High-resolution proteomic analysis of medulloblastoma clinical samples identifies therapy-resistant subgroups and MYC immunohistochemistry as a powerful outcome predictor

BACKGROUND: While international consensus and the 2021 WHO classification recognize multiple molecular medulloblastoma subgroups, these are difficult to identify in clinical practice utilizing routine approaches. As a result, biology-driven risk stratification and therapy assignment for medulloblastoma remains a major clinical challenge. Here, we report mass spectrometry-based analysis of clinical samples for medulloblastoma subgroup discovery, highlighting a MYC-driven prognostic signature and MYC immunohistochemistry (IHC) as a clinically tractable method for improved risk stratification. METHODS: We analyzed 56 formalin fixed paraffin embedded (FFPE) medulloblastoma samples by data-independent acquisition mass spectrometry identifying a MYC proteome signature in therapy-resistant group 3 medulloblastoma. We validated MYC IHC prognostic and predictive value across 2 groups of 3/4 medulloblastoma clinical cohorts (n = 362) treated with standard therapies. RESULTS: After the exclusion of WNT tumors, MYC IHC was an independent predictor of therapy resistance and death [HRs 23.6 and 3.23; 95% confidence interval (CI) 1.04-536.18 and 1.84-5.66; P = .047 and <.001]. Notably, only ~50% of the MYC IHC-positive tumors harbored MYC amplification. Accordingly, cross-validated survival models incorporating MYC IHC outperformed current risk stratification schemes including MYC amplification, and reclassified ~20% of patients into a more appropriate very high-risk category. CONCLUSIONS: This study provides a high-resolution proteomic dataset that can be used as a reference for future biomarker discovery. Biology-driven clinical trials should consider MYC IHC status in their design. Integration of MYC IHC in classification algorithms for non-WNT tumors could be rapidly adopted on a global scale, independently of advanced but technically challenging molecular profiling techniques.