Abstract A030: Lessons learned from the Metformin and Active Surveillance Trial (MAST): An opportunity to address intra-patient heterogeneity for biomarker development and precision medicine in low-risk prostate cancer

Abstract Low-risk prostate cancer (PCa) is slowly progressing and can often be managed by Active Surveillance (AS), however about 30% of patients will progress to definitive treatment despite good long-term outcomes. Given that the prostate and prostate tumors are metabolically unique, biomarkers and interventions related to metabolism may sustain adherence to AS and delay progression. However, intra-patient heterogeneity and long-term monitoring has posed a challenge for precision medicine. This is evident in the recent findings from the Metformin and Active Surveillance (MAST) trial, where metformin, a biguanide antihyperglycemic agent, was evaluated for its ability to delay progression in low-risk PCa. No overall relationship between metformin exposure and progression was observed over 36 months, but subgroup analysis revealed that participants with a high BMI (≥30 kg/m2) on metformin were more likely to progress (HR 2.36, p=0.028), suggesting underlying metabolic differences. We hypothesized that certain metabolic profiles are related to progression risk, but that intra-patient heterogeneity must be considered during the development of precision strategies. To explore this, correlative clinical data from the MAST trial have been modelled with respect to variability over time and in association with metformin exposure, BMI, and progression using Linear Mixed Models and Cox PH regression analysis. These findings have been overlaid with Olink® HT Proximity Extension Assays (Thermo Fisher Scientific), which bridge antibody-driven target detection with quantitative PCR-based readouts to measure >5000 proteins at once. In total, 408 patients were randomized to either placebo or metformin (850 mg BID) and followed for up to 36 months. Multivariable regression analysis revealed that progression in those with a BMI ≥30 related to treatment was also related to PSA (p=0.022); whereas with a BMI <30, progression was related to number of cores positive (p=0.001), PSA (p=0.007), and prostate volume (p=0.006), but not treatment (p=0.07). HBA1C levels were used as a crude readout of metabolic state and metformin response. In general, HBA1C values were statistically similar at each time point for treatment and BMI subgroups, but the coefficient of variability (CV) between patients averaged 1.8% (SD=1.43) and ranged from 0 (no variability) to 12.9%, suggesting underlying changes. Indeed, paired time-dependent changes in HBA1C were reflective of both metformin response (p=0.002) and BMI (p=0.023). Further proteomic analysis revealed underlying metabolic features associated with risk based on clinical factors, including Leptin and Insulin-related proteins in and those related to carcinoembryonic antigen (CEA) proteins. Taken together, underlying metabolic features contributing to low-risk PCa progression need to be addressed appropriately to address intra-patient variability. Further development of biomarkers that transcend intra-tumour heterogeneity using robust proteomic approaches may overcome these challenges. Citation Format: Jessica G Cockburn, Aurora Mejia, Clare O'Connell, Katherine Lajkosz, Rui Bernardino, Aingeshaan Kubendran, Doron Berlin, Rafa Mongenegro Burke, Neil E Fleshner. Lessons learned from the Metformin and Active Surveillance Trial (MAST): An opportunity to address intra-patient heterogeneity for biomarker development and precision medicine in low-risk prostate cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Functional and Genomic Precision Medicine in Cancer: Different Perspectives, Common Goals; 2025 Mar 11-13; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(5 Suppl):Abstract nr A030.

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