DoD Prostate Cancer, Data Science Award | W81XWH 21 PCRP DSA

FY21 DoD Prostate Cancer Research Program (PCRP) Data Science Award: FAQs

1) What is the FY21 PCRP Data Science Award?

The FY21 DoD Prostate Cancer Research Program (PCRP) Data Science Award is a research funding opportunity focused on using modern quantitative and analytical methods to extract new knowledge from large or complex prostate cancer datasets. The goal is to create or adapt data-science-driven tools and analyses that meaningfully advance prostate cancer research and, ultimately, patient care.

2) What kinds of projects are a good fit for this award?

Projects that are data-intensive and computationally driven, including efforts that:

• Develop new tools, resources, or computational processes for prostate cancer research using data science approaches.
• Apply, adapt, or improve existing computational methods in a way that clearly advances the field.
• Integrate multiple data types (for example, combining imaging, digital pathology, genomics/omics, and clinically annotated outcomes) to produce deeper insights than any single dataset could provide.
• Create resources/tools that improve access to standardized and harmonized datasets for research, clinical care, and patient communities, including real-time clinical care applications.

3) What research areas and disciplines does the opportunity emphasize?

The award emphasizes computational and quantitative approaches in areas such as computational biology, bioinformatics, artificial intelligence and machine learning, epidemiology, omics analyses, medical imaging, digital pathology, and other clinically annotated datasets relevant to prostate cancer.

4) Does the project need to address specific program priorities?

Yes. Funded work is expected to push progress against one or more of the FY21 PCRP Overarching Challenges. The program also places strong emphasis on producing results or products that are clearly differentiated from what already exists and that can outperform current approaches.

5) Can applicants create new datasets as part of the project?

No. This mechanism cannot be used to create new datasets. Projects must rely on existing, established retrospective data sources.

6) Are retrospective studies allowed?

Yes. Retrospective studies using existing data are explicitly within scope, as long as the work aligns with the data science focus and other requirements of the award.

7) Are prospective human subject studies or clinical trials allowed?

No. Prospective recruitment of human subjects and clinical trials are not allowed.

8) How does the program define a clinical trial for the purposes of this award?

The opportunity uses a standard clinical trial definition: a clinical trial involves the prospective assignment of human subjects to interventions to evaluate health-related outcomes.

9) What if the project uses de-identified existing data only?

The opportunity notes that certain uses of de-identified existing data may fall outside CDMRP-defined clinical research (for example, research consistent with IRB Exemption 4). Even so, projects still need to follow applicable oversight processes for DoD-funded work as described in the opportunity.

10) What expectations are there for methodological rigor and evaluation?

Proposals are expected to be rigorous and defensible from a data science standpoint. The program encourages clear validation and evaluation plans, such as benchmarking against existing methods, head-to-head comparisons, or other quality and utility assessments that demonstrate reliability, generalizability, and practical value.

11) How large does the dataset need to be?

The dataset must be sufficiently large to support appropriate statistical and analytical power. The application should be grounded in large-scale evidence rather than small or underpowered datasets.

12) Do applicants need to prove they can access the required data?

Yes. Applicants must document that they have access to the necessary datasets and/or patient samples. If access depends on external repositories, retrospective datasets, or resources associated with ongoing studies, letters of support are encouraged to demonstrate the data will be available as proposed.

13) What is the emphasis on tools and resources versus one-off analyses?

The award strongly prefers durable, reusable contributions. Applications are expected to describe how outputs will be shared and maintained and how the approach can incorporate additional data produced by future studies so the tools and methods can be refined over time, rather than being limited to a one-time analysis.

14) Are there required plans for sharing tools, resources, and outputs?

Yes. A major requirement is openness and sharing. Any tools, resources, and computational processes developed are expected to be made available for public use so the broader research and patient communities can benefit.

15) What is a Data and Resource Sharing Plan, and what must it cover?

Applications must include a Data and Resource Sharing Plan that describes:

• How tools, resources, and computational processes will be shared for public use.
• How shared outputs will be maintained.
• How the design will incorporate additional data produced by future studies so methods/tools can be refined over time.

16) Is collaboration encouraged, and how can teams structure leadership?

Yes. Collaboration is strongly encouraged through a formal Partnering Principal Investigator option that supports two PIs with distinct and complementary contributions, such as partnerships between data scientists and clinicians.

17) How does the Partnering PI model work?

One PI serves as the Initiating PI and handles most submission administration. The Partnering PI is an equal scientific contributor. If funded, each PI is named on an individual award within their organization.

18) What type of funding instrument will be used?

Awards are made as assistance agreements, issued either as a grant or a cooperative agreement. The specific instrument depends on how much substantial involvement the Department of Defense anticipates having during the project.

19) What is the maximum budget allowed?

Direct costs for the full period of performance are capped at $1.0 million.

20) How much total funding was the program planning to allocate, and how many awards were expected?

The program planned to allocate about $4.8 million total to fund roughly three awards.

21) What were the key dates mentioned for this opportunity?

The original application closing date was July 29, 2021. Awards were anticipated no later than September 30, 2022. FY21 funds were expected to remain available for use until September 30, 2027.

22) What regulatory and ethics reviews apply if the project involves human-related materials or data?

For any DoD-funded work involving human subjects, human anatomical substances, or human cadavers, projects must undergo additional review by the USAMRDC Office of Research Protections/Human Research Protection Office (HRPO) before research begins, in addition to local IRB or ethics review.

23) How long should applicants plan for DoD human research protection review?

Applicants are advised to plan for a multi-month regulatory timeline for the additional HRPO review before research can begin.

24) What is required for multi-institution projects regarding IRB review?

For multi-institution projects, a single-IRB plan is required.

25) What populations or communities should the proposed work be relevant to?

Proposed work must be relevant to active-duty Service Members, Veterans, military beneficiaries, and/or the broader American public.

26) Are collaborations with military or VA institutions encouraged?

Yes. Collaborations between military/VA institutions and non-military institutions are encouraged to leverage unique populations, infrastructure, and clinical resources.

27) Can applicants align their ideas with the Metastatic Cancer Task Force recommendations?

Applicants are encouraged to consider ideas aligned with recommendations from the congressionally mandated Metastatic Cancer Task Force, as long as proposals remain within the scope and limitations of this data science-focused award.

28) What makes an application stand out according to the opportunity description?

Applications are positioned to be competitive when they propose data science work that is clearly differentiated from existing approaches, demonstrates stronger performance than current methods (with a convincing validation plan), uses sufficiently large established retrospective datasets, and produces reusable tools or resources that are openly shared and designed to improve over time as new data becomes available.