准确评估肿瘤的病理亚型对诊断、治疗和预后至关重要。以往病理亚型的诊断主要依赖HE染色法和免疫组织化学法,而随着测序技术的不断发展,对患者进行基因型和表型特点的个体分析成为可能,将肿瘤病理分型与基因分型结合用于疾病分型、诊治选择和疗效判断的精准医学研究逐渐兴起。不同病理亚型的肿瘤细胞来源、致癌因素和临床表型均不尽相同,其在基因组上会留下特异“印迹”,即突变特征。本研究通过整合癌症基因组数据库(The Cancer Genome Atlas, TCGA)中肾癌、肺癌和食管癌的外显子测序数据,分别对3种肿瘤通过肿瘤基因突变特征进行肿瘤病理分型聚类和预测。首先通过非监督聚类方法将3种肿瘤分别按照24种突变特征进行聚类分析,其次通过随机森林法从24种突变特征中进一步选择对于区分不同病理亚型有显著性的突变特征并进行聚类分析,构建突变特征对3种肿瘤病理亚型的分型模型。在肾癌中,该模型准确率达到了100% (95% confidence interval (CI): 0.93~1.00),肺癌和食管癌中分别达到了78% (95% CI: 0.66~0.86)和84% (95% CI: 0.60~0.97)。以上研究结果表明,突变特征作为新型分子标记物,对肿瘤的病理分型、诊断,尤其是早诊具有一定的参考意义。

It holds great promises to precisely stratify cancer subtypes to improve cancer diagnosis, therapy and prognosis. In the past, the diagnosis of pathological subtypes mainly relied on hematoxylin-eosin staining and immunohistochemistry. With the development of sequencing technologies, genotype and phenotype analysis of individuals has become possible and precision medicine is on the rise in healthcare. As different tumor subtypes have different cell-of-origin, risk factors and clinical phenotypes, they generate unique combinations of mutation types, termed “Mutational Signatures”. Herein, using the exome sequencing data from The Cancer Genome Atlas (TCGA), we evaluated the utility of mutational landscape for differentiating cell-of-origin within three common cancers (kidney, lung and esophageal cancers). We found that mutational signatures predicted histological subtypes of kidney cancers, clear cell renal cell carcinoma (KIRC) vs. chromophobe renal cell carcinoma (KICH), which had different cell-of-origin, with 100% accuracy (95% CI: 0.93-1.00). The mutational signatures also predicted histological subtypes of lung cancers (lung adenocarcinoma vs. lung squamous cell carcinoma) and esophageal cancers (esophageal adenocarcinoma vs. esophageal squamous cell carcinoma) with 78% (95% CI: 0.66-0.86) and 84% accuracy (95% CI: 0.60-0.97), respectively. Collectively, mutational signatures-based subtyping is good at pathological classification, personalized diagnosis, especially early detection for common cancers.