Circulating free insulin-like growth factor-I and prostate cancer: a case-control study nested in the European prospective investigation into cancer and nutrition.

BACKGROUND: Circulating total insulin-like growth factor-I (IGF-I) is an established risk factor for prostate cancer. However, only a small proportion of circulating IGF-I is free or readily dissociable from IGF-binding proteins (its bioavailable form), and few studies have investigated the association of circulating free IGF-I with prostate cancer risk. METHODS: We analyzed data from 767 prostate cancer cases and 767 matched controls nested within the European Prospective Investigation into Cancer and Nutrition cohort, with an average of 14-years (interquartile range = 2.9) follow-up. Matching variables were study center, length of follow-up, age, and time of day and fasting duration at blood collection. Circulating free IGF-I concentration was measured in serum samples collected at recruitment visit (mean age 55 years old; standard deviation = 7.1) using an enzyme-linked immunosorbent assay (ELISA). Conditional logistic regressions were performed to examine the associations of free IGF-I with risk of prostate cancer overall and subdivided by time to diagnosis (≤ 14 and > 14 years), and tumor characteristics. RESULTS: Circulating free IGF-I concentrations (in fourths and as a continuous variable) were not associated with prostate cancer risk overall (odds ratio [OR] = 1.00 per 0.1 nmol/L increment, 95% CI: 0.99, 1.02) or by time to diagnosis, or with prostate cancer subtypes, including tumor stage and histological grade. CONCLUSIONS: Estimated circulating free IGF-I was not associated with prostate cancer risk. Further research may consider other assay methods that estimate bioavailable IGF-I to provide more insight into the well-substantiated association between circulating total IGF-I and subsequent prostate cancer risk.

Identifying proteomic risk factors for overall, aggressive, and early onset prostate cancer using Mendelian Randomisation and tumour spatial transcriptomics.

BACKGROUND: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. METHODS: We investigated the association of 2002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomisation (MR) and colocalisation. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalisation were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumour tissue to assess their role in tumour aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. FINDINGS: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which the majority replicated where data were available. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirmed an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also found an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that comparatively had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk also mapped to existing therapeutic interventions. INTERPRETATION: Our findings emphasise the importance of proteomics for improving our understanding of prostate cancer aetiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumours. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer. FUNDING: This work was supported by Cancer Research UK (grant no. C8221/A29017).

Alcohol intake and endogenous sex hormones in women: Meta-analysis of cohort studies and Mendelian randomization.

BACKGROUND: The mechanisms underlying alcohol-induced breast carcinogenesis are not fully understood but may involve hormonal changes. METHODS: Cross-sectional associations were investigated between self-reported alcohol intake and serum or plasma concentrations of estradiol, estrone, progesterone (in premenopausal women only), testosterone, androstenedione, dehydroepiandrosterone sulfate, and sex hormone binding globulin (SHBG) in 45 431 premenopausal and 173 476 postmenopausal women. Multivariable linear regression was performed separately for UK Biobank, European Prospective Investigation into Cancer and Nutrition, and Endogenous Hormones and Breast Cancer Collaborative Group, and meta-analyzed the results. For testosterone and SHBG, we also conducted Mendelian randomization and colocalization using the ADH1B (alcohol dehydrogenase 1B) variant (rs1229984). RESULTS: Alcohol intake was positively, though weakly, associated with all hormones (except progesterone in premenopausal women), with increments in concentrations per 10 g/day increment in alcohol intake ranging from 1.7% for luteal estradiol to 6.6% for postmenopausal dehydroepiandrosterone sulfate. There was an inverse association of alcohol with SHBG in postmenopausal women but a small positive association in premenopausal women. Two-sample randomization identified positive associations of alcohol intake with total testosterone (difference per 10 g/day increment: 4.1%; 95% CI, 0.6-7.6) and free testosterone (7.8%; 4.1-11.5), and an inverse association with SHBG (-8.1%; -11.3% to -4.9%). Colocalization suggested a shared causal locus at ADH1B between alcohol intake and higher free testosterone and lower SHBG (posterior probability for H4, 0.81 and 0.97, respectively). CONCLUSIONS: Alcohol intake was associated with small increases in sex hormone concentrations, including bioavailable fractions, which may contribute to its effect on breast cancer risk.

Genome-wide analysis to assess if heavy alcohol consumption modifies the association between SNPs and pancreatic cancer risk.

BACKGROUND: Pancreatic cancer is a leading cause of cancer-related death globally. Risk factors for pancreatic cancer include common genetic variants and potentially heavy alcohol consumption. We assessed if genetic variants modify the association between heavy alcohol consumption and pancreatic cancer risk. METHODS: We conducted a genome-wide interaction analysis of single nucleotide polymorphisms (SNP) by heavy alcohol consumption (more than 3 drinks per day) for pancreatic cancer in European ancestry populations from genome-wide association studies (GWAS). Our analysis included 3,707 cases and 4,167 controls from case-control studies and 1,098 cases and 1,162 controls from cohort studies. Fixed effect meta-analyses were conducted. RESULTS: A potential novel region of association on 10p11.22, lead SNP rs7898449 (Pinteraction = 5.1 x 10-8 in the meta-analysis, Pinteraction = 2.1x10-9 in the case-control studies, Pinteraction = 0.91 cohort studies) was identified. A SNP correlated with this lead SNP is an eQTL for the NRP1 gene. Of the 17 genomic regions with genome-wide significant evidence of association with pancreatic cancer in prior studies, we observed suggestive evidence that heavy alcohol consumption modified the association for one SNP near LINC00673, rs11655237 on 17q25.1 (Pinteraction = 0.004). CONCLUSIONS: We identified a novel genomic region that may be associated with pancreatic cancer risk in conjunction with heavy alcohol consumption located near an eQTL for the NRP1, a protein that plays an important role in the development and progression of pancreatic cancer Impact: This work can provide insight into the etiology of pancreatic cancer particularly in heavy drinkers.

Geographic variation of mutagenic exposures in kidney cancer genomes.

International differences in the incidence of many cancer types indicate the existence of carcinogen exposures that have not yet been identified by conventional epidemiology make a substantial contribution to cancer burden1. In clear cell renal cell carcinoma, obesity, hypertension and tobacco smoking are risk factors, but they do not explain the geographical variation in its incidence2. Underlying causes can be inferred by sequencing the genomes of cancers from populations with different incidence rates and detecting differences in patterns of somatic mutations. Here we sequenced 962 clear cell renal cell carcinomas from 11 countries with varying incidence. The somatic mutation profiles differed between countries. In Romania, Serbia and Thailand, mutational signatures characteristic of aristolochic acid compounds were present in most cases, but these were rare elsewhere. In Japan, a mutational signature of unknown cause was found in more than 70% of cases but in less than 2% elsewhere. A further mutational signature of unknown cause was ubiquitous but exhibited higher mutation loads in countries with higher incidence rates of kidney cancer. Known signatures of tobacco smoking correlated with tobacco consumption, but no signature was associated with obesity or hypertension, suggesting that non-mutagenic mechanisms of action underlie these risk factors. The results of this study indicate the existence of multiple, geographically variable, mutagenic exposures that potentially affect tens of millions of people and illustrate the opportunities for new insights into cancer causation through large-scale global cancer genomics.

Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank.

The availability of protein measurements and whole exome sequence data in the UK Biobank enables investigation of potential observational and genetic protein-cancer risk associations. We investigated associations of 1463 plasma proteins with incidence of 19 cancers and 9 cancer subsites in UK Biobank participants (average 12 years follow-up). Emerging protein-cancer associations were further explored using two genetic approaches, cis-pQTL and exome-wide protein genetic scores (exGS). We identify 618 protein-cancer associations, of which 107 persist for cases diagnosed more than seven years after blood draw, 29 of 618 were associated in genetic analyses, and four had support from long time-to-diagnosis ( > 7 years) and both cis-pQTL and exGS analyses: CD74 and TNFRSF1B with NHL, ADAM8 with leukemia, and SFTPA2 with lung cancer. We present multiple blood protein-cancer risk associations, including many detectable more than seven years before cancer diagnosis and that had concordant evidence from genetic analyses, suggesting a possible role in cancer development.

Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers.

Circulating proteins can reveal key pathways to cancer and identify therapeutic targets for cancer prevention. We investigate 2,074 circulating proteins and risk of nine common cancers (bladder, breast, endometrium, head and neck, lung, ovary, pancreas, kidney, and malignant non-melanoma) using cis protein Mendelian randomisation and colocalization. We conduct additional analyses to identify adverse side-effects of altering risk proteins and map cancer risk proteins to drug targets. Here we find 40 proteins associated with common cancers, such as PLAUR and risk of breast cancer [odds ratio per standard deviation increment: 2.27, 1.88-2.74], and with high-mortality cancers, such as CTRB1 and pancreatic cancer [0.79, 0.73-0.85]. We also identify potential adverse effects of protein-altering interventions to reduce cancer risk, such as hypertension. Additionally, we report 18 proteins associated with cancer risk that map to existing drugs and 15 that are not currently under clinical investigation. In sum, we identify protein-cancer links that improve our understanding of cancer aetiology. We also demonstrate that the wider consequence of any protein-altering intervention on well-being and morbidity is required to interpret any utility of proteins as potential future targets for therapeutic prevention.

Association between circulating inflammatory markers and adult cancer risk: a Mendelian randomization analysis.

BACKGROUND: Tumour-promoting inflammation is a "hallmark" of cancer and conventional epidemiological studies have reported links between various inflammatory markers and cancer risk. The causal nature of these relationships and, thus, the suitability of these markers as intervention targets for cancer prevention is unclear. METHODS: We meta-analysed 6 genome-wide association studies of circulating inflammatory markers comprising 59,969 participants of European ancestry. We then used combined cis-Mendelian randomization and colocalisation analysis to evaluate the causal role of 66 circulating inflammatory markers in risk of 30 adult cancers in 338,294 cancer cases and up to 1,238,345 controls. Genetic instruments for inflammatory markers were constructed using genome-wide significant (P < 5.0 × 10-8) cis-acting SNPs (i.e., in or ±250 kb from the gene encoding the relevant protein) in weak linkage disequilibrium (LD, r2 < 0.10). Effect estimates were generated using inverse-variance weighted random-effects models and standard errors were inflated to account for weak LD between variants with reference to the 1000 Genomes Phase 3 CEU panel. A false discovery rate (FDR)-corrected P-value ("q-value") <0.05 was used as a threshold to define "strong evidence" to support associations and 0.05 ≤ q-value < 0.20 to define "suggestive evidence". A colocalisation posterior probability (PPH4) >70% was employed to indicate support for shared causal variants across inflammatory markers and cancer outcomes. Findings were replicated in the FinnGen study and then pooled using meta-analysis. FINDINGS: We found strong evidence to support an association of genetically-proxied circulating pro-adrenomedullin concentrations with increased breast cancer risk (OR: 1.19, 95% CI: 1.10-1.29, q-value = 0.033, PPH4 = 84.3%) and suggestive evidence to support associations of interleukin-23 receptor concentrations with increased pancreatic cancer risk (OR: 1.42, 95% CI: 1.20-1.69, q-value = 0.055, PPH4 = 73.9%), prothrombin concentrations with decreased basal cell carcinoma risk (OR: 0.66, 95% CI: 0.53-0.81, q-value = 0.067, PPH4 = 81.8%), and interleukin-1 receptor-like 1 concentrations with decreased triple-negative breast cancer risk (OR: 0.92, 95% CI: 0.88-0.97, q-value = 0.15, PPH4 = 85.6%). These findings were replicated in pooled analyses with the FinnGen study. Though suggestive evidence was found to support an association of macrophage migration inhibitory factor concentrations with increased bladder cancer risk (OR: 2.46, 95% CI: 1.48-4.10, q-value = 0.072, PPH4 = 76.1%), this finding was not replicated when pooled with the FinnGen study. For 22 of 30 cancer outcomes examined, there was little evidence (q-value ≥0.20) that any of the 66 circulating inflammatory markers examined were associated with cancer risk. INTERPRETATION: Our comprehensive joint Mendelian randomization and colocalisation analysis of the role of circulating inflammatory markers in cancer risk identified potential roles for 4 circulating inflammatory markers in risk of 4 site-specific cancers. Contrary to reports from some prior conventional epidemiological studies, we found little evidence of association of circulating inflammatory markers with the majority of site-specific cancers evaluated. FUNDING: Cancer Research UK (C68933/A28534, C18281/A29019, PPRCPJT∖100005), World Cancer Research Fund (IIG_FULL_2020_022), National Institute for Health Research (NIHR202411, BRC-1215-20011), Medical Research Council (MC_UU_00011/1, MC_UU_00011/3, MC_UU_00011/6, and MC_UU_00011/4), Academy of Finland Project 326291, European Union's Horizon 2020 grant agreement no. 848158 (EarlyCause), French National Cancer Institute (INCa SHSESP20, 2020-076), Versus Arthritis (21173, 21754, 21755), National Institutes of Health (U19 CA203654), National Cancer Institute (U19CA203654).

Impact of weight loss on cancer-related proteins in serum: results from a cluster randomised controlled trial of individuals with type 2 diabetes.

BACKGROUND: Type 2 diabetes is associated with higher risk of several cancer types. However, the biological intermediates driving this relationship are not fully understood. As novel interventions for treating and managing type 2 diabetes become increasingly available, whether they also disrupt the pathways leading to increased cancer risk is currently unknown. We investigated the effect of a type 2 diabetes intervention, in the form of intentional weight loss, on circulating proteins associated with cancer risk to gain insight into potential mechanisms linking type 2 diabetes and adiposity with cancer development. METHODS: Fasting serum samples from participants with diabetes enrolled in the Diabetes Remission Clinical Trial (DiRECT) receiving the Counterweight-Plus weight-loss programme (intervention, N = 117, mean weight-loss 10 kg, 46% diabetes remission) or best-practice care by guidelines (control, N = 143, mean weight-loss 1 kg, 4% diabetes remission) were subject to proteomic analysis using the Olink Oncology-II platform (48% of participants were female; 52% male). To identify proteins which may be altered by the weight-loss intervention, the difference in protein levels between groups at baseline and 1 year was examined using linear regression. Mendelian randomization (MR) was performed to extend these results to evaluate cancer risk and elucidate possible biological mechanisms linking type 2 diabetes and cancer development. MR analyses were conducted using independent datasets, including large cancer meta-analyses, UK Biobank, and FinnGen, to estimate potential causal relationships between proteins modified during intentional weight loss and the risk of colorectal, breast, endometrial, gallbladder, liver, and pancreatic cancers. FINDINGS: Nine proteins were modified by the intervention: glycoprotein Nmb; furin; Wnt inhibitory factor 1; toll-like receptor 3; pancreatic prohormone; erb-b2 receptor tyrosine kinase 2; hepatocyte growth factor; endothelial cell specific molecule 1 and Ret proto-oncogene (Holm corrected P-value <0.05). Mendelian randomization analyses indicated a causal relationship between predicted circulating furin and glycoprotein Nmb on breast cancer risk (odds ratio (OR) = 0.81, 95% confidence interval (CI) = 0.67-0.99, P-value = 0.03; and OR = 0.88, 95% CI = 0.78-0.99, P-value = 0.04 respectively), though these results were not supported in sensitivity analyses examining violations of MR assumptions. INTERPRETATION: Intentional weight loss among individuals with recently diagnosed diabetes may modify levels of cancer-related proteins in serum. Further evaluation of the proteins identified in this analysis could reveal molecular pathways that mediate the effect of adiposity and type 2 diabetes on cancer risk. FUNDING: The main sources of funding for this work were Diabetes UK, Cancer Research UK, World Cancer Research Fund, and Wellcome.

International Pooled Analysis of Leisure-Time Physical Activity and Premenopausal Breast Cancer in Women From 19 Cohorts.

PURPOSE: There is strong evidence that leisure-time physical activity is protective against postmenopausal breast cancer risk but the association with premenopausal breast cancer is less clear. The purpose of this study was to examine the association of physical activity with the risk of developing premenopausal breast cancer. METHODS: We pooled individual-level data on self-reported leisure-time physical activity across 19 cohort studies comprising 547,601 premenopausal women, with 10,231 incident cases of breast cancer. Multivariable Cox regression was used to estimate hazard ratios (HRs) and 95% CIs for associations of leisure-time physical activity with breast cancer incidence. HRs for high versus low levels of activity were based on a comparison of risk at the 90th versus 10th percentiles of activity. We assessed the linearity of the relationship and examined subtype-specific associations and effect modification across strata of breast cancer risk factors, including adiposity. RESULTS: Over a median 11.5 years of follow-up (IQR, 8.0-16.1 years), high versus low levels of leisure-time physical activity were associated with a 6% (HR, 0.94 [95% CI, 0.89 to 0.99]) and a 10% (HR, 0.90 [95% CI, 0.85 to 0.95]) reduction in breast cancer risk, before and after adjustment for BMI, respectively. Tests of nonlinearity suggested an approximately linear relationship (Pnonlinearity = .94). The inverse association was particularly strong for human epidermal growth factor receptor 2-enriched breast cancer (HR, 0.57 [95% CI, 0.39 to 0.84]; Phet = .07). Associations did not vary significantly across strata of breast cancer risk factors, including subgroups of adiposity. CONCLUSION: This large, pooled analysis of cohort studies adds to evidence that engagement in higher levels of leisure-time physical activity may lead to reduced premenopausal breast cancer risk.

Observational and genetic associations between cardiorespiratory fitness and cancer: a UK Biobank and international consortia study.

BACKGROUND: The association of fitness with cancer risk is not clear. METHODS: We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for risk of lung, colorectal, endometrial, breast, and prostate cancer in a subset of UK Biobank participants who completed a submaximal fitness test in 2009-12 (N = 72,572). We also investigated relationships using two-sample Mendelian randomisation (MR), odds ratios (ORs) were estimated using the inverse-variance weighted method. RESULTS: After a median of 11 years of follow-up, 4290 cancers of interest were diagnosed. A 3.5 ml O2⋅min-1⋅kg-1 total-body mass increase in fitness (equivalent to 1 metabolic equivalent of task (MET), approximately 0.5 standard deviation (SD)) was associated with lower risks of endometrial (HR = 0.81, 95% CI: 0.73-0.89), colorectal (0.94, 0.90-0.99), and breast cancer (0.96, 0.92-0.99). In MR analyses, a 0.5 SD increase in genetically predicted O2⋅min-1⋅kg-1 fat-free mass was associated with a lower risk of breast cancer (OR = 0.92, 95% CI: 0.86-0.98). After adjusting for adiposity, both the observational and genetic associations were attenuated. DISCUSSION: Higher fitness levels may reduce risks of endometrial, colorectal, and breast cancer, though relationships with adiposity are complex and may mediate these relationships. Increasing fitness, including via changes in body composition, may be an effective strategy for cancer prevention.

Evaluation of circulating plasma proteins in breast cancer using Mendelian randomisation.

Biomarkers for early detection of breast cancer may complement population screening approaches to enable earlier and more precise treatment. The blood proteome is an important source for biomarker discovery but so far, few proteins have been identified with breast cancer risk. Here, we measure 2929 unique proteins in plasma from 598 women selected from the Karolinska Mammography Project to explore the association between protein levels, clinical characteristics, and gene variants, and to identify proteins with a causal role in breast cancer. We present 812 cis-acting protein quantitative trait loci for 737 proteins which are used as instruments in Mendelian randomisation analyses of breast cancer risk. Of those, we present five proteins (CD160, DNPH1, LAYN, LRRC37A2 and TLR1) that show a potential causal role in breast cancer risk with confirmatory results in independent cohorts. Our study suggests that these proteins should be further explored as biomarkers and potential drug targets in breast cancer.

Identifying proteomic risk factors for overall, aggressive and early onset prostate cancer using Mendelian randomization and tumor spatial transcriptomics.

Background: Understanding the role of circulating proteins in prostate cancer risk can reveal key biological pathways and identify novel targets for cancer prevention. Methods: We investigated the association of 2,002 genetically predicted circulating protein levels with risk of prostate cancer overall, and of aggressive and early onset disease, using cis-pQTL Mendelian randomization (MR) and colocalization. Findings for proteins with support from both MR, after correction for multiple-testing, and colocalization were replicated using two independent cancer GWAS, one of European and one of African ancestry. Proteins with evidence of prostate-specific tissue expression were additionally investigated using spatial transcriptomic data in prostate tumor tissue to assess their role in tumor aggressiveness. Finally, we mapped risk proteins to drug and ongoing clinical trials targets. Results: We identified 20 proteins genetically linked to prostate cancer risk (14 for overall [8 specific], 7 for aggressive [3 specific], and 8 for early onset disease [2 specific]), of which a majority were novel and replicated. Among these were proteins associated with aggressive disease, such as PPA2 [Odds Ratio (OR) per 1 SD increment = 2.13, 95% CI: 1.54-2.93], PYY [OR = 1.87, 95% CI: 1.43-2.44] and PRSS3 [OR = 0.80, 95% CI: 0.73-0.89], and those associated with early onset disease, including EHPB1 [OR = 2.89, 95% CI: 1.99-4.21], POGLUT3 [OR = 0.76, 95% CI: 0.67-0.86] and TPM3 [OR = 0.47, 95% CI: 0.34-0.64]. We confirm an inverse association of MSMB with prostate cancer overall [OR = 0.81, 95% CI: 0.80-0.82], and also find an inverse association with both aggressive [OR = 0.84, 95% CI: 0.82-0.86] and early onset disease [OR = 0.71, 95% CI: 0.68-0.74]. Using spatial transcriptomics data, we identified MSMB as the genome-wide top-most predictive gene to distinguish benign regions from high grade cancer regions that had five-fold lower MSMB expression. Additionally, ten proteins that were associated with prostate cancer risk mapped to existing therapeutic interventions. Conclusion: Our findings emphasize the importance of proteomics for improving our understanding of prostate cancer etiology and of opportunities for novel therapeutic interventions. Additionally, we demonstrate the added benefit of in-depth functional analyses to triangulate the role of risk proteins in the clinical aggressiveness of prostate tumors. Using these integrated methods, we identify a subset of risk proteins associated with aggressive and early onset disease as priorities for investigation for the future prevention and treatment of prostate cancer.

Kidney Function and Risk of Renal Cell Carcinoma.

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.

Alcohol intake and endogenous sex hormones in women: meta-analysis of cohort studies and Mendelian randomization.

Background: The mechanisms underlying alcohol-induced breast carcinogenesis are not fully understood but may involve hormonal changes. Methods: We investigated cross-sectional associations between self-reported alcohol intake and serum or plasma concentrations of oestradiol, oestrone, progesterone (in pre-menopausal women only), testosterone, androstenedione, DHEAS (dehydroepiandrosterone sulphate) and SHBG (sex hormone binding globulin) in 45 431 pre-menopausal and 173 476 post-menopausal women. We performed multivariable linear regression separately for UK Biobank, EPIC (European Prospective Investigation into Cancer and Nutrition) and EHBCCG (Endogenous Hormones and Breast Cancer Collaborative Group), and meta-analysed the results. For testosterone and SHBG, we also conducted two-sample Mendelian Randomization (MR) and colocalisation using the ADH1B (Alcohol Dehydrogenase 1B) variant (rs1229984). Results: Alcohol intake was positively, though weakly, associated with all hormones (except progesterone in pre-menopausal women), with increments in concentrations per 10 g/day increment in alcohol intake ranging from 1.7% for luteal oestradiol to 6.6% for post-menopausal DHEAS. There was an inverse association of alcohol with SHBG in post-menopausal women but a small positive association in pre-menopausal women. MR identified positive associations of alcohol intake with total testosterone (difference per 10 g/day increment: 4.1%; 95% CI: 0.6%, 7.6%) and free testosterone (7.8%; 4.1%, 11.5%), and an inverse association with SHBG (-8.1%; -11.3%, -4.9%). Colocalisation suggested a shared causal locus at ADH1B between alcohol intake and higher free testosterone and lower SHBG (PP4: 0.81 and 0.97 respectively). Conclusions: Alcohol intake was associated with small increases in sex hormone concentrations, including bioavailable fractions, which may contribute to its effect on breast cancer risk.

Genetic predisposition to metabolically unfavourable adiposity and prostate cancer risk: A Mendelian randomization analysis.

BACKGROUND: The associations of adiposity with aggressive prostate cancer risk are unclear. Using two-sample Mendelian randomization, we assessed the association of metabolically unfavourable adiposity (UFA), favourable adiposity (FA) and for comparison body mass index (BMI), with prostate cancer, including aggressive prostate cancer. METHODS: We examined the association of these genetically predicted adiposity-related traits with risk of prostate cancer overall, aggressive and early onset disease using outcome summary statistics from the PRACTICAL consortium (including 15,167 aggressive cases). RESULTS: In inverse-variance weighted models, there was little evidence that genetically predicted one standard deviation higher UFA, FA and BMI were associated with aggressive prostate cancer [OR: 0.85 (95% CI:0.61-1.19), 0.80 (0.53-1.23) and 0.97 (0.88-1.08), respectively]; these associations were largely consistent in sensitivity analyses accounting for horizontal pleiotropy. There was no strong evidence that genetically determined UFA, FA or BMI were associated with overall prostate cancer or early age of onset prostate cancer. CONCLUSIONS: We did not find differences in the associations of UFA and FA with prostate cancer risk, which suggest that adiposity is unlikely to influence prostate cancer via the metabolic factors assessed; however, these did not cover some aspects related to metabolic health that may link obesity with aggressive prostate cancer, which should be explored in future studies.

Reproductive and hormonal factors and risk of renal cell carcinoma among women in the European Prospective Investigation into Cancer and Nutrition.

BACKGROUND: Renal cell carcinoma (RCC) is twice as common among men compared with women, and hormonal factors have been suggested to partially explain this difference. There is currently little evidence on the roles of reproductive and hormonal risk factors in RCC aetiology. MATERIALS & METHODS: We investigated associations of age at menarche and age at menopause, pregnancy-related factors, hysterectomy and ovariectomy and exogenous hormone use with RCC risk among 298,042 women in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. RESULTS: During 15 years of follow-up, 438 RCC cases were identified. Parous women had higher rates of RCC compared with nulliparous women (HR = 1.71, 95% CI 1.18, 2.46), and women who were older at age of first pregnancy had lower rates of RCC (30 years + vs. <20 years HR = 0.53, 95% CI 0.34, 0.82). Additionally, we identified a positive association for hysterectomy (HR = 1.43 95% CI 1.09, 1.86) and bilateral ovariectomy (HR = 1.67, 95% CI 1.13, 2.47), but not unilateral ovariectomy (HR = 0.99, 95% CI 0.61, 1.62) with RCC risk. No clear associations were found for age at menarche, age at menopause or exogenous hormone use. CONCLUSION: Our results suggest that parity and reproductive organ surgeries may play a role in RCC aetiology.

Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools.

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.

Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosis.

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.