Gut：常见遗传变异对结直肠癌风险具有累积影响

　　英国一项研究表明，基因型资料可提供年龄、性别和家族史等结直肠癌危险因素之外的附加信息，用以预测结直肠癌的发病风险。论文于4月22日在线发表于《消化道》（Gut）杂志。

　　研究纳入9个欧洲人群数据库，制定和验证了结直肠癌风险预测模型。

　　研究者利用二元逻辑回归分析法，评估了年龄、性别、家族史和10个易感基因位点基因型的综合效应，并利用病例对照资料得出风险模型，然后，利用内部交叉验证和外部独立样本评估模型的鉴别力，最后，通过基因型和家族史模型分别估算了特定年龄和性别人群的10年绝对风险。

　　结果显示，病例的风险等位基因的中位数量多于对照，并且被外部验证人群所证实。平均每等位基因风险升高为9%。虽然不同风险谱下，模型判别表现不佳，但在特定人群，模型具有发现预期10年绝对风险>5%亚组人群的实用性。

　　研究者认为，尽管目前使用模型发现结直肠癌高风险人群已成为可能，但个体化遗传风险预测尚不可行。

与结直肠癌相关的拓展阅读：

• Nature reviews：2012年结直肠癌研究的里程碑式突破
• J Clin Oncol：子宫内膜癌患者结直肠癌发生的风险多大？
• JCO：晚期结直肠癌伴肝转移患者的新选择
• Clin Cancer Res：索拉菲尼对于结直肠癌效果不佳 更多信息请点击：有关结直肠癌更多资讯

Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42 103 individuals
Objective
Colorectal cancer (CRC) has a substantial heritable component. Common genetic variation has been shown to contribute to CRC risk. A study was conducted in a large multi-population study to assess the feasibility of CRC risk prediction using common genetic variant data combined with other risk factors. A risk prediction model was built and applied to the Scottish population using available data.
Design
Nine populations of European descent were studied to develop and validate CRC risk prediction models. Binary logistic regression was used to assess the combined effect of age, gender, family history (FH) and genotypes at 10 susceptibility loci that individually only modestly influence CRC risk. Risk models were generated from case-control data incorporating genotypes alone (n=39 266) and in combination with gender, age and FH (n=11 324). Model discriminatory performance was assessed using 10-fold internal cross-validation and externally using 4187 independent samples. The 10-year absolute risk was estimated by modelling genotype and FH with age- and gender-specific population risks.
Results
The median number of risk alleles was greater in cases than controls (10 vs 9, p<2.2×10−16), confirmed in external validation sets (Sweden p=1.2×10−6, Finland p=2×10−5). The mean per-allele increase in risk was 9% (OR 1.09; 95% CI 1.05 to 1.13). Discriminative performance was poor across the risk spectrum (area under curve for genotypes alone 0.57; area under curve for genotype/age/gender/FH 0.59). However, modelling genotype data, FH, age and gender with Scottish population data shows the practicalities of identifying a subgroup with >5% predicted 10-year absolute risk.
Conclusion
Genotype data provide additional information that complements age, gender and FH as risk factors, but individualised genetic risk prediction is not currently feasible. Nonetheless, the modelling exercise suggests public health potential since it is possible to stratify the population into CRC risk categories, thereby informing targeted prevention and surveillance.