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1.
Cell ; 184(8): 2239-2254.e39, 2021 04 15.
Artigo em Inglês | MEDLINE | ID: mdl-33831375

RESUMO

Intra-tumor heterogeneity (ITH) is a mechanism of therapeutic resistance and therefore an important clinical challenge. However, the extent, origin, and drivers of ITH across cancer types are poorly understood. To address this, we extensively characterize ITH across whole-genome sequences of 2,658 cancer samples spanning 38 cancer types. Nearly all informative samples (95.1%) contain evidence of distinct subclonal expansions with frequent branching relationships between subclones. We observe positive selection of subclonal driver mutations across most cancer types and identify cancer type-specific subclonal patterns of driver gene mutations, fusions, structural variants, and copy number alterations as well as dynamic changes in mutational processes between subclonal expansions. Our results underline the importance of ITH and its drivers in tumor evolution and provide a pan-cancer resource of comprehensively annotated subclonal events from whole-genome sequencing data.


Assuntos
Heterogeneidade Genética , Neoplasias/genética , Variações do Número de Cópias de DNA , DNA de Neoplasias/química , DNA de Neoplasias/metabolismo , Bases de Dados Genéticas , Resistencia a Medicamentos Antineoplásicos/genética , Humanos , Neoplasias/patologia , Polimorfismo de Nucleotídeo Único , Sequenciamento Completo do Genoma
2.
PLoS Comput Biol ; 17(1): e1008400, 2021 01.
Artigo em Inglês | MEDLINE | ID: mdl-33465079

RESUMO

Tumors contain multiple subpopulations of genetically distinct cancer cells. Reconstructing their evolutionary history can improve our understanding of how cancers develop and respond to treatment. Subclonal reconstruction methods cluster mutations into groups that co-occur within the same subpopulations, estimate the frequency of cells belonging to each subpopulation, and infer the ancestral relationships among the subpopulations by constructing a clone tree. However, often multiple clone trees are consistent with the data and current methods do not efficiently capture this uncertainty; nor can these methods scale to clone trees with a large number of subclonal populations. Here, we formalize the notion of a partially-defined clone tree (partial clone tree for short) that defines a subset of the pairwise ancestral relationships in a clone tree, thereby implicitly representing the set of all clone trees that have these defined pairwise relationships. Also, we introduce a special partial clone tree, the Maximally-Constrained Ancestral Reconstruction (MAR), which summarizes all clone trees fitting the input data equally well. Finally, we extend commonly used clone tree validity conditions to apply to partial clone trees and describe SubMARine, a polynomial-time algorithm producing the subMAR, which approximates the MAR and guarantees that its defined relationships are a subset of those present in the MAR. We also extend SubMARine to work with subclonal copy number aberrations and define equivalence constraints for this purpose. Further, we extend SubMARine to permit noise in the estimates of the subclonal frequencies while retaining its validity conditions and guarantees. In contrast to other clone tree reconstruction methods, SubMARine runs in time and space that scale polynomially in the number of subclones. We show through extensive noise-free simulation, a large lung cancer dataset and a prostate cancer dataset that the subMAR equals the MAR in all cases where only a single clone tree exists and that it is a perfect match to the MAR in most of the other cases. Notably, SubMARine runs in less than 70 seconds on a single thread with less than one Gb of memory on all datasets presented in this paper, including ones with 50 nodes in a clone tree. On the real-world data, SubMARine almost perfectly recovers the previously reported trees and identifies minor errors made in the expert-driven reconstructions of those trees. The freely-available open-source code implementing SubMARine can be downloaded at https://github.com/morrislab/submarine.


Assuntos
Algoritmos , Biologia Computacional/métodos , Mutação/genética , Neoplasias , Simulação por Computador , Evolução Molecular , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Neoplasias/classificação , Neoplasias/genética , Sequenciamento Completo do Genoma
3.
Nat Methods ; 18(2): 144-155, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33398189

RESUMO

Subclonal reconstruction from bulk tumor DNA sequencing has become a pillar of cancer evolution studies, providing insight into the clonality and relative ordering of mutations and mutational processes. We provide an outline of the complex computational approaches used for subclonal reconstruction from single and multiple tumor samples. We identify the underlying assumptions and uncertainties in each step and suggest best practices for analysis and quality assessment. This guide provides a pragmatic resource for the growing user community of subclonal reconstruction methods.


Assuntos
DNA de Neoplasias/genética , Neoplasias/genética , Análise de Sequência de DNA/métodos , Algoritmos , Humanos , Polimorfismo de Nucleotídeo Único
4.
Nat Commun ; 11(1): 731, 2020 02 05.
Artigo em Inglês | MEDLINE | ID: mdl-32024834

RESUMO

The type and genomic context of cancer mutations depend on their causes. These causes have been characterized using signatures that represent mutation types that co-occur in the same tumours. However, it remains unclear how mutation processes change during cancer evolution due to the lack of reliable methods to reconstruct evolutionary trajectories of mutational signature activity. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we present TrackSig, a new method that reconstructs these trajectories using optimal, joint segmentation and deconvolution of mutation type and allele frequencies from a single tumour sample. In simulations, we find TrackSig has a 3-5% activity reconstruction error, and 12% false detection rate. It outperforms an aggressive baseline in situations with branching evolution, CNA gain, and neutral mutations. Applied to data from 2658 tumours and 38 cancer types, TrackSig permits pan-cancer insight into evolutionary changes in mutational processes.


Assuntos
Biologia Computacional/métodos , Mutação , Neoplasias/genética , Simulação por Computador , Evolução Molecular , Frequência do Gene , Genoma Humano , Humanos , Neoplasias/patologia , Polimorfismo de Nucleotídeo Único , Sequenciamento Completo do Genoma
5.
Nature ; 578(7793): 122-128, 2020 02.
Artigo em Inglês | MEDLINE | ID: mdl-32025013

RESUMO

Cancer develops through a process of somatic evolution1,2. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes3. Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)4, we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.


Assuntos
Evolução Molecular , Genoma Humano/genética , Neoplasias/genética , Reparo do DNA/genética , Dosagem de Genes , Genes Supressores de Tumor , Variação Genética , Humanos , Mutagênese Insercional/genética
6.
Nat Biotechnol ; 38(1): 97-107, 2020 01.
Artigo em Inglês | MEDLINE | ID: mdl-31919445

RESUMO

Tumor DNA sequencing data can be interpreted by computational methods that analyze genomic heterogeneity to infer evolutionary dynamics. A growing number of studies have used these approaches to link cancer evolution with clinical progression and response to therapy. Although the inference of tumor phylogenies is rapidly becoming standard practice in cancer genome analyses, standards for evaluating them are lacking. To address this need, we systematically assess methods for reconstructing tumor subclonality. First, we elucidate the main algorithmic problems in subclonal reconstruction and develop quantitative metrics for evaluating them. Then we simulate realistic tumor genomes that harbor all known clonal and subclonal mutation types and processes. Finally, we benchmark 580 tumor reconstructions, varying tumor read depth, tumor type and somatic variant detection. Our analysis provides a baseline for the establishment of gold-standard methods to analyze tumor heterogeneity.


Assuntos
Algoritmos , Neoplasias/patologia , Células Clonais , Simulação por Computador , Variações do Número de Cópias de DNA/genética , Dosagem de Genes , Genoma , Humanos , Mutação/genética , Neoplasias/genética , Polimorfismo de Nucleotídeo Único/genética , Padrões de Referência
7.
Cell ; 173(4): 1003-1013.e15, 2018 05 03.
Artigo em Inglês | MEDLINE | ID: mdl-29681457

RESUMO

The majority of newly diagnosed prostate cancers are slow growing, with a long natural life history. Yet a subset can metastasize with lethal consequences. We reconstructed the phylogenies of 293 localized prostate tumors linked to clinical outcome data. Multiple subclones were detected in 59% of patients, and specific subclonal architectures associate with adverse clinicopathological features. Early tumor development is characterized by point mutations and deletions followed by later subclonal amplifications and changes in trinucleotide mutational signatures. Specific genes are selectively mutated prior to or following subclonal diversification, including MTOR, NKX3-1, and RB1. Patients with low-risk monoclonal tumors rarely relapse after primary therapy (7%), while those with high-risk polyclonal tumors frequently do (61%). The presence of multiple subclones in an index biopsy may be necessary, but not sufficient, for relapse of localized prostate cancer, suggesting that evolution-aware biomarkers should be studied in prospective studies of low-risk tumors suitable for active surveillance.


Assuntos
Neoplasias da Próstata/patologia , Biomarcadores Tumorais/sangue , Sequenciamento de Nucleotídeos em Larga Escala , Proteínas de Homeodomínio/genética , Proteínas de Homeodomínio/metabolismo , Humanos , Masculino , Gradação de Tumores , Recidiva Local de Neoplasia , Polimorfismo de Nucleotídeo Único , Modelos de Riscos Proporcionais , Estudos Prospectivos , Neoplasias da Próstata/classificação , Neoplasias da Próstata/genética , Proteínas de Ligação a Retinoblastoma/genética , Proteínas de Ligação a Retinoblastoma/metabolismo , Serina-Treonina Quinases TOR/genética , Serina-Treonina Quinases TOR/metabolismo , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo , Ubiquitina-Proteína Ligases/genética , Ubiquitina-Proteína Ligases/metabolismo
8.
Bioinformatics ; 31(8): 1305-6, 2015 Apr 15.
Artigo em Inglês | MEDLINE | ID: mdl-25481007

RESUMO

MOTIVATION: Kablammo is a web-based application that produces interactive, vector-based visualizations of sequence alignments generated by BLAST. These visualizations can illustrate many features, including shared protein domains, chromosome structural modifications and genome misassembly. AVAILABILITY AND IMPLEMENTATION: Kablammo can be used at http://kablammo.wasmuthlab.org. For a local installation, the source code and instructions are available under the MIT license at http://github.com/jwintersinger/kablammo. CONTACT: jeff@wintersinger.org.


Assuntos
Gráficos por Computador , Genes de Helmintos/genética , Alinhamento de Sequência/métodos , Software , Animais , Genoma Helmíntico , Haemonchus/genética , Internet , Linguagens de Programação , Análise de Sequência de DNA
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