Symposium to Announce Finalists of NCI's "Up for a Challenge? Stimulating Innovation in Breast Cancer Genetic Epidemiology"
In order to encourage innovative approaches to more fully explain the genomic basis of breast cancer, the Epidemiology and Genomics Research Program (EGRP) in NCI's Division of Cancer Control and Population Sciences partnered with Sage Bionetworks to launch "Up for a Challenge (U4C)—Stimulating Innovation in Breast Cancer Genetic Epidemiology" in 2015. Participating research teams used genetic epidemiologic data from thousands of breast cancer cases and controls from ethnically diverse populations to discover genetic pathways involved in breast cancer risk. For more information about the Challenge research question, data sets, application process, and other FAQs, visit the U4C website.
A symposium to announce the finalists of the Challenge was held on September 12, 2016, on the NIH main campus in Bethesda, MD. Finalists were chosen based on the identification of novel findings, replication of findings, innovation of approach, evidence of novel biological hypotheses, and collaboration.
The names of the top teams and the individual researchers are listed below, along with brief descriptions of their projects and findings.
Elizabeth Gillanders, National Cancer Institute (NCI)
Advancing Innovation in Biomedical Research
Sandeep Patel, Health and Human Services (HHS)
Introducing U4C and NCI Goals
Leah Mechanic, NCI
State of Breast Cancer Genetic Epidemiology
Sara Lindstroem, University of Washington
Needs for Innovation and Trends in U4C Submissions
Jason Moore, The University of Pennsylvania
Josh Hoffman, Ph.D., M.S., University of California, San Francisco
Yunxian (Fureya) Liu, Ph.D., University of Virginia
Chad Myers, Ph.D., University of Minnesota
Finalists and Project Descriptions
Team Captain: John Witte, Ph.D., University of California, San Francisco
Team Members: Nima Emami, Ph.D.; Rebecca Graff, Ph.D.; Dexter Hadley, M.D., Ph.D.; Josh Hoffman, Ph.D., M.S.; Donglei Hu, Ph.D.; Scott Huntsman, M.S.; Lancelote Leong, B.A.; Arunabha Majumdar, Ph.D.; Michael Passarelli, Ph.D., M.P.H.; Caroline Tai, Ph.D., M.P.H.; Noah Zaitlen, Ph.D.; Elad Ziv, M.D.
Team UCSF used all the designated GWAS datasets provided and performed a traditional GWAS to reproduce previous published findings, followed by a genome-wide association of gene expression (GWAGE) and admixture mapping to identify new genes. Using the GWAGE approach, they identified novel associations with the ACAP1 and RTNK2 genes and breast cancer. These findings were replicated in the UK biobank study. ACAP1 and RTKN2 are in the same gene family. Moreover, ACAP1 interacts with the third cytoplasmic loop of SLC2A4/GLUT4, while RTKN2 is implicated in the activation of NF-κB pathway, suggesting possible biological mechanisms for these findings.
Team Captain: Michael Guertin, Ph.D., University of Virginia
Team Members: Mete Civelek, Ph.D.; Mikhail Dozmorov, Ph.D.; Yunxian (Fureya) Liu, Ph.D.; Stephen Rich, Ph.D.
Team Transcription employed a novel integrative genomics approach to explore the hypothesis that many of the non-coding single nucleotide polymorphisms (SNPs) identified by GWAS alter transcription factor (TF) binding sites and mediate effect on disease by modulating TF binding and gene regulation. This team identified a SNP, rs4802200, in perfect linkage disequilibrium with a GWAS-identified SNP, which is predicted to disrupt ZNF143 transcription factor binding within a breast cancer-relevant regulatory element. This SNP is a strong expression quantitative trait loci (eQTL) of ZNF404 in breast tissue. This pipeline can be used as a general framework to identify candidate causal variants with regulatory regions and TF binding sites that confer phenotypic variation and disease risk.
Team Captain: Chad Myers, Ph.D., University of Minnesota
Team Members: Carol Lange, Ph.D.; Wen Wang, Ph.D.; Zhiyuan Xu, Ph.D.
Team UMN-CSBIO used an innovative computational approach to search for pathway level interactions, instead of examining individual variants or genes. By examining pathway interactions using two of the U4C designated GWAS datasets, the team identified steroid hormone biosynthesis as a major hub of interactions and this pathway was implicated as interacting with many pathways, including the gene set previously associated with acute myeloid leukemia (AML). Several existing studies reported the chemotherapy treatment for breast cancer as a risk factor for AML. Importantly, these interactions would have been missed using traditional approaches.
Slides from the Symposium presentations are available upon request; please email UpForAChallenge@mail.nih.gov.
- Elizabeth Gillanders, Ph.D., Genomic Epidemiology Branch, EGRP
- Leah Mechanic, Ph.D., M.P.H., Genomic Epidemiology Branch, EGRP
For additional information about the Challenge, please contact the Planning Committee at UpForAChallenge@mail.nih.gov.