PROMISE Consortium

Filling Genomic Knowledge Gaps Through Team Science

The Prostate Cancer Precision Medicine Multi-Institutional Collaborative Effort (PROMISE) Consortium, made up of leading prostate cancer centers across the country, was formed to develop a repository of real world clinical-genomic data in order to better understand the interplay between molecular features, prognosis, and response to prostate cancer therapies. There are already FDA approved therapies for prostate cancers with DNA repair defects resulting from 15 gene aberrations, however, the impact of specific genes is not well understood.

Why This Study Is Important

Given that molecular findings are increasingly being used to define clinically relevant prostate cancer subgroups, it is of paramount importance that we understand the influence these mutations have on the overall clinical course to inform future precision medicine clinical trial design.

The first targeted therapies (olaparib and rucaparib) have been approved for metastatic castration-resistant prostate cancer. Additional alterations have also been proposed as candidate biomarkers for personalized selection of therapy and are subject to ongoing trials.

Data has shown that the presence of certain HR mutations (e.g. BRCA 1, BRCA 2) appear to be associated with increased risk for prostate cancer and an aggressive disease course. As such, studies targeting HR deficient prostate cancer patients should factor in these observations when designing prospective trials.

In addition, a more nuanced understanding of the role genomic factors play in determining outcomes will allow clinicians to better counsel patients on prognosis.

Mission

To bridge the knowledge gap between real-world tumor genomic profiling and outcomes of patients with advanced prostate cancer.

Goals

To harness the existing knowledge from clinical care experience to better inform future clinical care and precision medicine approaches by:

  • Establishing a repository of completely de-identified clinical and genomic patient data linked to various disease-related outcomes.
  • Investigating prognostic and potentially predictive biomarkers (e.g. genomic, transcriptomic, proteomic, metabolomics) of response and resistance to therapy.