Computational Biology Approach To Predict Hypomethylating Agent (Hma) Response Using Genomic And Clinical Characteristics In Myelodsyplastic Syndromes (Mds)

Background: HMAs (e.g., azacitidine (AZA), decitabine (DAC)) are approved agents for the treatment of patients with MDS. Despite their widespread use, only 50% of patients respond to HMA without reliable biomarkers of response despite knowing the patient's genomic and clinical (non-genomic) characteristics prior to treatment. Unfortunately, no comprehensive method exists to predict HMA response in a patient using both genomic and non-genomic parameters, though we recently developed a genomics-informed computational biology method (CBM) for MDS (PMID 27855285). Predicting treatment response would improve management of MDS patients by empowering the clinician to restrict treatment-related adverse events to those who would benefit and also reduce health care costs.