bims-tumhet 2025-11-30 papers

Issue of 2025–11–30
six papers selected by
Sergio Marchini, Humanitas Research

bioRxiv. 2025 Oct 14. pii: 2025.10.13.681986. [Epub ahead of print]

Tissue specificity and chromosomal alterations shape divergent immune programs in HRD tumors.

Doga C Gulhan, David Barras, Marco Mina, Eleonora Ghisoni, Yoo-Na Kim, Vinay Viswanadham, Hu Jin, Florian Huber, Krisztian Homicsko, Michal Bassani-Sternberg, Giovanni Ciriello, Peter J Park, Denarda Dangaj Laniti.

Homologous recombination deficiency (HRD) activates pro-inflammatory cGAS/STING signaling, positioning it as a biomarker for combining immune checkpoint blockade (ICB) and PARP inhibition (PARPi). However, the consequences of HRD on the immune landscape across cancers remain unclear. Here, we applied a pan-cancer HRD classifier to >10,000 tumors from The Cancer Genome Atlas and uncovered striking heterogeneity in immune activity. Compared to HR-proficient tumors, HRD tumors showed elevated inflammation in breast, ovarian, and endometrial cancers. These tumors exhibited robust activation of innate and adaptive immune pathways (IFN, NF-κB) and transcriptional hallmarks of senescence, angiogenesis, and adenosine signaling. In contrast, lung, head and neck, and melanoma HRD tumors displayed suppressed inflammation and evidence of immune escape through large-scale loss-of-heterozygosity (LOH) at IFNA/B, STING, and other loci. These tumors also frequently presented HLA LOH and oncogene amplifications, suggesting selection under immune pressure and replication stress. Together, our study resolves HRD tumors into two immune archetypes, immune-inflamed and immune-evasive, linked to chromosomal instability and lineage, informing biomarker-driven evaluation of immune checkpoint blockade/PARPi combinatorial therapies.

DOI: https://doi.org/10.1101/2025.10.13.681986

BMC Cancer. 2025 Nov 25. 25(1): 1816

Cell-free DNA methylation and fragmentomics-based liquid biopsy for accurate esophageal cancer detection.

Jingyun Wang, Wenjing Zhang, Haohao Li, Yiting Wang, Jian Qi, Jinfu Nie.

BACKGROUND: Cell-free DNA is a promising source of biomarkers for early cancer detection and carries tumor-driven methylation and fragmentation features that have achieved good diagnostic efficacy across various cancers. However, there were no studies that detected both of them for esophageal cancer diagnosis.
METHODS: In this study, we analyzed the cfDNA methylation and fragmentation markers for accurate esophageal cancer detection. Using cfMeDIP-seq, we profiled 145 plasma samples from healthy controls and esophageal cancer patients. We used multiple algorithms to identify cfDNA methylation markers and fragmentation markers to evaluate the efficacy of early esophageal cancer detection.
RESULTS: Finally, we identified 25 cfDNA methylation and fragmentation markers and constructed a machine-learning model, which achieved a sensitivity of 99% and specificity of 97.82% in an independent cohort. These results indicate that methylation and fragmentomics biomarkers based on cfMeDIP-seq can accurately distinguish esophageal cancer patients from non-tumor controls.
CONCLUSION: Our study based on cfMeDIP-seq highlights the efficacy of cfDNA methylation and fragmentation histology markers in diagnosing esophageal cancer and provides a direction for subsequent research.

Keywords: Cancer early detection; Cell-free DNA; Esophageal cancer; Fragmentomics; Methylation; cfMeDIP-seq

DOI: https://doi.org/10.1186/s12885-025-15150-4

bioRxiv. 2025 Oct 21. pii: 2025.10.20.683167. [Epub ahead of print]

Knowledge-informed multimodal cfDNA analysis improves sensitivity and generalization in cancer detection.

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.

DOI: https://doi.org/10.1101/2025.10.20.683167

Int J Mol Sci. 2025 Nov 13. pii: 10982. [Epub ahead of print]26(22):

Genomics and Epigenomics Approaches for the Quantification of Circulating Tumor DNA in Liquid Biopsy: Relevance of a Multimodal Strategy.

Elisa De Paolis, Alessia Perrucci, Gabriele Albertini Petroni, Alessandra Conca, Matteo Corsi, Andrea Urbani, Angelo Minucci.

The adoption of liquid biopsy approaches in clinical practice has triggered a significant paradigm shift in the diagnostic, prognostic, and predictive outcomes for cancer patients. Circulating tumor DNA (ctDNA) is considered a valuable biomarker for monitoring tumor burden and its mutational dynamics. In this context, not all cell-free DNA (cfDNA) molecules are derived from tumor cells. Furthermore, due to tumor heterogeneity, not all ctDNA molecules contain cancer-associated alleles, complicating the direct quantification of the circulating tumor allele fraction (cTF) within the total cfDNA. Cancer arises from the accumulation of multiple genetic and epigenetic changes. Each of these molecular features can be exploited as the basis of methodological strategies used in ctDNA quantification. Different layers of omics data, from genomics, evaluating mutational analysis of somatic single-nucleotide variants and copy number alterations, to epigenomics, primarily consisting of the evaluation of methylation profiles and fragmentation patterns, can be used for this purpose. Some of these approaches can be effective in a multi-modal manner. To date, the quantification approaches for estimating cTF vary enormously, making direct comparisons and an assessment of their translational value challenging. Moreover, the lack of regulatory approval for many of these assays is a critical barrier to their widespread clinical adoption. This review explores the different omics approaches described for ctDNA quantification, outlining strengths and limitations, and highlighting their valuable applications in clinical settings.

Keywords: NGS; cTF; cfDNA; ctDNA; epigenomics; genomics; liquid biopsy; methylation; omics; transcriptomics

DOI: https://doi.org/10.3390/ijms262210982

bioRxiv. 2025 Oct 09. pii: 2025.10.08.676897. [Epub ahead of print]

Integrated single-cell whole genome sequencing and spatial transcriptomics reveal latent intra-tumoral heterogeneity in ovarian cancer.

Rania Bassiouni, Yuxin Jin, Lee D Gibbs, Jing Qian, Solomon O Rotimi, Heather Miller, Michelle G Webb, Seeta Rajpara, Javier Arias-Stella, David W Craig, Lynda Roman, John D Carpten.

The mortality rate of ovarian cancer remains disproportionately high compared to its incidence. This is partly due to a high level of intra-tumoral heterogeneity that promotes disease recurrence and treatment failure. In this study, we describe degrees of heterogeneity revealed by single-cell whole genome sequencing and spatial transcriptomics of five epithelial ovarian carcinomas. At the cellular level, we describe pseudo-diploid cells that match the malignant cell population in both somatic variant and copy number patterns. At the clonal and subclonal levels, we describe diversification associated with copy number gains and whole genome doubling. In multi-clonal samples, we infer evolutionary relationships from single cell copy number, loss of heterozygosity analysis, and somatic variant detection, and correlate these with tissue histology and gene expression programs. In one sample, we identify functionally consequential copy number alterations that contribute to molecular diversity, cell proliferation, and inflammation in a minor clone that persisted without major expansion alongside a more complex major clone. In another, we describe a complex evolutionary history including a spontaneous reversion of a driver mutation in a secondary clone, which correlated with a switch in oncogenic expression programs.

DOI: https://doi.org/10.1101/2025.10.08.676897

bioRxiv. 2025 Nov 13. pii: 2025.11.06.686988. [Epub ahead of print]

A chromatin fiber model explains cell-free DNA fragmentation signatures of active regulatory elements.

Alexis Yang, Garyoung Gary Lee, Surya Chhetri, Razane El Hajj Chehade, Gunsagar Gulati, Medha Pandey, Doris Fu, Yoo-Na Kim, Shahab Sotudian, Hunter Savignano, Sadia D Shahbazi, Chelsea Philpot, Taylor A Hoggood, Doga C Gulhan, Manolis Kellis, Jacob E Berchuck, Sylvan C Baca.

Circulating cell-free DNA (cfDNA) assays are being widely adopted in oncology and maternal-fetal medicine. Patterns of cfDNA fragmentation can provide useful information about gene regulation and expression in human disease from a blood draw. Here, we demonstrate that enhancer RNA expression - a marker of enhancer activity - can be inferred from local patterns of cfDNA fragmentation. We define a transcriptional activation score (TAS) that predicts expression of enhancers and genes based on cfDNA fragment sizes and positions near transcriptional start sites (TSSs). The TAS identifies activity of cancer-associated enhancers in patients with cancer, distinguishes clinically relevant cancer subtypes, and identifies activation of enhancers associated with treatment resistance and therapy response. We propose a simple model to account for our findings based on chromatin fiber structure and the depletion of H1 histone proteins near active TSSs. Our model provides a unified framework that reconciles seemingly conflicting observations from prior fragmentomics studies. Broadly, this work enables blood-based assessments of gene regulation in cancer and non-oncologic diseases to inform pathobiology, diagnosis, and treatment selection.

DOI: https://doi.org/10.1101/2025.11.06.686988