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Summary: Background: Opium consumption has recently been identified as a carcinogen, but the impact of opium use on cancer burden is unknown. We aimed to evaluate the fraction of cancers that could be attributed to opium use alone and in combination with cigarette smoking in a region where opium is widely used. Methods: 50,045 Iranian adults were recruited to this prospective cohort study between 2004 and 2008 and were followed through January 2022. We assessed the association between using opium and/or cigarette smoking and various cancers using proportional hazards regression models. We then calculated population attributable fractions (PAFs) for all cancers and for groups of cancers causally linked to opium and cigarette smoking. Findings: Of the total participants, 8% only used opium, 8.3% only smoked cigarettes, and 9% used both substances. During a median 14 years of follow-up, 2195 individuals were diagnosed with cancer, including 215 opium-related cancers (lung, larynx, and bladder) and 1609 tobacco-related cancers (20 types). Opium use alone was estimated to cause 35% (95% CI: 26%–45%) of opium-related cancers, while smoking cigarettes alone was estimated to cause 9% (6%–12%) of tobacco-related cancers in this population. Using opium and/or cigarettes was estimated to cause 13% (9%–16%) of all cancers, 58% (49%–66%) of opium-related cancers, and 15% (11%–18%) of tobacco-related cancers. Moreover, joint exposure to opium and cigarettes had the greatest impact on cancers of the larynx, pharynx, lung, and bladder, with PAFs ranging from 50% to 77%. Interpretation: Using opium and smoking cigarettes account for a large proportion of cancers in this population. To reduce the cancer burden, prevention policies should aim to decrease the use of both substances through public awareness campaigns and interventional efforts. Funding: The Golestan Cohort Study work was funded by the Tehran University of Medical Sciences, Cancer Research UK, U.S. National Cancer Institute, International Agency for Research on Cancer. The presented analysis was supported by the International HundredK+ Cohorts Consortium (IHCC).

Summary: Background: To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis. Methods: We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only. Findings: There were 86 proteins nominally associated with mortality (p

BackgroundExcess bodyweight and related metabolic perturbations have been implicated in kidney cancer aetiology, but the specific molecular mechanisms underlying these relationships are poorly understood. In this study, we sought to identify circulating metabolites that predispose kidney cancer and to evaluate the extent to which they are influenced by body mass index (BMI).Methods and findingsWe assessed the association between circulating levels of 1,416 metabolites and incident kidney cancer using pre-diagnostic blood samples from up to 1,305 kidney cancer case-control pairs from 5 prospective cohort studies. Cases were diagnosed on average 8 years after blood collection. We found 25 metabolites robustly associated with kidney cancer risk. In particular, 14 glycerophospholipids (GPLs) were inversely associated with risk, including 8 phosphatidylcholines (PCs) and 2 plasmalogens. The PC with the strongest association was PC ae C34:3 with an odds ratio (OR) for 1 standard deviation (SD) increment of 0.75 (95% confidence interval [CI]: 0.68 to 0.83, p = 2.6 × 10-8). In contrast, 4 amino acids, including glutamate (OR for 1 SD = 1.39, 95% CI: 1.20 to 1.60, p = 1.6 × 10-5), were positively associated with risk. Adjusting for BMI partly attenuated the risk association for some-but not all-metabolites, whereas other known risk factors of kidney cancer, such as smoking and alcohol consumption, had minimal impact on the observed associations. A mendelian randomisation (MR) analysis of the influence of BMI on the blood metabolome highlighted that some metabolites associated with kidney cancer risk are influenced by BMI. Specifically, elevated BMI appeared to decrease levels of several GPLs that were also found inversely associated with kidney cancer risk (e.g., -0.17 SD change [ßBMI] in 1-(1-enyl-palmitoyl)-2-linoleoyl-GPC (P-16:0/18:2) levels per SD change in BMI, p = 3.4 × 10-5). BMI was also associated with increased levels of glutamate (ßBMI: 0.12, p = 1.5 × 10-3). While our results were robust across the participating studies, they were limited to study participants of European descent, and it will, therefore, be important to evaluate if our findings can be generalised to populations with different genetic backgrounds.ConclusionsThis study suggests a potentially important role of the blood metabolome in kidney cancer aetiology by highlighting a wide range of metabolites associated with the risk of developing kidney cancer and the extent to which changes in levels of these metabolites are driven by BMI-the principal modifiable risk factor of kidney cancer.

Background: Malignant Pleural Mesothelioma (MPM) is an aggressive disease related to asbestos exposure, with no effective therapeutic options. Methods: We undertook unsupervised analyses of RNA-sequencing data of 284 MPMs, with no assumption of discreteness. Using immunohistochemistry, we performed an orthogonal validation on a subset of 103 samples and a biological replication in an independent series of 77 samples. Findings: A continuum of molecular profiles explained the prognosis of the disease better than any discrete model. The immune and vascular pathways were the major sources of molecular variation, with strong differences in the expression of immune checkpoints and pro-angiogenic genes; the extrema of this continuum had specific molecular profiles: a “hot” bad-prognosis profile, with high lymphocyte infiltration and high expression of immune checkpoints and pro-angiogenic genes; a “cold” bad-prognosis profile, with low lymphocyte infiltration and high expression of pro-angiogenic genes; and a “VEGFR2+/VISTA+” better-prognosis profile, with high expression of immune checkpoint VISTA and pro-angiogenic gene VEGFR2. We validated the gene expression levels at the protein level for a subset of five selected genes belonging to the immune and vascular pathways (CD8A, PDL1, VEGFR3, VEGFR2, and VISTA), in the validation series, and replicated the molecular profiles as well as their prognostic value in the replication series. Interpretation: The prognosis of MPM is best explained by a continuous model, which extremes show specific expression patterns of genes involved in angiogenesis and immune response. Keywords: Pleural mesothelioma, Immunotherapy, Angiogenesis, MESOMICS project, French MESOBANK