Background: Multi-dimensional cancer genotyping is increasingly needed for clinical diagnostics and trial selection. Whole genome copy number is relevant for glioblastoma and other brain tumors but routine prospective FFPE-based multiplex copy number and somatic mutation genotyping for clinical trials has not been achieved. Methods: Using novel DNA extraction protocols we implemented whole genome copy number (Agilent aCGH stock 1M feature arrays) and somatic mutation profiling (Oncomap v4.4 Sequenom) assays into a CLIA-certified laboratory setting. Twenty-three copy number aberrations (CNAs) relevant to brain tumors were reported from whole genome data and Oncomap results were reported for 471 cancer-related mutations in 41 genes. Results: During the initial eight months of our combined copy number and mutation-testing program, aCGH and Oncomap were performed prospectively on 239 and 157 brain tumor patients respectively. Copy number was reported in 90% of patients (214/239) and failures were due to insufficient DNA from small biopsies. GBMs (n = 94) harbored gains and losses at expected rates and included amplifications of common drug targets (EGFR, EGFRv3, MET, MDM2, MDM4, PDGFRA, CDK4, CDK6). Emerging candidate drug targets were identified including variant EGFR deletions (e.g. EGFRv2) and FGFR3-TACC3 gains for which other clinical tests are not available. Oncomap results for 78 GBMs revealed mutations in TP53, PTEN and IDH1. Less frequent mutations occurred in BRAF, RB1, PIK3CA, PIK3R1 and KRAS. We integrated copy number and mutation data for 27 GBMs, allowing for improved evaluation of tumor suppressor inactivation status (e.g. PTEN mutation with monosomy 10). In combination, our assays reported data on 10 clinically actionable drug targets relevant for 15 clinical trials open at the time of patient testing. Conclusions: We implemented complementary assays for efficiently detecting genome-wide CNAs and mutations on FFPE brain tumor samples in a CLIA-certified environment. Systematic integration of these results broadens the range of diagnostic and actionable data available to identify patients for trials of targeted therapeutics.