Classifying Medulloblastoma Subgroups Based on Small, Clinically Achievable Gene Sets

Sivan Gershanov, Shreyas Madiwale, Galina Feinberg-Gorenshtein, Igor Vainer, Tamar Nehushtan, Shalom Michowiz, Nitza Goldenberg-Cohen, Yehudit Birger, Helen Toledano, Mali Salmon-Divon*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


As treatment protocols for medulloblastoma (MB) are becoming subgroup-specific, means for reliably distinguishing between its subgroups are a timely need. Currently available methods include immunohistochemical stains, which are subjective and often inconclusive, and molecular techniques—e.g., NanoString, microarrays, or DNA methylation assays—which are time-consuming, expensive and not widely available. Quantitative PCR (qPCR) provides a good alternative for these methods, but the current NanoString panel which includes 22 genes is impractical for qPCR. Here, we applied machine-learning–based classifiers to extract reliable, concise gene sets for distinguishing between the four MB subgroups, and we compared the accuracy of these gene sets to that of the known NanoString 22-gene set. We validated our results using an independent microarray-based dataset of 92 samples of all four subgroups. In addition, we performed a qPCR validation on a cohort of 18 patients diagnosed with SHH, Group 3 and Group 4 MB. We found that the 22-gene set can be reduced to only six genes (IMPG2, NPR3, KHDRBS2, RBM24, WIF1, and EMX2) without compromising accuracy. The identified gene set is sufficiently small to make a qPCR-based MB subgroup classification easily accessible to clinicians, even in developing, poorly equipped countries.

Original languageEnglish
Article number637482
JournalFrontiers in Oncology
StatePublished - 10 Jun 2021


  • biomarkers
  • gene expression
  • machine learning
  • medulloblastoma
  • subgroup classification


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