Identification of multiple myeloma resistant cells using machine learning and laser tweezers Raman spectroscopy

Abstract

Multiple myeloma may develop resistance to certain drugs during chemotherapy, which have a fatal impact on treatment efficacy. At present, the drug resistance detection methods for multiple myeloma, such as proteomic identification and clone formation analysis, are relatively complex, and the accuracy and detection time are not ideal. In our work, laser tweezers Raman spectroscopy was used to collect 412 groups of spectra of two kinds of cells, namely, MM.1R and MM.1S, which were respectively resistant to dexamethasone and sensitive to dexamethasone. We selected support vector machine, random forest, linear discriminant analysis and other algorithms to train the pretreated Raman spectra, and the recognition accuracy on the test set was above 95%. This result shows that the combination of laser tweezers Raman spectroscopy and artificial intelligence algorithm can quickly detect drug resistance of cancer cells.