Biology subjects

Hopper, J. L.

Publications and source records attributed to Hopper, J. L..

7 recordsLinked to original sources

bioRxiv2021

Ability of known colorectal cancer susceptibility SNPs to predict colorectal cancer risk: A cohort study within the UK Biobank

Colorectal cancer risk stratification is crucial to improve screening and risk-reducing recommendations, and consequently do better than a one-size-fits-all screening regimen. Current screening guidelines in the UK, USA and Australia focus solely on family history and age for risk prediction, even though the vast majority of the population do not have any family history. We investigated adding a polygenic risk score based on 45 single-nucleotide polymorphisms to a family history model (combined model) to quantify how it improves the stratification and discriminatory performance of 10-year risk and full lifetime risk using a prospective population-based cohort within the UK Biobank. For both 10-year and full lifetime risk, the combined model had a wider risk distribution compared with family history alone, resulting in improved risk stratification of nearly 2-fold between the top and bottom risk quintiles of the full lifetime risk model. Importantly, the combined model can identify people (n=72,019) who do not have family history of colorectal cancer but have a predicted risk that is equivalent to having at least one affected first-degree relative (n=44,950). We also confirmed previous findings by showing that the combined full lifetime risk model significantly improves discriminatory accuracy compared with a simple family history model 0.673 (95% CI 0.664-0.682 versus 0.666 (95% CI 0.657-0.675), p=0.0065. Therefore, a combined polygenic risk score and first-degree family history model could be used to improve risk stratified population screening programs.

genomics↗

bioRxiv2020

A combined proteomics and Mendelian randomization approach to investigate the effects of aspirin-targeted proteins on colorectal cancer

BackgroundEvidence for aspirins chemopreventative properties on colorectal cancer (CRC) is substantial, but its mechanism of action is not well-understood. We combined a proteomic approach with Mendelian randomization (MR) to identify possible new aspirin targets that decrease CRC risk. MethodsHuman colorectal adenoma cells (RG/C2) were treated with aspirin (24 hours) and a stable isotope labelling with amino acids in cell culture (SILAC) based proteomics approach identified altered protein expression. Protein quantitative trait loci (pQTLs) from INTERVAL (N=3,301) and expression QTLs (eQTLs) from the eQTLGen Consortium (N=31,684) were used as genetic proxies for protein and mRNA expression levels. Two-sample MR of mRNA/protein expression on CRC risk was performed using eQTL/pQTL data combined with CRC genetic summary data from the Colon Cancer Family Registry (CCFR), Colorectal Transdisciplinary (CORECT), Genetics and Epidemiology of Colorectal Cancer (GECCO) consortia and UK Biobank (55,168 cases and 65,160 controls). ResultsAltered expression was detected for 125/5886 proteins. Of these, aspirin decreased MCM6, RRM2 and ARFIP2 expression and MR analysis showed that a standard deviation increase in mRNA/protein expression was associated with increased CRC risk (OR:1.08, 95% CI:1.03-1.13, OR:3.33, 95% CI:2.46-4.50 and OR:1.15, 95% CI:1.02-1.29, respectively). ConclusionMCM6 and RRM2 are involved in DNA repair whereby reduced expression may lead to increased DNA aberrations and ultimately cancer cell death, whereas ARFIP2 is involved in actin cytoskeletal regulation indicating a possible role in aspirins reduction of metastasis. ImpactOur approach has shown how laboratory experiments and population-based approaches can combine to identify aspirin-targeted proteins possibly affecting CRC risk.

cancer biology↗

bioRxiv2019

Physical activity and risks of breast and colorectal cancer: A Mendelian randomization analysis

Physical activity has been associated with lower risks of breast and colorectal cancer in epidemiological studies; however, it is unknown if these associations are causal or confounded. In two-sample Mendelian randomization analyses, using summary genetic data from the UK Biobank and GWA consortia, we found that a one standard deviation increment in average acceleration was associated with lower risks of breast cancer (odds ratio [OR]: 0.59, 95% confidence interval [CI]: 0.42 to 0.84, P-value=0.003) and colorectal cancer (OR: 0.66, 95% CI: 0.53 to 0.82, P-value=2*E-4). We found similar magnitude inverse associations by breast cancer subtype and by colorectal cancer anatomical site. Our results support a potentially causal relationship between higher physical activity levels and lower risks of breast cancer and colorectal cancer. Based on these data, the promotion of physical activity is probably an effective strategy in the primary prevention of these commonly diagnosed cancers.\n\nDisclaimerWhere 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.

genetics↗

bioRxiv2019

A new colorectal cancer risk prediction model incorporating family history, personal and environmental factors

PurposeReducing colorectal cancer (CRC) incidence and mortality through early detection would improve efficacy if targeted. A CRC risk-prediction model incorporating personal, family, genetic and environmental risk factors could enhance prediction.\n\nMethodsWe developed risk-prediction models using population-based CRC cases (N=4,445) and controls (N=3,967) recruited by the Colon Cancer Family Registry Cohort (CCFRC). A familial risk profile (FRP) was calculated to summarize individuals risk based on their CRC family history, family structure, germline mutation probability in major susceptibility genes, and a polygenic component. Using logistic regression, we developed risk models including individuals FRP or a binary CRC family-history (FH), and risk factors collected at recruitment. Model validation used follow-up data for population-(N=12,052) and clinic-based (N=5,584) relatives with no cancer history at recruitment, assessing calibration (E/O) and discrimination (AUC).\n\nResultsThe E/O (95% confidence interval [CI]) for FRP models for population-based relatives were 1.04 (0.74-1.45) and 0.86 (0.64-1.20) for men and women, and for clinic-based relatives 1.15 (0.87-1.58) and 1.04 (0.76-1.45). The age-adjusted AUC (95% CI) for FRP models in population-based relatives were 0.69 (0.60-0.78) and 0.70 (0.62-0.77), and for clinic-based relatives 0.77 (0.69-0.84) and 0.68 (0.60-0.76). The incremental values of AUC (95% CI) for FRP over FH models for population-based relatives were 0.08 (0.01-0.15) and 0.10 (0.04-0.16), and for clinic-based relatives 0.11 (0.05-0.17) and 0.11 (0.06-0.17).\n\nConclusionThe FRP-based model and FH-based model calibrate well in both settings. The FRP-based model provided better risk-prediction and discrimination than the FH-based model. A detailed family history may be useful for targeted risk-based screening and clinical management.

epidemiology↗

bioRxiv2018

Genome-wide association study of peripheral blood DNA methylation and conventional mammographic density measures

Age- and body mass index (BMI)-adjusted mammographie density is one the strongest breast cancer risk factors. DNA methylation is a molecular mechanism that could underlie interindividual variation in mammographic density. We aimed to investigate the association between breast cancer risk-predicting mammographic density measures and blood DNA methylation. For 436 women from the Australian Mammographic Density Twins and Sisters Study and 591 women from the Melbourne Collaborative Cohort Study, mammographic density (dense area, non-dense area and percentage dense area) defined by the conventional brightness threshold was measured using the CUMULUS software, and peripheral blood DNA methylation was measured using the HumanMethylation450 (HM450) BeadChip assay. Associations between DNA methylation at >400,000 sites and mammographic density measures adjusted for age and BMI were assessed within each cohort and pooled using fixed-effect meta-analysis. Associations with methylation at genetic loci known to be associated with mammographic density were also examined. We found no genome-wide significant(P<10-7)association for any mammographic density measure from the meta-analysis, or from the cohort-specific analyses. None of the 299 methylation sites located at genetic loci associated with mammographic density was associated with any mammographic density measure after adjusting for multiple testing (all P>0.05/299 = 1.7 x 10-4). In summary, our study did not detect associations between blood DNA methylation, as measured by the HM450 assay, and conventional mammographic density measures that predict breast cancer risk.

epidemiology↗

bioRxiv2018

Associations between environmental breast cancer risk factors and DNA methylation-based risk-predicting measures

BackgroundGenome-wide average DNA methylation (GWAM) and epigenetic age acceleration have been suggested to predict breast cancer risk. We aimed to investigate the relationships between these putative risk-predicting measures and environmental breast cancer risk factors.\n\nMethodsUsing the Illumina HumanMethylation450K assay methylation data, we calculated GWAM and epigenetic age acceleration for 132 female twin pairs and their 215 sisters. Linear regression was used to estimate associations between these risk-predicting measures and multiple breast cancer risk factors. Within-pair analysis was performed for the 132 twin pairs.\n\nResultsGWAM was negatively associated with number of live births, and positively with age at first live birth (both P<0.05). Epigenetic age acceleration was positively associated with body mass index (BMI), smoking, alcohol drinking and age at menarche, and negatively with age at first live birth (all P<0.05), and the associations with BMI, alcohol drinking and age at first live birth remained in the within-pair analysis.\n\nConclusionsThis exploratory study shows that lifestyle and hormone-related breast cancer risk factors are associated with DNA methylation-based measures that could predict breast cancer risk. The associations of epigenetic age acceleration with BMI, alcohol drinking and age at first live birth are unlikely to be due to familial confounding.

epidemiology↗

bioRxiv2017

Inference about causation between body mass index and DNA methylation in blood from a twin family study

BackgroundSeveral studies have reported DNA methylation in blood to be associated with body mass index (BMI), but only a few have investigated causal aspects of the association. We used a twin family design to assess this association at two life points and applied a novel analytical approach to investigate the evidence for causality.\n\nMethodsThe methylation profile of DNA from peripheral blood was measured for 479 Australian women (mean age 56 years) from 130 twin families. Linear regression was used to estimate the associations of methylation at ~410 000 cytosine-guanine dinucleotides (CpG), and of the average methylation at ~20 000 genes, with current BMI, BMI at age 18-21 years, and the change between the two (BMI change). A novel regression-based methodology for twins, Inference about Causation through Examination of Familial Confounding (ICE FALCON), was used to assess causation.\n\nResultsAt 5% false discovery rate, nine, six and 12 CpGs at 24 loci were associated with current BMI, BMI at age 18-21 years and BMI change, respectively. The average methylation of BHLHE40 and SOCS3 loci was associated with current BMI, and of PHGDH locus was associated with BMI change. From the ICE FALCON analyses with BMI as the predictor and methylation as the outcome, a womans methylation level was associated with her co-twins BMI, and the association disappeared conditioning on her own BMI, consistent with BMI causing methylation. To the contrary, using methylation as the predictor and BMI as the outcome, a womans BMI was not associated with her co-twins methylation level, consistent with methylation not causing BMI.\n\nConclusionFor middle-aged women, peripheral blood DNA methylation at several genomic locations is associated with current BMI, BMI at age 18-21 years and BMI change. Our study suggests that BMI has a causal effect on peripheral blood DNA methylation.

epidemiology↗