Convergent Evolution of Copy Number Alterations in Multi-Centric Hepatocellular Carcinoma

Carolin Lackner,Viola Paradiso,Monika Kovacova,Luigi Terracciano,Cristina Quintavalle,Luca Quagliata,Daniel Baumhoer,Sebastian Ribi,William Cross,Michal Kovac,Salvatore Piscuoglio,Karl Heinimann

doi:10.1038/s41598-019-40843-9

Carolin Lackner, Viola Paradiso + Show 10 more

Open Access

https://doi.org/10.1038/s41598-019-40843-9

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Abstract

In the recent years, new molecular methods have been proposed to discriminate multicentric hepatocellular carcinomas (HCC) from intrahepatic metastases. Some of these methods utilize sequencing data to assess similarities between cancer genomes, whilst other achieved the same results with transcriptome and methylome data. Here, we attempt to classify two HCC patients with multi-centric disease using the recall-rates of somatic mutations but find that difficult because their tumors share some chromosome-scale copy-number alterations (CNAs) but little-to-no single-nucleotide variants. To resolve the apparent conundrum, we apply a phasing strategy to test if those shared CNAs are identical by descent. Our findings suggest that the conflicting alterations occur on different homologous chromosomes, which argues for multi-centric origin of respective HCCs.

Highlights

The specific phasing algorithm is detailed in material and methods and on a Fig. 4 and is, at heart, a Welch’s two sample t-test that compares allele-frequency changes at polymorphic sites of candidate copy-number alterations (CNAs) of tumor samples and a tumor-free tissue, on the assumption that allele frequencies for each chromosomal haplotype are random variables with mean μ and variance σ2
We attempted to classify multi-centric hepatocellular carcinomas using the information that may be not be available for strategies that either compare tumor morphology or mutation burden
The use of the latter as proposed by Furuta et al can be sometimes misleading because the assignment of single or multiple origin category is possible only for tumors which share most or no somatic mutations, but otherwise there is no clear threshold to distinguish them

Summary

Introduction

We ensured that there was not a significant enrichment of false positive variants in our analysis by replicating 21/23 (91%) selected somatic mutations and re-assessing hematoxylin-eosin stains of tumor tissues to ensure that they contained at least 80% of tumor cells (Supplementary Table 7). We undertook a down-sampling approach to equalize read depth across all tumor regions and repeated the analysis with additional cancer-specialized calling algorithms, including MuTect[26] (i.e. GATK4 implementation of MuTect) and Strelka[7].

Results

Conclusion