Kocher, Jean‐Pierre

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.

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.

Abstract Motivation Genomic data are prevalent, leading to frequent encounters with uninterpreted variants or mutations with unknown mechanisms of effect. Researchers must manually aggregate data from multiple sources and across related proteins, mentally translating effects between the genome and proteome, to attempt to understand mechanisms. Materials and methods P2T2 presents diverse data and annotation types in a unified protein-centric view, facilitating the interpretation of coding variants and hypothesis generation. Information from primary sequence, domain, motif, and structural levels are presented and also organized into the first Paralog Annotation Analysis across the human proteome. Results Our tool assists research efforts to interpret genomic variation by aggregating diverse, relevant, and proteome-wide information into a unified interactive web-based interface. Additionally, we provide a REST API enabling automated data queries, or repurposing data for other studies. Conclusion The unified protein-centric interface presented in P2T2 will help researchers interpret novel variants identified through next-generation sequencing. Code and server link available at github.com/GenomicInterpretation/p2t2.

(1) Background: Arthrofibrosis is a common cause of patient debility and dissatisfaction after total knee arthroplasty (TKA). The diversity of molecular pathways involved in arthrofibrosis disease progression suggest that effective treatments for arthrofibrosis may require a multimodal approach to counter the complex cellular mechanisms that direct disease pathogenesis. In this study, we leveraged RNA-seq data to define genes that are suppressed in arthrofibrosis patients and identified adiponectin (ADIPOQ) as a potential candidate. We hypothesized that signaling pathways activated by ADIPOQ and the cognate receptors ADIPOR1 and ADIPOR2 may prevent fibrosis-related events that contribute to arthrofibrosis. (2) Methods: Therefore, ADIPOR1 and ADIPOR2 were analyzed in a TGFβ1 inducible cell model for human myofibroblastogenesis by both loss- and gain-of-function experiments. (3) Results: Treatment with AdipoRon, which is a small molecule agonist of ADIPOR1 and ADIPOR2, decreased expression of collagens (COL1A1, COL3A1, and COL6A1) and the myofibroblast marker smooth muscle α-actin (ACTA2) at both mRNA and protein levels in basal and TGFβ1-induced cells. (4) Conclusions: Thus, ADIPOR1 and ADIPOR2 represent potential drug targets that may attenuate the pathogenesis of arthrofibrosis by suppressing TGFβ-dependent induction of myofibroblasts. These findings also suggest that AdipoRon therapy may reduce the development of arthrofibrosis by mediating anti-fibrotic effects in joint capsular tissues.