Table2_Machine learning-based identification of a consensus immune-derived gene signature to improve head and neck squamous cell carcinoma therapy and outcome.xlsx

BackgroundHead and neck squamous cell carcinoma (HNSCC), an extremely aggressive tumor, is often associated with poor outcomes. The standard anatomy-based tumor–node–metastasis staging system does not satisfy the requirements for screening treatment-sensitive patients. Thus, an ideal biomarker leading to precise screening and treatment of HNSCC is urgently needed.
MethodsTen machine learning algorithms—Lasso, Ridge, stepwise Cox, CoxBoost, elastic network (Enet), partial least squares regression for Cox (plsRcox), random survival forest (RSF), generalized boosted regression modelling (GBM), supervised principal components (SuperPC), and survival support vector machine (survival-SVM)—as well as 85 algorithm combinations were applied to construct and identify a consensus immune-derived gene signature (CIDGS).
ResultsBased on the expression profiles of three cohorts comprising 719 patients with HNSCC, we identified 236 consensus prognostic genes, which were then filtered into a CIDGS, using the 10 machine learning algorithms and 85 algorithm combinations. The results of a study involving a training cohort, two testing cohorts, and a meta-cohort consistently demonstrated that CIDGS was capable of accurately predicting prognoses for HNSCC. Incorporation of several core clinical features and 51 previously reported signatures, enhanced the predictive capacity of the CIDGS to a level which was markedly superior to that of other signatures. Notably, patients with low CIDGS displayed fewer genomic alterations and higher immune cell infiltrate levels, as well as increased sensitivity to immunotherapy and other therapeutic agents, in addition to receiving better prognoses. The survival times of HNSCC patients with high CIDGS, in particular, were shorter. Moreover, CIDGS enabled accurate stratification of the response to immunotherapy and prognoses for bladder cancer. Niclosamide and ruxolitinib showed potential as therapeutic agents in HNSCC patients with high CIDGS.
ConclusionCIDGS may be used for stratifying risks as well as for predicting the outcome of patients with HNSCC in a clinical setting.

背景 头颈部鳞状细胞癌（Head and neck squamous cell carcinoma, HNSCC）是一类极具侵袭性的肿瘤，患者通常预后不佳。当前基于解剖学的标准肿瘤-淋巴结-转移分期系统无法满足筛选治疗敏感型患者的需求，因此亟需开发可用于头颈部鳞状细胞癌精准筛选与治疗的理想生物标志物。

方法 本研究采用10种机器学习算法——套索回归（Lasso）、岭回归（Ridge）、逐步Cox回归（stepwise Cox）、CoxBoost算法、弹性网络（Enet）、Cox偏最小二乘回归（plsRcox）、随机生存森林（RSF）、广义提升回归模型（GBM）、监督主成分分析（SuperPC）以及生存支持向量机（survival-SVM），联合85种算法组合，构建并鉴定出一致性免疫源性基因特征（CIDGS）。

结果 基于包含719名头颈部鳞状细胞癌患者的三个队列的基因表达谱数据，本研究通过上述10种机器学习算法及85种算法组合，筛选得到236个一致性预后基因，并进一步整合为一致性免疫源性基因特征（CIDGS）。训练队列、两个测试队列及荟萃队列的分析结果均一致表明，CIDGS可精准预测头颈部鳞状细胞癌患者的预后。将多项核心临床特征与51种已报道的基因特征纳入CIDGS后，其预测效能显著优于其他基因特征。值得注意的是，低CIDGS评分患者的基因组变异更少、免疫细胞浸润水平更高，对免疫治疗及其他治疗药物的敏感性更强，且预后更佳；而高CIDGS评分患者的生存时间则更短。此外，CIDGS可精准分层膀胱癌患者的免疫治疗应答情况与预后。氯硝柳胺（Niclosamide）与芦可替尼（ruxolitinib）在高CIDGS评分的头颈部鳞状细胞癌患者中显示出潜在治疗价值。

结论 一致性免疫源性基因特征（CIDGS）可用于头颈部鳞状细胞癌患者的风险分层与临床预后预测，具备临床应用潜力。