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2023 Kovler Family Foundation – PCF Young Investigator Award

Validation and Integration of Optimized Risk Stratification and Novel Biomarkers for the Personalization of Prostate Cancer Treatment

Soumyajit Roy, MBBS, MSc
Rush University Medical Center

Mentors: Daniel Spratt, Felix Feng, Alison Tree

Description:

  • Prostate cancer is the leading cause of cancer treatment-associated disability worldwide. Despite multi-modality treatment approaches, half of the deaths from prostate cancer are due to failure to cure patients with presumed non-metastatic disease at diagnosis.
  • To avoid both under- and over-treatment, more accurate ways are needed to distinguish patients who need aggressive therapy from those who don’t are needed.
  • While novel strategies to better risk-stratify patients at the time of diagnosis are being developed, they require validation.
  • Soumyajit Roy is working to validate the STAR-CAP system, a newly built prostate cancer prognostication model that uses more granular information on patient’s age, clinical stage, tumor grade, biopsy results, and PSA levels, to divide patients into nine stages each of which has different outcome probability compared to others. This system has outperformed current classification systems in preliminary studies for predicting patients’ long-term chances of dying from prostate cancer with standard curative treatments.
  • In this project, Dr. Roy will validate the STAR-CAP system for adoption in national guidelines and compare it to existing risk stratification criteria, using data compiled from phase III randomized trials and will see how the trial results are translated in the context of STAR-CAP prognostic model.
  • Further, he aims to integrate the STAR-CAP system with other risk-stratification and treatment selection biomarkers such as Decipher and Artera AI (an AI-based pathological feature-based biomarker), to optimize treatment selection for individual patients.
  • If successful, this project will result in new national guidelines for prostate cancer prognostication and treatment selection at the time of diagnosis with localized disease. This will improve patient outcomes by better matching patients to the most appropriate treatment or management plans, reducing both under- and over-treatment.

What this means to patients: Prostate cancer prognostication models are used to guide treatment and management strategies at the time of diagnosis, and are meant to identify patients who can safely undergo active surveillance from those needing more aggressive and immediate treatment. However, these models are not highly accurate, and many patients end up being either over- or under-treated. Dr. Roy’s project will validate a new prostate cancer prognostication model that will improve the ability to tailor individualized risk-directed treatment, which will not only help reduce the burden of recurrent metastatic prostate cancer but also avoid undue treatment related morbidities in long-term prostate cancer survivors across the world.