Abstract 1893: Deep learning supported high content analysis of primary patient samples identifies ALK inhibition as a novel mechanism of action in a subset of ovarian cancers

Abstract

Background: High unmet need of ovarian cancer (OC) suggests the discovery of new targeted therapeutics is crucial to improve patient prognosis. Unlike artificial model systems such as cell lines, primary cancer samples recapitulate the complexity of the original microenvironment consisting of cancer cells as well as stromal and immune cells; this is especially important when evaluating IO targets and signalling pathways. Supported by our previous success predicting therapy for late stage haematological cancer patients in the EXALT-I trial using AI-supported functional single cell quantification of drug action (Kornauth et. al. 2021) we set out to systematically reveal novel targets and pathways in OC using small molecule drugs (SMDs) as tools. Single cell phenotypic screening of OC MPAs (malignant pleural effusion and ascites) was enabled by the quantification of drug effects using an end-to-end scalable deep learning driven image analysis tool chain. This custom state-of-the-art AI software is critical to enable robust primary cell screening given the diversity of cells within each sample. This revealed anaplastic lymphoma kinase (ALK), as well as structurally related targets and pathway associated proteins, as being potential novel targets in a subset of OC patient samples. There is sparse literature evidence for therapeutic utilisation of the ALK pathway in OC, and the diversity of responses indicates a further novel patient selection method. Methods: MPAs from OC patients (n = 20) were collected and the sensitivity of the cancer cells to 85 SMDs was evaluated using high content microscopy. Individual cells were segmented and classified using convolutional neural networks and drug responses were estimated from the resulting cell counts. The integration of these results with whole exome and RNA sequencing guided target and pathway prioritisation. Results: Screening for novel sensitivities using SMDs as tools uncovered inhibitors of ALK and related targets as having strong cancer cell cytotoxic effects, recapitulated in solid tumour biopsies. Transcriptomic profiling revealed pathway correlations to ALK inhibitor sensitivity, however non-annotated polypharmacological effects of each drug cannot yet be excluded. Conclusions: Quantifying SMD sensitivity in a disease relevant model system identified ALK as a promising and overlooked target in OC, providing an upstream and potentially more specific target to the recently suggested MEK, PI3K and STAT3 (Papp et. al. 2018, Izar et al. 2020). While further work to confirm the target is required, this study supports a notion of patient-centric drug development using disease relevant models and deep learning. Our work introduces a novel patient-centric tool to advance understanding of the OC target landscape and provides a resource for the development of novel therapeutic approaches. Citation Format: Irene Gutierrez-Perez, Joost Van Ham, Valentin Aranha, Rin Okumura, Elisabeth Waltenberger, Isabella Alt, Claudia Baumgaertler, Maja Stulic, Edgar Petru, Christoph Minichsdorfer, Lukas Hefler, Judith Lafleur, Nikolaus Krall, Thorsten Füreder, Gregory Ian Vladimer, Robert Sehlke, Bojan Vilagos. Deep learning supported high content analysis of primary patient samples identifies ALK inhibition as a novel mechanism of action in a subset of ovarian cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1893.