Taxonomy of hepatobiliary cancer (HBC) categorizes tumors by location or histopathology (tissue of origin, TO). Tumors originating from different TOs can also be grouped by overlapping genomic alterations (GA) into molecular subtypes (MS). The aim of this study was to create novel HBC MSs. Next-generation sequencing (NGS) data from the AACR-GENIE database were used to examine the genomic landscape of HBCs. Machine learning and gene enrichment analysis identified MSs and their oncogenomic pathways. Descriptive statistics were used to compare subtypes and their associations with clinical and molecular variables. Integrative analyses generated three MSs with different oncogenomic pathways independent of TO (n= 324;p< 0.05). HC-1 “hyper-mutated-proliferative state” MS had rapidly dividing cells susceptible to chemotherapy; HC-2 “adaptive stem cell-cellular senescence” MS had epigenomic alterations to evade immune system and treatment-resistant mechanisms; HC-3 “metabolic-stress pathway” MS had metabolic alterations. The discovery of HBC MSs is the initial step in cancer taxonomy evolution and the incorporation of genomic profiling into the TNM system. The goal is the development of a precision oncology machine learning algorithm to guide treatment planning and improve HBC outcomes. Future studies should validate findings of this study, incorporate clinical outcomes, and compare the MS classification to the AJCC 8th staging system.
肝胆癌（HBC）的分类依据肿瘤位置或组织病理学（起源组织，TO）进行。起源于不同TO的肿瘤也可根据重叠的基因组改变（GA）归为分子亚型（MS）。本研究旨在创建新型HBC分子亚型。通过AACR-GENIE数据库中的新一代测序（NGS）数据，我们分析了HBC的基因组特征。采用机器学习与基因富集分析鉴定分子亚型及其致癌基因组通路。通过描述性统计比较各亚型及其与临床和分子变量的关联。整合分析产生了三种具有不同致癌基因组通路的分子亚型，这些亚型独立于起源组织（n=324；p<0.05）。HC-1型“高突变-增殖状态”分子亚型具有快速分裂的细胞，对化疗敏感；HC-2型“适应性干细胞-细胞衰老”分子亚型表现表观基因组改变，可逃避免疫系统并具有治疗抵抗机制；HC-3型“代谢应激通路”分子亚型存在代谢改变。HBC分子亚型的发现是癌症分类体系演进的第一步，也是将基因组谱整合至TNM分期系统的初步尝试。其目标在于开发精准肿瘤学机器学习算法，以指导治疗规划并改善HBC临床结局。未来研究需验证本研究成果，纳入临床结局数据，并将此分子亚型分类与美国癌症联合委员会第八版分期系统进行比较。
肿瘤(癌症)患者之家