Skip to main content

COVID-19 is an emerging, rapidly evolving situation.

What people with cancer should know: https://www.cancer.gov/coronavirus

Guidance for cancer researchers: https://www.cancer.gov/coronavirus-researchers

Get the latest public health information from CDC: https://www.coronavirus.gov

Get the latest research information from NIH: https://www.nih.gov/coronavirus

Grant Details

Grant Number: 5R01CA204954-05 Interpret this number
Primary Investigator: Freedman, Matthew
Organization: Dana-Farber Cancer Inst
Project Title: Identifying Causal Variants and Genes Underlying Breast Cancer Risk Loci
Fiscal Year: 2020


Abstract

 DESCRIPTION (provided by applicant): In stark contrast to Mendelian disorders, the majority of complex trait-associated common variants map to non-protein coding regions. Since there is a less well-developed genetic code for the much larger non- protein coding portion of the genome, identifying the gene(s) and causal alleles underlying non- Mendelian/complex traits presents a challenge. Given the rapidity with which genome wide association studies (GWAS) are discovering regions associated with complex traits, gene and causal allele identification have become severe bottlenecks. The overall goal of this proposal is to outline a rigorous strategy to discover functionally causal variants and genes underlying complex traits. While the proposal focuses on breast cancer, the strategies are generic and can be applied to any non-protein coding locus. The central hypothesis is that cancer risk loci are regulatory elements. Recent data convincingly demonstrate that GWAS loci are enriched for regulatory elements. Regulatory elements control the level of expression of genes. Causal genes and variants are difficult to discover because the scientific community is less adept at annotating the non-protein coding portion of the genome. This proposal seeks to utilize three powerful tools, expression quantitative trait loci (eQTL), circular chromosome conformation capture (4C) and genome editing to identify causal genes and alleles. Both Aims 1 and 2 are logically and structurally similar - identify enhancer-target gene interactions (using eQTL in Aim 1 and 4C/TALE-LSD1 in Aim 2), identify candidate causal variants using case-control fine mapping data intersected with epigenetic profiling, and perform genome editing on candidate causal variants. The variant that affects the predetermined readout - changes in gene expression (Aim 1) or allele- specific expression (Aim 2) - will be deemed a causal functional polymorphism. In parallel, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) evaluation of the candidate causal variants will be performed. Information from these assays will be integrated with genetic and epigenetic data to define the functionally causal variant. Aim 3 will test the target genes in cell based models to understand their influence on cancer-related phenotypes, such as proliferation and invasion. At the completion of this project, we fully anticipate that we will have begun to unravel the genes/pathways that initiate human breast cancer. Discovering the mechanisms underlying prostate cancer will not only inform the biology of this disease, but may also reveal opportunities to more rationally intervene in treatment and prevention.



Publications

Ovarian Cancer Risk Variants Are Enriched in Histotype-Specific Enhancers and Disrupt Transcription Factor Binding Sites.
Authors: Jones M.R. , Peng P.C. , Coetzee S.G. , Tyrer J. , Reyes A.L.P. , Corona R.I. , Davis B. , Chen S. , Dezem F. , Seo J.H. , et al. .
Source: American journal of human genetics, 2020-10-01; 107(4), p. 622-635.
EPub date: 2020-09-17.
PMID: 32946763
Related Citations

Allele-Specific QTL Fine Mapping with PLASMA.
Authors: Wang A.T. , Shetty A. , O'Connor E. , Bell C. , Pomerantz M.M. , Freedman M.L. , Gusev A. .
Source: American journal of human genetics, 2020-02-06; 106(2), p. 170-187.
EPub date: 2020-01-30.
PMID: 32004450
Related Citations

Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes.
Authors: Fachal L. , Aschard H. , Beesley J. , Barnes D.R. , Allen J. , Kar S. , Pooley K.A. , Dennis J. , Michailidou K. , Turman C. , et al. .
Source: Nature genetics, 2020 01; 52(1), p. 56-73.
EPub date: 2020-01-07.
PMID: 31911677
Related Citations

A transcriptome-wide association study of high-grade serous epithelial ovarian cancer identifies new susceptibility genes and splice variants.
Authors: Gusev A. , Lawrenson K. , Lin X. , Lyra P.C. , Kar S. , Vavra K.C. , Segato F. , Fonseca M.A.S. , Lee J.M. , Pejovic T. , et al. .
Source: Nature genetics, 2019 05; 51(5), p. 815-823.
EPub date: 2019-05-01.
PMID: 31043753
Related Citations

A Transcriptome-Wide Association Study Among 97,898 Women to Identify Candidate Susceptibility Genes for Epithelial Ovarian Cancer Risk.
Authors: Lu Y. , Beeghly-Fadiel A. , Wu L. , Guo X. , Li B. , Schildkraut J.M. , Im H.K. , Chen Y.A. , Permuth J.B. , Reid B.M. , et al. .
Source: Cancer research, 2018-09-15; 78(18), p. 5419-5430.
EPub date: 2018-07-27.
PMID: 30054336
Related Citations

A Somatically Acquired Enhancer of the Androgen Receptor Is a Noncoding Driver in Advanced Prostate Cancer.
Authors: Takeda D.Y. , Spisák S. , Seo J.H. , Bell C. , O'Connor E. , Korthauer K. , Ribli D. , Csabai I. , Solymosi N. , Szállási Z. , et al. .
Source: Cell, 2018-07-12; 174(2), p. 422-432.e13.
EPub date: 2018-06-14.
PMID: 29909987
Related Citations

Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer.
Authors: Milne R.L. , Kuchenbaecker K.B. , Michailidou K. , Beesley J. , Kar S. , Lindström S. , Hui S. , Lemaçon A. , Soucy P. , Dennis J. , et al. .
Source: Nature genetics, 2017 Dec; 49(12), p. 1767-1778.
EPub date: 2017-10-23.
PMID: 29058716
Related Citations

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.
Authors: Phelan C.M. , Kuchenbaecker K.B. , Tyrer J.P. , Kar S.P. , Lawrenson K. , Winham S.J. , Dennis J. , Pirie A. , Riggan M.J. , Chornokur G. , et al. .
Source: Nature genetics, 2017 May; 49(5), p. 680-691.
EPub date: 2017-03-27.
PMID: 28346442
Related Citations




Back to Top