fundamental unsolved problem in tumorigenesis, which can substantially further our understanding of cancer mechanisms and inform the development of targeted therapies. Most current approaches to address this problem use primarily somatic mutations, not fully exploiting additional layers of biological information. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating multiple data types (protein-protein interactions, mutual exclusivity of mutations or copy number alterations, transcriptional co-regulation, and RNA co-expression) into a single probabilistic model. To efficiently search the exponential space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods. For breast cancer subtypes, the inferred modules recapitulate known molecular mechanisms and suggest novel subtype-specific functionalities. These findings are supported by an independent patient cohort, as well as independent proteomic and phosphoproteomic datasets.
1,975 downloads cancer biology
Although combination therapy has been a mainstay of cancer treatment for decades, it remains challenging, both to identify novel effective combinations of drugs and to determine the optimal combination for a particular patient's tumor. While there have been several recent efforts to test drug combinations in vitro, examining the immense space of possible combinations is far from being feasible. Thus, it is crucial to develop data-driven techniques to computationally identify the optimal drug combination for a patient. We introduce TreeCombo, an extreme gradient boosted tree-based approach to predict synergy of novel drug combinations, using chemical and physical properties of drugs and gene expression levels of cell lines as features. We find that TreeCombo significantly outperforms three other state-of-the-art approaches, including the recently developed DeepSynergy, which uses the same set of features to predict synergy using deep neural networks. Moreover, we found that the predictions from our approach were interpretable, with genes having well-established links to cancer serving as important features for prediction of drug synergy.
1,961 downloads cancer biology
Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, non-coding RNAs, post-translational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations, once thought to be crucial only for the malignant phenotype maintenance, are now recognized as critical also for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. While associated with cancer, the precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems, and bioinformatics algorithms for their analysis. Advances in bioinformatics data integration techniques that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer, and are therefore also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations to cause tumorigenesis initiation and progression. This deeper understanding is essential to future studies that will precisely infer patients prognosis and select patients who will be responsive to emerging epigenetic therapies.
1,957 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.
1,953 downloads cancer biology
Maria C Vladoiu, Ibrahim El-Hamamy, Laura K Donovan, Hamza Farooq, Borja L Holgado, Vijay Ramaswamy, Stephen C Mack, John JY Lee, Sachin Kumar, David Przelicki, Antony Michealraj, Kyle Juraschka, Patryk Skowron, Betty Luu, Hiromichi Suzuki, A Sorana Morrissy, Florence MG Cavalli, Livia Garzia, Craig Daniels, Xiaochong Wu, Maleeha A Qazi, Sheila K Singh, Jennifer A Chan, Marco A. Marra, David Malkin, Peter Dirks, Trevor Pugh, Faiyaz Notta, Claudia L. Kleinman, Alexandra Joyner, Nada Jabado, Lincoln Stein, Michael D Taylor
The study of the origin and development of cerebellar tumours has been hampered by the complexity and heterogeneity of cerebellar cells that change over the course of development. We used single-cell transcriptomics to study >60,000 cells from the developing murine cerebellum, and show that different molecular subgroups of childhood cerebellar tumors mirror the transcription of cells from distinct, temporally restricted cerebellar lineages. Sonic Hedgehog medulloblastoma transcriptionally mirrors the granule cell hierarchy as expected, whereas Group 3 medulloblastoma resemble Nestin+ve stem cells, Group 4 medulloblastomas resemble unipolar brush cells, and PFA/PFB ependymoma and cerebellar pilocytic astrocytoma resemble the pre-natal gliogenic progenitor cells. Furthermore, single-cell transcriptomics of human childhood cerebellar tumors demonstrates that many bulk tumors contain a mixed population of cells with divergent differentiation. Our data highlight cerebellar tumors as a disorder of early brain development, and provide a proximate explanation for the peak incidence of cerebellar tumors in early childhood.
1,950 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.
1,949 downloads cancer biology
Microsatellite instability (MSI) refers to the hypermutability of the cancer genome due to impaired DNA mismatch repair. Although MSI has been studied for decades, the large amount of sequencing data now available allows us to examine the molecular fingerprints of MSI in greater detail. Here, we analyze ~8000 exome and ~1000 whole-genome pairs across 23 cancer types. Our pan-cancer analysis reveals that the prevalence of MSI events is highly variable within and across tumor types including some in which MSI is not typically examined. We also identify genes in DNA repair and oncogenic pathways recurrently subject to MSI and uncover non-coding loci that frequently display MSI events. Finally, we propose an exome-based predictive model for the MSI phenotype that achieves high sensitivity and specificity. These results advance our understanding of the genomic drivers and consequences of MSI, and a comprehensive catalog of tumor-type specific MSI loci we have generated enables efficient panel-based MSI testing to identify patients who are likely to benefit from immunotherapy.
1,943 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.
1,923 downloads cancer biology
Oliver A. Zill, Kimberly C. Banks, Stephen R. Fairclough, Stefanie A. Mortimer, James V. Vowles, Reza Mokhtari, David R Gandara, Philip C Mack, Justin I Odegaard, Rebecca J. Nagy, Arthur M. Baca, Helmy Eltoukhy, Darya I. Chudova, Richard B Lanman, AmirAli Talasaz
Cell-free DNA (cfDNA) sequencing provides a non-invasive method for obtaining actionable genomic information to guide personalized cancer treatment, but the presence of multiple alterations in circulation related to treatment and tumor heterogeneity pose analytical challenges. We present the somatic mutation landscape of 70 cancer genes from cfDNA deep-sequencing analysis of 21,807 patients with treated, late-stage cancers across >50 cancer types. Patterns and prevalence of cfDNA alterations in major driver genes for non-small cell lung, breast, and colorectal cancer largely recapitulated those from tumor tissue sequencing compendia (TCGA and COSMIC), with the principle differences in alteration prevalence being due to patient treatment. This highly sensitive cfDNA sequencing assay revealed numerous subclonal tumor-derived alterations, expected as a result of clonal evolution, but leading to an apparent departure from mutual exclusivity in treatment-naive tumors. To facilitate interpretation of this added complexity, we developed methods to identify cfDNA copy-number driver alterations and cfDNA clonality. Upon applying these methods, robust mutual exclusivity was observed among predicted truncal driver cfDNA alterations, in effect distinguishing tumor-initiating alterations from secondary alterations. Treatment-associated resistance, including both novel alterations and parallel evolution, was common in the cfDNA cohort and was enriched in patients with targetable driver alterations. Together these retrospective analyses of a large set of cfDNA deep-sequencing data reveal subclonal structures and emerging resistance in advanced solid tumors.
1,894 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.
1,878 downloads cancer biology
Transcriptional dysregulation is a key feature of cancer. Transcription factors (TFs) are the main link between signalling pathways and the transcriptional regulatory machinery of the cell, positioning them as key oncogenic inductors and therefore potential targets of therapeutic intervention. We implemented a computational pipeline to infer TF regulatory activities from basal gene expression and applied it to publicly available and newly generated RNA-seq data from a collection of 1,010 cancer cell lines and 9,250 primary tumors. We show that the predicted TF activities recapitulate known mechanisms of transcriptional dysregulation in cancer and dissect mutant-specific effects in driver genes. Importantly, we show the potential for predicted TF activities to be used as markers of sensitivity to the inhibition of their upstream regulators. Furthermore, combining these inferred activities with existing pharmacogenomic markers significantly improves the stratification of sensitive and resistant cell lines for several compounds. Our approach provides a framework to link driver genomic alterations with transcriptional dysregulation that helps to predict drug sensitivity in cancer and to dissect its mechanistic determinants.
1,874 downloads cancer biology
Amy Li, Rebecca H. Herbst, David Canner, Jason M Schenkel, Olivia C Smith, Jonathan Y Kim, Michelle Hillman, Arjun Bhutkar, Michael S Cuoco, C. Garrett Rappazzo, Patricia Rogers, Celeste Dang, Orit Rozenblatt-Rosen, Le Cong, Michael Birnbaum, Aviv Regev, Tyler Jacks
Regulatory T cells (Tregs) can impair anti-tumor immune responses and are associated with poor prognosis in multiple cancer types. Tregs in human tumors span diverse transcriptional states distinct from those of peripheral Tregs, but their contribution to tumor development remains unknown. Here, we used single cell RNA-Seq to longitudinally profile conventional CD4+ T cells (Tconv) and Tregs in a genetic mouse model of lung adenocarcinoma. Tissue-infiltrating and peripheral CD4+ T cells differed, highlighting divergent pathways of activation during tumorigenesis. Longitudinal shifts in Treg heterogeneity suggested increased terminal differentiation and stabilization of an effector phenotype over time. In particular, effector Tregs had enhanced expression of the interleukin 33 receptor ST2. Treg-specific deletion of ST2 reduced effector Tregs, increased infiltration of CD8+ T cells into tumors, and decreased tumor burden. Our study shows that ST2 plays a critical role in Treg-mediated immunosuppression in cancer, highlighting new potential paths for therapeutic intervention.
1,869 downloads cancer biology
Consequential events in cancer progression are typically rare and occur in the unobserved past. Detailed cell phylogenies can capture the history and chronology of such transient events - including metastasis. Here, we applied our Cas9-based lineage tracer to study metastatic progression in a lung cancer xenograft mouse model, revealing the underlying rates, routes, and patterns of metastasis. We report deeply resolved phylogenies for tens of thousands of metastatically disseminated cancer cells. We observe surprisingly diverse metastatic phenotypes, ranging from metastasis-incompetent to highly aggressive populations, and these differences are associated with characteristic changes in transcriptional state, including differential expression of metastasis-related genes like IFI27 and ID3. We further show that metastases transit via tissue routes that are diverse, complex, and multidirectional, and identify examples of reseeding, seeding cascades, and parallel seeding topologies. More broadly, we demonstrate the power of next-generation lineage tracers to record cancer evolution at high resolution and vast scale. ### Competing Interest Statement J.S.W. is an advisor and/or has equity in KSQ Therapeutics, Maze Therapeutics, Amgen, Tenaya, and 5 AM Ventures. T.G.B. is an advisor to Novartis, Astrazeneca, Revolution Medicines, Array, Springworks, Strategia, Relay, Jazz, Rain and receives research funding from Novartis and Revolution Medicines.
1,857 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 G.W. 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.
1,856 downloads cancer biology
Marc Zapatka, Ivan Borozan, Daniel S. Brewer, Murat Iskar, Adam Grundhoff, Malik Alawi, Nikita Desai, Holger Sültmann, 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.
1,855 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.
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