Abstract 139: Exploiting interactions between ploidy and host physiology

Abstract

Abstract Whole-genome doubling facilitates rapid tumor evolution and is a hallmark of many cancers, including Glioblastoma (GBM) where it has an incidence of ~14%. GBM remains uniformly lethal, with a median overall survival of 14-16 months, despite aggressive therapy. In people aged 65 and above, prognosis is even more dismal, with median survival of less than 7 months. While comorbidities explain some of the increased age-related risk, age-related changes in the tumor microenvironment likely also contribute. Aging brains have higher incidence of polyploidy (Pearson r=0.80; P=0.031; Nandakumar et al., 2021), hypoxia related gene expression (Pearson r =0.22, P =0.008) and Glucose levels (Ding et al., 2021) as well as decreased stiffness (Hiscox et al., 2018). The average ploidy of 20 different cancer types correlates with the Oxygen levels recorded in their respective tissue of origin (Spearman r = 0.66, P = 0.002). Together these results suggest that availability of metabolic substrates in the GBM environment drives different cell fate decisions in cancer cells of different ploidy and shapes GBM disease initiation. We address this hypothesis with integrated in-vitro and in-silico experiments and multi-omic and imaging analysis of primary and recurrent GBM. We genetically engineered cell lines to obtain diploid and tetraploid lineages for each and tested how exposure to variable gradients of glucose, hypoxia and phosphate affect their migratory patterns. We performed RNA-seq and WGS of multiple regions obtained from two patient-matched primary and recurrent GBM. Pre- and post-surgery MRI data was available from both surgeries and registered to a T1 map Atlas to quantify stiffness and oxygenation in the brain. This data was used to calibrate a Stochastic State-Space Model (S3MB) we developed to recapitulate re-growth of enhancing primary tumor margins into the respective recurrent tumor. The model domain is initiated from MRI data and consists of 4mm3 voxels. Each voxel state is defined by 8+ variables that are updated over time: Stiffness, Oxygen, Phosphate, Glucose, Vasculature, Dead cells, migrating cells and proliferating cells of various ploidies. In-vitro results, multi-omics results and similarity between observed and simulated recurrent tumors were used to infer model parameters (resource consumption-, migration-, death and proliferation rates). Running the model backwards in time, we predict the local point of origin (LPO) of each GBM. We found that resource availability in the predicted LPO was correlated to WGD status, suggesting conditions in the local environment in which a GBM originates shape the outcome of an early competition between diploid and tetraploid cells. Citation Format: Noemi Andor. Exploiting interactions between ploidy and host physiology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 139.