Philosophy
Our research group emphasizes translational science with immediate potential clinical application. Our focus is on computational cancer genomics, the application of massively parallel sequencing to precision cancer medicine, and resistance to cancer therapeutics.
We have research activities in the following areas:
We have research activities in the following areas:
Prospective clinical interpretation. A primary focus of our lab is the development and application of algorithms that analyze and interpret complex 'omics data from individual patients for prospective clinical uses. We developed and applied the first algorithm that interprets genomic data with a clinical lens from whole exome sequencing, and are expanding to complex and integrated molecular, imaging, and clinical modalities.
In addition, we are expanding efforts in machine learning and genomic media. This includes creating human-computer interaction approaches for physician consumption of genomic data at the point of care, and enabling knowledge sharing and crowd-sourcing of data for researchers, physicians, and ultimately, patients.
In addition, we are expanding efforts in machine learning and genomic media. This includes creating human-computer interaction approaches for physician consumption of genomic data at the point of care, and enabling knowledge sharing and crowd-sourcing of data for researchers, physicians, and ultimately, patients.
Clinical response to therapies. A key research area in our lab involves the 'omic characterization of tumor and germline samples from patients who exhibit a spectrum of responses to existing and emerging cancer therapies. We have developed computational methods that enable this research for targeted therapies (e.g. RAF inhibitors and BRAF-mutant melanoma, androgen deprivation therapy in prostate cancer), conventional chemotherapies (e.g. cisplatin and urothelial carcinoma), and immunotherapies (e.g. ipilimumab and melanoma) in multiple disease types. We are actively expanding these efforts across therapeutics and clinical scenarios, including highly collaborative efforts in the genitourinary malignancies (prostate, urothelial, renal, and testicular cancers), among others.
Integrative genomics. We have multiple efforts that emphasize the integration of somatic and germline sequencing data, along with emerging sources of additional data (e.g. histopathology images, clinical data) for multiple purposes. This includes expanding our understanding of how functional germline variants mediate response to cancer therapies, enabling discovery of new germline drivers of disease in patients with multiple primary tumors, and exploring ways that familial genomic studies may inform the cancer genome-environment relationship. We are also expanding integration using machine learning techniques applied to large cancer data sets for biological interpretation and clinical prediction.