Background: Residual cancer burden (RCB) is a continuous score that captures the amount of residual cancer after neoadjuvant chemotherapy and predicts disease recurrence and survival across all breast cancer subtypes. RCB score 0 corresponds to pathological complete response (pCR; ypT0, ypN0). We hypothesize that comparison of the distributions of RCB scores between randomized treatment arms of a trial could predict treatment effect on recurrence free survival better than comparison of pCR rates only.

‍Methods:The cancer Treatment Efficacy Score (TES) compares efficacies of two treatments using non-continuous RCB results. We examined (i) area between cumulative distribution (ABC) functions; (ii) density ratio of RCB scores; and (iii) density difference of RCB scores from two treatments, to select the most efficient metric to compute TES. A random permutation procedure was used to estimate the p-value from each test. These methods were applied to data from the durvalumab/olaparib arm and corresponding controls of the I-SPY2 trial, separately by molecular subtype. In subsampling and simulation experiments we assessed robustness of results including power and false positive rate control under variable sample sizes to select the most robust TES metric. The other 11 experimental arms of I-SPY2 were used to assess the performance of the final metric. We calculated correlation between TES and (i) pCR rate difference, and 3- and 5-year (ii) event-free (EFS) and (iii) distant recurrence free survivals (DRFS).

‍Results: RCB scores are multimodal and do not follow normal distribution.In simulated data ABC provided more stable results than the other methods, had good power, performed well with small sample sizes, resulted in low false positive rate, required the least computational time, and therfore was selected as the TES metric for validation in 11 arms of I-SPY2. We found a high correlation between difference in pCR rate and TES value across all molecular subtypes in each of the 11 trial arms (r = 0.92, p = 1.7e-8). There was also significant linear relationship between TES and survival estimates in EFS (r = 0.58, p = 9.3e-3 for 3-years survival; r = 0.62, p = 4.8e-3 for 5-years survival) and DRFS (r = 0.56, p = 1.2e-2 for 3-years survival; r = 0.54, p = 1.8e-2 for 5-years survival). Statistically significant TES score correlated significantly with higher benefit in 3-years survival (p = 9.7e-4 for EFS; p = 5.7e-3 for DRFS) and 5-years survival (p = 9.7e-4 for EFS; p = 3.0e-3 for DRFS). In most instances, this correlation with survival was higher than seen with pCR difference.

‍Conclusions: TES is a novel more optimal metric to identify the more effective cytotoxic neoadjuvant regimen from the entire distribution of pathologic response that significantly correlates with event and recurrence free survival and may serve as a better surrogate than pCR rate difference.