HKUST Shenzhen Research Institute, 9 Yuexing First Road, Nanshan, Shenzhen, China.
Division of Life Science and Applied Genomics Center, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.
BMC Cancer. 2020 Jan 31;20(1):84. doi: 10.1186/s12885-020-6546-8.
Cancer subtyping has mainly relied on pathological and molecular means. Massively parallel sequencing-enabled subtyping requires genomic markers to be developed based on global features rather than individual mutations for effective implementation.
In the present study, the whole genome sequences (WGS) of 110 liver cancers of Japanese patients published with different pathologies were analyzed with respect to their single nucleotide variations (SNVs) comprising both gain-of-heterozygosity (GOH) and loss-of-heterozygosity (LOH) mutations, the signatures of combined GOH and LOH mutations, along with recurrent copy number variations (CNVs).
The results, obtained based on the WGS sequences as well as the Exome subset within the WGSs that covered ~ 2.0% of the WGS and the AluScan-subset within the WGSs that were amplifiable by Alu element-consensus primers and covered ~ 2.1% of the WGS, indicated that the WGS samples could be employed with the mutational parameters of SNV load, LOH%, the Signature α%, and survival-associated recurrent CNVs (srCNVs) as genomic markers for subtyping to stratify liver cancer patients prognostically into the long and short survival subgroups. The usage of the AluScan-subset data, which could be implemented with sub-micrograms of DNA samples and vastly reduced sequencing analysis task, outperformed the usage of WGS data when LOH% was employed as stratifying criterion.
Thus genomic subtyping performed with novel genomic markers identified in this study was effective in predicting patient-survival duration, with cohorts of hepatocellular carcinomas alone and those including intrahepatic cholangiocarcinomas. Such relatively heterogeneity-insensitive genomic subtyping merits further studies with a broader spectrum of cancers.
癌症亚型主要依赖于病理和分子手段。基于大规模平行测序的分型需要开发基于全局特征而非单个突变的基因组标记,以实现有效实施。
在本研究中,分析了 110 例日本患者肝癌的全基因组序列(WGS),这些患者的病理不同,分析内容包括包含杂合性增益(GOH)和杂合性丢失(LOH)突变的单核苷酸变异(SNV)、GOH 和 LOH 突变的组合特征以及反复出现的拷贝数变异(CNV)。
基于 WGS 序列以及 WGS 中覆盖约 2.0%的外显子集和 WGS 中可通过 Alu 元件一致引物扩增并覆盖约 2.1%的 AluScan 子集获得的结果表明,WGS 样本可以使用 SNV 负荷、LOH%、Signatureα%和与生存相关的反复出现的 CNV(srCNV)等突变参数作为基因组标记进行分型,将肝癌患者按预后分为长生存和短生存亚组。当 LOH% 作为分层标准时,使用 AluScan 子集数据(可以使用亚微克 DNA 样本和大大减少测序分析任务来实现)的效果优于使用 WGS 数据的效果。
因此,本研究中确定的新型基因组标记进行的基因组亚型分析可有效预测患者的生存时间,单独使用肝细胞癌队列和包括肝内胆管癌在内的队列均如此。这种相对不易受异质性影响的基因组亚型分析值得进一步研究更广泛的癌症谱。