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Genomic Analysis, Machine Learning Leads to Chemotherapy Response Features in IDH-Wild Type Glioblastoma

A Hong Kong University of Science and Technology- and Samsung Medical Center-led team reporting in Genome Medicine outlines molecular features linked to chemotherapy resistance in forms of glioblastoma (GBM) containing wild-type isocitrate dehydrogenase (IDH-wt) genes. Using exome sequencing, GliomaSCAN targeted panel sequencing, and/or RNA sequencing combined with machine learning (ML), the researchers characterized molecular features found in patient-derived glioma stem-like cells (GSCs) originating from 29 temozolomide chemotherapy-resistant IDH-wild type GBM cases and 40 IDH-wild type GBM cases that were susceptible to the adjuvant chemotherapy treatment. In the process, they came up with a combined tumor signature coinciding with temozolomide chemotherapy response, highlighting a handful of genes with enhanced expression in the temozolomide-resistant cells, along with other chemotherapy resistance- or sensitivity-related features. With multisector temozolomide screening, meanwhile, the authors show that chemotherapy response can vary within tumor samples from the same individual. "We identified molecular characteristics associated with [temozolomide] sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs," the authors write.