Abstract
Interrogating the stochastic events underlying tumor evolution from non-malignant precursors is crucial for understanding therapy resistance. Current methods are complicated by chromosomal instability, obscuring driver identification and yielding non-representative genetics. Inspired by patient tumors that evolve without chromosomal instability, we developed Stochastically Emergent Tumors (SETs) by inducing mismatch repair deficiency in non-malignant precursors, then engrafting in mice. Barcoded SETs exhibited increased tumoral and drug target heterogeneity over current models. SETs delineated the stochastic contributions, mutational landscapes, and selective pressures distinguishing tumorigenesis from non-malignant precursor in vitro growth. SETs are an unlimited source for diverse Stochastically Emergent cell Lines (SELs), bolstering under-represented cancers. Since SETs composition dynamically reflects therapy exposure, they are a whole-genome platform for precision oncology. We identified three novel genetic drivers (ZFHX3, CIC, KMT2D) of differential prostate cancer therapy responses. These alterations are enriched in patients of African and Chinese ancestry and correlate with significant differences in survival.