Department of General Surgery, Guangdong Provincial Key Laboratory of Precision Medicine for Gastrointestinal Tumor, Nanfang Hospital, The First School of Clinical Medicine, Southern Medical University, Guangzhou, China.
School of Science, Jimei University, Xiamen, China.
Cancer Sci. 2022 Jul;113(7):2409-2424. doi: 10.1111/cas.15385. Epub 2022 May 11.
Collagen in the tumor microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed using multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (163 consecutive patients) was used to validate the model. The collagen signature comprised four collagen features significantly associated with pCR both in the primary and validation cohorts (p < 0.001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors. The nomogram showed good discrimination with area under the ROC curve (AUC) of 0.891 in the primary cohort and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (AUC = 0.908) and good calibration. Decision curve analysis demonstrated that the nomogram was clinically useful. In conclusion, the collagen signature in the tumor microenvironment of pretreatment biopsy is significantly associated with pCR. The nomogram based on the collagen signature and clinicopathological predictors could be used for individualized prediction of pCR in LARC patients before nCRT.
肿瘤微环境中的胶原蛋白被认为是预测治疗反应的潜在生物标志物。本研究旨在探讨接受新辅助放化疗(nCRT)的局部晚期直肠癌(LARC)患者中,胶原特征是否与病理完全缓解(pCR)相关,并建立和验证预测 pCR 的个体化预测模型。该预测模型是在一个主要队列(353 例连续患者)中开发的。从预处理活检的多光子图像中总共提取了 142 个胶原特征,并应用最小绝对收缩和选择算子(Lasso)回归进行特征选择和胶原特征构建。使用多变量分析开发了列线图。根据其判别能力、校准和临床实用性评估列线图的性能。使用独立队列(163 例连续患者)验证模型。胶原特征由四个与主要和验证队列中 pCR 显著相关的胶原特征组成(p<0.001)。个体化预测列线图中的预测因子包括胶原特征和临床病理预测因子。列线图在主要队列中的 AUC 为 0.891,具有良好的判别能力,且校准良好。在验证队列中应用列线图仍具有良好的判别能力(AUC=0.908)和良好的校准。决策曲线分析表明该列线图具有临床实用性。总之,预处理活检肿瘤微环境中的胶原特征与 pCR 显著相关。基于胶原特征和临床病理预测因子的列线图可用于 nCRT 前 LARC 患者 pCR 的个体化预测。