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Background: Epidemiological studies of associations between metabolites and cancer risk have typically focused on specific cancer types separately. Here, we designed a multivariate pan-cancer analysis to identify metabolites potentially associated with multiple cancer types, while also allowing the investigation of cancer type-specific associations.

Methods: We analysed targeted metabolomics data available for 5828 matched case-control pairs from cancer-specific case-control studies on breast, colorectal, endometrial, gallbladder, kidney, localized and advanced prostate cancer, and hepatocellular carcinoma nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. From pre-diagnostic blood levels of an initial set of 117 metabolites, 33 cluster representatives of strongly correlated metabolites and 17 single metabolites were derived by hierarchical clustering. The mutually adjusted associations of the resulting 50 metabolites with cancer risk were examined in penalized conditional logistic regression models adjusted for body mass index, using the data-shared lasso penalty.

Results: Out of the 50 studied metabolites, (i) six were inversely associated with the risk of most cancer types: glutamine, butyrylcarnitine, lysophosphatidylcholine a C18:2, and three clusters of phosphatidylcholines (PCs); (ii) three were positively associated with most cancer types: proline, decanoylcarnitine, and one cluster of PCs; and (iii) 10 were specifically associated with particular cancer types, including histidine that was inversely associated with colorectal cancer risk and one cluster of sphingomyelins that was inversely associated with risk of hepatocellular carcinoma and positively with endometrial cancer risk.

Conclusions: These results could provide novel insights for the identification of pathways for cancer development, in particular those shared across different cancer types.

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.

Pooling metabolomics data across studies is often desirable to increase the statistical power of the analysis. However, this can raise methodological challenges as several preanalytical and analytical factors could introduce differences in measured concentrations and variability between datasets. Specifically, different studies may use variable sample types (e.g., serum versus plasma) collected, treated, and stored according to different protocols, and assayed in different laboratories using different instruments. To address these issues, a new pipeline was developed to normalize and pool metabolomics data through a set of sequential steps: (i) exclusions of the least informative observations and metabolites and removal of outliers; imputation of missing data; (ii) identification of the main sources of variability through principal component partial R-square (PC-PR2) analysis; (iii) application of linear mixed models to remove unwanted variability, including samples’ originating study and batch, and preserve biological variations while accounting for potential differences in the residual variances across studies. This pipeline was applied to targeted metabolomics data acquired using Biocrates AbsoluteIDQ kits in eight case-control studies nested within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort. Comprehensive examination of metabolomics measurements indicated that the pipeline improved the comparability of data across the studies. Our pipeline can be adapted to normalize other molecular data, including biomarkers as well as proteomics data, and could be used for pooling molecular datasets, for example in international consortia, to limit biases introduced by inter-study variability. This versatility of the pipeline makes our work of potential interest to molecular epidemiologists.

The number of national reference populations that are whole-genome sequenced are rapidly increasing. Partly driving this development is the fact that genetic disease studies benefit from knowing the genetic variation typical for the geographical area of interest. A whole-genome sequenced Swedish national reference population (n = 1000) has been recently published but with few samples from northern Sweden. In the present study we have whole-genome sequenced a control population (n = 300) (ACpop) from Västerbotten County, a sparsely populated region in northern Sweden previously shown to be genetically different from southern Sweden. The aggregated variant frequencies within ACpop are publicly available (DOI 10.17044/NBIS/G000005) to function as a basic resource in clinical genetics and for genetic studies. Our analysis of ACpop, representing approximately 0.11% of the population in Västerbotten, indicates the presence of a genetic substructure within the county. Furthermore, a demographic analysis showed that the population from which samples were drawn was to a large extent geographically stationary, a finding that was corroborated in the genetic analysis down to the level of municipalities. Including ACpop in the reference population when imputing unknown variants in a Västerbotten cohort resulted in a strong increase in the number of high-confidence imputed variants (up to 81% for variants with minor allele frequency < 5%). ACpop was initially designed for cancer disease studies, but the genetic structure within the cohort will be of general interest for all genetic disease studies in northern Sweden.

Adverse environmental conditions are detrimental to plant growth and development. Acclimation to abiotic stress conditions involves activation of signaling pathways which often results in changes in gene expression via networks of transcription factors (TFs). Mediator is a highly conserved co-regulator complex and an essential component of the transcriptional machinery in eukaryotes. Some Mediator subunits have been implicated in stress-responsive signaling pathways; however, much remains unknown regarding the role of plant Mediator in abiotic stress responses. Here, we use RNA-seq to analyze the transcriptional response of Arabidopsis thaliana to heat, cold and salt stress conditions. We identify a set of common abiotic stress regulons and describe the sequential and combinatorial nature of TFs involved in their transcriptional regulation. Furthermore, we identify stress-specific roles for the Mediator subunits MED9, MED16, MED18 and CDK8, and putative TFs connecting them to different stress signaling pathways. Our data also indicate different modes of action for subunits or modules of Mediator at the same gene loci, including a co-repressor function for MED16 prior to stress. These results illuminate a poorly understood but important player in the transcriptional response of plants to abiotic stress and identify target genes and mechanisms as a prelude to further biochemical characterization.

G-quadruplex (G4) structures are stable noncanonical DNA structures that are implicated in the regulation of many cellular pathways. We show here that the G4-stabilizing compound PhenDC3 causes growth defects in Schizosaccharomyces pombe cells, especially during S-phase in synchronized cultures. By visualizing individual DNA molecules, we observed shorter DNA fragments of newly replicated DNA in the PhenDC3-treated cells, suggesting that PhenDC3 impedes replication fork progression. Furthermore, a novel single DNA molecule damage assay revealed increased single-strand DNA lesions in the PhenDC3-treated cells. Moreover, chromatin immunoprecipitation showed enrichment of the leading-strand DNA polymerase at sites of predicted G4 structures, suggesting that these structures impede DNA replication. We tested a subset of these sites and showed that they form G4 structures, that they stall DNA synthesis in vitro and that they can be resolved by the breast cancerassociated Pif1 family helicases. Our results thus suggest that G4 structures occur in S. pombe and that stabilized/unresolved G4 structures are obstacles for the replication machinery. The increased levels of DNA damage might further highlight the association of the human Pif1 helicase with familial breast cancer and the onset of other human diseases connected to unresolved G4 structures.

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.

Even small variations in dNTP concentrations decrease DNA replication fidelity, and this observation prompted us to analyze genomic cancer data for mutations in enzymes involved in dNTP metabolism. We found that sterile alpha motif and histidine-aspartate domain-containing protein 1 (SAMHD1), a deoxyribonucleoside triphosphate triphosphohydrolase that decreases dNTP pools, is frequently mutated in colon cancers, that these mutations negatively affect SAMHD1 activity, and that severalSAMHD1mutations are found in tumors with defective mismatch repair. We show that minor changes in dNTP pools in combination with inactivated mismatch repair dramatically increase mutation rates. Determination of dNTP pools in mouse embryos revealed that inactivation of oneSAMHD1allele is sufficient to elevate dNTP pools. These observations suggest that heterozygous cancer-associatedSAMHD1mutations increase mutation rates in cancer cells.

Five-year survival for patients with oral cancer has been disappointingly stable during the last decades, creating a demand for new biomarkers and treatment targets. Lately, much focus has been set on immunomodulation as a possible treatment or an adjuvant increasing sensitivity to conventional treatments. The objective of this study was to evaluate the prognostic importance of response to radiotherapy in tongue carcinoma patients as well as the expression of the CXC-chemokines in correlation to radiation response in the same group of tumours. Thirty-eight patients with tongue carcinoma that had received radiotherapy followed by surgery were included. The prognostic impact of pathological response to radiotherapy, N-status, T-stage, age and gender was evaluated using Cox's regression models, Kaplan-Meier survival curves and chi-square test. The expression of 23 CXC-chemokine ligands and their receptors were evaluated in all patients using microarray and qPCR and correlated with response to treatment using logistic regression. Pathological response to radiotherapy was independently associated to overall survival with a 2-year survival probability of 81% for patients showing a complete pathological response, while patients with a non-complete response only had a probability of 42% to survive for 2 years (p = 0.016). The expression of one CXC-chemokine, CXCL10, was significantly associated with response to radiotherapy and the group of patients with the highest CXCL10 expression responded, especially poorly (p = 0.01). CXCL10 is a potential marker for response to radiotherapy and overall survival in patients with squamous cell carcinoma of the tongue.

Background  The pathogenesis of oral lichen planus (OLP), a chronic inflammatory disease, is not fully understood. It is known that OLP has autoimmune features, and it is suggested to be an autoimmune disease. ELF-3 is involved in differentiation of keratinocytes and deregulated in different tumours and inflammatory diseases. CXCR-3 and its ligands CXCL-10 and CXCL-11 are increased in autoimmune diseases and linked to Th-1 immune response. Objectives  To analyse and compare expression of ELF-3, CXCR-3, CXCL-10 and CXCL-11 in OLP lesions and controls in whole and microdissected epithelium. Methods  Tissue biopsies from 20 patients clinically and histologically diagnosed with OLP and 20 healthy controls were studied using whole tissues or microdissected epithelium. By the use of qRT-PCR, mRNA levels of ELF-3, CXCR-3, CXCL-10 and CXCL-11 were studied. Western blot was used for analysis of ELF-3 protein expression. Sera from 19 OLP patients and 20 controls were analysed with ELISA in search for autoantibodies. Results  The upregulation of CXCR-3, CXCL-10 and CXCL-11 found in OLP is similar to previous findings showing an autoimmune phenotype in lichen planus (LP) and lichen sclerosus. Decreased expression of the differentiation-related transcription factor ELF-3 was also seen in OLP lesions, and we further demonstrate presence of circulating autoantibodies against the ELF-3 protein in sera from 3 of 19 (16%) LP patients tested. Conclusions  On the basis of these findings, we confirm that OLP shows features of an autoimmune disease and suggest deregulated differentiation of keratinocytes to be one of the causes of the disease phenotype.