Citations

Summary Cancer genome sequencing is essential for understanding tumor evolution and advancing precision medicine. 1 However, reference gaps and germline variants obscure detection of small and large somatic variants and methylation in repetitive regions. 1–3 It is common for tumor cells to gain or lose chromosome arms due to somatic structural changes that occur inside highly repetitive satellite DNA sequences in the centromeres. 4 To identify the full spectrum of somatic variants, including complex rearrangements, we construct and curate near-complete, haplotype-resolved assemblies of the most recent common ancestor of an early-passage broadly-consented hypodiploid pancreatic cancer cell line and matched normal tissues. The tumor assembly completely recapitulates all 35 tumor chromosomes observed with karyotyping, with multiple translocation-induced hybrid chromosomes. The hybrid chromosomes contain putative functional dicentric and fused centromeres, nested foldback inversions causing 14 breakpoints with a haplotype switch in a single event, and centromeric satellite tandem duplications up to 136 kbp. Direct comparison of tumor and normal assembly haplotypes uncovers >7,000 variants altering >1 Mbp of sequence in repetitive regions that have been hidden by reference gaps and germline variants. 44 % of somatic small variants change representation because they alter germline variants on GRCh38, impacting mutational signatures and kataegis/omikli clusters. Most somatic LINE insertions originate from two hypomethylated non-reference germline LINE insertions, highlighting their impact on insertion mutation burden. These assemblies demonstrate that centromeric, acrocentric, and telomeric regions conventionally excluded from analysis harbor extensive somatic and epigenetic changes. Resolving complete tumor genomes enables a deeper understanding of cancer structural plasticity and the endpoints of breakage-fusion-bridge cycles. These assembled, curated paired normal-tumor benchmarks will serve as a critical foundation for developing future algorithms to characterize the most intractable regions of cancer genomes.

Abstract We present chromosome-level, phased diploid genome assemblies of two widely used human fibroblast cell lines: BJ (46,XY) and IMR-90 (46,XX). Using Oxford Nanopore, PacBio HiFi, and Hi-C sequencing data, we generated assemblies spanning 5.9 and 6.0 Gbp with diploid quality values exceeding QV 60. To validate structural integrity, we developed KaryoScope, an alignment-free tool for generating computational karyograms from k-mer feature databases. We identify >50 000 structural variants relative to T2T-CHM13v2.0, the majority of which are heterozygous and cell-line-specific. Combining reference-based and de novo gene annotation, we uncover a previously unreported 1 Mbp homozygous duplication at the 16p11.2 locus in BJ, demonstrating that even karyotypically normal cell lines can harbor clinically relevant submicroscopic rearrangements. We show that mapping publicly available short-read, RNA-seq, and ChIP-seq data to sample-matched diploid assemblies substantially improves read alignment and enables haplotype phasing of 23%–28% of short reads. The BJ and IMR-90 assemblies and associated variant calls are publicly available as a resource for the research community.

Abstract Background: Hepatocellular carcinoma (HCC), the most prevalent primary liver cancer, develops through multiple pathways; however, the extent to which HBV, HCV, and non-viral etiologies shape tumor biology and influence treatment responses remain incompletely understood. The availability of viral detection methods from sequencing data and multiomics workflows give us a new opportunities to study how etiology influences the molecular landscape of HCC. Here, we analyze TCGA's LIHC samples categorized into HBV-, HCV-, and non-viral tumors to compare genomic, transcriptomic and methylation profile of these HCC tumors to identify biological pathways specific to each group. Methods: Whole-exome, RNA-seq, and clinical data from TCGA-LIHC (n=325) were analyzed. Viral status was assigned using Exogene (≥5 HBV/HCV reads; mixed infections excluded; non-viral = 0 reads). Mutation and clinical comparisons were performed using cBioPortal. RNA-seq data were processed with edgeR for 3-way differential expression (median count ≥25; |log2FC|>2; FDR<0.05). Pathway enrichment used ShinyGO. Findings were validated using the GSE62232 microarray cohort analyzed with limma. Results: Demographic patterns reflected known risk profiles: HBV patients presented younger (mean 53.9 years) and were predominantly male (78.8%) and Asian (92.9%), while HCV and non-viral groups were older and primarily White. Non-viral HCC showed the highest proportion of females (42.7%) and nearly half lacked significant fibrosis, consistent with metabolic disease. In contrast, HCV had the highest cirrhosis burden (47.2%), and HBV tumors showed the greatest proportion of poorly differentiated histology (52.2%).The comparison of the expressed genes between the 3 tumor subtypes revealed distinct etiology-associated pathways. Metabolic and mitochondrial including fatty acid β-oxidation differential expressed in Non-viral HCC. Activation of antiviral and interferon signaling, including type I IFN response and OAS-mediated defense pathways are observed in HCV-HCC. Finally, Cell-cycle and proliferation pathways are differentially expressed in HBV-positive, a finding consistent with effects of viral integration. Clustering analysis separated HBV and non-viral tumors into distinct groups. These signatures were reproduced in a validation cohort. Conclusion: Our analyses highlight that HBV-, HCV-, and non-viral HCC harbor distinct transcriptional and signaling pathways. These etiology-specific signatures provide a framework for developing more precise biomarkers and for tailoring therapeutic strategies to the molecular biology of each etiology. Acknowledgement: We acknowledge the late Dr. Sean Cleary, MD, for his invaluable contributions to this project. Disclosure: AI has been used to trim sentences in order to fit in the word limit. Citation Format: Amerti Guta, Daniel O'Brien, Aditya V. Bhagwate, Jean-Pierre A. Kocher, Lewis R. Roberts. Multiomic dissection of HBV-, HCV-, and non-viral HCC reveals distinct proliferation, interferon, and metabolic signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 2682.

Abstract Single-cell sequencing technologies provide us with information at the level of individual cells. Combining single-cell RNA-seq and single-cell TCR-seq profiling enables the exploration of cell heterogeneity and T-cell receptor repertoires simultaneously. Integrating both types of data can play a crucial role in enhancing our understanding of T-cell-mediated immunity and, in turn, facilitate the advancement of immunotherapy. Here, we present immunopipe, a comprehensive and flexible pipeline to perform integrated analysis of scRNA-seq and scTCR-seq data. In addition to the command line tool, we provide a user-friendly web interface for pipeline configuration and execution monitoring, benefiting researchers without extensive programming experience. With its comprehensive functionality and ease of use, immunopipe empowers researchers to uncover valuable insights from scRNA-seq and scTCR-seq data, ultimately advancing the understanding of immune responses and immunotherapy development.

Introduction: The lack of understanding about the genetic and molecular basis of young stroke (<55 years of age) has hindered development of diagnostic, management, and therapeutic approaches. Whole exome sequencing (WES) can be leveraged as a tool for studying etiological processes underlying stroke. At the time of study inception, this is the single largest WES study in this unique young stroke population to uncover elusive stroke etiologies. Methods: The Mayo Clinic TAPESTRY database is a research program conducting whole exome sequencing of 100,000 participants. Of these, 406 participants met the criteria for young stroke (18-54 years of age) based on ICD-9 and ICD-10 diagnostic codes. Controls were TAPESTRY participants without a stroke-related ICD diagnosis. Cases were matched for age, gender, and race for a 1:4 case-control ratio. Ischemic stroke etiology was classified using TOAST classification. To uncover genetic mechanisms, a gene centric approach was used to identify variants in our cohort. A list of 66 genes known to be associated with young stroke was curated based on existing genetic databases. Various filtration strategies were used to identify potentially damaging variants (graphic 1). Results: There were 406 cases (363 ischemic and 43 hemorrhagic; 291 female; age at stroke onset 39.8±10.4; 368 were White) and 3,857 controls. TOAST classification yielded 20 patients with large artery atherosclerosis, 58 cardioembolic, 10 small vessel disease, 148 other determined (58 patent foramen ovale, 50 dissection, 48 hypercoagulable state, 11 Moyamoya, 11 vasculitis, 10 fibromuscular dysplasia), and 165 undetermined etiologies. Several individuals had multiple competing etiologies. Of cerebral hemorrhages, there were 6 aneurysmal, 6 arteriovenous malformation, 5 hypertensive, 2 substance abuse, 3 trauma, and 21 other etiologies. Across the entire cohort, 35,054 variants were found within the 66 genes identified from the literature. Filtration identified 48 potentially damaging variants across 30 genes in 44 patients (20 cryptogenic and 21 other: 12 patent foramen ovale, 5 vasculitis, 4 dissection, 4 cardioembolic) that were absent in controls. Conclusion: WES is a powerful tool that can be used to uncover potential genetic mechanisms in the young stroke population. An improved understanding of etiologies would improve diagnostics and pave the way for new preventive and therapeutic approaches, thus improving the overall care of young stroke patients.

Abstract Introduction: Single-cell RNA-sequencing (scRNA-seq) pairing with single-cell T-Cell-Receptor-sequencing (scTCR-seq) enables profiling of gene expression and TCR repertoires in individual T cells, elevating our understanding of T-cell-mediated immunity. Here, we present immunopipe, a comprehensive and flexible pipeline (https://github.com/pwwang/immunopipe) for analyzing the paired data. Besides the command-line tool, it offers a user-friendly web interface that allows users with varying programming expertise to configure, initiate, and monitor the pipeline. By combining extensive functionality with ease of use, immunopipe empowers users to unlock valuable insights from the data, which notably advances our understanding of immune response and fosters the development of innovative immunotherapies. Results: Immunopipe is a comprehensive analytical pipeline for scRNA-seq and scTCR-seq data, offering various modules leveraging existing tools and novel methods to ensure data quality and enable downstream analytics. It performs quality control for scRNA-seq data, clusters cells based on gene expression to facilitate the identification of cell populations and provides insights into T-cell heterogeneity and functional diversity through T-cell selection and clustering. Enrichment analysis identifies markers and pathways for T-cell characterization. Our automated T-cell annotation tool annotates cell types accurately and hierarchically. For scTCR-seq data, immunopipe analyzes TCR clones, including clonality, diversity, gene usage, and repertoire overlap. Clone residency analysis examines the presence of clones across samples. Integrative analyses combine scRNA-seq and scTCR-seq data, providing a comprehensive understanding of the cellular landscape. Immunopipe incorporates diverse cell group information to analyze T-cell populations, which enables granular perspectives of the data for comprehensive insights. Immunopipe offers a flexible platform, allowing users to customize the analyses as needed. It supports both core and optional analyses through configuration, where users have control over the adjustable parameters. The pipeline relies on cell grouping information that can be referenced from metadata or modified to generate new variables for analysis and data filtering. Immunopipe can be executed locally or on scheduler platforms, and a docker image is provided for compatibility and simplified installation. It also offers a web interface that facilitates configuration, execution monitoring, and access to results. Conclusion: Immunopipe is a flexible and user-friendly pipeline for analyzing scRNA-seq and scTCR-seq data. It empowers researchers to gain valuable insights that could enhance the understanding of immune responses and facilitate the development of innovative immunotherapies. Citation Format: Panwen Wang, Haidong Dong, Yue Yu, Shuwen Zhang, Zhifu Sun, Jean-Pierre A. Kocher, Junwen Wang, Lin Yi, Ying Li. Immunopipe: A comprehensive and flexible scRNA-seq and scTCR-seq data analysis pipeline [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4959.

Introduction: Telomere biology disorders (TBD) are multisystem diseases often associated with bone marrow failure (BMF) and pulmonary fibrosis. TBD are clinically diagnosed when mean telomere lengths (TL) in blood cells measured by clinical flowFISH assays fall &lt;1 st percentile, in comparison to age-matched reference controls. This information has limitations, e.g. TL in blood cells may not represent TL in other organs (e.g. lung, liver) and moreover, individuals with high clinical suspicion of TBD can show borderline average TL which complicates their diagnosis. The likely reason for this is that cell senescence can be triggered by shortening of a subset, or even a single chromosome arm telomere, a situation that may be missed when measuring average TL. Previous approaches to measure individual chromosome-arm telomere length (CA-TL) are only available for a small number of chromosome arms or can only estimate the number of shortened telomeres without matching them to specific chromosome arms. Taking advantage of recent developments in long-read next generation sequencing (NGS) and the availability of the recently published telomere-to-telomere (T2T) reference human genome, our group developed Telogator, (Stephens Z. et al, 2022) a bioinformatic tool that estimates CA-TL for most chromosome arms using long-read NGS data. Here we use Telogator to describe for the first time the CA-TL pattern in TBD patients in comparison with a control non-TBD cohort. Methods: TBD patients were identified through the Pre-Myeloid and Bone Marrow Failure Specialty Clinic at Mayo Clinic. All patients underwent flowFISH and genetic testing. Age-matched non-TBD patients identified at the Center for Individualized Medicine (Mayo Clinic) were used as reference controls. Informed consent was obtained from all individuals with approval from the Mayo Clinic Institutional Review Board. DNA from peripheral blood mononuclear cells was sequenced using PacBio Sequel II following the manufacturer indications for HiFi sequencing. BAM files were aligned to the T2T reference and CA-TL was calculated using Telogator as described in Stephens Z. et al. 2022. Average TL, CA-TL, and percent of short telomeres (both defined as &lt;5 kb and &lt;3.2 kb) were calculated. Comparisons of average TL and percent of short telomeres were performed using t-test, while Wilcoxon rank-sum test was used to compare CA-TL. Results: Four TBD patients [median age = 58.5 (52-60), 50% female] and 6 non-TBD controls [Median age = 56 (50-63), 33% female] were included in the analysis. All TBD patients presented average TL below the 1 st centile in both lymphocytes and granulocytes, and TBD-related phenotype (100% with pulmonary fibrosis, 50% with BMF, 25% with liver fibrosis). Three had a pathogenic variant in a TBD related genes [2 in TERT (NM_198253.3: c.2030G&gt;A, p.(Gly677Asp) and c.2515_2533del, p.(Thr839Alafs*29)) and 1 in NHP2 (NM_017838.3: c.459_460delAT; p.(X154Argext29))]. The average TL calculated by Telogator showed high correlation with flowFISH values (r 2 = 0.9883, p = 0.0059) and was lower in TBD patients than controls (p = 0.0012). Next, when comparing individual CA-TL between groups, only half of CA-TL was statistically different in the TBD group compared to controls (1p, 3q, 4p, 5p, 5q, 6q, 7p, 7q, 8q, 9p, 9q, 10q, 11p, 12q, 12q, 16p, 16q, 18q, 19p, 19q, 20p, 20q, 21q - Figure 1). The percentage of telomeres &lt;5kb and &lt;3 kb was significantly increased in TBD patients compared to controls (p= 0.014 and 0.0333, respectively). Discussion: Using a novel informatic tool, we replicate previous results obtained using classic approaches and were able to expand on these studies by measuring specific CA-TL in TBD patients. We identified statistically significant differences at the CA-TL level in these patients when compared to age-matched controls, while other CA-TL were not statistically different. Future studies and the inclusion of additional TBD patients will help define the clinical relevance of the differential CA-TL shortening and its prevalence among TBD patients. While limited by the small number of samples in the study, we show the feasibility of using this approach to study TL with unprecedented resolution and show that Telogator offers important advantages compared to classic methods to study telomere biology that can be used in other relevant fields like aging and/or cancer. Our results pave the way to advance the mechanistic understanding of TBD.

Abstract Telomeres are regions of repetitive DNA at the ends of linear chromosomes which protect chromosome ends from degradation. Telomere lengths have been extensively studied in the context of aging and disease, though most studies use average telomere lengths which are of limited utility. We present a method for identifying all 92 telomere alleles from long read sequencing data. Individual telomeres are identified using variant repeats proximal to telomere regions, which are unique across alleles. This high-throughput and high-resolution characterization of telomeres could be foundational to future studies investigating the roles of specific telomeres in aging and disease.

Abstract Motivation Telomeres are the repetitive sequences found at the ends of eukaryotic chromosomes and are often thought of as a ‘biological clock,’ with their average length shortening during division in most cells. In addition to their association with senescence, abnormal telomere lengths are well known to be associated with multiple cancers, short telomere syndromes and as risk factors for a broad range of diseases. While a majority of methods for measuring telomere length will report average lengths across all chromosomes, it is known that aberrations in specific chromosome arms are biomarkers for certain diseases. Due to their repetitive nature, characterizing telomeres at this resolution is prohibitive for short read sequencing approaches, and is challenging still even with longer reads. Results We present Telogator: a method for reporting chromosome-specific telomere length from long read sequencing data. We demonstrate Telogator’s sensitivity in detecting chromosome-specific telomere length in simulated data across a range of read lengths and error rates. Telogator is then applied to 10 germline samples, yielding a high correlation with short read methods in reporting average telomere length. In addition, we investigate common subtelomere rearrangements and identify the minimum read length required to anchor telomere/subtelomere boundaries in samples with these haplotypes. Availability and implementation Telogator is written in Python3 and is available at github.com/zstephens/telogator. Supplementary information Supplementary data are available at Bioinformatics online.