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

Ben Ho; Anthony Arnoldo; Yvonne Zhong; Mei Lu; Jonathon Torchia; Fupan Yao; Cynthia Hawkins; Annie Huang

doi:10.1093/noajnl/vdae004

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

Neurooncol Adv. 2024 Jan 16;6(1):vdae004. doi: 10.1093/noajnl/vdae004. eCollection 2024 Jan-Dec.

Authors

Ben Ho^{1

2

3}, Anthony Arnoldo⁴, Yvonne Zhong⁴, Mei Lu^{1

2}, Jonathon Torchia⁵, Fupan Yao^{2

6}, Cynthia Hawkins^{1

2

4}, Annie Huang^{7

2

3

6}

Affiliations

¹ Division of Cell Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.
² Arthur and Sonia Labatt Brain Tumor Research Centre, The Hospital for Sick Children, Toronto, Ontario, Canada.
³ Department of Laboratory Medicine and Pathobiology, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
⁴ Division of Pathology, Hospital for Sick Children, Toronto, Ontario, Canada.
⁵ Cantata Bio, LLC, Scott's Valley, California, USA.
⁶ Department of Medical Biophysics, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
⁷ Division of Hematology and Oncology, The Hospital for Sick Children, Toronto, Ontario, Canada.

Abstract

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.

Keywords: CNS neoplasm; gene expression profiling; molecular typing; rhabdoid tumor; tumor biomarkers.