Abstract
Genomic characterization has begun to redefine diagnostic classifications of cancers. However, it remains a challenge to infer disease phenotypes from genomic alterations alone. To help realize the promise of genomics, we have performed a quantitative proteomics investigation using Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) and 41 tissue samples spanning the 4 genomically based 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 subgroup specific protein isoforms. Integrating our proteomic measurements with genomic data, we calculate a poor correlation between mRNA and protein abundance. Using EPIC 850 k methylation array data on the same tissues, we also investigate the influence of copy number alterations and DNA methylation on the proteome in an attempt to characterize the impact of these genetic features on the proteome. Reciprocally, we are able to use the proteome to identify which genomic alterations result in altered protein abundance and thus are most likely to impact biology. Finally, we are able to assemble protein-based pathways yielding potential avenues for clinical intervention. From these, we validate the EIF4F cap-dependent translation pathway as a novel druggable pathway in medulloblastoma. Thus, quantitative proteomics complements genomic platforms to yield a more complete understanding of functional tumor biology and identify novel therapeutic targets for medulloblastoma.
Highlights
In a series of landmark papers, microarray transcriptome characterization subdivided the malignant childhood brain tumor medulloblastoma into at least four distinct entities [11, 31, 32, 49, 66]
We created a pooled super-SILAC reference atlas, termed the Labeled Atlas of Medulloblastoma Proteins (LAMP), containing labeled proteins from 8 primary and established cell lines chosen to represent the breadth of medulloblastoma across the four genomic subgroups (Methods)
SILAC proteomic analysis We created a pooled super-SILAC reference atlas, termed the Labeled Atlas of Medulloblastoma Proteins (LAMP), by combining equal amounts of isotopically labeled proteins from 8 primary and established cell lines chosen to represent the breadth of medulloblastoma across the four genomic subgroups (DAOY, D556, D283, R026, R032, R060, MB002, and MB004)
Summary
In a series of landmark papers, microarray transcriptome characterization subdivided the malignant childhood brain tumor medulloblastoma into at least four distinct entities [11, 31, 32, 49, 66]. Chromosomal copy number alterations, clinical characteristics such as age and survival, and methylation array data correlated well with the transcriptomic classification, reinforcing the identity of Rivero-Hinojosa et al Acta Neuropathologica Communications (2018) 6:48 proteins more directly determine the functional state of the cell. A large number of post-transcriptional regulatory mechanisms from interfering RNAs to selective RNA binding proteins affect differential translation in response to a wide range of cellular conditions [63]. The first paper published from the CPTAC (Clinical Proteomic Tumor Analysis Consortium) initiative showed a correlation coefficient of 0.23 between transcript and protein abundance [77] and subsequent publications have replicated this experience [45, 78]. It is becoming increasingly recognized that cancer cells utilize alternative pathways of translation initiation that prioritize proteins important in the cellular response to stress [41, 60, 63]. Most importantly, in order to interfere with that biology, proteins remain the most actionable targets of pharmaceutical and immunotherapeutic intervention
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