Abstract B026: Modeling karyotype-driven adaptations to metabolic restrictions predicts therapeutic response and immunogenicity in cancer

Abstract Cancer cells adapt to environmental and therapeutic pressures through karyotypic plasticity, including whole genome doubling, aneuploidy, and structural genome changes. Polyploid/polyaneuploid giant cancer cells (PGCCs/PACCs) represent an extreme state of this plasticity, arising through endoreplication as a common response to chemotherapy, irradiation, viral infection, hypoxia, or nutrient restriction. PGCCs act as drug-tolerant reservoirs that can later depolyploidize to repopulate tumors, underscoring their role in recurrence and therapy failure. Yet, how readily tumor cells access the PGCC state and how karyotype dynamics sculpt drug sensitivity remain poorly understood. To address this gap, we integrated (i) long-term evolution experiments (LTEEs), (ii) mathematical modeling, and (iii) computational analysis into a unified framework to define karyotype evolution as a predictive biomarker of therapy response. To study karyotype evolution under metabolic stress, we developed CLONEID, an LTEE platform that combines bright-field imaging, single-cell karyotyping and a neutral-drift framework distinguishing drift from selection. Its computer-vision module classifies PGCC versus proliferative states while PCA of other morphometrics revealed stress-specific phenotypes even without whole-genome doubling or overt ploidy change. LTEEs (6 months) under glucose deprivation, phosphate restriction or hypoxia revealed stress- and ploidy-dependent trajectories. Glucose deprivation promoted whole-genome doubling whereas phosphate restriction and hypoxia drove chromosome loss toward near-diploid states. Strikingly, our in vivo studies confirmed hypoxia-induced ploidy reduction, which was predicted by LTEE trajectories within the 36 days and recapitulated by our model. Moreover, long-term adaptations also shifted therapy response. Glucose-deprived cultures gained resistance to gemcitabine and topotecan while phosphate-deprived cultures became more sensitive to taxanes and carboplatin. Furthermore, our functional assays showed that evolved cultures enhanced T-cell activation while control condition suppressed immunogenicity through tryptophan metabolism. To complement LTEEs, we built an ODE linear chain trick (LCT) model of the cell cycle, calibrated with isogenic diploid and tetraploid TNBC lines. Our model successfully captured ploidy-conditioned therapeutic responses. Specifically, gemcitabine, among 74 tested anticancer agents, eliminated near-diploid cells, but spared tetraploids that entered PGCC state and resumed proliferation after drug withdrawal. Overall, our findings identified that whole-genome doubling, polyploidy, and PGCC formation shape both long-term evolution and acute therapy responses. Integrating LTEEs with LCT modeling successfully links ploidy, karyotypic adaptation, and drug sensitivity. Our approach establishes the ground for “Drug–Karyotype” pairs as biomarkers and shows how metabolic niches sculpt KFLs, offering a path toward personalized, evolution-informed oncology. Citation Format: Vural Tagal, Jackson Cole, Richard J. Beck, Didem Ilter, Daria Myroshnychenko, Konstantin Martin, Andriy Marusyk, Ana P. Gomes, Noemi Andor. Modeling karyotype-driven adaptations to metabolic restrictions predicts therapeutic response and immunogenicity in cancer abstract. In: Proceedings of the AACR Special Conference in Cancer Research: Cancer Evolution: The Dynamics of Progression and Persistence; 2025 Dec 4-6; Albuquerque, NM. Philadelphia (PA): AACR; Cancer Res 2025;85 (23Suppl): Abstract nr B026.