ArticleBMC genomics2020
Comparing copy-number profiles under multi-copy amplifications and deletions.
Article in BMC genomics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- DICE: fast and accurate distance-based reconstruction of single-cell copy number phylogenies.Life science alliance · 2025Article
- A zero-agnostic model for copy number evolution in cancer.PLoS computational biology · 2023Article
- CNAsim: improved simulation of single-cell copy number profiles and DNA-seq data from tumors.Bioinformatics (Oxford, England) · 2023Article
- A zero-agnostic model for copy number evolution in cancer.bioRxiv : the preprint server for biology · 2023Article
- Association of Androgen-Receptor Gene Mutations with the Copy Number of Androgen-Receptor Silk Protein A Complex and Glutathione-S-Transferases T1 and M1 in Prostate Cancer Patients.Genetics research · 2023Article
- SCONCE2: jointly inferring single cell copy number profiles and tumor evolutionary distances.BMC bioinformatics · 2022Article
- Copy number evolution with weighted aberrations in cancer.Bioinformatics (Oxford, England) · 2020Article
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Authors and funding
2 authors.
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Abstract
backgroundDuring cancer progression, malignant cells accumulate somatic mutations that can lead to genetic aberrations. In particular, evolutionary events akin to segmental duplications or deletions can alter the copy-number profile (CNP) of a set of genes in a genome. Our aim is to compute the evolutionary distance between two cells for which only CNPs are known. This asks for the minimum number of segmental amplifications and deletions to turn one CNP into another. This was recently formalized into a model where each event is assumed to alter a copy-number by 1 or -1, even though these events can affect large portions of a chromosome.
resultsWe propose a general cost framework where an event can modify the copy-number of a gene by larger amounts. We show that any cost scheme that allows segmental deletions of arbitrary length makes computing the distance strongly NP-hard. We then devise a factor 2 approximation algorithm for the problem when copy-numbers are non-zero and provide an implementation called cnp2cnp. We evaluate our approach experimentally by reconstructing simulated cancer phylogenies from the pairwise distances inferred by cnp2cnp and compare it against two other alternatives, namely the MEDICC distance and the Euclidean distance.
conclusionsThe experimental results show that our distance yields more accurate phylogenies on average than these alternatives if the given CNPs are error-free, but that the MEDICC distance is slightly more robust against error in the data. In all cases, our experiments show that either our approach or the MEDICC approach should preferred over the Euclidean distance.
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