State Key Laboratory of Oral Diseases, National Clinical Research Center for Oral Diseases, Research Unit of Oral Carcinogenesis and Management, Chinese Academy of Medical Sciences, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Department of Pathology, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
J Dent Res. 2023 Mar;102(3):270-279. doi: 10.1177/00220345221134605. Epub 2022 Nov 4.
Immune subtyping is an important way to reveal immune heterogeneity, which may contribute to the diversity of the progression and treatment in head and neck squamous cell carcinoma (HNSCC). However, reported immune subtypes mainly focus on levels of immune infiltration and are mostly based on a mono-omics profile. This study aimed to identify a comprehensive immune subtype for HNSCC via multi-omics clustering and build a novel subtype prediction system for clinical application. Data were obtained from The Cancer Genome Atlas database and our independent multicenter cohort. Multi-omics clustering was performed to identify 3 clusters of 499 patients in The Cancer Genome Atlas based on immune-related gene expression and somatic mutations. The immune characteristics and biological features of the obtained clusters were revealed by bioinformatics, and 3 immune subtypes were identified: 1) adaptive immune activation subtype predominantly enriched in T cells, 2) innate immune activation subtype predominantly enriched in macrophages, and 3) immune desert subtype. Subsequently, the clinical implications of each subtype were analyzed per clinical epidemiology. We found that adaptive immune activation showed better survival outcomes and had a similar response to chemotherapy with innate immune activation, whereas immune desert might be relatively resistant to chemotherapy. Moreover, a subtype prediction system was developed by deep learning with whole slide images and named HISMD: HNSCC Immune Subtypes via Multi-omics and Deep Learning. We endowed HISMD with interpretability through image-based key feature extraction. The clinical implications, biological significances, and predictive stability of HISMD were successfully verified by using our independent multicenter cohort data set. In summary, this study revealed the immune heterogeneity of HNSCC and obtained a novel, highly accurate, and interpretable immune subtyping prediction system. For clinical implementation in the future, additional validation and utility studies are warranted.
免疫亚群分型是揭示免疫异质性的重要方法,这可能有助于解释头颈部鳞状细胞癌(HNSCC)进展和治疗的多样性。然而,已报道的免疫亚群主要侧重于免疫浸润水平,且大多基于单一组学特征。本研究旨在通过多组学聚类鉴定用于 HNSCC 的综合免疫亚群,并建立用于临床应用的新型亚群预测系统。数据来自癌症基因组图谱(TCGA)数据库和我们的独立多中心队列。基于免疫相关基因表达和体细胞突变,对 TCGA 中的 499 例患者进行多组学聚类,以识别出 3 个聚类。通过生物信息学揭示获得的聚类的免疫特征和生物学特征,并鉴定出 3 种免疫亚群:1)适应性免疫激活亚群,主要富含 T 细胞;2)固有免疫激活亚群,主要富含巨噬细胞;3)免疫荒漠亚群。随后,按临床流行病学对每种亚群的临床意义进行分析。我们发现,适应性免疫激活具有更好的生存结局,与固有免疫激活对化疗的反应相似,而免疫荒漠可能对化疗相对耐药。此外,我们通过全切片图像和深度学习开发了一种亚群预测系统,命名为 HISMD:通过多组学和深度学习的 HNSCC 免疫亚群。我们通过基于图像的关键特征提取赋予 HISMD 可解释性。使用我们的独立多中心队列数据集成功验证了 HISMD 的临床意义、生物学意义和预测稳定性。总之,本研究揭示了 HNSCC 的免疫异质性,并获得了一种新型、高度准确且可解释的免疫亚群预测系统。未来在临床实施时,需要进行更多的验证和实用性研究。