Epigenetic mechanisms of lung carcinogenesis involve differentially methylated CpG sites beyond those associated with smoking.

Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case-control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.

Dietary intakes of dioxins and polychlorobiphenyls (PCBs) and breast cancer risk in 9 European countries.

BACKGROUND: Dioxins and polychlorobiphenyls (PCBs) are persistent organic pollutants that have demonstrated endocrine disrupting properties. Several of these chemicals are carcinogenic and positive associations have been suggested with breast cancer risk. In general population, diet represents the main source of exposure. METHODS: Associations between dietary intake of 17 dioxins and 35 PCBs and breast cancer were evaluated in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort from nine European countries using multivariable Cox regressions. The present study included 318,607 women (mean ± SD age: 50.7 ± 9.7) with 13,241 incident invasive breast cancers and a median follow-up of 14.9 years (IQR = 13.5-16.4). Dietary intake of dioxins and PCBs was assessed combining EPIC food consumption data with food contamination data provided by the European Food Safety Authority. RESULTS: Exposure to dioxins, dioxins + Dioxin-Like-PCBs, Dioxin-Like-PCBs (DL-PCBs), and Non-Dioxin-Like-PCBs (NDL-PCBs) estimated from reported dietary intakes were not associated with breast cancer incidence, with the following hazard ratios (HRs) and 95% confidence intervals for an increment of 1 SD: HRdioxins = 1.00 (0.98 to 1.02), HRdioxins+DL-PCB = 1.01 (0.98 to 1.03), HRDL-PCB = 1.01 (0.98 to 1.03), and HRNDL-PCB = 1.01 (0.99 to 1.03). Results remained unchanged when analyzing intakes as quintile groups, as well as when analyses were run separately per country, or separating breast cancer cases based on estrogen receptor status or after further adjustments on main contributing food groups to PCBs and dioxins intake and nutritional factors. CONCLUSIONS: This large European prospective study does not support the hypothesis of an association between dietary intake of dioxins and PCBs and breast cancer risk.

Integrative, multi-omics, analysis of blood samples improves model predictions: applications to cancer.

BACKGROUND: Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific ("individual") patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as "shared" or "joint". In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case-control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case-control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas. RESULTS: Our results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development. CONCLUSIONS: In the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.

Prospective Identification of Elevated Circulating CDCP1 in Patients Years before Onset of Lung Cancer.

Increasing evidence points to a role for inflammation in lung carcinogenesis. A small number of circulating inflammatory proteins have been identified as showing elevated levels prior to lung cancer diagnosis, indicating the potential for prospective circulating protein concentration as a marker of early carcinogenesis. To identify novel markers of lung cancer risk, we measured a panel of 92 circulating inflammatory proteins in 648 prediagnostic blood samples from two prospective cohorts in Italy and Norway (women only). To preserve the comparability of results and protect against confounding factors, the main statistical analyses were conducted in women from both studies, with replication sought in men (Italian participants). Univariate and penalized regression models revealed for the first time higher blood levels of CDCP1 protein in cases that went on to develop lung cancer compared with controls, irrespective of time to diagnosis, smoking habits, and gender. This association was validated in an additional 450 samples. Associations were stronger for future cases of adenocarcinoma where CDCP1 showed better explanatory performance. Integrative analyses combining gene expression and protein levels of CDCP1 measured in the same individuals suggested a link between CDCP1 and the expression of transcripts of LRRN3 and SEM1. Enrichment analyses indicated a potential role for CDCP1 in pathways related to cell adhesion and mobility, such as the WNT/ß-catenin pathway. Overall, this study identifies lung cancer-related dysregulation of CDCP1 expression years before diagnosis. SIGNIFICANCE: Prospective proteomics analyses reveal an association between increased levels of circulating CDCP1 and lung carcinogenesis irrespective of smoking and years before diagnosis, and integrating gene expression indicates potential underlying mechanisms.See related commentary by Itzstein et al., p. 3441.