Sun, Zhifu

ABSTRACT Germline variation shapes cancer risk, yet its influence on the evolutionary dynamics of established tumors remains poorly understood. In multiple myeloma, subclonal diversification drives disease progression and treatment failure, but the heritable factors that modulate this process are unknown. Here, we show that germline variation is associated with tumor evolutionary features, implicating inherited regulation in subclonal expansion. Integrating germline variation with tumor evolutionary parameters identifies variants associated with evolutionary features, with signals enriched in regulatory regions, consistent with a transcriptional basis. We further identify TBKBP1 as a key locus linking germline variation to tumor evolution and clinical outcome. Germline variation at this locus is associated with TBKBP1 expression and subclonal expansion, and TBKBP1 expression correlates with adverse prognosis, consistent across independent cohorts. Functional analyses demonstrate that TBKBP1 promotes proliferation and activates MYC, mTORC1 and non-canonical NF-κB signaling pathway. Together, these findings establish germline regulatory variation as a determinant of tumor evolutionary dynamics and identify TBKBP1 as a mediator linking inherited variation to subclonal expansion and disease progression in multiple myeloma.

Abstract Introduction: Protein truncating somatic ASXL1 mutations in CMML are associated with high-risk features and poor outcomes. We have previously demonstrated that ASXL1-mutant (mt) CMML is characterized by permissive chromatin states and overexpression of leukemia stemness genes (Binder et al. Nat Commun; 2022). DLK1 (delta-like homolog 1) is a pseudo-NOTCH like ligand that is maternally imprinted and paternally expressed and is physiologically not expressed beyond embryogenesis. Aberrant DLK1 expression has been associated with poor outcomes in solid tumors and we have shown differential overexpression in ASXL1mt versus wild-type (wt) CMML. Both the promoter and the last exon of DLK1 contain CpG islands that play a role in regulating DLK1. The CpG island within the fifth exon of DLK1, known as the DLK1-differentially methylated region (DMR) [chr14:100,726,892-100,738,224] is completely unmethylated on the maternal allele and partially methylated on the paternal allele. Previous work (Developmental Cell; 2021) has also identified a DLK1 imprinting control region (ICR) known as the IG-DMR [chr14:100,811,001–100,811,037]. DNA methylation state of the IG-DMR has been shown to impact the expression of MEG3 mRNA (lncRNA) which functions as a cofactor to direct PRC2 to the DLK1 promoter, suppressing DLK1 expression. Since the regulation of DLK1 in ASXL1mt CMML is not well understood, we conducted this multiomic analyses to define epigenetic mechanisms of DLK1 overexpression in ASXL1mt CMML. Methods: After IRB approval, we performed bulk RNA-seq, ChIP-seq (H3K4me1/me3, H3K27ac, H3K27me3) and Illumina MethylationEPIC array on bone marrow (BM) mononuclear cells of 40 CMML patients (19 ASXL1mt, 21 wt). scRNA-seq was conducted on 5 ASXL1mt and 3 wt cases. All libraries were sequenced on an Illumina NovaSeq instrument. We assessed these samples for differential gene expression (RNA-seq) and changes in histone modifications (ChIP-seq) between ASXL1mt and wt CMML, specifically at the DLK1-DMR and IG-DMRregulatory loci. We conducted ddPCR (digital droplet PCR) to determine allelic status of DLK1 in ASXL1mtand wt cases. Global and allele/sequence specific DNA methylation changes were determined using Biomodal's duet EvoC methodology (6 base sequencing, including 5mC and 5hmC) in 5 ASXL1mt and 5 wt CMML cases. Results: We validated the overexpression of DLK1 in 19 ASXL1mt versus 21 wt (log2 fold change +4.85, FDR 4.67e-13) CMML BM samples. scRNA-seq on 5 ASXL1mt versus 3 wt samples demonstrated that DLK1 is only expressed in mutant cases, with the expression being limited to HSPCs. ChIP-seq analysis at the DLK1 regulatory loci validated (a) reduced occupancy of H3K27me3, correlating with ASXL1mt associated disruption of the PRC2-complex, and (b) increased co-occupancy of H3K4me1 and H3K27ac in mt cases, validating a permissive chromatin state in ASXL1mt cases at the DLK1 regulatory regions. Through correlation analysis of RNA-seq and ChIP-seq, we defined the promoter for DLK1, characterized by high Pearson correlation coefficients (R > 0.5) and enrichment of H3K27ac and H3K4me3. Differential methylation analysis identified global hypomethylation around the DLK1-DMR and promoter regions. We observed a mean reduction of ~40% in methylation status at the DLK1 promoter in ASXL1mt versus wt cases, suggesting a loss of epigenetic silencing at this locus. This was corroborated by significant enrichment of 5hmC at the fifth exon of DLK1 indicating active DNA demethylation. Conversely, we observed a gain in methylation at the IG-DMR, suggesting loss of imprinting control through MEG3 silencing and DLK1 overexpression. To further determine the impact of imprinting deregulation on overexpression of DLK1 in ASXL1mt CMML, allele-specific methylation was profiled using the Biomodal whole genome methylation data. This demonstrated that the methylation patterns in ASXL1mt CMML cases were significantly deregulated at the DLK1-DMR, with >30% loss of methylation compared to GRCh38 reference alleles. Furthermore, ddPCR was able to confirm bi-allelic expression of DLK1 in ASXL1mt CMML, suggesting that the observed methylation dysregulation leads to loss of imprinting of the maternal allele. Conclusion: In summary, we demonstrate that ASXL1 mutations in CMML disrupt epigenetic pathways, promoting DNA demethylation, resulting in permissive chromatin states and an aberrant and oncogenic re-expression of maternal DLK1, due to loss of imprinting control.

Abstract Aberrant DNA methylation and copy-number alterations (CNA) drive Barrett’s esophagus progression to esophageal adenocarcinoma; however, their combined utility for early detection is unclear. We aimed to identify and validate methylated DNA markers (MDM) and CNAs to distinguish esophageal adenocarcinoma/high-grade dysplasia (HGD) from nondysplastic Barrett’s esophagus (NDBE). In this multiphase, multicenter study, we discovered and validated MDMs and quantified CNAs utilizing whole-genome methylation sequencing of esophageal brushings. DNA biomarkers identified from discovery were further validated in independent patients with paired esophageal brushing and swallowed capsule sponge samples. MDMs were filtered against a reduced representation bisulfite sequencing dataset obtained from independent tissue samples to advance only concordant candidates. CNA burden was quantified using ichorCNA-derived aneuploidy scores (AS). Two hundred MDMs discovered in HGD (N = 18) and esophageal adenocarcinoma (N = 18) versus NDBE brushing samples (N = 18) were tested in independent samples (N = 146). A 52-MDM panel achieved a cross-validated AUC of 0.88 [95% confidence interval (CI), 0.82–0.95]; the addition of AS improved discrimination of HGD/esophageal adenocarcinoma from NDBE to 0.91 (95% CI, 0.86–0.97) AUC. At 80% specificity, the combined model detected 93% of esophageal adenocarcinoma and 88% of HGD cases. In paired capsule sponge samples, a 58-MDM panel achieved a cross-validated AUC of 0.77 (95% CI, 0.66–0.88); a combined 58-MDM and AS model achieved AUC 0.80 (95% CI, 0.7–0.9). MDMs and AS discerned HGD/esophageal adenocarcinoma from normal esophagus/NDBE in endoscopic brushing and capsule sponge samples. This approach may improve Barrett’s esophagus surveillance. Prevention Relevance: This study demonstrates that combining epigenetic and genomic biomarkers across minimally invasive sampling methods can accurately distinguish HGD/esophageal adenocarcinoma from nondysplastic Barrett’s esophagus, offering promising, less invasive strategies to improve BE surveillance and enable endoscopic therapy for esophageal adenocarcinoma prevention and treatment.

Background/Objectives: Human endogenous retroviruses (ERVs) are genomic sequences integrated into the human genome from ancestral exogenous retroviruses and are epigenetically silenced under normal conditions. Growing evidence has shown that they can be reactivated in human diseases such as cancers and autoimmune diseases. However, their clinical implications in colon cancer are yet to be explored. Methods: RNA-seq data were downloaded from RNA Atlas and TCGA for cell lines and tissue samples, respectively. After alignment, ERV expression was quantified against comprehensively compiled ERVs (3220). ERV expression profiles were compared between sequencing protocols, cancer and normal cells, and matched tumor and normal tissue pairs. Unsupervised clustering was used to identify ERV-defined tumor subtypes and their associations with clinical and other molecular features. ERV association with disease-specific survival (DSS) was performed using the Cox regression model. Results: PolyA and total RNA protocols were comparable in ERV expression detection. Cancer cells had significantly increased ERV expression and reactivation. Upregulated ERVs were significantly enriched in viral protein interactions with cytokine and cytokine receptors. ERV expression-defined tumor classes were significantly associated with tumor mutation burden and immuno-phenotypes such as antigen processing and presenting machinery and tumor immune infiltration score. Survival analysis identified 152 ERVs to be independently associated with DSS. Conclusions: ERV abnormal expression is common in colon cancer. The ERV-defined subtypes are associated with tumor immunity, and some ERVs are independently associated with patient outcomes.

This study reveals a novel role for Treg-derived IL-6R in supporting epithelial repair. Despite preserved immune-suppressive capacity, deletion of IL-6R from Tregs impairs epithelial transcription and worsens injury in colitis. We demonstrate that human intestinal organoids preferentially respond to trans- over classic IL-6 signaling. These findings introduce a Treg-specific role in immune-epithelial cross talk relevant to mucosal healing and inflammatory bowel disease.

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.

Background: The epigenome, the set of modifications to DNA and associated molecules that control gene expression, cellular identity, and function, plays a major role in mediating cellular responses to outside factors. Thus, evaluation of the epigenetic state can provide insights into cellular adaptions occurring over the course of disease. Methods: We performed epigenome-wide association studies of primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) using the Illumina MethylationEPIC Bead Chip. Results: We found evidence of increased epigenetic age acceleration and differences in predicted immune cell composition in patients with PSC and PBC. Epigenetic profiles demonstrated differences in predicted protein levels including increased levels of tumor necrosis factor receptor superfamily member 1B in patients with cirrhotic compared to noncirrhotic PSC and PBC. Epigenome-wide association studies of PSC discovered strongly associated 5′-C-phosphate-G-3′ sites in genes including vacuole membrane protein 1 and SOCS3, and epigenome-wide association studies of PBC found strong 5′-C-phosphate-G-3′ associations in genes including NOD-like receptor family CARD domain containing 5, human leukocyte antigen-E, and PSMB8. Analyses identified disease-associated canonical pathways and upstream regulators involved with immune signaling and activation of macrophages and T-cells. A comparison of PSC and PBC data found relatively little overlap at the 5′-C-phosphate-G-3′ and gene levels with slightly more overlap at the level of pathways and upstream regulators. Conclusions: This study provides insights into methylation profiles of patients that support current concepts of disease mechanisms and provide novel data to inspire future research. Studies to corroborate our findings and expand into other -omics layers will be invaluable to further our understanding of these rare diseases with the goal to improve and individualize prognosis and treatment.

Abstract Introduction: Barrett’s esophagus (BE) confers increased cancer risk. Endoscopic surveillance is recommended to distinguish non-dysplastic BE (NDBE), low grade dysplasia (LGD), high grade dysplasia (HGD), or adenocarcinoma (EAC) for endoscopic intervention. However, this paradigm is limited by sampling error, leading to missed/interval EAC. Herein, we aimed to classify these samples more accurately by performing whole genome methylation sequencing (WGMS) and utilizing multiple genomic features on endoscopic brush samples. Materials and methods: Endoscopic brush samples from patients with NDBE (n=18), HGD (18), EAC (18), and normal controls (18) were collected and underwent WGMS (discovery dataset). Sequence reads were processed through internal pipeline to extract CpG methylation and read level methylation ratio (alpha value). Copy number aberration (CNA) detection was performed using both CNVkit and ichorCNA. Differential methylated regions (DMRs) were identified through comparing EAC/HGD with NDBE and normal samples. Targeted methylation sequencing for selected DMRs and shallow WGMS were performed on EAC (41), HGD (26), LGD (23), NDBE (42), and normal controls (37) as validation. Ratio of the reads with alpha value>0.6 for each DMR was used for further analysis. Detected CNA segments were summarized into chromosome arm loss or gain (aneuploidy score or AS representing the number of chromosome arms gained or lost in a sample) and trimmed median absolute deviation (tMAD). Classification models were developed using the discovery set and the best performing one was applied to the validation dataset. Results: Genome-wide DNA methylation largely segregated different groups of samples as diagnosed; however, some unexpected group crossings were observed. Top 200 DMRs were identified between EAC/HGD and NDBE and normal samples. CNA changes were widespread in EAC, very common in HGD but rare in NDBE (17/18 EACs,14/18 HGDs, 5/18 NDBEs with AS>0). With tMAD > 0.015, 16/18 EACs, 8/18 HGDs but none of 18 NDBEs passed the threshold. EAC or HGD samples without detectable CNAs had very low “tumor” fraction as determined by methylation-based cell type deconvolution or ichorCNA. Similar results were found in the validation dataset. A multi-omics (AS, tMAD, estimated tumor fraction, and DMR methylation) model could predict EAC, HGD, or NDBE with 0.81 accuracy through cross validation (10X) and 0.73 in the independent dataset. This increased to 0.98 and 0.90 in separating high risk EAC/HGD from NDBE, respectively. Conclusion: Multi-omics data obtained from single WGMS allow for a comprehensive analysis of BE samples with varying dysplasia levels. Using classification modeling, these characteristics can be effectively used to accurately identify and distinguish between EAC and HGD from NDBE, suggesting their utility as adjuncts to endoscopic surveillance biopsies. Citation Format: Zhifu Sun, Caroline L. Matchett, Seth W. Slettedahl, William R. Taylor, Panwen Wang, Calise Berger, Caryn E. Anderson, Melissa A. Passe, Ramona Lansing, Collin E. Chalmers, Patrick H. Foote, Jeanette E. Eckel Passow, Doug W. Mahoney, John B. Kisiel, Prasad G. Iyer. Multi-omics cancer risk assessment of Barrett’s esophagus with DNA methylation sequencing from endoscopic brush samples [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB249.

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.