bims-ovdlit 2026-09-20 papers

Issue of 2026–09–20
seven papers selected by
Lara Paracchini, Humanitas Research

Annu Rev Pathol. 2026 Sep 17.

The Pathogenesis of Epithelial Ovarian Cancer.

Epithelial ovarian cancer is not a single disease but a group of biologically distinct malignancies that include serous (high-grade and low-grade), endometrioid, clear cell, and mucinous carcinomas, along with other rare subtypes. Integrating clinicopathological analyses, genomic and multiomic data, and experimental investigations in model systems has revealed the pathogenesis of the various histologic subtypes. A unique feature of epithelial ovarian cancer is that most of these tumors are now recognized to arise not from ovarian tissue but from the fallopian tube or endometrium, the latter in the context of ovarian endometriosis. Studies of precursor lesions have revealed complex evolutionary trajectories and the earliest molecular events in their development. Recent single-cell and spatial technologies further elucidate the roles of intratumoral heterogeneity and the tumor microenvironment in disease progression. This review summarizes these advances from the perspective of tissue of origin and highlights their implications for prevention, early detection, and therapeutic development.

DOI: https://doi.org/10.1146/annurev-pathmechdis-032025-021500

Cancer Lett. 2026 Sep 18. pii: S0304-3835(26)00617-8. [Epub ahead of print] 218853

Plasma cfDNA fragmentomics enables non-invasive detection and preoperative risk stratification of bladder cancer in a multicenter study.

Kaihua Liu, Hua Bao, Guangqi Li, Wenwen Ran, Ping Yang, Zongliang Zhang, Wenxian Li, Ningyou Li, Minhui Ren, Tao Ding, Peng He, Shanshan Yang, Xinyue Hong, Ke Wang, Xiaoming Xing.

Accurate non-invasive tools for bladder cancer detection and preoperative assessment of muscle invasion remain limited. We developed a plasma cell-free DNA (cfDNA) fragmentomics assay to support two clinically related tasks: detection of bladder cancer and preoperative estimation of muscle-invasive risk. Using low-pass whole-genome sequencing, we extracted fragment size coverage, copy-number variation, and nucleosome positioning features. The multicenter cohort included 656 participants and an external extension of 31 cancer cases (total, 687). An integrated classifier was first trained for cancer detection. Subsequently, a separate non-muscle-invasive bladder cancer (NMIBC) versus muscle-invasive bladder cancer (MIBC) classifier was developed using definitively staged cases from the training cohort with internal threshold selection and independent external validation. For cancer detection, the model achieved AUCs of 0.9484 (training), 0.9446 (internal validation), and 0.9390 (external validation). At the locked threshold of 0.592, selected in the internal validation cohort, sensitivities were 81.0%, 80.0%, and 84.3%, with specificities of 95.0%, 94.7%, and 91.8% against healthy controls and 93.3% and 91.1% against benign disease controls in internal and external validation, respectively. For invasion stratification, the NMIBC/MIBC classifier achieved AUCs of 0.9254, 0.9004, and 0.8845 in the training, internal validation, and expanded external validation cohorts, respectively, with 90.3% sensitivity and 77.4% specificity for MIBC detection externally. The assay demonstrated high reproducibility across storage conditions and sequencing batches. This blood-based framework supports multicenter bladder cancer detection and preoperative stratification of muscle invasion as an adjunctive translational approach, while prospective validation in defined clinical use settings remains necessary.

Keywords: bladder cancer; cfDNA fragmentomics; early detection; liquid biopsy; muscle invasion

DOI: https://doi.org/10.1016/j.canlet.2026.218853

Lancet. 2026 Sep 19. pii: S0140-6736(26)01543-6. [Epub ahead of print]408(10560): 1088-1089

Unmasking inequity: emergency presentations in ovarian cancer care - Authors' reply.

Lancet Ovarian Cancer Commission

DOI: https://doi.org/10.1016/S0140-6736(26)01543-6

Lancet. 2026 Sep 18. pii: S0140-6736(26)01669-7. [Epub ahead of print]

Precision prevention and early detection: framework for a new clinical cancer control pathway.

Timothy R Rebbeck.

Stage at diagnosis remains the strongest determinant of cancer survival, yet only 13% of cancers are detected through guideline-based screening, and 20-50% first present in an emergency department. Precision prevention and early detection (PPED) tailors cancer control by asking who is at elevated risk of developing cancer, what preventive intervention is appropriate, when should intervention be applied and at what intensity, and how and where preventive services should be delivered. De-escalation of preventive interventions in low-risk individuals is as important as preventive care escalation in high-risk individuals. The hereditary cancer syndromes demonstrate that this logic works when cancer risk is sufficiently enriched. Although the science must continue to mature, the health-system infrastructure and workforce required to sustain longitudinal prevention planning are a defining cancer control challenge in the next decade.

DOI: https://doi.org/10.1016/S0140-6736(26)01669-7

Cancer. 2026 Sep 15. 132(18): e70588

Clinical management of clonal hematopoiesis.

Kelly S Chien, Guillermo Garcia-Manero.

Clonal hematopoiesis, particularly clonal hematopoiesis of indeterminate potential and clonal cytopenia of undetermined significance, is an age-related premalignant condition characterized by the expansion of hematopoietic clones carrying somatic mutations. Because of the accessibility of peripheral blood, it has been increasingly detected, even in otherwise healthy individuals. Although often asymptomatic, specific clonal hematopoiesis-associated mutations and clonal features are associated with an increased risk of progression to hematologic malignancies, including myelodysplastic syndromes and acute myeloid leukemia. Beyond malignant transformation, clonal hematopoiesis also has systemic implications and has been linked to a variety of nonmalignant conditions, such as cardiovascular and inflammatory disease. In this review, we focus on the clinical management of clonal hematopoiesis, including approaches to diagnosis, tools for risk stratification, and strategies for surveillance and comorbidity mitigation. In the absence of consensus guidelines and approved therapies, management remains individualized, underscoring the need for continued research and clinical trials to guide evidence-based care and improve patient outcomes.

Keywords: CCUS; CHIP; clonal hematopoiesis

DOI: https://doi.org/10.1002/cncr.70588

Cancer Cell. 2026 Sep 14. pii: S1535-6108(26)00357-0. [Epub ahead of print]44(9): 1715-1720

Molecular residual disease: Charting a new path for treating resectable solid tumors.

Shin Kobayashi, Tadayoshi Hashimoto, Hideaki Bando, Yoshiaki Nakamura, Aparna Parikh, Jeanne Tie, Qian Shi, Eiji Oki, Takayuki Yoshino.

Molecular residual disease (MRD), detected via circulating tumor DNA (ctDNA) after curative-intent surgery, identifies resectable solid tumor patients at high recurrence risk, thereby enabling enriched trials with larger effect sizes and smaller samples. Yet enrichment narrows the eligible population and thus challenges conventional randomized designs. We propose an MRD Registry as an external control cohort that integrates quantitative ctDNA data with outcomes to validate ctDNA dynamics as response indicators-facilitating efficient MRD-based trial design and accelerating novel therapy development.

DOI: https://doi.org/10.1016/j.ccell.2026.08.001

Nat Commun. 2026 08 17. pii: 9841. [Epub ahead of print]17(1):

Decoding the sequence determinants of locus-specific DNA methylation across human tissues.

Junru Jin, Ding Wang, Jianbo Qiao, Wenjia Gao, Yuhang Liu, Siqi Chen, Quan Zou, Shu Wu, Ran Su, Leyi Wei.

DNA methylation is a fundamental epigenetic modification that plays crucial roles in transcriptional regulation, cellular differentiation, and genome stability. However, how locus-specific DNA methylation is determined by intrinsic DNA sequence remains poorly understood. Here, we introduce Melody, a deep learning framework that predicts DNA methylation from 10-kb genomic sequences, enabling the integration of both local and long-range sequence signals. Across 39 human tissues, Melody accurately predicts methylation profiles and consistently outperforms existing state-of-the-art methods in whole-chromosome, hypomethylated-region, and cell-type-specific benchmarks. Melody also generalizes to methylation quantitative trait locus (meQTL) effect prediction and identifies regulatory sequence motifs associated with methylation variability. To extend prediction beyond profiled tissues, we further develop Melody-G, which incorporates single-cell RNA-seq foundation model embeddings to infer methylation states in previously unseen cell types from transcriptomic data. Together, Melody provides a unified framework for linking genomic sequence and cellular state to DNA methylation and offers new insights into the regulatory logic governing the human methylome.

DOI: https://doi.org/10.1038/s41467-026-76744-5