EPID-02. BLOOD-BASED CLASSIFICATION OF IDH-STATUS OF NON-ENHANCING GLIOMAS: A MACHINE LEARNING APPROACH

Abstract BACKGROUND Non-invasive determination of IDH mutational status in patients with glioma could offer significant therapeutic opportunities. While IDH wildtype (WT) tumors typically show enhancement on MRI, IDH mutant (MUT) tumors often lack this enhancement. However, relying solely on anatomic radiology may lead to misclassification, and currently, tissue acquisition is the primary method for assessing IDH-status in gliomas. These limitations hamper the development of neoadjuvant or intraoperative therapeutic strategies based on IDH-status; thus, more minimally invasive methods to determine IDH-status are needed. In this study, we assessed peripheral immune cell proportions, which vary by glioma subtype, from pre-surgery peripheral blood samples as an alternative method of classifying the IDH-status in gliomas without enhancement. MATERIALS AND METHODS We employed a highly accurate large language model (GPT-4-Turbo-128k) with over 99% accuracy to read radiology notes and exclude patients with enhancing gliomas. Then, we identified 12 immune cell subtypes from whole blood using deconvolution algorithms based on DNA methylation data. These immune cell subtypes, along with patient age, were integrated into a machine learning model (random forest) to predict IDH-status (WT vs. MUT), leveraging conditional Generative Adversarial Networks to generate synthetic data and mitigate bias in the datasets. Two independent datasets were included for training and validating the model. RESULTS The random forest model had an AUC of 0.90 and accurately identified IDH-status in 81% of a training set of 287 gliomas (65 WT and 222 MUT at varied time points after diagnosis). In an independent validation data set of 99 gliomas (6 WT and 93 MUT) from pre-surgery blood samples, the AUC was 0.93, and accurately predicted IDH-status in 92% of cases with a sensitivity of 93% and specificity of 83%. CONCLUSIONS We predicted the IDH-status of non-enhancing gliomas using patients’ age and immunomethylomic data from pre-surgical peripheral blood samples. This minimally invasive approach is a promising step toward reducing risk and enabling earlier therapies based on IDH-status.