Pawitan Yudi, Bjöhle Judith, Amler Lukas, Borg Anna-Lena, Egyhazi Suzanne, Hall Per, Han Xia, Holmberg Lars, Huang Fei, Klaar Sigrid, Liu Edison T, Miller Lance, Nordgren Hans, Ploner Alexander, Sandelin Kerstin, Shaw Peter M, Smeds Johanna, Skoog Lambert, Wedrén Sara, Bergh Jonas
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Breast Cancer Res. 2005;7(6):R953-64. doi: 10.1186/bcr1325. Epub 2005 Oct 3.
Adjuvant breast cancer therapy significantly improves survival, but overtreatment and undertreatment are major problems. Breast cancer expression profiling has so far mainly been used to identify women with a poor prognosis as candidates for adjuvant therapy but without demonstrated value for therapy prediction.
We obtained the gene expression profiles of 159 population-derived breast cancer patients, and used hierarchical clustering to identify the signature associated with prognosis and impact of adjuvant therapies, defined as distant metastasis or death within 5 years. Independent datasets of 76 treated population-derived Swedish patients, 135 untreated population-derived Swedish patients and 78 Dutch patients were used for validation. The inclusion and exclusion criteria for the studies of population-derived Swedish patients were defined.
Among the 159 patients, a subset of 64 genes was found to give an optimal separation of patients with good and poor outcomes. Hierarchical clustering revealed three subgroups: patients who did well with therapy, patients who did well without therapy, and patients that failed to benefit from given therapy. The expression profile gave significantly better prognostication (odds ratio, 4.19; P = 0.007) (breast cancer end-points odds ratio, 10.64) compared with the Elston-Ellis histological grading (odds ratio of grade 2 vs 1 and grade 3 vs 1, 2.81 and 3.32 respectively; P = 0.24 and 0.16), tumor stage (odds ratio of stage 2 vs 1 and stage 3 vs 1, 1.11 and 1.28; P = 0.83 and 0.68) and age (odds ratio, 0.11; P = 0.55). The risk groups were consistent and validated in the independent Swedish and Dutch data sets used with 211 and 78 patients, respectively.
We have identified discriminatory gene expression signatures working both on untreated and systematically treated primary breast cancer patients with the potential to spare them from adjuvant therapy.
辅助性乳腺癌治疗显著提高了生存率,但过度治疗和治疗不足是主要问题。迄今为止,乳腺癌表达谱分析主要用于识别预后不良的女性作为辅助治疗的候选对象,但尚未证明其对治疗预测有价值。
我们获取了159例来自人群的乳腺癌患者的基因表达谱,并使用层次聚类来识别与预后及辅助治疗影响相关的特征,辅助治疗影响定义为5年内发生远处转移或死亡。76例接受治疗的来自人群的瑞典患者、135例未接受治疗的来自人群的瑞典患者以及78例荷兰患者的独立数据集用于验证。定义了来自人群的瑞典患者研究的纳入和排除标准。
在159例患者中,发现一个由64个基因组成的子集能够对预后良好和不良的患者进行最佳区分。层次聚类揭示了三个亚组:接受治疗后情况良好的患者、未接受治疗但情况良好的患者以及未能从给定治疗中获益的患者。与埃尔斯顿 - 埃利斯组织学分级(2级与1级、3级与1级的优势比分别为2.81和3.32;P = 0.24和0.16)、肿瘤分期(2期与1期、3期与1期的优势比分别为1.11和1.28;P = 0.83和0.68)以及年龄(优势比为0.11;P = 0.55)相比,该表达谱给出了显著更好的预后预测(优势比为4.19;P = 0.007)(乳腺癌终点优势比为10.64)。风险组在分别包含211例和78例患者的独立瑞典和荷兰数据集中是一致且经过验证的。
我们已经识别出在未经治疗和接受系统治疗的原发性乳腺癌患者中均起作用的具有区分性的基因表达特征,这些特征有可能使他们无需接受辅助治疗。