Research Philosophy in the Van Allen Lab

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:

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 via the Molecular Oncology Almanac.

In addition, we are expanding efforts in machine learning and genomic media for precision cancer medicine. This includes creating novel artificial intelligence driven strategies to match patients to therapies, 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 multi-omic study of tumor and germline samples from patients who exhibit a spectrum of responses to existing and emerging cancer therapies to discover novel biological factors driving selective response that may also nominate new therapeutic targets. 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, and employing emerging profiling modalities paired with functional and clinical validation approaches.

 

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 AI-guided analyses. This includes expanding our understanding of how functional germline variants mediate response to cancer therapies, enabling discovery of new germline drivers of cancer in large patient cohorts, and exploring ways that familial genomic studies may inform the cancer genome-environment relationship. We are also expanding integration using biologically informed AI applied to large cancer data sets for biological interpretation and clinical prediction.