Citations

Background and Aims: Cholangiocarcinoma (CCA) is one of the most lethal cancers, characterized by molecular heterogeneity and treatment resistance. To uncover new biological signals and therapeutic opportunities, we employed multiomic characterization combined with machine learning. Approach and Results: We profiled all anatomical CCA subtypes using whole exome sequencing, mRNA sequencing, and proteome/phosphoproteome analysis. Integrative dimensional reduction revealed RNA, protein, and phosphoprotein features driving tumor heterogeneity, enabling clustering. Machine learning algorithms identified molecular features for each cluster and mapped external datasets and patient-derived xenograft (PDX) models onto these clusters. Kinase enrichment analysis highlighted targetable kinases active in each cluster. In vivo validation was performed in cluster-specific PDX models using the selective TNK1 inhibitor, TP-5801. We identified 3 molecular clusters with distinct pathway characterization: immunomodulatory (cluster 1), metabolic (cluster 2), and gene regulation/cellular fate (cluster 3). Cluster assignment was independent of anatomic subtype but correlated with overall survival following curative-intent resection. We also identified multiomic features and pathways linked to overall survival and lymph node metastases, crucial for patient treatment selection. Kinase enrichment analysis pinpointed TNK1 as a highly active kinase in the metabolic cluster. Treatment with TP-5801 significantly reduced tumor growth in a metabolic PDX model, but not in models representing the other clusters. Combining internal data with publicly available datasets, we identified the immunomodulatory cluster as most responsive to gemcitabine/cisplatin therapy, confirmed in vivo using cluster-specific PDX models. Conclusions: Integrated multiomic characterization provides translational insights by defining unique molecular subtypes associated both with therapeutic response and overall clinical outcomes. This approach identified TNK1 as a previously unrecognized therapeutic target in a defined subset of CCA tumors.

Background: Lung cancer remains the leading cause of cancer-related mortality globally, largely due to delayed diagnosis in its early stages. While conventional diagnostic tools like low-dose CT and tissue biopsy are routinely used, they suffer from limitations including invasiveness, radiation exposure, cost, and limited sensitivity for early-stage detection. Liquid biopsy, a minimally invasive alternative that captures circulating tumor-derived biomarkers such as ctDNA, cfRNA, and exosomes from body fluids, offers promising diagnostic potential—yet its sensitivity in early disease remains suboptimal. Recent advances in Artificial Intelligence (AI) and radiomics are poised to bridge this gap. Objective: This review aims to explore how AI, in combination with radiomics, enhances the diagnostic capabilities of liquid biopsy for early detection of lung cancer and facilitates personalized monitoring strategies. Content Overview: We begin by outlining the molecular heterogeneity of lung cancer, emphasizing the need for earlier, more accurate detection strategies. The discussion then transitions into liquid biopsy and its key analytes, followed by an in-depth overview of AI techniques—including machine learning (e.g., SVMs, Random Forest) and deep learning models (e.g., CNNs, RNNs, GANs)—that enable robust pattern recognition across multi-omics datasets. The role of radiomics, which quantitatively extracts spatial and morphological features from imaging modalities such as CT and PET, is explored in conjunction with AI to provide an integrative, multimodal approach. This convergence supports the broader vision of precision medicine by integrating omics data, imaging, and electronic health records. Discussion: The synergy between AI, liquid biopsy, and radiomics signifies a shift from traditional diagnostics toward dynamic, patient-specific decision-making. Radiomics contributes spatial information, while AI improves pattern detection and predictive modeling. Despite these advancements, challenges remain—including data standardization, limited annotated datasets, the interpretability of deep learning models, and ethical considerations. A push toward rigorous validation and multimodal AI frameworks is necessary to facilitate clinical adoption. Conclusion: The integration of AI with liquid biopsy and radiomics holds transformative potential for early lung cancer detection. This non-invasive, scalable, and individualized diagnostic paradigm could significantly reduce lung cancer mortality through timely and targeted interventions. As technology and regulatory pathways mature, collaborative research is crucial to standardize methodologies and translate this innovation into routine clinical practice.

Background: It is crucial to evaluate liver fibrosis in metabolic dysfunction-associated steatotic liver disease (MASLD). Digital pathology, an automated method for quantitative fibrosis measurement, provides valuable support to pathologists by providing refined continuous metrics and addressing inter-observer variability. Although non-invasive tests (NITs) have been validated as consistent with manual pathology, the relationship between digital pathology and NITs remains unexplored. Methods: This study included 99 biopsy-proven MASLD patients. Quantitative-fibrosis (Q-Fibrosis) used second-harmonic generation/two-photon excitation fluorescence microscopy (SHG/TPEF) to quantify fibrosis parameters (q-FPs). Correlations between eight NITs and q-FPs were analyzed. Results: Using manual pathology as standard, Q-Fibrosis exhibited excellent diagnostic performance in fibrosis stages assessment with area under the receiver operating characteristic curves (AUCs) ranging from 0.924 to 0.967. In addition, magnetic resonance elastography (MRE) achieved the highest diagnostic accuracy (AUC: 0.781–0.977) among the eight NITs. Furthermore, MRE-assessed liver stiffness measurement (MRE-LSM) showed the strongest correlation with q-FPs, particularly adjusted by string length, string width, and the number of short and thick strings within the portal region. Conclusions: Both MRE and digital pathology demonstrated excellent diagnostic accuracy. MRE-LSM was primarily determined by collagen extent, location and pattern, which provide a new perspective for understanding the relationship between the change in MRE and histological fibrosis reverse.

Abstract Background Physical activity is associated with lower colorectal cancer (CRC) risk, but its association with molecular subtypes defined by genetic and epigenetic alterations of the disease is unclear. Such information may enhance the understanding of the mechanisms related to the benefits of physical activity. Methods Pooled observational (cases: n = 5386; controls: n = 6798; studies n = 5) and genome-wide association data (cases: n = 8178; controls: n = 10 472; studies n = 5) were used. We used multivariable logistic regression models and Mendelian randomization to assess the association between physical activity and the risk of CRC subtypes defined by individual tumor markers (and marker combinations), namely microsatellite instability status, CpG island methylator phenotype status, and BRAF and KRAS mutations. We used case-only analysis to test for differences between molecular subtypes. We applied Bonferroni correction to account for multiple tests. Results In the pooled observational analysis, higher levels of physical activity were associated with lower CRC risk (Obs-per 1SD, odds ratio [OR] = 0.94, 95% confidence interval [CI] = 0.90 to 0.97), with an association that was stronger in males (Obs-per 1SD, OR = 0.91, 95% CI = 0.87 to 0.96) than in females (Obs-per 1SD, OR = 0.97, 95% CI = 0.91 to 1.03; Pinteraction = .04). Higher physical activity was associated with a lower risk of CRC across all molecular subtypes, especially in males. There was no difference in the associations by subtypes by pooled observational or Mendelian randomization analyses. The findings did not differ by study design, anatomical site, and early or late age onset of CRC. Conclusions Our findings suggest that physical activity is not differentially associated with the 4 major molecular subtypes involved in colorectal carcinogenesis, indicating that its benefits extend broadly across colorectal cancer pathogenesis.

Abstract Pancreatic adenocarcinoma (PDA) has poor response to chemotherapy with a strong likelihood of cancer recurrence. While previous studies have examined patient samples collected before and after chemotherapy, few have analyzed matched samples from the same patients that allow us to deconvolute interpatient heterogeneity, a large limitation to PDA studies. Here, we study the effects of chemotherapy on tumor cells and their surrounding microenvironment using scRNA sequencing of matched pre- and post- treatment tissue biopsies from 5 patients. We utilized the Seurat v4 pipeline to observe transcriptomic differences and corresponding putative cellular interactions in both treatment states. Unsurprisingly, we found insufficient tumor epithelial cells post-treatment due to the intended cytotoxic effects of chemotherapy, leading us to focus on the tumor microenvironment where we had sufficient cells to analyze. We identified that immune cells in the TME are heterogeneously affected by chemotherapy treatment with patient specific differences, but we did identify a unanimous increase in CXCR4 in CD4 and CD8 positive T cells after treatment. Analysis of CAFs revealed that a subset of fibroblasts lose myofibroblastic features and increasingly acquire 'neural'-like features after treatment. This suggests that chemotherapy-altered fibroblasts may contribute to a pro-tumor microenvironment that facilitates recurrence through neuron–CAF–tumor interactions. Through inferred ligand-receptor pairs identified with the CellChat v2 pipeline, we observed that increased CXCL12 in these neural CAFs interacts with the increased CXCR4 on T-cells. We validated the expression of these genes in patients by staining human patient PDAC tissues (both treated and treatment naïve tissues) for neural genes that were highly enriched post-treatment in our single cell sequencing dataset. We have also begun inhibiting these genes of interest in CAFs and are exploring the effects of these ‘neural’-CAFs on patient-derived tumor organoids in culture. Citation Format: Aylin Z. Henstridge, Padma Kadiyala, Ahmed Elhoissiny, Jianhua Liu, Georgina Branch, Nandini Arya, Vaibhav Sahai, Nicole Peterson, Jorge Machicado, Richard Kwon, Allison Schulman, Erik Wamsteker, George Phillips, Martin Fernandez-Zapico, Mark Truty, Timothy Frankel, Fillip Bednar, Marina Pasca di Magliano, Costas Lyssiotis, Eileen S. Carpenter. An increase in neural cancer associated fibroblasts following exposure to chemotherapy in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B079.

Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with limited treatment options and poor survival. This study explores how a medically supervised ketogenic diet (MSKD) impacts the gut microbiome and treatment response in patients with newly diagnosed metastatic PDAC receiving gemcitabine, nab-paclitaxel, and cisplatin chemotherapy. Methods: As part of a randomized Phase II trial, 32 evaluable patients (median age 65.9 years; 53% male), 16 were randomized to either MSKD or standard diet (SD) combined with chemotherapy. Stool samples were collected from 4 patients in the MSKD group (13 samples) and 8 patients in the SD group (23 samples). Deep shotgun metagenomic sequencing was conducted using NovaSeq (Illumina). Taxonomical and functional microbiome characterization was performed using MetaPhlAn 4.0 and HUMAnN 3.6 (The Huttenhower Lab, Harvard). Comprehensive bioinformatics analyses, using QIIME2, were performed to analyze microbiome composition and function over time. Results: Patients in the MSKD group showed better treatment responses overall. Microbiome profiles between the two groups remained distinct throughout therapy. Though not statistically significant due to sample size, MSKD patients showed lower alpha diversity (Shannon diversity; p-value=0.1339), likely reflecting reduced intake of fermentable fiber. Differential abundance analysis using the ANCOM-BC method revealed a significant enrichment of beneficial taxa in the MSKD group compared to the SD group (p<0.05, log-fold change [lfc]≥2), including Akkermansia muciniphila (linked to improved response to immune checkpoint inhibitors; p=0.017931, q=1), Intestinimonas butyriciproducens (a short-chain fatty acid [SCFA] and butyrate producer; p=3.09E-05, q=0.017), and Streptococcus thermophilus (a supporter of gut mucosal health; p=1.15E-05, q=0.006). During the course of the study, only MSKD patients showed sustained increases in Roseburia hominis (SFCA/butyrate producer; p=3.94E-05, q=0.021) and Lacticaseibacillus rhamnosus (with anti-inflammatory and immune-modulating properties; p=7.28E-19, q=3.98E-16), along with a marked reduction in potentially pro-inflammatory Actinomyces spp.(p≤4.47E-05, q≤0.024). Functional profiling showed increased abundance of cancer-relevant microbial pathways in MSKD patients, including phosphatidate metabolism (PWY-7039; involved in lipid signaling and immune regulation; p=4.36E-04, q=0.20) and allantoin degradation (PWY-5705; linked to redox balance; p=9.16E-06, q=0.004). Conversely, L-ascorbate biosynthesis V (PWY-6415) was reduced (p=3.80E-04, q=0.17), likely due to decreased D-galacturonate substrate availability from fiber restriction. Conclusion: These findings suggest that an MSKD may beneficially modulate the gut microbiome in ways that support and enhance therapeutic response in patients with PDAC. Further validation in a larger cohort is warranted to confirm these results and improve analytical resolution. Citation Format: Keehoon Lee, Derek Cridebring, Gayle S. Jameson, Denis J. Roe, Erkut Borazanci, Diana L. Hanna, Caroline G.P. Roberts, Meredith S. Pelster, Richard C. Frank, Angela T. Alistar, Alan M. Miller, J. Erin Wiedmeier-Nutor, Sandra D. Algaze, Alison R. Zoller, Sarah J. Hallberg, Betsy C. Wertheim, Joshua D. Rabinowitz, Stephen Gately, Jennifer Keppler, Sunil Sharma, Drew W. Rasco, Daniel D. Von Hoff. Ketogenic Diet Modulates Gut Microbiome Composition and Enhances Treatment Response in Patients with Advanced Pancreatic Cancer: A Phase II Study [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research—Emerging Science Driving Transformative Solutions; Boston, MA; 2025 Sep 28-Oct 1; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl_3):Abstract nr B094.

ABSTRACT Background and Aims Clinically significant portal hypertension (CSPH; hepatic venous pressure gradient (HVPG) ≥ 10 mmHg) is a severe complication of chronic liver disease, with increased risk of variceal bleeding and liver decompensation (at HVPG ≥ 12 mmHg). This study evaluated the performance of multiparametric (mp)MRI for diagnosing CSPH and predicting liver decompensation, compared to ultrasound‐based shear wave elastography (SWE) and blood tests. Methods In this prospective single‐centre study (2018–2022), 59 patients (M 30, mean age 52.7 years) underwent mpMRI and HVPG measurement, with an average interval of 32 ± 31 days. The mpMRI protocol included 2D/3D MR elastography (MRE), T 1 /proprietary iron‐compensated T 1 (cT 1 )/T 1ρ mapping, gadoxetate‐enhanced dynamic contrast‐enhanced MRI (DCE‐MRI) of the liver and spleen, SWE and blood tests. Statistical analyses included Mann–Whitney U tests, ROC analysis, logistic regression and Cox proportional hazards models. Results CSPH was present in 13 patients (22%). Several MRI parameters showed high diagnostic performance for CSPH (AUC ≥ 0.8), with spleen stiffness‐2D MRE (AUC 0.88, 95% confidence interval (CI) 0.78–0.98) and liver uptake on DCE‐MRI (AUC 0.83, 95% CI 0.70–0.96) performing best, while SWE had AUCs of 0.63 (95% CI 0.45–0.81) for liver and 0.67 (95% CI 0.44–0.89) for spleen. Combining spleen stiffness‐2D MRE and liver uptake achieved an AUC of 0.93 (95% CI 0.86–1.00) for diagnosing CSPH. For predicting decompensation, spleen stiffness‐3D MRE and liver uptake rate had AUCs of 0.83 (95% CI 0.68–0.99) and 0.83 (95% CI 0.70–0.95), respectively, outperforming SWE. Conclusions Spleen stiffness measured with MRE combined with gadoxetate liver uptake (measured with DCE‐MRI) can diagnose CSPH and predict liver decompensation, with superior performance compared to SWE. Trial Registration ClinicalTrials.gov identifier: NCT03436550

Abstract Hepatocellular carcinoma (HCC) mortality is increasing globally, partly due to the growing prevalence of nonviral liver diseases. Genome‐wide association studies (GWAS) have identified genetic variants associated with HCC development. Leveraging GWAS summary statistics and linkage disequilibrium score regression (LDSR), we investigated disease co‐development with hepatitis C virus‐negative (HCV‐negative) HCC to provide unique insights into HCC etiology and prioritize relationships for further causal inquiry. We utilized the LDSR statistical framework to estimate the genetic correlation and heritability between HCV‐negative HCC with 901 epidemiologic, behavioral, and clinical traits from the United Kingdom Biobank (UKBB). First, we set the threshold for observed scale heritability of each trait at 0.02 to ensure reliable inferences with adequate study power. Next, we observed significant positive genetic correlations between HCV‐negative HCC and blood‐based biomarkers of liver injury (ALT, GGT) and allostatic load (including glycated hemoglobin, blood pressure, and total albumin). We also identified a positive genetic correlation between HCV‐negative HCC and diseases associated with metabolic dysfunction‐associated steatotic liver disease (MASLD), including diabetes, hypertension, chronic ischemic heart disease, and others. Taken together, our results help to identify polygenic and pleiotropic signals related to different phenotypic traits associated with HCC and support further exploration of the predictive power of blood‐based biomarkers identified in this study for inferring HCC development among HCV‐negative individuals.

Abstract Background Delirium is often underdiagnosed in clinical practice and is not routinely coded for billing. While manual chart review can identify delirium, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) can analyze unstructured text in electronic health records (EHRs) to extract meaningful clinical information. Methods To support national integration of NLP for EHR-based delirium identification across different institutions, we launched the Delirium Interest Group within the national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) NLP Working Group. This paper outlines our initial efforts to standardize, evaluate, and translate an NLP-based delirium detection model into the i2b2/ENACT platform. Results Multisite contextual inquiry identified several key challenges, including variations in local screening practices (eg, tools used, documentation frequency, and quality control), the need for harmonized definitions in the context of EHRs, and the complexity of modeling temporal logic. Multisite NLP evaluation revealed variable performance degradation driven by differences in delirium screening practices, clinical documentation patterns and semantics, and note syntactic structures. Conclusion Our work represents an important first step toward enabling scalable and standardized NLP-based delirium detection across institutions. By engaging diverse institutions through the ENACT NLP Working Group, we identified shared challenges and site-specific variations that impact model implementation and performance. Our collaborative approach enabled the development of a more robust framework for delirium identification across heterogeneous EHR systems. Future efforts will build on this foundation to enhance the validity, usability, and translational impact of delirium detection.