Abstract 4877: Morphological detection of chemotherapy resistance in cancer cell lines using AI-based analysis
Authors: Andreja Jovic, Manisha Ray, Ryan Carelli, Kiran Saini, Tiffine Pham, Christian Corona, Stephane C. Boutet, Chassidy Johnson, Mahyar Salek, Maddison Masaeli, Matt Barnes, Cyril Y. Ramathal
Journal: Cancer Research (2024)

Abstract Chemoresistance can develop during the course of treatment of many cancers, which can limit the utility of standard therapeutics and lead to poor outcomes. The mechanism by which resistance arises is not well understood, and developing models to study resistance can be challenging. Resistance can spontaneously arise in cell lines, however, it is difficult to separate resistant from sensitive cells using traditional biomarkers, hampering their utility for studying mechanisms. Morphology presents another modality to examine resistance, independently of protein markers, and may solve a previously intractable problem of separating resistant from sensitive cells for further study. Here we sought to study the mechanism of chemoresistance in cell lines using multi-parameter morphology in combination with gene expression and other standard molecular approaches. We induced resistance to specific chemotherapeutic drugs in parent cancer cell lines from lung, ovarian, and melanoma cancers. IC-50s of each drug increased by ≥1 order of magnitude in the resistant cells. Whole exome sequencing and bulk RNAseq were performed to examine differentially expressed genes and pathways between the parent and resistant cell lines. Each cell line was analyzed using multi-dimensional morphology using the Deepcell platform, which extracts &amp;gt;100 morphological features from images of unlabeled single cells using artificial intelligence (AI), advanced imaging, and microfluidics. The morphology features represent both Deep Learning (DL)-derived features and human-interpretable morphometric features, such as size and shape. Unique morphotypes were observed differentiating each combination of parent and resistant cell lines, with notable changes in granularity distinguishing them. A subset of images was used to train a random forest classifier to predict the resistant state of each cell image, which performed with up to 85% accuracy on independent cell images. Strikingly, the combination of DL plus morphometric features outperformed the use of morphometric features alone, demonstrating the increased utility of combined analysis. The top gene pathways and top morphological features were examined to infer potential mechanisms underlying chemoresistance. This work demonstrates that morphology alone can be used to distinguish drug resistant vs. parent populations without the use of markers. Morphology analysis can be applied to understanding complex phenotypes, and future platform improvements will enable sorting of these resistant populations to better identify molecular pathways in both cell lines and in primary tumor samples. Citation Format: Andreja Jovic, Manisha Ray, Ryan Carelli, Kiran Saini, Tiffine Pham, Christian Corona, Stephane C. Boutet, Chassidy Johnson, Mahyar Salek, Maddison Masaeli, Matt Barnes, Cyril Y. Ramathal. Morphological detection of chemotherapy resistance in cancer cell lines using AI-based analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4877.