Showing papers by "Robert S. Fulton published in 2022"

The Human Pangenome Project: a global resource to map genomic diversity

Abstract: The human reference genome is the most widely used resource in human genetics and is due for a major update. Its current structure is a linear composite of merged haplotypes from more than 20 people, with a single individual comprising most of the sequence. It contains biases and errors within a framework that does not represent global human genomic variation. A high-quality reference with global representation of common variants, including single-nucleotide variants, structural variants and functional elements, is needed. The Human Pangenome Reference Consortium aims to create a more sophisticated and complete human reference genome with a graph-based, telomere-to-telomere representation of global genomic diversity. Here we leverage innovations in technology, study design and global partnerships with the goal of constructing the highest-possible quality human pangenome reference. Our goal is to improve data representation and streamline analyses to enable routine assembly of complete diploid genomes. With attention to ethical frameworks, the human pangenome reference will contain a more accurate and diverse representation of global genomic variation, improve gene-disease association studies across populations, expand the scope of genomics research to the most repetitive and polymorphic regions of the genome, and serve as the ultimate genetic resource for future biomedical research and precision medicine.

A draft human pangenome reference

Abstract: Abstract Here the Human Pangenome Reference Consortium presents a first draft of the human pangenome reference. The pangenome contains 47 phased, diploid assemblies from a cohort of genetically diverse individuals 1 . These assemblies cover more than 99% of the expected sequence in each genome and are more than 99% accurate at the structural and base pair levels. Based on alignments of the assemblies, we generate a draft pangenome that captures known variants and haplotypes and reveals new alleles at structurally complex loci. We also add 119 million base pairs of euchromatic polymorphic sequences and 1,115 gene duplications relative to the existing reference GRCh38. Roughly 90 million of the additional base pairs are derived from structural variation. Using our draft pangenome to analyse short-read data reduced small variant discovery errors by 34% and increased the number of structural variants detected per haplotype by 104% compared with GRCh38-based workflows, which enabled the typing of the vast majority of structural variant alleles per sample.

Semi-automated assembly of high-quality diploid human reference genomes

Abstract: The current human reference genome, GRCh38, represents over 20 years of effort to generate a high-quality assembly, which has benefitted society1,2. However, it still has many gaps and errors, and does not represent a biological genome as it is a blend of multiple individuals3,4. Recently, a high-quality telomere-to-telomere reference, CHM13, was generated with the latest long-read technologies, but it was derived from a hydatidiform mole cell line with a nearly homozygous genome5. To address these limitations, the Human Pangenome Reference Consortium formed with the goal of creating high-quality, cost-effective, diploid genome assemblies for a pangenome reference that represents human genetic diversity6. Here, in our first scientific report, we determined which combination of current genome sequencing and assembly approaches yield the most complete and accurate diploid genome assembly with minimal manual curation. Approaches that used highly accurate long reads and parent-child data with graph-based haplotype phasing during assembly outperformed those that did not. Developing a combination of the top-performing methods, we generated our first high-quality diploid reference assembly, containing only approximately four gaps per chromosome on average, with most chromosomes within ±1% of the length of CHM13. Nearly 48% of protein-coding genes have non-synonymous amino acid changes between haplotypes, and centromeric regions showed the highest diversity. Our findings serve as a foundation for assembling near-complete diploid human genomes at scale for a pangenome reference to capture global genetic variation from single nucleotides to structural rearrangements.

Spatially restricted drivers and transitional cell populations cooperate with the microenvironment in untreated and chemo-resistant pancreatic cancer

Abstract: Abstract Pancreatic ductal adenocarcinoma is a lethal disease with limited treatment options and poor survival. We studied 83 spatial samples from 31 patients (11 treatment-naïve and 20 treated) using single-cell/nucleus RNA sequencing, bulk-proteogenomics, spatial transcriptomics and cellular imaging. Subpopulations of tumor cells exhibited signatures of proliferation, KRAS signaling, cell stress and epithelial-to-mesenchymal transition. Mapping mutations and copy number events distinguished tumor populations from normal and transitional cells, including acinar-to-ductal metaplasia and pancreatic intraepithelial neoplasia. Pathology-assisted deconvolution of spatial transcriptomic data identified tumor and transitional subpopulations with distinct histological features. We showed coordinated expression of TIGIT in exhausted and regulatory T cells and Nectin in tumor cells. Chemo-resistant samples contain a threefold enrichment of inflammatory cancer-associated fibroblasts that upregulate metallothioneins. Our study reveals a deeper understanding of the intricate substructure of pancreatic ductal adenocarcinoma tumors that could help improve therapy for patients with this disease.

NIH SenNet Consortium to map senescent cells throughout the human lifespan to understand physiological health.

Abstract: Cells respond to many stressors by senescing, acquiring stable growth arrest, morphologic and metabolic changes, and a proinflammatory senescence-associated secretory phenotype. The heterogeneity of senescent cells (SnCs) and senescence-associated secretory phenotype are vast, yet ill characterized. SnCs have diverse roles in health and disease and are therapeutically targetable, making characterization of SnCs and their detection a priority. The Cellular Senescence Network (SenNet), a National Institutes of Health Common Fund initiative, was established to address this need. The goal of SenNet is to map SnCs across the human lifespan to advance diagnostic and therapeutic approaches to improve human health. State-of-the-art methods will be applied to identify, define and map SnCs in 18 human tissues. A common coordinate framework will integrate data to create four-dimensional SnC atlases. Other key SenNet deliverables include innovative tools and technologies to detect SnCs, new SnC biomarkers and extensive public multi-omics datasets. This Perspective lays out the impetus, goals, approaches and products of SenNet.

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

Abstract: •Non-cirrhotic HCC genomically resembles cirrhotic HCC•Comprehensive genome- and transcriptome-wide profiling allows detection of novel structural variants, fusions, and undiagnosed viral infections•NR1H4 fusions may represent a novel mechanism for tumorigenesis in HCC•Non-cirrhotic HCC is characterized by genotoxic mutational signatures and dysregulated liver metabolism•Clinical history and comprehensive omic profiling incompletely explain underlying etiologies for non-cirrhotic HCC highlighting the need for further research Worldwide, there are approximately 750,000 new cases of hepatocellular carcinoma (HCC) each year [[1]Ferlay J. Soerjomataram I. Dikshit R. Eser S. Mathers C. Rebelo M. et al.Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.Int J Cancer. 2015; 136: E359-E386Crossref PubMed Scopus (19883) Google Scholar]. Although HCC has the 5th highest incidence rate in men and 9th highest incidence rate in women, it has the second highest mortality rate of all cancer types [[1]Ferlay J. Soerjomataram I. Dikshit R. Eser S. Mathers C. Rebelo M. et al.Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012.Int J Cancer. 2015; 136: E359-E386Crossref PubMed Scopus (19883) Google Scholar]. HCC is traditionally associated with inflammation-inducing risk factors, which promote liver cirrhosis including: chronic hepatitis infections, such as hepatitis b virus (HBV) and hepatitis c virus (HCV), alcohol abuse, and non-alcoholic fatty liver disease [[2]Fattovich G. Stroffolini T. Zagni I. Donato F. Hepatocellular carcinoma in cirrhosis: incidence and risk factors.Gastroenterology. 2004; 127: S35-S50Abstract Full Text Full Text PDF PubMed Scopus (1835) Google Scholar]. However, approximately 20% of patients present with non-cirrhotic HCC in the absence of these risk factors [[3]Alkofer B. Lepennec V Chiche L. Hepatocellular cancer in the non-cirrhotic liver.J Visc Surg. 2011; 148: 3-11Crossref PubMed Google Scholar]. If diagnosed early, patients with non-cirrhotic HCC maintain adequate liver function, allowing for effective tumor resection with exceptional prognosis when compared to patients with cirrhotic HCC [[4]Maeda T. Shimada M. Harimoto N. Tsujita E. Aishima S.-I. Tanaka S. et al.Prognosis of early hepatocellular carcinoma after hepatic resection.Hepatogastroenterology. 2008; 55: 1428-1432PubMed Google Scholar]. However, late-stage diagnosis of non-cirrhotic HCC typically presents with larger and more aggressive tumors that are prone to metastasis [[5]Llovet J.M. Brú C. Bruix J. Prognosis of hepatocellular carcinoma: the BCLC staging classification.Semin Liver Dis. 1999; 19: 329-338Crossref PubMed Google Scholar]. Even with extensive tumor resection, approximately 50% of patients relapse within three years post-treatment [[6]Shah S.A. Cleary S.P. Wei A.C. Yang I. Taylor B.R. Hemming A.W. et al.Recurrence after liver resection for hepatocellular carcinoma: risk factors, treatment, and outcomes.Surgery. 2007; 141: 330-339Abstract Full Text Full Text PDF PubMed Scopus (307) Google Scholar]. Using high-throughput sequencing, researchers have previously characterized the genomic landscape of cirrhotic HCC [7Schulze K. Imbeaud S. Letouzé E. Alexandrov L.B. Calderaro J. Rebouissou S. et al.Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets.Nat Genet. 2015; 47: 505-511Crossref PubMed Scopus (872) Google Scholar, 8Fujimoto A. Furuta M. Totoki Y. Tsunoda T. Kato M. Shiraishi Y. et al.Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer.Nat Genet. 2016; 48: 500-509Crossref PubMed Scopus (412) Google Scholar, 9Cancer Genome Atlas Research Network. Electronic address: [email protected], Cancer Genome Atlas Research Network. Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma.Cell. 2017; 169 (e23): 1327-1341Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, 10Laurent-Puig P. Zucman-Rossi J. Genetics of hepatocellular tumors.Oncogene. 2006; 25: 3778-3786Crossref PubMed Scopus (298) Google Scholar, 11Guichard C. Amaddeo G. Imbeaud S. Ladeiro Y. Pelletier L. Maad I.B. et al.Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma.Nat Genet. 2012; 44: 694-698Crossref PubMed Scopus (961) Google Scholar, 12Kan Z. Zheng H. Liu X. Li S. Barber T.D. Gong Z. et al.Whole-genome sequencing identifies recurrent mutations in hepatocellular carcinoma.Genome Res. 2013; 23: 1422-1433Crossref PubMed Scopus (349) Google Scholar, 13Fujimoto A. Totoki Y. Abe T. Boroevich K.A. Hosoda F. Nguyen H.H. et al.Whole-genome sequencing of liver cancers identifies etiological influences on mutation patterns and recurrent mutations in chromatin regulators.Nat Genet. 2012; 44: 760-764Crossref PubMed Scopus (638) Google Scholar]. These studies included whole genome, whole exome, and/or transcriptome sequencing with a focus on analyzing HCC induced by HBV, HCV, and/or cirrhosis. Prior studies, which have evaluated the genomics of cirrhotic and non-cirrhotic HCC, report that among the most significant and recurrent alterations are TERT mutations which typically occur at the promoter region [[8]Fujimoto A. Furuta M. Totoki Y. Tsunoda T. Kato M. Shiraishi Y. et al.Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer.Nat Genet. 2016; 48: 500-509Crossref PubMed Scopus (412) Google Scholar,[9]Cancer Genome Atlas Research Network. Electronic address: [email protected], Cancer Genome Atlas Research Network. Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma.Cell. 2017; 169 (e23): 1327-1341Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar,[14]Jhunjhunwala S. Jiang Z. Stawiski E.W. Gnad F. Liu J. Mayba O. et al.Diverse modes of genomic alteration in hepatocellular carcinoma.Genome Biol. 2014; 15: 436PubMed Google Scholar]. Mutations within this region have been observed in a variety of cancer types beyond cirrhotic HCC, suggesting a common role of activating TERT promoter variants in oncogenesis and metastasis [15Mosrati M.A. Malmström A. Lysiak M. Krysztofiak A. Hallbeck M. Milos P. et al.TERT promoter mutations and polymorphisms as prognostic factors in primary glioblastoma.Oncotarget. 2015; 6: 16663-16673Crossref PubMed Google Scholar, 16Huang F.W. Hodis E. Xu M.J. Kryukov G.V. Chin L. Garraway L.A. Highly recurrent TERT promoter mutations in human melanoma.Science. 2013; 339: 957-959Crossref PubMed Scopus (1225) Google Scholar, 17Hosen I. Rachakonda P.S. Heidenreich B. de Verdier P.J. Ryk C. Steineck G. et al.Mutations in TERT promoter and FGFR3 and telomere length in bladder cancer.Int J Cancer. 2015; 137: 1621-1629Crossref PubMed Google Scholar]. TERT expression in terminally differentiated cells promotes telomere maintenance and elongation [[18]Jafri M.A. Ansari S.A. Alqahtani M.H. Shay J.W. Roles of telomeres and telomerase in cancer, and advances in telomerase-targeted therapies.Genome Med. 2016; 8: 69Crossref PubMed Scopus (295) Google Scholar]. Telomere maintenance is required for late stage cancer propagation with TERT misregulation being harnessed by human cancers to evade mitotic catastrophe and apoptosis [[19]Blasco M.A. Telomeres and human disease: ageing, cancer and beyond.Nat Rev Genet. 2005; 6: 611-622Crossref PubMed Scopus (1166) Google Scholar]. Previous studies have recognized that increases in TERT expression could serve as a proxy for telomere maintenance; however, late-stage tumors exhibit shortened telomeres in comparison to their normal counterparts, due to high turnover rates [[20]Saini N. Srinivasan R. Chawla Y. Sharma S. Chakraborti A. Rajwanshi A. Telomerase activity, telomere length and human telomerase reverse transcriptase expression in hepatocellular carcinoma is independent of hepatitis virus status.Liver Int. 2009; 29: 1162-1170Crossref PubMed Google Scholar,[21]Fredriksson N.J. Ny L. Nilsson J.A. Larsson E. Systematic analysis of noncoding somatic mutations and gene expression alterations across 14 tumor types.Nat Genet. 2014; 46: 1258-1263Crossref PubMed Scopus (188) Google Scholar]. While counterintuitive, the presence of shortened telomeres in tumors with TERT overexpression is thought to arise in one of two ways. One manner is when somatic cells with critically short telomeres undergo senescence and selective pressure leading to the acquisition of the TERT promoter mutations and regeneration of telomerase to overcome telomeric crisis [[22]Heidenreich B. Kumar R. TERT promoter mutations in telomere biology.Mutat Res - Rev Mut Res. 2017; 771: 15-31Crossref PubMed Scopus (0) Google Scholar]. Another pathway to shortened telomeres is that a TERT promoter mutation is acquired by the pre-cancerous cell. At first, TERT and telomerase levels are marginal and do not prohibit telomere shortening. Critically short telomeres start accumulating and cells with TERT promoter mutations can then gradually upregulate TERT to stabilize critically short telomeres [[23]Lorbeer F.K. Hockemeyer D. TERT promoter mutations and telomeres during tumorigenesis.Curr Opin Genet Dev. 2020; 60: 56-62Crossref PubMed Scopus (19) Google Scholar]. Among studies specific to cirrhotic HCC, the putative mechanisms of TERT activation can be divided into three categories: 1) HBV integration events in the TERT promoter [[8]Fujimoto A. Furuta M. Totoki Y. Tsunoda T. Kato M. Shiraishi Y. et al.Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer.Nat Genet. 2016; 48: 500-509Crossref PubMed Scopus (412) Google Scholar,[24]Jiang Z. Jhunjhunwala S. Liu J. Haverty P.M. Kennemer M.I. Guan Y. et al.The effects of hepatitis B virus integration into the genomes of hepatocellular carcinoma patients.Genome Res. 2012; 22: 593-601Crossref PubMed Scopus (199) Google Scholar], 2) point mutations (C228T and C250T) in the promoter region mutually exclusive of HBV integration [[9]Cancer Genome Atlas Research Network. Electronic address: [email protected], Cancer Genome Atlas Research Network. Comprehensive and Integrative Genomic Characterization of Hepatocellular Carcinoma.Cell. 2017; 169 (e23): 1327-1341Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar,[25]Kawai-Kitahata F. Asahina Y. Tanaka S. Kakinuma S. Murakawa M. Nitta S. et al.Comprehensive analyses of mutations and hepatitis B virus integration in hepatocellular carcinoma with clinicopathological features.J Gastroenterol. 2016; 51: 473-486Crossref PubMed Google Scholar], and 3) structural variations of the TERT promoter region [[8]Fujimoto A. Furuta M. Totoki Y. Tsunoda T. Kato M. Shiraishi Y. et al.Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer.Nat Genet. 2016; 48: 500-509Crossref PubMed Scopus (412) Google Scholar,[14]Jhunjhunwala S. Jiang Z. Stawiski E.W. Gnad F. Liu J. Mayba O. et al.Diverse modes of genomic alteration in hepatocellular carcinoma.Genome Biol. 2014; 15: 436PubMed Google Scholar]. This study characterizes biomarkers and elucidates recurrent anomalies in non-cirrhotic HCC. We identified somatic variants in 117 tumor samples whereby 52 samples were cirrhotic, 63 samples were non-cirrhotic, and 2 samples had an unspecified cirrhotic status. Using this cohort, we analyzed single nucleotide variants (SNVs), insertions and deletions (INDELs), structural variation (SV), copy number variation (CNV), loss of heterozygosity (LOH), differential expression, and viral integration events. This comprehensive approach uncovered the genomic features implicated in non-cirrhotic HCC to improve its diagnosis, prognosis, and treatment. The discovery cohort consisted of 30 primary tumor and adjacent matched non-tumor liver samples obtained through surgical resection from adult patients diagnosed with HCC between 2000 to 2011 at the Washington University School of Medicine. Within this cohort, 13 were male and 17 were female. Additionally, 2 were African American and 28 were Caucasian. None of these samples exhibited evidence of hepatocellular adenoma (HCA) and the non-cirrhotic samples did not show signs of advanced fibrosis. 1 sample was HBV positive and 4 samples were HCV positive according to clinical data. All other samples within the discovery cohort had an unknown clinical etiology. The extension-alpha and extension-beta cohorts had 16 HCC tumors with matched non-tumor liver and 71 tumor-only HCC samples, respectively. Discovery and extension-alpha cohort samples were flash-frozen prior to banking and extension-beta samples were derived from formalin fixed paraffin embedded (FFPE) blocks. Across both extension cohorts, 27 were female and 58 were male. Furthermore, 2 were Asian, 13 were African American, and 70 were Caucasian. Within the extension-alpha cohort, two samples were HCV positive, one had chronic cholestasis, and the others had no known clinical etiology. Clinical data for the extension-beta cohort was as follows: 5 had known alcohol use, 8 were HBV positive, 29 were HCV positive, 2 were diagnosed with primary sclerosing cholangitis (PSC), and 6 samples were diagnosed with non-alcoholic steatohepatitis (NASH). From the extension-alpha cohort, 2 patients did not provide information on race and gender (Table S1). All patient samples were acquired after informed consent to an approved study by the Washington University School of Medicine Institutional Review Board (IRB 201106388). DNA and RNA from samples in the discovery cohort were extracted using the QIAamp DNA Mini kit and Qiagen RNeasy Mini kit, respectively. Whole genome sequencing libraries were constructed using Kapa HYPER kits for use on the Illumina HiSeq 2000 platform. The Ovation RNA-seq System V2 (NuGen Inc) kit was used to generate RNAseq libraries. Resulting barcoded libraries were pooled prior to Illumina sequencing. To validate variants identified from WGS, a hybrid capture panel (CAP1) was designed and executed on the Illumina platform to capture fragments from the WGS libraries. The QIAamp DNA Mini kit was used to extract DNA from extension-alpha samples, which was subsequently sequenced using the CAP1 strategy. Finally, CAP1 sequencing was used to identify variants from the DNA extracted from extension-beta samples with the QIAamp DNA FFPE Tissue kit. A second hybrid capture panel (CAP2) utilized Nimblegen and spiked-in IDT probes that hybridized to the TERT promoter locus and HBV genome (designed against a consensus sequence for 10 common HBV strains, see supplementary methods). CAP2 sequencing was employed on all 117 samples. TERT promoter variants were also detected in the discovery and extension-alpha cohorts with Sanger sequencing. cDNA capture was performed on pooled samples from the extension cohorts. WGS and CAP1 data were aligned to GRCh37 via the Genome Modeling System (GMS) using BWA [[26]Griffith M. Griffith O.L. Smith S.M. Ramu A. Callaway M.B. Brummett A.M. et al.Genome Modeling System: A Knowledge Management Platform for Genomics.PLoS Comput Biol. 2015; 11e1004274Crossref Scopus (53) Google Scholar,[27]Li H. Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.Bioinformatics. 2009; 25: 1754-1760Crossref PubMed Scopus (23536) Google Scholar]. Reads from the CAP2 data were competitively aligned using BWA [[27]Li H. Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform.Bioinformatics. 2009; 25: 1754-1760Crossref PubMed Scopus (23536) Google Scholar] against the human reference genome (GRCh37) along with ten HBV genotypes for which complete genomes were available. RNAseq data were aligned with bowtie/tophat and expression was evaluated with cufflinks [[28]Trapnell C. Williams B.A. Pertea G. Mortazavi A. Kwan G. van Baren M.J. et al.Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation.Nat Biotechnol. 2010; 28: 511-515Crossref PubMed Scopus (9695) Google Scholar,[29]Trapnell C. Pachter L. Salzberg S.L. TopHat: discovering splice junctions with RNA-Seq.Bioinformatics. 2009; 25: 1105-1111Crossref PubMed Scopus (8604) Google Scholar]. All raw RNAseq reads from the discovery cohort were also aligned against the HBV genomes for evidence of HBV expression at the RNA level. The predominant HBV strain was determined using relative coverage for competitive alignments. The precise location of the HBV integration site was identified from discordant read pairs from realigning HBV CAP2 reads to GRCh37 and the predominant HBV strain's genome. A similar procedure was performed for HCV whereby both WGS and RNAseq reads were aligned against six HCV genotypes. The predominant HCV strain was determined using the total read support. To detect AAV1 and AAV2 integration, RNAseq reads were competitively aligned using kallisto [[30]Bray N.L. Pimentel H. Melsted P. Pachter L. Near-optimal probabilistic RNA-seq quantification.Nat Biotechnol. 2016; 34: 525-527Crossref PubMed Scopus (3035) Google Scholar] against AAV1 and AAV2 sequences. Telomeric tumor:normal read ratios were determined from WGS data using the GMS and visualized in R. A Wilcoxon-Mann-Whitney test measured the significance of differences between telomere length in tumor and normal samples. Somatic variant analysis for single nucleotide variants (SNV) and insertions/deletions (INDEL) were performed on all three cohorts while germline variant analysis for these variants was performed on the discovery and extension-alpha cohort. Several computational tools within and outside of the GMS [[31]Griffith M. Griffith O.L. Smith S.M. Ramu A. Callaway M.B. Brummett A.M. et al.Genome Modeling System: A Knowledge Management Platform for Genomics.PLoS Comput Biol. 2015; 11e1004274Crossref Scopus (53) Google Scholar] were employed to facilitate variant calling and subsequent filtering based on variables including variant allele frequency, read count, and predicted pathogenicity. WGS data from samples within the discovery cohort were analyzed for structural variants (SV), copy number variation (CNV), and loss of heterozygosity (LOH). Manta [[32]Chen X. Schulz-Trieglaff O. Shaw R. Barnes B. Schlesinger F. Källberg M. et al.Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications.Bioinformatics. 2016; 32: 1220-1222Crossref PubMed Scopus (589) Google Scholar] was used to identify SV events. Manta-reported breakpoints, along with a 10kb flank were annotated with biomaRt and ensembl (GRCh37.p13). Regions of CNV were identified with the GMS and LOH were identified using VarScan2 [[31]Griffith M. Griffith O.L. Smith S.M. Ramu A. Callaway M.B. Brummett A.M. et al.Genome Modeling System: A Knowledge Management Platform for Genomics.PLoS Comput Biol. 2015; 11e1004274Crossref Scopus (53) Google Scholar,[33]Koboldt D.C. Zhang Q. Larson D.E. Shen D. McLellan M.D. Lin L. et al.VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing.Genome Res. 2012; 22: 568-576Crossref PubMed Scopus (2656) Google Scholar]. The DNAcopy circular binary segmentation algorithm generated segments of LOH and CNV, which served as input for GISTIC [[34]Mermel C.H. Schumacher S.E. Hill B. Meyerson M.L. Beroukhim R. Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers.Genome Biol. 2011; 12: R41Crossref PubMed Scopus (1487) Google Scholar] to conduct a recurrence analysis. Fusion detection algorithms identified samples in the discovery cohort harboring gene fusions from RNAseq data. Fusion predictions involving NR1H4 were validated across all 117 samples using a NanoString nCounter® Elements™ TagSets assay. Sequences for predicted transcripts of the fusion calls that met certain read support criteria (≥10 spanning + encompassing reads and ≥1 spanning read) were sent to NanoString for probe design. The R “survival” package [[35]Therneau T.M. Grambsch P.M. Modeling Survival Data: Extending the Cox Model. Springer Science & Business Media, 2013Google Scholar] was used to associate SV-affected genes and CNV/LOH-affected genomic regions with overall survival and recurrence free survival. Only mutated genes and genomic regions occuring in ≥ 4 discovery cohort samples were included in this analysis. A survival analysis was also applied to SNV/INDELs observed in all non-cirrhotic samples from the three cohorts. All Kaplan-Meier survival plots were created in R. Fisher's exact test was used to test for clinical associations with variables: lymphovascular space invasion (LVSI), tumor differentiation status, cirrhosis, and liver disease. Samples without relevant clinical data were excluded. Significance was measured with a multiple test correction using the FDR methodology (q-value < 0.05). Read counts for genes mutated in non-cirrhotic tumors and matched normal samples of the discovery cohort were used by the DEseq2 Bioconductor package [[36]Love M.I. Huber W. Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.Genome Biol. 2014; 15https://doi.org/10.1186/s13059-014-0550-8Crossref Scopus (24864) Google Scholar] to perform differential gene expression analysis using a negative binomial distribution with samples as a blocking factor. Significance was measured with a Wald test and Benjamini & Hochberg multiple test correction (q-value < 0.5). Pathway analysis was performed using log2 differential expression data. There were 30 patients included in the discovery cohort with tumors which were surgically resectable. These surgically resectable tumors were untreated, providing the opportunity to study HCC in the absence of chemotherapeutic intervention, which is normally incorporated in the treatment of cirrhotic HCC. Three of the patients within this cohort developed HCC in the setting of cirrhosis, all of which had been previously diagnosed with HCV. The remaining 27 individuals developed non-cirrhotic HCC, two of these individuals were diagnosed with HBV and another two individuals were diagnosed with HCV. To elucidate the genomic landscape of resected, primarily non-cirrhotic HCC, we performed whole genome sequencing (WGS), hybrid capture sequencing (CAP1), and transcriptome sequencing (RNAseq) on these 30 samples (Table 1). WGS failed for one tumor sample in the discovery cohort, therefore the final data for this cohort included WGS and CAP1 data for 29 samples (26 non-cirrhotic, 3 cirrhotic), and RNAseq data for 30 samples (27 non-cirrhotic, 3 cirrhotic). The sequencing analysis revealed a single previously unknown and undiagnosed HBV case with viral integration occurring at the TERT promoter (Figure S1, Table S1). Median haploid coverage for WGS data was 35.6x (range: 28.5-39.3) and 58.4x (range: 46.8-94.4) for normal and tumor samples, respectively.Table 1Discovery (N=29)Extension-alpha (N=16)Extension-beta (N=71)Sample TypeTumor/Non-tumorTumor/Non-tumorTumorWGSYesNoNoCAP1YesYesYesCAP2YesYesYesRNAseqYesNoNo Open table in a new tab After filtering, we observed a median mutation burden of 1.31 mutations/Mb (range: 0.033-3.28), comprised of 2,633 SNVs and INDELs across all samples (range: 2-200, median: 77.5, mean=87.8) (Figure 1, Table S1). These variants were discovered across 2,245 genes with 258 of these genes mutated in more than one sample. Using WGS data from the 26 non-cirrhotic samples, we identified 6 genes that were significantly mutated above background mutation rates according to MuSiC: ALB, APOB, CTNNB1, TP53, RB1, and RPS6KA3 (Figure 1, Table S1). With regards to all methods of sequencing (WGS, RNAseq, CAP1, and CAP2), the most frequently encountered variant was a SNV in the telomerase reverse transcriptase (TERT) promoter (C228T; G1295228A), which was identified in 17/30 samples and resulted in overexpression of TERT (Figure S2, Table S2). Within the exome, TP53 was the most recurrently mutated gene and was observed in 8/29 of samples (Table S1). Beta catenin 1 (CTNNB1) was also significantly mutated within this cohort (6/29), whereby the majority of variants occurred at amino acids S37 and S45, both of which reside in a putative GSK3B phosphorylation site in exon 3 (ENST00000349496) (Figure S3) [[37]Miyoshi Y. Iwao K. Nagasawa Y. Aihara T. Sasaki Y. Imaoka S. et al.Activation of the beta-catenin gene in primary hepatocellular carcinomas by somatic alterations involving exon 3.Cancer Res. 1998; 58: 2524-2527PubMed Google Scholar]. Frameshift mutations in APOB were observed in 4/29 of samples (Table S1). Mutation signatures using the COSMIC database for the discovery cohort were investigated. Signatures 5 (unknown etiology), 4 (smoking damage association), 16 (unknown etiology), and 12 (liver damage association) were most prevalent and contributed to the overall cohort signature at 23%, 14%, 8%, and 7%, respectively (Figure S4). Differential gene expression analysis performed on the non-cirrhotic samples revealed that 11% of genes, including TERT, were upregulated (4,468/39,392) and 10% of genes, including CTNNB1 and WISP2, were downregulated (4,114/39,392) compared to adjacent non-tumor liver tissue (q-value < 0.1) (Table S1). Comparison of gene log2 fold changes derived from the differential expression analysis revealed the cell cycle pathway as upregulated in the KEGG signaling and metabolism database (q-value ≤ 0.05). Similarly, we observed 16 pathways as down-regulated (q-value ≤ 0.05), most of which are related to metabolic liver processes. Genes such as ADH5 and EHHADH were observed with reduced expression levels and participate in 38% (6/16) of these pathways. Using the Gene Ontology biological process database, we observed 107 pathways as significantly upregulated (q-value ≤ 0.05). The majority of the upregulated pathways were related to cellular division and DNA repair. In addition, 28 pathways were identified as significantly downregulated (q-value ≤ 0.05), many of which were related to liver metabolism (Table S2). When evaluating the samples within the discovery cohort for telomere length at the DNA level, we observed that the majority of tumor samples exhibited shortened telomeres compared to their paired normal sample (p-value = 0.00011) (Figure S2). One exception was seen in sample HCC16_D, which was distinguished by abnormally high expression of TERT (FPKM=36) (Figure S5). We observed recurrent large scale amplification of the q-arm of chromosome 1 in ≥ 50% of the discovery cohort. Similarly, large scale deletions of the p-arms of chromosomes 8 and 17 were found in ≥ 40% of the cohort (Figure 2). In total, analysis with GISTIC and subsequent manual review revealed 75 unique regions across 17 chromosomes as recurrently amplified and 45 unique regions across 17 chromosomes as significantly deleted (q < 0.05) (Table S1). No significant associations with tumor differentiation status were made (α=0.05). Each CNV and LOH event was tested for their association with overall survival and recurrence free survival but no significant association could be made following multiple test correction. A total of 33 genes identified as recurrently deleted by GISTIC showed concordant decreased expression in tumor samples (Table S1). These include genes previously characterized as relevant to HCC development and progression: HEYL [[38]Kuo K.-K. Jian S.-F. Li Y.-J. Wan S.-W. Weng C.-C. Fang K. et al.Epigenetic inactivation of transforming growth factor-β1 target gene HEYL, a novel tumor suppressor, is involved in the P53-induced apoptotic pathway in hepatocellular carcinoma.Hepatol Res. 2014; 45: 782-793Crossref PubMed Scopus (11) Google Scholar] (q-value = 0.032), UQCRH [[39]Park E.-R. Kim S.-B. Lee J.-S. Kim Y.-H. Lee D.-H. Cho E.-H. et al.The mitochondrial hinge protein, UQCRH, is a novel prognostic factor for hepatocellular carcinoma.Cancer Med. 2017; 6: 749-760Crossref PubMed Scopus (19) Google Scholar] (q-value = 0.032), and MUTYH [[40]Krupa R. Czarny P. Wigner P. Wozny J. Jablkowski M. Kordek R. et al.The Relationship Between Single-Nucleotide Polymorphisms, the Expression of DNA Damage Response Genes, and Hepatocellular Carcinoma in a Polish Population.DNA Cell Biol. 2017; https://doi.org/10.1089/dna.2017.3664Crossref PubMed Scopus (13) Google Scholar] (q-value = 0.048). A subset of these genes have also been implicated in tumorigenesis, metastasis, and progression of other cancer types and may prove to be relevant for HCC development and progression: RPL11 [[41]Takafuji T. Kayama K. Sugimoto N. Fujita M. GRWD1, a new player among oncogenesis-related ribosomal/nucleolar proteins.Cell Cycle. 2017; : 1-7Google Scholar] (q-value = 0.048), UBE2D3 [[42]Guan G.G. Wang W.B. Lei B.X. Wang Q.L. Wu L. Fu Z.M. et al.UBE2D3 is a positive prognostic factor and is negatively correlated with hTERT expression in esophageal cancer.Oncol Lett. 2015; 9: 1567-1574Crossref PubMed Scopus (17) Google Scholar] (q-value = 0.032), ARRB1 [[43]Miele E. Po A. Begalli F. Antonucci L. Mastronuzzi A. Marras C.E. et al.β-arrestin1-mediated acetylation of Gli1 regulates Hedgehog/Gli signaling and modulates self-renewal of SHH medulloblastoma cancer stem cells.BMC Cancer. 2017; 17: 488Crossref PubMed Scopus (0) Google Scholar] (q-value = 0.032), ENG [[44]Kokaji E. Shimomura A. Minamisaka T. Nakajima T. Miwa S. Hatta H. et al.Endoglin (CD105) and SMAD4 regulate spheroid formation and the suppression of the invasive ability of human pancreatic cancer cells.Int J Oncol. 2018; 52: 892-900PubMed Google Scholar] (q-value = 0.049), and ABLIM2 [[45]Hwang S.J. Lee H.W. Kim H.R. Song H.J. Lee D.H. Lee H. et al.Overexpression of microRNA-95-3p suppresses

Convergent Clonal Evolution of Signaling Gene Mutations Is a Hallmark of Myelodysplastic Syndrome Progression.

Abstract: Progression from myelodysplastic syndromes (MDS) to secondary acute myeloid leukemia (AML) is associated with the acquisition and expansion of subclones. Our understanding of subclone evolution during progression, including the frequency and preferred order of gene mutation acquisition, remains incomplete. Sequencing of 43 paired MDS and secondary AML samples identified at least one signaling gene mutation in 44% of MDS and 60% of secondary AML samples, often below the level of standard sequencing detection. In addition, 19% of MDS and 47% of secondary AML patients harbored more than one signaling gene mutation, almost always in separate, coexisting subclones. Signaling gene mutations demonstrated diverse patterns of clonal evolution during disease progression, including acquisition, expansion, persistence, and loss of mutations, with multiple patterns often coexisting in the same patient. Multivariate analysis revealed that MDS patients who had a signaling gene mutation had a higher risk of AML progression, potentially providing a biomarker for progression. SIGNIFICANCE Subclone expansion is a hallmark of progression from MDS to secondary AML. Subclonal signaling gene mutations are common at MDS (often at low levels), show complex and convergent patterns of clonal evolution, and are associated with future progression to secondary AML. See related article by Guess et al., p. 316 (33). See related commentary by Romine and van Galen, p. 270.

Nine out of ten samples were mistakenly switched by The Orang-utan Genome Consortium

Abstract: Abstract The Sumatran orang-utan (Pongo abelii) reference genome was first published in 2011, in conjunction with ten re-sequenced genomes from unrelated wild-caught individuals. Together, these published data have been utilized in almost all great ape genomic studies, plus in much broader comparative genomic research. Here, we report that the original sequencing Consortium inadvertently switched nine of the ten samples and/or resulting re-sequenced genomes, erroneously attributing eight of these to the wrong source individuals. Among them is a genome from the recently identified Tapanuli (P. tapanuliensis) species: thus, this genome was sequenced and published a full six years prior to the species’ description. Sex was wrongly assigned to five known individuals; the numbers in one sample identifier were swapped; and the identifier for another sample most closely resembles that of a sample from another individual entirely. These errors have been reproduced in countless subsequent manuscripts, with noted implications for studies reliant on data from known individuals.

Neoantigen Landscape Supports Feasibility of Personalized Cancer Vaccine for Follicular Lymphoma

Abstract: Personalized cancer vaccines designed to target neoantigens represent a promising new treatment paradigm in oncology. In contrast to classical idiotype vaccines, we hypothesized that polyvalent vaccines could be engineered for the personalized treatment of follicular lymphoma (FL) using neoantigen discovery by combined whole exome sequencing (WES) and RNA sequencing (RNA-Seq). Fifty-eight tumor samples from 57 patients with FL underwent WES and RNA-Seq. Somatic and B-cell clonotype neoantigens were predicted and filtered to identify high-quality neoantigens. B-cell clonality was determined by alignment of B-cell receptor (BCR) CDR3 regions from RNA-Seq data, grouping at the protein level, and comparison to the BCR repertoire of RNA-Seq data from healthy individuals. An average of 52 somatic mutations per patient (range: 2-172) were identified, and two or more (median: 15) high-quality neoantigens were predicted for 56 of 58 samples. The predicted neoantigen peptides were composed of missense mutations (76%), indels (9%), gene fusions (3%), and BCR sequences (11%). Building off of these preclinical analyses, we initiated a pilot clinical trial using personalized neoantigen vaccination combined with PD-1 blockade in patients with relapsed or refractory FL (#NCT03121677). Synthetic long peptide (SLP) vaccines were successfully synthesized for and administered to all four patients enrolled to date. Initial results demonstrate feasibility, safety, and potential immunologic and clinical responses. Our study suggests that a genomics-driven personalized cancer vaccine strategy is feasible for patients with FL, and this may overcome prior challenges in the field.