Machine-Learning Provides Patient-Specific Prediction of Metastatic Risk Based on Innovative, Mechanobiology Assay

Abstract

Cancer mortality is mostly related to metastasis. Metastasis is currently prognosed via histopathology, disease-statistics, or genetics; those are potentially inaccurate, not rapidly available and require known markers. We had developed a rapid (~ 2 h) mechanobiology-based approach to provide early prognosis of the clinical likelihood for metastasis. Specifically, invasive cell-subsets seeded on impenetrable, physiological-stiffness polyacrylamide gels forcefully indent the gels, while non-invasive/benign cells do not. The number of indenting cells and their attained depths, the mechanical invasiveness, accurately define the metastatic risk of tumors and cell-lines. Utilizing our experimental database, we compare the capacity of several machine learning models to predict the metastatic risk. Models underwent supervised training on individual experiments using classification from literature and commercial-sources for established cell-lines and clinical histopathology reports for tumor samples. We evaluated 2-class models, separating invasive/non-invasive (e.g. benign) samples, and obtained sensitivity and specificity of 0.92 and 1, respectively; this surpasses other works. We also introduce a novel approach, using 5-class models (i.e. normal, benign, cancer-metastatic-non/low/high) that provided average sensitivity and specificity of 0.69 and 0.91. Combining our rapid, mechanical invasiveness assay with machine learning classification can provide accurate and early prognosis of metastatic risk, to support choice of treatments and disease management.