Vargas-Aguilar Víctor Manuel, Arroyo-Alvarez Karina
Instituto Mexicano del Seguro Social, Unidad de Medicina Familiar No. 17, Servicio de Planificación Familiar. Ciudad de México, México
Rev Med Inst Mex Seguro Soc. 2018 Mar-Apr;56(2):180-185.
Gene signatures quantify hormone receptors and proliferation genes, combining multivariate prediction models. Hormone-negative tumors have greater proliferation and the prognostic value is limited. The first generation of prognostic signatures (Oncotype DX, MammaPrint, Genomic Degree Index) predict recurrence at 5 years. Subsequent tests (Prosigna, EndoPredict, Breast Cancer Index) have better prognostic value for recurrence and are predictive of early relapse. There are no useful prognostic genetic tests for hormone-negative tumors, or predictors of response to treatment. The recent expansion of high-performance technology platforms including the low-cost sequencing of tumor-derived DNA and circulating RNA and the reliable rapid quantification of microRNAs offer new opportunities to build prediction models.
基因特征结合多变量预测模型对激素受体和增殖基因进行量化。激素阴性肿瘤具有更高的增殖率,其预后价值有限。第一代预后特征(Oncotype DX、MammaPrint、基因组程度指数)可预测5年复发率。后续检测(Prosigna、EndoPredict、乳腺癌指数)对复发具有更好的预后价值,并可预测早期复发。对于激素阴性肿瘤,尚无有用的预后基因检测方法或治疗反应预测指标。包括肿瘤来源DNA和循环RNA的低成本测序以及微小RNA的可靠快速定量在内的高性能技术平台的近期扩展,为构建预测模型提供了新机遇。