Abstract A043: Identifying tumor heterogeneity and drug sensitivity in primary prostate cancer towards personalized medicine

Abstract

Abstract Introduction: Prostate cancer (PCa) is a highly heterogeneous disease with multiple and distinct cancer foci within the same prostate gland. We hypothesize that heterogeneous lesions, may be linked to different functional properties e.g. differential drug response sensitivity. Here we investigate which histopathological and molecular properties are associated with ability to maintain PCa cells as ex vivo organoid cultures and possibly specific drug responses. Methods: To simultaneously assess the histopathology, genetic profile and organoid drug sensitivity, we employed mirror biopsies for FFPE and cell preparations. Four cores of prostate tissue from radical prostatectomies (N=13) were used for generating patient-derived organoids (PDOs). Targeted genomic sequencing was performed on PDOs and their parental FFPE tissues. Drug response was evaluated by ATP-based viability assay after treatment with standard-of-care (AR inhibitors), and approved compounds for other malignancies (DNA synthesis, receptor tyrosine kinase (RTK) and mTOR inhibitors). RNA sequencing was performed on the parental cores. Associations among mutations, transcriptomic profile and PDO responses were being further explored using machine learning (ML) algorithms towards drug response prediction. Results: Histologically heterogeneous cores with different Gleason Score (GS range 6 to 9) per prostate were found in 76% of cases (N=10/13). A fraction of 78% of tumor-containing cores (N=22/28) and 66% of benign cores (N=16/24) gave rise to PDOs, showing a high formation efficiency. PCa somatic mutations were found in 19/32 cores, 11 cores of which shared mutations with their matching PDOs. The transcriptomic profile of each case was overall preserved among different cores. Drug responses in PDOs revealed not only inter- but also intra-patient heterogeneity with only 2 patient cases (N=2/13) having positive correlation within the different cores of the same case (Pearson’s R =0.58, 0.72 based on z-score viability). Instead, RTK inhibitors, Crizotinib and Ponatinib, showed high efficacy in the majority of cases (p<0.01 over vehicle, N=29/32 and N=30/32, respectively). Statistically significant correlations were found among 8 genomic mutations (mainly FGFR1, JAK2 and cell cycle members) and sensitivity to 5 drug compounds. Different features were tested for ML model, with RNA-seq outperforming the rest features; on average 260-1800 genes per drugs were selected for the drug prediction. ML linear regression models showed high Pearson’s correlation (R>0.8) between predicted and observed drug response values in 13/13 patients and 10/11 compounds tested. Conclusions: The majority of primary PCa cases showed genetic and histopathological multifocality, representing a highly heterogeneous cohort. PDOs recapitulated the genetic signature of the parental tissues. Drug responses of PDOs did not follow patient-specific patterns but rather revealed high inter- and intra-patient heterogeneity. Therapy response could be predicted based on transcriptomic profile for personalized treatment decision. Citation Format: Juening Kang, Sofia Karkampouna, Katja Ovchinnikova, Panagiotis Chouvardas, George Thalmann, Marianna Kruithof-de Julio. Identifying tumor heterogeneity and drug sensitivity in primary prostate cancer towards personalized medicine [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A043.