bims-dinmec 2026-03-22 papers

Issue of 2026–03–22
three papers selected by
Lorena Ancona, Humanitas Research

EMBO Mol Med. 2026 Mar 19.

ctDNA monitoring using tumor-informed copy number analysis.

Ze Zhou, Ros Cutts, Sarah Hrebien, Christina X Zhang, Isaac Garcia-Murillas, Woody Z Zhang, Alexander M Frankell, Wendy N Cooper, Amit Roshan, Nicholas C Turner, Tommy Kaplan, Nitzan Rosenfeld, Hui Zhao.

Methods to detect circulating tumor DNA (ctDNA) enable minimally invasive responsive monitoring of cancer dynamics. However, sensitive and cost-effective methods are still lacking. Current methods for detecting cancer signals in shallow whole-genome sequencing (sWGS) data from cell-free DNA (cfDNA) via copy number aberration (CNA) analysis typically have a limit of detection of approximately 3% tumor fraction (TF). We developed informCNA, a bioinformatics method that leverages CNA information from sWGS of tumor or pre-treatment plasma samples with high TF as references, enabling ctDNA detection down to 0.2% TF across multiple cancer types. In 177 serial plasma samples from 18 patients with ovarian cancer, informCNA showed high concordance with the standard serum protein marker CA-125 and identified recurrence a median of 3.7 months earlier than CA-125 test. These results demonstrate the potential of personalized CNA analysis through sWGS for estimating ctDNA burden, enabling precise and cost-effective disease monitoring and early detection of relapse.

Keywords: Copy Number Aberration (CNA); Liquid Biopsy; Tumor-informed; cfDNA; ctDNA

DOI: https://doi.org/10.1038/s44321-026-00399-4

Brief Bioinform. 2026 Mar 01. pii: bbag111. [Epub ahead of print]27(2):

Sensitive detection of copy number alterations in low-pass liquid biopsy sequencing data.

Lotta Eriksson, Eszter Lakatos.

Liquid biopsies, coupled with analysis of copy number alterations (CNAs), have emerged as a promising tool for non-invasive monitoring of cancer progression and tumor composition. However, methods utilizing CNA data from liquid biopsies are limited by the low signal in the samples, caused by a low percentage of cancer DNA in the blood, and inherent noise introduced in the sequencing. To address this challenge, we developed BayesCNA, a method designed to improve signal extraction from low-pass liquid biopsy sequencing data, by utilizing a Bayesian changepoint detection algorithm. We use information of the posterior changepoint probabilities to identify likely changepoints, where a changepoint indicates a shift in the copy number state. The signal is then reconstructed using the identified partition. We show the effectiveness of the method on synthetically generated datasets and compare the method with state-of-the-art bioinformatics tools under noisy conditions. Our results show that this novel approach increases sensitivity in detecting CNAs, particularly in low-quality cases.

Keywords: Bayesian changepoint detection; copy number alterations; liquid biopsies; low-pass sequencing

DOI: https://doi.org/10.1093/bib/bbag111

BMC Res Notes. 2026 Mar 19.

Baseline epigenetic clock measures in the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA).

Claire Hill, Claire Potter, Laura Smyth, Jill Kilner, Angela Scott, Charlotte Neville, Frank Kee, Bernadette McGuinness, Amy Jayne McKnight.

OBJECTIVES: DNA methylation (DNAm) can change dynamically in response to intrinsic or external exposures. Characteristic methylation profiles are associated with accelerated biological ageing and poorer health outcomes. CpG methylation levels for individuals within the Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) were previously generated from baseline blood-derived DNA typed on the Infinium MethylationEPIC array. Harnessing this methylation data, epigenetic clock measures were determined, adjusting for chronological age and white blood cell count (CD8T, CD4T, Natural killer, B-cell, Monocytes and Granulocytes).
DATA DESCRIPTION: The NICOLA study was designed to be representative of the Northern Ireland population at Wave 1 and involved the recruitment of 8,283 people from across Northern Ireland. Participants over the age of 50 were recruited. Other household members (spouses/partners) were allowed to take part, even if they were under the age of 50. 2.3% of participants were under the age of 50. Around 50% of participants were aged between 50 and 65, and 10% of participants were over the age of 80 years. This dataset includes epigenetic clock measures for a subset of 1,870 (48% male) participants within the NICOLA cohort, with a mean age of 64.1 ± 9.37 years. The following 10 epigenetic clocks were generated: (± PC)PhenoAge, (± PC)GrimAge, (± PC)Horvath1, (± PC)Hannum, PCDNAmTL and DunedinPACE. ±PC represents the presence or absence of principal component adjustment. This document describes NICOLA's epigenetic clock dataset, which is ~ 2.5Gb in size.

Keywords: Ageing; Association; EPIC; Epigenetic; Epigenetic clock; Methylation; NICOLA

DOI: https://doi.org/10.1186/s13104-026-07771-0