Abstract 2392: Multi-dimensional morphology analysis enables identification and label-free enrichment of heterogeneous tumor cell populations

Abstract

Abstract Studying tumor heterogeneity at single cell resolution is crucial for elucidating tumor progression and treatment response. Classically, tumor cells are characterized with a limited set of biomarkers that cannot cover the full extent of the tumor cell heterogeneity. The advent of single cell genomics has enabled scientists to study heterogeneity at high resolution, but gene expression signatures may not translate to readily available biomarkers for functional studies. Multi-dimensional morphology analysis has the potential for higher resolution than cell surface biomarkers while also reducing cell manipulations. The Deepcell platform enables multi-dimensional morphology analysis of unlabeled single cells using artificial intelligence (AI), imaging, and microfluidics, allowing for higher resolution of population heterogeneity beyond protein expression markers. Using the Deepcell platform, we trained an AI model to identify and sort for malignant cells from non-small cell lung cancer cells (NSCLC) tumor biopsies based on morphology. scRNA-seq, CNV, and targeted mutation analysis verified enrichment of carcinoma cells. Sorted cells had gene expression signatures indicative of tumor cells (e.g. EpCAM expression), increased genomic alterations by CNV analysis, and increased allele frequency of P53 and KRAS mutations relative to pre-sorted samples. Overall, the data indicate brightfield images of cells can be used to detect macro-level changes in cell morphology resulting from the molecular events involved in tumorigenesis. In addition, we induced chemotherapeutic drug resistance of lung cancer cell lines in vitro, imaged the resulting cultures on the Deepcell platform, and used the bright-field cell images to generate multi-dimensional morphological embeddings that can be visualized by UMAP. The resulting UMAPs showed distinct clusters of cells for both the parent and resistant cell lines, indicating that there are morphological differences associated with drug resistance. We developed a random forest classifier to identify the top differential morphological features between parental and resistant cell lines. These top features can distinguish between the populations with high accuracy (87%). Finally, we performed multi-dimensional morphology analysis to compare lung adenocarcinoma (LA) and squamous cell carcinoma (SCC) cell lines. The classifier predicted the correct cell type with 75% accuracy, suggesting that differences between these two carcinomas are reflected in their morphology. Together, our data suggest that AI-detected morphological differences between cell populations may show a biologically significant link between morphology and phenotype. As such, multi-dimensional morphology analysis will bolster the understanding of complex disease states and tumor microenvironment, particularly in patient derived biopsies. Citation Format: Andreja Jovic, Kiran Saini, Ryan Carelli, Tiffine Pham, Christian Corona, Jeanette Mei, Michael Phelan, Stephane C. Boutet, Kevn Jacobs, Julie Kim, Manisha Ray, Chassidy Johnson, Nianzhen Li, Mahyar Salek, Maddison Masaeli, Matt Barnes, Cyril Ramathal. Multi-dimensional morphology analysis enables identification and label-free enrichment of heterogeneous tumor cell populations [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 2392.