Session: 632. Chronic Myeloid Leukemia: Clinical and Epidemiological: Poster III
Hematology Disease Topics & Pathways:
Research, adult, Translational Research, Clinical Research, health outcomes research, patient-reported outcomes, survivorship, Biological Processes, molecular biology, Study Population, Human
In this study, we used a Cox Proportional Hazard (CoxPH) analysis to identify 22 out of a pool of 35 pre-validated miRNAs which were associated with IM nonresponse in CD34+ CML cells of 80 patients at diagnosis. These patients were later classified as IM-responders or IM-nonresponders based on the latest European LeukemiaNet (ELN) guidelines. Interestingly, a Welch t-test revealed 16 of these 22 IM-response associated miRNAs to be differentially expressed between IM-responders and IM-nonresponders. Between IM-responders and IM-nonresponders we also found 7 clinical parameters that were associated with IM-response of which 3 matched parameters had significantly different values including CFC assay outputs (p=0.0015), Sokal scores (p=0.0070) and white blood cell counts (WBC, p=0.0028). We then trained a machine learning model employing the random-forest (RF), gradient-boosting (GBM) and naïve-bayes (NB) algorithms with different combinations of the 16 miRNAs with and without the clinical parameters of these patients to identify panels with high predictive performance based on area-under-curve (AUC) values of receiver-operating-characteristic (ROC) and precision-recall (PR) curves. Notably, the multivariable panel consisting of both miRNAs and clinical features (AUC-ROC RF=0.83, GBM=0.83, NB=0.84, AUC-PR RF=0.68, GBM=0.67, NB=0.70) performed better than either miRNA (AUC-ROC RF=0.72, GBM=0.73, NB=0.78, AUC-PR RF=0.49, GBM=0.51, NB=0.4) or clinical (AUC-ROC RF=0.82, GBM=0.84, NB=0.84, AUC-PR RF=0.64, GBM=0.67, NB=0.64) panels alone. Interestingly, 2 miRNAs in this panel, miR-185 and miR-145, were also significant classifiers for our Nilotinib predictive study suggesting that expression patterns of these miRNAs may have predictive properties for multiple TKI responses.
Thus, we show that predictive accuracy of biomarkers may be supplemented by inclusion of multivariable parameters, and our findings may inform future studies on developing predictive panels for more optimized treatment plans in the clinic.
Disclosures: No relevant conflicts of interest to declare.
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