Winners of NCI's "Up for a Challenge? (U4C) Stimulating Innovation in Breast Cancer Genetic Epidemiology"
Back row: Jason Moore, Ph.D.; Leah Mechanic, Ph.D., M.P.H.; Sara Lindström, MSc., Ph.D.
Middle row: Joshua Hoffman, Ph.D.; Michael Guertin, Ph.D.; Elizabeth Gillanders, Ph.D.
Front row: Yunxian (Fureya) Liu, Ph.D.; Chad Myers, Ph.D.; Wen Wang, Ph.D.
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.
NCI selected the winners of the U4C in October 2016. Details are providing regarding the winning teams and individual researchers are listed below, along with brief descriptions of their projects and findings. Winners were chosen based on the identification of novel findings, replication of findings, innovation of approach, evidence of novel biological hypotheses, and collaboration. The grand prize, second prize, and runners-up have been invited to submit a manuscript for publication in PLoS Genetics describing their approach and results. All Challenge participants will be acknowledged in a special issue of the journal, pending acceptance.
A symposium to announce the finalists of the Challenge was held on September 12, 2016, on the NIH main campus in Bethesda, MD.
Winning Teams and Project Descriptions
GRAND PRIZE: Team UCSF
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.; Arunabha Majumdar, Ph.D.; Michael Passarelli, Ph.D., M.P.H.; Sarah Sawyer, Ph.D.; 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.
GRAND PRIZE: UMN-CSBIO
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.
SECOND PLACE: Team Transcription
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: U4C Maroons
Team Captain: DeZheng Huo, MD., Ph.D., University of Chicago
Team Members: Guimin Gao, Ph.D.; Hae Kyung Im, Ph.D.; Olufunmilayo Olopade, Ph.D.; Brandon Pierce, Ph.D.
Team Captain: Knut M. Wittkowksi, Ph.D., Sc.D., The Rockefeller University
Team Members: Christina Dadurian, B.A.
- 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.