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Lai J, Yang Y, Liu Y, Scharpf RB, Karchin R. Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction. BIOINFORMATICS ADVANCES 2024; 4:vbae094. [PMID: 38948008 PMCID: PMC11213631 DOI: 10.1093/bioadv/vbae094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 05/28/2024] [Accepted: 06/15/2024] [Indexed: 07/02/2024]
Abstract
Summary Neoplastic tumors originate from a single cell, and their evolution can be traced through lineages characterized by mutations, copy number alterations, and structural variants. These lineages are reconstructed and mapped onto evolutionary trees with algorithmic approaches. However, without ground truth benchmark sets, the validity of an algorithm remains uncertain, limiting potential clinical applicability. With a growing number of algorithms available, there is urgent need for standardized benchmark sets to evaluate their merits. Benchmark sets rely on in silico simulations of tumor sequence, but there are no accepted standards for simulation tools, presenting a major obstacle to progress in this field. Availability and implementation All analysis done in the paper was based on publicly available data from the publication of each accessed tool.
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Affiliation(s)
- Jiaying Lai
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yi Yang
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Yunzhou Liu
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
| | - Robert B Scharpf
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
| | - Rachel Karchin
- Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD 21218, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, United States
- Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, MD 21231, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, United States
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Miura S, Vu T, Deng J, Buturla T, Oladeinde O, Choi J, Kumar S. Power and pitfalls of computational methods for inferring clone phylogenies and mutation orders from bulk sequencing data. Sci Rep 2020; 10:3498. [PMID: 32103044 PMCID: PMC7044161 DOI: 10.1038/s41598-020-59006-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 01/23/2020] [Indexed: 12/13/2022] Open
Abstract
Tumors harbor extensive genetic heterogeneity in the form of distinct clone genotypes that arise over time and across different tissues and regions in cancer. Many computational methods produce clone phylogenies from population bulk sequencing data collected from multiple tumor samples from a patient. These clone phylogenies are used to infer mutation order and clone origins during tumor progression, rendering the selection of the appropriate clonal deconvolution method critical. Surprisingly, absolute and relative accuracies of these methods in correctly inferring clone phylogenies are yet to consistently assessed. Therefore, we evaluated the performance of seven computational methods. The accuracy of the reconstructed mutation order and inferred clone groupings varied extensively among methods. All the tested methods showed limited ability to identify ancestral clone sequences present in tumor samples correctly. The presence of copy number alterations, the occurrence of multiple seeding events among tumor sites during metastatic tumor evolution, and extensive intermixture of cancer cells among tumors hindered the detection of clones and the inference of clone phylogenies for all methods tested. Overall, CloneFinder, MACHINA, and LICHeE showed the highest overall accuracy, but none of the methods performed well for all simulated datasets. So, we present guidelines for selecting methods for data analysis.
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Affiliation(s)
- Sayaka Miura
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Tracy Vu
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jiamin Deng
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Tiffany Buturla
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Olumide Oladeinde
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Jiyeong Choi
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA.,Department of Biology, Temple University, Philadelphia, PA, 19122, USA
| | - Sudhir Kumar
- Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA, 19122, USA. .,Department of Biology, Temple University, Philadelphia, PA, 19122, USA. .,Center for Excellence in Genome Medicine and Research, King Abdulaziz University, Jeddah, Saudi Arabia.
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