Emerging patterns of somatic mutations in cancer
TL;DR: The developing statistical approaches that are used to identify significantly mutated genes are highlighted, and the emerging biological and clinical insights from such analyses are discussed, as well as the future challenges of translating these genomic data into clinical impacts.
Abstract: Recent advances in technological tools for massively parallel, high-throughput sequencing of DNA have enabled the comprehensive characterization of somatic mutations in a large number of tumour samples. In this Review, we describe recent cancer genomic studies that have assembled emerging views of the landscapes of somatic mutations through deep-sequencing analyses of the coding exomes and whole genomes in various cancer types. We discuss the comparative genomics of different cancers, including mutation rates and spectra, as well as the roles of environmental insults that influence these processes. We highlight the developing statistical approaches that are used to identify significantly mutated genes, and discuss the emerging biological and clinical insights from such analyses, as well as the future challenges of translating these genomic data into clinical impacts.
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Washington University in St. Louis1, Johns Hopkins University2, Discovery Institute3, Genome Institute of Singapore4, University of Miami5, Broad Institute6, Harvard University7, University of Texas MD Anderson Cancer Center8, Memorial Sloan Kettering Cancer Center9, Cornell University10, University of Cambridge11, Catalan Institution for Research and Advanced Studies12, University of California, Santa Cruz13, Baylor College of Medicine14
TL;DR: This study reports a PanCancer and PanSoftware analysis spanning 9,423 tumor exomes (comprising all 33 of The Cancer Genome Atlas projects) and using 26 computational tools to catalog driver genes and mutations, identifying 299 driver genes with implications regarding their anatomical sites and cancer/cell types.
1,623 citations
Cites background from "Emerging patterns of somatic mutati..."
...…issues that confound individual statistical analyses to find driver genes, such as heterogeneous mutation rate across the genome (Lawrence et al., 2013), inflated significance for long genes (Watson et al., 2013), and false positive calls in cancers with high mutation rates (Tokheim et al., 2016b)....
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TL;DR: WES data indicate that a larger subclonal mutation fraction may be associated with increased likelihood of postsurgical relapse in patients with localized lung adenocarcinomas, and different mutations are present in different regions of any given lung cancer, and their pattern may predict patient relapse.
Abstract: Cancers are composed of populations of cells with distinct molecular and phenotypic features, a phenomenon termed intratumor heterogeneity (ITH). ITH in lung cancers has not been well studied. We applied multiregion whole-exome sequencing (WES) on 11 localized lung adenocarcinomas. All tumors showed clear evidence of ITH. On average, 76% of all mutations and 20 out of 21 known cancer gene mutations were identified in all regions of individual tumors, which suggested that single-region sequencing may be adequate to identify the majority of known cancer gene mutations in localized lung adenocarcinomas. With a median follow-up of 21 months after surgery, three patients have relapsed, and all three patients had significantly larger fractions of subclonal mutations in their primary tumors than patients without relapse. These data indicate that a larger subclonal mutation fraction may be associated with increased likelihood of postsurgical relapse in patients with localized lung adenocarcinomas.
841 citations
TL;DR: By investigating false heterozygous calls in the haploid genome, the erroneous realignment in low-complexity regions and the incomplete reference genome with respect to the sample are identified as the two major sources of errors, which press for continued improvements in these two areas.
Abstract: Motivation: Whole-genome high-coverage sequencing has been widely used for personal and cancer genomics as well as in various research areas. However, in the lack of an unbiased whole-genome truth set, the global error rate of variant calls and the leading causal artifacts still remain unclear even given the great efforts in the evaluation of variant calling methods.
Results: We made 10 single nucleotide polymorphism and INDEL call sets with two read mappers and five variant callers, both on a haploid human genome and a diploid genome at a similar coverage. By investigating false heterozygous calls in the haploid genome, we identified the erroneous realignment in low-complexity regions and the incomplete reference genome with respect to the sample as the two major sources of errors, which press for continued improvements in these two areas. We estimated that the error rate of raw genotype calls is as high as 1 in 10–15 kb, but the error rate of post-filtered calls is reduced to 1 in 100–200 kb without significant compromise on the sensitivity.
Availability and implementation: BWA-MEM alignment and raw variant calls are available at http://bit.ly/1g8XqRt scripts and miscellaneous data at https://github.com/lh3/varcmp.
Contact: gro.etutitsnidaorb@ilgneh
Supplementary information: Supplementary data are available at Bioinformatics online.
801 citations
Cites methods from "Emerging patterns of somatic mutati..."
...…with the Illumina technologies (Bentley et al., 2008; Wang et al., 2008), resequencing has been widely used for personal and cancer genomics (Watson et al., 2013), for the discovery of de novo mutations associated with Mendelian diseases (Bamshad et al., 2011), for the reconstruction of…...
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TL;DR: How transcriptional control is disrupted by genetic alterations in cancer cells, why transcriptional dependencies can develop as a consequence of dysregulated programs, and how these dependencies provide opportunities for novel therapeutic interventions in cancer are discussed.
767 citations
Cites background from "Emerging patterns of somatic mutati..."
...…alterations that affect cell signaling, transcription factors, enhancer elements, chromatin regulators, and chromosome structure (Garraway and Lander, 2013; Kandoth et al., 2013; Lawrence et al., 2014; Stratton et al., 2009; Sur and Taipale, 2016; Vogelstein et al., 2013; Watson et al., 2013)....
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...A number of excellent reviews describe these alterations that affect cell signaling, transcription factors, enhancer elements, chromatin regulators, and chromosome structure (Garraway and Lander, 2013; Kandoth et al., 2013; Lawrence et al., 2014; Stratton et al., 2009; Sur and Taipale, 2016; Vogelstein et al., 2013; Watson et al., 2013)....
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...Many excellent reviews have described an ever-expanding catalog of these alterations (Bywater et al., 2013; Garraway and Lander, 2013; Kandoth et al., 2013; Lawrence et al., 2014; Stratton et al., 2009; Sur and Taipale, 2016; Vogelstein et al., 2013; Watson et al., 2013)....
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TL;DR: The spectrum of gene fusions in cancer and how the methods to identify them have evolved are described, and the conceptual implications of current, sequencing-based approaches for detection are discussed.
Abstract: Structural chromosome rearrangements may result in the exchange of coding or regulatory DNA sequences between genes. Many such gene fusions are strong driver mutations in neoplasia and have provided fundamental insights into the disease mechanisms that are involved in tumorigenesis. The close association between the type of gene fusion and the tumour phenotype makes gene fusions ideal for diagnostic purposes, enabling the subclassification of otherwise seemingly identical disease entities. In addition, many gene fusions add important information for risk stratification, and increasing numbers of chimeric proteins encoded by the gene fusions serve as specific targets for treatment, resulting in dramatically improved patient outcomes. In this Timeline article, we describe the spectrum of gene fusions in cancer and how the methods to identify them have evolved, and also discuss conceptual implications of current, sequencing-based approaches for detection.
514 citations
Cites background from "Emerging patterns of somatic mutati..."
...These findings spurred interest in the cytogenetic analysis of other haematological neoplasms, with a steadily increasing number of characteristic balanced rearrangements, in particular translocations, soon being described in vari ous disorders during the 1970s, including t(8;14)(q24;q32), t(2;8)(p11;q24) and t(8;22) (q24;q11) in Burkitt lymphoma11–14, t(4;11) (q21;q23) in acute lymphoblastic leukaemia (ALL)15, t(15;17)(q22;q21) in acute pro myelocytic leukaemia (APL)16, and t(14;18) (q32;q21) in follicular lymphoma17....
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References
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TL;DR: Recognition of the widespread applicability of these concepts will increasingly affect the development of new means to treat human cancer.
51,099 citations
TL;DR: A new method and the corresponding software tool, PolyPhen-2, which is different from the early tool polyPhen1 in the set of predictive features, alignment pipeline, and the method of classification is presented and performance, as presented by its receiver operating characteristic curves, was consistently superior.
Abstract: To the Editor:
Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow.
Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naive Bayes classifier (Supplementary Methods).
Figure 1
PolyPhen-2 pipeline and prediction accuracy. (a) Overview of the algorithm. (b) Receiver operating characteristic (ROC) curves for predictions made by PolyPhen-2 using five-fold cross-validation on HumDiv (red) and HumVar3 (light green). UniRef100 (solid ...
We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging.
We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2).
One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations.
For a mutation, PolyPhen-2 calculates Naive Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods).
The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging.
11,571 citations
TL;DR: BRAF somatic missense mutations in 66% of malignant melanomas and at lower frequency in a wide range of human cancers, with a single substitution (V599E) accounting for 80%.
Abstract: Cancers arise owing to the accumulation of mutations in critical genes that alter normal programmes of cell proliferation, differentiation and death. As the first stage of a systematic genome-wide screen for these genes, we have prioritized for analysis signalling pathways in which at least one gene is mutated in human cancer. The RAS RAF MEK ERK MAP kinase pathway mediates cellular responses to growth signals. RAS is mutated to an oncogenic form in about 15% of human cancer. The three RAF genes code for cytoplasmic serine/threonine kinases that are regulated by binding RAS. Here we report BRAF somatic missense mutations in 66% of malignant melanomas and at lower frequency in a wide range of human cancers. All mutations are within the kinase domain, with a single substitution (V599E) accounting for 80%. Mutated BRAF proteins have elevated kinase activity and are transforming in NIH3T3 cells. Furthermore, RAS function is not required for the growth of cancer cell lines with the V599E mutation. As BRAF is a serine/threonine kinase that is commonly activated by somatic point mutation in human cancer, it may provide new therapeutic opportunities in malignant melanoma.
9,785 citations
Daniel C. Koboldt1, Robert S. Fulton1, Michael D. McLellan1, Heather Schmidt1 +352 more•Institutions (35)
TL;DR: The ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity.
Abstract: We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.
9,355 citations
7,570 citations