The quality of cancer genomic and proteomic data relies upon the quality of the clinical specimens examined. Here, we show that data derived from non-microdissected glioblastoma multiforme tumor tissue is either masked or not accurate, producing correlations between genomic and proteomic data that lead to false classifications for therapeutic stratification. We analyzed the level of 133 key signaling proteins and phosphoproteins in laser capture microdissected (LCM) primary tumors from a study set of tissues used for the Cancer Genome Atlas (TCGA) profiling efforts, comparing the results to tissue-matched, nontumor cell-enriched lysates from adjacent sections. Among the analytes, 44%, including targets for clinically important inhibitors, such as phosphorylated mTOR, AKT, STAT1, VEGFR2, or BCL2, differed between matched tumor cell-enriched and nonenriched specimens (even in tumor sections with 90% tumor cell content). While total EGFR protein levels were higher in tumors with EGFR mutations, regardless of tumor cell enrichment, EGFR phosphorylation was increased only in LCM-enriched tumor specimens carrying EGFR mutations. Phosphorylated and total PTEN, which is highly expressed in normal brain, was reduced only in LCM-enriched tumor specimens with either PTEN mutation or loss in PTEN copy number, with no differences observed in non-microdissected samples. These results were confirmed in an independent, non-microdissected, publicly available protein data set from the TCGA database. Our findings highlight the necessity for careful upfront cellular enrichment in biospecimens that form the basis for targeted therapy selection and for molecular characterization efforts such as TCGA.