Head and neck squamous cell carcinoma (HNSCC) has the sixth highest incidence among cancers worldwide and results in roughly 380,000 deaths annually. Unfortunately, outcomes have been relatively stagnant for patients with locally advanced HNSCC over the past 15 years. Locoregional recurrence is a major driver for mortality from HNSCC, but factors contributing to treatment resistance are not well understood. As such, new diagnostic approaches that accurately capture the appearance, development, and behavior of HNSCC are critical for understanding the biology of these tumors to devise novel treatment strategies. Furthermore, preclinical testing of novel biomarkers in animal models that recapitulate human disease is essential for successful translation of basic science findings to improved clinical outcomes.

My objective is to develop novel computational and mathematical methods to interrogate the biology and treatment resistance of HNSCC. In particular, I will investigate the high-throughput extraction and analysis of mathematical features from radiological images (i.e., radiomics) and digital pathology images (i.e., pathomics) to derive a multi-scale mathematical representation of the HNSCC phenotype.

In Aim 1, I will investigate the radiomic and pathomic expression of patients with HNSCC to identify and characterize quantitative markers of radiation resistance and metabolic response to therapy. These methodologies will be experimentally confirmed and further interrogated in Aim 2, using genetically-engineered mouse models of HNSCC, where I will analyze radiomic and pathomic data with molecular-level data from DNA and RNA sequencing to dissect mechanisms of resistance to standard therapies and interrogate the role of immune dysregulation in HNSCC recurrence. My central hypothesis is that tumor-specific radiopathomic expression patterns of HNSCC are linked to the underlying biological and molecular processes responsible for radiation resistance. The rationale for this research is that it will ultimately lead to a better understanding of HNSCC biology to guide improved treatment approaches for this urgent unmet clinical need.

As a physicist, imaging scientist, and cancer researcher, I am very interested in computational and mathematical approaches to precision oncology. My work focuses on the development of computational tools and mathematical methods to facilitate the discovery of quantitative biomarkers otherwise dormant in biomedical images. My long-term career objective is to become a successful Principal Investigator and leader in the emerging field of computational oncology. My 5-year plan is to establish an independent research program focused on the development and translation of new computational approaches to predict disease progression, quantify treatment response, and enable personalized therapy. With this Peer Reviewed Cancer Research Program (PRCRP) Career Development Award, I will obtain new training in cancer biology – to compliment my expertise in imaging biomarkers – such that I can bridge an existing knowledge gap between abstract mathematical image representation and the underlying biologic phenotype of cancer. To successfully launch my independent research program, I will use this PRCRP Career Development Award to achieve my short-term career objectives: (1) to develop new skills in the cancer biology; (2) to develop an understanding of high-throughput sequencing technology and associated genomic data; and (3) to receive focused training in grant writing, leadership, mentoring, and professional development. My proposed career development plan will facilitate excellent in these training areas, allowing me to complete my short-term objectives, start my independent research program, and accomplish my 5-year plan.

Successful completion of the proposed research will result in an image-guided computational platform for mechanistic and therapeutic preclinical and clinical HNSCC research and biomarker discovery. I anticipate that my results will greatly improve our overall understanding of HNSCC treatment resistance and facilitate the design of improved treatment strategies for Service members, their families, Veterans, other military beneficiaries, and the American public. Translation of this research into clinical practice will likely lead to increased cancer control rates, improved acute and long-term toxicity profiles, and a better understanding of how survival is attained. Collectively, this will have a profound impact on the basic health, well-being, and overall survival of patients being treated for HNSCC, in particular Veterans who are at higher risk of developing the disease relative to the general population. Confirmation of my hypotheses would be an important advancement in computational oncology.