Abstract

The MSKCC Center for Translational Cancer Genomic Analysis, a Genome Data Analysis Center type B (GDAC-B), aims to develop novel integrative analysis methods for studying cancer genomic data, thereby enabling the translation of genomic insights into new clinical applications. The planned novel translational genomics analyses are presented in four specific aims organized around four themes: Subtype discovery. Pathway analysis, Therapy nomination, and Software development. These methods will be critical to helping the Cancer Genome Atlas (TCGA) project meet its stated objective of accelerating our understanding of the molecular basis of cancer, and improving our ability to diagnose, treat, and prevent cancer. The analysis work and method development will be closely coordinated with the GDACs working group (GDAC-WG), software tools will be fully integrated with the TCGA analytic pipelines and results of analyses will be made freely available to the scientific community via TCGA internet portals, with well-established plans for caBIG interoperability. The work of this GDAC-B will build on the flow of data from Genome Characterization Centers and Genome Sequencing Centers, as integrated by the GDAC-A data analysis centers. The applicant group of the MSKCC Center for Translational Cancer Genomic Analysis has a strong track record in large scale collaborative cancer genomics within the TCGA pilot phase and other consortia, and benefits from a computational biology program that is uniquely embedded in a comprehensive cancer center with a major focus on basic and translational research.

Public Health Relevance

Over the next five years, The Cancer Genome Atlas (TCGA) will generate the most detailed information ever obtained on the abnormalities present in many different types of cancer. To make sense of this unprecedented amount of information, the MSKCC Center for Translational Cancer Genomic Analysis will develop new ways of analyzing this information, to enable and hasten the translation of these novel insights into new clinical advances in the diagnosis and treatment of cancer.

  Funding Agency

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
5U24CA143840-04
Application #
8328074
Study Section
Special Emphasis Panel (ZCA1-SRLB-U (O1))
Program Officer
Yang, Liming
Project Start
2009-09-28
Project End
2014-07-31
Budget Start
2012-08-01
Budget End
2013-07-31
Support Year
4
Fiscal Year
2012
Total Cost
$1,582,181
Indirect Cost
$969,632

  Institution

Name
Sloan-Kettering Institute for Cancer Research
Department
Type
DUNS #
064931884
City
New York
State
NY
Country
United States
Zip Code
10065

  Publications

Way, Gregory P; Sanchez-Vega, Francisco; La, Konnor et al. (2018) Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas. Cell Rep 23:172-180.e3
Ricketts, Christopher J; De Cubas, Aguirre A; Fan, Huihui et al. (2018) The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma. Cell Rep 23:313-326.e5
Knijnenburg, Theo A; Wang, Linghua; Zimmermann, Michael T et al. (2018) Genomic and Molecular Landscape of DNA Damage Repair Deficiency across The Cancer Genome Atlas. Cell Rep 23:239-254.e6
Peng, Xinxin; Chen, Zhongyuan; Farshidfar, Farshad et al. (2018) Molecular Characterization and Clinical Relevance of Metabolic Expression Subtypes in Human Cancers. Cell Rep 23:255-269.e4
Huang, Kuan-Lin; Mashl, R Jay; Wu, Yige et al. (2018) Pathogenic Germline Variants in 10,389 Adult Cancers. Cell 173:355-370.e14
Ding, Li; Bailey, Matthew H; Porta-Pardo, Eduard et al. (2018) Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics. Cell 173:305-320.e10
Seiler, Michael; Peng, Shouyong; Agrawal, Anant A et al. (2018) Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types. Cell Rep 23:282-296.e4
Liu, Yang; Sethi, Nilay S; Hinoue, Toshinori et al. (2018) Comparative Molecular Analysis of Gastrointestinal Adenocarcinomas. Cancer Cell 33:721-735.e8
Jayasinghe, Reyka G; Cao, Song; Gao, Qingsong et al. (2018) Systematic Analysis of Splice-Site-Creating Mutations in Cancer. Cell Rep 23:270-281.e3
Saltz, Joel; Gupta, Rajarsi; Hou, Le et al. (2018) Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Rep 23:181-193.e7

Showing the most recent 10 out of 103 publications