Debyani Chakravarty

ORCID: 0000-0001-8629-5732
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About
Contact & Profiles
Research Areas
  • Cancer Genomics and Diagnostics
  • Lung Cancer Treatments and Mutations
  • Genetic factors in colorectal cancer
  • Genomics and Rare Diseases
  • RNA modifications and cancer
  • Pancreatic and Hepatic Oncology Research
  • Radiomics and Machine Learning in Medical Imaging
  • Cholangiocarcinoma and Gallbladder Cancer Studies
  • Cancer Mechanisms and Therapy
  • Epigenetics and DNA Methylation
  • Colorectal Cancer Treatments and Studies
  • Sarcoma Diagnosis and Treatment
  • Health Systems, Economic Evaluations, Quality of Life
  • PARP inhibition in cancer therapy
  • Glioma Diagnosis and Treatment
  • Ferroptosis and cancer prognosis
  • Biomedical Text Mining and Ontologies
  • Renal cell carcinoma treatment
  • Lung Cancer Diagnosis and Treatment
  • Protein Degradation and Inhibitors
  • Cancer-related Molecular Pathways
  • Computational Drug Discovery Methods
  • Peptidase Inhibition and Analysis
  • Bioinformatics and Genomic Networks
  • Health and Medical Research Impacts

Memorial Sloan Kettering Cancer Center
2016-2025

Molecular Oncology (United States)
2016-2025

Kettering University
2013-2024

Northwestern Memorial Hospital
2017

NorthShore University HealthSystem
2017

University of Calgary
2017

The University of Texas MD Anderson Cancer Center
2017

Massachusetts General Hospital
2017

Georgetown University Medical Center
2004-2016

Georgetown University
2004-2016

Cameron Brennan Roel G.W. Verhaak Aaron McKenna Benito Campos Houtan Noushmehr and 95 more Sofie R. Salama Siyuan Zheng Debyani Chakravarty Zack Sanborn Samuel H. Berman Rameen Beroukhim Brady Bernard Chang‐Jiun Wu Giannicola Genovese Ilya Shmulevich Jill S. Barnholtz‐Sloan Lihua Zou Rahulsimham Vegesna Sachet A. Shukla Giovanni Ciriello W. K. Alfred Yung Wei Zhang Carrie Sougnez Tom Mikkelsen Kenneth Aldape Darell D. Bigner Erwin G. Van Meir Michael D. Prados Andrew E. Sloan Keith L. Black Jennifer Eschbacher Gaetano Finocchiaro William A. Friedman David W. Andrews Abhijit Guha Mary Iacocca Brian Patrick O’Neill Greg Foltz Jerome Myers Daniel J. Weisenberger Robert Penny Raju Kucherlapati Charles M. Perou D. Neil Hayes Richard A. Gibbs Marco A. Marra Gordon B. Mills Eric S. Lander Paul T. Spellman Richard K. Wilson Chris Sander John N. Weinstein Matthew Meyerson Stacey Gabriel Peter W. Laird David Haussler Gad Getz Lynda Chin Christopher C. Benz Jill S. Barnholtz‐Sloan Wendi Barrett Quinn T. Ostrom Yingli Wolinsky Keith L. Black Bikash Bose Paul T. Boulos Madgy Boulos Jenn Brown Christine Czerinski Matthew Eppley Mary Iacocca Thelma Kempista Teresa Kitko Yakov Koyfman Brenda Rabeno Pawan Rastogi Michael C. Sugarman Patricia Swanson Kennedy Yalamanchii Ilana P. Otey Yingchun Spring Liu Yonghong Xiao J. Todd Auman Peng‐Chieh Chen Angela Hadjipanayis Eunjung Lee Semin Lee Peter J. Park Jonathan G. Seidman Lixing Yang Raju Kucherlapati Steven N. Kalkanis Tom Mikkelsen Laila Poisson Aditya Raghunathan Lisa Scarpace Brady Bernard Ryan Bressler Andrea Eakin Lisa Iype

10.1016/j.cell.2013.09.034 article EN publisher-specific-oa Cell 2013-10-01
Ahmet Zehir Ryma Benayed Ronak Shah Aijazuddin Syed Sumit Middha and 95 more Hyunjae R. Kim Preethi Srinivasan Jianjiong Gao Debyani Chakravarty Sean M. Devlin Matthew D. Hellmann David Barron Alison M. Schram Meera Hameed Snjezana Doğan Dara S. Ross Jaclyn F. Hechtman Deborah F. DeLair JinJuan Yao Diana Mandelker Donavan T. Cheng Raghu Chandramohan Abhinita Mohanty Ryan Ptashkin Gowtham Jayakumaran Meera Prasad Mustafa Syed Anoop Balakrishnan Rema Zhen Y. Liu Khédoudja Nafa Laetitia Borsu Justyna Sadowska Jacklyn Casanova Ruben Bacares Iwona Kiecka Anna Razumova Julie B Son Lisa Stewart Tessara Baldi Kerry Mullaney Hikmat Al‐Ahmadie Efsevia Vakiani Adam Abeshouse Alexander Penson Philip Jonsson Niedzica Camacho Matthew T. Chang Helen Won Benjamin Groß Ritika Kundra Zachary Heins Hsiao‐Wei Chen Sarah Phillips Hongxin Zhang Jiaojiao Wang Angelica Ochoa Jonathan Wills Michael Eubank Stacy B. Thomas Stuart M. Gardos Dalicia N. Reales Jesse Galle Robert Durany Roy Cambria Wassim Abida Andrea Cercek Darren R. Feldman Mrinal M. Gounder A. Ari Hakimi James J. Harding Gopa Iyer Yelena Y. Janjigian Emmet Jordan Ciara M. Kelly Maeve A. Lowery Luc G.T. Morris Antonio Omuro Nitya Raj Pedram Razavi Alexander N. Shoushtari Neerav Shukla Tara E. Soumerai Anna M. Varghese Rona Yaeger Jonathan Coleman Bernard H. Bochner Gregory J. Riely Leonard B. Saltz Howard I. Scher Paul Sabbatini Mark E. Robson David S. Klimstra Barry S. Taylor José Baselga Nikolaus Schultz David M. Hyman Maria E. Arcila David B. Solit Marc Ladanyi Michael F. Berger

10.1038/nm.4333 article EN Nature Medicine 2017-05-08

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical contain key features representing the democratized nature collection process. To ensure proper use this large dataset associated genomic features, we developed standardized named Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major outcome endpoints. In...

10.1016/j.cell.2018.02.052 article EN cc-by-nc-nd Cell 2018-04-01

Highlights•Alteration map of 10 signaling pathways across 9,125 samples from 33 cancer types•Reusable, curated pathway templates that include a catalogue driver genes•57% tumors have at least one potentially actionable alteration in these pathways•Co-occurrence alterations suggests combination therapy opportunitiesSummaryGenetic control cell-cycle progression, apoptosis, and cell growth are common hallmarks cancer, but the extent, mechanisms, co-occurrence differ between individual tumor...

10.1016/j.cell.2018.03.035 article EN cc-by-nc-nd Cell 2018-04-01

We conducted comprehensive integrative molecular analyses of the complete set tumors in The Cancer Genome Atlas (TCGA), consisting approximately 10,000 specimens and representing 33 types cancer. performed clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, miRNA expression levels reverse-phase protein arrays, which all, except for revealed primarily organized by histology, tissue type, or anatomic origin. influence cell type was evident...

10.1016/j.cell.2018.03.022 article EN cc-by-nc-nd Cell 2018-04-01

Cancer progression involves the gradual loss of a differentiated phenotype and acquisition progenitor stem-cell-like features. Here, we provide novel stemness indices for assessing degree oncogenic dedifferentiation. We used an innovative one-class logistic regression (OCLR) machine-learning algorithm to extract transcriptomic epigenetic feature sets derived from non-transformed pluripotent stem cells their progeny. Using OCLR, were able identify previously undiscovered biological mechanisms...

10.1016/j.cell.2018.03.034 article EN cc-by-nc-nd Cell 2018-04-01

The AACR Project GENIE is an international data-sharing consortium focused on generating evidence base for precision cancer medicine by integrating clinical-grade genomic data with clinical outcome tens of thousands patients treated at multiple institutions worldwide. In conjunction the first public release from approximately 19,000 samples, we describe goals, structure, and standards report conclusions high-level analysis initial phase data. We also provide examples utility data, such as...

10.1158/2159-8290.cd-17-0151 article EN Cancer Discovery 2017-06-02

Aneuploidy, whole chromosome or arm imbalance, is a near-universal characteristic of human cancers. In 10,522 cancer genomes from The Cancer Genome Atlas, aneuploidy was correlated with TP53 mutation, somatic mutation rate, and expression proliferation genes. Aneuploidy anti-correlated immune signaling genes, due to decreased leukocyte infiltrates in high-aneuploidy samples. Chromosome arm-level alterations show cancer-specific patterns, including loss 3p squamous We applied genome...

10.1016/j.ccell.2018.03.007 article EN cc-by-nc-nd Cancer Cell 2018-04-01

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings tumor-infiltrating lymphocytes (TILs) based on H&E from 13 tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches images. Affinity propagation revealed local spatial structure in patterns correlation with overall survival. map...

10.1016/j.celrep.2018.03.086 article EN cc-by-nc-nd Cell Reports 2018-04-01

Our comprehensive analysis of alternative splicing across 32 The Cancer Genome Atlas cancer types from 8,705 patients detects events and tumor variants by reanalyzing RNA whole-exome sequencing data. Tumors have up to 30% more than normal samples. Association somatic with confirmed known trans associations in SF3B1 U2AF1 identified additional trans-acting (e.g., TADA1, PPP2R1A). Many tumors thousands not detectable samples; on average, we ≈930 exon-exon junctions ("neojunctions") typically...

10.1016/j.ccell.2018.07.001 article EN cc-by-nc-nd Cancer Cell 2018-08-01

The Cancer Genome Atlas (TCGA) cancer genomics dataset includes over 10,000 tumor-normal exome pairs across 33 different types, in total >400 TB of raw data files requiring analysis. Here we describe the Multi-Center Mutation Calling Multiple Cancers project, our effort to generate a comprehensive encyclopedia somatic mutation calls for TCGA enable robust cross-tumor-type analyses. Our approach accounts variance and batch effects introduced by rapid advancement DNA extraction,...

10.1016/j.cels.2018.03.002 article EN cc-by Cell Systems 2018-03-01

Tumor genetic testing is standard of care for patients with advanced lung adenocarcinoma, but the fraction who derive clinical benefit remains undefined. Here, we report experience 860 metastatic adenocarcinoma analyzed prospectively mutations in >300 cancer-associated genes. Potentially actionable events were stratified into one four levels based upon published or laboratory evidence that mutation question confers increased sensitivity to investigational therapies. Overall, 37.1% (319/860)...

10.1158/2159-8290.cd-16-1337 article EN Cancer Discovery 2017-03-24

In order to facilitate implementation of precision medicine in clinical management cancer, there is a need harmonise and standardise the reporting interpretation clinically relevant genomics data.

10.1093/annonc/mdy263 article EN publisher-specific-oa Annals of Oncology 2018-07-26

Gene fusions represent an important class of somatic alterations in cancer. We systematically investigated 9,624 tumors across 33 cancer types using multiple fusion calling tools. identified a total 25,664 fusions, with 63% validation rate. Integration gene expression, copy number, and annotation data revealed that involving oncogenes tend to exhibit increased whereas tumor suppressors have the opposite effect. For kinases, we found 1,275 intact kinase domain, proportion which varied...

10.1016/j.celrep.2018.03.050 article EN cc-by Cell Reports 2018-04-01

Glioblastoma (GBM) is distinguished by a high degree of intratumoral heterogeneity, which extends to the pattern expression and amplification receptor tyrosine kinases (RTKs). Although most GBMs harbor RTK amplifications, clinical trials small-molecule inhibitors targeting individual RTKs have been disappointing date. Activation multiple within provides theoretical mechanism resistance; however, spectrum functional dependence among tumor cell subpopulations in actual tumors unknown. We...

10.1073/pnas.1114033109 article EN Proceedings of the National Academy of Sciences 2012-02-08

We analyzed 921 adenocarcinomas of the esophagus, stomach, colon, and rectum to examine shared distinguishing molecular characteristics gastrointestinal tract (GIACs). Hypermutated tumors were distinct regardless cancer type comprised those enriched for insertions/deletions, representing microsatellite instability cases with epigenetic silencing MLH1 in context CpG island methylator phenotype, plus elevated single-nucleotide variants associated mutations POLE. Tumors chromosomal diverse,...

10.1016/j.ccell.2018.03.010 article EN cc-by-nc-nd Cancer Cell 2018-04-01

Purpose A long natural history and a predominant osseous pattern of metastatic spread are impediments to the adoption precision medicine in patients with prostate cancer. To establish feasibility clinical genomic profiling this disease, we performed targeted deep sequencing tumor normal DNA from locoregional, noncastrate, castration-resistant Patients Methods consented analysis their germline DNA. hybridization capture-based assay was used identify single-nucleotide variations, small...

10.1200/po.17.00029 article EN JCO Precision Oncology 2017-07-07

Hippo signaling has been recognized as a key tumor suppressor pathway. Here, we perform comprehensive molecular characterization of 19 core genes in 9,125 samples across 33 cancer types using multidimensional "omic" data from The Cancer Genome Atlas. We identify somatic drivers among and the related microRNA (miRNA) regulators, functional genomic approaches, experimentally characterize YAP TAZ mutation effects miR-590 miR-200a regulation for TAZ. pathway activity is best characterized by...

10.1016/j.celrep.2018.10.001 article EN cc-by-nc-nd Cell Reports 2018-10-01

We characterized the epigenetic landscape of genes encoding long noncoding RNAs (lncRNAs) across 6,475 tumors and 455 cancer cell lines. In stark contrast to CpG island hypermethylation phenotype in cancer, we observed a recurrent hypomethylation 1,006 lncRNA including EPIC1 (epigenetically-induced lncRNA1). Overexpression is associated with poor prognosis luminal B breast patients enhances tumor growth vitro vivo. Mechanistically, promotes cell-cycle progression by interacting MYC through...

10.1016/j.ccell.2018.03.006 article EN cc-by-nc-nd Cancer Cell 2018-04-01

Hotspot mutations in splicing factor genes have been recently reported at high frequency hematological malignancies, suggesting the importance of RNA cancer. We analyzed whole-exome sequencing data across 33 tumor types The Cancer Genome Atlas (TCGA), and we identified 119 with significant non-silent mutation patterns, including over-representation, recurrent loss function (tumor suppressor-like), or hotspot profile (oncogene-like). Furthermore, analysis revealed altered events associated...

10.1016/j.celrep.2018.01.088 article EN cc-by-nc-nd Cell Reports 2018-04-01

Highlights•MYC paralogs are significantly amplified (28% of all samples)•MYC antagonists mutated (MGA, 4% samples) or deleted (MNT, 10% alterations mutually exclusive with PIK3CA, PTEN, APC, BRAF alterations•Expression analysis reveals pan-cancer and tumor-specific MYC-associated pathwaysSummaryAlthough the MYC oncogene has been implicated in cancer, a systematic assessment MYC, related transcription factors, co-regulatory proteins, forming proximal network (PMN), across human cancers is...

10.1016/j.cels.2018.03.003 article EN cc-by Cell Systems 2018-03-01

Advanced human thyroid cancers, particularly those that are refractory to treatment with radioiodine (RAI), have a high prevalence of BRAF (v-raf murine sarcoma viral oncogene homolog B1) mutations. However, the degree which these cancers dependent on expression is still unclear. To address this question, we generated mice expressing one most commonly detected mutations in papillary carcinomas (BRAF(V600E)) follicular cells doxycycline-inducible (dox-inducible) manner. Upon dox induction...

10.1172/jci46382 article EN Journal of Clinical Investigation 2011-11-21
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