Biology subjects

Trani, L.

Publications and source records attributed to Trani, L..

3 recordsLinked to original sources

bioRxiv2021

Genomic and transcriptomic somatic alterations of hepatocellular carcinoma in non-cirrhotic livers

BackgroundLiver cancer is the second leading cause of cancer-related deaths worldwide. Hepatocellular carcinoma (HCC) risk factors include chronic hepatitis, cirrhosis, and alcohol abuse, whereby tumorigenesis is induced through inflammation and subsequent fibrotic response. However, a subset of HCC arises in non-cirrhotic livers. We characterized the genomic and transcriptomic landscape of non-cirrhotic HCC to identify features underlying the diseases development and progression. MethodsWhole genome and transcriptome sequencing was performed on 30 surgically resectable tumors comprised of primarily of non-cirrhotic HCC and adjacent normal tissue. Using somatic variants, capture reagents were created and employed on an additional 87 cases of mixed cirrhotic/non-cirrhotic HCC. Cases were analyzed to identify viral integrations, single nucleotide variants (SNVs), insertions and deletions (INDELS), copy number variants, loss of heterozygosity, gene fusions, structural variants, and differential gene expression. ResultsWe detected 3,750 SNVs/INDELS and extensive CNVs and expression changes. Recurrent TERT promoter mutations occurred in >52% of non-cirrhotic discovery samples. Frequently mutated genes included TP53, CTNNB1, and APOB. Cytochrome P450 mediated metabolism was significantly downregulated. Structural variants were observed at MACROD2, WDPCP and NCKAP5 in >20% of samples. Furthermore, NR1H4 fusions involving gene partners EWSR1, GNPTAB, and FNIP1 were detected and validated in 2 non-cirrhotic samples. ConclusionGenomic analysis can elucidate mechanisms that may contribute to non-cirrhotic HCC tumorigenesis. The comparable mutational landscape between cirrhotic and non-cirrhotic HCC supports previous work suggesting a convergence at the genomic level during disease progression. It is therefore possible genomic-based treatments can be applied to both HCC subtypes with progressed disease. HighlightsO_LINon-cirrhotic HCC genomically resembles cirrhotic HCC C_LIO_LIComprehensive genome- and transcriptome-wide profiling allows detection of novel structural variants, fusions, and undiagnosed viral infections C_LIO_LINR1H4 fusions may represent a novel mechanism for tumorigenesis in HCC C_LIO_LINon-cirrhotic HCC is characterized by genotoxic mutational signatures and dysregulated liver metabolism C_LIO_LIClinical history and comprehensive omic profiling incompletely explain underlying etiologies for non-cirrhotic HCC highlighting the need for further research C_LI Short DescriptionThis study characterizes the genomic landscape of hepatocellular carcinomas (HCCs) in non-cirrhotic livers. Using 117 HCCs tumor/normal pairs, we identified 3,750 SNVs/INDELS with high variant frequency in TERT, TP53, CTNNB1, and APOB. CYP450 was significantly downregulated and many structural variants were observed. This characterization could assist in elucidating non-cirrhotic HCC tumorigenesis.

cancer biology↗

bioRxiv2021

de novo variant calling identifies cancer mutation profiles in the 1000 Genomes Project

Detection of de novo variants (DNVs) is critical for studies of disease-related variation and mutation rates. We developed a GPU-based workflow to rapidly call DNVs (HAT) and demonstrated its effectiveness by applying it to 4,216 Simons Simplex Collection (SSC) whole-genome sequenced parent-child trios from DNA derived from blood. In our SSC DNV data, we identified 78 {+/-} 15 DNVs per individual, 18% {+/-} 5% at CpG sites, 75% {+/-} 9% phased to the paternal chromosome of origin, and an average allele balance of 0.49. These calculations are all in line with DNV expectations. We sought to build a control DNV dataset by running HAT on 602 whole-genome sequenced parent-child trios from DNA derived from lymphoblastoid cell lines (LCLs) from the publicly available 1000 Genomes Project (1000G). In our 1000G DNV data, we identified 740 {+/-} 967 DNVs per individual, 14% {+/-} 4% at CpG sites, 61% {+/-} 11% phased to the paternal chromosome of origin, and an average allele balance of 0.41. Of the 602 trios, 80% had > 100 DNVs and we hypothesized the excess DNVs were cell line artifacts. Several lines of evidence in our data suggest that this is true and that 1000G does not appear to be a static reference. By mutation profile analysis, we tested whether these cell line artifacts were random and found that 40% of individuals in 1000G did not have random DNV profiles; rather they had DNV profiles matching B-cell lymphoma. Furthermore, we saw significant excess of protein-coding DNVs in 1000G in the gene IGLL5 that has already been implicated in this cancer. As a result of cell line artifacts, 1000G has variants present in DNA repair genes and at Clinvar pathogenic or likely-pathogenic sites. Our study elucidates important implications of the use of sequencing data from LCLs for both reference building projects as well as disease-related projects whereby these data are used in variant filtering steps.

genomics↗

bioRxiv2018

Standard operating procedure for somatic variant refinement of tumor sequencing data

PurposeManual review of aligned sequencing reads is required to develop a high-quality list of somatic variants from massively parallel sequencing data (MPS). Despite widespread use in analyzing MPS data, there has been little attempt to describe methods for manual review, resulting in high inter- and intra-lab variability in somatic variant detection and characterization of tumors.\n\nMethodsOpen source software was used to develop an optimal method for manual review setup. We also developed a systemic approach to visually inspect each variant during manual review.\n\nResultsWe present a standard operating procedures for somatic variant refinement for use by manual reviewers. The approach is enhanced through representative examples of 4 different manual review categories that indicate a reviewers confidence in the somatic variant call and 19 annotation tags that contextualize commonly observed sequencing patterns during manual review. Representative examples provide detailed instructions on how to classify variants during manual review to rectify lack of confidence in automated somatic variant detection.\n\nConclusionStandardization of somatic variant refinement through systematization of manual review will improve the consistency and reproducibility of identifying true somatic variants after automated variant calling.

genetics↗