a, Schema for translating a predictive model from cell lines to patients using few-shot learning. The model is trained over successive rounds of data, each with fewer samples but closer to the desired clinical context. b, Challenge 2. Predictive models were pretrained using responses of breast cancer cell lines to targeted perturbations with a particular drug (Table 1). Few-shot learning was then performed on 0–10 PDTC breast tumor samples exposed to that drug (x axis), and model accuracy (Pearson’s correlation, y axis, mean ± 95% CI) validated using the remaining held-out PDTC samples. Results averaged across 48 drugs on n = 83 PDTC models. c, Predictive accuracy (x axis) is displayed separately for each drug model (y axis, mean ± 95% CI) on n = 83 PDTC models. Colors as in previous figures.