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. 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. 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. Key metabolic differences exist across adult glioma subtypes.

Glioma is a rare brain tumor with a poor prognosis. Familial glioma is a subset of glioma with a strong genetic predisposition that accounts for approximately 5% of glioma cases. We performed whole-genome sequencing on an exploratory cohort of 203 individuals from 189 families with a history of familial glioma and an additional validation cohort of 122 individuals from 115 families. We found significant enrichment of rare deleterious variants of seven genes in both cohorts, and the most significantly enriched gene was HERC2 ( P = 0.0006). Furthermore, we identified rare noncoding variants in both cohorts that were predicted to affect transcription factor binding sites or cause cryptic splicing. Last, we selected a subset of discovered genes for validation by CRISPR knockdown screening and found that DMBT1, HP1BP3 , and ZCH7B3 have profound impacts on proliferation. This study performs comprehensive surveillance of the genomic landscape of familial glioma.

Background: Targeted surveillance of at-risk individuals in families with increased risk of hereditary cancer is an effective prevention strategy if relatives are identified, informed and enrolled in screening programs. Despite the potential benefits, many eligible at-risk relatives remain uninformed of their cancer risk. This study describes the general public’s opinion on disclosure of hereditary colorectal cancer (CRC) risk information, as well as preferences on the source and the mode of information. Methods: A random sample of the general public was assessed through a Swedish citizen web-panel. Respondents were presented with scenarios of being an at-risk relative in a family that had an estimated increased hereditary risk of CRC; either 10% (moderate) or 70% (high) lifetime risk. A colonoscopy was presented as a preventive measure. Results were analysed to identify significant differences between groups using the Pearson’s chi-square (χ²) test. Results: Of 1800 invited participants, 977 completed the survey (54%). In the moderate and high-risk scenarios, 89.2 and 90.6% respectively, would like to receive information about a potential hereditary risk of CRC (χ2, p = .755). The desire to be informed was higher among women (91.5%) than men (87.0%, χ2, p = .044). No significant differences were found when comparing different age groups, educational levels, place of residence and having children or not. The preferred source of risk information was a healthcare professional in both moderate and high-risk scenarios (80.1 and 75.5%). However, 18.1 and 20.1% respectively would prefer to be informed by a family member. Assuming that healthcare professionals disclosed the information, the favoured mode of information was letter and phone (38.4 and 33.2%). Conclusions: In this study a majority of respondents wanted to be informed about a potential hereditary risk of CRC and preferred healthcare professionals to communicate this information. The two presented levels of CRC lifetime risk did not significantly affect the interest in being informed. Our data offer insights into the needs and preferences of the Swedish population, providing a rationale for developing complementary healthcare-assisted communication pathways to realise the full potential of targeted prevention of hereditary CRC.

Here, we present a strategy for early molecular marker pattern detection—Subset analysis of Matched Repeated Time points (SMART)—used in a mass-spectrometry-based metabolomics study of repeated blood samples from future glioma patients and their matched controls. The outcome from SMART is a predictive time span when disease-related changes are detectable, defined by time to diagnosis and time between longitudinal sampling, and visualization of molecular marker patterns related to future disease. For glioma, we detect significant changes in metabolite levels as early as eight years before diagnosis, with longitudinal follow up within seven years. Elevated blood plasma levels of myo-inositol, cysteine, N-acetylglucosamine, creatinine, glycine, proline, erythronic-, 4-hydroxyphenylacetic-, uric-, and aceturic acid were particularly evident in glioma cases. We use data simulation to ensure non-random events and a separate data set for biomarker validation. The latent biomarker, consisting of 15 interlinked and significantly altered metabolites, shows a strong correlation to oxidative metabolism, glutathione biosynthesis and monosaccharide metabolism, linked to known early events in tumor development. This study highlights the benefits of progression pattern analysis and provide a tool for the discovery of early markers of disease.

Aggregation of genome-wide common risk variants, such as polygenic risk score (PRS), can measure genetic susceptibility to cancer. A better understanding of how common germline variants associate with somatic alterations and clinical features could facilitate personalized cancer prevention and early detection. We constructed PRSs from 14 genome-wide association studies (median n = 64,905) for 12 cancer types by multiple methods and calibrated them using the UK Biobank resources (n = 335,048). Meta-analyses across cancer types in The Cancer Genome Atlas (n = 7,965) revealed that higher PRS values were associated with earlier cancer onset and lower burden of somatic alterations, including total mutations, chromosome/arm somatic copy-number alterations (SCNA), and focal SCNAs. This contrasts with rare germline pathogenic variants (e.g., BRCA1/2 variants), showing heterogeneous associations with somatic alterations. Our results suggest that common germline cancer risk variants allow early tumor development before the accumulation of many somatic alterations characteristic of later stages of carcinogenesis. Significance: Meta-analyses across cancers show that common germline risk variants affect not only cancer predisposition but the age of cancer onset and burden of somatic alterations, including total mutations and copy-number alterations.