A novel statistical framework for surrogate endpoint prediction of survival in neoadjuvant breast cancer trials

Introduction Pathological complete response (pCR) is a strong prognostic marker, but survival outcomes comparing treatment to control do not reliably align with treatment-control differences in pCR rates in breast cancer. A novel Bayesian hierarchical framework models treatments within trials, allowing us to predict treatment effects on distant recurrence-free survival (DRFS) from pCR with greater accuracy. Methods We analyzed 12 neoadjuvant breast cancer trials (6,000 patients, all HR/HER2 subtypes; including I-SPY2). The framework of Burzykowski, Molenberghs & Buyse (2005) is extended to a novel arm-based hierarchical structure providing a distribution of pCR and DRFS treatment effects controlling for subtype (HR/HER2), N and T stage, grade, and calendar year. Three held-out trials (877 patients; 26 regimens; med follow-up >4 years) validate predictions of DRFS treatment benefit from pCR. All data was used to estimate treatment-effect correlation and the surrogate threshold effect (STE). Analyses were repeated for Residual Cancer Burden Index (binary RCB01, continuous RCB). Results Predicted probability of DRFS benefit closely matched actual DRFS follow-up (mean absolute error 0.06; Pearson r = 0.9). Across all trials, pCR showed moderate surrogacy (ρ = 0.82, R 2 = 0.67). A 60% increase in pCR odds achieves ≥95% probability of DRFS benefit (STE = OR of 1.60). RCB outperforms pCR across all metrics, with 92% sensitivity and 93% specificity for detecting DRFS benefit in 26 validation regimens (Table 1). Conclusion This novel Bayesian meta-analytic framework reveals that pCR, RCB01 and continuous RCB reliably predict survival benefit across heterogeneous trials, with RCB performing the best in an external validation. This provides a statistical foundation for accelerated approval decisions using robust early biomarkers in modern neoadjuvant trial designs by accurately predicting survival at the treatment arm level. Table 1 Predicted vs. Actual DRFS Benefit: 26 Held Out Regimens Overall Data Surrogacy Measures Sensitivity Specificity Mean Absolute Error Pearson r rho Median Pr[rho>0.5] R 2 Median PR[R 2 >0.5] STE pCR 0.83 0.71 0.06 0.90 0.82 (0.87) 0.67 (0.67) OR 1.60 RCB01 0.91 0.80 0.06 0.94 0.90 (0.95) 0.80 (0.83) OR 1.37 RCB 0.92 0.93 0.05 0.95 0.89(0.96) 0.80 (0.82) -9.3% Pr[DRFS benefit] > 0.5 Decision threshold used for Sensitivity and Specificity. STE Surrogate Threshold Effect: improvement needed for ≥95% posterior probability of DRFS benefit