Li Lian-Fang, Xu Xiao-Jing, Zhao Ying, Liu Zhe-Bing, Shen Zhen-Zhou, Jin Wei-Rong, Shao Zhi-Ming
Department of Oncology, Breast Cancer Institute, Cancer Hospital/Cancer Institute, Shanghai Medical College, Institutes of Biomedical Science, Fudan University, 270 Dong'an Road, Shanghai, 200032, P.R. China.
Breast Cancer Res Treat. 2009 Jan;113(2):231-7. doi: 10.1007/s10549-008-9925-4. Epub 2008 Feb 16.
Gene expression data has in recent years demonstrated the superior capacity to predict the prognosis of breast cancer patients unreceiving adjuvant chemotherapy comparing to the information available from traditional clinical and pathological sources. Meanwhile, adjuvant chemotherapy can significantly improve survival of breast cancer. It would be inappropriate to ignore its effect on prognosis. We hypothesized that an integrated gene expression profile can predict the prognosis of breast cancer patients receiving chemotherapy. Therefore, we screened the specific gene markers and constructed an integrated 24-gene signature by low-density microarray including the "poor signature" and genes related to resistance to chemotherapy. The gene signature stratified correctly patients into good prognosis group and poor prognosis group. In addition, the Kaplan-Meier analyses for disease-free survival as a function of the 24-gene signature showed highly significant differences between the two groups (Log Rank test P < 0.0001 = Univariate and multivariate Cox's proportional-hazards regression analyses indicated that the signature represents the strongest independent prognostic factor for breast cancer patients. When compared with single signature, such as Oncotype DX and 70 poor signature, the integrated signature showed more predominant power of predication in breast cancer patients receiving chemotherapy. Such integrated signature will critically aid clinical decision making at the level of individualization for most breast cancer patients receiving chemotherapy.
近年来，基因表达数据已显示出相较于传统临床和病理来源信息，在预测未接受辅助化疗的乳腺癌患者预后方面具有更卓越的能力。同时，辅助化疗可显著提高乳腺癌患者的生存率。忽视其对预后的影响是不合适的。我们推测，整合的基因表达谱能够预测接受化疗的乳腺癌患者的预后。因此，我们筛选了特定的基因标志物，并通过低密度微阵列构建了一个包含“不良特征”和化疗耐药相关基因的整合24基因特征谱。该基因特征谱能正确地将患者分为预后良好组和预后不良组。此外，根据24基因特征谱对无病生存期进行的Kaplan-Meier分析显示，两组之间存在高度显著差异（对数秩检验P<0.0001），单因素和多因素Cox比例风险回归分析表明，该特征谱是乳腺癌患者最强的独立预后因素。与单一特征谱（如Oncotype DX和70基因不良特征谱）相比，整合特征谱在接受化疗的乳腺癌患者中显示出更强的预测能力。这种整合特征谱将对大多数接受化疗的乳腺癌患者在个体化层面的临床决策起到关键的辅助作用。
