The lateral cerebellum is preferentially sensitive to high sonic hedgehog signaling and medulloblastoma formation

The main cell of origin of the Sonic hedgehog (SHH) subgroup of medulloblastoma (MB) is granule cell precursors (GCPs), a SHH-dependent transient amplifying population in the developing cerebellum. SHH-MBs can be further subdivided based on molecular and clinical parameters, as well as location since SHH-MBs occur preferentially in the lateral cerebellum (hemispheres). Our analysis of adult patient data suggests that tumors with Smoothened (SMO) mutations form more specifically in the hemispheres than those with Patched 1 (PTCH1) mutations. Using sporadic mouse models of SHH-MB with the two mutations commonly seen in adult MB, constitutive activation of Smo (SmoM2) or loss-of-Ptch1, we found that regardless of timing of induction or type of mutation, tumors developed primarily in the hemispheres with SmoM2-mutants indeed showing a stronger specificity. We further uncovered that GCPs in the hemispheres are more susceptible to high level SHH signaling compared to GCPs in the medial cerebellum (vermis), as more SmoM2 or Ptch1-mutant hemisphere cells remain undifferentiated and show increased tumorigenicity when transplanted. Finally, we identified location-specific GCP gene expression profiles, and found that deletion of the genes most highly expressed in the hemispheres (Nr2f2) or vermis (Engrailed1) showed opposing effects on GCP differentiation. Our studies thus provide new insights into intrinsic differences within GCPs that impact on SHH-MB progression. The P8 wild type GCPs or SmoM2-mutant GCPs (Atoh1-CreER/+; R26LSL-SmoM2-YFP; 200ug/g Tm at P2) from either cerebellar hemispheres or vermis were isolated by Percoll gradient. 5 mice were pooled for each replicate. The MSKCC Integrated Genomics Operation (IGO) core facility performed RNA isolation and microarray analysis. Briefly, high quality RNA were interrogated with Affymetrix microarrays (GeneChip™ Mouse Genome 430 2.0 Array). GeneChip CEL files were analyzed with the Partek Genomics Suite and Gene Pattern, including unsupervised hierarchical clustering strategies and PCA. Supervised analyses comprised LIMMA, ANOVA, and SAM to infer specific genesets