bims-ovdlit 2026-10-11 papers

Nature. 2026 Oct 07.

Genome accessibility reveals disease risk that gene expression misses.

Keywords: Bioinformatics; Genetics; Genomics

DOI: https://doi.org/10.1038/d41586-026-03072-5

Nat Commun. 2026 09 05. pii: 10565. [Epub ahead of print]17(1):

Benchmarking copy number alteration inference methods for spatial transcriptomics.

Shi Han, Zhixi Xiong, Ying Zhou, Can Yang.

Copy number alterations (CNAs), gains or losses of genomic regions, contribute to malignant progression and tumor heterogeneity. Advances in spatial transcriptomics have expanded opportunities to study clonal structure in situ, but direct spatial genomic profiling remains difficult in practice, motivating the increasing use of computational methods to infer CNAs from spatial transcriptomics data. However, their performance across diverse spatial transcriptomics settings remains unclear. Here, we present a benchmark of nine CNA inference methods across 69 spatial transcriptomics tissue sections from six cancer types and four spatial transcriptomics platforms. By evaluating these methods across four key tasks, we show that no single method consistently outperforms all others, with performance depending on the analytical goal and data characteristics. We therefore provide task-specific and data-aware guidance to help users select appropriate methods in practical settings. More broadly, this benchmark provides a basis for the future development and optimization of CNA inference methods.

DOI: https://doi.org/10.1038/s41467-026-77500-5

J Natl Cancer Inst. 2026 Oct 03. pii: djag309. [Epub ahead of print]

Clonal hematopoiesis and biological aging: an interaction that increases cancer risk?

Jake Tobin, George S Vassiliou.

DOI: https://doi.org/10.1093/jnci/djag309

Cancer Gene Ther. 2026 Oct 08.

Whole-genome DNA methylation analysis reveals distinct subtype characteristics in epithelial ovarian cancer.

Qiaoling Ren, Yingqing Deng, Caizhou Zhong, Dongmei Zhou, Renci Liu, Xiujie Sheng, Deqiang Sun.

Epithelial ovarian cancer (EOC) is characterized by high mortality, insidious onset, and pronounced heterogeneity, underscoring the critical need for effective biomarkers for early detection and subtype-specific diagnosis. While DNA methylation represents a stable epigenetic marker, prior studies have largely relied on profiling techniques with limited genomic coverage and have often overlooked subtype-specific differences essential for guiding treatment decisions. To address this, we performed whole-genome bisulfite sequencing on 44 normal ovarian tissues and 89 EOC tissues encompassing all five histological subtypes, integrated with transcriptome sequencing, to comprehensively evaluate the diagnostic and prognostic utility of DNA methylation across EOC subtypes. Our analyses revealed that genome-wide methylation profiles effectively distinguished normal from tumor tissues and captured the substantial heterogeneity inherent to EOC. Differentially methylated regions (DMRs) and differentially expressed genes were enriched in immune-related processes and epithelial differentiation, aligning with the increased proportions of epithelial and immune cells identified through deconvolution analysis. Subtype-specific DMRs further implicated hormonal pathways in endometrioid (EC) and clear cell (CCOC) carcinomas, and linked mucinous ovarian cancer (MOC) to fluid regulation. Integrative analysis of DMRs and cellular composition indicated that epithelial cells in high-grade serous (HG), low-grade serous (LG), and EC tumors most closely resembled fallopian tube, ovarian, and endometrial epithelial cells, respectively, whereas MOC and CCOC epithelial cells showed similarity to gastrointestinal and renal epithelia. Moreover, weighted gene co-expression network analysis identified epithelia-associated DMRs that achieved up to 90% specificity for subtype classification in internal validation and demonstrated prognostic relevance in independent public datasets. In conclusion, the subtype-specific DNA methylation markers identified in this study accurately reflect the distinct histological features of each EOC subtype and hold promise for application in subtype classification and prognosis prediction, thereby providing a scientific foundation for future clinical interventions in EOC.

DOI: https://doi.org/10.1038/s41417-026-01079-8

Cancer Discov. 2026 Oct 08. OF1-OF9

Universal Germline Genetic Testing after a Diagnosis of Cancer.

Age at diagnosis among patients with cancer is a predictor of pathogenic germline variant prevalence, yet age thresholds often misclassify genetic risk. We performed germline sequencing of 39,184 unselected patients with solid malignancies spanning 32 tumor types, interrogating >90 cancer predisposition genes independent of clinical suspicion. Patients were stratified into early-, average-, and late-onset groups based on standard deviations from tumor-specific mean age at diagnosis. Pathogenic variant prevalence inversely correlated with age at diagnosis: 18.4% in early-onset tumors, 15.6% in average-onset tumors, and 12.3% in late-onset tumors (P < 0.001). Variants in high/moderate-penetrance genes accounted for the enrichment in early-onset cases (12.4%, early-onset; 8.9%, average-onset; 5.1%, late-onset; P < 0.001). Restricting hereditary cancer testing to patients diagnosed before age 50, as is typically done, would miss 4,601 pathogenic variant carriers, 72% of all variants detected. These findings support broad-based germline testing beyond conventional age-based criteria, reflecting the burden of hereditary risk even among late-onset cases.
SIGNIFICANCE: Across 32 solid tumor types in a pan-cancer cohort, we found enrichment of germline pathogenic variants among patients with subtype-specific early-onset cancer. Using age 50 as a testing cutoff misses most patients with inherited predisposition, supporting universal germline genetic testing for all individuals diagnosed with cancer.

DOI: https://doi.org/10.1158/2159-8290.CD-26-0971