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Erdmann-Pham DD, Batra SS, Turkalo TK, Durbin J, Blanchette M, Yeh I, Shain H, Bastian BC, Song YS, Rokhsar DS, Hockemeyer D. Tracing cancer evolution and heterogeneity using Hi-C. Nat Commun 2023; 14:7111. [PMID: 37932252 PMCID: PMC10628133 DOI: 10.1038/s41467-023-42651-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 10/09/2023] [Indexed: 11/08/2023] Open
Abstract
Chromosomal rearrangements can initiate and drive cancer progression, yet it has been challenging to evaluate their impact, especially in genetically heterogeneous solid cancers. To address this problem we developed HiDENSEC, a new computational framework for analyzing chromatin conformation capture in heterogeneous samples that can infer somatic copy number alterations, characterize large-scale chromosomal rearrangements, and estimate cancer cell fractions. After validating HiDENSEC with in silico and in vitro controls, we used it to characterize chromosome-scale evolution during melanoma progression in formalin-fixed tumor samples from three patients. The resulting comprehensive annotation of the genomic events includes copy number neutral translocations that disrupt tumor suppressor genes such as NF1, whole chromosome arm exchanges that result in loss of CDKN2A, and whole-arm copy-number neutral loss of homozygosity involving PTEN. These findings show that large-scale chromosomal rearrangements occur throughout cancer evolution and that characterizing these events yields insights into drivers of melanoma progression.
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Affiliation(s)
- Dan Daniel Erdmann-Pham
- Department of Mathematics, University of California, Berkeley, CA, 94720, USA
- Department of Statistics, Stanford University, Stanford, CA, 94305, USA
| | - Sanjit Singh Batra
- Computer Science Division, University of California, Berkeley, CA, 94720, USA
| | - Timothy K Turkalo
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
| | - James Durbin
- Dovetail Genomics, Enterprise Way, Scotts Valley, CA, 95066, USA
| | - Marco Blanchette
- Dovetail Genomics, Enterprise Way, Scotts Valley, CA, 95066, USA
| | - Iwei Yeh
- Department of Dermatology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94143, USA
- Department of Pathology, University of California, San Francisco, CA, 94143, USA
| | - Hunter Shain
- Department of Dermatology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94143, USA
| | - Boris C Bastian
- Department of Dermatology and Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94143, USA
- Department of Pathology, University of California, San Francisco, CA, 94143, USA
| | - Yun S Song
- Computer Science Division, University of California, Berkeley, CA, 94720, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
- Department of Statistics, University of California, Berkeley, CA, 94720, USA.
| | - Daniel S Rokhsar
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
- Innovative Genomics Institute, University of California, Berkeley, CA, 94720, USA.
- Okinawa Institute for Science and Technology, Tancha, Okinawa, Japan.
| | - Dirk Hockemeyer
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA.
- Chan Zuckerberg Biohub, San Francisco, CA, 94158, USA.
- Innovative Genomics Institute, University of California, Berkeley, CA, 94720, USA.
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Khalil AIS, Chattopadhyay A, Sanyal A. Analysis of Aneuploidy Spectrum From Whole-Genome Sequencing Provides Rapid Assessment of Clonal Variation Within Established Cancer Cell Lines. Cancer Inform 2021; 20:11769351211049236. [PMID: 34671179 PMCID: PMC8521761 DOI: 10.1177/11769351211049236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 09/02/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND The revolution in next-generation sequencing (NGS) technology has allowed easy access and sharing of high-throughput sequencing datasets of cancer cell lines and their integrative analyses. However, long-term passaging and culture conditions introduce high levels of genomic and phenotypic diversity in established cell lines resulting in strain differences. Thus, clonal variation in cultured cell lines with respect to the reference standard is a major barrier in systems biology data analyses. Therefore, there is a pressing need for a fast and entry-level assessment of clonal variations within cell lines using their high-throughput sequencing data. RESULTS We developed a Python-based software, AStra, for de novo estimation of the genome-wide segmental aneuploidy to measure and visually interpret strain-level similarities or differences of cancer cell lines from whole-genome sequencing (WGS). We demonstrated that aneuploidy spectrum can capture the genetic variations in 27 strains of MCF7 breast cancer cell line collected from different laboratories. Performance evaluation of AStra using several cancer sequencing datasets revealed that cancer cell lines exhibit distinct aneuploidy spectra which reflect their previously-reported karyotypic observations. Similarly, AStra successfully identified large-scale DNA copy number variations (CNVs) artificially introduced in simulated WGS datasets. CONCLUSIONS AStra provides an analytical and visualization platform for rapid and easy comparison between different strains or between cell lines based on their aneuploidy spectra solely using the raw BAM files representing mapped reads. We recommend AStra for rapid first-pass quality assessment of cancer cell lines before integrating scientific datasets that employ deep sequencing. AStra is an open-source software and is available at https://github.com/AISKhalil/AStra.
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Affiliation(s)
| | - Anupam Chattopadhyay
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Amartya Sanyal
- School of Biological Sciences, Nanyang Technological University, Singapore, Singapore
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Kim K, Kim M, Kim Y, Lee D, Jung I. Hi-C as a molecular rangefinder to examine genomic rearrangements. Semin Cell Dev Biol 2021; 121:161-170. [PMID: 33992531 DOI: 10.1016/j.semcdb.2021.04.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/26/2021] [Accepted: 04/27/2021] [Indexed: 11/16/2022]
Abstract
The mammalian genome is highly packed into the nucleus. Over the past decade, the development of Hi-C has contributed significantly to our understanding of the three-dimensional (3D) chromatin structure, uncovering the principles and functions of higher-order chromatin organizations. Recent studies have repositioned its property in spatial proximity measurement to address challenging problems in genome analyses including genome assembly, haplotype phasing, and the detection of genomic rearrangements. In particular, the power of Hi-C in detecting large-scale structural variations (SVs) in the cancer genome has been demonstrated, which is challenging to be addressed solely with short-read-based whole-genome sequencing analyses. In this review, we first provide a comprehensive view of Hi-C as an intuitive and effective SV detection tool. Then, we introduce recently developed bioinformatics tools utilizing Hi-C to investigate genomic rearrangements. Finally, we discuss the potential application of single-cell Hi-C to address the heterogeneity of genomic rearrangements and sub-population identification in the cancer genome.
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Affiliation(s)
- Kyukwang Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Mooyoung Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Yubin Kim
- Department of Life Science, University of Seoul, Seoul 02504, Republic of Korea
| | - Dongsung Lee
- Department of Life Science, University of Seoul, Seoul 02504, Republic of Korea.
| | - Inkyung Jung
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
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