Variation of Positive Predictive Values of Fecal Immunochemical Tests by Polygenic Risk Score in a Large Screening Cohort

Faecal haemoglobin: Measurement, applications, and future potential

Faecal hemoglobin concentrations (f-Hb) can be quantitated using faecal immunochemical test for haemoglobin (FIT) analytical systems. FIT are of proven value and widely used in colorectal cancer (CRC) screening. Several factors affect f-Hb including sex, age, deprivation, geographical region, and FIT system. Thus, FIT data may not be transferable. Women are disadvantaged in programmes using a single f-Hb threshold for all participants, but risk scoring or sex stratified thresholds could be used to minimise this problem. In addition, low but detectable f-Hb, below the threshold, implies future risk of CRC. In several countries, where colonoscopy resources are constrained, FIT are now accepted as of added value in assessment of patients presenting in primary or secondary care with symptoms, although some serious colorectal disease is missed. Elevated f-Hb in the absence of any discernible colorectal lesions is common and has been found in several diseases with a systemic inflammatory component, including circulatory, respiratory, digestive, neuropsychological, blood and endocrine diseases, and others. There is growing evidence for the value of f-Hb in post-polypectomy surveillance, potentially saving costs and colonoscopy. There may be a role for FIT systems which have lower limits of detection than currently available methods. The faecal material remaining in FIT specimen collection devices could be used for further studies, including assessment of the microbiome. The estimation of f-Hb is now a mature investigative tool but further research will undoubtedly expand applications of value.

Integrating genome-wide polygenic risk scores and non-genetic risk to predict colorectal cancer diagnosis using UK Biobank data: population based cohort study

OBJECTIVE

To evaluate the benefit of combining polygenic risk scores with the QCancer-10 (colorectal cancer) prediction model for non-genetic risk to identify people at highest risk of colorectal cancer.

DESIGN

Population based cohort study.

SETTING

Data from the UK Biobank study, collected between March 2006 and July 2010.

PARTICIPANTS

434 587 individuals with complete data for genetics and QCancer-10 predictions were included in the QCancer-10 plus polygenic risk score modelling and validation cohorts.

MAIN OUTCOME MEASURES

Prediction of colorectal cancer diagnosis by genetic, non-genetic, and combined risk models. Using data from UK Biobank, six different polygenic risk scores for colorectal cancer were developed using LDpred2 polygenic risk score software, clumping, and thresholding approaches, and a model based on genome-wide significant polymorphisms. The top performing genome-wide polygenic risk score and the score containing genome-wide significant polymorphisms were combined with QCancer-10 and performance was compared with QCancer-10 alone. Case-control (logistic regression) and time-to-event (Cox proportional hazards) analyses were used to evaluate risk model performance in men and women.

RESULTS

Polygenic risk scores derived using the LDpred2 program performed best, with an odds ratio per standard deviation of 1.584 (95% confidence interval 1.536 to 1.633), and top age and sex adjusted C statistic of 0.733 (95% confidence interval 0.710 to 0.753) in logistic regression models in the validation cohort. Integrated QCancer-10 plus polygenic risk score models out-performed QCancer-10 alone. In men, the integrated LDpred2 model produced a C statistic of 0.730 (0.720 to 0.741) and explained variation of 28.2% (26.3 to 30.1), compared with 0.693 (0.682 to 0.704) and 21.0% (18.9 to 23.1) for QCancer-10 alone. In women, the C statistic for the integrated LDpred2 model was 0.687 (0.673 to 0.702) and explained variation was 21.0% (18.7 to 23.7), compared with 0.645 (0.631 to 0.659) and 12.4% (10.3 to 14.6) for QCancer-10 alone. In the top 20% of individuals at highest absolute risk, the sensitivity and specificity of the integrated LDpred2 models for predicting colorectal cancer diagnosis was 47.8% and 80.3% respectively in men, and 42.7% and 80.1% respectively in women, with increases in absolute risk in the top 5% of risk in men of 3.47-fold and in women of 2.77-fold compared with the median. Illustrative decision curve analysis indicated a small incremental improvement in net benefit with QCancer-10 plus polygenic risk score models compared with QCancer-10 alone.

CONCLUSIONS

Integrating polygenic risk scores with QCancer-10 modestly improves risk prediction over use of QCancer-10 alone. Given that QCancer-10 data can be obtained relatively easily from health records, use of polygenic risk score in risk stratified population screening for colorectal cancer currently has no clear justification. The added benefit, cost effectiveness, and acceptability of polygenic risk scores should be carefully evaluated in a real life screening setting before implementation in the general population.

Combined performance of fecal immunochemical tests and a genetic risk score for advanced neoplasia detection

Fecal immunochemical tests (FITs) are increasingly used as noninvasive screening tests in colorectal cancer (CRC) screening programs. Polygenic risk scores (PRS) are increasingly propagated for risk stratification in CRC screening. We aimed to assess the potential of combining FIT results and PRS to enhance diagnostic accuracy of detecting advanced neoplasia (AN) compared to using FIT results alone. Of 10,362 participants of screening colonoscopy in Southern Germany who conducted either one of two quantitative FITs, genotyping was done in all participants with advanced neoplasia (CRC or advanced adenoma) and a random subset of controls. Among 5,306 individuals, a PRS was calculated based on the number of risk alleles in 140 single nucleotide polymorphisms. Partial areas under the receiver operating characteristics (ROC) curves (pAUCs) were computed for FIT and PRS alone and combined, focusing on a specificity range of 100%-80%. Both FITs showed similar performance characteristics with pAUCs (95%CIs) of 0.661 (0.625-0.698) (Ridascreen Hemoglobin) and 0.682 (0.661-0.701) (FOB Gold) for AN detection. PRS alone reached a pAUC (95%CI) of 0.524 (0.499-0.550) and 0.530 (0.516-0.545), respectively, and its addition to FIT did not improve pAUCs (0.659 (0.622-0.697) and 0.667 (0.650-0.687), respectively). This finding was confirmed by investigating sensitivities at fixed specificities at 85%, 90% and 95%. Partial AUCs also did not improve when adding the weighted PRS to FIT instead of the unweighted PRS. In summary, the combination with PRS did not improve diagnostic accuracy of FIT-based screening in a large asymptomatic CRC screening population from South-Western Germany.