Bhagwate, Aditya

The intestinal epithelium is exposed to diverse combinations of microbiota-derived compounds; however, the mechanisms by which the host integrates these signals remain poorly defined. Studying two highly abundant microbial metabolites, we identified the purine metabolite hypoxanthine as an effector metabolite that directly drives signaling and the short-chain fatty acid butyrate as a regulatory metabolite that conditions host responsiveness. Specifically, hypoxanthine activates the adenosine A1 receptor–TRPC4 axis in enterochromaffin (EC) cells, triggering calcium influx and serotonin release resulting in accelerated gastrointestinal transit locally and increased platelet activation systemically. In contrast, butyrate epigenetically upregulates specific G protein–coupled receptors and ion channels to enhance response to hypoxanthine and the neurotransmitters norepinephrine and dopamine. These findings define a cooperative signaling framework and highlight the role of EC cells as a distinct epithelial signaling hub that senses and integrates microbial metabolite signals to drive physiological responses. Our findings provide a mechanistic foundation for therapeutic strategies that leverage combinatorial microbial signaling.

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.

Obesity is an epidemic with myriad health effects, but little is understood regarding individual obese phenotypes and how they may respond to therapy. Epigenetic changes associated with obesity have been detected in blood, liver, pancreas, and adipose tissues. Previous work using human organoids found that dietary glucose hyperabsorption is a steadfast trait in cultures derived from some obese subjects, but detailed transcriptional or epigenomic features of the intestinal epithelia associated with this persistent phenotype are unknown. This study evaluated differentially expressed genes and relative chromatin accessibility in intestinal organoids established from donors classified as non-obese, obese, or obese hyperabsorptive by body mass index and glucose transport assays. Transcriptomic analysis indicated that obese hyperabsorptive subject organoids have significantly upregulated dietary nutrient absorption transcripts and downregulated type I interferon targets. Chromatin accessibility and transcription factor footprinting predicted that enhanced HNF4G binding may promote the obese hyperabsorption phenotype. Quantitative RT-PCR assessment in organoids representing a larger subject cohort suggested that intestinal epithelial expression of CUBN, GIP, SLC5A11, and SLC2A5 were highly correlated with hyperabsorption. Thus, the obese hyperabsorption phenotype was characterized by transcriptional changes that support increased nutrient uptake by intestinal epithelia, potentially driven by differentially accessible chromatin. Recognizing unique intestinal phenotypes in obesity provides a new perspective in considering therapeutic targets and options with which to manage the disease.

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.

Abstract Background Malignant peripheral nerve sheath tumors (MPNSTs) can arise from atypical neurofibromas (ANF). Loss of the polycomb repressor complex 2 (PRC2) is a common event. Previous studies on PRC2-regulated genes in MPNST used genetic add-back experiments in highly aneuploid MPNST cell lines which may miss PRC2-regulated genes in NF1-mutant ANF-like precursor cells. A set of PRC2-regulated genes in human Schwann cells (SCs) has not been defined. We hypothesized that PRC2 loss has direct and indirect effects on gene expression resulting in MPNST, so we sought to identify PRC2-regulated genes in immortalized human Schwann cells (iHSCs). Methods We engineered NF1-deficient iHSCs with loss of function SUZ12 or EED mutations. RNA sequencing revealed 1327 differentially expressed genes to define PRC2-regulated genes. To investigate MPNST pathogenesis, we compared genes in iHSCs to consistent gene expression differences between ANF and MPNSTs. Chromatin immunoprecipitation sequencing was used to further define targets. Methylome and proteomic analyses were performed to further identify enriched pathways. Results We identified potential PRC2-regulated drivers of MPNST progression. Pathway analysis indicates many upregulated cancer-related pathways. We found transcriptional evidence for activated Notch and Sonic Hedgehog (SHH) signaling in PRC2-deficient iHSCs. Functional studies confirm that Notch signaling is active in MPNST cell lines, patient-derived xenografts, and transient cell models of PRC2 deficiency. A combination of MEK and γ-secretase inhibition shows synergy in MPNST cell lines. Conclusions We identified PRC2-regulated genes and potential drivers of MPNSTs. Our findings support the Notch pathway as a druggable target in MPNSTs. Our identification of PRC2-regulated genes and pathways could result in more novel therapeutic approaches.

Abstract Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses.

Abstract Purpose: Benign breast disease (BBD) is viewed as a non-obligate precursor stage in the development of breast cancer (BC). Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks has been routinely used in BC research. However, RNA-seq of heterogeneous bulk samples represents an averaged expression profile across all cell mixtures. Downstream analyses, such as biomarker discovery, are likely confounded by differences in cell proportions. There is a lack of research in optimizing strategies for cell type deconvolution especially for FFPE samples. We evaluated and optimized strategies for cell type deconvolution using existing single cell RNA-seq (scRNA-seq) and single nucleus RNA-seq (snRNA-seq) breast tissue references, and applied these to a cohort of BBD FFPE samples to assess risk of subsequent BC. Methods: 7 scRNA-seq and 3 snRNA-seq public samples from women’s breast tissue were normalized using SCTransform, then integrated using Harmony to form a complete reference set. Bioinformatics evaluations examined the performance of existing statistical and deep learning-based deconvolution methods. Computational simulations were conducted to evaluate those methods on three scenarios: 1) baseline; 2) incomplete reference set; 3) FFPE artifacts. FFPE artifacts were simulated based on a generalized additive model using 7 in-house paired FFPE-fresh frozen samples. Finally, we applied the best performing method to our BBD cohort of 62 samples. Differential expression analysis adjusting for cell proportions was performed to identify transcriptomic aberrations in BBD cases. Results: snRNA-seq was more sensitive in capturing adipocytes, while scRNA-seq was more sensitive in detecting lymphocytes in breast tissue. The median number of detectable genes per cell was higher for scRNA-seq than snRNA-seq before integration (1264 vs 558). Marker gene-based regression methods (e.g. CIBERSORT) displayed higher variance and lower bias compared to methods directly using scRNA-seq data (e.g. SCDC). Incomplete reference data led to increased bias and little effect on variance. FFPE artifacts had a higher impact on performance compared to incomplete reference data. Overall, Scaden, a deep learning-based method, was the top performing method across all simulation scenarios and was especially robust to FFPE artifacts. 59 differential expressed genes (DEGs) were identified with fold change > 1.5 and p value < 0.01. DEGs involved in BC, e.g. S100A8, WNT4 and HIST1H3H, would be left out if cell proportions were not properly adjusted. Conclusions: We constructed the first complete single cell reference data for women’s breast tissue and benchmarked existing cell type deconvolution methods. Compared to analyses not adjusting for cell proportions, we observed substantial differences in perturbed genes and pathways associated with BC risk for BBD patients. Citation Format: Yuanhang Liu, Chen Wang, Stacey J. Winham, Aditya Bhagwate, Robert A. Vierkant, Julie M. Cunningham, Bryan M. McCauley, William A. Jons, Derek C. Radisky, Amy C. Degnim. Deconvolution of cell type composition in FFPE tissue: Application to benign breast disease. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4291.