Rapid, economical diagnostic classification of ATRT molecular subgroup using NanoString nCounter platform

Abstract

Despite genomic simplicity, recent studies have reported at least three major Atypical Teratoid Rhabdoid Tumor (ATRT) subgroups with distinct molecular and clinical features. Reliable ATRT subgrouping in clinical settings remains challenging due to a lack of suitable biological markers, sample rarity, and relatively high cost of conventional subgrouping methods. This study aimed to develop a reliable ATRT molecular stratification method that can be implemented across clinical settings. We have developed an ATRT subgroup predictor assay using a custom genes panel for the NanoString nCounter System and a flexible machine learning classifier package. 71 ATRT primary tumors with matching gene expression array and NanoString data were used to construct a multi-algorithms ensemble classifier. Additional validation was performed using an independent gene expression array against independently generated dataset. We also analyzed 11 extra-cranial rhabdoid tumors with our classifier and compared our approach against DNA methylation classification to evaluate the result consistency with existing methods. We have demonstrated that our novel ensemble classifier has an overall average of 93.6% accuracy in the validation dataset, and a striking 98.9% accuracy was achieved with the high prediction score samples. Using our classifier, all analyzed extra-cranial rhabdoid tumors are classified as MYC subgroups. Compared with the DNA methylation classification, the results show high agreement, with 84.5% concordance and up to 95.8% concordance for high-confidence predictions. Here we present a rapid, cost-effective, and accurate ATRT subgrouping assay applicable for clinical use.