RESUMO
Background: Despite genomic simplicity, recent studies have reported at least 3 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 the relatively high cost of conventional subgrouping methods. This study aimed to develop a reliable ATRT molecular stratification method to implement in clinical settings. Methods: 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. Seventy-one 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 the 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. Results: 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. Conclusions: Here we present a rapid, cost-effective, and accurate ATRT subgrouping assay applicable for clinical use.
RESUMO
Infant gliomas have paradoxical clinical behavior compared to those in children and adults: low-grade tumors have a higher mortality rate, while high-grade tumors have a better outcome. However, we have little understanding of their biology and therefore cannot explain this behavior nor what constitutes optimal clinical management. Here we report a comprehensive genetic analysis of an international cohort of clinically annotated infant gliomas, revealing 3 clinical subgroups. Group 1 tumors arise in the cerebral hemispheres and harbor alterations in the receptor tyrosine kinases ALK, ROS1, NTRK and MET. These are typically single-events and confer an intermediate outcome. Groups 2 and 3 gliomas harbor RAS/MAPK pathway mutations and arise in the hemispheres and midline, respectively. Group 2 tumors have excellent long-term survival, while group 3 tumors progress rapidly and do not respond well to chemoradiation. We conclude that infant gliomas comprise 3 subgroups, justifying the need for specialized therapeutic strategies.