Increased expression of individual genes in whole blood is associated with late-stage lung cancer at and close to diagnosis.

Lung cancer (LC) mortality rates are still increasing globally. As survival is linked to stage, there is a need to identify markers for earlier LC diagnosis and individualized treatment. The whole blood transcriptome of LC patients represents a source of potential LC biomarkers. We compared expression of > 60,000 genes in whole blood specimens taken from LC cases at diagnosis (n = 128) and controls (n = 62) using genome-wide RNA sequencing, and identified 14 candidate genes associated with LC. High expression of ANXA3, ARG1 and HP was strongly associated with lower survival in late-stage LC cases (hazard ratios (HRs) = 2.81, 2.16 and 2.54, respectively). We validated these markers in two independent population-based studies with pre-diagnostic whole blood specimens taken up to eight years prior to LC diagnosis (n = 163 cases, 184 matched controls). ANXA3 and ARG1 expression was strongly associated with LC in these specimens, especially with late-stage LC within two years of diagnosis (odds ratios (ORs) = 3.47 and 5.00, respectively). Additionally, blood CD4 T cells, NK cells and neutrophils were associated with LC at diagnosis and improved LC discriminative ability beyond candidate genes. Our results indicate that in whole blood, increased expression levels of ANXA3, ARG1 and HP are diagnostic and prognostic markers of late-stage LC.

Altered plasma metabolite levels can be detected years before a glioma diagnosis.

Genetic and metabolic changes in tissue and blood are reported to occur several years before glioma diagnosis. Since gliomas are currently detected late, a liquid biopsy for early detection could affect the quality of life and prognosis of patients. Here, we present a nested case-control study of 550 prediagnostic glioma cases and 550 healthy controls from the Northern Sweden Health and Disease study (NSHDS) and the European Prospective Investigation into Cancer and Nutrition (EPIC) study. We identified 93 significantly altered metabolites related to glioma development up to 8 years before diagnosis. Out of these metabolites, a panel of 20 selected metabolites showed strong disease correlation and a consistent progression pattern toward diagnosis in both the NSHDS and EPIC cohorts, and they separated future cases from controls independently of biological sex. The blood metabolite panel also successfully separated both lower-grade glioma and glioblastoma cases from controls, up to 8 years before diagnosis in patients within the NSHDS cohort and up to 2 years before diagnosis in EPIC. Pathway enrichment analysis detected metabolites related to the TCA cycle, Warburg effect, gluconeogenesis, and cysteine, pyruvate, and tyrosine metabolism as the most affected.

Legacy perfluoroalkyl acids and their oxidizable precursors in plasma samples of Norwegian women.

Humans are exposed to perfluoroalkyl acids (PFAA) mainly through direct pathways, such as diet and drinking water, but indirect exposure also occurs when PFAA precursors break down to form legacy PFAA. Exposure to PFAA precursors raises particular concern, as neither the exposure nor the precursors themselves have been well described. In the present study, we aimed to assess the indirect contribution of oxidizable PFAA precursors to the total per- and polyfluoroalkyl substances (PFAS) burden in human plasma following the voluntary phase-out of production of long-chain PFAS. In addition, multiple logistic regression was used to explore associations between selected lifestyle and dietary factors and the oxidizable PFAA precursors fraction. This study included 302 cancer-free participants of the Norwegian Women and Cancer postgenome cohort. PFAS analyses were performed in plasma samples to determine PFAS concentrations before and after oxidation with the Total Oxidizable Precursor (TOP) assay. In pre-TOP analyses, perfluorooctane sulfonic acid (PFOS) was the dominant compound, followed by perfluorooctanoic acid (PFOA).The vast majority (98%) of the study population had increased post-TOP concentrations for at least one PFAA. The formation of PFAA accounted for 12% of the total PFAS burden, with seven PFAA observed post-TOP in at least 30% of study participants. PFHpA, br- PFOA, and PFDA were only detected in post-TOP analyses and showed the highest increase in concentrations. Of the PFAA with increased concentrations, we noted significant associations for year of birth, parity, BMI, and some dietary factors, although they were not consistent between the different PFAA. These results indicate that while the TOP assay might not provide a complete assessment of total PFAS burden in humans, it offers comprehensive assessment of unknown PFAA precursors that might be present in plasma, and it could therefore be implemented as an auxiliary tool in this regard.

Increased levels of microRNA-320 in blood serum and plasma is associated with imminent and advanced lung cancer.

Lung cancer (LC) incidence is increasing globally and altered levels of microRNAs (miRNAs) in blood may contribute to identification of individuals with LC. We identified miRNAs differentially expressed in peripheral blood at LC diagnosis and evaluated, in pre-diagnostic blood specimens, how long before diagnosis expression changes in such candidate miRNAs could be detected. We identified upregulated candidate miRNAs in plasma specimens from a hospital-based study sample of 128 patients with confirmed LC and 62 individuals with suspected but confirmed negative LC (FalsePos). We then evaluated the expression of candidate miRNAs in pre-diagnostic plasma or serum specimens of 360 future LC cases and 375 matched controls. There were 1663 miRNAs detected in diagnostic specimens, nine of which met our criteria for candidate miRNAs. Higher expression of three candidates, miR-320b, 320c, and 320d, was associated with poor survival, independent of LC stage and subtype. Moreover, miR-320c and miR-320d expression was higher in pre-diagnostic specimens collected within 2 years of LC diagnosis. Our results indicated that elevated levels of miR-320c and miR-320d may be early indications of imminent and advanced LC.

A New Pipeline for the Normalization and Pooling of Metabolomics Data.

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.

Systemic inflammation markers and cancer incidence in the UK Biobank.

Systemic inflammation markers have been linked to increased cancer risk and mortality in a number of studies. However, few studies have estimated pre-diagnostic associations of systemic inflammation markers and cancer risk. Such markers could serve as biomarkers of cancer risk and aid in earlier identification of the disease. This study estimated associations between pre-diagnostic systemic inflammation markers and cancer risk in the prospective UK Biobank cohort of approximately 440,000 participants recruited between 2006 and 2010. We assessed associations between four immune-related markers based on blood cell counts: systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and risk for 17 cancer sites by estimating hazard ratios (HR) using flexible parametric survival models. We observed positive associations with risk for seven out of 17 cancers with SII, NLR, PLR, and negative associations with LMR. The strongest associations were observed for SII for colorectal and lung cancer risk, with associations increasing in magnitude for cases diagnosed within one year of recruitment. For instance, the HR for colorectal cancer per standard deviation increment in SII was estimated at 1.09 (95% CI 1.02-1.16) in blood drawn five years prior to diagnosis and 1.50 (95% CI 1.24-1.80) in blood drawn one month prior to diagnosis. We observed associations between systemic inflammation markers and risk for several cancers. The increase in risk the last year prior to diagnosis may reflect a systemic immune response to an already present, yet clinically undetected cancer. Blood cell ratios could serve as biomarkers of cancer incidence risk with potential for early identification of disease in the last year prior to clinical diagnosis.

ICAM1 expression is induced by proinflammatory cytokines and associated with TLS formation in aggressive breast cancer subtypes.

Intratumoral formation of tertiary lymphoid structures (TLS) within the tumor microenvironment is considered to be a consequence of antigen challenge during anti-tumor responses. Intracellular adhesion molecule 1 (ICAM1) has been implicated in a variety of immune and inflammatory responses, in addition to associate with triple negative breast cancer (TNBC). In this study, we detected TLS in the aggressive tumor phenotypes TNBC, HER2+ and luminal B, whereas the TLS negative group contained solely tumors of the luminal A subtype. We show that ICAM1 is exclusively expressed in TNBC and HER2 enriched subtypes known to be associated with inflammation and the formation of TLS. Furthermore, cell from normal mammary epithelium and breast cancer cell lines expressed ICAM1 upon stimulation with the proinflammatory cytokines TNFα, IL1ß and IFNγ. ICAM1 overexpression was induced in MCF7, MDA-MB-468 and SK-BR-3 cells regardless of hormone receptor status. Taken together, our findings show that ICAM1 is expressed in aggressive subtypes of breast cancer and its expression is inducible by well-known proinflammatory cytokines. ICAM1 may be an attractive molecular target for TNBC, but further investigations elucidating the role of ICAM1 in targeted therapies have to take into consideration selective subtypes of breast cancer.

Digital marine bioprospecting: mining new neurotoxin drug candidates from the transcriptomes of cold-water sea anemones.

Marine bioprospecting is the search for new marine bioactive compounds and large-scale screening in extracts represents the traditional approach. Here, we report an alternative complementary protocol, called digital marine bioprospecting, based on deep sequencing of transcriptomes. We sequenced the transcriptomes from the adult polyp stage of two cold-water sea anemones, Bolocera tuediae and Hormathia digitata. We generated approximately 1.1 million quality-filtered sequencing reads by 454 pyrosequencing, which were assembled into approximately 120,000 contigs and 220,000 single reads. Based on annotation and gene ontology analysis we profiled the expressed mRNA transcripts according to known biological processes. As a proof-of-concept we identified polypeptide toxins with a potential blocking activity on sodium and potassium voltage-gated channels from digital transcriptome libraries.