Response to Li and Hopper

Risk-Stratified Screening for Colorectal Cancer Using Genetic and Environmental Risk Factors: A Cost-Effectiveness Analysis Based on Real-World Data

BACKGROUND & AIMS

Previous studies on the cost-effectiveness of personalized colorectal cancer (CRC) screening were based on hypothetical performance of CRC risk prediction and did not consider the association with competing causes of death. In this study, we estimated the cost-effectiveness of risk-stratified screening using real-world data for CRC risk and competing causes of death.

METHODS

Risk predictions for CRC and competing causes of death from a large community-based cohort were used to stratify individuals into risk groups. A microsimulation model was used to optimize colonoscopy screening for each risk group by varying the start age (40-60 years), end age (70-85 years), and screening interval (5-15 years). The outcomes included personalized screening ages and intervals and cost-effectiveness compared with uniform colonoscopy screening (ages 45-75, every 10 years). Key assumptions were varied in sensitivity analyses.

RESULTS

Risk-stratified screening resulted in substantially different screening recommendations, ranging from a one-time colonoscopy at age 60 for low-risk individuals to a colonoscopy every 5 years from ages 40 to 85 for high-risk individuals. Nevertheless, on a population level, risk-stratified screening would increase net quality-adjusted life years gained (QALYG) by only 0.7% at equal costs to uniform screening or reduce average costs by 1.2% for equal QALYG. The benefit of risk-stratified screening improved when it was assumed to increase participation or costs less per genetic test.

CONCLUSIONS

Personalized screening for CRC, accounting for competing causes of death risk, could result in highly tailored individual screening programs. However, average improvements across the population in QALYG and cost-effectiveness compared with uniform screening are small.

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.

Polygenic risk for prostate cancer: Decreasing relative risk with age but little impact on absolute risk

Polygenic risk scores (PRSs) for a variety of diseases have recently been shown to have relative risks that depend on age, and genetic relative risks decrease with increasing age. A refined understanding of the age dependency of PRSs for a disease is important for personalized risk predictions and risk stratification. To further evaluate how the PRS relative risk for prostate cancer depends on age, we refined analyses for a validated PRS for prostate cancer by using 64,274 prostate cancer cases and 46,432 controls of diverse ancestry (82.8% European, 9.8% African American, 3.8% Latino, 2.8% Asian, and 0.8% Ghanaian). Our strategy applied a novel weighted proportional hazards model to case-control data to fully utilize age to refine how the relative risk decreased with age. We found significantly greater relative risks for younger men (age 30-55 years) compared with older men (70-88 years) for both relative risk per standard deviation of the PRS and dichotomized according to the upper 90^th percentile of the PRS distribution. For the largest European ancestral group that could provide reliable resolution, the log-relative risk decreased approximately linearly from age 50 to age 75. Despite strong evidence of age-dependent genetic relative risk, our results suggest that absolute risk predictions differed little from predictions that assumed a constant relative risk over ages, from short-term to long-term predictions, simplifying implementation of risk discussions into clinical practice.

Body Mass Index and Birth Weight Improve Polygenic Risk Score for Type 2 Diabetes

One of the major challenges in the post-genomic era is elucidating the genetic basis of human diseases. In recent years, studies have shown that polygenic risk scores (PRS), based on aggregated information from millions of variants across the human genome, can estimate individual risk for common diseases. In practice, the current medical practice still predominantly relies on physiological and clinical indicators to assess personal disease risk. For example, caregivers mark individuals with high body mass index (BMI) as having an increased risk to develop type 2 diabetes (T2D). An important question is whether combining PRS with clinical metrics can increase the power of disease prediction in particular from early life. In this work we examined this question, focusing on T2D. We present here a sex-specific integrated approach that combines PRS with additional measurements and age to define a new risk score. We show that such approach combining adult BMI and PRS achieves considerably better prediction than each of the measures on unrelated Caucasians in the UK Biobank (UKB, n = 290,584). Likewise, integrating PRS with self-reports on birth weight (n = 172,239) and comparative body size at age ten (n = 287,203) also substantially enhance prediction as compared to each of its components. While the integration of PRS with BMI achieved better results as compared to the other measurements, the latter are early-life measurements that can be integrated already at childhood, to allow preemptive intervention for those at high risk to develop T2D. Our integrated approach can be easily generalized to other diseases, with the relevant early-life measurements.