Abstract 1117: Rapid label-free imaging-based evaluation of cancer dependencies in zero-passage primary cells

Abstract

Abstract To successfully map cancer dependencies, it is essential to conduct genetic and pharmacological screenings in a diversity of cell models. However, existing model development approaches require long periods of culture time and it is difficult to create long-term models of many cancers, greatly limiting the share of patient samples that can be studied. To enable high-throughput perturbation screens in primary cells without an intermediate model generation step, we are developing a label-free imaging-based platform for early living tissue perturbation. Here, we present our ex vivo system for the preservation and morphological characterization of malignant ascites from patients with gastroesophageal cancer, whose prognosis remains poor and there is an urgent need for rapid evidence-based therapeutic discovery. First, we established a workflow to acquire and perturb cells within 1 day of sample collection. We found that mixing ascites fluid with organoid media improves the preservation of cellular composition and viability of the samples. Next, we hypothesized that label-free microscopy can be a potential alternative for fluorescence-based biomarkers of which signal fades over time in live-cell imaging. To test this, we designed a systematic approach for data generation to assess the reproducibility of measures, and we built a dataset consisting of over 1.0M cells from 14 samples (10 unique patients). For training input, we extracted morphological features of each identified cell by generating several projections from z-stacks of label-free bright-field microscopy images. For training labels, we used fluorescence labels to annotate cell type and viability during the imaging screens. Then, using solely bright-field morphological data as input, we trained models to infer cell identity and viability, we found that the accuracy of predictions was 92% and 82%, respectively. Strong correlations were found between tumor fractions determined by flow cytometry and the prediction of tumor cell fraction from label-free morphology only. Based on single-cell RNA sequencing data, we designed a candidate panel of 28 compounds that are anticipated to exhibit antitumor activity via different mechanisms that are of relevance to our study cohort. We observed that label-free inference of compound activity showed a strong correlation (R2 > 0.8) with fluorescent-based predictions. We are now expanding the scale of our rapid screens by finding the minimal number of necessary z-stacks of bright-field and fluorescent channels maintaining the prediction accuracy of cell identity and viability. By finding the appropriate minimum setting for imaging setup, the throughput of the system can be increased both in the compound and sample number. Our approach couples the timing of the perturbation with the preservation of subcellular heterogeneity, it will serve as a strong foundation for preclinical studies. Citation Format: Mushriq Al-Jazrawe, Csaba Molnar, Elisabeth Abeyta, Steven Blum, Niklas Rindtorff, Kathryn Cebula, Sean Misek, Maria Alimova, William Colgan, Carmen Rios, Moony Tseng, James McFarland, Aviv Regev, Beth Cimini, Anne Carpenter, Adam Bass, Samuel Klempner, Jesse Boehm. Rapid label-free imaging-based evaluation of cancer dependencies in zero-passage primary cells [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 1117.