Genomic subtyping of liver cancers with prognostic application

Abstract

Background: 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. Methods: 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). Results: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 elementconsensus 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. Conclusions: 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.

From genome sequencing to the discovery of potential biomarkers in liver disease

“…In addition, a large-scale study is underway to confirm the correlation between abnormality including mutation in the genome and liver disease ( 3 , 6 ). For example, copy-number variations (CNVs) in 38 types of cancers were found as a part of the pan-cancer analysis of whole genomes (PCAWG) consortium analyzing 2,658 cancers and the result suggested that the CNVs could be used as diagnostic markers in the early stage of cancers ( 86 ).…”

“…In addition, somatic mutations found in the hepatocellular carcinoma (HCC) were related to highly expressed hepato-specific genes, providing evidence of liver tumorigenesis ( 7 ). A research revealed that genomic markers of liver cancer could also be identified with WGS by genomic subtyping ( 3 ). In the process, they figured out the correlation between single nucleotide variations (SNVs) load and two types of heterozygosity mutations – gain-of-heterozygosity (GOH) and loss-of-heterozygosity (LOH) – by categorizing the SNV loads of 110 liver cancers obtained by paired blood-tumor WGS ( 3 ).…”

“…A research revealed that genomic markers of liver cancer could also be identified with WGS by genomic subtyping ( 3 ). In the process, they figured out the correlation between single nucleotide variations (SNVs) load and two types of heterozygosity mutations – gain-of-heterozygosity (GOH) and loss-of-heterozygosity (LOH) – by categorizing the SNV loads of 110 liver cancers obtained by paired blood-tumor WGS ( 3 ). Additionally, it was showed that the recurrent somatic survival-related CNVs (srCNVs) are linked to the LOHs, as they are more relevant to HCC short survival ( 3 ).…”

“…In the process, they figured out the correlation between single nucleotide variations (SNVs) load and two types of heterozygosity mutations – gain-of-heterozygosity (GOH) and loss-of-heterozygosity (LOH) – by categorizing the SNV loads of 110 liver cancers obtained by paired blood-tumor WGS ( 3 ). Additionally, it was showed that the recurrent somatic survival-related CNVs (srCNVs) are linked to the LOHs, as they are more relevant to HCC short survival ( 3 ). The analysis of WGS data along with prognostic survival analysis indicated that malignant cancers tend to have a large number of SNV, LOHs, and CNV mutations ( 3 ).…”

“…The analysis of WGS data along with prognostic survival analysis indicated that malignant cancers tend to have a large number of SNV, LOHs, and CNV mutations ( 3 ). Based on the result, SNV load, LOH%, Signature a%, or srCNV were suggested as remarkable factors as genomic markers ( 3 ).…”