Multimodal artificial intelligence (MMAI) model to identify benefit from 2nd-generation androgen receptor pathway inhibitors (ARPI) in high-risk non-metastatic prostate cancer patients from STAMPEDE.

Abstract

5001 Background: The STAMPEDE trials showed that adding abiraterone acetate + prednisolone (AAP) ± enzalutamide (ENZ) to standard of care androgen deprivation therapy (SOC) improves metastasis-free survival (MFS) in high-risk non-metastatic (M0) prostate cancer (PCa) patients (pts). However, variable responses & adverse events underscore the need for prognostic & predictive biomarkers. We evaluated performance of a validated MMAI algorithm (ArteraAI Prostate Test v1.2) to identify pts who benefit most from the addition of AAP ± ENZ (ARPI). Methods: High-risk M0 STAMPEDE pts treated with SOC+ARPI (N=555) or SOC (N=781) with sufficient quality H&E biopsy images & clinical data (T stage, age, PSA) were included. MMAI score association with PCa specific mortality (PCSM, primary outcome measure) & distant metastasis (DM) was analyzed using Fine-Gray regression & cumulative incidence curves, with other cause mortality treated as competing risks. MFS was assessed using Cox regression & Kaplan-Meier curves. An optimal cut-point was identified via grid search to maximize ARPI benefit separation across biomarker positive (pos, MMAI in top quartile) & negative (neg) subgroups. Hazard ratios [95% CI] & p values are reported. Results: PCSM median follow-up was 6.0 years (N=1336). Continuous MMAI scores were statistically significantly associated with poorer PCSM (1.65 [1.43-1.90], p<0.001), MFS (1.42 [1.29-1.56], p<0.001) & DM (1.54 [1.36-1.74], p<0.001). Using clinically-established prognostic cut-offs, 89% of pts were MMAI high-risk. The optimal ARPI MMAI cut-point identified 334 biomarker-pos pts who had significantly higher PCSM than biomarker-neg pts. A statistically significant biomarker-treatment interaction for PCSM (p-int=0.04) revealed that biomarker-pos pts treated with ARPI had improved PCSM (0.42 [0.24-0.74], p=0.003), while biomarker-neg pts did not derive a treatment benefit (0.85 [0.56-1.29], p=0.45). Estimated 5-year PCSM was 9% for biomarker-pos pts receiving ARPI vs. 17% with SOC, compared to 4% & 7% for biomarker-neg pts, respectively, with similar results observed in M0N0 pts (Table 1). Conclusions: For the first time, we demonstrate that a validated MMAI algorithm can identify high-risk non-metastatic PCa pts most likely to benefit from the addition of ARPI. Notably we identify a positive biomarker-treatment interaction in the highest MMAI score quartile, which in cases of clinical equipoise could inform clinical decision-making. We highlight MMAI’s potential to optimize treatment decisions & spare biomarker-neg pts from unnecessary therapy & toxicities. Clinical trial information: NCT00268476 . Estimated 5-yr absolute risk reduction from ARPI vs SOC-treated patients by biomarker groups in M0 (M0N0) pts. Biomarker-neg Biomarker-pos PCSM 3% (1%) 8% (9%) MFS 2% (-1%) 17% (16%) DM 5% (3%) 12% (15%)