Head and neck squamous-cell carcinoma (HNSCC) is a disease with a generally poor prognosis; half of treated patients eventually develop recurrent and/or metastatic (R/M) disease. Patients with R/M HNSCC generally have incurable disease with a median survival of 10 to 15 months. Although immune-checkpoint blockade (ICB) has improved outcomes in patients with R/M HNSCC, identifying patients who are likely to benefit from ICB remains a challenge. Biomarkers in current clinical use include tumor mutational burden and immunohistochemistry for programmed death-ligand 1, both of which have only modest predictive power. Machine learning (ML) has the potential to aid in clinical decision-making as an approach to estimate a tumor’s likelihood of response or a patient’s likelihood of experiencing clinical benefit from therapies such as ICB. Previously, we described a random forest ML model that had value in predicting ICB response using 11 or 16 clinical, laboratory, and genomic features in a pan-cancer development cohort. However, its applicability to certain cancer types, such as HNSCC, has been unknown, due to a lack of cancer-type-specific validation. Here, we present the first validation of a random forest ML tool to predict the likelihood of ICB response in patients with R/M HNSCC. The tool had adequate predictive power for tumor response (area under the receiver operating characteristic curve = 0.65) and was able to stratify patients by overall (HR = 0.53 [95% CI 0.29–0.99],p= 0.045) and progression-free (HR = 0.49 [95% CI 0.27–0.87],p= 0.016) survival. The overall accuracy was 0.72. Our study validates an ML predictor in HNSCC, demonstrating promising performance in a novel cohort of patients. Further studies are needed to validate the generalizability of this algorithm in larger patient samples from additional multi-institutional contexts.
头颈部鳞状细胞癌（HNSCC）是一种预后普遍较差的疾病；接受治疗的患者中约半数最终会发展为复发性和/或转移性（R/M）疾病。R/M HNSCC患者通常无法治愈，中位生存期仅为10至15个月。尽管免疫检查点阻断（ICB）疗法改善了R/M HNSCC患者的临床结局，但如何识别可能从ICB治疗中获益的患者仍是临床面临的挑战。目前临床使用的生物标志物包括肿瘤突变负荷和程序性死亡配体1的免疫组化检测，但两者的预测能力均有限。机器学习（ML）作为一种评估肿瘤治疗反应可能性或患者从ICB等疗法中获得临床获益概率的方法，在辅助临床决策方面具有潜力。此前，我们曾报道一种随机森林机器学习模型，该模型在泛癌种开发队列中利用11或16项临床、实验室及基因组特征对ICB治疗反应具有预测价值。然而，由于缺乏针对特定癌症类型（如HNSCC）的验证，其在该类癌症中的适用性尚未明确。本研究首次验证了随机森林机器学习工具在预测R/M HNSCC患者ICB治疗反应概率方面的效能。该工具对肿瘤反应具有良好的预测能力（受试者工作特征曲线下面积=0.65），并能根据总生存期（HR=0.53[95%CI 0.29–0.99]，p=0.045）和无进展生存期（HR=0.49[95%CI 0.27–0.87]，p=0.016）对患者进行分层。总体准确率为0.72。本研究验证了机器学习预测模型在HNSCC中的应用，在新患者队列中展现出良好的预测性能。未来需要更多研究在来自多机构的更大规模患者样本中验证该算法的普适性。
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