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.
Abstract Background: The relationship between kidney function and risk of renal cell carcinoma (RCC) is not well understood. In this study, we evaluated the association between estimated glomerular filtration rate (eGFR) and risk of incident RCC, and assessed whether this association depends on time between eGFR measurement and RCC diagnosis. We also sought to evaluate if eGFR may be useful to predict RCC risk. Methods: We conducted this study in the UK Biobank cohort based on 440,983 participants of whom 984 were diagnosed with RCC during 4,552,747 person-years of follow-up. The temporal relation between kidney function and RCC was evaluated with flexible parametric survival models for eGFR calculated from creatinine, cystatin C and both, adjusted for C-reactive protein (CRP) and common RCC risk factors. We also assessed the benefit of combining CRP and eGFR with a published RCC risk prediction model by estimating calibration and discrimination using a resampling algorithm as internal validation. Results: We found that a lower eGFR - an indication of poor kidney function - was associated with higher RCC risk when measured up to five years prior to diagnosis. We estimated the RCC hazard ratio per standard deviation decrease in eGFR when measured one year before diagnosis at 1.22 (95% confidence interval [95% CI]: 1.11-1.34), and at 1.14 (95% CI: 1.05-1.19) when measured five years before diagnosis. The corresponding RCC HR for eGFR measured ten years before diagnosis was 1.03 (95% CI: 0.95-1.12). Adding eGFR to the RCC risk model provided a small improvement in risk discrimination 2 years before diagnosis with a C-index of 0.76 (95% CI: 0.71-0.81) compared to the published model (0.73, 95% CI: 0.68-0.79). Conclusion: This study demonstrated that markers of kidney function are robustly associated with RCC risk when measured within the last five years leading up to diagnosis. However, kidney function markers do not seem to provide important improvements in RCC risk discrimination beyond established risk factors. Citation Format: Karine Alcala, Nicolas Alcala, Richard Martin, Paul Brennan, David Muller, Hilary A. Robbins, Mattias Johansson. Kidney function and risk of renal cell carcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4202.
The Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) program is an NCI-funded initiative with an objective to develop tools to optimize 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 1,161 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 any history of smoking and cases were diagnosed within 3 years of blood draw. The Nodule Malignancy project measured 1,077 proteins among participants with a heavy smoking history within 4 LDCT screening studies (n=425 cases within 5 years of blood draw, 398 benign-nodule controls, and 430 nodule-free controls). The INTEGRAL panel will enable absolute quantification of 21 proteins. We will evaluate its lung cancer discriminative performance in the Risk Biomarker project using a case-cohort study including 14 cohorts (n=1,696 cases and 2,926 subcohort representatives), and in the Nodule Malignancy project within 5 LDCT screening studies (n=675 cases, 648 benign-nodule controls, and 680 nodule-free controls). Future progress to advance lung cancer early detection biomarkers will require carefully designed validation, translational, and comparative studies.
BackgroundMalignant Pleural Mesothelioma (MPM) is an aggressive disease related to asbestos exposure, with no effective therapeutic options.MethodsWe 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.FindingsA 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.InterpretationThe prognosis of MPM is best explained by a continuous model, which extremes show specific expression patterns of genes involved in angiogenesis and immune response.
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 [ß BMI ] 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 (ß BMI : 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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