IMMU-34. PREDICTION OF TUMOR-ASSOCIATED ANTIGENS USING RADIOGENOMICS

Abstract INTRODUCTION Glioblastoma remains a disease with dismal prognosis. Immunotherapy demonstrated promising results against some refractory cancers. However, comparative results have not been accomplished in glioblastoma therapy due to lack of suitable tumor targets and considerable clonal heterogeneity. OBJECTIVE We propose leveraging radiogenomics using machine learning based on Pre-Op MRI to predict tumor antigen expression profiles displaying specific targets for immunotherapy. METHODS We collected RNAseq data and Pre-Op Axial T2-FLAIR images in 29 patients from IVY Glioblastoma Atlas Project. MRIs were segmented using LIFEx software as follows: 1) tumor + edema (T+E) and 2) whole brain (WB). The intended prediction was for tumor-associated antigens. H2O.ai software was used for prediction using an automated supervised machine learning algorithm. Antigens that achieved >55% area under the curve (i.e., prediction) were reanalyzed adjusting for reproducibility, time of analysis and maximum precision. RESULTS An average of 53.2 T2-FLAIR axial slices per patient were used for 3D reconstruction. We selected the following genes (ERBB2, TP53, IL10RA, CD163, EGFR and MGMT) for downstream analysis given their clinical, immunologic, and prognostic relevance. Regarding tumor-associated antigen prediction for ERBB2, TP53, IL10RA, CD163, EGFR and MGMT predicted 52%, 56%, 66%, 66%, 56% and 0%, on T+E segmentation respectively; The values were 49%, 93%, 87%, 47%, 37% and 0% for the respective WB group. Reanalysis revealed 100% prediction accuracy for the presence of mutant TP53 and IL10RA in both segmentation groups (T+E, WB). CD163 and EGFR prediction was 54% and 61% for the T+E group and 48% for both antigens in the WB group. CONCLUSION We successfully predicted the presence of two out of six antigens (i.e., mutant TP53 and IL10RA) in this patient cohort. Our approach can provide expeditious antigen identification to enable cellular therapy manufacturing for patients with unresectable newly diagnosed tumorsor those which relapsed geographically. Furthermore, this MRI-based methodology might provide a precision medicine approach to places that cannot afford costly testing (i.e., RNAseq).