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Supplementary Table 7: S-PrediXcan statistics at each brain tissue for the 31 identified gene associations.

Supplementary Table 6: Genes and their TWAS P-values for association with (a) GBM glioma (b) non-GBM glioma. s.d., standard deviation. Detailed are the S-MultiXcan P-value for association between gene expression and GBM/non-GBM risk and the corresponding Z-scores quantifying this relationship (e.g. a positive score indicates increased gene expression increases risk of GBM or non-GBM glioma). N and Nindep indicate the total number of single-tissue results used for S-MultiXcan analysis and the number of independent components after singular value decomposition, respectively. "p_i_best" and "t_i_best" correspond to the best single-tissue P-value and tissue model respectively, accordingly "p_i_worst" and "t_i_worset" correspond to the worst. "eigen_max" and "eigen_min" correspond to the maximum and minimum eigenvalue in the covariance matrix respectively, with "eigen_min_kept" corresponding to the minimum eigenvalue that survived thresholding.

Genome-wide association studies (GWAS) have so far identified 25 loci associated with glioma risk, with most showing specificity for either glioblastoma (GBM) or non-GBM tumors. The majority of these GWAS susceptibility variants reside in noncoding regions and the causal genes underlying the associations are largely unknown. Here we performed a transcriptome-wide association study to search for novel risk loci and candidate causal genes at known GWAS loci using Genotype-Tissue Expression Project (GTEx) data to predict cis-predicted gene expression in relation to GBM and non-GBM risk in conjunction with GWAS summary statistics on 12,488 glioma cases (6,183 GBM and 5,820 non-GBM) and 18,169 controls. Imposing a Bonferroni-corrected significance level of P < 5.69 × 10−6, we identified 31 genes, including GALNT6 at 12q13.33, as a candidate novel risk locus for GBM (mean Z = 4.43; P = 5.68 × 10−6). GALNT6 resides at least 55 Mb away from any previously identified glioma risk variant, while all other 30 significantly associated genes were located within 1 Mb of known GWAS-identified loci and were not significant after conditioning on the known GWAS-identified variants. These data identify a novel locus (GALNT6 at 12q13.33) and 30 genes at 12 known glioma risk loci associated with glioma risk, providing further insights into glioma tumorigenesis.Significance:This study identifies new genes associated with glioma risk, increasing understanding of how these tumors develop.

Supplementary Figure 1: Quantile-Quantile Plots of -log10 (P-value) associations. (a) TWAS for GBM glioma; (b) TWAS for GBM glioma (lower 90% of associations); (c) TWAS for non-GBM glioma; (d) TWAS for non-GBM glioma (lower 90% of associations); (e) GWAS meta-analysis for GBM glioma; (f) GWAS meta-analysis for GBM glioma (lower 90% of associations); (g) GWAS meta-analysis for non-GBM glioma; (h) GWAS meta-analysis for non-GBM glioma (lower 90% of associations). Supplementary Figure 2: Manhattan Plots of SNP genomic co-ordinates against GWAS meta-analysis -log10 (P-value). (a) GBM glioma; (b) Non-GBM glioma. The red line represents the Bonferroni-corrected threshold of P{less than or equal to}5Ã-10-8. Supplementary Figure 3: Dendograms to display gene module clustering in brain tissues. Clustering was performed using the WGCNA (11) package for each tissue. (a) Amygdala; (b) Anterior cingulate cortex (BA24); (c) Caudate (basal ganglia); (d) Cerebellar Hemisphere; (e) Cerebellum; (f) Cortex; (g) Frontal Cortex (BA9); (h) Hippocampus; (i) Hypothalamus; (j) Nucleus accumbens (basal ganglia); (k) Putamen (basal ganglia); (l) Spinal cord (cervical c-1); (m) Substantia nigra. Supplementary Figure 4: Heatmaps illustrating gene module correlations in brain tissues. Darker colours indicate stronger associations. (a) Amygdala; (b) Anterior cingulate cortex (BA24); (c) Caudate (basal ganglia); (d) Cerebellar Hemisphere; (e) Cerebellum; (f) Cortex; (g) Frontal Cortex (BA9); (h) Hippocampus; (i) Hypothalamus; (j) Nucleus accumbens (basal ganglia); (k) Putamen (basal ganglia); (l) Spinal cord (cervical c-1); (m) Substantia nigra. Supplementary Figure 5: Module clustering of brain tissues with amygdala. Modules were created by clustering according to the amygdala tissue using WGCNA (11). These modules were applied to each tissue in turn and the Z statistic is a quantitative measure of module preservation. (a) Amygdala (reference); (b) Anterior cingulate cortex (BA24); (c) Caudate (basal ganglia); (d) Cerebellar Hemisphere; (e) Cerebellum; (f) Cortex; (g) Frontal Cortex (BA9); (h) Hippocampus; (i) Hypothalamus; (j) Nucleus accumbens (basal ganglia); (k) Putamen (basal ganglia); (l) Spinal cord (cervical c-1); (m) Substantia nigra. Supplementary Figure 6: TWAS power plots. Simulation analysis based on 12,488 glioma cases (6,183 GBM, 5,820 non-GBM) and 18,169 controls. Gene expression was generated from the distribution of gene expression levels from the respective brain tissue. Statistical power was calculated at P 0.4, case and control MAF > 0.01, PHWE in controls > 1x10-8. For the meta-analysis we considered all SNPs meeting the above criteria in all eight studies as well as having heterogeneity I2 < 75 in GBM/non-GBM glioma. a For the UCSF/Mayo study an imputation threshold of > 0.8 was applied. Supplementary Table 2: Prediction models trained on GTEx v7 data and covariance files obtained from http://predictdb.org/. Genes were retained if nested cross-validated correlation between predicted and actual levels > 0.10 (R2>1%) and P-value of the correlation test < 0.05. Supplementary Table 3: Previously reported glioma risk SNPs. Supplementary Table 4: Brain tissue, soft power threshold and number of independent genes. Soft power thresholds were chosen at the 90% threshold unless otherwise indicated. + For Cerebellar Hemisphere and Cerebellum, the 90% threshold was not achieved and 80% was imposed. For this reason such tissues were excluded from median calculations for soft power and number of independent genes. Supplementary Table 8: S-PrediXcan results for 31 genes in 922 whole-blood samples from the Depression Genes and Networks (DGN) study. Supplementary Table 9: Annotation of identified genes by presence within COSMIC cancer gene census and overlap with tumour copy number changes. *Cosmic cancer gene census annotation for CDKN2A was used.

Supplementary Table 5: Table of module color with module size and associated Z-statistics. Modules were created by clustering according to the Amygdala tissue using WGCNA11. These modules of gene number "moduleSize" were applied to each tissue in turn and the Z-statistic ("Zsummary.pres") is a quantitive measure of module preservation relative to Amygdala. (a) Anterior cingulate cortex (BA24); (b) Caudate (basal ganglia); (c) Cerebellar Hemisphere; (d) Cerebellum; (e) Cortex; (f) Frontal Cortex (BA9); (g) Hippocampus; (h) Hypothalamus; (i) Nucleus accumbens (basal ganglia); (j) Putamen (basal ganglia); (k) Spinal cord (cervical c-1); (l) Substantia nigra.

The genetic aetiology of familial glioma is largely unknown. To gain further insight into the role of rare disruptive variants we performed whole exome sequencing (WES) of 17 glioma families, identifying two families where loss of function mutations in dynein axonemal heavy chain 11 (DNAH11) co-segregated with glioma (DNAH11 p.Ser1470ArgfsTer6 and p.Thr3900Lys) and single families implicating Telomere Replication Complex Component 1 (CTC1 p.Leu1058ProfsTer32) and filamin alpha (FLNA p.Ser1356Phe). We complemented linkage analysis by WES of 150 additional unrelated familial glioma cases and conducting gene burden tests leveraging WES data on 691 cases of glioma from TCGA. Our analysis does not provide support for a hitherto unidentified major cancer gene for glioma but highlights a series of novel candidate predisposition variants/genes, worthy of further investigation to shed light on glioma risk.

International audience; B-cell non-Hodgkin's lymphoma (NHL) risk associations had been mainly attributed to family history of the disease, inflammation, and immune components including human leukocyte antigen (HLA) genetic variations. Nevertheless, a broad range of genome-wide association studies (GWAS) have shed light into the identification of several genetic variants presumptively associated with B-cell NHL etiologies, survival or shared genetic risk with other diseases. The present review aims to overview HLA structure and diversity and summarize the evidence of genetic variations, by GWAS, on five NHL subtypes (diffuse large B-cell lymphoma DLBCL, follicular lymphoma FL, chronic lymphocytic leukemia CLL, marginal zone lymphoma MZL, and primary central nervous system lymphoma PCNSL). Evidence indicates that the HLA zygosity status in B-cell NHL might promote immune escape and that genome-wide significance variants can give biological insight but also potential therapeutic markers such as WEE1 in DLBCL. However, additional studies are needed, especially for non-DLBCL, to replicate the associations found to date.

Background Cerebral cavernous malformations (CCMs) are common sporadic and inherited vascular malformations of the central nervous system. Although familial CCMs are linked to loss-of-function mutations in KRIT1 (CCM1), CCM2, or PDCD10 (CCM3), the genetic cause of sporadic CCMs, representing 80% of cases, remains incompletely understood. Methods We developed two mouse models harboring mutations identified in human meningiomas with the use of the prostaglandin D2 synthase (PGDS) promoter. We performed targeted DNA sequencing of surgically resected CCMs from patients and confirmed our findings by droplet digital polymerase-chain-reaction analysis. Results We found that in mice expressing one of two common genetic drivers of meningioma - Pik3ca H1047R or AKT1 E17K - in PGDS-positive cells, a spectrum of typical CCMs develops (in 22% and 11% of the mice, respectively) instead of meningiomas, which prompted us to analyze tissue samples from sporadic CCMs from 88 patients. We detected somatic activating PIK3CA and AKT1 mutations in 39% and 1%, respectively, of lesion tissue from the patients. Only 10% of lesions harbored mutations in the CCM genes. We analyzed lesions induced by the activating mutations Pik3ca H1074R and AKT1 E17K in mice and identified the PGDS-expressing pericyte as the probable cell of origin. Conclusions In tissue samples from sporadic CCMs, mutations in PIK3CA were represented to a greater extent than mutations in any other gene. The contribution of somatic mutations in the genes that cause familial CCMs was comparatively small. (Funded by the Fondation ARC pour la Recherche contre le Cancer and others.).

Activation-induced cytidine deaminase, AICDA or AID, is a driver of somatic hypermutation and class-switch recombination in immunoglobulins. In addition, this deaminase belonging to the APOBEC family, may have off-target effects genome-wide, but its effects at pan-cancer level are not well elucidated. Here, we used different pan-cancer datasets, totaling more than 50,000 samples analyzed by whole-genome, whole-exome or targeted sequencing. AID synergizes initial hotspot mutations by a second composite mutation. Analysis of 2.5 million cells, normal and oncogenic, revealed AICDA expression activation after oncogenic transformation and cell cycle regulation loss. AID mutational load was found to be independently associated with favorable outcome in immune-checkpoint inhibitors (ICI) treated patients across cancers after analyzing 2,000 samples. Finally, we found that AID related neoepitopes, resulting from mutations at more frequent hotspots if compared to other mutational signatures, enhance CXCL13/CCR5 expression, immunogenicity and T-cell exhaustion, which may increase ICI sensitivity.In BriefA combined bulk and single cell multi-omic analysis of over 50,000 patients and 2.5 million cells across 80 tumor types reveals oncogenic acquired AICDA expression inducing composite mutations and clonal immunogenic neoepitopes that are associated with favorable outcome in patients treated by immune-checkpoint inhibitors.Highlights•Pan-cancer analysis of AID mutations using > 50,000 samples, 2,000 ICI treated cases and 2.5 million cells with genome, exome and transcriptome data•Oncogenic transient AICDA expression induces mutations mainly during transcription of its off-target genes in virtually all cancers•AID is implicated in composite mutations on weakly functional alleles and immunogenic clonal neoepitopes at hotspots with greater positive selection•AID mutational load predicts response and is associated with favorable outcome in ICI treated patients

Molecular classification of cancer has entered clinical routine to inform diagnosis, prognosis, and treatment decisions. At the same time, new tumor entities have been identified that cannot be defined histologically. For central nervous system tumors, the current World Health Organization classification explicitly demands molecular testing, e.g., for 1p/19q-codeletion or IDH mutations, to make an integrated histomolecular diagnosis. However, a plethora of sophisticated technologies is currently needed to assess different genomic and epigenomic alterations and turnaround times are in the range of weeks, which makes standardized and widespread implementation difficult and hinders timely decision making. Here, we explored the potential of a pocket-size nanopore sequencing device for multimodal and rapid molecular diagnostics of cancer. Low-pass whole genome sequencing was used to simultaneously generate copy number (CN) and methylation profiles from native tumor DNA in the same sequencing run. Single nucleotide variants in IDH1, IDH2, TP53, H3F3A, and the TERT promoter region were identified using deep amplicon sequencing. Nanopore sequencing yielded ~0.1X genome coverage within 6 h and resulting CN and epigenetic profiles correlated well with matched microarray data. Diagnostically relevant alterations, such as 1p/19q codeletion, and focal amplifications could be recapitulated. Using ad hoc random forests, we could perform supervised pan-cancer classification to distinguish gliomas, medulloblastomas, and brain metastases of different primary sites. Single nucleotide variants in IDH1, IDH2, and H3F3A were identified using deep amplicon sequencing within minutes of sequencing. Detection of TP53 and TERT promoter mutations shows that sequencing of entire genes and GC-rich regions is feasible. Nanopore sequencing allows same-day detection of structural variants, point mutations, and methylation profiling using a single device with negligible capital cost. It outperforms hybridization-based and current sequencing technologies with respect to time to diagnosis and required laboratory equipment and expertise, aiming to make precision medicine possible for every cancer patient, even in resource-restricted settings. Electronic supplementary material The online version of this article (doi:10.1007/s00401-017-1743-5) contains supplementary material, which is available to authorized users.

To examine for a causal relationship between vitamin D and glioma risk we performed an analysis of genetic variants associated with serum 25-hydroxyvitamin D (25(OH)D) levels using Mendelian randomisation (MR), an approach unaffected by biases from confounding. Two-sample MR was undertaken using genome-wide association study data. Single nucleotide polymorphisms (SNPs) associated with 25(OH)D levels were used as instrumental variables (IVs). We calculated MR estimates for the odds ratio (OR) for 25(OH)D levels with glioma using SNP-glioma estimates from 12,488 cases and 18,169 controls, using inverse-variance weighted (IVW) and maximum likelihood estimation (MLE) methods. A non-significant association between 25(OH)D levels and glioma risk was shown using both the IVW (OR = 1.21, 95% confidence interval [CI] = 0.90-1.62, P = 0.201) and MLE (OR = 1.20, 95% CI = 0.98-1.48, P = 0.083) methods. In an exploratory analysis of tumour subtype, an inverse relationship between 25(OH)D levels and glioblastoma (GBM) risk was identified using the MLE method (OR = 0.62, 95% CI = 0.43-0.89, P = 0.010), but not the IVW method (OR = 0.62, 95% CI = 0.37-1.04, P = 0.070). No statistically significant association was shown between 25(OH)D levels and non-GBM glioma. Our results do not provide evidence for a causal relationship between 25(OH)D levels and all forms of glioma risk. More evidence is required to explore the relationship between 25(OH)D levels and risk of GBM.

BackgroundWe characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.MethodsPublished and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentrationResultsThe PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable.ConclusionsGenetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. Apparent discordance between genetic associations and trial outcome for T2DM might be explained lack by a of statistical precision, or differences in the nature and duration of genetic versus pharmacological perturbation of PCSK9.FundingThis research was funded by the British Heart Foundation (SP/13/6/30554, RG/10/12/28456, FS/18/23/33512), UCL Hospitals NIHR Biomedical Research Centre, by the Rosetrees and Stoneygate Trusts.Condensed abstractEvidence on the long-term efficacy and safety of therapeutic inhibition of PCSK9 is lacking. To explore potential long-term effects of PCSK9 inhibition, we characterised the phenotypic consequence of LDL-cholesterol lowering variants at the PCSK9 locus. A PCSK9 gene score comprising 4 SNPs recapitulated the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and risk of myocardial infarction, and was associated with an increased risk of type 2 diabetes. No associations with safety outcomes such as cancer, COPD, Alzheimer’s disease or atrial fibrillation were identified. Our findings suggest PCSK9 inhibition may be safe and effective during prolonged use.

International audience; Background: Primary central nervous system lymphoma (PCNSL) represents a particular entity within non-Hodgkin lymphomas and is associated with poor outcome. The present study addresses the potential clinical relevance of chimeric transcripts in PCNSL discovered by using RNA sequencing (RNA-seq).Methods: Seventy-two immunocompetent and newly diagnosed PCNSL cases were included in the present study. Among them, 6 were analyzed by RNA-seq to detect new potential fusion transcripts. We confirmed the results in the remaining 66 PCNSL. The gene fusion was validated by fluorescence in situ hybridization (FISH) using formalin-fixed paraffin-embedded (FFPE) samples. We assessed the biological and clinical impact of one new gene fusion.Results: We identified a novel recurrent gene fusion, E26 transformation-specific translocation variant 6-immunoglobulin heavy chain (ETV6-IgH). Overall, ETV6-IgH was found in 13 out of 72 PCNSL (18%). No fusion conserved an intact functional domain of ETV6, and ETV6 was significantly underexpressed at gene level, suggesting an ETV6 haploinsufficiency mechanism. The presence of the gene fusion was also validated by FISH in FFPE samples. Finally, PCNSL samples harboring ETV6-IgH showed a better prognosis in multivariate analysis, P = 0.03, hazard ratio = 0.33, 95% CI = 0.12-0.88. The overall survival at 5 years was 69% for PCNSL harboring ETV6-IgH versus 29% for samples without this gene fusion.Conclusions: ETV6-IgH is a new potential surrogate marker of PCNSL with favorable prognosis with ETV6 haploinsufficiency as a possible mechanism. The potential clinical impact of ETV6-IgH should be validated in larger prospective studies.

BACKGROUND: Obesity and related factors have been implicated as possible aetiological factors for the development of glioma in epidemiological observation studies. We used genetic markers in a Mendelian randomisation framework to examine whether obesity-related traits influence glioma risk. This methodology reduces bias from confounding and is not affected by reverse causation.METHODS: Genetic instruments were identified for 10 key obesity-related risk factors, and their association with glioma risk was evaluated using data from a genome-wide association study of 12,488 glioma patients and 18,169 controls. The estimated odds ratio of glioma associated with each of the genetically defined obesity-related traits was used to infer evidence for a causal relationship.RESULTS: No convincing association with glioma risk was seen for genetic instruments for body mass index, waist-to-hip ratio, lipids, type-2 diabetes, hyperglycaemia or insulin resistance. Similarly, we found no evidence to support a relationship between obesity-related traits with subtypes of glioma-glioblastoma (GBM) or non-GBM tumours.CONCLUSIONS: This study provides no evidence to implicate obesity-related factors as causes of glioma.

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 x 10(-9), odds ratio (OR) = 1.22), 11q14.1 (rs11233250; P = 9.95 x 10(-10), OR = 1.24), 16p13.3 (rs2562152; P = 1.93 x 10-8, OR = 1.21), 16q12.1 (rs10852606; P = 1.29 x 10(-11), OR = 1.18) and 22q13.1 (rs2235573; P = 1.76 x 10(-10), OR = 1.15), as well as eight loci for non-GBM tumors at 1q32.1 (rs4252707; P = 3.34 x 10(-9), OR = 1.19), 1q44 (rs12076373; P = 2.63 x 10(-10), OR = 1.23), 2q33.3 (rs7572263; P = 2.18 x 10(-10), OR = 1.20), 3p14.1 (rs11706832; P = 7.66 x 10(-9), OR = 1.15), 10q24.33 (rs11598018; P = 3.39 x 10-8, OR = 1.14), 11q21 (rs7107785; P = 3.87 x 10(-10), OR = 1.16), 14q12 (rs10131032; P = 5.07 x 10(-11), OR = 1.33) and 16p13.3 (rs3751667; P = 2.61 x 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.

Little is known about the genomic basis of primary central nervous system lymphoma (PCNSL) tumorigenesis. To investigate the mutational profile of PCNSL, we analyzed nine paired tumor and germline DNA samples from PCNSL patients by high throughput exome sequencing. Eight genes of interest have been further investigated by focused resequencing in 28 additional PCNSL tumors to better estimate their incidence. Our study identified recurrent somatic mutations in 37 genes, some involved in key signaling pathways such as NFKB, B cell differentiation and cell cycle control. Focused resequencing in the larger cohort revealed high mutation rates for genes already described as mutated in PCNSL such as MYD88 (38%), CD79B (30%), PIM1 (22%) and TBL1XR1 (19%) and for genes not previously reported to be involved in PCNSL tumorigenesis such as ETV6 (16%), IRF4 (14%), IRF2BP2 (11%) and EBF1 (11%). Of note, only 3 somatically acquired SNVs were annotated in the COSMIC database. Our results demonstrate a high genetic heterogeneity of PCNSL and mutational pattern similarities with extracerebral diffuse large B cell lymphomas, particularly of the activated B-cell (ABC) subtype, suggesting shared underlying biological mechanisms. The present study provides new insights into the mutational profile of PCNSL and potential targets for therapeutic strategies.

Background. The 1p19q non-codeleted gliomas with IDH mutation, defined as “molecular astrocytomas,” display frequent TP53 mutations and have an intermediate prognosis. We investigated the prognostic impact of copy number-neutral loss of heterozygosity (CNLOH) in 17p in this population. Methods. We analyzed 793 gliomas (206 grade II, 377 grade III, and 210 grade IV) by single nucleotide polymorphism array and for TP53 mutations. Results. Homodisomy revealed by CNLOH was observed in 156 cases (19.7%). It was more frequent in astrocytomas and oligoastrocytomas (98/256, 38%) than oligodendrogliomas (28/327, 8.6%; p < .0001) or glioblastoma multiforme (30/210, 14.3%; p < .0001), tightly associated with TP53 mutation (69/71 vs. 20/79; p = 2 × 10−16), and mutually exclusive with 1p19q codeletion (1/156 vs. 249/556; p < .0001). In the group of IDH-mutated 1p19q non-codeleted gliomas, CNLOH 17p was associated with longer survival (86.3 vs. 46.2 months; p = .004), particularly in grade III gliomas (overall survival >100 vs. 37.9 months; p = .007). These data were confirmed in an independent dataset from the Cancer Genome Atlas. Conclusion. CNLOH 17p is a prognostic marker and further refines the molecular classification of gliomas.

BACKGROUND: Low-grade gliomas (LGG) are divided into three histo-molecular groups: i) IDHwt, ii) IDHmut and 1p19q intact and iii) 1p19q co-deleted with IDH1 mutation. The current classification has improved the clinical stratification and its reproducibility. However, LGGs are still associated with an important degree of clinical heterogeneity.We sough to analyze the genomic and genetic heterogeneity by re-analyzing next-generation sequencing data of two different cohorts of LGG to dissect the potential role of clonal architecture on the clinical evolution of LGG. MATERIAL AND METHODS: We have re-analyzed whole-exome sequencing (WES) using LGG TCGA dataset and a cohort of 40 LGG analyzed by WES from the POLA Network. We have obtained the clonal architecture, the mutation burden, the number of clones, the clonal and subclonal distribution of genetic and genomic alterations and the distribution of the somatic signatures. In addition, we have also analyzed 5 1p19q co-deleted samples at different timepoints to further dissect the clonal evolution of this entity. RESULTS: As expected, the mutation burden of LGG is low (median somatic mutation per sample 34). Surprisingly, the number the subclones was higher in the 1p19q co-deleted subgroup.LGG were enriched in somatic signatures associated with hydrolytic deamination of methylated CpG and with deficiency of mismatch repair. Interestingly, only within 1p19q co-deleted gliomas, the number of subclones, the mutant-allele tumor heterogeneity (MATH) score and the signature associated with hydrolytic deamination of methylated CpGwere associated with poorer outcome in univariate analysis. Finally, only the enrichment of the mutational signature associated with hydrolytic deamination of methylated CpG had prognostic impact in 1p19q co-deleted LGG in Cox multivariate analysis afteradjusting by age, mutational clones number, MATH score and loss of CDKN2A loss. CONCLUSION: The clonal architecture of LGG may provide additional clinical relevant data within 1p19q co-deleted tumors. The enrichment of a somatic signature associated with hydrolytic deamination of methylated CpG is independently associated with poor overall survival.

BACKGROUND: Glioma accounts for ~27% of all primary brain tumors and is responsible for ~13,000 cancer-related deaths in the US each year. Glioma tumors can be broadly classified into glioblastoma (GBM) and lower-grade non-GBM. Typically gliomas have a poor prognosis irrespective of medical care, with the most common form, GBM, having a five-year survival rate of only 5%. While genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, individual studies have had limited power to identify risk loci. METHODS: We performed the largest glioma GWAS to date, comprising a meta-analysis of six existing GWAS (6,405 cases, 14,100 controls) as well as new GWAS from the Glioma International Case Control Consortium (GICC; 4,572 cases and 3,286 controls) and University of California, San Francisco (UCSF)-Mayo (1,519 cases, 804 controls), totaling 12,496 cases (6,191 classified as GBM, 5,819 as non-GBM) and 18,190 controls. RESULTS: We identified five new risk loci for GBM at 1p31.3 (rs12752552; near JAK1, P=2.04×10-9, odds ratio (OR)=1.22), 11q14.1 (rs11233250; P=9.95×10-10, OR=1.24), 16p13.3 (rs2562152; near MPG, P=1.93x10-8, OR=1.21), 16q12.1 (rs10852606; HEATR3, P=1.29×10-11, OR=1.18), 22q13.1 (rs2235573; P=1.76×10-10, OR=1.15) and eight for non-GBM at 1q32.1 (rs4252707; MDM4, P=3.34×10-9, OR=1.19), 1q44 (rs12076373; AKT3, P=2.63×10-10, OR=1.23), 2q33.3 (rs7572263; near IDH1, P=2.18×10-10, OR=1.20), 3p14.1 (rs11706832; LRIG1, P=7.66×10-9, OR=1.15), 10q24.33 (rs11598018; OBFC1, P=3.39×10-8, OR=1.14), 11q21 (rs7107785; P=3.87×10-10, OR=1.16), 14q12 (rs10131032; P=5.07x10-11, OR=1.33) and 16p13.3 (rs3751667; P=2.61×10-9, OR=1.18). Case-only analyses confirmed the specificity of 11q14.1, 16p13.3 and 22q13.1 associations for GBM and 1q44, 2q33.3, 3p14.1, 11q21 and 14q12 for non-GBM tumors. In the combined meta-analysis, among previously published glioma risk SNPs, those for all glioma at 17p13.1 (TP53), GBM at 5p15.33 (TERT), 7p11.2 (EGFR), 9p21.3 (CDKN2B-AS1) and 20q13.33 (RTEL1) and for non-GBM at 8q24.21 (CCDC26), 11q23.2, 11q23.3 (PHLDB1) and 15q24.2 (ETFA) showed even greater evidence for association. SNPs at 10q25.2 and 12q12.1 for non-GBM tumors retained genome-wide significance (i.e. P CONCLUSIONS: Our findings substantiate genetic susceptibility to GBM and non-GBM glioma being highly distinct, consistent with their distinctive molecular profiles presumably resulting from different etiological pathways. Functional analyses should lead to further insights into the biological basis of the different glioma histologies. Such information can inform gene discovery initiatives and therefore have a measurable impact on the successful development of new therapeutic agents. Citation Format: Ben Kinnersley, Beatrice S. Melin, Jill S. Barnholtz-Sloan, Margaret R. Wrensch, Christoffer Johansen, Dora Il’yasova, Quinn Ostrom, and members of GICC, Karim Labreche, Jeanette E. Eckel-Passow, Paul A. Decker, Marianne Labussière, Ahmed Idbaih, Khe Hoang-Xuan, Anna-Luisa Di Stefano, Karima Mokhtari, Jean-Yves Delattre, Pilar Galan, Konstantinos Gousias, Johannes Schramm, Minouk J. Schoemaker, Sarah J. Fleming, Stefan Herms, Stefanie Heilmann, Marcus M. Nöthen, Heinz-Erich Wichmann, Stefan Schreiber, Anthony Swerdlow, Mark Lathrop, Matthias Simon, Marc Sanson, Preetha Rajaraman, Stephen Chanock, Martha Linet, Zhaoming Wang, Meredith Yeager, Rose K. Lai, Elizabeth B. Claus, Sara H. Olson, Robert B. Jenkins, Richard S. Houlston, Melissa L. Bondy. Genome-wide association study of glioma reveals specific differences in genetic susceptibility to glioblastoma and non-glioblastoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1302. doi:10.1158/1538-7445.AM2017-1302

Background: Primary central nervous system lymphoma (PCNSL) is a rare extranodal diffuse large B-cell lymphoma (DLBCL). Little is known about the genomic basis of tumorigenesis in PCNSL partly explained by the rarity of biological material available for research studies. PCNSL patients display poor outcomes compared to systemic DLBCL and it is still unclear whether this is linked to the organ-specific microenvironment or reflects a specific, aggressive, intrinsic biological behavior. To our knowledge, the mutational landscape of PCNSL has never been investigated Experimental design: We analyzed 9 paired tumor and germline DNA samples from PCNSL patients by high throughput exome sequencing. Then, we performed focused sequencing in 28 additional PCNSL tumors to validate somatic genomic alterations and better estimate their incidence. Mutational data were correlated with copy number variations (CNV) identified by SNP and CGH arrays and clinical data. Results: We identified recurrent somatic mutations in genes involved in key signalling pathways such as NFκB (i.e. MYD88, CD79B, TBL1XR1), B cell differentiation (i.e. ETV6, EBF1, IRF4) and cell cycle control (i.e. BTG1, PIM1). We found somatic mutations on MYD88 (38%), CD79B (30%), TBL1XR1 (22%) or ETV6 (16%) genes and described bi-allelic inactivation of TBL1XR1 and ETV6 genes. Interestingly, the correlation with the outcome showed a significant negative impact of TBL1XR1 mutation on survival in a multivariate analysis. Conclusions: The present study showed that PCNSLs have high genetic heterogeneity and that their mutational patterns display similarities with extracerebral DLBCL, particularly of the ABC subtype, suggesting shared underlying biological mechanisms. These results provide new insights into the mutational landscape of PCNSL and potential targets for therapeutic strategies. This work benefited from the LOC study group network (réseau national de centres experts des lymphomes primitifs du SNC, INCa). Citation Format: Aurélie Bruno, Blandine Boisselier, Karim Labreche, Yannick Marie, Marc Polivka, Anne Jouvet, Clovis Adam, Dominique Figarella-Branger, Catherine Miquel, Anne Vital, Caroline Houillier, Carole Soussain, Karima Mokhtari, Romain Daveau, Khê Hoang-Xuan. Mutational analysis of primary central nervous system lymphoma. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5192. doi:10.1158/1538-7445.AM2014-5192