Abstract PO-089: Morphokinetic single-cell analysis and machine learning as a tool to characterize breast cancer cell motility and response to therapy
Authors: Tsarfaty Ilan , Megides Or , Doron Ohad , Alaloof Hagar , Moskowitz Ori , Horev Judith 
Published: March 2021

Abstract
Triple-negative breast cancer (TNBC) is a type of aggressive breast cancer lacking the expression of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor-2 (HER-2). TNBC patients have a high propensity for distant metastasis and limited treatment options. Cancer cell motility and invasion are fundamental steps in metastasis. MET, a tyrosine kinase receptor, and its ligand, Hepatocyte Growth Factor/Scatter Factor (HGF/SF), induce specific signal transduction in tumor cells leading to cell motility and metastasis. MET's induced tumorigenesis and metastatic processes make it an ideal target for anti-cancer therapy. We characterized the motility patterns of m-Cherry labeled TNBC cells (BT-549, MDA-MB-231) and ER-positive cells (MCF7) subjected to time-lapse fluorescence wound healing assay. We also studied the effect of MET inhibition, chemotherapy, and their combined impact on breast cancer motility. Time-lapse images were subjected to commercial packages for segmentation and tracking - allowing us to extract morphokinetic (MK) information at a single-cell resolution. To better understand cell motility patterns, we developed the Tool for Analysis of Single-Cell (TASC) infrastructure, which utilizes unsupervised machine learning methods to demonstrate a high-dimensional feature set at a single-cell resolution. We compared our experimental MK results to simulations of three modified classical physical models: Lévy flight (LF), fractal Brownian motion, and random walk (RW) with or without a back-propagating wave (BPW). TASC analysis demonstrated a fundamental difference between ER-positive and TNBC cells: both cell types contained two subpopulations harboring similar MK characteristics. TNBC cells presented an additional subpopulation characterized by 1) dominantly increasing cumulative kinetics values (mean square displacement - MSD), 2) highest temporal wave values (velocity starting-time), and 3) a decrease in non-cumulative kinetics values. The combined treatment of TNBC cells with MET inhibition and chemotherapy eliminates this high-motility group. Cell motility models and motion simulations were compared using Wasserstein's analysis. Untreated TNBC cells showed high similarities to RW with BPW simulations, while MET-activated TNBC cells transformed into LF without BPW simulations, indicating more aggressive behavior. TASC analysis revealed a previously undefined high motility subpopulation of TNBC that was eradicated by combining MET inhibition and chemotherapy. The primary benefit of TASC is the ability to cluster and characterize similar behaviors across different motility models while still detecting subtle changes within each one. MK analysis combined with TASC can lead to discovering novel targets for precise therapy. Moreover, this infrastructure may determine patient tumor susceptibility to chemotherapy and biological treatments and operate as an analytical tool for personalized, targeted therapy.
Citation Format: Ilan Tsarfaty, Or Megides, Ohad Doron, Hagar Alaloof, Ori Moskowitz, Judith Horev. Morphokinetic single-cell analysis and machine learning as a tool to characterize breast cancer cell motility and response to therapy [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-089.