Multiple Myeloma is a malignant proliferation of monoclonal plasma cells that are derived from post-germinal-center B cells. Myeloma cells produce monoclonal paraproteins and cause lytic bone lesions, anemia and renal failure. Myeloma accounts for almost 14% of all hematologic cancers. Despite intensive study, the etiology of Multiple Myeloma is unknown. Reports of substantial familial clustering of myeloma cases have been reported, including by our own team. These data are consistent with the existence of specific risk genes that predispose to Familial Myeloma and associated malignancies. Analogous to the BRCA1 breast cancer constitutional risk gene, which affects treatment decisions (surgical management and PARP inhibitors), surveillance (annual breast MRI) and prevention (oophorectomy), identification of Familial Myeloma risk genes is likely to provide important new mechanistic insights that can also significantly impact important clinical decision making for both affected individuals and at-risk family members. Unfortunately, there are currently no known constitutional familial or sporadic myeloma risk genes. Here, we will use an innovative strategy incorporating previously untapped computational resources to discover and rigorously validate novel constitutional cancer risk genes in one of the largest Familial Myeloma clinical and genetic resources in the world. We will use an innovative tiered whole exome and full genome sequencing strategy of well- characterized Familial Myeloma probands and available biospecimens to help discover, prioritize and validate causative constitutional mutation candidates. Our overall goal is to discover and validate the first constitutional Familial Myeloma risk genes in clinically well- characterized kindreds. This is anticipated to increase the number of patients and their at-risk family members who can benefit from increased cancer surveillance, early detection and cancer prevention.

Public Health Relevance

Despite intensive study, the etiology of Multiple Myeloma is unknown. Here, we will use an innovative strategy incorporating previously untapped computational resources to discover and rigorously validate novel constitutional cancer risk genes in one of the largest Familial Myeloma clinical and genetic resources in the world. Our overall goal is to discover and validate the first constitutional Familial Myeloma risk genes in clinically well-characterized kindreds.

Agency
National Institute of Health (NIH)
Type
Research Project (R01)
Project #
5R01CA167824-03
Application #
8681393
Study Section
Epidemiology of Cancer Study Section (EPIC)
Program Officer
Mechanic, Leah E
Project Start
Project End
Budget Start
Budget End
Support Year
3
Fiscal Year
2014
Total Cost
Indirect Cost
Name
Weill Medical College of Cornell University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
City
New York
State
NY
Country
United States
Zip Code
10065
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Guo, Yu; Wei, Xiaomu; Das, Jishnu et al. (2013) Dissecting disease inheritance modes in a three-dimensional protein network challenges the "guilt-by-association" principle. Am J Hum Genet 93:78-89
Khurana, Ekta; Fu, Yao; Colonna, Vincenza et al. (2013) Integrative annotation of variants from 1092 humans: application to cancer genomics. Science 342:1235587
Meyer, Michael J; Das, Jishnu; Wang, Xiujuan et al. (2013) INstruct: a database of high-quality 3D structurally resolved protein interactome networks. Bioinformatics 29:1577-9
Vijai, Joseph; Kirchhoff, Tomas; Schrader, Kasmintan A et al. (2013) Susceptibility loci associated with specific and shared subtypes of lymphoid malignancies. PLoS Genet 9:e1003220