University of Groningen, University Medical Center Groningen, Department of Pathology & Medical Biology, Hanzeplein 1, 9713 GZ Groningen, the Netherlands.
University of Groningen, University Medical Center Groningen, Department of Otorhinolaryngology and Head and Neck Surgery, Hanzeplein 1, 9713 GZ Groningen, the Netherlands.
Oral Oncol. 2024 Jun;153:106830. doi: 10.1016/j.oraloncology.2024.106830. Epub 2024 May 7.
Conventional clinicopathological characteristics insufficiently predict prognosis in oral squamous cell carcinoma (OSCC). We aimed to assess the added predictive value of tumor microenvironment immune cell composition (TMICC) in addition to conventional clinicopathological characteristics.
Primary tumor samples of 290 OSCC patients were immunohistochemically stained for CD4, CD8, CD20, CD68, CD163, CD57, FoxP3 and Programmed cell Death Ligand 1. Additionally, clinicopathological characteristics were obtained from patients' medical files. Predictive models were trained and validated by conducting Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses with cross-validation. To quantify the added predictive power of TMICC within models, receiver operating characteristic (ROC) analyses were used.
Recurrence occurred in 74 patients (25.5%). Conventional clinicopathological characteristics (tumor localization, pathological T-stage, pathological N-stage, extracapsular spread, resection margin, differentiation grade, perineural invasion, lymphovascular invasion) and treatment modality, were used to build a LASSO logistic regression-based predictive model. Addition of TMICC to the model resulted in a comparable AUC of respectively 0.79 (±0.01) and 0.76 (±0.1) in the training and test sets. The model indicated that high numbers of CD4+ T cells protected against recurrence. Lymph node metastasis, extracapsular spread, perineural invasion, positive surgical margins and reception of adjuvant treatment were associated with increased risk for recurrence.
The TMICC, specifically the number of CD4+ T cells, is an independent predictor , however, addition to conventional clinicopathological characteristics does not improve the performance of a predictive model for recurrence in OSCC.
传统的临床病理特征不足以预测口腔鳞状细胞癌（OSCC）的预后。我们旨在评估肿瘤微环境免疫细胞组成（TMICC）除了传统的临床病理特征之外，对预后的额外预测价值。
对 290 例 OSCC 患者的原发肿瘤样本进行 CD4、CD8、CD20、CD68、CD163、CD57、FoxP3 和程序性细胞死亡配体 1 的免疫组织化学染色。此外，还从患者的病历中获得了临床病理特征。通过交叉验证进行最小绝对收缩和选择算子（LASSO）回归分析来训练和验证预测模型。为了量化 TMICC 在模型中的附加预测能力，使用接收者操作特征（ROC）分析。
74 例患者（25.5%）发生复发。传统的临床病理特征（肿瘤定位、病理 T 分期、病理 N 分期、包膜外扩散、切缘、分化程度、神经周围侵犯、血管侵犯）和治疗方式，用于构建 LASSO 逻辑回归预测模型。将 TMICC 添加到模型中，在训练集和测试集中，AUC 分别为 0.79（±0.01）和 0.76（±0.1）。该模型表明，CD4+T 细胞数量较多可预防复发。淋巴结转移、包膜外扩散、神经周围侵犯、阳性切缘和接受辅助治疗与复发风险增加相关。
TMICC，特别是 CD4+T 细胞的数量，是一个独立的预测因素，但除了传统的临床病理特征外，不能提高 OSCC 复发预测模型的性能。
