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.

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, IL1β 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.

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Ocean acidification threatens to disrupt interactions between organisms throughout marine ecosystems. The diversity of reef-building organisms decreases as seawater CO 2 increases along natural gradients, yet soft-bodied animals, such as sea anemones, are often resilient. We sequenced the polyA-enriched transcriptome of adult sea anemone Anemonia viridis and its dinoflagellate symbiont sampled along a natural CO 2 gradient in Italy to assess stress levels in these organisms. We found that about 1.4% of the anemone transcripts, but only ~0.5% of the Symbiodinium sp. transcripts were differentially expressed. Processes enriched at high seawater CO 2 were mainly linked to cellular stress, including significant up-regulation of protective cellular functions and deregulation of metabolic pathways. Transposable elements were differentially expressed at high seawater CO 2 , with an extreme up-regulation (> 100-fold) of the BEL -family of long terminal repeat retrotransposons. Seawater acidified by CO 2 generated a significant stress reaction in A . viridis , but no bleaching was observed and Symbiodinium sp. appeared to be less affected. These observed changes indicate the mechanisms by which A . viridis acclimate to survive chronic exposure to ocean acidification conditions. We conclude that many organisms that are common in acidified conditions may nevertheless incur costs due to hypercapnia and/or lowered carbonate saturation states.

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.