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Project: Machine Learning to Improve Targeted Cancer Therapy

Advances in genomics have created many new cancer therapies that target specific genetic or molecular features, raising the potential for personalized treatment that improves effectiveness and decreases side effects. However, the majority of patients treated with targeted therapies do not respond as predicted, and detailed patient genomic information is expensive to acquire.

This collaboration proposes to improve targeting of cancer therapies by developing new AI approaches that recommend the best treatment based on a combination of genetic and pathology data. Researchers will build new methods drawing upon computer vision and machine learning to gather essential contextual information about individual cancers from tumor samples, utilizing both genomic and image-based features. This work will advance both artificial intelligence and cancer-focused data science by developing innovative solutions to emerging machine learning problems in cancer research, ultimately benefiting patients through more targeted, effective treatments.

Mentors: Samantha Riesenfeld and Alex Pearson

Medical oncologist Alexander T. Pearson, MD, PhD, cares for adults with head and neck cancers, especially squamous cell carcinoma, a common form of cancer that develops in the squamous cells that make up the middle and outer layer lining the mouth and throat. He also specializes in salivary gland cancers.

His research combines laboratory experiments and mathematical models to more fully understand how head and neck cancers form and how to better design treatments for these cancers. He is currently the principal investigator on an NIH-funded study on the development of combination therapies for head and neck cancer.

Dr. Pearson has more than a dozen peer-reviewed publications in the primary literature, has had several abstracts selected for oral presentation and is editor of the Oncology Boards Flash Review, 2nd edition.