Identification of multivariable microRNA and clinical biomarker panels to predict imatinib response in chronic myeloid leukemia at diagnosis.

Imatinib Mesylate (imatinib) was once hailed as the magic bullet for chronic myeloid leukemia (CML) and remains a front-line therapy for CML to this day alongside other tyrosine kinase inhibitors (TKIs). However, TKI treatments are rarely curative and patients are often required to receive life-long treatment or otherwise risk relapse. Thus, there is a growing interest in identifying biomarkers in patients which can predict TKI response upon diagnosis. In this study, we analyze clinical data and differentially expressed miRNAs in CD34+ CML cells from 80 patients at diagnosis who were later classified as imatinib-responders or imatinib-nonresponders. A Cox Proportional Hazard (CoxPH) analysis identified 16 miRNAs that were associated with imatinib nonresponse and differentially expressed in these patients. We also trained a machine learning model with different combinations of the 16 miRNAs with and without clinical parameters and identified a panel with high predictive performance based on area-under-curve values of receiver-operating-characteristic and precision-recall curves. Interestingly, the multivariable panel consisting of both miRNAs and clinical features performed better than either miRNA or clinical panels alone. Thus, our findings may inform future studies on predictive biomarkers and serve as a tool to develop more optimized treatment plans for CML patients in the clinic.

Identification of key microRNAs as predictive biomarkers of Nilotinib response in chronic myeloid leukemia: a sub-analysis of the ENESTxtnd clinical trial.

Despite the effectiveness of tyrosine kinase inhibitors (TKIs) against chronic myeloid leukemia (CML), they are not usually curative as some patients develop drug-resistance or are at risk of disease relapse when treatment is discontinued. Studies have demonstrated that primitive CML cells display unique miRNA profiles in response to TKI treatment. However, the utility of miRNAs in predicting treatment response is not yet conclusive. Here, we analyzed differentially expressed miRNAs in CD34+ CML cells pre- and post-nilotinib (NL) therapy from 58 patients enrolled in the Canadian sub-analysis of the ENESTxtnd phase IIIb clinical trial which correlated with sensitivity of CD34+ cells to NL treatment in in vitro colony-forming cell (CFC) assays. We performed Cox Proportional Hazard (CoxPH) analysis and applied machine learning algorithms to generate multivariate miRNA panels which can predict NL response at treatment-naïve or post-treatment time points. We demonstrated that a combination of miR-145 and miR-708 are effective predictors of NL response in treatment-naïve patients whereas miR-150 and miR-185 were significant classifiers at 1-month and 3-month post-NL therapy. Interestingly, incorporation of NL-CFC output in these panels enhanced predictive performance. Thus, this novel predictive model may be developed into a prognostic tool for use in the clinic.

The miR-185/PAK6 axis predicts therapy response and regulates survival of drug-resistant leukemic stem cells in CML.

Overcoming drug resistance and targeting cancer stem cells remain challenges for curative cancer treatment. To investigate the role of microRNAs (miRNAs) in regulating drug resistance and leukemic stem cell (LSC) fate, we performed global transcriptome profiling in treatment-naive chronic myeloid leukemia (CML) stem/progenitor cells and identified that miR-185 levels anticipate their response to ABL tyrosine kinase inhibitors (TKIs). miR-185 functions as a tumor suppressor: its restored expression impaired survival of drug-resistant cells, sensitized them to TKIs in vitro, and markedly eliminated long-term repopulating LSCs and infiltrating blast cells, conferring a survival advantage in preclinical xenotransplantation models. Integrative analysis with mRNA profiles uncovered PAK6 as a crucial target of miR-185, and pharmacological inhibition of PAK6 perturbed the RAS/MAPK pathway and mitochondrial activity, sensitizing therapy-resistant cells to TKIs. Thus, miR-185 presents as a potential predictive biomarker, and dual targeting of miR-185-mediated PAK6 activity and BCR-ABL1 may provide a valuable strategy for overcoming drug resistance in patients.

Nutrient Deprivation Affects Salmonella Invasion and Its Interaction with the Gastrointestinal Microbiota.

Salmonella enterica serovar Typhimurium (S. Typhimurium) is a foodborne enteric pathogen and a major cause of gastroenteritis in humans. It is known that molecules derived from the human fecal microbiota downregulate S. Typhimurium virulence gene expression and induce a starvation-like response. In this study, S. Typhimurium was cultured in minimal media to mimic starvation conditions such as that experienced by S. Typhimurium in the human intestinal tract, and the pathogen's virulence in vitro and in vivo was measured. S. Typhimurium cultured in minimal media displayed a reduced ability to invade human epithelial cells in a manner that was at least partially independent of the Salmonella Pathogenicity Island 1 (SPI-1) type III secretion system. Nutrient deprivation did not, however, alter the ability of S. Typhimurium to replicate and survive inside epithelial cells. In a murine model of S. Typhimurium-induced gastroenteritis, prior cultivation in minimal media did not alter the pathogen's ability to colonize mice, nor did it affect levels of gastrointestinal inflammation. Upon examining the post-infection fecal gastrointestinal microbiota, we found that specifically in the 129Sv/ImJ murine strain S. Typhimurium cultured in minimal media induced differential microbiota compositional shifts compared to that of S. Typhimurium cultured in rich media. Together these findings demonstrate that S. Typhimurium remains a potent pathogen even in the face of nutritional deprivation, but nevertheless that nutrient deprivation encountered in this environment elicits significant changes in the bacterium genetic programme, as well as its capacity to alter host microbiota composition.