My laboratory seeks to understand how innate differences (such as germline variants) in DNA replication and/or repair impact two critical areas in precision oncology: (1) what underlies an individual’s cancer risk, and (2) how an individual will respond to cancer treatment. Currently my work focuses on colorectal and renal tumors, but results from my work are applicable to multiple tumor types.

In the area of cancer risk prediction, we seek to identify and functionally assess novel or rare ‘risk’ germline variants in the population. Knowing which germline variants confer risk and which don’t is important because physicians don’t know how to act upon the massive number of variants of uncertain significance that have been identified by sequencing studies. We currently are evaluating variants in replicative DNA polymerases, POLE and POLD1, as these are known to cause mutagenesis, genetic instability and cancer (such as colorectal, endometrial).

Our approach to solving the ‘too many uncertain variants’ problem is based on a novel hypothesis, that germline POLE and POLD1 variants that are dysfunctional in the tumor also have a detectable dysfunctional signature in the germline. We are leveraging big data of tumor and germline to build a Germline Signature, which can be adopted into clinical practice to guide risk prediction and allow for application of cancer prevention strategies (e.g., lifestyle interventions) or early detection (e.g. by improving cancer screening panels). Collaborating with clinical partners, we plan to improve and validate this tool in prospective studies. Longer term, we plan to use the Germline Signature to build risk prediction models.

In the area of individual response to cancer treatment, we are integrating germline variation data with biological assessment of innate capacity to repair DNA damage to develop a ‘signature’ that predicts the magnitude of benefit from chemoradiation therapy. My approach is particularly novel because tumor-specific signatures have been sought but no biomarker has ever been established; this may be because prior studies did not evaluate innate factors. This work will deliver preclinical evidence that could allow for precision stratification of patients to therapies.

Currently, my two research directions are relatively separate. But I envision they will eventually meet. By pursuing both it may be possible to identify specific innate factors that influence both risk and response to treatment. Results may elucidate mechanisms common to many cancers and/or underlying predisposition to a certain tumor type.