Announcing the recipients of the 2021 Shipley Pilot Grant Awards:
Jonathan Wisco, PhD, Associate Professor of Anatomy and Neurobiology
Prostate cancer is the most common cancer among men, except for skin cancer: According to the American Cancer Society, approximately 60 percent of cases are diagnosed in men over age 65 and the disease rarely occurs in those younger than 40. Prostate cancer is disproportionately diagnosed in black men, and amongst the social determinants of health that influence prostate gland access screening is the availability of best practices technology. This study will improve upon ultrasound and magnetic resonance imaging procedures that can contribute to increasing accessibility for better prostate cancer diagnosis.
Rachel L. Flynn, PhD, Assistant Professor of Pharmacology & Experimental Therapeutics
Sustained androgen-deprivation therapy (ADT) can drive the progression of localized prostate cancer to metastatic prostate cancer. Thus, long-term ADT selects for a more aggressive prostate cancer subtype. Safety net hospitals such as Boston Medical Center treat a disproportionate number of aggressive prostate cancers, thus new biomarkers that help understand mechanism and risk for progression and metastasis in those prostate cancer patients are urgently needed. The goal of the study is to identify novel biomarkers that can predict this progression to metastatic disease and ultimately, shift the standard of care for the treatment of this more aggressive subtype.
Alla Grishok, PhD, Associate Professor of Biochemistry
This multi-center, multi-PI initiative hopes to identify coding and non-coding genomic alterations that are associated with more aggressive disease in Black men with prostate cancer. The goal is to establish a pipeline that will integrate the genomic data with other biomarker measurements, including the presence of circulating tumor DNA, alterations in the gut microbiome, and overall clinical outcomes to create a comprehensive snapshot of these patients both at the time of diagnosis and following treatment. The integration of these datasets may allow for improved prediction of a patient’s response to a specific therapeutic intervention.