Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification
Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification
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DOI：
10.1007/s10549-015-3406-3
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发表时间：
2015-06-01
影响因子：
3.8
通讯作者：
Rakha, E. A.
中科院分区：
文献类型：
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作者：
Aleskandarany, Mohammed A.;Soria, D.;Rakha, E. A.
准确的远处转移 (DM) 预测对于乳腺癌 (BC) 的风险分层和有效的治疗决策至关重要。许多基于组织标志物研究的预后标志物/模型不断出现，使用传统的统计方法分析与 DM/不良预后相关的复杂/维度数据。然而，其中很少有具有令人满意的临床应用证据的。本研究旨在为 BC 患者建立 DM 风险评估算法。一系列经过充分表征的早期侵入性原发性可手术 BC (n = 1902)，以及一组生物标志物的免疫组织化学表达 (n = 31) 构成了本研究的材料。使用 WEKA 软件计算决策树算法，利用定量生物标志物的表达和远处转移的存在/不存在。 15 种生物标志物与 DM 显着相关，其中 6 个时间亚组根据 DM 发展时间（随访时间从 < 1 年到 > 15 年）进行表征。在这 15 种生物标志物中，10 种具有显着的表达模式，其中 Ki67LI、HER2、p53、N-钙粘蛋白、P-钙粘蛋白、PIK3CA 和 TOMM34 在 DM 早期发展中表现出显着较高的表达。相反，ER、PR 和 BCL2 的较高表达与 DM 的延迟发生相关。 DM 预测算法是利用 15 个重要标记的信息案例构建的。对患者的四个风险组进行了表征。根据软件生成的截断值，p53、HER2 和 BCL2 三个标记物预测 DM 的概率，阳性预测值的准确率为 81.1%，阴性预测值的准确率为 77.3%。该算法重申了这三种标记物报告的预后价值，并强调了它们在 BC 进展中的核心生物学作用。因此，有必要对这一经过修剪的生物标志物组进行进一步的独立验证。
Accurate distant metastasis (DM) prediction is critical for risk stratification and effective treatment decisions in breast cancer (BC). Many prognostic markers/models based on tissue marker studies are continually emerging using conventional statistical approaches analysing complex/dimensional data association with DM/poor prognosis. However, few of them have fulfilled satisfactory evidences for clinical application. This study aimed at building DM risk assessment algorithm for BC patients. A well-characterised series of early invasive primary operable BC (n = 1902), with immunohistochemical expression of a panel of biomarkers (n = 31) formed the material of this study. Decision tree algorithm was computed using WEKA software, utilising quantitative biomarkers' expression and the absence/presence of distant metastases. Fifteen biomarkers were significantly associated with DM, with six temporal subgroups characterised based on time to development of DM ranging from < 1 to > 15 years of follow-up. Of these 15 biomarkers, 10 had a significant expression pattern where Ki67LI, HER2, p53, N-cadherin, P-cadherin, PIK3CA and TOMM34 showed significantly higher expressions with earlier development of DM. In contrast, higher expressions of ER, PR and BCL2 were associated with delayed occurrence of DM. DM prediction algorithm was built utilising cases informative for the 15 significant markers. Four risk groups of patients were characterised. Three markers p53, HER2 and BCL2 predicted the probability of DM, based on software-generated cut-offs, with a precision rate of 81.1 % for positive predictive value and 77.3 %, for the negative predictive value. This algorithm reiterates the reported prognostic values of these three markers and underscores their central biological role in BC progression. Further independent validation of this pruned panel of biomarkers is therefore warranted.