Citations

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.

Gut microbiota regulate the intestinal expression of drug-metabolizing enzymes, transporters, and barrier properties that determine oral drug absorption and disposition. This study compares germ-free Swiss Webster mice to mice colonized with fecal microbiota from a single human donor, evaluating microbiota-driven differences in colonic gene expression and P-gp protein expression, as well as small intestinal mucosal permeability. Transcriptomic analysis of colonic tissue revealed microbiota-induced upregulation of genes encoding P-gp and other drug transporters, including MCT1 and OCTN2. Immunofluorescence also indicated greater P-gp expression and apical localization in colonized mice. Gene expression of drug-metabolizing enzymes was also higher in colonized mice, including phase I enzymes (carboxylesterase 2 paralogs), phase II enzymes (UDP-glucuronosyltransferases and glutathione S-transferases), and enzymes responsible for synthesizing their co-substrates, UDP-glucuronic acid and glutathione. Small intestinal mucosal explants demonstrated lower permeability to 14C-labeled polyethylene glycol 4000 in colonized mice compared to germ-free mice. In the colon, transcriptomic analysis identified microbiota-dependent changes in genes regulating paracellular tight junctions and actomyosin contractility. These results show that human-derived gut microbiota alter gene expression in the mouse colon, identifying candidate microbiota-responsive genes relevant to oral drug absorption and disposition.

Background Tumor-infiltrating lymphocytes (TILs) are established determinants of clinical outcomes in breast cancer. 1–6 While stromal TILs (sTILs) are commonly evaluated due to easier scoring and higher interobserver reproducibility, intraepithelial TILs may provide additional insights into anti-tumor immunity. 7 We aimed to comprehensively characterize immunotype-associated cellular landscapes in triple-negative breast cancer (TNBC) and identify features associated with clinical outcomes. Methods 212 TNBC tumors were classified into three immunotypes based on immune infiltrate location and density: immune desert (ID), immune excluded (IE), and immune inflamed (IN). 8 Single-cell spatial transcriptomics was performed on 75 tumors to define the cellular composition and functional states of the tumor microenvironment across immunotypes. Findings were validated and integrated with bulk RNA sequencing data and clinical outcome datasets from FinXX, CALGB-40603, I-SPY 2, and Real-World Clinico-Genomic Data (RWCGD) cohorts. Results Patients with IN tumors had significantly improved outcomes compared with ID tumors. Despite high sTILs, IE tumors showed poor outcomes similar to ID tumors. Single-cell spatial analysis revealed that ID and IE tumors exhibited reduced major histocompatibility complex I/II expression and fewer tumor-resident plasmacytoid dendritic cells (pDCs) compared with IN tumors, which were enriched for interferon-alpha (IFNα) and interferon-gamma (IFNγ) responses. High IFN response scores were associated with favorable outcomes across multiple therapy types in independent datasets. Deconvolution of RWCGD bulk RNA-seq data confirmed that pDC abundance correlated with improved outcomes specifically in hormone receptor–negative subtypes. Conclusions Our study highlights pDCs and IFN signaling as hallmarks of effective anti-tumor immunity in TNBC. Immunotype-based profiling underscores the limited prognostic value of sTILs alone in immune-excluded tumors and supports pDCs and IFN pathways as potential biomarkers for prognosis and therapeutic development in TNBC.

Abstract Background: The increasing volume and diversity of clinical genomic reports pose a significant challenge across healthcare institutions for the interpretation and proper implementation of precision oncology research. Reports from multiple different vendors (e.g., Invitae, Ambry Genetics, Foundation Medicine) are typically PDFs and exhibit substantial heterogeneity in panel design, gene coverage, and reporting standards, which hinders efficient retrospective data mining and patient cohort identification within the electronic medical records. We sought to develop a framework to interrogate all of these reports. Methods: We extracted genomic reports from patients with breast cancer treated with neoadjuvant chemotherapy and developed MolHarmonizer, a novel, scalable framework leveraging Python and Gemini LLMs, designed to process and harmonize genomic data from disparate multi-vendor reports. Gemini LLMs are employed explicitly for robust information extraction, normalization, and structuring of key genomic features, transforming unstructured data into a unified, queryable dataset. Results: Our MolHarmonizer framework successfully processed 1,147 genomic reports from 1703 breast cancer patients (2006-2023) from 23 different companies, demonstrating robust capability to extract and standardize critical actionable biomarkers. Data sources included Invitae (n=554), Ambry Genetics (n=189), Natera (n=95), Mayo Clinic (n=88), Tempus (n=63), Guardant Health (n=47), and Foundation Medicine (n=37), with others contributing less than 20 reports. Of the samples, 827/1147 (72.1%) were germline (blood/saliva). A majority of the patients were tested using the panels due to a personal/family history (n=925). Overall, 413/1147 (36.0%) reports identified at least one mutation. For breast cancer, 75 reports showed BRCA1/2 mutations (37 BRCA1, 37 BRCA2, and one patient with both BRCA1 and 2). Other mutations identified included: PIK3CA (n=40), TP53 (n=96), PTEN (n=21), ESR1 (n=13) and AKT1/2 (n=8). Conclusion: MolHarmonizer, a powerful framework leveraging Gemini LLMs, effectively addresses genomic data heterogeneity by automating biomarker extraction and harmonization. This enables rapid cohort identification and deep retrospective analyses for clinical insights, biomarker discovery, understanding disease history, facilitating novel pattern discovery, e.g., predicting BRCA1 mutations from WSI, and accelerating research within our neoadjuvant BC cohort. Future plans include expanding to include over 20,000 breast cancer patients, developing a user-friendly chatbot, and ensuring inter-institutional adaptability for a variety of complex diseases. Citation Format: Krishna Rani Kalari, Xiaojia Tang, Thanmayee Boyapati, Tanya L. Hoskin, Sumathilatha Myla, Sumedha G. Penheiter, Richard M. Weinshilboum, Liewei Wang, Hamid R. Tizhoosh, Karthik Vikram Giridhar, Matthew P. Goetz, Judy C. Boughey. Development of an LLM framework for analysis of heterogeneous breast cancer patients genomic reports [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 2756.

Abstract Background: DNA methyltransferase (DNMT) isoforms 1, 3A and 3B are enzymes that regulate DNA methylation, controlling gene expression, chromatin stability and genetic imprinting. We have previously shown that DNMT3A protein expression in early triple negative breast cancer (TNBC) is associated with more aggressive clinicopathologic features (higher tumor grade, tumor proliferation and frequency of nodal metastases) and worse clinical outcomes in the absence of chemotherapy (Leon-Ferre et al, SABCS 2024). Pre-clinically, we have shown that DNMT3A protein expression predicts sensitivity to DNMT inhibitors (Yu J et al., J Clin Invest 2018), and that DNMT inhibitor treatment leads to downregulation of DNMT3A, enhanced expression of immune-related pathways, and halted tumor growth, particularly in combination with immunotherapy and paclitaxel. Methods: This is a phase 1, open-label, multicenter study evaluating the safety and recommended phase 2 dose (RP2D) of the oral DNMT inhibitor ASTX727 + paclitaxel + pembrolizumab in metastatic TNBC. ASTX727 is an oral fixed dose combination (FDC) tablet of 35 mg decitabine (active agent) and 100 mg cedazuridine (a cytidine deaminase inhibitor that prevents decitabine degradation in the bowel and liver, allowing oral delivery). The study consists of two parts: Part 1 is a dose-finding cohort, using a 3+3 design, with 5 dose levels (Table), and its primary objective is to determine the RP2D. Part 2 is an expansion cohort (n=6), with the primary objective to confirm tolerability of the regimen at the RP2D. Secondary objectives are to record the antitumor activity of the regimen, describe the AE profile, explore the association of baseline gene expression profiles with clinical benefit, and to evaluate impact of treatment on tumor and ctDNA methylation. Exploratory objectives will evaluate the association of baseline DNMT3A protein expression and antitumor activity, and the impact of treatment on the tumor and peripheral blood immune phenotype. Eligible patients are adults 18 years or older with metastatic TNBC (ER/PR ≤10%, and HER2-negative per ASCO/CAP guidelines), independent of PD-L1 expression. Patients are allowed to have received 0-3 prior lines of systemic therapy in the metastatic setting, including prior immune checkpoint inhibitors in the early and/or metastatic settings. Patients must be eligible for taxanes and have adequate organ and bone marrow function. Patients with treated brain metastases are eligible if previously treated without evidence of progression after 4 weeks. Leptomeningeal carcinomatosis is not allowed. Patients must have an ECOG performance status of ≤2 and have no gastrointestinal disorders impacting absorption of oral medications. As of June 20, 2025, the study is enrolling patients, with 10 patients dosed in the dose-finding phase. Citation Format: R. Leon-Ferre, D. M. Zahrieh, P. P. Advani, D. Quiroga, K. V. Giridhar, P. J. Dizona, D. Stover, X. Wang, A. J. Tevaarwerk, G. M. Choong, S. Yasir, K. R. Kalari, J. Foldi, R. Parajuli, M. H. Hackney, H. K. Chew, R. Wajeeha, H. Bear, E. Sharon, B. Ko, G. I. Shapiro, S. Gore, L. Pelosof, L. Wang, M. P. Goetz. Trial in progress: Phase 1 Study Targeting DNA Methyltransferases in Metastatic Triple-Negative Breast Cancer (NCI Protocol #10546) [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS5-07-20.

Integrating artificial intelligence (AI) with healthcare data is rapidly transforming medical diagnostics and driving progress toward precision medicine. However, effectively leveraging multimodal data, particularly digital pathology whole slide images (WSIs) and genomic sequencing, remains a significant challenge due to the intrinsic heterogeneity of these modalities and the need for scalable and interpretable frameworks. Existing diagnostic models typically operate on unimodal data, overlooking critical cross-modal interactions that can yield richer clinical insights. We introduce MarbliX (Multimodal Association and Retrieval with Binary Latent Indexed matriX), a self-supervised framework that learns to embed WSIs and immunogenomic profiles into compact, scalable binary codes, termed “monogram.” By optimizing a triplet contrastive objective across modalities, MarbliX captures high-resolution patient similarity in a unified latent space, enabling efficient retrieval of clinically relevant cases and facilitating case-based reasoning. In lung cancer, MarbliX achieves 85%–89% across all evaluation metrics, outperforming histopathology (69%–71%) and immunogenomics (73%–76%). In kidney cancer, real-valued monograms yield the strongest performance (F1: 80%–83%, Accuracy: 87%–90%), with binary monograms slightly lower (F1: 78%–82%).

Abstract Background Brain insulin resistance and cerebrovascular dysfunction emerge early in late-onset Alzheimer’s disease, but how amyloid-β (Aβ) disrupts insulin signaling at the cerebrovascular blood–brain barrier—a major site of insulin receptor signaling and transport into the brain—remains unclear. Methods We exposed two distinct human blood-brain-barrier endothelial cell models to soluble Aβ40 or Aβ42 for 1 h, followed by 100 nM insulin for 10 min. Protein and phosphoprotein responses were quantified by reverse-phase protein array, and differential expression was evaluated using linear models. Results Aβ40 reduced insulin-stimulated Akt activation and converted insulin’s normal inhibition of AMPK into modest stimulation. Aβ42 did not alter insulin-stimulated Akt signaling but moderately suppressed basal Akt activation. Conclusions These findings suggest that Aβ40 acutely impairs insulin signal transduction in BBB endothelial cells, supporting a model in which vascular Aβ exposure contributes directly to the early development of brain insulin resistance in AD.

Abstract Cutaneous squamous cell carcinoma (cSCC) poses significant clinical challenges due to its rising incidence and potential for metastasis. Histopathologic risk stratification is further limited by substantial inter-observer variability. Unsupervised AI approaches based on content-based image retrieval offer scalable and interpretable decision support for diagnostic pathology. The objective of this study was to evaluate the use of image retrieval within histopathology atlases to stratify cSCC tumor differentiation from whole-slide images (WSIs), while comparing different patch selection and feature extraction strategies. This retrospective study included 552 archived WSIs comprising 385 well-differentiated, 102 moderately differentiated, and 66 poorly differentiated cases collected across Mayo Clinic sites in Arizona, Florida, and Minnesota. Image atlases were constructed using multiple patch aggregation strategies (Mosaic, Collage, and Montage) and deep learning encoders (KimiaNet, PathDino, and H-Optimus-0). A leave-one-WSI-out evaluation framework was used to assess differentiation classification performance using accuracy, specificity, sensitivity, and F1 score. Mosaic combined with KimiaNet achieved the highest Top-1 accuracy (74.9%) and specificity (92.6%), while Mosaic with H-Optimus-0 yielded the best Top-5 accuracy (79.0%) and macro-F1 score (62.6%). Collage combined with KimiaNet produced the highest Top-5 specificity (99.5%). The generalizability of the evaluated AI models varied across hospitals, reflecting differences in imaging protocols, staining practices, and patient populations. Overall, unsupervised image search and retrieval provides effective, annotation-free support for cSCC differentiation and has the potential to enhance dermatopathology workflows when appropriate combinations of patch selection and feature ex-traction methods are employed.

Abstract Background Plasma biomarkers offer improved accessibility compared to established diagnostic modalities for Alzheimer's disease (AD). However, current plasma biomarker candidates have not been tested comprehensively in diverse populations, and represent only a fraction of pathways dysregulated in AD. This study seeks to address these knowledge gaps through untargeted plasma biomarker discovery in African Americans (AA), an underrepresented group in AD research despite having a higher risk of developing dementia compared to non‐Hispanic Whites (NHW). Method Untargeted proteomics were generated using the SomaScan 7k platform on plasma samples from AA study participants clinically diagnosed as AD dementia ( n = 181) or cognitively unimpaired (CU; n = 142). Machine learning was implemented to identify a set of plasma proteins to distinguish between AD dementia and CU. Predictive models were constructed by elastic net regression optimized with nested cross‐validation on 6,853 proteins and evaluated by receiver operating characteristic analysis. The generalizability of plasma protein predictors was evaluated in an external Alzheimer's Disease Research Center cohort of 369 NHW and AA study participants diagnosed as AD dementia or CU. Result Thirty‐six proteins were selected as predictors. Proteins selected by machine learning were enriched for processes previously associated with AD including synaptic dysfunction, inflammation, vascular dysfunction, and amyloid processing. The 36 plasma proteins alone achieved an area under the curve (AUC) of 0.94 to classify AD dementia in the discovery cohort, representing a 22% improvement over a model including age and sex. The 36 proteins were validated in an external cohort also achieving AUC=0.94, a 35% improvement over age and sex. Conclusion Biomarker discovery combining untargeted proteomics and machine learning identified a set of plasma proteins that could serve as a novel and highly accurate diagnostic plasma biomarker panel for AD. Work is underway to assess the association of the predictive panel with p ‐tau217, medical comorbidities, and social/structural determinants of health.

Abstract Deconvolution models are a powerful tool for extracting cell-type-specific information from bulk gene expression profiles. Current methods leverage advanced machine learning models and high-resolution sequencing, like single-cell RNA-sequencing, showing promising results across diverse tissues and conditions. However, they still present important limitations: First, many depend on selecting a robust reference, which can strongly affect the deconvolution. Second, pseudobulk data used for training and real bulk RNA-seq samples often exhibit strong distribution shifts, which are currently unaccounted for. Finally, most deconvolution approaches behave as black boxes, which can compromise the reliability of the results. Here, we present Sweetwater, an adaptive and interpretable autoencoder that efficiently deconvolves bulk samples leveraging multiple classes of reference data. Moreover, we propose an improved way of generating training data from a mixture of FACS-sorted FASTQ files, reducing platform-specific biases and outperforming current single-cell-based references. Furthermore, we introduce a gold standard dataset to facilitate fair and accurate evaluation of deconvolution approaches. Finally, we demonstrate that Sweetwater adapts effectively to deconvolved samples during training, uncovering biologically meaningful patterns and enhancing result’s reliability. Sweetwater is available at https://doi.org/10.6084/m9.figshare.29609180, and we anticipate it will expedite the accurate examination of high-throughput clinical data across diverse applications.