Abstract 1303: AI driven single cell analysis of drug action in solid tumor material: An entry point to functional precision medicine

Abstract

Introduction: The paradigm of precision oncology is the selection of optimal therapy for individual patients. However, disease complexity prevents most cases from having a clear correlation between genetics and response, and programs using driver mutations to stratify patients into treatment arms have shown lackluster results (Middleton et al 2020). In contrast, prior work by us and colleagues demonstrated that functional analysis of drug action on single cells in unmanipulated primary tumor tissues with automated microscopy and advanced image analysis can identify effective therapies for patients with hematological cancers. In a prospective interventional trial (EXALT1 NCT03096821 Snijder et al 2017 N=17, Staber et al 2020 ASH N=56) patients treated with drugs ranked by differential ex vivo response resulted in a 55% ORR and >1.3-fold PFS improvement compared to the prior therapy in 54%. However, the scope has been limited to hematological cancers and solid tumor ascites/effusions.Adapting analysis to tumor biopsies required major advances in wet lab and computation, including robust quantification of 3D confocal images. Here, we present first biological evidence from a newly developed pipeline for the ex vivo treatment and single cell interrogation of singular cell + microaggregate suspensions from ovarian cancer biopsies. Methods and results: Viable tissue from debulking surgery was obtained after consent and dissociated. The resulting cells and microaggregates were treated with >85 small molecules in 7 concentrations in triplicate for 72h, fixed, and stained for cancer surface markers. Cells were imaged with automated confocal microscopy at 20x across multiple 384 well plates, with z-resolution of <17 planes. Images were segmented with a modular analysis pipeline with custom neural networks in a scalable cloud environment, yielding precise characterization (e.g. viability, size, marker intensity) and localization of each cell, elucidating the complexity of biopsies. Single cell data in turn was used to determine the ability of drugs to induce targeted cytotoxicity and modify the immune system/pathways, cell morphology, and the 3D environment. Conclusions: High content single cell phenotypic analysis of solid tumors can enable the study of drug action in primary tissues. Compared to organoids where cells are outgrown, working with primary cells in short term incubation enables analysis of drug activity prior to culture adaptation, and the presence of immune cells enables the study of I/O drugs, in a close-to-patient setting. A challenge to achieve single cell resolution of complex mixtures of aggregate/single and adherent/non-adherent cells was overcome by confocal microscopy and AI driven 3D image analysis. The screening and follow-up data indicates this could support preclinical research, biomarker discovery, and precision medicine. Citation Format: Valentin Aranha, Diogo Tomaz, Irene Gutierrez Perez, Florian Rohrer, Joost Van Ham, Lukas Hefler, Laudia Hadjari, Judith Lafleur, Nikolaus Krall, Robert Sehlke, Bojan Vilagos, Gregory Ian Vladimer. AI driven single cell analysis of drug action in solid tumor material: An entry point to functional precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1303.