Tumour evolution inferred by single-cell sequencing

Navin, Nicholas E.; Kendall, Jude; Troge, Jennifer; Andrews, Peter M.; Rodgers, Linda; McIndoo, Jeanne; Cook, Kerry; Stepansky, Asya; Levy, Dan; Esposito, Diane; Muthuswamy, Lakshmi; Krasnitz, A.; McCombie, W. Richard; Hicks, James; Wigler, Michael

doi:10.1038/nature09807

Cited by 2,356 publications

(2,348 citation statements)

References 24 publications

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“…In addition, the imbalances of SNP allele frequencies were used to correctly predict an LOH on chromosome 3 in only a subset of the tumour samples. Recent studies found genomic heterogeneity in breast cancer 10,24 , pancreatic cancer 25,26 , and B-cell chronic lymphocytic leukemia 9 , as well as mosaic amplifications of tyrosine kinase receptor genes in glioblastoma 27 . Together, these findings provide compelling evidence for clonal evolution as a general mechanism in cancer development.…”

Section: Discussionmentioning

confidence: 99%

Reliable detection of subclonal single-nucleotide variants in tumour cell populations

et al. 2012

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According to the clonal evolution model, tumour growth is driven by competing subclones in somatically evolving cancer cell populations, which gives rise to genetically heterogeneous tumours. Here we present a comparative targeted deep-sequencing approach combined with a customised statistical algorithm, called deepsnV, for detecting and quantifying subclonal single-nucleotide variants in mixed populations. We show in a rigorous experimental assessment that our approach is capable of detecting variants with frequencies as low as 1/10,000 alleles. In selected genomic loci of the TP53 and VHL genes isolated from matched tumour and normal samples of four renal cell carcinoma patients, we detect 24 variants at allele frequencies ranging from 0.0002 to 0.34. moreover, we demonstrate how the allele frequencies of known single-nucleotide polymorphisms can be exploited to detect loss of heterozygosity. our findings demonstrate that genomic diversity is common in renal cell carcinomas and provide quantitative evidence for the clonal evolution model.

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Section: Discussionmentioning

confidence: 99%

Reliable detection of subclonal single-nucleotide variants in tumour cell populations

et al. 2012

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“…5). Relatively new techniques, such as single-cell and highdepth sequencing 70,71 , imaging 72 and cytometry time-of-flight 73 , could prove especially valuable for monitoring the number, properties and behavior of different tumor subclones (Fig. 2d).…”

Section: P E R S P E C T I V Ementioning

confidence: 99%

Navigating cancer network attractors for tumor-specific therapy

et al. 2012

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“…A cancer is a mass of cells that has gone through multiple rounds of selection and clonal expansion[103, 104] and has both heterogeneous and homogeneous components to its mutation profile[105]. Arising from a single cell, with its own spectrum of unique somatic mutations that accumulated over the more than 50 rounds of cell division since fertilization of the egg, a million or more cells arise that share the same set of clonally amplified somatic mutations (both drivers and passengers).…”

Section: Mps Applications In Mutation Analysismentioning

confidence: 99%

“…Multiple aCGH experiments[36, 37], and recently a MPS experiment[104], have used single-cell approaches to profile CNVs in individual human normal and tumor cells, but base-pair resolution genotyping of single eukaryotic cells has not yet been shown. Massively parallel sequencing of single-cells has the potential to revolutionize reproductive medicine, cancer research, developmental biology and aging research.…”

Section: Mps Applications In Mutation Analysismentioning

confidence: 99%

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

Gundry

Vijg

2012

Mutation Research/Fundamental and Molecular Mechanisms of Mutag

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DNA mutations are the source of genetic variation within populations. The majority of mutations with observable effects are deleterious. In humans mutations in the germ line can cause genetic disease. In somatic cells multiple rounds of mutations and selection lead to cancer. The study of genetic variation has progressed rapidly since the completion of the draft sequence of the human genome. Recent advances in sequencing technology, most importantly the introduction of massively parallel sequencing (MPS), have resulted in more than a hundred-fold reduction in the time and cost required for sequencing nucleic acids. These improvements have greatly expanded the use of sequencing as a practical tool for mutation analysis. While in the past the high cost of sequencing limited mutation analysis to selectable markers or small forward mutation targets assumed to be representative for the genome overall, current platforms allow whole genome sequencing for less than $5,000. This has already given rise to direct estimates of germline mutation rates in multiple organisms including humans by comparing whole genome sequences between parents and offspring. Here we present a brief history of the field of mutation research, with a focus on classical tools for the measurement of mutation rates. We then review MPS, how it is currently applied and the new insight into human and animal mutation frequencies and spectra that has been obtained from whole genome sequencing. While great progress has been made, we note that the single most important limitation of current MPS approaches for mutation analysis is the inability to address low-abundance mutations that turn somatic tissues into mosaics of cells. Such mutations are at the basis of intra-tumor heterogeneity, with important implications for clinical diagnosis, and could also contribute to somatic diseases other than cancer, including aging. Some possible approaches to gain access to low-abundance mutations are discussed, with a brief overview of new sequencing platforms that are currently waiting in the wings to advance this exploding field even further.

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Tumour evolution inferred by single-cell sequencing

Cited by 2,356 publications

References 24 publications

Reliable detection of subclonal single-nucleotide variants in tumour cell populations

Reliable detection of subclonal single-nucleotide variants in tumour cell populations

Navigating cancer network attractors for tumor-specific therapy

Direct mutation analysis by high-throughput sequencing: From germline to low-abundant, somatic variants

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