David Quigley, PhD

I am a translational geneticist focused on understanding metastatic cancer, with a focus on prostate cancer. My research in Alan Ashworth’s laboratory at the UCSF Helen Diller Comprehensive Cancer Center, and in collaboration with the UCSF SU2C/PCF West Coast Dream Team, focuses on:
  1. how alterations in DNA repair genes affect tumor genomes
  2. detecting and overcoming resistance to targeted therapy
Our 2017 paper in Cancer Discovery provided the first demonstration of multi-clonal BRCA2 reversion mutations in metastatic prostate cancer patients who developed resistance to talazoparib and olaparib using liquid biopsy. This year I was a lead author on the largest whole genome study of metastatic prostate cancer. This paper was recently published in Cell.

During my graduate training with Anne-Lise Børresen-Dale (University of Oslo), Allan Balmain (UCSF), and Vessela Kristensen (University of Oslo), I identified candidate mechanisms of germline cancer susceptibility variants in mouse models of skin cancer and human breast cancer by combining bioinformatic and genetic approaches. To this end, I developed methods for expression Quantitative Trait Loci (eQTL) network analysis to identify germline variants that influence genes with a common functional role. eQTL network analysis identifies the causal effects of individual variants on organism-level phenotypes while simultaneously identifying a mechanism of action.

EmailDavid.Quigley at ucsf.edu
Address UCSF Helen Diller Comprehensive Cancer Center
1450 Third St. Room 207
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., Molecular Cancer Research 2014).


Dr. Felix Feng, Genetics of Prostate Cancer
Dr. Eric Small, Genetics of Prostate Cancer
Dr. Allan Balmain, PhD, Genetics of skin cancer
Dr. Anne-Lise Børresen-Dale, PhD, Genetics of breast cancer
Dr. Anders Persson, PhD, neural stem cells and glioma
Dr. Vessela Kristensen, PhD, Genetics of breast cancer
Dr. Anders Persson, PhD, Glioblastoma
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 June 2017