Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer
Association of the Collagen Signature in the Tumor Microenvironment With Lymph Node Metastasis in Early Gastric Cancer
复制标题
肿瘤微环境中的胶原蛋白特征与早期胃癌淋巴结转移的关系
DOI：
10.1001/jamasurg.2018.5249
复制
发表时间：
2019-03-01
期刊：
影响因子：
16.9
通讯作者：
Yan, Jun
中科院分区：
文献类型：
--
作者：
Chen, Dexin;Chen, Gang;Yan, Jun
淋巴结状态是早期胃癌（EGC）治疗决策的主要决定因素。目前的评估方法不足以估计EGC的淋巴结转移（LNM）。目的建立并验证一种基于肿瘤微环境中完全定量的胶原特征的预测模型，以估计EGC中LNM的个体风险。设计、设置和参与者本回顾性研究于2016年8月1日至2018年5月10日在中国的2家医学中心（南方医院和福建省立医院）进行。参与者包括2008年1月1日至2012年12月31日期间接受根治性胃切除术并接受T1胃癌诊断的组织学确诊胃癌连续患者的主要队列（n = 232）。排除了接受新辅助放疗、化疗或放化疗的患者。另一个连续队列（n = 143）从2011年1月1日至2013年12月31日接受相同诊断，入组以提供验证。收集每例患者的基线临床病理学数据。使用多光子成像提取标本中的胶原蛋白特征，并构建胶原蛋白签名。开发了基于胶原蛋白特征的LNM预测模型，并进行了内部和外部验证。主要结果和指标分析预测模型和决策曲线的受试者工作特征曲线下面积（AUROC），以估计LNM。结果共纳入375例患者。主要队列包括232例连续患者，其中LNM率为16.4%（n = 38; 25例男性[65.8%]，平均[SD]年龄为57.82 [10.17]岁）。验证队列包括143例连续患者，其中LNM率为20.9%（n = 30; 20例男性[66.7%]，平均[SD]年龄为54.10 [13.19]岁）。胶原标记与LNM在统计学上显著相关（比值比，5.470; 95%CI，3.315-9.026; P <0.001）。多因素分析显示，肿瘤浸润深度、肿瘤分化程度和胶原蛋白特征是LNM的独立预测因素。将这3个预测因子纳入新的预测模型，并建立诺模图。该模型在主要队列（AUROC，0.955; 95%CI，0.919-0.991）和验证队列（AUROC，0.938; 95%CI，0.897-0.981）中显示出良好的区分度。在主要队列中选择最佳临界值，其灵敏度为86.8%，特异性为93.3%，准确性为92.2%，阳性预测值为71.7%，阴性预测值为97.3%。验证队列的敏感性为90.0%，特异性为90.3%，准确性为90.2%，阳性预测值为71.1%，阴性预测值为97.1%。在375例患者中，发现敏感性为87.3%，特异性为92.1%，准确性为91.2%，阳性预测值为72.1%，阴性预测值为96.9%。结论和相关性本研究的发现表明，肿瘤微环境中的胶原特征是EGC中LNM的独立指标，并且基于该胶原标记的预测模型可用于EGC患者的治疗决策。
Importance Lymph node status is the primary determinant in treatment decision making in early gastric cancer (EGC). Current evaluation methods are not adequate for estimating lymph node metastasis (LNM) in EGC. Objective To develop and validate a prediction model based on a fully quantitative collagen signature in the tumor microenvironment to estimate the individual risk of LNM in EGC. Design, Setting, and Participants This retrospective study was conducted from August 1, 2016, to May 10, 2018, at 2 medical centers in China (Nanfang Hospital and Fujian Provincial Hospital). Participants included a primary cohort (n = 232) of consecutive patients with histologically confirmed gastric cancer who underwent radical gastrectomy and received a T1 gastric cancer diagnosis from January 1, 2008, to December 31, 2012. Patients with neoadjuvant radiotherapy, chemotherapy, or chemoradiotherapy were excluded. An additional consecutive cohort (n = 143) who received the same diagnosis from January 1, 2011, to December 31, 2013, was enrolled to provide validation. Baseline clinicopathologic data of each patient were collected. Collagen features were extracted in specimens using multiphoton imaging, and the collagen signature was constructed. An LNM prediction model based on the collagen signature was developed and was internally and externally validated. Main Outcomes and Measures The area under the receiver operating characteristic curve (AUROC) of the prediction model and decision curve were analyzed for estimating LNM. Results In total, 375 patients were included. The primary cohort comprised 232 consecutive patients, in whom the LNM rate was 16.4% (n = 38; 25 men [65.8%] with a mean [SD] age of 57.82 [10.17] years). The validation cohort consisted of 143 consecutive patients, in whom the LNM rate was 20.9% (n = 30; 20 men [66.7%] with a mean [SD] age of 54.10 [13.19] years). The collagen signature was statistically significantly associated with LNM (odds ratio, 5.470; 95% CI, 3.315-9.026; P < .001). Multivariate analysis revealed that the depth of tumor invasion, tumor differentiation, and the collagen signature were independent predictors of LNM. These 3 predictors were incorporated into the new prediction model, and a nomogram was established. The model showed good discrimination in the primary cohort (AUROC, 0.955; 95% CI, 0.919-0.991) and validation cohort (AUROC, 0.938; 95% CI, 0.897-0.981). An optimal cutoff value was selected in the primary cohort, which had a sensitivity of 86.8%, a specificity of 93.3%, an accuracy of 92.2%, a positive predictive value of 71.7%, and a negative predictive value of 97.3%. The validation cohort had a sensitivity of 90.0%, a specificity of 90.3%, an accuracy of 90.2%, a positive predictive value of 71.1%, and a negative predictive value of 97.1%. Among the 375 patients, a sensitivity of 87.3%, a specificity of 92.1%, an accuracy of 91.2%, a positive predictive value of 72.1%, and a negative predictive value of 96.9% were found. Conclusions and Relevance This study’s findings suggest that the collagen signature in the tumor microenvironment is an independent indicator of LNM in EGC, and the prediction model based on this collagen signature may be useful in treatment decision making for patients with EGC.