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Background: Lung cancer risk prediction models might efficiently identify individuals who should be offered lung cancer screening. However, their performance has not been comprehensively evaluated in Europe. We aimed to externally validate and evaluate the performance of several risk prediction models that predict lung cancer incidence or mortality in prospective European cohorts., Methods: We analysed 240 137 participants aged 45-80 years with a current or former smoking history from nine European countries in four prospective cohorts from the pooled database of the Lung Cancer Cohort Consortium: the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (Finland), the Nord-Trøndelag Health Study (Norway), CONSTANCES (France), and the European Prospective Investigation into Cancer and Nutrition (Denmark, Germany, Italy, Spain, Sweden, the Netherlands, and Norway). We evaluated ten lung cancer risk models, which comprised the Bach, the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial 2012 model (PLCO m2012 ), the Lung Cancer Risk Assessment Tool (LCRAT), the Lung Cancer Death Risk Assessment Tool (LCDRAT), the Nord-Trøndelag Health Study (HUNT), the Optimized Early Warning Model for Lung Cancer Risk (OWL), the University College London-Death (UCLD), the University College London-Incidence (UCLI), the Liverpool Lung Project version 2 (LLP version 2), and the Liverpool Lung Project version 3 (LLP version 3) models. We quantified model calibration as the ratio of expected to observed cases or deaths and discrimination using the area under the receiver operating characteristic curve (AUC). For each model, we also identified risk thresholds that would screen the same number of individuals as each of the US Preventive Services Task Force 2021 (USPSTF-2021), the US Preventive Services Task Force 2013 (USPSTF-2013), and the Nederlands-Leuvens Longkanker Screenings Onderzoek (NELSON) criteria., Findings: Among the participants, 1734 lung cancer cases and 1072 lung cancer deaths occurred within five years of enrolment. Most models had reasonable calibration in most countries, although the LLP version 2 overpredicted risk by more than 50% in eight countries (expected to observed ≥1·50). The PLCO m2012 , LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI models showed similar discrimination in most countries, with AUCs ranging from 0·68 (95% CI 0·59-0·77) to 0·83 (0·78-0·89), whereas the LLP version 2 and LLP version 3 showed lower discrimination, with AUCs ranging from 0·64 (95% CI 0·57-0·72) to 0·78 (0·74-0·83). When pooling data from all countries (but excluding the HUNT cohort), 33·9% (73 313 of 216 387) of individuals were eligible by USPSTF-2021 criteria, which included 74·8% (1185) of lung cancers and 76·3% (730) of lung cancer deaths occurring over 5 years. Fewer individuals were selected by USPSTF-2013 and NELSON criteria. After applying thresholds to select a population of equal size to USPSTF-2021, the PLCO m2012 , LCDRAT, LCRAT, Bach, HUNT, OWL, UCLD, and UCLI, models identified 77·6%-79·1% of future cases, although they selected slightly older individuals compared with USPSTF-2021 criteria. Results were similar for USPSTF-2013 and NELSON., Interpretation: Several lung cancer risk prediction models showed good performance in European countries and might improve the efficiency of lung cancer screening if used in place of categorical eligibility criteria., Funding: US National Cancer Institute, l'Institut National du Cancer, Cancer Research UK., Competing Interests: Declaration of interests We declare no competing interests. Where authors are identified as personnel of the International Agency for Research on Cancer or WHO, the authors alone are responsible for the views expressed in this Article and they do not necessarily represent the decisions, policy, or views of those organisations., (This is an Open Access article published under the CC BY NC ND 3.0 IGO license which permits users to download and share the article for non-commercial purposes, so long as the article is reproduced in the whole without changes, and provided the original source is properly cited. This article shall not be used or reproduced in association with the promotion of commercial products, services or any entity. There should be no suggestion that WHO endorses any specific organisation, products or services. The use of the WHO logo is not permitted. This notice should be preserved along with the article's original URL.)

Background: We evaluated the temporal association between kidney function, assessed by estimated glomerular filtration rate (eGFR), and the risk of incident renal cell carcinoma (RCC). We also evaluated whether eGFR could improve RCC risk discrimination beyond established risk factors., Methods: We analyzed the UK Biobank cohort, including 463,178 participants of whom 1,447 were diagnosed with RCC during 5,696,963 person-years of follow-up. We evaluated the temporal association between eGFR and RCC risk using flexible parametric survival models, adjusted for C-reactive protein and RCC risk factors. eGFR was calculated from creatinine and cystatin C levels., Results: Lower eGFR, an indication of poor kidney function, was associated with higher RCC risk when measured up to 5 years prior to diagnosis. The RCC HR per SD decrease in eGFR when measured 1 year before diagnosis was 1.26 [95% confidence interval (95% CI), 1.16-1.37], and 1.11 (95% CI, 1.05-1.17) when measured 5 years before diagnosis. Adding eGFR to the RCC risk model provided a small improvement in risk discrimination 1 year before diagnosis with an AUC of 0.73 (95% CI, 0.67-0.84) compared with the published model (0.69; 95% CI, 0.63-0.79)., Conclusions: This study demonstrated that kidney function markers are associated with RCC risk, but the nature of these associations are consistent with reversed causality. Markers of kidney function provided limited improvements in RCC risk discrimination beyond established risk factors., Impact: eGFR may be of potential use to identify individuals in the extremes of the risk distribution., (©2023 The Authors; Published by the American Association for Cancer Research.)

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)., Competing Interests: Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization. The authors declare no conflicts of interest., (© 2023 International Agency for Research on Cancer (IARC - WHO).)

Background: We sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test., Methods: We designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who were assayed for 302 proteins. We used 470 case-control pairs from 4 cohorts to select proteins and train a protein-based risk model. We subsequently used 154 case-control pairs from 2 cohorts to compare the risk-discriminatory performance of the protein-based model with that of the Early Cancer Detection Test (EarlyCDT)-Lung and the PLCOm2012 model using receiver operating characteristics analysis and by estimating models' sensitivity. All tests were 2-sided., Results: The area under the curve for the protein-based risk model in the validation sample was 0.75 (95% confidence interval [CI] = 0.70 to 0.81) compared with 0.64 (95% CI = 0.57 to 0.70) for the PLCOm2012 model (Pdifference = .001). The EarlyCDT-Lung had a sensitivity of 14% (95% CI = 8.2% to 19%) and a specificity of 86% (95% CI = 81% to 92%) for incident lung cancer. At the same specificity of 86%, the sensitivity for the protein-based risk model was estimated at 49% (95% CI = 41% to 57%) and 30% (95% CI = 23% to 37%) for the PLCOm2012 model., Conclusion: Circulating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung., (© The Author(s) 2023. Published by Oxford University Press.)

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 < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10-1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61-0.66), compared with 0.62 (95% CI: 0.59-0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: -0.003 to 0.035)., Interpretation: Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information., Funding: No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute (U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden (AMP19-962), and Swedish Department of Health Ministry., Competing Interests: Declaration of interests Jian-Min Yuan has a declaration on NIH grant funding, and the other authors have no conflicts of interest., (Copyright © 2023 World Health Organization. Published by Elsevier B.V. All rights reserved.)

Background: The relation between blood pressure and kidney cancer risk is well established but complex and different study designs have reported discrepant findings on the relative importance of diastolic blood pressure (DBP) and systolic blood pressure (SBP). In this study, we sought to describe the temporal relation between diastolic and SBP with renal cell carcinoma (RCC) risk in detail., Methods: Our study involved two prospective cohorts: the European Prospective Investigation into Cancer and Nutrition study and UK Biobank, including >700 000 participants and 1692 incident RCC cases. Risk analyses were conducted using flexible parametric survival models for DBP and SBP both separately as well as with mutuality adjustment and then adjustment for extended risk factors. We also carried out univariable and multivariable Mendelian randomization (MR) analyses (DBP: ninstruments = 251, SBP: ninstruments = 213) to complement the analyses of measured DBP and SBP., Results: In the univariable analysis, we observed clear positive associations with RCC risk for both diastolic and SBP when measured ≥5 years before diagnosis and suggestive evidence for a stronger risk association in the year leading up to diagnosis. In mutually adjusted analysis, the long-term risk association of DBP remained, with a hazard ratio (HR) per standard deviation increment 10 years before diagnosis (HR10y) of 1.20 (95% CI: 1.10-1.30), whereas the association of SBP was attenuated (HR10y: 1.00, 95% CI: 0.91-1.10). In the complementary multivariable MR analysis, we observed an odds ratio for a 1-SD increment (ORsd) of 1.34 (95% CI: 1.08-1.67) for genetically predicted DBP and 0.70 (95% CI: 0.56-0.88) for genetically predicted SBP., Conclusion: The results of this observational and MR study are consistent with an important role of DBP in RCC aetiology. The relation between SBP and RCC risk was less clear but does not appear to be independent of DBP., (© World Health Organization, 2022. All rights reserved. The World Health Organization has granted the Publisher permission for the reproduction of this article.)

Background: Excess 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 Findings: We 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., Conclusions: This 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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: CL is an Academic Editor on PLOS Medicine’s editorial board; ASB reports institutional grants outside of this work from AstraZeneca, Bayer, Biogen, BioMarin, Bioverativ, Novartis, Regeneron and Sanofi; during the course of this project, PS became a full-time employee of GSK. No other conflicts of interest have been declared by the authors.

Background: The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no study has compared the performance of lung cancer risk models in the UK., Methods: We analysed current and former smokers aged 40-80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC)., Results: Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81-0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79-0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79-0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14-1.27) to 2.16 for LLPv2 (95% CI = 2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%)., Conclusion: In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.

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., (Copyright © 2018. Published by Elsevier B.V.)