VIRUSBreakend: Viral Integration Recognition Using Single Breakends

A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory

BACKGROUND & AIMS

Hepatocellular carcinoma (HCC) is a highly fatal cancer characterized by high intra-tumor heterogeneity (ITH). A panoramic understanding of its tumor evolution, in relation to its clinical trajectory, may provide novel prognostic and treatment strategies.

METHODS

Through the Asia-Pacific Hepatocellular Carcinoma trials group (NCT03267641), we recruited one of the largest prospective cohorts of patients with HCC, with over 600 whole genome and transcriptome samples from 123 treatment-naïve patients.

RESULTS

Using a multi-region sampling approach, we revealed seven convergent genetic evolutionary paths governed by the early driver mutations, late copy number variations and viral integrations, which stratify patient clinical trajectories after surgical resection. Furthermore, such evolutionary paths shaped the molecular profiles, leading to distinct transcriptomic subtypes. Most significantly, although we found the coexistence of multiple transcriptomic subtypes within certain tumors, patient prognosis was best predicted by the most aggressive cell fraction of the tumor, rather than by overall degree of transcriptomic ITH level - a phenomenon we termed the 'bad apple' effect. Finally, we found that characteristics throughout early and late tumor evolution provide significant and complementary prognostic power in predicting patient survival.

CONCLUSIONS

Taken together, our study generated a comprehensive landscape of evolutionary history for HCC and provides a rich multi-omics resource for understanding tumor heterogeneity and clinical trajectories.

IMPACT AND IMPLICATIONS

This prospective study, utilizing comprehensive multi-sector, multi-omics sequencing and clinical data from surgically resected hepatocellular carcinoma (HCC), reveals critical insights into the role of tumor evolution and intra-tumor heterogeneity (ITH) in determining the prognosis of HCC. These findings are invaluable for oncology researchers and clinicians, as they underscore the influence of distinct evolutionary paths and the 'bad apple' effect, where the most aggressive tumor fraction dictates disease progression. These insights not only enhance prognostic accuracy post-surgical resection but also pave the way for personalized treatment strategies tailored to specific tumor evolutionary and transcriptomic profiles. The coexistence of multiple subtypes within the same tumor prompts a re-appraisal of the utilities of depending on single samples to represent the entire tumor and suggests the need for clinical molecular imaging. This research thus marks a significant step forward in the clinical understanding and management of HCC, underscoring the importance of integrating tumor evolutionary dynamics and multi-omics biomarkers into therapeutic decision-making.

CLINICAL TRIAL NUMBER

NCT03267641 (Observational cohort).

Most large structural variants in cancer genomes can be detected without long reads

Short-read sequencing is the workhorse of cancer genomics yet is thought to miss many structural variants (SVs), particularly large chromosomal alterations. To characterize missing SVs in short-read whole genomes, we analyzed 'loose ends'-local violations of mass balance between adjacent DNA segments. In the landscape of loose ends across 1,330 high-purity cancer whole genomes, most large (>10-kb) clonal SVs were fully resolved by short reads in the 87% of the human genome where copy number could be reliably measured. Some loose ends represent neotelomeres, which we propose as a hallmark of the alternative lengthening of telomeres phenotype. These pan-cancer findings were confirmed by long-molecule profiles of 38 breast cancer and melanoma cases. Our results indicate that aberrant homologous recombination is unlikely to drive the majority of large cancer SVs. Furthermore, analysis of mass balance in short-read whole genome data provides a surprisingly complete picture of cancer chromosomal structure.

Integrated genetic analyses of immunodeficiency-associated Epstein-Barr virus- (EBV) positive primary CNS lymphomas

Immunodeficiency-associated primary CNS lymphoma (PCNSL) represents a distinct clinicopathological entity, which is typically Epstein-Barr virus-positive (EBV+) and carries an inferior prognosis. Genetic alterations that characterize EBV-related CNS lymphomagenesis remain unclear precluding molecular classification and targeted therapies. In this study, a comprehensive genetic analysis of 22 EBV+ PCNSL, therefore, integrated clinical and pathological information with exome and RNA sequencing (RNASeq) data. EBV+ PCNSL with germline controls carried a median of 55 protein-coding single nucleotide variants (SNVs; range 24-217) and 2 insertions/deletions (range 0-22). Genetic landscape was largely shaped by aberrant somatic hypermutation with a median of 41.01% (range 31.79-53.49%) of SNVs mapping to its target motifs. Tumors lacked established SNVs (MYD88, CD79B, PIM1) and copy number variants (CDKN2A, HLA loss) driving EBV- PCNSL. Instead, EBV+ PCNSL were characterized by SOCS1 mutations (26%), predicted to disinhibit JAK/STAT signaling, and mutually exclusive gain-of-function NOTCH pathway SNVs (26%). Copy number gains were enriched on 11q23.3, a locus directly targeted for chromosomal aberrations by EBV, that includes SIK3 known to protect from cytotoxic T-cell responses. Losses covered 5q31.2 (STING), critical for sensing viral DNA, and 17q11 (NF1). Unsupervised clustering of RNASeq data revealed two distinct transcriptional groups, that shared strong expression of CD70 and IL1R2, previously linked to tolerogenic tumor microenvironments. Correspondingly, deconvolution of bulk RNASeq data revealed elevated M2-macrophage, T-regulatory cell, mast cell and monocyte fractions in EBV+ PCNSL. In addition to novel insights into the pathobiology of EBV+ PCNSL, the data provide the rationale for the exploration of targeted therapies including JAK-, NOTCH- and CD70-directed approaches.

Re-analysis of hepatitis B virus integration sites reveals potential new loci associated with oncogenesis in hepatocellular carcinoma

BACKGROUND

Hepatitis B virus (HBV) is a major cause of hepatocellular carcinoma (HCC). HBV DNA can get integrated into the hepatocyte genome to promote carcinogenesis. However, the precise mechanism by which the integrated HBV genome promotes HCC has not been elucidated.

AIM

To analyze the features of HBV integration in HCC using a new reference database and integration detection method.

METHODS

Published data, consisting of 426 Liver tumor samples and 426 paired adjacent non-tumor samples, were re-analyzed to identify the integration sites. Genome Reference Consortium Human Build 38 (GRCh38) and Telomere-to-Telomere Consortium CHM13 (T2T-CHM13 (v2.0)) were used as the human reference genomes. In contrast, human genome 19 (hg19) was used in the original study. In addition, GRIDSS VIRUSBreakend was used to detect HBV integration sites, whereas high-throughput viral integration detection (HIVID) was applied in the original study (HIVID-hg19).

RESULTS

A total of 5361 integration sites were detected using T2T-CHM13. In the tumor samples, integration hotspots in the cancer driver genes, such as TERT and KMT2B, were consistent with those in the original study. GRIDSS VIRUSBreakend detected integrations in more samples than by HIVID-hg19. Enrichment of integration was observed at chromosome 11q13.3, including the CCND1 pro-moter, in tumor samples. Recurrent integration sites were observed in mitochondrial genes.

CONCLUSION

GRIDSS VIRUSBreakend using T2T-CHM13 is accurate and sensitive in detecting HBV integration. Re-analysis provides new insights into the regions of HBV integration and their potential roles in HCC development.

Molecular Evolution of Classic Hodgkin Lymphoma Revealed Through Whole-Genome Sequencing of Hodgkin and Reed Sternberg Cells

The rarity of malignant Hodgkin and Reed Sternberg (HRS) cells in classic Hodgkin lymphoma (cHL) limits the ability to study the genomics of cHL. To circumvent this, our group has previously optimized fluorescence-activated cell sorting to purify HRS cells. Using this approach, we now report the whole-genome sequencing landscape of HRS cells and reconstruct the chronology and likely etiology of pathogenic events leading to cHL. We identified alterations in driver genes not previously described in cHL, APOBEC mutational activity, and the presence of complex structural variants including chromothripsis. We found that high ploidy in cHL is often acquired through multiple, independent chromosomal gains events including whole-genome duplication. Evolutionary timing analyses revealed that structural variants enriched for RAG motifs, driver mutations in B2M, BCL7A, GNA13, and PTPN1, and the onset of AID-driven mutagenesis usually preceded large chromosomal gains. This study provides a temporal reconstruction of cHL pathogenesis.

SIGNIFICANCE

Previous studies in cHL were limited to coding sequences and therefore not able to comprehensively decipher the tumor complexity. Here, leveraging cHL whole-genome characterization, we identify driver events and reconstruct the tumor evolution, finding that structural variants, driver mutations, and AID mutagenesis precede chromosomal gains. This article is highlighted in the In This Issue feature, p. 171.

AlcoR: alignment-free simulation, mapping, and visualization of low-complexity regions in biological data

BACKGROUND

Low-complexity data analysis is the area that addresses the search and quantification of regions in sequences of elements that contain low-complexity or repetitive elements. For example, these can be tandem repeats, inverted repeats, homopolymer tails, GC-biased regions, similar genes, and hairpins, among many others. Identifying these regions is crucial because of their association with regulatory and structural characteristics. Moreover, their identification provides positional and quantity information where standard assembly methodologies face significant difficulties because of substantial higher depth coverage (mountains), ambiguous read mapping, or where sequencing or reconstruction defects may occur. However, the capability to distinguish low-complexity regions (LCRs) in genomic and proteomic sequences is a challenge that depends on the model's ability to find them automatically. Low-complexity patterns can be implicit through specific or combined sources, such as algorithmic or probabilistic, and recurring to different spatial distances-namely, local, medium, or distant associations.

FINDINGS

This article addresses the challenge of automatically modeling and distinguishing LCRs, providing a new method and tool (AlcoR) for efficient and accurate segmentation and visualization of these regions in genomic and proteomic sequences. The method enables the use of models with different memories, providing the ability to distinguish local from distant low-complexity patterns. The method is reference and alignment free, providing additional methodologies for testing, including a highly flexible simulation method for generating biological sequences (DNA or protein) with different complexity levels, sequence masking, and a visualization tool for automatic computation of the LCR maps into an ideogram style. We provide illustrative demonstrations using synthetic, nearly synthetic, and natural sequences showing the high efficiency and accuracy of AlcoR. As large-scale results, we use AlcoR to unprecedentedly provide a whole-chromosome low-complexity map of a recent complete human genome and the haplotype-resolved chromosome pairs of a heterozygous diploid African cassava cultivar.

CONCLUSIONS

The AlcoR method provides the ability of fast sequence characterization through data complexity analysis, ideally for scenarios entangling the presence of new or unknown sequences. AlcoR is implemented in C language using multithreading to increase the computational speed, is flexible for multiple applications, and does not contain external dependencies. The tool accepts any sequence in FASTA format. The source code is freely provided at https://github.com/cobilab/alcor.

Viral Integration Plays a Minor Role in the Development and Prognostication of Oral Squamous Cell Carcinoma

Viruses are well known drivers of several human malignancies. A causative factor for oral cavity squamous cell carcinoma (OSCC) in patients with limited exposure to traditional risk factors, including tobacco use, is yet to be identified. Our study aimed to comprehensively evaluate the role of viral drivers in OSCC patients with low cumulative exposure to traditional risk factors. Patients under 50 years of age with OSCC, defined using strict anatomic criteria were selected for WGS. The WGS data was interrogated using viral detection tools (Kraken 2 and BLASTN), together examining >700,000 viruses. The findings were further verified using tissue microarrays of OSCC samples using both immunohistochemistry and RNA in situ hybridisation (ISH). 28 patients underwent WGS and comprehensive viral profiling. One 49-year-old male patient with OSCC of the hard palate demonstrated HPV35 integration. 657 cases of OSCC were then evaluated for the presence of HPV integration through immunohistochemistry for p16 and HPV RNA ISH. HPV integration was seen in 8 (1.2%) patients, all middle-aged men with predominant floor of mouth involvement. In summary, a wide-ranging interrogation of >700,000 viruses using OSCC WGS data showed HPV integration in a minority of male OSCC patients and did not carry any prognostic significance.

Comprehensive genomic and epigenomic analysis in cancer of unknown primary guides molecularly-informed therapies despite heterogeneity

The benefit of molecularly-informed therapies in cancer of unknown primary (CUP) is unclear. Here, we use comprehensive molecular characterization by whole genome/exome, transcriptome and methylome analysis in 70 CUP patients to reveal substantial mutational heterogeneity with TP53, MUC16, KRAS, LRP1B and CSMD3 being the most frequently mutated known cancer-related genes. The most common fusion partner is FGFR2, the most common focal homozygous deletion affects CDKN2A. 56/70 (80%) patients receive genomics-based treatment recommendations which are applied in 20/56 (36%) cases. Transcriptome and methylome data provide evidence for the underlying entity in 62/70 (89%) cases. Germline analysis reveals five (likely) pathogenic mutations in five patients. Recommended off-label therapies translate into a mean PFS ratio of 3.6 with a median PFS1 of 2.9 months (17 patients) and a median PFS2 of 7.8 months (20 patients). Our data emphasize the clinical value of molecular analysis and underline the need for innovative, mechanism-based clinical trials.

The identification of molecular biomarkers in cancer of unknown primary site (CUP) cases may enable the improvement of prognosis in these patients. Here, the authors integrate whole genome/exome, transcriptome and methylome data in 70 CUP patients, recommend therapies based on their analysis and report clinical outcome data.

Potential multi-modal effects of provirus integration on HIV-1 persistence: lessons from other viruses

Despite antiretroviral therapy (ART), HIV-1 persists as proviruses integrated into the genomic DNA of CD4⁺ T cells. The mechanisms underlying the persistence and clonal expansion of these cells remain incompletely understood. Cases have been described in which proviral integration can alter host gene expression to drive cellular proliferation. Here, we review observations from other genome-integrating human viruses to propose additional putative modalities by which HIV-1 integration may alter cellular function to favor persistence, such as by altering susceptibility to cytotoxicity in virus-expressing cells. We propose that signals implicating such mechanisms may have been masked thus far by the preponderance of defective and/or nonreactivatable HIV-1 proviruses, but could be revealed by focusing on the integration sites of intact proviruses with expression potential.

The third international hackathon for applying insights into large-scale genomic composition to use cases in a wide range of organisms

In October 2021, 59 scientists from 14 countries and 13 U.S. states collaborated virtually in the Third Annual Baylor College of Medicine & DNANexus Structural Variation hackathon. The goal of the hackathon was to advance research on structural variants (SVs) by prototyping and iterating on open-source software. This led to nine hackathon projects focused on diverse genomics research interests, including various SV discovery and genotyping methods, SV sequence reconstruction, and clinically relevant structural variation, including SARS-CoV-2 variants. Repositories for the projects that participated in the hackathon are available at https://github.com/collaborativebioinformatics.

Isling: a tool for detecting integration of wild-type viruses and clinical vectors

Detecting viral and vector integration events is a key step when investigating interactions between viral and host genomes. This is relevant in several fields, including virology, cancer research and gene therapy. For example, investigating integrations of wild-type viruses such as human papillomavirus and hepatitis B virus has proven to be crucial for understanding the role of these integrations in cancer. Furthermore, identifying the extent of vector integration is vital for determining the potential for genotoxicity in gene therapies. To address these questions, we developed isling, the first tool specifically designed for identifying viral integrations in both wild-type and vector from next-generation sequencing data. Isling addresses complexities in integration behaviour including integration of fragmented genomes and integration junctions with ambiguous locations in a host or vector genome, and can also flag possible vector recombinations. We show that isling is up to 1.6-fold faster and up to 170% more accurate than other viral integration tools, and performs well on both simulated and real datasets. Isling is therefore an efficient and application-agnostic tool that will enable a broad range of investigations into viral and vector integration. These include comparisons between integrations of wild-type viruses and gene therapy vectors, as well as assessing the genotoxicity of vectors and understanding the role of viruses in cancer.

Causes and Consequences of HPV Integration in Head and Neck Squamous Cell Carcinomas: State of the Art

A constantly increasing incidence in high-risk Human Papillomaviruses (HPV)s driven head and neck squamous cell carcinomas (HNSCC)s, especially of oropharyngeal origin, is being observed. During persistent infections, viral DNA integration into the host genome may occur. Studies are examining if the physical status of the virus (episomal vs. integration) affects carcinogenesis and eventually has further-reaching consequences on disease progression and outcome. Here, we review the literature of the most recent five years focusing on the impact of HPV integration in HNSCCs, covering aspects of detection techniques used (from PCR up to NGS approaches), integration loci identified, and associations with genomic and clinical data. The consequences of HPV integration in the human genome, including the methylation status and deregulation of genes involved in cell signaling pathways, immune evasion, and response to therapy, are also summarized.

GRIDSS2: comprehensive characterisation of somatic structural variation using single breakend variants and structural variant phasing

GRIDSS2 is the first structural variant caller to explicitly report single breakends-breakpoints in which only one side can be unambiguously determined. By treating single breakends as a fundamental genomic rearrangement signal on par with breakpoints, GRIDSS2 can explain 47% of somatic centromere copy number changes using single breakends to non-centromere sequence. On a cohort of 3782 deeply sequenced metastatic cancers, GRIDSS2 achieves an unprecedented 3.1% false negative rate and 3.3% false discovery rate and identifies a novel 32-100 bp duplication signature. GRIDSS2 simplifies complex rearrangement interpretation through phasing of structural variants with 16% of somatic calls phasable using paired-end sequencing.

Clinical Validation of Whole Genome Sequencing for Cancer Diagnostics

Whole genome sequencing (WGS) using fresh-frozen tissue and matched blood samples from cancer patients may become the most complete genetic tumor test. With the increasing availability of small biopsies and the need to screen more number of biomarkers, the use of a single all-inclusive test is preferable over multiple consecutive assays. To meet high-quality diagnostics standards, we optimized and clinically validated WGS sample and data processing procedures, resulting in a technical success rate of 95.6% for fresh-frozen samples with sufficient (≥20%) tumor content. Independent validation of identified biomarkers against commonly used diagnostic assays showed a high sensitivity (recall; 98.5%) and precision (positive predictive value; 97.8%) for detection of somatic single-nucleotide variants and insertions and deletions (across 22 genes), and high concordance for detection of gene amplification (97.0%; EGFR and MET) as well as somatic complete loss (100%; CDKN2A/p16). Gene fusion analysis showed a concordance of 91.3% between DNA-based WGS and an orthogonal RNA-based gene fusion assay. Microsatellite (in)stability assessment showed a sensitivity of 100% with a precision of 94%, and virus detection (human papillomavirus), an accuracy of 100% compared with standard testing. In conclusion, whole genome sequencing has a >95% sensitivity and precision compared with routinely used DNA techniques in diagnostics, and all relevant mutation types can be detected reliably in a single assay.