Abstract 4956: Discovering novel targetable pathways by combining functional and multi-omic data from primary ovarian cancer samples

Background: There is a critical necessity to reveal novel and tractable targets for anti-cancer treatments in indications with high unmet medical need, such as high grade serous ovarian cancer (HGSOC). However, standard process for target discovery using models such as outgrown cell lines and well-averaged readouts has yielded a less than 5% approval rate for drugs entering trials (Thomas et al. 2016 Bio.org).

Here, we describe patient-centric target discovery through the use of disease relevant primary OC samples and single cell functional characterization using a platform with proven hemonc translatability (Kornauth et al. 2021, Snijder et al. 2017). We integrate data from our functional drug testing platform under multiple drug perturbations with matching genomic and transcriptomic data to reveal associations with novel downstream regulators of sensitivity.

Methods: Sensitivity of the cancer cell compartment in primary malignant ascites samples (n = 20; 75% HGSOC) to 85 small molecule drugs, was evaluated using a proprietary and translatable deep learning-driven single cell imaging platform (Vladimer et al. 2017). Cancer cell sensitivity from the drugs was combined with WES, bulk-RNAseq and drug induced changes in phosphoproteome, and single cell RNAseq transcriptome to identify perturbed targets and pathways.

Results: Here we describe a family of TKIs including ALKi that induce cytotoxicity of cancer cells in primary samples, not previously captured in publicly available cell line drug sensitivity screening data (Iorio et al. 2016). We report novel sensitivity of OC driven by non-canonical targets of ceritinib such as FAK1 or IGF1R, mediated by the downstream signaling hub YBX1 (Kuenzi et al. 2017), involved in NFB pathway regulation (Motolani et al. 2021). Indeed, transcriptomic scRNA analysis upon ceritinib treatment of primary OC cells revealed rapid perturbation of numerous NFB pathway members, alongside YBX1 inactivation.

Conclusions: Combining functional endpoints and single cell-based differential expression analysis of primary OC samples, we have identified the NFB pathway and the regulator YBX1 as a promising novel sensitivity for HGSOC treatment development. These and several other important targetable nodes identified, sit outside the recently suggested JAK/STAT pathway (Izar et al. 2020), thereby demonstrating a pipeline towards novel drug target and pathway discovery driven by patient-centric, disease relevant models of high-need indications.

Citation Format: Irene Gutierrez-Perez, Bekir Ergüner, Pisanu Buphamalai, Joost Van Ham, Paul Heinz, Valentin Aranha, Rin Okumura, Elisabeth Waltenberger, Isabella Alt, Claudia Baumgaertler, Maja Stulic, Edgar Petru, Christoph Minichsdorfer, Judith Lafleur, Lukas Hefler, Laudia Hadjari, Lucia Dzurillova, Jozef Sufliarsky, Nikolaus Krall, Thorsten Füreder, Gregory Ian Vladimer, Bojan Vilagos, Robert Sehlke. Discovering novel targetable pathways by combining functional and multi-omic data from primary ovarian cancer samples. [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 4956.

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

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.

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

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.