Citations

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.

Abstract Introduction: Screening for prostate cancer (PCa) with molecular markers is hindered by low tumor allele fraction (TAF) in liquid biopsy samples such as blood or urine. Others have suggested that tumor allele enrichment can be achieved by evaluating fragment-level methylation patterns generated by next generation sequencing (NGS). Here, we test this concept using target capture NGS on cfDNA from prediagnostic collected urine samples to discriminate PCa from otherwise healthy men. Methods: Buffered urines were collected from 23 patients with biopsy confirmed PCa (Gleason score 6-9) and 24 otherwise healthy age matched cancer-free controls. cfDNA was isolated and enzymatic methyl-seq libraries were prepared and enriched by hybrid capture using probes mapping to 197 differentially methylated regions (DMRs) from a previous PCa/normal tissue marker discovery study in our lab. These DMRs were subsequently vetted independently using a methylome-wide dataset from a cohort of 36 clean and disease control urines to ensure low non-cancer background signal (<1%). Indexed libraries were sequenced at 560X deduplicated coverage and fragment-level CpG methylation information was extracted from individual sample .bam files. Mapped sequencing reads exhibiting concordant methylation were used to derive a quantitative α-score. Reads had to contain at minimum 5 CpGs and have an α-value (% 5mC per read) > 60%. Positivity for the 197 markers required an α-score greater than 6 sigma (σ) above the mean of the control arm. Values were variance-adjusted to ensure equal weighting. Beta (β) analysis (mean % 5mC across all reads) of the data was also performed for comparison. Results: Of the 197 markers, 187 were positive for one or more PCa patients. Tumor-derived fragments were detected in 22 of 23 cases, which included 8 G-6, 9 G-7, 2 G-8, and 3 G-9 patients. 1 G-7 was missed, however the sequencing coverage for this sample was significantly lower than the others due to poor library enrichment. 2 of the 24 healthy controls were positive, one with extremely high fragment counts. Upon re-review of the full control cohort, this patient had been diagnosed with bladder cancer 2 years after urine collection. The 2nd patient had was diagnosed with lung cancer in a similar timeframe. The other 22 confirmed cancer-free controls had α-scores below the 6σ threshold. Using a co-captured reference gene (B3GALT6) to mimic genomic equivalents, the TAFs ranged from 0.015% to 8%. β analysis of the data detected only 13 PCa patients (>6σ) and marker positivity was lower than the fragment-level approach. Conclusion: Target capture NGS coupled with fragment-level analysis is a rational approach for sensitive and specific detection of low tumor-derived DNA in liquid biopsy samples. For PCa assay in urine, we were able to uncover positivity in cancer patients approaching 1 in 10,000 ctDNA dilution levels. These results warrant further validation studies with this and other cancers. Citation Format: Tibor Bedekovics, William R. Taylor, Zhifu Sun, Saurabh Baheti, Xiaoming Cao, Kelli N. Burger, Karen A. Doering, Patrick H. Foote, Benjamin Gochanour, Douglas, W. Mahoney, Matthew T. Gettman, John B. Kisiel. Detection of prostate cancer in urine by fragment-level DNA methylation analysis [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 2424.

Abstract Background The development of Crohn’s disease [CD] involves immune cell signalling pathways regulated by epigenetic modifications. Aberrant DNA methylation has been identified in peripheral blood and bulk intestinal tissue from CD patients. However, the DNA methylome of disease-associated intestinal CD4+ lymphocytes has not been evaluated. Materials and Methods Genome-wide DNA methylation sequencing was performed from terminal ileum CD4+ cells from 21 CD patients and 12 age- and sex-matched controls. Data were analysed for differentially methylated CpGs [DMCs] and methylated regions [DMRs]. Integration was performed with RNA-sequencing data to evaluate the functional impact of DNA methylation changes on gene expression. DMRs were overlapped with regions of differentially open chromatin [by ATAC-seq] and CCCTC-binding factor [CTCF] binding sites [by ChIP-seq] between peripherally derived Th17 and Treg cells. Results CD4+ cells in CD patients had significantly increased DNA methylation compared to those from the controls. A total of 119 051 DMCs and 8113 DMRs were detected. While hypermethylated genes were mostly related to cell metabolism and homeostasis, hypomethylated genes were significantly enriched within the Th17 signalling pathway. The differentially enriched ATAC regions in Th17 cells [compared to Tregs] were hypomethylated in CD patients, suggesting heightened Th17 activity. There was significant overlap between hypomethylated DNA regions and CTCF-associated binding sites. Conclusions The methylome of CD patients shows an overall dominant hypermethylation yet hypomethylation is more concentrated in proinflammatory pathways, including Th17 differentiation. Hypomethylation of Th17-related genes associated with areas of open chromatin and CTCF binding sites constitutes a hallmark of CD-associated intestinal CD4+ cells.

AbstractFerroptosis is a newly identified cell death mechanism and potential biomarker for hepatocellular carcinoma (HCC) therapy; however, its clinical relevance and underlying mechanism remain unclear. In this study, transcriptome and methylome data from 374 HCC cases were investigated for 41 ferroptosis‐related genes to identify ferroptosis activity‐associated subtypes. These subtypes were further investigated for associations with clinical and pathological variables, gene mutation landscapes, deregulated pathways and tumour microenvironmental immunity. A gene expression signature and predictive model were developed and validated using an additional 232 HCC cases from another independent cohort. Two distinct ferroptosis phenotypes (Ferroptosis‐H and Ferroptosis‐L) were identified according to ferroptosis gene expression and methylation in the patients with HCC. Patients with the Ferroptosis‐H had worse overall and disease‐specific survival, and the molecular subtypes were significantly associated with different clinical characteristics, mRNA expression patterns, tumour mutation profiles and microenvironmental immune status. Furthermore, a 15‐gene ferroptosis‐related prognostic model (FPM) for HCC was developed and validated which demonstrated accurate risk stratification ability. A nomogram included the FPM risk score, ECOG PS and hepatitis B status was developed for eventual clinical translation. Our results suggest that HCC subtypes defined by ferroptosis gene expression and methylation may be used to stratify patients for clinical decision‐making.

Long non-coding RNAs (lncRNAs) are a large class of gene transcripts that do not code proteins; however, their functions are largely unknown and many new lncRNAs are yet to be discovered. Taking advantage of our previously developed, super-fast, novel lncRNA discovery pipeline, UClncR, and rich resources of GTEx RNA-seq data, we performed systematic novel lincRNA discovery for over 8000 samples across 30 tissue types. We conducted novel detection for each major tissue type first and then consolidated the novel discoveries from all tissue types. These novel lincRNs were profiled and analyzed along with known genes to identify tissue-specific genes in 30 major human tissue types. Thirteen sub-brain regions were also analyzed in a similar manner. Our analysis revealed thousands to tens of thousands of novel lincRNAs for each tissue type. These lincRNAs could define each tissue type’s identity and demonstrated their reliability and tissue-specific expression. Tissue-specific genes were identified for each major tissue type and sub-brain region. The tissue-specific genes clearly defined each respective tissue’s unique function and could be used to expand the interpretation of non-coding SNPs from genome-wide association (GWAS) studies.