Abstract
The genomic characterization of medulloblastoma at the level of the chromosome, epigenome and transcriptome has radically redefined diagnostic classifications and reoriented disease definitions toward a biological underpinning. However, it still remains a challenge to leverage this wealth of data toward a true understanding of disease biology and subsequently to capitalize on the promise of biologically based therapies. One reason for this difficulty is the loose correlation between the genetic and the functional aspects of the cancer cell resulting from multilayered regulatory mechanisms governing protein production, the predominant functional moiety of the cell. To better actualize the promise of genomics, it is necessary to add an understanding of the proteomic dimension to cancer cell biology. We have undertaken a super-SILAC based quantitative proteomics survey of 42 tissue samples spanning the 4 genomic subgroups of medulloblastoma and control cerebellum. We have identified and quantitated thousands of proteins across these groups and find that we are able to recapitulate the genomic subgroups based upon subgroup restricted and differentially abundant proteins while also identifying one additional subgroup. Adding array gene expression data to this analysis, we calculate a relatively poor correlation between mRNA and protein abundance with the best concordance found among stable proteins. EPIC 850k methylation array data performed on the same samples extend this analysis to the epigenome yielding chromosomal copy number variations and correlations to gene promoter methylation. From the quantitative proteomic data, we are able to discern networks of functional pathways with differential activation between the medulloblastoma subgroups and normal control cerebellum yielding potential avenues for clinical intervention. This work demonstrates that quantitative proteomics can further elucidate the biology inherent in a genomically based classification regime.