Abstract 7494: Pan-cancer analysis of circulating tumor organoid drug sensitivity revealed differential distribution of viability dependence among cancer hallmark modules

Abstract

Abstract Background: Patient-derived organoids represent an opportunity into deciphering human cancer biology at the personal-level resolution. Phenotyping living cell properties, such as drug sensitivity, complements deep sequencing to improve the prediction of cancer treatment response and holds promises for functional precision medicine. Modern cancer therapeutics target genetic alterations in modular networks, also known as cancer hallmarks, which drive biological essentialities for cancer progression. However, it remains elusive the relative robustness of each module among the naturally occurring solid tumors. Methods: In order to explore this issue, we analyzed a drug sensitivity database of a collection of circulating tumor cells-derived organoids (CDO) from 532 patients with advanced/metastatic solid tumors to 15 chemicals. In this collection, each CDO was derived from 20 ml peripheral blood on a binary colloid crystal cell culture system over 4 weeks. CDO were tested for survival fraction at the Cmax of the designated chemicals. Cell viability was measured with ATP abundance normalized to vehicle controls. For each chemical tested, the probability density function (PDF) of drug sensitivity was fitted to log normal distribution and goodness of fit was analyzed with Kolmogorov Smirnov test with the significance cut-off set to 0.05. The targeted biological pathway of each chemical was mapped to canonical human pathways in the Gene Ontology and KEGG databases. Strength of the specific evolution conserved module (ECM) was evaluated with the published analysis using clustering by inferred models of evolution (CLIME) algorithm. Results: There were 25 types of tumors in the database. The top 3 tumor types were soft-tissue sarcoma, breast cancers, central nervous system tumor. The average number in each tumor type was 21. The PDF of commonly known chemotherapeutic agents, such as gemcitabine, doxorubicin, docetaxel followed log normal distribution. In contrast, the PDF of commonly known biological targeted agents such as everolimus, cabozantinib or enzalutamide do not. For chemicals whose PDF followed a log normal distribution, the biological pathways that were affected tend to have a higher strength of ECM value. Conclusions: Our early analysis revealed the feasibility to quantitatively estimate the robustness of biological pathways in the cancer hallmark modules with a CDO bank. The biological implication of deviation from classical log normal distribution for biologically targeted agents warrants further investigation. Citation Format: Yihsuan Chen, Shih-Pei Wu, Yin-Ju Chen, Long-Sheng Lu. Pan-cancer analysis of circulating tumor organoid drug sensitivity revealed differential distribution of viability dependence among cancer hallmark modules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7494.