The benefit of a complete reference genome for cancer structural variant analysis.

The complexities of cancer genomes are becoming more easily interpreted due to advancements in sequencing technologies and improved bioinformatic analysis. Structural variants (SVs) represent an important subset of somatic events in tumors. While detection of SVs has been markedly improved by the development of long-read sequencing, somatic variant identification and annotation remains challenging. We hypothesized that use of a completed human reference genome (CHM13-T2T) would improve somatic SV calling. Our findings in a tumour/normal matched benchmark sample and two patient samples show that the CHM13-T2T improves SV detection and prioritization accuracy compared to GRCh38, with a notable reduction in false positive calls. We also overcame the lack of annotation resources for CHM13-T2T by lifting over CHM13-T2T-aligned reads to the GRCh38 genome, therefore combining both improved alignment and advanced annotations. In this process, we assessed the current SV benchmark set for COLO829/COLO829BL across four replicates sequenced at different centers with different long-read technologies. We discovered instability of this cell line across these replicates; 346 SVs (1.13%) were only discoverable in a single replicate. We identify 49 somatic SVs, which appear to be stable as they are consistently present across the four replicates. As such, we propose this consensus set as an updated benchmark for somatic SV calling and include both GRCh38 and CHM13-T2T coordinates in our benchmark. The benchmark is available at: 10.5281/zenodo.10819636 Our work demonstrates new approaches to optimize somatic SV prioritization in cancer with potential improvements in other genetic diseases.

Genomic structures and regulation patterns at HPV integration sites in cervical cancer.

Human papillomavirus (HPV) integration has been implicated in transforming HPV infection into cancer, but its genomic consequences have been difficult to study using short-read technologies. To resolve the dysregulation associated with HPV integration, we performed long-read sequencing on 63 cervical cancer genomes. We identified six categories of integration events based on HPV-human genomic structures. Of all HPV integrants, defined as two HPV-human breakpoints bridged by an HPV sequence, 24% contained variable copies of HPV between the breakpoints, a phenomenon we termed heterologous integration. Analysis of DNA methylation within and in proximity to the HPV genome at individual integration events revealed relationships between methylation status of the integrant and its orientation and structure. Dysregulation of the human epigenome and neighboring gene expression in cis with the HPV-integrated allele was observed over megabase-ranges of the genome. By elucidating the structural, epigenetic, and allele-specific impacts of HPV integration, we provide insight into the role of integrated HPV in cervical cancer.

The Drivers, Mechanisms, and Consequences of Genome Instability in HPV-Driven Cancers.

Human papillomavirus (HPV) is the causative driver of cervical cancer and a contributing risk factor of head and neck cancer and several anogenital cancers. HPV's ability to induce genome instability contributes to its oncogenicity. HPV genes can induce genome instability in several ways, including modulating the cell cycle to favour proliferation, interacting with DNA damage repair pathways to bring high-fidelity repair pathways to viral episomes and away from the host genome, inducing DNA-damaging oxidative stress, and altering the length of telomeres. In addition, the presence of a chronic viral infection can lead to immune responses that also cause genome instability of the infected tissue. The HPV genome can become integrated into the host genome during HPV-induced tumorigenesis. Viral integration requires double-stranded breaks on the DNA; therefore, regions around the integration event are prone to structural alterations and themselves are targets of genome instability. In this review, we present the mechanisms by which HPV-dependent and -independent genome instability is initiated and maintained in HPV-driven cancers, both across the genome and at regions of HPV integration.

Analysis of Ugandan cervical carcinomas identifies human papillomavirus clade-specific epigenome and transcriptome landscapes.

Cervical cancer is the most common cancer affecting sub-Saharan African women and is prevalent among HIV-positive (HIV+) individuals. No comprehensive profiling of cancer genomes, transcriptomes or epigenomes has been performed in this population thus far. We characterized 118 tumors from Ugandan patients, of whom 72 were HIV+, and performed extended mutation analysis on an additional 89 tumors. We detected human papillomavirus (HPV)-clade-specific differences in tumor DNA methylation, promoter- and enhancer-associated histone marks, gene expression and pathway dysregulation. Changes in histone modification at HPV integration events were correlated with upregulation of nearby genes and endogenous retroviruses.

Computational approach to predict species-specific type III secretion system (T3SS) effectors using single and multiple genomes.

BACKGROUND: Many gram-negative bacteria use type III secretion systems (T3SSs) to translocate effector proteins into host cells. T3SS effectors can give some bacteria a competitive edge over others within the same environment and can help bacteria to invade the host cells and allow them to multiply rapidly within the host. Therefore, developing efficient methods to identify effectors scattered in bacterial genomes can lead to a better understanding of host-pathogen interactions and ultimately to important medical and biotechnological applications. RESULTS: We used 21 genomic and proteomic attributes to create a precise and reliable T3SS effector prediction method called Genome Search for Effectors Tool (GenSET). Five machine learning algorithms were trained on effectors selected from different organisms and a trained (voting) algorithm was then applied to identify other effectors present in the genome testing sets from the same (GenSET Phase 1) or different (GenSET Phase 2) organism. Although a select group of attributes that included the codon adaptation index, probability of expression in inclusion bodies, N-terminal disorder, and G + C content (filtered) were better at discriminating between positive and negative sets, algorithm performance was better when all 21 attributes (unfiltered) were used. Performance scores (sensitivity, specificity and area under the curve) from GenSET Phase 1 were better than those reported for six published methods. More importantly, GenSET Phase 1 ranked more known effectors (70.3%) in the top 40 ranked proteins and predicted 10-80% more effectors than three available programs in three of the four organisms tested. GenSET Phase 2 predicted 43.8% effectors in the top 40 ranked proteins when tested on four related or unrelated organisms. The lower prediction rates from GenSET Phase 2 may be due to the presence of different translocation signals in effectors from different T3SS families. CONCLUSIONS: The species-specific GenSET Phase 1 method offers an alternative approach to T3SS effector prediction that can be used with other published programs to improve effector predictions. Additionally, our approach can be applied to predict effectors of other secretion systems as long as these effectors have translocation signals embedded in their sequences.