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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.
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Índice de Massa Corporal , Neoplasias Renais/sangue , Metaboloma , Obesidade/sangue , Idoso , Biomarcadores/sangue , Estudos de Casos e Controles , Europa (Continente)/epidemiologia , Feminino , Humanos , Incidência , Neoplasias Renais/diagnóstico , Neoplasias Renais/epidemiologia , Neoplasias Renais/genética , Masculino , Análise da Randomização Mendeliana , Metabolômica , Pessoa de Meia-Idade , Obesidade/diagnóstico , Obesidade/epidemiologia , Obesidade/genética , Estudos Prospectivos , Medição de Risco , Fatores de Risco , Vitória/epidemiologiaRESUMO
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.
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Detecção Precoce de Câncer/métodos , Neoplasias Pulmonares/diagnóstico , Seleção de Pacientes , Adulto , Idoso , Calibragem , Estudos de Coortes , Detecção Precoce de Câncer/normas , Feminino , Humanos , Neoplasias Pulmonares/epidemiologia , Masculino , Pessoa de Meia-Idade , Modelos Estatísticos , Valor Preditivo dos Testes , Medição de Risco , Fatores de Risco , Classe Social , Medicina Estatal , Reino Unido/epidemiologiaRESUMO
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.
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Biomarcadores/sangue , Inflamação/sangue , Inflamação/imunologia , Neoplasias/epidemiologia , Adulto , Idoso , Bancos de Espécimes Biológicos , Biomarcadores Tumorais/análise , Contagem de Células Sanguíneas , Estudos de Coortes , Feminino , Humanos , Incidência , Contagem de Linfócitos , Masculino , Pessoa de Meia-Idade , Neoplasias/sangue , Neutrófilos/patologia , Estudos Prospectivos , Reino Unido/epidemiologiaRESUMO
Cell-mediated immune suppression may play an important role in lung carcinogenesis. We investigated the associations for circulating levels of tryptophan, kynurenine, kynurenine:tryptophan ratio (KTR), quinolinic acid (QA) and neopterin as markers of immune regulation and inflammation with lung cancer risk in 5,364 smoking-matched case-control pairs from 20 prospective cohorts included in the international Lung Cancer Cohort Consortium. All biomarkers were quantified by mass spectrometry-based methods in serum/plasma samples collected on average 6 years before lung cancer diagnosis. Odds ratios (ORs) and 95% confidence intervals (CIs) for lung cancer associated with individual biomarkers were calculated using conditional logistic regression with adjustment for circulating cotinine. Compared to the lowest quintile, the highest quintiles of kynurenine, KTR, QA and neopterin were associated with a 20-30% higher risk, and tryptophan with a 15% lower risk of lung cancer (all ptrend < 0.05). The strongest associations were seen for current smokers, where the adjusted ORs (95% CIs) of lung cancer for the highest quintile of KTR, QA and neopterin were 1.42 (1.15-1.75), 1.42 (1.14-1.76) and 1.45 (1.13-1.86), respectively. A stronger association was also seen for KTR and QA with risk of lung squamous cell carcinoma followed by adenocarcinoma, and for lung cancer diagnosed within the first 2 years after blood draw. This study demonstrated that components of the tryptophan-kynurenine pathway with immunomodulatory effects are associated with risk of lung cancer overall, especially for current smokers. Further research is needed to evaluate the role of these biomarkers in lung carcinogenesis and progression.
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Adenocarcinoma de Pulmão/diagnóstico , Biomarcadores Tumorais/sangue , Carcinoma de Células Grandes/diagnóstico , Carcinoma de Células Escamosas/diagnóstico , Inflamação/complicações , Neoplasias Pulmonares/diagnóstico , Carcinoma de Pequenas Células do Pulmão/diagnóstico , Adenocarcinoma de Pulmão/sangue , Adenocarcinoma de Pulmão/etiologia , Adulto , Idoso , Carcinoma de Células Grandes/sangue , Carcinoma de Células Grandes/etiologia , Carcinoma de Células Escamosas/sangue , Carcinoma de Células Escamosas/etiologia , Estudos de Casos e Controles , Feminino , Seguimentos , Humanos , Inflamação/sangue , Inflamação/imunologia , Cinurenina/sangue , Neoplasias Pulmonares/sangue , Neoplasias Pulmonares/etiologia , Masculino , Pessoa de Meia-Idade , Neopterina/sangue , Prognóstico , Estudos Prospectivos , Fatores de Risco , Carcinoma de Pequenas Células do Pulmão/sangue , Carcinoma de Pequenas Células do Pulmão/etiologia , Triptofano/sangueRESUMO
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 240â137 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 (PLCOm2012), 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 PLCOm2012, 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 PLCOm2012, 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.
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Detecção Precoce de Câncer , Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/mortalidade , Europa (Continente)/epidemiologia , Idoso , Masculino , Feminino , Pessoa de Meia-Idade , Estudos Prospectivos , Medição de Risco , Idoso de 80 Anos ou mais , Incidência , Fatores de RiscoRESUMO
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.
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Carcinoma de Células Renais , Neoplasias Renais , Insuficiência Renal Crônica , Humanos , Carcinoma de Células Renais/epidemiologia , Taxa de Filtração Glomerular/fisiologia , Rim , Fatores de Risco , Neoplasias Renais/epidemiologia , Creatinina , Insuficiência Renal Crônica/complicaçõesRESUMO
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).
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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.
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Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/diagnóstico , Prognóstico , Modelos de Riscos Proporcionais , França , Suécia , Antígenos de Neoplasias , Moléculas de Adesão CelularRESUMO
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.
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Neoplasias Pulmonares , Proteômica , Humanos , Medição de Risco , Estudos de Casos e Controles , Estudos Prospectivos , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/epidemiologia , Pulmão , Detecção Precoce de CâncerRESUMO
The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize low-dose CT (LDCT) lung cancer screening. Here, we describe the rationale and design for the Risk Biomarker and Nodule Malignancy projects within INTEGRAL. The overarching goal of these projects is to systematically investigate circulating protein markers to include on a panel for use (i) pre-LDCT, to identify people likely to benefit from screening, and (ii) post-LDCT, to differentiate benign versus malignant nodules. To identify informative proteins, the Risk Biomarker project measured 1161 proteins in a nested-case control study within 2 prospective cohorts (n = 252 lung cancer cases and 252 controls) and replicated associations for a subset of proteins in 4 cohorts (n = 479 cases and 479 controls). Eligible participants had a current or former history of smoking and cases were diagnosed up to 3 years following blood draw. The Nodule Malignancy project measured 1078 proteins among participants with a heavy smoking history within four LDCT screening studies (n = 425 cases diagnosed up to 5 years following blood draw, 430 benign-nodule controls, and 398 nodule-free controls). The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n = 1696 cases and 2926 subcohort representatives), and in the Nodule Malignancy project within five LDCT screening studies (n = 675 cases, 680 benign-nodule controls, and 648 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.
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Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/diagnóstico , Neoplasias Pulmonares/epidemiologia , Neoplasias Pulmonares/etiologia , Estudos de Casos e Controles , Detecção Precoce de Câncer , Estudos de Coortes , Estudos Prospectivos , Tomografia Computadorizada por Raios X , Pulmão , BiomarcadoresRESUMO
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 >700â000 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.
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Carcinoma de Células Renais , Hipertensão , Neoplasias Renais , Pressão Sanguínea , Carcinoma de Células Renais/epidemiologia , Humanos , Hipertensão/epidemiologia , Neoplasias Renais/epidemiologia , Estudos Prospectivos , Fatores de RiscoRESUMO
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.