ReMixT: clone-specific genomic structure estimation in cancer

McPherson, Andrew; Roth, Andrew; Ha, Gavin; Chauve, Cédric; Steif, Adi; Souza, Camila P. E. de; Eirew, Peter; Bouchard‐Côté, Alexandre; Aparicio, Samuel; Şahinalp, S. Cenk; Shah, Sohrab P.

doi:10.1186/s13059-017-1267-2

Cited by 35 publications

(75 citation statements)

References 33 publications

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“…In our benchmarking experiments (see Methods), JaBbA inferred JCN with consistently higher fidelity than previously published genome graph-based methods (ReMixT (McPherson et al, 2017), Weaver , PREGO (Oesper et al, 2012)) across a wide range of tumor purities ( Fig. S1C-D).…”

Section: Resultssupporting

confidence: 53%

Novel patterns of complex structural variation revealed across thousands of cancer genome graphs

Hadi

Yao

et al. 2019

Preprint

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Cancer genomes often harbor hundreds of somatic DNA rearrangement junctions, many of which cannot be easily classified into simple (e.g. deletion, translocation) or complex (e.g. chromothripsis, chromoplexy) structural variant classes. Applying a novel genome graph computational paradigm to analyze the topology of junction copy number (JCN) across 2,833 tumor whole genome sequences (WGS), we introduce three complex rearrangement phenomena: pyrgo, rigma, and tyfonas. Pyrgo are "towers" of low-JCN duplications associated with early replicating regions and superenhancers, and are enriched in breast and ovarian cancers. Rigma comprise "chasms" of low-JCN deletions at late-replicating fragile sites in esophageal and other gastrointestinal (GI) adenocarcinomas. Tyfonas are "typhoons" of high-JCN junctions and fold back inversions that are enriched in acral but not cutaneous melanoma and associated with a previously uncharacterized mutational process of non-APOBEC kataegis. Clustering of tumors according to genome graph-derived features identifies subgroups associated with DNA repair defects and poor prognosis. Cancer genomics | Structural variation | DNA rearrangements | Mutational Processes | Genome graphs | Copy number alterationsCorrespondence: mski@mskilab.org K. Hadi, X.Yao, J. Behr et al. | bioRχiv | November 12, 2019 | 1-8

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Section: Resultssupporting

confidence: 53%

Novel patterns of complex structural variation revealed across thousands of cancer genome graphs

Hadi

Yao

et al. 2019

Preprint

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“…The single-cell view of allele-and haplotype-specific copy numbers provided by CHISEL offers the opportunity for deeper analysis of tumor evolution. CHISEL enables the identification of allele-specific CNAs, including copy-neutral LOHs and whole-genome duplications (WGDs), and haplotype-specific CNAs, including mirrored-subclonal CNAs, in individual cells without the limitations of bulk tumor-samples where inference of tumor ploidy and purity from admixed signals is extremely challenging [20][21][22][23][24][25][26][27][28][29][30][31][32] . In addition, previous analysis of haplotype-specific CNAs have been restricted to the special case where these CNAs are present in different samples from the same patient 51 .…”

Section: Discussionmentioning

confidence: 99%

“…Second, BAF estimation could be further improved by using larger haplotype blocks that one could infer from the results of CHISEL in regions of allelic imbalance or using the signal from sequencing reads that cover multiple SNPs. Third, a more refined model of CNA evolution might be integrated in the inference of haplotype-specific copy numbers, for example reconstructing the full copy-number tree from the model of interval events 19 or integrating the additional signal from breakpoints 32 . Fourth, one could improve techniques for classifying cells with highly aberrant copy-number profiles, designing classifiers to distinguish actively replicating regions 6, 8 from cell doublets.…”

Section: Discussionmentioning

confidence: 99%

“…CHISEL leverages information across hundreds-thousands of individual cells to overcome the low sequencing coverage (ă0.05X) per cell. As in DNA sequencing of bulk samples [20][21][22][23][24][25][26][27][28][29][30][31][32] , CHISEL uses two quantities derived from aligned reads to estimate the number of copies, p c t and q c t , of the two alleles of each genomic region t. The first quantity, the read-depth ratio (RDR) x t , is directly proportional to the total copy number c t " p c t`q c t . The second quantity, the B-allele frequency (BAF) y t , measures the imbalance between the number of copies of the two alleles and corresponds to either p ct ct or q ct ct .…”

Section: Chisel Algorithmmentioning

confidence: 99%

“…c t,i « γ i x t,i for some cell-specific scale factor γ i . CHISEL computes x t,i by appropriate normalization of the number of reads aligned to sufficiently large bins (5Mb in this work) -accounting for GC bias and other biases -similar to other approaches for CNA detection from bulk [20][21][22][23][24][25][26][27][28][29][30][31][32] or single-cell 6-8, 45-50 sequencing (Supplementary Note C.1).…”

Section: Computation Of Read-depth Ratio and B-allele Frequencymentioning

confidence: 99%

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Characterizing the allele- and haplotype-specific copy number landscape of cancer genomes at single-cell resolution with CHISEL

Zaccaria

Raphael

2019

Preprint

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Single-cell barcoding technologies have recently been used to perform whole-genome sequencing of thousands of individual cells in parallel. These technologies provide the opportunity to characterize genomic heterogeneity at single-cell resolution, but their extremely low sequencing coverage (ă0.05X per cell) has thus far restricted their use to identification of the total copy number of large multi-megabase segments in individual cells. However, total copy numbers do not distinguish between the two homologous chromosomes in humans, and thus provide a limited view of tumor heterogeneity and evolution missing important events such as copy-neutral loss-of-heterozygosity (LOH).We introduce CHISEL, the first method to infer allele-and haplotype-specific copy numbers in single cells and subpopulations of cells by aggregating sparse signal across thousands of individual cells. We applied CHISEL to 10 single-cell sequencing datasets from 2 breast cancer patients, each dataset containing «2 000 cells. We identified extensive allele-specific copy-number aberrations (CNAs) in these samples including copy-neutral LOH, whole-genome duplications (WGDs), and mirrored-subclonal CNAs in subpopulations of cells. These allele-specific CNAs alter the copy number of genomic regions containing well-known breast cancer genes including TP53, BRCA2, and PTEN but are invisible to total copy number analysis. We utilized CHISEL's allele-and haplotype-specific copy numbers to derive a more refined reconstruction of tumor evolution: timing allele-specific CNAs before and after WGDs, identifying low-frequency subclones distinguished by unique CNAs, and uncovering evidence of convergent evolution. This reconstruction is supported by orthogonal analysis of somatic single-nucleotide variants (SNVs) obtained by pooling barcoded reads across clones defined by CHISEL.1 Single-cell DNA sequencing is a promising technology to quantify tumor heterogeneity and evolution with unprecedented resolution, enabling the identification of rare subpopulations of cells with distinct mutations and the inference of the evolutionary dynamics of cancer 1-4 . Recently, single-cell barcoding technologies, including Chromium Single Cell CNV Solution from 10X Genomics 5, 6 and direct library preparation 7, 8 , have been used to perform low-coverage whole-genome sequencing of thousands of individual cells in parallel, overcoming the limited number of cells and the amplification/coverage biases of previous techniques 4 . Due to technical and financial limitations, these technologies have extremely low sequencing coverage (ă0.05X per cell) which has thus far limited their application to the detection of large («3-5Mb) copy-number aberrations (CNAs) in individual cells. CNAs alter the number of copies of genomic regions, are frequent somatic mutations that drive cancer development 9-12 , play a crucial role in cancer treatment and prognosis 13,14 , and provide important markers for reconstruction of cancer evolution [15][16][17][18][19] .Since the human genome is diploid, ea...

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Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer

Zhang

McPherson²,

Milne³

et al. 2018

Cell

Self Cite

292

256

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High-grade serous ovarian cancer (HGSC) exhibits extensive malignant clonal diversity with widespread but non-random patterns of disease dissemination. We investigated whether local immune microenvironment factors shape tumor progression properties at the interface of tumor-infiltrating lymphocytes (TILs) and cancer cells. Through multi-region study of 212 samples from 38 patients with whole-genome sequencing, immunohistochemistry, histologic image analysis, gene expression profiling, and T and B cell receptor sequencing, we identified three immunologic subtypes across samples and extensive within-patient diversity. Epithelial CD8+ TILs negatively associated with malignant diversity, reflecting immunological pruning of tumor clones inferred by neoantigen depletion, HLA I loss of heterozygosity, and spatial tracking between T cell and tumor clones. In addition, combinatorial prognostic effects of mutational processes and immune properties were observed, illuminating how specific genomic aberration types associate with immune response and impact survival. We conclude that within-patient spatial immune microenvironment variation shapes intraperitoneal malignant spread, provoking new evolutionary perspectives on HGSC clonal dispersion.

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ReMixT: clone-specific genomic structure estimation in cancer

Cited by 35 publications

References 33 publications

Novel patterns of complex structural variation revealed across thousands of cancer genome graphs

Novel patterns of complex structural variation revealed across thousands of cancer genome graphs

Characterizing the allele- and haplotype-specific copy number landscape of cancer genomes at single-cell resolution with CHISEL

Interfaces of Malignant and Immunologic Clonal Dynamics in Ovarian Cancer

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