BACKGROUND The prediction of clinical behavior, response to therapy, and outcome of infiltrative glioma is challenging. On the basis of previous studies of tumor biology, we defined five glioma molecular groups with the use of three alterations: mutations in the TERT promoter, mutations in IDH, and codeletion of chromosome arms 1p and 19q (1p/19q codeletion). We tested the hypothesis that within groups based on these features, tumors would have similar clinical variables, acquired somatic alterations, and germline variants. METHODS We scored tumors as negative or positive for each of these markers in 1087 gliomas and compared acquired alterations and patient characteristics among the five primary molecular groups. Using 11,590 controls, we assessed associations between these groups and known glioma germline variants. RESULTS Among 615 grade II or III gliomas, 29% had all three alterations (i.e., were triplepositive), 5% had TERT and IDH mutations, 45% had only IDH mutations, 7% were triple-negative, and 10% had only TERT mutations; 5% had other combinations. Among 472 grade IV gliomas, less than 1% were triple-positive, 2% had TERT and IDH mutations, 7% had only IDH mutations, 17% were triple-negative, and 74% had only TERT mutations. The mean age at diagnosis was lowest (37 years) among patients who had gliomas with only IDH mutations and was highest (59 years) among patients who had gliomas with only TERT mutations. The molecular groups were independently associated with overall survival among patients with grade II or III gliomas but not among patients with grade IV gliomas. The molecular groups were associated with specific germline variants. CONCLUSIONS Gliomas were classified into five principal groups on the basis of three tumor markers. The groups had different ages at onset, overall survival, and associations with germline variants, which implies that they are characterized by distinct mechanisms of pathogenesis.

Genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, but individual studies have had limited power to identify risk loci. We performed a meta-analysis of existing GWAS and two new GWAS, which totaled 12,496 cases and 18,190 controls. We identified five new loci for glioblastoma (GBM) at 1p31.3 (rs12752552; P = 2.04 × 10−9, odds ratio (OR) = 1.22), 11q14.1 (rs11233250; P = 9.95 × 10−10, OR = 1.24), 16p13.3 (rs2562152; P = 1.93 × 10−8, OR = 1.21), 16q12.1 (rs10852606; P = 1.29 × 10−11, OR = 1.18) and 22q13.1 (rs2235573; P = 1.76 × 10−10, OR = 1.15), as well as eight loci for non-GBM tumors at 1q32.1 (rs4252707; P = 3.34 × 10−9, OR = 1.19), 1q44 (rs12076373; P = 2.63 × 10−10, OR = 1.23), 2q33.3 (rs7572263; P = 2.18 × 10−10, OR = 1.20), 3p14.1 (rs11706832; P = 7.66 × 10−9, OR = 1.15), 10q24.33 (rs11598018; P = 3.39 × 10−8, OR = 1.14), 11q21 (rs7107785; P = 3.87 × 10−10, OR = 1.16), 14q12 (rs10131032; P = 5.07 × 10−11, OR = 1.33) and 16p13.3 (rs3751667; P = 2.61 × 10−9, OR = 1.18). These data substantiate that genetic susceptibility to GBM and non-GBM tumors are highly distinct, which likely reflects different etiology.

IMPORTANCEPer the World Health Organization 2016 integrative classification, newly diagnosed glioblastomas are separated into isocitrate dehydrogenase gene 1 or 2 (IDH)-wild-type and IDH-mutant subtypes, with median patient survival of 1.2 and 3.6 years, respectively. Although maximal resection of contrast-enhanced (CE) tumor is associated with longer survival, the prognostic importance of maximal resection within molecular subgroups and the potential importance of resection of non-contrast-enhanced (NCE) disease is poorly understood.OBJECTIVE To assess the association of resection of CE and NCE tumors in conjunction with molecular and clinical information to develop a new road map for cytoreductive surgery.

Germline BRCA1 mutations predispose to breast cancer. To identify genetic modifiers of this risk, we performed a genome-wide association study in 1,193 individuals with BRCA1 mutations who were diagnosed with invasive breast cancer under age 40 and 1,190 BRCA1 carriers without breast cancer diagnosis over age 35. We took forward 96 SNPs for replication in another 5,986 BRCA1 carriers (2,974 individuals with breast cancer and 3,012 unaffected individuals). Five SNPs on 19p13 were associated with breast cancer risk (Ptrend = 2.3 × 10−9 to Ptrend = 3.9 × 10−7), two of which showed independent associations (rs8170, hazard ratio (HR) = 1.26, 95% CI 1.17–1.35; rs2363956 HR = 0.84, 95% CI 0.80–0.89). Genotyping these SNPs in 6,800 population-based breast cancer cases and 6,613 controls identified a similar association with estrogen receptor–negative breast cancer (rs2363956 per-allele odds ratio (OR) = 0.83, 95% CI 0.75–0.92, Ptrend = 0.0003) and an association with estrogen receptor–positive disease in the opposite direction (OR = 1.07, 95% CI 1.01–1.14, Ptrend = 0.016). The five SNPs were also associated with triple-negative breast cancer in a separate study of 2,301 triple-negative cases and 3,949 controls (Ptrend = 1 × 10−7 to Ptrend = 8 × 10−5; rs2363956 per-allele OR = 0.80, 95% CI 0.74–0.87, Ptrend = 1.1 × 10−7).

Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation and useful visualization tools are demonstrated via case study of complex biological samples assessed using the iTRAQ™ relative labeling protocol. KeywordsProteomics; ANOVA; iTRAQ™; Normalization; relative labeling protocol; Missing data; GaussSiedel; Backfitting; Fixed effects model; Mixed effects model A. INTRODUCTIONThe objective of global proteomics via mass spectrometry is to detect and quantify all proteins present in a biological sample. Proteins that exhibit an increase/decrease in abundance between two or more groups of interest, (e.g., diseased and non-diseased) are considered candidate CORRESPONDING AUTHOR FOOTNOTE Ann L. Oberg, Mayo Clinic, Cancer Center Statistics, 200 First St SW, Rochester, MN 55905. Telephone (507) 538-1556; Fax (507) biomarkers. However, experimental factors such as differences in sample collection, sample characteristics such as cellular concentration, variations in sample processing and the experimental process add variability to the observed abundances. Experimental variability hinders the comparison of effects of interest, and if not accounted for during the design and analysis stages, can lead the researcher down an erroneous path of discovery.Several mass spectrometry (MS) techniques have been developed that allow greater control over experimental factors that introduce variability and ultimately decrease the quality of the data. Recently, focus has centered on the ability to assess multiple samples within a single MS experiment. Binary sample labeling techniques such as 16 O/ 18 O (1) , ICAT™ (2) , and SILAC (3) were developed to evaluate paired samples whereas iTRAQ™ (4) was developed to simultaneously analyze four, and more recently, eight samples (5) . The binary labeling techniques add complexity to the acquired spectra and to their interpretation by introducing additional peaks into the mass spectra. Furthermore, overlapping isotopic clusters require further analytical techniques to deconvolute the resulting spectrum and the associated protein/ peptide abundances (4,6,7) . The iTRAQ™ labeling system overcomes this to some extent since the labeled species are isobaric and protein abundances are measured only in the resulting MS/ MS fragmentation spectra.Although sample labeling techniques allow greater control over experimental variability within an MS experiment, the analysis of multiple MS experiments remains difficult. Within an experiment, it is important that equal amounts of total protein are labeled under each labeling condition to ensure that the observed abundances are not influenced by total protein concentration. Once the samples are labeled and mixed together for MS analysis, labeling methods naturally control for instrument variability. The same principles apply when performing multiple experiments; with th...