Raman classification of selected subtypes of acute lymphoblastic leukemia (ALL)

Abstract

B-cell precursor acute lymphoblastic leukemia (BCP-ALL) with chromosome translocations like KMT2A gene rearrangement (KMT2A-r) and BCR-ABL1 fusion gene have been recognized as crucial drivers in both BCP-ALL leukemogenesis and treatment management. Standard diagnostic protocols for proliferative diseases of the hematopoietic system, like KMT2A-r-ALL, are genetically based and strongly molecularly oriented. Therefore, an efficient diagnostic procedure requires not only experienced and multidisciplinary laboratory staff but also considerable instrumentation and material costs. In recent years, a Raman spectroscopy method has been increasingly used to detect subtle chemical changes in individual cells resulting from stress or disease. Therefore, the objective of this study was to identify Raman signatures for the molecular subtypes and to develop a classification method based on the unique spectroscopic profile of in vitro models that represent specific aberrations aimed at KMT2A-r (RS4;11, and SEM) and the BCR-ABL1 fusion gene (SUP-B15, BV-173, and SD-1). Data analysis was based on chemometric methods, i.e. principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and support vector machine (SVM). The PCA-based multivariate model was used for pattern recognition of each investigated group of cells while PLS-DA and SVM were used to build models for the discrimination of spectra from the studied BCP-ALL molecular subtypes. The results showed that the studied molecular subtypes of ALL have characteristic spectroscopic profiles reflecting their peculiar biochemical state. The content of lipids (1600 cm⁻¹), nucleic acids (789 cm⁻¹), and haemoproteins (754, 1130, and 1315 cm⁻¹), which are crucial in cell metabolism, was indicated as the main source of differentiation between subtypes. Identification of spectroscopic markers of cells with BCR-ABL1 or KMT2A-r may be useful in pharmacological studies to monitor the effectiveness of chemotherapy and further to understand differences in molecular responses between leukemia primary cells and cell lines.