Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States.
Bobby R. Alford Department of Otolaryngology- Head and Neck Surgery, Baylor College of Medicine, Houston, TX, United States.
Oral Oncol. 2023 Aug;143:106459. doi: 10.1016/j.oraloncology.2023.106459. Epub 2023 Jun 10.
Matching treatment intensity to tumor biology is critical to precision oncology for head and neck squamous cell carcinoma (HNSCC) patients. We sought to identify biological features of tumor cell multinucleation, previously shown by us to correlate with survival in oropharyngeal (OP) SCC using a machine learning approach.
Hematoxylin and eosin images from an institutional OPSCC cohort formed the training set (D). TCGA HNSCC patients (oral cavity, oropharynx and larynx/hypopharynx) formed the validation set (D). Deep learning models were trained in D to calculate a multinucleation index (MuNI) score. Gene set enrichment analysis (GSEA) was then used to explore correlations between MuNI and tumor biology.
MuNI correlated with overall survival. A multivariable nomogram that included MuNI, age, race, sex, T/N stage, and smoking status yielded a C-index of 0.65, and MuNI was prognostic of overall survival (2.25, 1.07-4.71, 0.03), independent of the other variables. High MuNI scores correlated with depletion of effector immunocyte subsets across all HNSCC sites independent of HPV and TP53 mutational status although the correlations were strongest in wild-type TP53 tumors potentially due to aberrant mitotic events and activation of DNA-repair mechanisms.
MuNI is associated with survival in HNSCC across subsites. This may be driven by an association between high levels of multinucleation and a suppressive (potentially exhausted) tumor immune microenvironment. Mechanistic studies examining the link between multinucleation and tumor immunity will be required to characterize biological drivers of multinucleation and their impact on treatment response and outcomes.
为实现头颈鳞癌（HNSCC）患者的精准肿瘤学，将治疗强度与肿瘤生物学相匹配至关重要。我们试图确定肿瘤细胞多核化的生物学特征，此前我们曾使用机器学习方法在口咽（OP）SCC 中显示其与生存相关。
来自机构性 OP-SCCO 队列的苏木精和伊红图像构成了训练集（D）。TCGA-HNSCC 患者（口腔、口咽和喉/下咽）构成了验证集（D）。在 D 中训练深度学习模型以计算多核化指数（MuNI）评分。然后使用基因集富集分析（GSEA）来探索 MuNI 与肿瘤生物学之间的相关性。
MuNI 与总生存期相关。一个包含 MuNI、年龄、种族、性别、T/N 分期和吸烟状况的多变量列线图得出的 C 指数为 0.65，并且 MuNI 是总生存期的预后因素（2.25、1.07-4.71、0.03），独立于其他变量。高 MuNI 评分与所有 HNSCC 部位的效应免疫细胞亚群耗竭相关，与 HPV 和 TP53 突变状态无关，但在野生型 TP53 肿瘤中相关性最强，这可能是由于异常有丝分裂事件和激活 DNA 修复机制所致。
MuNI 与 HNSCC 跨亚部位的生存相关。这可能是由于高水平多核化与抑制性（可能耗尽）肿瘤免疫微环境之间存在关联。需要进行机制研究，以检查多核化与肿瘤免疫之间的联系，从而确定多核化的生物学驱动因素及其对治疗反应和结局的影响。
