bioRxiv [Preprint]. 2025 Nov 4:2025.11.03.686234. doi: 10.1101/2025.11.03.686234.

ABSTRACT

Pancreatic ductal adenocarcinoma (PDAC) arises from heterogeneous precursor lesions, including intraductal papillary mucinous neoplasms (IPMNs), but the features distinguishing indolent from progressive lesions remain unclear. We performed an integrative analysis of transcriptomic, genomic, and microenvironmental profiles of IPMNs to define multi-omic phenotypes. Using transfer learning, we projected IPMN-derived transcriptional programs onto spatial transcriptomic datasets from IPMNs and pancreatic intraepithelial neoplasias (PanINs). We identified two major phenotypes: one associated with cancer-associated fibroblasts and epithelial-to-mesenchymal transition, shared across IPMN, PanIN, and PDAC; and a second, glycolysis-enriched phenotype with a unique somatic mutation profile specific to IPMN. Spatial mapping further revealed grade-specific enrichment of transcriptional programs and distinct interactions with stromal and immune subtypes, underscoring the role of the precancer microenvironment in progression. These findings establish multi-omic phenotypes that unify genetic, transcriptional, and microenvironmental heterogeneity, providing a framework for distinguishing progressive from indolent precancers and a web-based public atlas for future exploration of these data and transcriptional phenotypes.

PMID:41279473 | PMC:PMC12637499 | DOI:10.1101/2025.11.03.686234

bioRxiv [Preprint]. 2025 Oct 21:2025.10.20.683167. doi: 10.1101/2025.10.20.683167.

ABSTRACT

Liquid biopsy offers a minimally invasive opportunity to detect and monitor cancers through analysis of cell-free DNA (cfDNA). However, current approaches face challenges of limited sensitivity at low tumor fractions, technical variability, and poor generalization across cohorts. Tumor-informed targeted methods offer high specificity but suffer from low sensitivity due to random sampling, tumor evolution and adaptation (including resistance mechanisms), and other sources of heterogeneity. Conversely, tumor-naive genome-wide methods can increase sensitivity but often sacrifice specificity, particularly at low tumor fractions. We developed Fragmentomics Analysis for Tumor Evaluation with AI (Fate-AI), a multimodal framework that integrates fragmentomic and methylation-derived features from low-pass whole-genome sequencing (LPWGS) and cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq). It employs a knowledge-informed strategy to select recurrently altered genomic regions and tissue-specific methylation loci to combine the advantages of tumor-naive approaches with the specificity of tumor-informed approaches. This approach derives robust per-sample normalized features that mitigate batch effects and enhance cross-cohort reproducibility. We evaluated Fate-AI on a total of 1,219 plasma samples spanning ten cancer types and healthy controls from multiple laboratories and sequencing centers, including 432 newly profiled cases (280 with both cfMeDIP-seq and LPWGS) together with 787 samples from four independent public datasets. Fate-AI achieved superior sensitivity and specificity compared to state-of-the-art methods, detecting tumor-derived signals at fractions as low as 10^-5 in experimental dilutions. Fate-AI scores correlated with disease stage and tracked longitudinal progression, anticipating relapse months before clinical progression. Furthermore, Fate-AI enabled tissue-of-origin classification, with AUCs ranging from 0.84 to 0.97 across six cancer types. Collectively, our results demonstrate that Fate-AI provides a sensitive, generalizable, and clinically actionable platform for early detection, minimal residual disease monitoring, and tissue-of-origin classification, supporting its potential as a liquid biopsy framework in precision oncology.

PMID:41278930 | PMC:PMC12633305 | DOI:10.1101/2025.10.20.683167

Commun Med (Lond). 2025 Nov 13. doi: 10.1038/s43856-025-01231-9. Online ahead of print.

ABSTRACT

BACKGROUND: Although germline testing for DNA mismatch repair (MMR) genes is routinely performed, clinical guidelines highlight evidence gaps due to limited populations and biases. We examined germline pathogenic variants of MMR genes (MLH1, MSH2, MSH6, and PMS2) in 112,927 unselected individuals from BioBank Japan.

METHODS: We analyzed 74,085 cancer patients with 23 cancer types and 38,842 controls matched by sex, age, and hospital area from BioBank Japan, collected between April 2003 and March 2018. Germline pathogenic variants in the coding regions and 2 bp flanking intronic sequences of MMR genes were identified using a multiplex PCR-based target sequencing method. We examined associations with cancer types and demographic characterization of the pathogenic variants, comparing findings to existing clinical guidelines.

RESULTS: Here we show 228 pathogenic variants identified in MMR genes, with pathogenic MSH6 variants most frequently observed in endometrial cancer and 12 other significant associations. Twelve other significant associations are noted across a broad range of odds ratios, whereas pancreatic cancer exhibits no such association. Pathogenic variant carriers are diagnosed up to 12.4 years earlier than non-carriers, and colorectal and gastric cancers are diagnosed up to 16.4 years later than indicated by the guidelines. Higher carrier frequencies are observed in patients with both colorectal and endometrial cancers (24.8%) and in those with endometrial cancer and a family history of endometrial (26.0%) or colorectal (16.1%) cancers.

CONCLUSIONS: This study provides critical insights for clinical guidelines on the associations between cancer types, age at diagnosis, and carrier frequency.

PMID:41258140 | DOI:10.1038/s43856-025-01231-9

bioRxiv [Preprint]. 2025 Oct 3:2025.10.01.679230. doi: 10.1101/2025.10.01.679230.

ABSTRACT

The pancreas plays a central role in major human diseases, yet our understanding of its cellular diversity and plasticity remains incomplete. Here, we present a single-cell multiomics atlas of the human pancreas, profiling over four million cells and nuclei from 57 donors across fetal development, adult homeostasis, and type 2 diabetes (T2D). Integrating sc/snRNA-seq, snATAC-seq, VASA-seq, spatial transcriptomics (Xenium), and multiplexed proteomics (CODEX), we resolve gene expression, chromatin accessibility, and spatial organization at high resolution. We identify transcriptionally plastic centroacinar-like cells (pCACs) in adults with fetal-like features, delineate endocrine and exocrine lineage trajectories during development, and uncover HNF1A-defined beta cell epigenetic states. In T2D, we observe shifts in beta cell subtypes and altered regulatory programs. Glucose perturbation of healthy islets reveals cell-type-specific adaptation and stress responses. This atlas provides a foundational framework to understand pancreas biology and the role of cellular plasticity in regeneration and disease.

PMID:41256699 | PMC:PMC12622017 | DOI:10.1101/2025.10.01.679230