Timing of tumor initation predicts medulloblastoma heterogeneity, stem cell composition and probability of relapse [array]

Patients with medulloblastoma are typically treated with a narrow range of therapies, but may experience widely divergent outcomes; 80-90% become long-term survivors while 20% develop incurable recurrence. Transcriptomic profiling has identified four subgroups with different recurrence risks, but outcomes remain variable for individual patients within each subgroup. To gain new insight into why patients with similar-appearing tumors have variable outcomes, we examined how the timing of tumor initiation effects medulloblastomas triggered by a single, common driver mutation. We genetically-engineered mice to express an oncogenic Smo allele starting early in development in the broad lineage of neural stem cells, or later, in the more committed lineage of cerebellar granule neuron progenitors. Both groups developed medulloblastomas and no other tumors. We compared medulloblastoma progression, response to therapy, gene expression profile and cellular heterogeneity, determined by single cell transcriptomic analysis (scRNA-seq). The average transcriptomic profiles of the tumors were similar. However, stem cell-triggered medulloblastomas progressed faster, contained more OLIG2-expressing tumor stem cells, and consistently showed radioresistance. In contrast, progenitor-triggered MBs progressed slower, lost stem cell character over time and were radiosensitive. Progenitor-triggered medulloblastomas also contained more diverse stromal populations, including tumor-associated macrophages, indicating that the timing of oncogenesis affected the subsequent interactions between the tumor and microenvironment. Our findings show that developmental events in tumorigenesis may be impossible to infer from transcriptomic profile, but while remaining cryptic can nevertheless influence tumor composition and the outcome of therapy. Precise understanding of medulloblastoma pathogenesis and prognosis requires supplementing transcriptomic data with biomarkers of cellular heterogeneity.

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Malawsky DS, Weir SJ, Ocasio JK, Babcock B, Dismuke T, Cleveland AH, Donson AM, Vibhakar R, Wilhelmsen K, Gershon TR