CNSigs: An R Package for the Identification of Copy Number Mutational Signatures
- 1Ohio State University
Abstract
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. Bioinformatic advances have identified underlying single nucleotide variant mutational signatures resulting from distinct mutational processes, yet development of algorithms able to uncover similar signatures for CNAs remains less advanced. Using segmented data files from DNA sequencing, six copy number features are extracted for signature determination: segment size, breakpoints, copy number oscillation, changepoint size, copy number, and breakpoints per chromosome arm, along with ploidy. Mixed model approaches and non-negative matrix factorization are utilized to derive CNA signatures across cancer types. The full methodology was packaged in a publicly available, robust R package, CNSigs. To verify reproducibility, we derived five signatures from two independent breast cancer datasets (total n>3000), demonstrating high accuracy (average cosine similarity = 0.89). Pan-cancer application of CNSigs in TCGA resulted in derivation of 13 pan-cancer signatures which were significantly associated with disease-specific survival. Benchmarking CNSigs to two other CNA signature approaches within TCGA demonstrated non-overlapping signatures and favorable compute speed for CNSigs. We evaluated n=24 pairs of tumor and circulating tumor DNA (ctDNA) that demonstrated that CNSigs are detectable and reproducible via ctDNA, with significant association of CNSig11 with metastatic triple-negative breast cancer progression-free survival specifically for taxane chemotherapy. CNSigs association with immunophenotype was evaluated in low-grade glioma and CNSig3 was found to be highly prognostic yet complementary to immune features. The CNSigs allows researchers to easily analyze their own samples to derive copy number signatures and evaluate clinical associations. We demonstrate its 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 CNA fingerprints.
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BibTeX
@misc{tallman2026cnsigs,
title = {{CNSigs: An R Package for the Identification of Copy Number Mutational Signatures}},
author = {D. Tallman and S. Striker and A. M. Byappanahalli and S. Stockard and J. Jenison and K. A. Collier and E. Blige and M. Vater and D. G. Stover},
howpublished = {bioRxiv},
year = {2026},
month = jun,
doi = {10.64898/2026.06.21.733646},
url = {https://www.biorxiv.org/content/10.64898/2026.06.21.733646v2},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - CNSigs: An R Package for the Identification of Copy Number Mutational Signatures
AU - D. Tallman
AU - S. Striker
AU - A. M. Byappanahalli
AU - S. Stockard
AU - J. Jenison
AU - K. A. Collier
AU - E. Blige
AU - M. Vater
AU - D. G. Stover
PY - 2026
DA - 2026/06/21
PB - bioRxiv
DO - 10.64898/2026.06.21.733646
UR - https://www.biorxiv.org/content/10.64898/2026.06.21.733646v2
AB - 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. Bioinformatic advances have identified underlying single nucleotide variant mutational signatures resulting from distinct mutational processes, yet development of algorithms able to uncover similar signatures for CNAs remains less advanced. Using segmented data files from DNA sequencing, six copy number features are extracted for signature determination: segment size, breakpoints, copy number oscillation, changepoint size, copy number, and breakpoints per chromosome arm, along with ploidy. Mixed model approaches and non-negative matrix factorization are utilized to derive CNA signatures across cancer types. The full methodology was packaged in a publicly available, robust R package, CNSigs. To verify reproducibility, we derived five signatures from two independent breast cancer datasets (total n>3000), demonstrating high accuracy (average cosine similarity = 0.89). Pan-cancer application of CNSigs in TCGA resulted in derivation of 13 pan-cancer signatures which were significantly associated with disease-specific survival. Benchmarking CNSigs to two other CNA signature approaches within TCGA demonstrated non-overlapping signatures and favorable compute speed for CNSigs. We evaluated n=24 pairs of tumor and circulating tumor DNA (ctDNA) that demonstrated that CNSigs are detectable and reproducible via ctDNA, with significant association of CNSig11 with metastatic triple-negative breast cancer progression-free survival specifically for taxane chemotherapy. CNSigs association with immunophenotype was evaluated in low-grade glioma and CNSig3 was found to be highly prognostic yet complementary to immune features. The CNSigs allows researchers to easily analyze their own samples to derive copy number signatures and evaluate clinical associations. We demonstrate its 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 CNA fingerprints.
ER -