Glinsky Gennadi V, Higashiyama Takuya, Glinskii Anna B
Sidney Kimmel Cancer Center, San Diego, California 92121, USA.
Clin Cancer Res. 2004 Apr 1;10(7):2272-83. doi: 10.1158/1078-0432.ccr-03-0522.
Selection of treatment options with the highest likelihood of successful outcome for individual breast cancer patients is based to a large degree on accurate classification into subgroups with poor and good prognosis reflecting a different probability of disease recurrence and survival after therapy. Here we propose a breast cancer classification algorithm taking into account three main prognostic features determined at the time of diagnosis: estrogen receptor (ER) status; lymph node (LN) status; and gene expression signatures associated with distinct therapy outcome.
Using microarray expression profiling and quantitative reverse transcription-PCR analyses, we compared expression profiles of the 70-gene breast cancer survival signature in established breast cancer cell lines and primary breast carcinomas from cancer patients. We classified 295 breast cancer patients using 14-, 13-, 6-, and 4-gene survival predictor signatures into subgroups having statistically distinct probability of therapy failure (P < 0.0001). We evaluated the prognostic power of breast cancer survival predictor signatures alone and in combination with ER and LN status using Kaplan-Meier analysis.
The breast cancer survival predictor algorithm allowed highly accurate classification into subgroups with dramatically distinct 5- and 10-year survival after therapy of a large cohort of 295 breast cancer patients with either ER+ or ER- tumors as well as LN+ or LN- disease (P < 0.0001, log-rank test).
Our data imply that quantitative laboratory tests measuring expression profiles of a limited set of identified small gene clusters may be useful in stratification of breast cancer patients at the time of diagnosis into subgroups with statistically distinct probability of positive outcome after therapy and assisting in selection of optimal treatment strategies. The estimated increase in survival due to the optimization of treatment protocols may reach many thousands of breast cancer survivors every year at the 10-year follow-up check point.
为个体乳腺癌患者选择最有可能获得成功治疗结果的方案，在很大程度上基于准确分类为预后不良和良好的亚组，这反映了治疗后疾病复发和生存的不同概率。在此，我们提出一种乳腺癌分类算法，该算法考虑了诊断时确定的三个主要预后特征：雌激素受体（ER）状态；淋巴结（LN）状态；以及与不同治疗结果相关的基因表达特征。
使用微阵列表达谱分析和定量逆转录 - PCR分析，我们比较了已建立的乳腺癌细胞系和癌症患者原发性乳腺癌中70基因乳腺癌生存特征的表达谱。我们使用14基因、13基因、6基因和4基因生存预测特征将295例乳腺癌患者分类为治疗失败概率具有统计学显著差异的亚组（P < 0.0001）。我们使用Kaplan - Meier分析评估了单独的乳腺癌生存预测特征以及与ER和LN状态联合使用时的预后能力。
乳腺癌生存预测算法能够将一大组295例ER + 或ER - 肿瘤以及LN + 或LN - 疾病的乳腺癌患者高度准确地分类为治疗后5年和10年生存率显著不同的亚组（P < 0.0001，对数秩检验）。
我们的数据表明，测量有限数量已鉴定小基因簇表达谱的定量实验室检测可能有助于在诊断时将乳腺癌患者分层为治疗后阳性结果概率具有统计学显著差异的亚组，并有助于选择最佳治疗策略。在10年随访检查点，由于治疗方案的优化，估计每年可增加数千名乳腺癌幸存者。
