Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer (Retracted Article)
Gene expression signatures, clinicopathological features, and individualized therapy in breast cancer (Retracted Article)
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DOI：
10.1001/jama.299.13.1574
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发表时间：
2008-04-02
影响因子：
120.7
通讯作者：
Potti, Anil
中科院分区：
文献类型：
--
作者：
Acharya, Chaitanya R.;Hsu, David S.;Potti, Anil
背景基因表达谱分析可能对乳腺癌的预后和治疗策略有帮助。目的：展示基因组信息与临床和病理危险因素相结合的价值，以改善预后，并改善早期乳腺癌的治疗策略。使用964个具有相应微阵列数据的临床注释的乳腺肿瘤样品（初始发现组中的573个和验证组中的391个）。所有患者均根据其各自的临床病理特征进行复发风险评分。将代表致癌途径活化和肿瘤生物学/微环境状态的特征应用于这些样品，以获得与复发风险评分相对应的失调模式，从而单独使用临床病理学预后模型来改善预后。化疗反应的预测因子也被应用于进一步表征早期乳腺癌中临床相关的异质性。主要结果测量早期乳腺癌中的基因表达特征和临床病理变量，以确定无复发生存率和对化疗敏感性的精确估计。结果在573例患者的初始数据集中，在低风险组中识别出代表致癌途径激活模式和肿瘤生物学/微环境状态的具有统计学意义的聚类（对数秩P=. 004），中等风险（对数秩P=. 01），和高风险（对数秩P=. 003)模型队列，代表乳腺癌的临床重要基因组亚表型。例如，在低风险组群中，在6个具有统计学显著性的聚类中，聚类4中的患者与聚类1中的患者相比具有较差的无复发存活率（对数秩P=. 004)和聚类5（log-rank P=. 03）。第4组患者的中位无复发生存期比第1组患者短16个月（95%CI，7.5- 24.5个月），比第5组患者短19个月（95%CI，10.5- 27.5个月）。多变量分析证实了基因组簇的独立预后价值（低风险，P=. 05;高风险，P=. 02）。在独立验证队列中，使用相关但不相同的聚类建立了这些通路失调模式在预测复发风险中的再现性和有效性。预后clinicgenomic集群也有独特的敏感性模式，常用的细胞毒therapy.Conclusions这些结果提供了初步证据，纳入临床风险分层的基因表达签名可以改善预后。需要前瞻性研究来确定这种方法对个体化治疗策略的价值。
Context Gene expression profiling may be useful for prognostic and therapeutic strategies in breast carcinoma.Objectives To demonstrate the value in integrating genomic information with clinical and pathological risk factors, to refine prognosis, and to improve therapeutic strategies for early stage breast cancer.Design, Setting, and Patients Retrospective study of patients with early stage breast carcinoma who were candidates for adjuvant chemotherapy; 964 clinically annotated breast tumor samples ( 573 in the initial discovery set and 391 in the validation cohort) with corresponding microarray data were used. All patients were assigned relapse risk scores based on their respective clinicopathological features. Signatures representing oncogenic pathway activation and tumor biology/ microenvironment status were applied to these samples to obtain patterns of deregulation that correspond with relapse risk scores to refine prognosis with the clinicopathological prognostic model alone. Predictors of chemotherapeutic response were also applied to further characterize clinically relevant heterogeneity in early stage breast cancer.Main Outcome Measures Gene expression signatures and clinicopathological variables in early stage breast cancer to determine a refined estimation of relapse- free survival and sensitivity to chemotherapy.Results In the initial data set of 573 patients, prognostically significant clusters representing patterns of oncogenic pathway activation and tumor biology/ microenvironment states were identified within the low- risk ( log- rank P=. 004), intermediate-risk ( log- rank P=. 01), and high- risk ( log- rank P=. 003) model cohorts, representing clinically important genomic subphenotypes of breast cancer. As an example, in the low- risk cohort, of 6 prognostically significant clusters, patients in cluster 4 had an inferior relapse- free survival vs patients in cluster 1 ( log- rank P=. 004) and cluster 5 ( log- rank P=. 03). Median relapse- free survival for patients in cluster 4 was 16 months less than for patients in cluster 1 ( 95% CI, 7.5- 24.5 months) and 19 months less than for patients in cluster 5 ( 95% CI, 10.5- 27.5 months). Multivariate analyses confirmed the independent prognostic value of the genomic clusters ( low risk, P=. 05; high risk, P=. 02). The reproducibility and validity of these patterns of pathway deregulation in predicting relapse risk was established using related but not identical clusters in the independent validation cohort. The prognostic clinicogenomic clusters also have unique sensitivity patterns to commonly used cytotoxic therapies.Conclusions These results provide preliminary evidence that incorporation of gene expression signatures into clinical risk stratification can refine prognosis. Prospective studies are needed to determine the value of this approach for individualizing therapeutic strategies.