Direct and rapid identification of T315I-Mutated BCR-ABL expressing leukemic cells using infrared microspectroscopy

miR-495-3p sensitizes BCR-ABL1-expressing leukemic cells to tyrosine kinase inhibitors by targeting multidrug resistance 1 gene in T315I mutated cells

Chronic myeloid leukemia (CML) is a clonal hematopoietic malignancy driven by the BCR-ABL1 fusion oncoprotein. The development of tyrosine kinase inhibitors (TKIs) has deeply increased long-term survival of CML patients. Nonetheless, one patient out of four will switch TKI off owing either to drug intolerance or resistance partly due to amplification or mutations of BCR-ABL1 oncogene and alteration in ATP-binding cassette (ABC) transporters. Increasing evidence suggests the involvement of the microRNA miR-495-3p in cancer-associated chemoresistance through multidrug resistance 1 (MDR1) gene, which encodes an ATP-dependent efflux pump. Our study aimed at investigating the potential role of miR-495-3p in CML TKI chemo-sensitivity and determining the underlying molecular circuitry involved. We first observed that miR-495-3p expression was lower in BCR-ABL1-expressing cellular models in vitro. Notably, loss-of-function experiments showed increased proliferation associated with a decreased number of nondividing cells (G0/G1) and resistance to Imatinib. Conversely, our data showed that miR-495-3p overexpression hindered leukemic cell growth and TKI resistance in Imatinib-resistant T315I-mutant cells, as well as drug efflux activity through MDR1 regulation. Further investigating the role of miR-495-3p in CML patients, we found that predicted miR-495-3p targets were upregulated in patients in blast crisis that were involved in protein phosphorylation and associated with the worst prognosis. Taken together, our results demonstrate that downregulation of miR-495-3p expression is important in the malignant phenotype of CML and TKI resistance mechanisms and could be a useful biomarker and a potential therapeutic target to eradicate CML.

Quasar: Easy Machine Learning for Biospectroscopy

Data volumes collected in many scientific fields have long exceeded the capacity of human comprehension. This is especially true in biomedical research where multiple replicates and techniques are required to conduct reliable studies. Ever-increasing data rates from new instruments compound our dependence on statistics to make sense of the numbers. The currently available data analysis tools lack user-friendliness, various capabilities or ease of access. Problem-specific software or scripts freely available in supplementary materials or research lab websites are often highly specialized, no longer functional, or simply too hard to use. Commercial software limits access and reproducibility, and is often unable to follow quickly changing, cutting-edge research demands. Finally, as machine learning techniques penetrate data analysis pipelines of the natural sciences, we see the growing demand for user-friendly and flexible tools to fuse machine learning with spectroscopy datasets. In our opinion, open-source software with strong community engagement is the way forward. To counter these problems, we develop Quasar, an open-source and user-friendly software, as a solution to these challenges. Here, we present case studies to highlight some Quasar features analyzing infrared spectroscopy data using various machine learning techniques.