Analyze Patient Data and Biospecimens from Past Clinical Trials to Predict Future Patient Outcomes
NCI has announced several funding opportunities that align with the Cancer Moonshot.See Funding Opportunities
The biological reasons underpinning why cancer patients with similar disease types may experience very different outcomes even though they receive the same standard of care remain poorly understood. Data from tumor samples and other biospecimens provided by thousands of patients who underwent standard cancer treatments, including those from racial/ethnic minority and underserved groups, may hold important clues about the molecular, genetic, and cellular features of tumors related to patient outcomes.
This recommendation supports retrospective analyses that combine patient clinical data with newly-generated specimen biological data, including expression patterns of genetic mutations, proteins, immune signatures, and cellular biochemistry. Such data may help researchers develop hypotheses about which tumor features may predict treatment benefit and affect patient outcomes, including resistance to treatments.
Ultimately, the knowledge gained from these analyses will aid researchers and clinicians in understanding prognostic stratification by developing hypotheses that could be tested in future clinical trials.
NCI is awarding funding to research projects that align with the panel's recommendation to analyze patient data and biospecimens from past clinical trials.
Molecular Profiling to Predict Response to Treatment (MP2PRT) Program: Retrospective Characterization and Analysis of Biospecimens Collected from NCI-Sponsored Trials
This program supports the characterization of biospecimens collected on NCI-sponsored clinical trials. It leverages the biospecimens and clinical outcomes from the NCI National Clinical Trials Network (NCTN) and NCI Community Oncology Research Program (NCORP). By analyzing tumors from cancer patients who were treated on completed clinical trials with published outcome results, the MP2PRT may lead to the development of predictive models about which patients will benefit from different cancer therapies.