David Quigley, PhD
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Research Summary

Introduction
I study how tumor progression and the tumor microenvironment affect the genetic architecture of cancer. I primarily use quantitative methods, though I have done wet lab work as well. I currently work in Allan Balmain's laboratory at the UCSF Helen Diller Comprehensive Cancer Center. In October 2014 I defended my PhD thesis in the Department of Genetics at the University of Oslo, supervised by Anne-Lise Børresen-Dale (University of Oslo), Allan Balmain, and Vessela Kristensen (University of Oslo).

I received my bachelor's degree in Computer Science from Carleton College in 1998 and a Masters's degree in Biomedical Informatics from Columbia University in 2006. In addition to eight years in cancer research, I have five years of professional experience as a software developer, building high-volume web-based financial data and publishing systems (CV).

Contact
Emaildquigley at cc.ucsf.edu
Address UCSF Helen Diller Comprehensive Cancer Center
1450 Third St. Room 320
Box 0128
San Francisco, CA 94143-0875

Genetics and Genomics of cancer susceptibility
The first step in understanding the genetic architecture of human disease is to identify germline and acquired genetic variants associated with disease initiation or progression.

A cancer susceptibility locus may encompass hundreds of genes. To identify the relevant targets of the variant we have taken a systems genetics approach that measures the influence of that locus on intermediate phenotypes such as mRNA expression or DNA copy number changes. This influence is then synthesized through a data-driven approach guided by a biological understanding of the affected tissue. I have used genetically heterogeneous mouse models of skin cancer to identify networks of expression Quantitative Trait Loci (QTL) associated with sets of genes that are functionally related because they produce proteins that are part of a common structure or because they are steps in a gene pathway. This approach is reviewed in (Quigley et al. Nature Reviews: Cancer 2009).

Combining these networks with phenotype QTL identified plausible candidate genes under common genetic control with matched phenotypes (e.g., hemoglobin pathway genes that share common genetic control with the Red Blood Cell count phenotype). We used this method to identify the stem cell marker Lgr5 as a candidate driver of normal hair follicle activity and the vitamin D receptor as a driver of skin tumor development (Quigley et al. Nature 2009).

Tumor context interacts with germline and somatic variation
As tumors develop, germline influence on gene expression is supplanted by the effects of somatic genome alterations and changes in the tumor microenvironment. The DMBA/TPA mouse skin tumor system develops frequent trisomy on chromosome seven. I showed that genes on this chromosome have a disproportionate loss of significant eQTL and corresponding increase in expression of Cyclin D1, a driver of tumorigenesis located on chromosome seven (Quigley et al. Genome Biology 2011). I have also used network eQTL methods to identify genetic variants only relevant in the context of a tumor. In my thesis work with Anne-Lise Børresen Dale and Vessela Kristensen at the University of Oslo, I identified a candidate mechanism for the breast cancer susceptibility locus at 5p12 (Quigley et al. Molecular Oncology 2014). We showed the risk genotype was associated with increased expression and decreased promoter methylation of MRPS30, a mitochondrial ribosomal protein. This association was exclusive in ER-positive breast tumors and not found in normal breast tissue. In an analysis of somatic TP53 mutation in the 2000 tumor METABRIC cohort, we showed that lymphocyte invasion in IC10/Basal-like breast tumors is associated with wild-type TP53 (Quigley et al., under review).

Collaborations

Dr. Anne-Lise Børresen-Dale, PhD, Genetics of breast cancer
Dr. Vessela Kristensen, PhD, Genetics of breast cancer
Dr. William Weiss, MD, PhD, Genetic models of neuroblastoma
Dr. Rosemary Akhurst, PhD, the role of TGF-Beta in squamous cell carcinomas
Dr. David Jablons, MD, lung adenocarcinoma genetic network analysis
Dr. Toni Hurtado, PhD, Estrogen signaling in breast cancer
Dr. Nancy Mcnamera, OD PhD, Inflammatory diseases in the eye
Dr. Tom Lietman, MD, stochastic modeling of trachoma

Updated August 2014