Grant Details
| Grant Number: |
1R01CA167824-01A1 Interpret this number |
| Primary Investigator: |
Lipkin, Steven |
| Organization: |
Weill Medical Coll Of Cornell Univ |
| Project Title: |
Whole Genome Sequencing to Discover Familial Myeloma Risk Genes |
| Fiscal Year: |
2012 |
Abstract
DESCRIPTION (provided by applicant): 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.
Publications
Ataxia telangiectasia-mutated rs56009889 and risk of common cancers.
Authors: Barnet-Griness O.
, Gruber S.B.
, Lipkin S.M.
, Shulman K.
, Haddad R.
, Galili R.
, Rennert G.
.
Source: European Journal Of Cancer Prevention : The Official Journal Of The European Cancer Prevention Organisation (ecp), 2025-03-01 00:00:00.0; 34(2), p. 124-129.
EPub date: 2024-07-16 00:00:00.0.
PMID: 39150041
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Germline rare deleterious variant load alters cancer risk, age of onset and tumor characteristics.
Authors: Esai Selvan M.
, Onel K.
, Gnjatic S.
, Klein R.J.
, Gümüş Z.H.
.
Source: Npj Precision Oncology, 2023-01-27 00:00:00.0; 7(1), p. 13.
EPub date: 2023-01-27 00:00:00.0.
PMID: 36707626
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A full-proteome, interaction-specific characterization of mutational hotspots across human cancers.
Authors: Chen S.
, Liu Y.
, Zhang Y.
, Wierbowski S.D.
, Lipkin S.M.
, Wei X.
, Yu H.
.
Source: Genome Research, 2022 Jan; 32(1), p. 135-149.
EPub date: 2021-12-28 00:00:00.0.
PMID: 34963661
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Sequencing at lymphoid neoplasm susceptibility loci maps six myeloma risk genes.
Authors: Waller R.G.
, Klein R.J.
, Vijai J.
, McKay J.D.
, Clay-Gilmour A.
, Wei X.
, Madsen M.J.
, Sborov D.W.
, Curtin K.
, Slager S.L.
, et al.
.
Source: Human Molecular Genetics, 2021-06-09 00:00:00.0; 30(12), p. 1142-1153.
PMID: 33751038
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Functional Common and Rare ERBB2 Germline Variants Cooperate in Familial and Sporadic Cancer Susceptibility.
Authors: Bao R.
, Ng A.
, Sasaki M.
, Esai Selvan M.
, Katti A.
, Lee H.
, Huang L.
, Skol A.D.
, Lavarino C.
, Salvador H.
, et al.
.
Source: Cancer Prevention Research (philadelphia, Pa.), 2021 Apr; 14(4), p. 441-454.
EPub date: 2021-01-08 00:00:00.0.
PMID: 33419763
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De novo missense variants disrupting protein-protein interactions affect risk for autism through gene co-expression and protein networks in neuronal cell types.
Authors: Chen S.
, Wang J.
, Cicek E.
, Roeder K.
, Yu H.
, Devlin B.
.
Source: Molecular Autism, 2020-10-08 00:00:00.0; 11(1), p. 76.
EPub date: 2020-10-08 00:00:00.0.
PMID: 33032641
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Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations.
Authors: Fragoza R.
, Das J.
, Wierbowski S.D.
, Liang J.
, Tran T.N.
, Liang S.
, Beltran J.F.
, Rivera-Erick C.A.
, Ye K.
, Wang T.Y.
, et al.
.
Source: Nature Communications, 2019-09-12 00:00:00.0; 10(1), p. 4141.
EPub date: 2019-09-12 00:00:00.0.
PMID: 31515488
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Exome sequencing identifies germline variants in DIS3 in familial multiple myeloma.
Authors: Pertesi M.
, Vallée M.
, Wei X.
, Revuelta M.V.
, Galia P.
, Demangel D.
, Oliver J.
, Foll M.
, Chen S.
, Perrial E.
, et al.
.
Source: Leukemia, 2019-04-09 00:00:00.0; , .
EPub date: 2019-04-09 00:00:00.0.
PMID: 30967618
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Inferring Protein-Protein Interaction Networks From Mass Spectrometry-Based Proteomic Approaches: A Mini-Review.
Authors: Yugandhar K.
, Gupta S.
, Yu H.
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Source: Computational And Structural Biotechnology Journal, 2019; 17, p. 805-811.
EPub date: 2019-06-20 00:00:00.0.
PMID: 31316724
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An interactome perturbation framework prioritizes damaging missense mutations for developmental disorders.
Authors: Chen S.
, Fragoza R.
, Klei L.
, Liu Y.
, Wang J.
, Roeder K.
, Devlin B.
, Yu H.
.
Source: Nature Genetics, 2018 07; 50(7), p. 1032-1040.
EPub date: 2018-06-11 00:00:00.0.
PMID: 29892012
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Germline mutations in lysine specific demethylase 1 (LSD1/KDM1A) confer susceptibility to multiple myeloma.
Authors: Wei X.
, Calvo-Vidal M.N.
, Chen S.
, Wu G.
, Revuelta M.V.
, Sun J.
, Zhang J.
, Walsh M.F.
, Nichols K.E.
, Joseph V.
, et al.
.
Source: Cancer Research, 2018-03-20 00:00:00.0; , .
EPub date: 2018-03-20 00:00:00.0.
PMID: 29559475
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Novel pedigree analysis implicates DNA repair and chromatin remodeling in multiple myeloma risk.
Authors: Waller R.G.
, Darlington T.M.
, Wei X.
, Madsen M.J.
, Thomas A.
, Curtin K.
, Coon H.
, Rajamanickam V.
, Musinsky J.
, Jayabalan D.
, et al.
.
Source: Plos Genetics, 2018 Feb; 14(2), p. e1007111.
EPub date: 2018-02-01 00:00:00.0.
PMID: 29389935
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Interactome INSIDER: a structural interactome browser for genomic studies.
Authors: Meyer M.J.
, Beltrán J.F.
, Liang S.
, Fragoza R.
, Rumack A.
, Liang J.
, Wei X.
, Yu H.
.
Source: Nature Methods, 2018-01-01 00:00:00.0; , .
EPub date: 2018-01-01 00:00:00.0.
PMID: 29355848
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GeMSTONE: orchestrated prioritization of human germline mutations in the cloud.
Authors: Chen S.
, Beltrán J.F.
, Esteban-Jurado C.
, Franch-Expósito S.
, Castellví-Bel S.
, Lipkin S.
, Wei X.
, Yu H.
.
Source: Nucleic Acids Research, 2017-07-03 00:00:00.0; 45(W1), p. W207-W214.
PMID: 28521008
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Iregnet3d: Three-dimensional Integrated Regulatory Network For The Genomic Analysis Of Coding And Non-coding Disease Mutations
Authors: Liang S.
, Tippens N.D.
, Zhou Y.
, Mort M.
, Stenson P.D.
, Cooper D.N.
, Yu H.
.
Source: Genome Biology, 2017-01-18 00:00:00.0; 18(1), p. 10.
PMID: 28100260
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Integrated Network Analysis Reveals Distinct Regulatory Roles Of Transcription Factors And Micrornas
Authors: Guo Y.
, Alexander K.
, Clark A.G.
, Grimson A.
, Yu H.
.
Source: Rna (new York, N.y.), 2016 Nov; 22(11), p. 1663-1672.
PMID: 27604961
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American Society of Clinical Oncology Policy Statement Update: Genetic and Genomic Testing for Cancer Susceptibility.
Authors: Robson M.E.
, Bradbury A.R.
, Arun B.
, Domchek S.M.
, Ford J.M.
, Hampel H.L.
, Lipkin S.M.
, Syngal S.
, Wollins D.S.
, Lindor N.M.
.
Source: Journal Of Clinical Oncology : Official Journal Of The American Society Of Clinical Oncology, 2015-11-01 00:00:00.0; 33(31), p. 3660-7.
EPub date: 2015-08-31 00:00:00.0.
PMID: 26324357
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ENCAPP: elastic-net-based prognosis prediction and biomarker discovery for human cancers.
Authors: Das J.
, Gayvert K.M.
, Bunea F.
, Wegkamp M.H.
, Yu H.
.
Source: Bmc Genomics, 2015-04-03 00:00:00.0; 16, p. 263.
EPub date: 2015-04-03 00:00:00.0.
PMID: 25887568
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Trimethylation of Lys36 on H3 restricts gene expression change during aging and impacts life span.
Authors: Pu M.
, Ni Z.
, Wang M.
, Wang X.
, Wood J.G.
, Helfand S.L.
, Yu H.
, Lee S.S.
.
Source: Genes & Development, 2015-04-01 00:00:00.0; 29(7), p. 718-31.
PMID: 25838541
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Phosphoproteomics reveals distinct modes of Mec1/ATR signaling during DNA replication.
Authors: Bastos de Oliveira F.M.
, Kim D.
, Cussiol J.R.
, Das J.
, Jeong M.C.
, Doerfler L.
, Schmidt K.H.
, Yu H.
, Smolka M.B.
.
Source: Molecular Cell, 2015-03-19 00:00:00.0; 57(6), p. 1124-32.
EPub date: 2015-03-19 00:00:00.0.
PMID: 25752575
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A massively parallel pipeline to clone DNA variants and examine molecular phenotypes of human disease mutations.
Authors: Wei X.
, Das J.
, Fragoza R.
, Liang J.
, Bastos de Oliveira F.M.
, Lee H.R.
, Wang X.
, Mort M.
, Stenson P.D.
, Cooper D.N.
, et al.
.
Source: Plos Genetics, 2014 Dec; 10(12), p. e1004819.
PMID: 25502805
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Elucidating common structural features of human pathogenic variations using large-scale atomic-resolution protein networks.
Authors: Das J.
, Lee H.R.
, Sagar A.
, Fragoza R.
, Liang J.
, Wei X.
, Wang X.
, Mort M.
, Stenson P.D.
, Cooper D.N.
, et al.
.
Source: Human Mutation, 2014 May; 35(5), p. 585-93.
PMID: 24599843
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Exploring mechanisms of human disease through structurally resolved protein interactome networks.
Authors: Das J.
, Fragoza R.
, Lee H.R.
, Cordero N.A.
, Guo Y.
, Meyer M.J.
, Vo T.V.
, Wang X.
, Yu H.
.
Source: Molecular Biosystems, 2014 Jan; 10(1), p. 9-17.
PMID: 24096645
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Predicting cancer prognosis using functional genomics data sets.
Authors: Das J.
, Gayvert K.M.
, Yu H.
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Source: Cancer Informatics, 2014; 13(Suppl 5), p. 85-8.
PMID: 25392695
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Integrative annotation of variants from 1092 humans: application to cancer genomics.
Authors: Khurana E.
, Fu Y.
, Colonna V.
, Mu X.J.
, Kang H.M.
, Lappalainen T.
, Sboner A.
, Lochovsky L.
, Chen J.
, Harmanci A.
, et al.
.
Source: Science (new York, N.y.), 2013-10-04 00:00:00.0; 342(6154), p. 1235587.
PMID: 24092746
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Dissecting disease inheritance modes in a three-dimensional protein network challenges the "guilt-by-association" principle.
Authors: Guo Y.
, Wei X.
, Das J.
, Grimson A.
, Lipkin S.M.
, Clark A.G.
, Yu H.
.
Source: American Journal Of Human Genetics, 2013-07-11 00:00:00.0; 93(1), p. 78-89.
EPub date: 2013-07-11 00:00:00.0.
PMID: 23791107
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INstruct: a database of high-quality 3D structurally resolved protein interactome networks.
Authors: Meyer M.J.
, Das J.
, Wang X.
, Yu H.
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Source: Bioinformatics (oxford, England), 2013-06-15 00:00:00.0; 29(12), p. 1577-9.
EPub date: 2013-06-15 00:00:00.0.
PMID: 23599502
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Cross-species protein interactome mapping reveals species-specific wiring of stress response pathways.
Authors: Das J.
, Vo T.V.
, Wei X.
, Mellor J.C.
, Tong V.
, Degatano A.G.
, Wang X.
, Wang L.
, Cordero N.A.
, Kruer-Zerhusen N.
, et al.
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Source: Science Signaling, 2013-05-21 00:00:00.0; 6(276), p. ra38.
EPub date: 2013-05-21 00:00:00.0.
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Susceptibility loci associated with specific and shared subtypes of lymphoid malignancies.
Authors: Vijai J.
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, Schrader K.A.
, Brown J.
, Dutra-Clarke A.V.
, Manschreck C.
, Hansen N.
, Rau-Murthy R.
, Sarrel K.
, Przybylo J.
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Source: Plos Genetics, 2013; 9(1), p. e1003220.
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Target essentiality and centrality characterize drug side effects.
Authors: Wang X.
, Thijssen B.
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Source: Plos Computational Biology, 2013; 9(7), p. e1003119.
PMID: 23874169
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HINT: High-quality protein interactomes and their applications in understanding human disease.
Authors: Das J.
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Source: Bmc Systems Biology, 2012-07-30 00:00:00.0; 6, p. 92.
EPub date: 2012-07-30 00:00:00.0.
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Genome-scale analysis of interaction dynamics reveals organization of biological networks.
Authors: Das J.
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Source: Bioinformatics (oxford, England), 2012-07-15 00:00:00.0; 28(14), p. 1873-8.
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Detecting overlapping protein complexes in protein-protein interaction networks.
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EPub date: 2012-03-18 00:00:00.0.
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Three-dimensional reconstruction of protein networks provides insight into human genetic disease.
Authors: Wang X.
, Wei X.
, Thijssen B.
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Source: Nature Biotechnology, 2012-01-15 00:00:00.0; 30(2), p. 159-64.
EPub date: 2012-01-15 00:00:00.0.
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