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[乳腺癌的基因特征、临床应用及治疗应用]

[Gene signatures for breast cancer, clinical utility and therapeutic applications].

作者信息

Vargas-Aguilar Víctor Manuel, Arroyo-Alvarez Karina

机构信息

Instituto Mexicano del Seguro Social, Unidad de Medicina Familiar No. 17, Servicio de Planificación Familiar. Ciudad de México, México

出版信息

Rev Med Inst Mex Seguro Soc. 2018 Mar-Apr;56(2):180-185.

Abstract

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.

摘要

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。

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