METHODS article
Front. Genet.
Sec. Cancer Genetics and Oncogenomics
Volume 16 - 2025 | doi: 10.3389/fgene.2025.1674138
CNAdjust: Enhancing CNA Calling Accuracy through Systematic Baseline Adjustment
Provisionally accepted
Hangjia Zhao1,2*
Michael Baudis1,2*- 1Universitat Zurich, Zürich, Switzerland
- 2Swiss Institute of Bioinformatics, Zurich, Switzerland
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Accurate determination of the genomic copy number baseline is crucial for identifying copy number alterations (CNAs) in cancer, yet it remains a significant challenge in tumors with complex karyotypes. To address this, we present CNAdjust, an integrated method to systematically detect and correct baseline inaccuracies in CNA data. CNAdjust employs a Bayesian framework that integrates cohort-specific CNA frequency priors with a data-driven plausibility score, ensuring that adjusted calls align with both biological cohort patterns and study-specific data. Performance validation using the TCGA pan-cancer dataset demonstrated improved alignment with absolute copy number estimates and enhanced CNA pattern interpretation. Furthermore, we revealed a strong correlation between chromosomal aneuploidy and baseline abnormalities, underscoring the prevalence of this issue in cancer genomics. By systematically improving the precision of CNA calls, CNAdjust serves as a critical tool for constructing harmonized reference datasets and advancing the progress of precision oncology. Its implementation as a standard, portable workflow enables the reproducible and scalable analysis of large, heterogeneous datasets, supporting large-scale genomic research. Source codes are available at: https://github. com/baudisgroup/CNAdjust.
Keywords: Copy number alterations, Baseline correction, Bayesian framework, Cancer genomics, Nextflow workflow
Received: 27 Jul 2025; Accepted: 10 Sep 2025.
Copyright: © 2025 Zhao and Baudis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Hangjia Zhao, Universitat Zurich, Zürich, Switzerland
Michael Baudis, Universitat Zurich, Zürich, Switzerland
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.