Reconstructing tumor evolution without chromosomal instability reveals in vivo determinants of therapeutic response
Year: 2025
Type: preprint
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.
Authors: Ruhollah Moussavi-Baygi, Matthew J. Ryan, Woogwang Sim, Samuel B. Hoelscher, Valbona Luga, Arun A. Chandrakumar, Lore Hoes, Junghwa Cha, Young Sun Lee, Katelyn Herm, Ben Doron, Danika Bakke, Sharon Wu, C.K. Cornelia Ding, Bradley A. Stohr, Peng Jin, Tejasveeta Nadkarni, Xiangyi Fang, Melita Haryono, An Nguyen, Wouter R. Karthaus, Charles Sawyers, Felix Y. Feng, Hani Goodarzi, Rohit Bose
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: green
Awards: Investigating How a Balance of Negative and Positive ETS Factors Controls Prostate Oncogenesis, Simulating Ancestrally Unbiased Tumor Evolution To Interrogate Drug Resistance, Cancer Center Support Grant, Weill Cornell/Rockefeller/Sloan Kettering MST Program, Investigating cell-intrinsic and extrinsic interactions in prostate cancer at the single cell level
OpenAlex ID: W4408593154