The effects of genetic variants related to insulin metabolism pathways and the interactions with lifestyles on colorectal cancer risk

Polygenic risk prediction models for colorectal cancer: a systematic review

BACKGROUND

Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors.

METHODS

We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement.

RESULTS

We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry.

CONCLUSIONS

Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed.

GASVeM: A New Machine Learning Methodology for Multi-SNP Analysis of GWAS Data Based on Genetic Algorithms and Support Vector Machines

Genome-wide association studies (GWAS) are observational studies of a large set of genetic variants in an individual’s sample in order to find if any of these variants are linked to a particular trait. In the last two decades, GWAS have contributed to several new discoveries in the field of genetics. This research presents a novel methodology to which GWAS can be applied to. It is mainly based on two machine learning methodologies, genetic algorithms and support vector machines. The database employed for the study consisted of information about 370,750 single-nucleotide polymorphisms belonging to 1076 cases of colorectal cancer and 973 controls. Ten pathways with different degrees of relationship with the trait under study were tested. The results obtained showed how the proposed methodology is able to detect relevant pathways for a certain trait: in this case, colorectal cancer.

Therapeutic Efficacy Evaluation of Pegylated Liposome Encapsulated With Vinorelbine Plus 111In Repeated Treatments in Human Colorectal Carcinoma With Multimodalities of Molecular Imaging

BACKGROUND/AIM

In precision therapy, liposomal encapsulated chemotherapeutic drugs have been developed to treat cancers by achieving higher drug accumulation in the tumor compared to normal tissues/organs.

MATERIALS AND METHODS

We developed a novel chemoradiotherapeutic approach via nanoliposomes conjugated with vinorelbine (VNB) and ¹¹¹In (¹¹¹In-VNB-liposome) and examined their pharmacokinetics, biodistribution, maximum tolerance dose, and toxicity in a NOD/SCID mouse model.

RESULTS

Pharmacokinetic results showed that the area under the curve (AUC) of PEGylated liposomes was about 17-fold higher than that of the free radioisotope. Tumor growth inhibition by ¹¹¹In-VNB-liposome was significantly higher than that of the control (p<0.05).

CONCLUSION

The tumors in NOD/SCID mice bearing HT-29/tk-luc xenografts were significantly suppressed by ¹¹¹In-VNB-liposomes. The study proposed repeated treatments with a novel liposome-mediated radiochemotherapy and validation of therapeutic efficacy via imaging.

External Validation of Risk Prediction Models Incorporating Common Genetic Variants for Incident Colorectal Cancer Using UK Biobank

The aim of this study was to compare and externally validate risk scores developed to predict incident colorectal cancer that include common genetic variants (SNPs), with or without established lifestyle/environmental (questionnaire-based/classical/phenotypic) risk factors. We externally validated 23 risk models from a previous systematic review in 443,888 participants ages 37 to 73 from the UK Biobank cohort who had 6-year prospective follow-up, no prior history of colorectal cancer, and data for incidence of colorectal cancer through linkage to national cancer registries. There were 2,679 (0.6%) cases of incident colorectal cancer. We assessed model discrimination using the area under the operating characteristic curve (AUC) and relative risk calibration. The AUC of models including only SNPs increased with the number of included SNPs and was similar in men and women: the model by Huyghe with 120 SNPs had the highest AUC of 0.62 [95% confidence interval (CI), 0.59-0.64] in women and 0.64 (95% CI, 0.61-0.66) in men. Adding phenotypic risk factors without age improved discrimination in men but not in women. Adding phenotypic risk factors and age increased discrimination in all cases (P < 0.05), with the best performing models including SNPs, phenotypic risk factors, and age having AUCs between 0.64 and 0.67 in women and 0.67 and 0.71 in men. Relative risk calibration varied substantially across the models. Among middle-aged people in the UK, existing polygenic risk scores discriminate moderately well between those who do and do not develop colorectal cancer over 6 years. Consideration should be given to exploring the feasibility of incorporating genetic and lifestyle/environmental information in any future stratified colorectal cancer screening program.

Night-Shift Work Duration and Risk of Colorectal Cancer According to IRS1 and IRS2 Expression

BACKGROUND

We hypothesized that the risk of colorectal cancer in night-shift workers might be different according to insulin receptor substrate status.

METHODS

Among 77,470 eligible women having night work assessed in the Nurses' Health Study, we documented a total of 1,397 colorectal cancer cases, of which 304 or 308 had available data on IRS1 and IRS2, respectively. We used duplication-method Cox proportional hazards regression analysis for competing risks to calculate HRs and 95% confidence intervals (CI) for each colorectal cancer subtype. We measured tumor IRS1 or IRS2 expression by immunohistochemistry (IHC).

RESULTS

Compared with women who never worked night shifts, those working ≥15 years night shifts had a marginal trend of increased overall risk of colorectal cancer (P trend = 0.06; multivariable HR = 1.20; 95% CI, 0.99-1.45). Longer duration of night-shift work was associated with a higher risk of IRS2-positive tumors (multivariable HR = 2.69; 95% CI, 1.48-4.89; P trend = 0.001, ≥15 years night shifts vs. never) but not with IRS2-negative tumors (multivariable HR = 0.90; 95% CI, 0.54-1.51; P trend = 0.72; P heterogeneity for IRS2 = 0.008). Similarly, the corresponding multivariable HRs were 1.81 for IRS1-positive tumors (95% CI, 0.94-3.48; P trend = 0.06) and 1.13 for IRS1-negative tumors (95% CI, 0.71-1.80; P trend = 0.56; P heterogeneity for IRS1 = 0.02).

CONCLUSIONS

Our molecular pathologic epidemiology data suggest a potential role of IRS in mediating carcinogenesis induced by night-shift work.

IMPACT

Although these findings need validation, rotating night shift might increase colorectal cancer risk in women with abnormal insulin receptor pathways.

Risk Prediction Models for Colorectal Cancer Incorporating Common Genetic Variants: A Systematic Review

Colorectal cancer screening reduces colorectal cancer incidence and mortality. Risk models based on phenotypic variables have relatively good discrimination in external validation and may improve efficiency of screening. Models incorporating genetic variables may perform better. In this review, we updated our previous review by searching Medline and EMBASE from the end date of that review (January 2014) to February 2019 to identify models incorporating at least one SNP and applicable to asymptomatic individuals in the general population. We identified 23 new models, giving a total of 29. Of those in which the SNP selection was on the basis of published genome-wide association studies, in external or split-sample validation the AUROC was 0.56 to 0.57 for models that included SNPs alone, 0.61 to 0.63 for SNPs in combination with other risk factors, and 0.56 to 0.70 when age was included. Calibration was only reported for four. The addition of SNPs to other risk factors increases discrimination by 0.01 to 0.06. Public health modeling studies suggest that, if determined by risk models, the range of starting ages for screening would be several years greater than using family history alone. Further validation and calibration studies are needed alongside modeling studies to assess the population-level impact of introducing genetic risk-based screening programs.