Development and validation of an integrated prognostic model
REGRESSION
Considering the previous results, we tested whether a model including baseline clinicopathological, immune-related features, and treatment-related information including response could predict EFS in NeoALTTO. BCR and TCR measures (details in METHODS) were included in the variable selection process, together with pCR information, clinicopathological variables and a set of gene signatures. The model was developed on a training cohort of 221 patients from the NeoALTTO trial with all included data available, using a forward stepwise approach with Akaike Information Criteria (AIC) to select the best model (Supplementary data The variables selected in the final model included BCR evenness, TIL levels, pCR status (ypT0/is), clinical nodal status and hormone receptor status (the final Cox regression model is shown in Supplementary data In NeoALTTO, the C-index calculated on the training cohort (The score was then divided into tertiles (cutoffs at −1.3763 and −0.8143), and Kaplan–Meier analysis was performed to estimate EFS in the groups identified (Supplementary Fig. 