个体化基因检测指导下的晚期三阴性乳腺癌化疗优势人群的选择模型建立

刘丹娜; 吴通; 陈露; 段方方; 周寒丽; 孔天东

doi:10.7619/jcmp.20210332

个体化基因检测指导下的晚期三阴性乳腺癌化疗优势人群的选择模型建立

Establishment of the model for selection of dominant advanced triple negative breast cancer patients with chemotherapy under the guidance of individualized gene detection

摘要

摘要:
目的建立基于个体化基因检测指导下的晚期三阴性乳腺癌（TNBC）的疗效预测模型。
方法入组训练集的97例患者均为2013年5月—2015年5月河南大学肿瘤医院乳腺中心开展的一项有关“基于个体化基因检测指导下的晚期TNBC化疗疗效”的临床研究参与者。患者均给予化疗，并检测ERCC1、TOP2A、TUBB3、TYMS基因表达情况。基于该群体建立疗效预测模型并进行内部验证。采用Logistic回归方法筛选疗效相关的预测因子，并最终建立预测模型及列线图。
结果单因素分析显示，组织学分级、脑转移、Ki-67指数、体力活动状态（PS）评分、ERCC1、TOP2A、TUBB3、TYMS表达情况与疗效有相关性（P < 0.05或P < 0.01）；多因素回归分析显示，组织学分级、脑转移以及ERCC1、TOP2A、TUBB3基因表达与疗效有相关性（P < 0.05或P < 0.01）。以多因素回归结果为基础，建立疗效预测模型并绘制列线图。模型曲线下面积（AUC）为0.861，95% CI为0.789~0.933，最佳截断值为0.739。拟合优度检验显示预测概率与实测概率差异无统计学意义（χ²=1.698，P=0.975）。
结论本研究建立的模型可预测晚期TNBC化疗优势人群，为临床决策提供参考依据。

Abstract:
Objective To establish a model for therapeutic prediction of advanced triple negative breast cancer (TNBC) patients with chemotherapy under the guidance of individualized gene detection.
Methods Training set included 97 patients from May 2013 to May 2015 in Breast Center of Cancer Hospital of Henan University, they all participated in a clinical study on "Efficacy of advanced TNBC patients with chemotherapy based on the guidance of individualized gene detection". All the patients were conducted with chemotherapy, and the expressions of ERCC1, TOP2A, TUBB3 and TYMS genes were detected. Based on this group of people, the efficacy prediction model was established and verified internally. Logistic method was used to screen the efficacy-related predictive factors, and the predictive equation model and Nomogram were established finally.
Results Univariate analysis showed that histological grade, brain metastasis, Ki-67 index, score of Performance Status (PS), and expressions of ERCC1, TOP2A, TUBB3 and TYMS genes were correlated with efficacy (P < 0.05 or P < 0.01). Multivariate regression analysis showed that histological grade, brain metastasis, and the expressions of ERCC1, TOP2A and TUBB3 genes were correlated with efficacy (P < 0.05 or P < 0.01). Based on the results of multivariate regression, the efficacy prediction model was established and the Nomogram was drawn. The area under curve (AUC) of the model was 0.861, 95% CI was 0.789 to 0.933, and the best cut-off value was 0.739. Chi-square goodness-of-fit test showed no significant difference between predicted probability and measured probability (χ²=1.698, P=0.975).
Conclusion The model established in this study can predict the dominant advanced TNBC patients with chemotherapy and provide references for clinical decision-making.

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