Machine learning classifier approaches for predicting response to RTK-type-III inhibitors demonstrate high accuracy using transcriptomic signatures and ex vivo data

Abstract

Motivation The application of machine learning (ML) techniques in the medical field have demonstrated both successes and challenges in the precision medicine era. The ability to accurately classify a subject as a potential responder versus a non-responder to a given therapy is still an active area of research pushing the field to create new approaches for applying machine learning techniques. In this study we leveraged publicly available data through the BeatAML initiative. Specifically, we used gene count data, generated via RNA-seq, from 451 individuals matched with ex vivo data generated from treatment with RTK-type-III inhibitors. Three feature selection techniques were tested, Principal Component Analysis (PCA), Shapley Additive Explanation technique (SHAP), and differential gene expression analysis (DGE), with three different classifiers, XGBoost, LightGBM, and Random Forest. Sensitivity versus specificity was analyzed using the area under the curve (AUC) - receiver operating curves (ROC) for every model developed. Results Our work demonstrated that feature selection technique, rather than the classifier, had the greatest impact on model performance. The SHAP technique outperformed the other feature selection techniques and was able to with high accuracy predict outcome response, with the highest performing model: Foretinib with 89% AUC using the SHAP technique and Random Forest classifier. Our ML pipelines demonstrate that at the time of diagnosis, a transcriptomics signature exists that can potentially predict response to treatment, demonstrating the potential of using ML applications in precision medicine efforts. Availability and implementation https://github.com/UD-CRPL/RCDML Supplementary information Supplementary data are available at Bioinformatics online.

Computational Characterization of Membrane Proteins as Anticancer Targets: Current Challenges and Opportunities

“…The structural analysis of the kinase domain, on the other hand, has been very useful to identify and prioritize small molecules targeting this domain, and to explain the reasons for resistance [62]. The wealth of experimental data for kinase inhibitors has made it possible to use ML models to screen not only potency for wildtype [63] and mutant RTKs [64], but also clinical responses associated with gene expression signatures [65]. Beyond small molecule screening, ML models have also been employed to generate de novo RTK inhibitors by combining 2D and 3D features of known kinase inhibitors [66].…”