The Weill Cornell Medicine-New York Genome Center (WCM-NYGC) for Functional and Clinical Interpretation of Tumor Profiles will perform integrative analyses of coding and non-coding variants to detect and unravel the function of specific classes of mutations and assess their clinical potential. As specified in the RFA, we have chosen to focus on two Core Competencies: (1) coding mutations and (2) non-coding mutations (germline). We will use WCM?s expertise in clinical genomics demonstrated by the first CLIA approved Whole Exome Sequencing test in New York State. We will leverage NYGC?s computational infrastructure with > 5,000 cores and 10Pb storage and data management expertise. We will focus our GDAC on specific classes of mutations: (1) coding mutations and their clinical relevance including relevance to immunotherapy; (2) driver non-coding point mutations and their role in transcriptional regulation; (3) the driving role of structural variations.
In Aim 1 we will annotate the clinical and functional impact of coding mutations including immunotherapy. First we will generate a clinical grade report containing clinical interpretation of mutations, viewable through a custom user interface. This will enable detailed statistics on number and frequency of clinically relevant variants. A new module will help contribute new variants to the knowledge base and community. Second, we will apply our analytical pipeline for unraveling the immune landscape together with a novel integrative immunoscore that predicts which patients are more likely to respond to immune checkpoint blockade, a therapy showing dramatic impact in a subset of cancer patients.
In Aim 2, we will annotate the driving role and impact of non-coding mutations on splicing. First, we will functionally characterize genetic variants outside of genes (promoters, enhancers) using the experimentally validated FunSeq pipeline. Second, we will analyze the transcriptional consequence of splice site alterations using a novel for integrating RNA-Seq data with predicted splice-altering variants from DNA sequencing.
In Aim 3 we will annotate the driving role and transcriptional impact of structural variations. We will annotate structural variants and gene fusions using a consensus-based approach with methods benchmarked in our group as well as novel methods. The output of this Aim will be comprehensive annotation and functional analysis of a critical class of non-coding events. In summary, the proposed analyses rely on existing pipelines and tools that will be used in a standard and automated way on the WCM-NYGC computational infrastructure. The objective of these analyses is to derive novel knowledge and correlation that will impact both clinical and research cancer genomics fields. The WCM-NYGC team will participate and be responsive to the cooperative partners in this Network.

Public Health Relevance

The joint Weill Cornell Medicine-New York Genome Center (WCM-NYGC) for Functional and Clinical Interpretation of Tumor Profiles will perform integrative analyses of coding and non-coding variants to detect and unravel the function of specific classes of mutations and assess their clinical potential.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Resource-Related Research Projects--Cooperative Agreements (U24)
Project #
5U24CA210989-04
Application #
9771392
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Yang, Liming
Project Start
2016-09-14
Project End
2021-08-31
Budget Start
2019-09-01
Budget End
2020-08-31
Support Year
4
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Weill Medical College of Cornell University
Department
Physiology
Type
Schools of Medicine
DUNS #
060217502
City
New York
State
NY
Country
United States
Zip Code
10065
Corces, M Ryan; Granja, Jeffrey M; Shams, Shadi et al. (2018) The chromatin accessibility landscape of primary human cancers. Science 362:
Liu, Hui; Murphy, Charles J; Karreth, Florian A et al. (2018) Identifying and Targeting Sporadic Oncogenic Genetic Aberrations in Mouse Models of Triple-Negative Breast Cancer. Cancer Discov 8:354-369
Bhinder, Bhavneet; Elemento, Olivier (2017) Towards a better cancer precision medicine: systems biology meets immunotherapy. Curr Opin Syst Biol 2:67-73
Doane, Ashley S; Elemento, Olivier (2017) Regulatory elements in molecular networks. Wiley Interdiscip Rev Syst Biol Med 9:
Amengual, Jennifer E; Prabhu, Sathyen A; Lombardo, Maximilian et al. (2017) Mechanisms of Acquired Drug Resistance to the HDAC6 Selective Inhibitor Ricolinostat Reveals Rational Drug-Drug Combination with Ibrutinib. Clin Cancer Res 23:3084-3096
Cuykendall, Tawny N; Rubin, Mark A; Khurana, Ekta (2017) Non-coding genetic variation in cancer. Curr Opin Syst Biol 1:9-15
Pyenson, Nora C; Gayvert, Kaitlyn; Varble, Andrew et al. (2017) Broad Targeting Specificity during Bacterial Type III CRISPR-Cas Immunity Constrains Viral Escape. Cell Host Microbe 22:343-353.e3
Dhingra, Priyanka; Martinez-Fundichely, Alexander; Berger, Adeline et al. (2017) Identification of novel prostate cancer drivers using RegNetDriver: a framework for integration of genetic and epigenetic alterations with tissue-specific regulatory network. Genome Biol 18:141
Du, Wei; Goldstein, Rebecca; Jiang, Yanwen et al. (2017) Effective Combination Therapies for B-cell Lymphoma Predicted by a Virtual Disease Model. Cancer Res 77:1818-1830