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Genome-wide association studies (GWAS) have contributed to our understanding of glioma susceptibility. To date, 25 risk loci for development of any of the glioma subtypes are known. However, GWAS studies reveal little about the molecular processes that lead to increased risk, especially for non-coding single nucleotide polymorphisms (SNP). A particular SNP in intron 2 of LRIG1, rs11706832, has been shown to increase the susceptibility for IDH1 mutated low-grade gliomas (LGG). Leucine-rich repeats and immunoglobulin-like domains protein 1 (LRIG1) is important in cancer development as it negatively regulates the epidermal growth factor receptor (EGFR); however, the mechanism responsible for this particular risk SNP and its potential effect on LRIG1 are not known. Using CRISPR-CAS9, we edited rs11706832 in HEK293T cells. Four HEK293T clones with the risk allele were compared to four clones with the non-risk allele for LRIG1 and SLC25A26 gene expression using RT-qPCR, for global gene expression using RNA-seq, and for metabolites using gas chromatography-mass spectrometry (GC–MS). The experiment did not reveal any significant effect of the SNP on the expression levels or splicing patterns of LRIG1 or SLC25A26. The global gene expression analysis revealed that the risk allele C was associated with upregulation of several mitochondrial genes. Gene enrichment analysis of 74 differentially expressed genes in the genome revealed a significant enrichment of type I interferon response genes, where many genes were downregulated for the risk allele C. Gene expression data of IDH1 mutated LGGs from the cancer genome atlas (TCGA) revealed a similar under expression of type I interferon genes associated with the risk allele. This study found the expression levels and splicing patterns of LRIG1 and SLC25A26 were not affected by the SNP in HEK293T cells. However, the risk allele was associated with a downregulation of genes involved in the innate immune response both in the HEK293T cells and in the LGG data from TCGA.

Hip fracture is the clinically most important fracture, but the genetic architecture of hip fracture is unclear. Here, we perform a large-scale hip fracture genome-wide association study meta-analysis and Mendelian randomization study using five cohorts from European biobanks. The results show that five genetic signals associate with hip fractures. Among these, one signal associates with falls, but not with bone mineral density (BMD), while four signals are in loci known to be involved in bone biology. Mendelian randomization analyses demonstrate a strong causal effect of decreased femoral neck BMD and moderate causal effects of Alzheimer's disease and having ever smoked regularly on risk of hip fractures. The substantial causal effect of decreased femoral neck BMD on hip fractures in both young and old subjects and in both men and women supports the use of change in femoral neck BMD as a surrogate outcome for hip fractures in clinical trials.

BACKGROUND: Gliomas are complex tumors with several genetic aberrations and diverse metabolic programs contributing to their aggressive phenotypes and poor prognoses. This study defines key metabolic features that can be used to differentiate between glioma subtypes, with potential for improved diagnostics and subtype targeted therapy.

METHODS: Cross-platform global metabolomic profiling coupled with clinical, genetic, and pathological analysis of glioma tissue from 224 tumors - oligodendroglioma (n=31), astrocytoma (n=31) and glioblastoma (n=162) - were performed. Identified metabolic phenotypes were evaluated in accordance with the WHO classification, IDH-mutation, 1p/19q-codeletion, WHO-grading 2-4, and MGMT promoter methylation.

RESULTS: Distinct metabolic phenotypes separate all six analyzed glioma subtypes. IDH-mutated subtypes, expressing 2-hydroxyglutaric acid, were clearly distinguished from IDH-wildtype subtypes. Considerable metabolic heterogeneity outside of the mutated IDH pathway were also evident, with key metabolites being high expression of glycerophosphates, inositols, monosaccharides and sugar alcohols and low levels of sphingosine and lysoglycerophospholipids in IDH-mutants. Among the IDH-mutated subtypes, we observed high levels of amino acids, especially glycine and 2-aminoadipic acid, in grade 4 glioma, and N-acetyl aspartic acid in low-grade astrocytoma and oligodendroglioma. Both IDH-wildtype and mutated oligodendroglioma and glioblastoma were characterized by high levels of acylcarnitines, likely driven by rapid cell growth and hypoxic features. We found elevated levels of 5-HIAA in gliosarcoma and a subtype of oligodendroglioma not yet defined as a specific entity, indicating a previously not described role for the serotonin pathway linked to glioma with bimorphic tissue.

CONCLUSION: Key metabolic differences exist across adult glioma subtypes.

Background: Understanding the trajectory and development of disease is important and the knowledge can be used to find novel targets for therapy and new diagnostic tools for early diagnosis.

Methods: Large cohorts from different parts of the world are unique assets for research as they have systematically collected plasma and DNA over long-time periods in healthy individuals, sometimes even with repeated samples. Over time, the population in the cohort are diagnosed with many different diseases, including brain tumors.

Results: Recent studies have detected genetic variants that are associated with increased risk of glioblastoma and lower grade gliomas specifically. The impact for genetic markers to predict disease in a healthy population has been deemed low, and a relevant question is if the genetic variants for glioma are associated with risk of disease or partly consist of genes associated to survival. Both metabolite and protein spectra are currently being explored for early detection of cancer.

Conclusions: We here present a focused review of studies of genetic variants, metabolomics, and proteomics studied in prediagnostic glioma samples and discuss their potential in early diagnostics.

No strong aetiological factors have been established for glioma aside from genetic mutations and variants, ionising radiation and an inverse relationship with asthmas and allergies. Our aim was to investigate the association between pre-diagnostic immune protein levels and glioma risk. We conducted a case–control study nested in the Northern Sweden Health and Disease Study cohort. We analysed 133 glioma cases and 133 control subjects matched by age, sex and date of blood donation. ELISA or Luminex bead-based multiplex assays were used to measure plasma levels of 19 proteins. Conditional logistic regression models were used to estimate the odds ratios and 95% CIs. To further model the protein trajectories over time, the linear mixed-effects models were conducted. We found that the levels of sVEGFR2, sTNFR2, sIL-2Rα and sIL-6R were associated with glioma risk. After adjusting for the time between blood sample collection and glioma diagnosis, the odds ratios were 1.72 (95% CI = 1.01–2.93), 1.48 (95% CI = 1.01–2.16) and 1.90 (95% CI = 1.14–3.17) for sTNFR2, sIL-2Rα and sIL-6R, respectively. The trajectory of sVEGFR2 concentrations over time was different between cases and controls (p-value = 0.031), increasing for cases (0.8% per year) and constant for controls. Our findings suggest these proteins play important roles in gliomagenesis.

I och med utveckling inom storskalig analys av blodprover har man idag insett nyttan av att omvandla biobanker med lagrade humanprover till data-banker där forskare snabbt kan få tillgång till data för att svara på forsknings-frågor. Problemet är att många av teknikerna för att skapa storskaliga data är semikvantitativa, värdena går inte att relatera till en absolut koncentration och är därmed svåra att slå samman och jämföra över tid. Randomisering, det vill säga att proverna analyseras i slumpvis inbördes ordning, är en av de viktigas-te aspekterna för att skapa data som går att slå samman och återanvända för många forskningsfrågor. Detta underlättar korrigering av oönskade analysva-riationer över tid. Utöver detta kan man använda sig av bryggningsprover, QC-prov (kvalitetskontrollprov) eller ankarprover, som analyseras upprepat både inom och mellan analystillfällen, vilket underlättar att lägga samman dataset som analyseras vid olika tillfällen.

Många kommersiella analysplattformar inkluderar ett eget QC-prov i analysen och vissa delar med sig av data för dessa prover. Det vore värdefullt om alla plattformar delade dessa data för kvalitetsutvärdering och eventuell korrige-ring av analysvariationer över tid. För alla semikvantitativa plattformar som undersöktes (Olink, Somalogic, Metabolon och Biocrates) var den tekniska variabiliteten mellan QC-proverna betydligt lägre än variabiliteten mellan ana-lyserade plasmaprover. Detta var tydligast för proteomikplattformarna, vilket antyder att förutsättningarna att upptäcka biologiska skillnader är bättre i pro-teomikdata. Undantaget från detta är en femte plattform, Nightingale, en kvan-titativ men smalare metabololmikmetod som anses generera stabila mätningar.

Vid all utveckling av biomarkörpaneler för att prediktera sjukdom behöver man göra upptäcktsanalyser, sedan valideringsstudier och därefter tester i den situation man tänker att testet ska fungera. De breda omikplattformarna läm-par sig för upptäckt och eventuellt validering, men för det faktiska kliniska tes-tet behövs en kvantitativ analys för att verkligen utvärdera att de proteiner eller metaboliter man vill använda är stabilt uppmätbara och fungerar för att pre-diktera sjukdom eller risk för sjukdom.

Background: Changes in immune marker levels in the blood could be used to improve the early detection of tumor-associated inflammatory processes. To increase predictiveness and utility in cancer detection, intraindividual long-term stability in cancer-free individuals is critical for biomarker candidates as to facilitate the detection of deviation from the norm.

Methods: We assessed intraindividual long-term stability for 19 immune markers (IL10, IL13, TNFa, CXCL13, MCP-3, MIP-1a, MIP-1b, fractalkine, VEGF, FGF-2, TGFa, sIL2Ra, sIL6R, sVEGF-R2, sTNF-R1, sTNF-R2, sCD23, sCD27, and sCD30) in 304 cancer-free individuals. Repeated blood samples were collected up to 20 years apart. Intraindividual reproducibility was assessed by calculating intraclass correlation coefficients (ICC) using a linear mixed model.

Results: ICCs indicated fair to good reproducibility (ICCs ≥ 0.40 and < 0.75) for 17 of 19 investigated immune markers, including IL10, IL13, TNFa, CXCL13, MCP-3, MIP-1a, MIP-1b, fractalkine, VEGF, FGF-2, TGFa, sIL2Ra, sIL6R, sTNF-R1, sTNF-R2, sCD27, and sCD30. Reproducibility was strong (ICC ≥ 0.75) for sCD23, while reproducibility was poor (ICC < 0.40) for sVEGF-R2. Using a more stringent criterion for reproducibility (ICC ≥ 0.55), we observed either acceptable or better reproducibility for IL10, IL13, CXCL13, MCP-3, MIP-1a, MIP-1b, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30.

Conclusions: IL10, IL13, CXCL13, MCP-3, MIP-1a, MIP-1b, VEGF, FGF-2, sTNF-R1, sCD23, sCD27, and sCD30 displayed ICCs consistent with intraindividual long-term stability in cancer-free individuals. Impact: Our data support using these markers in prospective longitudinal studies seeking early cancer detection biomarkers.

Introduction: Medulloblastoma is a malignant embryonal tumor of the cerebellum that occurs predominantly in children. To find germline genetic variants associated with medulloblastoma risk, we conducted a genome-wide association study (GWAS) including 244 medulloblastoma cases and 247 control subjects from Sweden and Denmark.

Methods: Genotyping was performed using Illumina BeadChips, and untyped variants were imputed using IMPUTE2.

Results: Fifty-nine variants in 11 loci were associated with increased medulloblastoma risk (p < 1 × 10–5), but none were statistically significant after adjusting for multiple testing (p < 5 × 10–8). Thirteen of these variants were genotyped, whereas 46 were imputed. Genotyped variants were further investigated in a validation study comprising 249 medulloblastoma cases and 629 control subjects. In the validation study, rs78021424 (18p11.23, PTPRM) was associated with medulloblastoma risk with OR in the same direction as in the discovery cohort (ORT = 1.59, pvalidation = 0.02). We also selected seven medulloblastoma predisposition genes for investigation using a candidate gene approach: APC, BRCA2, PALB2, PTCH1, SUFU, TP53, and GPR161. The strongest evidence for association was found for rs201458864 (PALB2, ORT = 3.76, p = 3.2 × 10–4) and rs79036813 (PTCH1, ORA = 0.42, p = 2.6 × 10–3).

Conclusion: The results of this study, including a novel potential medulloblastoma risk loci at 18p11.23, are suggestive but need further validation in independent cohorts.

Whole-genome sequencing is a promising approach for human autosomal dominant disease studies. However, the vast number of genetic variants observed by this method constitutes a challenge when trying to identify the causal variants. This is often handled by restricting disease studies to the most damaging variants, e.g. those found in coding regions, and overlooking the remaining genetic variation. Such a biased approach explains in part why the genetic causes of many families with dominantly inherited diseases, in spite of being included in whole-genome sequencing studies, are left unsolved today. Here we explore the use of a geographically matched control population to minimize the number of candidate disease-causing variants without excluding variants based on assumptions on genomic position or functional predictions. To exemplify the benefit of the geographically matched control population we apply a typical disease variant filtering strategy in a family with an autosomal dominant form of colorectal cancer. With the use of the geographically matched control population we end up with 26 candidate variants genome wide. This is in contrast to the tens of thousands of candidates left when only making use of available public variant datasets. The effect of the local control population is dual, it (1) reduces the total number of candidate variants shared between affected individuals, and more importantly (2) increases the rate by which the number of candidate variants are reduced as additional affected family members are included in the filtering strategy. We demonstrate that the application of a geographically matched control population effectively limits the number of candidate disease-causing variants and may provide the means by which variants suitable for functional studies are identified genome wide.

Background: Genome-wide association studies have identified germline genetic variants in 25 genetic loci that increase the risk of developing glioma in adulthood. It is not known if these variants increase the risk of developing glioma in children and adolescents and young adults (AYA). To date, no studies have performed genome-wide analyses to find novel genetic variants associated with glioma risk in children and AYA.

Methods: We investigated the association between 8,831,628 genetic variants and risk of glioma in 854 patients diagnosed up to the age of 29 years and 3,689 controls from Sweden and Denmark. Recruitment of patients and controls was population based. Genotyping was performed using Illumina BeadChips, and untyped variants were imputed with IMPUTE2. We selected 41 established adult glioma risk variants for detailed investigation.

Results: Three adult glioma risk variants, rs634537, rs2157719, and rs145929329, all mapping to the 9p21.3 (CDKN2B-AS1) locus, were associated with glioma risk in children and AYA. The strongest association was seen for rs634537 (odds ratio_G = 1.21; 95% confidence interval = 1.09–1.35; P = 5.8 × 10⁻⁴). In genome-wide analysis, an association with risk was suggested for 129 genetic variants (P <1 × 10⁻⁵).

Conclusions: Carriers of risk alleles in the 9p21.3 locus have an increased risk of glioma throughout life. The results from genome-wide association analyses require validation in independent cohorts.

Impact: Our findings line up with existing evidence that some, although not all, established adult glioma risk variants are associated with risk of glioma in children and AYA. Validation of results from genome-wide analyses may reveal novel susceptibility loci for glioma in children and AYA.