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PubMed2025

ZIPcnv: accurate and efficient inference of copy number variations from shallow whole-genome sequencing.

MOTIVATION: Shallow whole-genome sequencing (sWGS), a rapid and cost-effective sequencing technology, has gradually been widely adopted for CNV analyses. However, with genome‑wide coverage of only 0.1-5×, sWGS data display a pronounced zero‑inflation phenomenon-a large fraction of loci has zero sequencing reads. Zero inflation causes read counts to fluctuate by several‑fold between adjacent windows. As a result, random upward blips in coverage can be misinterpreted as copy‑number gains (false positives), and true deletions often become indistinguishable from pervasive zero‑coverage noise. In addition, existing CNV detection tools developed for sWGS data often struggle to adapt across different CNV sizes. These combined effects severely constrain the accuracy of CNV inference. RESULTS: To address above challenges, we propose ZIPcnv, a novel CNV detection tool specifically designed for sWGS data. First, we apply a segment sliding window to smooth the raw read depth signal, which transforms the original zero-inflated statistical characteristics into approximately normal distribution characteristics. We then design a statistical process model that robustly detects persistent shifts under high background noise using a cumulative sum strategy, classifying genomic regions into candidate and non-candidate CNV regions. Finally, dynamic sliding windows are used for one-pass detection of CNVs of varying lengths, with window size adapting to the CNV region size. We evaluated the performance of ZIPcnv on simulated data and 190 real whole-genome sequencing samples. Experimental results show that ZIPcnv consistently outperforms currently popular CNV detection tools. AVAILABILITY AND IMPLEMENTATION: The ZIPcnv source code is freely available at https://github.com/Nevermore233/ZIPcnv.

DNA Copy Number Variations↗

PubMed2026

sWGS Identifies a Copy-Number-High Subset of TP53-mutated Multiple-Classifier Endometrial Carcinomas With Adverse Clinicopathological Features.

TP53-mutated "multiple-classifier" endometrial carcinomas represent a diagnostically challenging subgroup within current molecular classification algorithms. Although these tumors are assigned to POLE-mutated or mismatch repair-deficient categories according to current ESGO/FIGO-based algorithms, their biological heterogeneity remains incompletely characterized. Herein, we retrospectively analyzed TP53-mutated multiple-classifier endometrial carcinomas identified through routine molecular profiling at our institution between 2022 and 2025 using an integrated histopathological, immunohistochemical, targeted sequencing, and shallow whole-genome sequencing approach. Copy-number alteration-high (CNA-high) status was defined as ≥5 large-scale genomic alterations, corresponding to copy-number gains or losses ≥3 Mb within a single chromosomal arm excluding whole-arm alterations. Among 33 analyzable TP53-mutated multiple-classifier endometrial carcinomas, sWGS identified 12 CNA-high tumors (36.4%) and 21 CNA-low tumors (63.6%). CNA-high tumors were more frequently non-endometrioid, high-grade, and advanced-stage according to FIGO 2023. They showed higher TP53 variant allele frequencies (VAF) and higher TP53 VAF-to-tumor-cellularity ratios. After a median follow-up of 12.8 months, recurrences (6/33; 18.2%) and disease-related deaths (3/33; 9.1%) were observed in the CNA-high subgroup, whereas no recurrence or disease-related death was observed among CNA-low patients. These findings indicate that TP53-mutated multiple-classifier endometrial carcinomas comprise biologically distinct subsets that are not fully captured by current 4-tier TCGA-based molecular classification and ESGO-based risk stratification. In this cohort, sWGS identified a CNA-high group with adverse clinicopathological features and clinical events suggesting a potentially more aggressive clinical course. Integration of genome-wide copy-number profiling may therefore refine the biological interpretation of TP53 alterations in multiple-classifier endometrial carcinomas and warrants validation in larger multicenter cohorts.

TP53↗

PubMed2026

Cell-free DNA genomic and fragmentomic features for early outcome prediction in large B cell lymphoma.

Curative-intent immunochemotherapy fails in ∼30% of patients with large B cell lymphoma (LBCL), yet no validated molecular tool enables early identification of high-risk individuals to guide treatment intensification. Using shallow whole-genome sequencing (sWGS) of plasma cell-free DNA from 190 LBCL patients, we develop and validate the ACT score (aberrations, composition of fragments, and terminal motif analyses), a composite classifier integrating genomic and fragmentomic features from a single post-cycle-1 sample. ACT-positive patients have worse 2-year outcomes versus ACT-negative patients: time-to-progression 29% vs. 83% (hazard ratio [HR]: 4.4, 95% confidence interval [CI]: 1.9-10.0; p = 1.5 × 10-4) and overall survival 47% vs. 93% (HR: 8.7, 95% CI: 3.0-25.4; p = 1.8 × 10-6). The ACT score is independently prognostic of the International Prognostic Index, and their combination identifies the highest risk patients. Unlike mutation-based approaches, this assay requires neither tumor tissue, germline control, nor a baseline plasma sample. Built on open-source tools and sWGS, the ACT score offers a feasible, scalable strategy for early risk stratification in aggressive LBCL.

Humans↗

PubMed2026

A stratified urine-based molecular diagnostic and prognostic model for non-muscle-invasive bladder cancer management.

BACKGROUND: Non-muscle-invasive bladder cancer (NMIBC) is characterized by a high recurrence rate requiring lifelong cystoscopic surveillance. Existing urine-based molecular assays mainly rely on mutations or methylation, which fail to capture large-scale genomic instability. Copy number variation (CNV) profiling offers complementary information on tumor evolution and aggressiveness, but its application in urinary diagnosis remains limited. We aimed to integrate CNV and DNA methylation signals from urinary DNA to establish a noninvasive and biologically informed stratified diagnostic model for NMIBC recurrence surveillance and risk stratification. METHODS: Urine samples were prospectively collected from 91 patients (75 evaluable) between June 2021 and August 2023. Shallow whole-genome sequencing (sWGS) was used to detect CNVs at chromosomal arm and focal gene levels, while ONECUT2 promoter methylation was quantified by qPCR. Diagnostic and prognostic performance was evaluated by ROC analysis, Kaplan-Meier survival, and stratified recurrence assessment. RESULTS: We evaluated a stratified diagnostic model combining CNV and ONECUT2 methylation testing in a cohort of 79 patients. CNV analysis alone showed high specificity (0.923) for NMIBC diagnosis. A combined model, using CNV as an initial screen followed by ONECUT2 methylation testing in CNV-positive cases, achieved a sensitivity of 0.783, specificity of 0.981, and a negative predictive value (NPV) of 0.911. This approach reduced the number of required ONECUT2 tests by 35% and identified a high proportion of true-negative patients (98.1%), which may help reduce unnecessary cystoscopy procedures. The model also demonstrated significant prognostic value, with the molecularly defined high-risk group showing significantly shorter recurrence-free survival (RFS) than the low-risk group (median RFS: 4.33 months vs. not reached; p < 0.001). Additional, in patients with initially negative cystoscopy after urine sample collection, the model demonstrated a predictive accuracy of 0.922 for recurrence, with molecular positivity observed a median of 9.6 months prior to clinical diagnosis. CONCLUSIONS: Integrating CNV and DNA methylation profiling from urinary DNA provides a powerful and noninvasive molecular framework for NMIBC surveillance. By combining early epigenetic changes with genomic instability signals, this approach enhances recurrence risk assessment and enables earlier detection compared with conventional cystoscopy. It offers a practical route toward personalized and adaptive post-treatment monitoring of NMIBC. TRIAL REGISTRATION: NCT04994197.

Humans↗

PubMed2025

Single-cell copy number calling and event history reconstruction.

MOTIVATION: Copy number alterations are driving forces of tumour development and the emergence of intra-tumour heterogeneity. A comprehensive picture of these genomic aberrations is therefore essential for the development of personalised and precise cancer diagnostics and therapies. Single-cell sequencing offers the highest resolution for copy number profiling down to the level of individual cells. Recent high-throughput protocols allow for the processing of hundreds of cells through shallow whole-genome DNA sequencing. The resulting low read-depth data poses substantial statistical and computational challenges to the identification of copy number alterations. RESULTS: We developed SCICoNE, a statistical model and MCMC algorithm tailored to single-cell copy number profiling from shallow whole-genome DNA sequencing data. SCICoNE reconstructs the history of copy number events in the tumour and uses these evolutionary relationships to identify the copy number profiles of the individual cells. We show the accuracy of this approach in evaluations on simulated data and demonstrate its practicability in applications to two breast cancer samples from different sequencing protocols. AVAILABILITY AND IMPLEMENTATION: SCICoNE is available at https://github.com/cbg-ethz/SCICoNE.

Single-Cell Analysis↗