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, Fujian, China.
Front Immunol. 2023 Sep 14;14:1269700. doi: 10.3389/fimmu.2023.1269700. eCollection 2023.
The Immunoscore can categorize patients into high- and low-risk groups for prognostication in colorectal cancer (CRC). Collagen plays an important role in immunomodulatory functions in the tumor microenvironment (TME). However, the correlation between collagen and the Immunoscore in the TME is unclear. This study aimed to construct a collagen signature to illuminate the relationship between collagen structure and Immunoscore.
A total of 327 consecutive patients with stage I-III stage CRC were included in a training cohort. The fully quantitative collagen features were extracted at the tumor center and invasive margin of the specimens using multiphoton imaging. LASSO regression was applied to construct the collagen signature. The association of the collagen signature with Immunoscore was assessed. A collagen nomogram was developed by incorporating the collagen signature and clinicopathological predictors after multivariable logistic regression. The performance of the collagen nomogram was evaluated via calibration, discrimination, and clinical usefulness and then tested in an independent validation cohort. The prognostic values of the collagen nomogram were assessed using Cox regression and the Kaplan-Meier method.
The collagen signature was constructed based on 16 collagen features, which included 6 collagen features from the tumor center and 10 collagen features from the invasive margin. Patients with a high collagen signature were more likely to show a low Immunoscore (Lo IS) in both cohorts (<0.001). A collagen nomogram integrating the collagen signature and clinicopathological predictors was developed. The collagen nomogram yielded satisfactory discrimination and calibration, with an AUC of 0.925 (95% CI: 0.895-0.956) in the training cohort and 0.911 (95% CI: 0.872-0.949) in the validation cohort. Decision curve analysis confirmed that the collagen nomogram was clinically useful. Furthermore, the collagen nomogram-predicted subgroup was significantly associated with prognosis. Moreover, patients with a low-probability Lo IS, rather than a high-probability Lo IS, could benefit from chemotherapy in high-risk stage II and stage III CRC patients.
The collagen signature is significantly associated with the Immunoscore in the TME, and the collagen nomogram has the potential to individualize the prediction of the Immunoscore and identify CRC patients who could benefit from adjuvant chemotherapy.
免疫评分可将结直肠癌（CRC）患者分为高风险和低风险组进行预后评估。胶原蛋白在肿瘤微环境（TME）的免疫调节功能中发挥重要作用。然而，TME 中胶原蛋白与免疫评分之间的相关性尚不清楚。本研究旨在构建一个胶原蛋白特征，以阐明胶原蛋白结构与免疫评分之间的关系。
共纳入 327 例连续的 I-III 期 CRC 患者作为训练队列。使用多光子成像在标本的肿瘤中心和浸润边缘提取全定量胶原蛋白特征。应用 LASSO 回归构建胶原蛋白特征。评估胶原蛋白特征与免疫评分的相关性。通过多变量逻辑回归纳入胶原蛋白特征和临床病理预测因子后，建立胶原蛋白列线图。通过校准、区分和临床实用性评估胶原蛋白列线图的性能，然后在独立验证队列中进行测试。通过 Cox 回归和 Kaplan-Meier 方法评估胶原蛋白列线图的预后价值。
基于 16 个胶原蛋白特征构建了胶原蛋白特征，其中包括肿瘤中心的 6 个胶原蛋白特征和浸润边缘的 10 个胶原蛋白特征。两个队列中，高胶原蛋白特征的患者更有可能表现出低免疫评分（Lo IS）（<0.001）。构建了整合胶原蛋白特征和临床病理预测因子的胶原蛋白列线图。该胶原蛋白列线图具有令人满意的区分度和校准度，在训练队列中的 AUC 为 0.925（95%CI：0.895-0.956），在验证队列中的 AUC 为 0.911（95%CI：0.872-0.949）。决策曲线分析证实，胶原蛋白列线图具有临床实用性。此外，根据胶原蛋白列线图预测的亚组与预后显著相关。此外，在高危 II 期和 III 期 CRC 患者中，低概率 Lo IS 患者而非高概率 Lo IS 患者可能受益于化疗。
胶原蛋白特征与 TME 中的免疫评分显著相关，胶原蛋白列线图有可能实现免疫评分的个体化预测，并确定可能受益于辅助化疗的 CRC 患者。
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