bims-ovdlit Biomed News
on Ovarian cancer: early diagnosis, liquid biopsy and therapy
Issue of 2026–09–13
ten papers selected by
Lara Paracchini, Humanitas Research
  1. DNA methylation biomarkers for early detection of ovarian cancer.
  2. How advances in machine learning drive early detection and risk prediction of early-onset colorectal cancer.
  3. Bayesian Integration of Tumor Mutational Signatures and Somatic Features Refines Pathogenicity Assessment of Germline Mismatch Repair Variants.
  4. Randomised phase III trials of cancer prevention drugs: a systematic review and prediction of trial success.
  5. A guide to understanding tumour evolution through the lens of population genetics.
  6. Recurrent mechanisms of biallelic epigenetic inactivation reveal new putative tumour suppressor genes in prostate cancer.
  7. Mechanisms underlying disease-causing variants in promoters and enhancers.
  8. Finding clonal hematopoiesis before sequencing.
  9. Family-based analyses show how children's genetics shape key traits.
  10. Identification and validation of a previously missed mutational signature in colorectal cancer.
  1. Mol Biol Rep. 2026 Sep 11. pii: 1562. [Epub ahead of print]53(1):
    DNA methylation biomarkers for early detection of ovarian cancer.
    Jeremy Yuan-Sheng Leow, Nancy Choon-Si Ng, Rebecca Shin-Yee Wong, Long Chiau Ming, Toong Lam Yap, Bey Hing Goh.
      Ovarian cancer (OC) remains difficult to detect at an early stage, and current screening approaches using CA125 and transvaginal ultrasonography have not demonstrated sufficient benefit for population screening. DNA methylation is a promising biomarker class because epigenetic alterations may arise early in tumourigenesis, can be detected in circulating cell-free DNA (cfDNA), and may provide tissue-of-origin information. This review critically evaluates recent evidence on DNA methylation biomarkers for early OC detection. PubMed/MEDLINE, Web of Science, and Scopus were searched for studies published between January 2020 and September 2025, supplemented by selected earlier studies of biological or methodological relevance. Evidence was synthesised across single-gene biomarkers, multi-locus panels, genome-wide signatures, assay platforms, and machine-learning classifiers, with emphasis on early-stage performance, histological representation, comparator populations, analytical methodology, and validation design. Single-gene markers such as BRCA1, RASSF1A, OPCML, HOXA9, and HIC1 show variable performance, while multi-gene and classifier-based approaches generally provide stronger discrimination. However, many studies remain limited by retrospective case-control designs, small FIGO stage I-II subsets, predominance of serous disease, and insufficient prospective validation. Integration with CA125 may improve sensitivity but can reduce specificity, which is critical in low-prevalence screening. Clinical translation will therefore require minimal and reproducible methylation signatures, standardised low-input cfDNA workflows, rigorous external validation, and prospective longitudinal evaluation in intended-use populations.
    Keywords:  Cell-free DNA; DNA methylation; Early detection; Ovarian cancer; Screening
    DOI:  https://doi.org/10.1007/s11033-026-12750-6
  2. Front Oncol. 2026 ;16 1916796
    How advances in machine learning drive early detection and risk prediction of early-onset colorectal cancer.
    Sudha S Murthy, Anna Schuh.
      Early-onset colorectal cancer (EOCRC), defined as colorectal cancer diagnosed before age 50, is rising across high- and middle-income settings whilst organised screening stays anchored to older age thresholds. Blood-based liquid biopsy, combined with machine learning, is the most plausible route to early detection in this group because it does not depend on bowel preparation, endoscopy capacity, or adherence to stool-based testing. The gap is structural: incidence climbs fastest in the population below the age at which any guideline-endorsed modality is offered. The analytical challenge is that early-stage tumour-derived signals in plasma are low in abundance and distributed across heterogeneous molecular layers: circulating tumour DNA mutations, aberrant methylation, cfDNA fragmentomics, and small non-coding RNA. Machine learning converts these into a single calibrated probability. This review examines where artificial intelligence (AI)-driven liquid biopsy genuinely adds diagnostic value in EOCRC, distinguishes components in which learned models are decorative from those in which they are mechanistically necessary, and identifies the validation deficit separating research cohorts from deployable clinical tools. It summarises the first-generation tools used clinically for early detection and post-treatment monitoring, then considers analytes from exosome-bound microRNAs to long-read whole-genome sequencing of circulating plasma DNA, which reads cytosine modification natively, resolves methylation and fragmentation on single molecules, and characterises structural events short reads cannot anchor. Any analyte can feed a learned model, but more diverse input yields better discrimination. The central argument is that approved, guideline-included blood tests were validated in populations aged 45 and above, and their performance in younger patients cannot be assumed.
    Keywords:  cfDNA fragmentomics; circulating tumour DNA; early cancer detection; early-onset colorectal cancer; liquid biopsy; long-read sequencing; machine learning
    DOI:  https://doi.org/10.3389/fonc.2026.1916796
  3. Hum Mutat. 2026 ;2026 7456837
    Bayesian Integration of Tumor Mutational Signatures and Somatic Features Refines Pathogenicity Assessment of Germline Mismatch Repair Variants.
    Yonatan Amzaleg, Kevin J McDonnell, Gregory E Idos, Christina Mathai, Stacy W Gray, Heather Hampel, David W Craig.
      Variants of uncertain significance (VUS) in mismatch repair (MMR) genes represent a persistent bottleneck in germline interpretation for Lynch syndrome, creating a critical opportunity to leverage tumor biology to refine pathogenicity assessment. Although tumor features such as microsatellite instability (MSI) and immunohistochemistry (IHC) are routinely evaluated, they are typically interpreted separately from germline classification, and their quantitative contribution within ACMG/AMP frameworks remains poorly defined. We therefore analyzed paired germline and tumor sequencing data from 1110 tumors across 1073 patients with colorectal or endometrial cancer to determine whether mismatch repair-deficient (MMR-d) mutational signatures can be quantitatively integrated into Bayesian germline variant interpretation. Using COSMIC single-base substitution signatures, tumors were classified as MMR-d or MMR proficient, and an empirically derived likelihood ratio (LR) quantified the association between MMR-d signatures and pathogenic germline MMR variants. The presence of an MMR-d signature increased the likelihood of an underlying pathogenic germline MMR variant approximately eightfold (LR ≈ 8; log10 LR ≈ 0.90), whereas its absence provided moderate-to-strong benign evidence (LR ≈ 0.156; log10 LR ≈ -0.81). Applying this integrative framework to 45 germline MMR VUS, joint modeling of tumor mutational signatures with additional somatic and variant-level evidence resulted in clinically significant reclassification of 38 (84.4%) variants, including three reclassified as pathogenic or likely pathogenic and 35 as likely benign. A total of 16 downgraded variants were independently downgraded by Invitae. These findings demonstrate that tumor mutational signatures can be formally incorporated into Bayesian germline interpretation, transforming tumor data into quantitative pathogenicity evidence and offering a principled strategy to reduce VUS burden in hereditary cancer genetics.
    DOI:  https://doi.org/10.1155/humu/7456837
  4. ESMO Open. 2026 Sep 08. pii: S2059-7029(26)02302-1. [Epub ahead of print]11(10): 108359
    Randomised phase III trials of cancer prevention drugs: a systematic review and prediction of trial success.
    ESMO Early Detection and Prevention of Cancer Task Force
       BACKGROUND: Pharmacological cancer risk reduction has received far less investment than therapeutic oncology and only a few agents have entered routine practice. Understanding the predictors of positive outcomes of previous trials and issues faced is critical to future developments.
    METHODS: This systematic review (PROSPERO CRD420250650276) included all phase III randomised controlled trials enrolling cancer-free adults over the past 45 years, evaluating pharmacological agents and whose primary or major secondary endpoint was directly related to cancer risk reduction. To predict trials' 'success', defined here as meeting the prespecified cancer risk reduction-related endpoint within the planned timeline, we used the Least Absolute Shrinkage and Selection Operator followed by multivariable logistic regression.
    RESULTS: Ninety-two trials comprising 659 904 participants were included, of which 46 (50.0%) met their primary endpoint. Success rates varied by therapeutic class: vitamins (9 of 38, 23.7%), endocrine therapies (15 of 20, 75.0%), nonsteroidal anti-inflammatory drugs (8 of 13, 61.5%), human papillomavirus vaccines (8 of 9, 88.9%) and anti-infective agents (5 of 8, 62.5%). Proportionally, breast (76.5%), cervical (72.7%) and oesophagogastric (58.3%) cancer risk reduction trials had the highest success rates versus 23.7% for vitamins trials. Adherence was highest for vaccines [median 95%, interquartile range (IQR) 93%-96%] and lowest for endocrine therapies (median 70%, IQR 65%-78%). Major safety issues arising for a dozen of the drugs limited their approval (n = 5) and use. In the multivariable analysis, the odds of success increased for trials with pharmaceutical sponsorship [odds ratio (OR) 5.30, 95% confidence interval (CI) 1.01-35.77] and decreased for non mechanism-specific drugs and for studies with lower pretrial evidence (OR 0.30, 95% CI 0.10-0.90).
    CONCLUSIONS: Although half of the pharmacological cancer risk reduction trials conducted so far have shown positive results, only a handful have led to regulatory approval. Trial success was driven by more targeted strategies and strong preliminary evidence, underscoring the need for mechanism-driven, strong-evidence development pathways in cancer risk reduction.
    Keywords:  cancer risk reduction; pharmacological; phase III randomised; prevention; trial success
    DOI:  https://doi.org/10.1016/j.esmoop.2026.108359
  5. Nat Rev Cancer. 2026 Sep 09.
    A guide to understanding tumour evolution through the lens of population genetics.
    Giulio Caravagna, Trevor A Graham, Andrea Sottoriva.
      Every cancer carries the history of its own evolution, hidden in its genome. Modern DNA sequencing can catalogue millions of mutations and profile tumours across space and time, but sequencing alone struggles to answer the questions that matter most: when did key adaptations emerge, how strongly were they selected, why do some tumours relapse whereas others do not, and how will the cancer evolve next? The reason is fundamental: sequencing is a snapshot, whereas evolution is a dynamic process. Bridging this gap requires moving beyond descriptive cancer genomics towards quantitative evolutionary inference. In this Review, we argue that population genetics provides the mathematical framework needed to extract evolutionary dynamics from cancer genomes. We show how models of mutation, selection and drift transform allele frequencies from descriptive measurements into quantitative estimates of clonal fitness and evolutionary timings. We discuss how these principles extend to epigenetic inheritance, plasticity and ecological interactions within the tumour ecosystem, and examine the assumptions and limitations for their application to modern sequencing data. By reframing cancer genomes as quantitative records of evolutionary processes rather than catalogues of mutations, researchers have used population genetics to provide a foundation for understanding - and ultimately predicting - the trajectories of cancer evolution.
    DOI:  https://doi.org/10.1038/s41568-026-00973-5
  6. Nat Commun. 2026 Sep 09. pii: 9646. [Epub ahead of print]17(1):
    Recurrent mechanisms of biallelic epigenetic inactivation reveal new putative tumour suppressor genes in prostate cancer.
    Pan Prostate Cancer Group (PPCG)
      The inactivation of tumour suppressor genes is a key step in cancer development, and is usually achieved by homozygous loss. In prostate cancer, however, large genomic regions are often hemizygously lost, which complicates the identification of putative tumour suppressors in these regions. Here, we develop Epi2Hit, an integrative computational method that leverages whole genome sequencing, epigenomic profiling and gene expression to identify biallelic inactivation of tumour suppressor genes involving DNA methylation of promoter and enhancer regions of one allele and genomic loss of the other allele. We apply Epi2Hit to a cohort of 2,021 prostate cancers to discover tumour suppressor genes. In particular, we identify epigenetic biallelic inactivation of ZFHX3 at a recurrence level similar to TP53. Biallelic inactivation of ZFHX3, a transcriptional repressor, leads to upregulation of oncogenes, including MYC and a shorter time to metastasis. Finally, we provide evidence that epigenetic silencing as 2nd hit is particularly enriched in regions with nearby essential genes, precluding homozygous loss.
    DOI:  https://doi.org/10.1038/s41467-026-72182-5
  7. Nat Genet. 2026 Sep 08.
    Mechanisms underlying disease-causing variants in promoters and enhancers.
    Hannah K Long, Kun Wu, Ryan S Barkham, Wendy A Bickmore.
      The study of human monogenic disorders has been a powerful tool for generating a deep understanding of protein function/dysfunction and for uncovering underlying biological mechanisms. Here we explore the insights that an expanding catalog of noncoding monogenic disease variants can provide into the functions of the noncoding genome. We focus on small genetic alterations (one to a few tens of base pairs) in cis-regulatory elements-promoters, enhancers and silencers-and their potential mechanisms of action, such as loss or gain of function. We discuss the challenges in determining pathogenicity for variants in the noncoding genome, discuss why there might be so few concrete examples and highlight the opportunities for advancing this area of human genetics by using experimental and machine-learning tools.
    DOI:  https://doi.org/10.1038/s41588-026-02743-3
  8. Blood Adv. 2026 Sep 22. 10(18): 6167-6169
    Finding clonal hematopoiesis before sequencing.
    Nancy Gillis, Jeffrey West.
      
    DOI:  https://doi.org/10.1182/bloodadvances.2026021419
  9. Nature. 2026 Sep 09.
    Family-based analyses show how children's genetics shape key traits.
      
    Keywords:  Depression; Genetics
    DOI:  https://doi.org/10.1038/d41586-026-02689-w
  10. Nat Commun. 2026 Aug 12. pii: 9671. [Epub ahead of print]17(1):
    Identification and validation of a previously missed mutational signature in colorectal cancer.
    Mariya Kazachkova, Burçak Otlu, Marcos Díaz-Gay, Ammal Abbasi, Sarah Moody, Zichen Jiang, Sandra Perdomo, David C Wedge, Paul Brennan, Michael R Stratton, Ludmil B Alexandrov.
      Mutational signature analysis has enhanced our understanding of mutagenic processes. In a recent study, we analyzed 802 microsatellite-stable colorectal cancers (CRC) and identified a de novo signature, SBS_D, which was decomposed into SBS18. Here, we re-evaluate this decomposition and provide evidence that SBS_D represents a distinct mutational process from SBS18. Through an analysis of 2,616 CRCs across three independent cohorts, we demonstrate that SBS_D is consistently present, suggesting this signature may have been previously overlooked. We illustrate that the pattern of SBS_D better aligns with signatures associated with deficiencies in DNA repair, despite evidence that SBS_D is not driven by canonical defects in these DNA repair pathways. Overall, this study identifies a previously unrecognized mutational signature in DNA repair-proficient CRC and proposes that its etiology may be linked to DNA repair infidelity emerging late in tumor development. SBS_D has been submitted to the COSMIC database and provisionally designated as SBS111.
    DOI:  https://doi.org/10.1038/s41467-026-76472-w
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