Discernable machine learning methods for Raman micro-spectroscopic stratification of mitoxantrone-induced drug-resistant cells in acute myeloid leukemia

Drug resistance plays a vital role in both cancer treatment and prognosis. Especially, early insights into such drug-induced resistance in acute myeloid leukemia (AML) can help to improve treatment plans, reduce costs, and bring overall positive outcomes for patients. Raman spectroscopy provides precise biomolecular information and can provide all these necessities effectively. In this study, we employed machine learning (ML) discrimination of Raman micro-spectroscopic data of myelocytic leukemia cell line HL-60 from its drug-resistant counterpart HL-60/MX2. Principal component analysis (PCA), linear discriminant analysis (LDA), and logistic regression (LR) methods were evaluated for their ability to identify and discriminate drug resistance in AML cells. Our study demonstrates the power of ML to classify drug-induced resistance in AML cells utilizing subtle variations in biomolecular information contained in molecular spectroscopic data by obtaining 94.11% and 97.05% classification accuracies by LDA and LR models, respectively. We also showed that the ML methods are discernable. Our findings depict the importance of automation and its optimal usage in cancer study and diagnosis. The results of our study are expected to take ML-assisted Raman spectroscopy one step closer to making it a generalized tool in medical diagnosis in the future.