Abstract Background: Copy number aberrations (CNAs) are gains and losses of large genomic segments present across most cancer types and are a hallmark of cancer genomic alterations. However, the processes underlying CNAs and characteristic patterns of CNAs are poorly understood. Using single nucleotide variant (SNV) data, bioinformatic advances have identified underlying mutational signatures resulting from distinct mutational processes. Mutational signatures have led to a variety of discoveries, several of which are being investigated in clinical management of cancer. The development of algorithms able to uncover similar signatures for CNAs, rather than SNVs, is still in its infancy. Here we present an analysis package for the R programming language called CNSigs that allows for the robust and reproducible derivation of copy number signatures and apply CNSigs in early and advanced breast cancer. Methods: Using segmented data files from DNA sequencing, six copy number features are extracted by CNSigs for signature determination: segment size, the number of breakpoints per 10 megabase bin, number of copy number oscillation events, average size of changepoints, average copy number, and number of breakpoints per chromsome arm, along with ploidy. Based on extracted copy number features, mixed model approaches and non-negative matrix factorization are utilized to derive CNA signatures across cancer types. CNSigs was applied to two large publicly-available cohorts of primary breast cancers (TCGA and METABRIC), a validation cohort of paired tissue and circulating tumor DNA/ctDNA (n=24 pairs), and ctDNA from a large cohort of patients with metastatic triple-negative breast cancer (mTNBC; n=171 patients). Results: To verify the reproducibility of the signatures, we derived five signatures from two independent breast cancer datasets that use distinct copy number segmentation approaches (TCGA - ABSOLUTE; METABRIC - ASCAT). From these two independent datasets, we identified five copy number signatures with high accuracy (average cosine similarity = 0.89). These five signatures are distinct from known breast cancer receptor-based or expression-based subtypes, yet reveal unique associations with underlying mutations, mutational processes, and transcriptional programs. We then took a pan-cancer approach to assess the robustness of the algorithm, and applied the pipeline to 11 different cancer types from TCGA dataset, deriving 13 pan-cancer signatures. To investigate potential application to ctDNA, we evaluated patients with paired tumor and ctDNA sequencing acquired at the same time (n=24 pairs), demonstrating that CNSigs are detectable via ctDNA. Exploratory application of CNSigs in a large cohort of mTNBC patients revealed association of presence of one specific signature, CNSig 11, with extended response to taxane chemotherapy. Conclusions: The CNSigs R package allows researchers to easily analyze their own samples to derive copy number signatures and evaluate clinical associations. We demonstrate potential application in ctDNA and association with treatment response. The development of this package allows further investigation of underlying processes that may be responsible for these CNA fingerprints. Citation Format: S. Striker, D. Tallman, K. Collier, E. Blige, M. Vater, D. G. Stover. Application of CNSigs, An R Package for the Identification of Copy Number Mutational Signatures, in Early and Advanced Breast Cancer abstract. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS2-09-15.
Striker et al. (Tue,) studied this question.