Programme Thesis
The DoW Prostate Cancer Data Science Award funds innovative data science approaches to advance prostate cancer research, diagnosis, treatment, and prevention. It exists to leverage computational methods and large-scale data analysis to address critical gaps in prostate cancer care, particularly for military beneficiaries and the broader population.
Selection Criteria
- Eligibility: Must be a U.S. entity (institution, nonprofit, or for-profit) or individual eligible to receive federal grants; international applicants are generally ineligible unless they have a U.S. partner or are specifically invited.
- Scoring rubric: Scientific merit (40%), impact on prostate cancer outcomes (30%), feasibility and data plan (20%), investigator qualifications and resources (10%).
- Reviewer priorities: Novelty of data science approach, potential for clinical translation, use of real-world or military health data, reproducibility, and team expertise in both oncology and computational methods.
Past Winners / Cohort Profiles
Past winners typically include U.S.-based academic researchers, data scientists at military medical centers, and small biotech firms with a focus on prostate cancer. Examples from similar DoD programs: teams from Johns Hopkins, University of Washington, and Walter Reed National Military Medical Center. Profiles often feature a PI with a track record in cancer bioinformatics or machine learning, and a co-investigator with clinical oncology expertise.
Ideal Candidate Fingerprint
The ideal applicant is a U.S.-based early-to-mid-career researcher with a PhD in computational biology, bioinformatics, or data science, affiliated with a university or military medical institution. They have a strong publication record in prostate cancer genomics or imaging, access to large patient datasets (e.g., from the Military Health System), and a clear plan for translating their model into clinical decision support.
Recommended Framing
Eniola Olutogun is not eligible for this grant as an independent Nigerian researcher without a U.S. institutional affiliation. However, if he were to partner with a U.S.-based collaborator (e.g., a prostate cancer researcher at a U.S. university), he could frame his CCT model as a novel data-driven framework for understanding addiction-related prostate cancer risk—specifically, how reward-memory encoding pathways (dopamine, opioid) intersect with prostate tumorigenesis. His Bayesian population dynamics and ODE modeling skills could be repurposed to analyze longitudinal PSA or treatment adherence data, offering a unique angle on prostate cancer progression in patients with substance use disorders.
Watch Out
Ineligibility due to non-U.S. status and lack of U.S. institutional affiliation; no prior prostate cancer research experience; the CCT model is focused on addiction, not prostate cancer, requiring a significant reframing and justification of relevance.