TrAp: a tree approach for fingerprinting subclonal tumor composition

Revealing the clonal composition of a single tumor is essential for identifying cell subpopulations with metastatic potential in primary tumors or with resistance to therapies in metastatic tumors. Sequencing technologies provide only an overview of the aggregate of numerous cells. Computational approaches to de-mix a collective signal composed of the aberrations of a mixed cell population of a tumor sample into its individual components are not available. We propose an evolutionary framework for deconvolving data from a single genome-wide experiment to infer the composition, abundance and evolutionary paths of the underlying cell subpopulations of a tumor. We have developed an algorithm (TrAp) for solving this mixture problem. In silico analyses show that TrAp correctly deconvolves mixed subpopulations when the number of subpopulations and the measurement errors are moderate. We demonstrate the applicability of the method using tumor karyotypes and somatic hypermutation data sets. We applied TrAp to Exome-Seq experiment of a renal cell carcinoma tumor sample and compared the mutational profile of the inferred subpopulations to the mutational profiles of single cells of the same tumor. Finally, we deconvolve sequencing data from eight acute myeloid leukemia patients and three distinct metastases of one melanoma patient to exhibit the evolutionary relationships of their subpopulations.     

Genetic evolution of nevus of Ota reveals clonal heterogeneity acquiring BAP1 and TP53 mutations

Melanoma presents molecular alterations based on its anatomical location and exposure to environmental factors. Due to its intrinsic genetic heterogeneity, a simple snapshot of a tumor's genetic alterations does not reflect the tumor clonal complexity or specific gene–gene cooperation. Here, we studied the genetic alterations and clonal evolution of a unique patient with a Nevus of Ota that developed into a recurring uveal‐like dermal melanoma. The Nevus of Ota and ulterior lesions contained GNAQ mutations were c‐KIT positive, and tumors showed an increased RAS pathway activity during progression. Whole‐exome sequencing of these lesions revealed the acquisition of BAP1 and TP53 mutations during tumor evolution, thereby unmasking clonal heterogeneity and allowing the identification of cooperating genes within the same tumor. Our results highlight the importance of studying tumor genetic evolution to identify cooperating mechanisms and delineate effective therapies.     

Detection of Clonal and Subclonal Copy-Number Variants in Cell-Free DNA from Patients with Breast Cancer Using a Massively Multiplexed PCR Methodology

We demonstrate proof-of-concept for the use of massively multiplexed PCR and next-generation sequencing (mmPCR-NGS) to identify both clonal and subclonal copy-number variants (CNVs) in circulating tumor DNA. This is the first report of a targeted methodology for detection of CNVs in plasma.Using an in vitro model of cell-free DNA, we show that mmPCR-NGS can accurately detect CNVs with average allelic imbalances as low as 0.5%, an improvement over previously reported whole-genome sequencing approaches. Our method revealed differences in the spectrum of CNVs detected in tumor tissue subsections and matching plasma samples from 11 patients with stage II breast cancer. Moreover, we showed that liquid biopsies are able to detect subclonal mutations that may be missed in tumor tissue biopsies. We anticipate that this mmPCR-NGS methodology will have broad applicability for the characterization, diagnosis, and therapeutic monitoring of CNV-enriched cancers, such as breast, ovarian, and lung cancer.     

Co-Evolution of Somatic Variation in Primary and Metastatic Colorectal Cancer May Expand Biopsy Indications in the Molecular Era

IntroductionMetastasis is thought to be a clonal event whereby a single cell initiates the development of a new tumor at a distant site. However the degree to which primary and metastatic tumors differ on a molecular level remains unclear. To further evaluate these concepts, we used next generation sequencing (NGS) to assess the molecular composition of paired primary and metastatic colorectal cancer tissue specimens.Methods468 colorectal tumor samples from a large personalized medicine initiative were assessed by targeted gene sequencing of 1,321 individual genes. Eighteen patients produced genomic profiles for 17 paired primary:metastatic (and 2 metastatic:metastatic) specimens.ResultsAn average of 33.3 mutations/tumor were concordant (shared) between matched samples, including common well-known genes (APC, KRAS, TP53). An average of 2.3 mutations/tumor were discordant (unshared) among paired sites. KRAS mutational status was always concordant. The overall concordance rate for mutations was 93.5%; however, nearly all (18/19 (94.7%)) paired tumors showed at least one mutational discordance. Mutations were seen in: TTN, the largest gene (5 discordant pairs), ADAMTS20, APC, MACF1, RASA1, TP53, and WNT2 (2 discordant pairs), SMAD2, SMAD3, SMAD4, FBXW7, and 66 others (1 discordant pair).ConclusionsWhereas primary and metastatic tumors displayed little variance overall, co-evolution produced incremental mutations in both. These results suggest that while biopsy of the primary tumor alone is likely sufficient in the chemotherapy-naïve patient, additional biopsies of primary or metastatic disease may be necessary to precisely tailor therapy following chemotherapy resistance or insensitivity in order to adequately account for tumor evolution.     

The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process

With the availability of next-generation sequencing (NGS) technology, it is expected that sequence variants may be called on a genomic scale. Here, we demonstrate that a deeper understanding of the distribution of the variant call frequencies at heterozygous loci in NGS data sets is a prerequisite for sensitive variant detection. We model the crucial steps in an NGS protocol as a stochastic branching process and derive a mathematical framework for the expected distribution of alleles at heterozygous loci before measurement that is sequencing. We confirm our theoretical results by analyzing technical replicates of human exome data and demonstrate that the variance of allele frequencies at heterozygous loci is higher than expected by a simple binomial distribution. Due to this high variance, mutation callers relying on binomial distributed priors are less sensitive for heterozygous variants that deviate strongly from the expected mean frequency. Our results also indicate that error rates can be reduced to a greater degree by technical replicates than by increasing sequencing depth.     

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma

Understanding tumor clonality is critical to understanding the mechanisms involved in tumorigenesis and disease progression. In addition, understanding the clonal composition changes that occur within a tumor in response to certain micro-environment or treatments may lead to the design of more sophisticated and effective approaches to eradicate tumor cells. However, tracking tumor clonal sub-populations has been challenging due to the lack of distinguishable markers. To address this problem, a VDJ-seq protocol was created to trace the clonal evolution patterns of diffuse large B cell lymphoma (DLBCL) relapse by exploiting VDJ recombination and somatic hypermutation (SHM), two unique features of B cell lymphomas.In this protocol, Next-Generation sequencing (NGS) libraries with indexing potential were constructed from amplified rearranged immunoglobulin heavy chain (IgH) VDJ region from pairs of primary diagnosis and relapse DLBCL samples. On average more than half million VDJ sequences per sample were obtained after sequencing, which contain both VDJ rearrangement and SHM information. In addition, customized bioinformatics pipelines were developed to fully utilize sequence information for the characterization of IgH-VDJ repertoire within these samples. Furthermore, the pipeline allows the reconstruction and comparison of the clonal architecture of individual tumors, which enables the examination of the clonal heterogeneity within the diagnosis tumors and deduction of clonal evolution patterns between diagnosis and relapse tumor pairs. When applying this analysis to several diagnosis-relapse pairs, we uncovered key evidence that multiple distinctive tumor evolutionary patterns could lead to DLBCL relapse. Additionally, this approach can be expanded into other clinical aspects, such as identification of minimal residual disease, monitoring relapse progress and treatment response, and investigation of immune repertoires in non-lymphoma contexts.     

Detection of low prevalence somatic mutations in solid tumors with ultra-deep targeted sequencing

Ultra-deep targeted sequencing (UDT-Seq) can identify subclonal somatic mutations in tumor samples. Early assays' limited breadth and depth restrict their clinical utility. Here, we target 71 kb of mutational hotspots in 42 cancer genes. We present novel methods enhancing both laboratory workflow and mutation detection. We evaluate UDT-Seq true sensitivity and specificity (> 94% and > 99%, respectively) for low prevalence mutations in a mixing experiment and demonstrate its utility using six tumor samples. With an improved performance when run on the Illumina Miseq, the UDT-Seq assay is well suited for clinical applications to guide therapy and study clonal selection in heterogeneous samples.     

Next-Generation Sequencing-Based HPV Genotyping Assay Validated in Formalin-Fixed,Paraffin-Embedded Oropharyngeal and Cervical Cancer Specimens

Available clinical human papilloma virus (HPV) diagnostics for head and neck cancer have limited sensitivity and/or fail to define the HPV genotype. Common HPV genotyping assays are costly and labor intensive. We sought to develop a next-generation sequencing (NGS)-based HPV genotyping assay that was sensitive enough to work on formalin-fixed paraffin-embedded (FFPE) samples. We developed an ion torrent NGS HPV genotyping assay using barcoded HPV PCR broad-spectrum general primers 5⁺/6⁺ (BSGP)5⁺/6⁺. To validate genotype specificity and use in archived clinical FFPE tumor samples, we compared NGS HPV genotyping at 2 sequencing centers with typing by Roche Linear Array assay in 42 oropharyngeal and cervical cancer specimens representing 10 HPV genotypes, as well as HPV-negative cases. To demonstrate the detection of a broad range of HPV genotypes, we genotyped a cohort of 266 cervical cancers. A comparison of NGS genotyping of FFPE cancer specimens with genotyping by Linear Array showed concordant results in 34/37 samples (92%) at sequencing site 1 and 39/42 samples (93%) at sequencing site 2. Concordance between sites was 92%. Designed for use with 10 ng genomic DNA, the assay detected HPV using as little as 1.25 ng FFPE-derived genomic DNA. In 266 cervical cancer specimens, the NGS assay identified 20 different HPV genotypes, including all 13 carcinogenic genotypes. This novel NGS assay provides a sensitive and specific high-throughput method to detect and genotype HPV in a range of clinical specimens derived from FFPE with low per-sample cost.     

Comparison of Sample Preparation Methods Used for the Next-Generation Sequencing of Mycobacterium tuberculosis

The advent and widespread application of next-generation sequencing (NGS) technologies to the study of microbial genomes has led to a substantial increase in the number of studies in which whole genome sequencing (WGS) is applied to the analysis of microbial genomic epidemiology. However, microorganisms such as Mycobacterium tuberculosis (MTB) present unique problems for sequencing and downstream analysis based on their unique physiology and the composition of their genomes. In this study, we compare the quality of sequence data generated using the Nextera and TruSeq isolate preparation kits for library construction prior to Illumina sequencing-by-synthesis. Our results confirm that MTB NGS data quality is highly dependent on the purity of the DNA sample submitted for sequencing and its guanine-cytosine content (or GC-content). Our data additionally demonstrate that the choice of library preparation method plays an important role in mitigating downstream sequencing quality issues. Importantly for MTB, the Illumina TruSeq library preparation kit produces more uniform data quality than the Nextera XT method, regardless of the quality of the input DNA. Furthermore, specific genomic sequence motifs are commonly missed by the Nextera XT method, as are regions of especially high GC-content relative to the rest of the MTB genome. As coverage bias is highly undesirable, this study illustrates the importance of appropriate protocol selection when performing NGS studies in order to ensure that sound inferences can be made regarding mycobacterial genomes.     

Building a Robust Tumor Profiling Program: Synergy between Next-Generation Sequencing and Targeted Single-Gene Testing

Next-generation sequencing (NGS) is a powerful platform for identifying cancer mutations. Routine clinical adoption of NGS requires optimized quality control metrics to ensure accurate results. To assess the robustness of our clinical NGS pipeline, we analyzed the results of 304 solid tumor and hematologic malignancy specimens tested simultaneously by NGS and one or more targeted single-gene tests (EGFR, KRAS, BRAF, NPM1, FLT3, and JAK2). For samples that passed our validated tumor percentage and DNA quality and quantity thresholds, there was perfect concordance between NGS and targeted single-gene tests with the exception of two FLT3 internal tandem duplications that fell below the stringent pre-established reporting threshold but were readily detected by manual inspection. In addition, NGS identified clinically significant mutations not covered by single-gene tests. These findings confirm NGS as a reliable platform for routine clinical use when appropriate quality control metrics, such as tumor percentage and DNA quality cutoffs, are in place. Based on our findings, we suggest a simple workflow that should facilitate adoption of clinical oncologic NGS services at other institutions.     

Biased estimates of clonal evolution and subclonal heterogeneity can arise from PCR duplicates in deep sequencing experiments

Accurate allele frequencies are important for measuring subclonal heterogeneity and clonal evolution. Deep-targeted sequencing data can contain PCR duplicates, inflating perceived read depth. Here we adapted the Illumina TruSeq Custom Amplicon kit to include single molecule tagging (SMT) and show that SMT-identified duplicates arise from PCR. We demonstrate that retention of PCR duplicate reads can imply clonal evolution when none exists, while their removal effectively controls the false positive rate. Additionally, PCR duplicates alter estimates of subclonal heterogeneity in tumor samples. Our method simplifies PCR duplicate identification and emphasizes their removal in studies of tumor heterogeneity and clonal evolution.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0420-4) contains supplementary material, which is available to authorized users.     

Clonal Architecture of Secondary Acute Myeloid Leukemia Defined by Single-Cell Sequencing

Next-generation sequencing has been used to infer the clonality of heterogeneous tumor samples. These analyses yield specific predictions—the population frequency of individual clones, their genetic composition, and their evolutionary relationships—which we set out to test by sequencing individual cells from three subjects diagnosed with secondary acute myeloid leukemia, each of whom had been previously characterized by whole genome sequencing of unfractionated tumor samples. Single-cell mutation profiling strongly supported the clonal architecture implied by the analysis of bulk material. In addition, it resolved the clonal assignment of single nucleotide variants that had been initially ambiguous and identified areas of previously unappreciated complexity. Accordingly, we find that many of the key assumptions underlying the analysis of tumor clonality by deep sequencing of unfractionated material are valid. Furthermore, we illustrate a single-cell sequencing strategy for interrogating the clonal relationships among known variants that is cost-effective, scalable, and adaptable to the analysis of both hematopoietic and solid tumors, or any heterogeneous population of cells.     

Evidence That Mutation Is Universally Biased towards AT in Bacteria

Mutation is the engine that drives evolution and adaptation forward in that it generates the variation on which natural selection acts. Mutation is a random process that nevertheless occurs according to certain biases. Elucidating mutational biases and the way they vary across species and within genomes is crucial to understanding evolution and adaptation. Here we demonstrate that clonal pathogens that evolve under severely relaxed selection are uniquely suitable for studying mutational biases in bacteria. We estimate mutational patterns using sequence datasets from five such clonal pathogens belonging to four diverse bacterial clades that span most of the range of genomic nucleotide content. We demonstrate that across different types of sites and in all four clades mutation is consistently biased towards AT. This is true even in clades that have high genomic GC content. In all studied cases the mutational bias towards AT is primarily due to the high rate of C/G to T/A transitions. These results suggest that bacterial mutational biases are far less variable than previously thought. They further demonstrate that variation in nucleotide content cannot stem entirely from variation in mutational biases and that natural selection and/or a natural selection-like process such as biased gene conversion strongly affect nucleotide content.     

Genotype‐free estimation of allele frequencies reduces bias and improves demographic inference from RADSeq data

Restriction‐site associated DNA sequencing (RADSeq) facilitates rapid generation of thousands of genetic markers at relatively low cost; however, several sources of error specific to RADSeq methods often lead to biased estimates of allele frequencies and thereby to erroneous population genetic inference. Estimating the distribution of sample allele frequencies without calling genotypes was shown to improve population inference from whole genome sequencing data, but the ability of this approach to account for RADSeq‐specific biases remains unexplored. Here we assess in how far genotype‐free methods of allele frequency estimation affect demographic inference from empirical RADSeq data. Using the well‐studied pied flycatcher (Ficedula hypoleuca) as a study system, we compare allele frequency estimation and demographic inference from whole genome sequencing data with that from RADSeq data matched for samples using both genotype‐based and genotype free methods. The demographic history of pied flycatchers as inferred from RADSeq data was highly congruent with that inferred from whole genome resequencing (WGS) data when allele frequencies were estimated directly from the read data. In contrast, when allele frequencies were derived from called genotypes, RADSeq‐based estimates of most model parameters fell outside the 95% confidence interval of estimates derived from WGS data. Notably, more stringent filtering of the genotype calls tended to increase the discrepancy between parameter estimates from WGS and RADSeq data, respectively. The results from this study demonstrate the ability of genotype‐free methods to improve allele frequency spectrum‐ (AFS‐) based demographic inference from empirical RADSeq data and highlight the need to account for uncertainty in NGS data regardless of sequencing method.     

Clonal Expansion Analysis of Transposon Insertions by High-Throughput Sequencing Identifies Candidate Cancer Genes in a PiggyBac Mutagenesis Screen

Somatic transposon mutagenesis in mice is an efficient strategy to investigate the genetic mechanisms of tumorigenesis. The identification of tumor driving transposon insertions traditionally requires the generation of large tumor cohorts to obtain information about common insertion sites. Tumor driving insertions are also characterized by their clonal expansion in tumor tissue, a phenomenon that is facilitated by the slow and evolving transformation process of transposon mutagenesis. We describe here an improved approach for the detection of tumor driving insertions that assesses the clonal expansion of insertions by quantifying the relative proportion of sequence reads obtained in individual tumors. To this end, we have developed a protocol for insertion site sequencing that utilizes acoustic shearing of tumor DNA and Illumina sequencing. We analyzed various solid tumors generated by PiggyBac mutagenesis and for each tumor >10⁶ reads corresponding to >10⁴ insertion sites were obtained. In each tumor, 9 to 25 insertions stood out by their enriched sequence read frequencies when compared to frequencies obtained from tail DNA controls. These enriched insertions are potential clonally expanded tumor driving insertions, and thus identify candidate cancer genes. The candidate cancer genes of our study comprised many established cancer genes, but also novel candidate genes such as Mastermind-like1 (Mamld1) and Diacylglycerolkinase delta (Dgkd). We show that clonal expansion analysis by high-throughput sequencing is a robust approach for the identification of candidate cancer genes in insertional mutagenesis screens on the level of individual tumors.     

BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality using next generation sequencing data

Tumors are characterized by properties of genetic instability, heterogeneity, and significant oligoclonality. Elucidating this intratumoral heterogeneity is challenging but important. In this study, we propose a framework, BubbleTree, to characterize the tumor clonality using next generation sequencing (NGS) data. BubbleTree simultaneously elucidates the complexity of a tumor biopsy, estimating cancerous cell purity, tumor ploidy, allele-specific copy number, and clonality and represents this in an intuitive graph. We further developed a three-step heuristic method to automate the interpretation of the BubbleTree graph, using a divide-and-conquer strategy. In this study, we demonstrated the performance of BubbleTree with comparisons to similar commonly used tools such as THetA2, ABSOLUTE, AbsCN-seq and ASCAT, using both simulated and patient-derived data. BubbleTree outperformed these tools, particularly in identifying tumor subclonal populations and polyploidy. We further demonstrated BubbleTree''s utility in tracking clonality changes from patients’ primary to metastatic tumor and dating somatic single nucleotide and copy number variants along the tumor clonal evolution. Overall, the BubbleTree graph and corresponding model is a powerful approach to provide a comprehensive spectrum of the heterogeneous tumor karyotype in human tumors. BubbleTree is R-based and freely available to the research community (https://www.bioconductor.org/packages/release/bioc/html/BubbleTree.html).     

multiSNV: a probabilistic approach for improving detection of somatic point mutations from multiple related tumour samples

Somatic variant analysis of a tumour sample and its matched normal has been widely used in cancer research to distinguish germline polymorphisms from somatic mutations. However, due to the extensive intratumour heterogeneity of cancer, sequencing data from a single tumour sample may greatly underestimate the overall mutational landscape. In recent studies, multiple spatially or temporally separated tumour samples from the same patient were sequenced to identify the regional distribution of somatic mutations and study intratumour heterogeneity. There are a number of tools to perform somatic variant calling from matched tumour-normal next-generation sequencing (NGS) data; however none of these allow joint analysis of multiple same-patient samples. We discuss the benefits and challenges of multisample somatic variant calling and present multiSNV, a software package for calling single nucleotide variants (SNVs) using NGS data from multiple same-patient samples. Instead of performing multiple pairwise analyses of a single tumour sample and a matched normal, multiSNV jointly considers all available samples under a Bayesian framework to increase sensitivity of calling shared SNVs. By leveraging information from all available samples, multiSNV is able to detect rare mutations with variant allele frequencies down to 3% from whole-exome sequencing experiments.     

VarWalker: Personalized Mutation Network Analysis of Putative Cancer Genes from Next-Generation Sequencing Data

A major challenge in interpreting the large volume of mutation data identified by next-generation sequencing (NGS) is to distinguish driver mutations from neutral passenger mutations to facilitate the identification of targetable genes and new drugs. Current approaches are primarily based on mutation frequencies of single-genes, which lack the power to detect infrequently mutated driver genes and ignore functional interconnection and regulation among cancer genes. We propose a novel mutation network method, VarWalker, to prioritize driver genes in large scale cancer mutation data. VarWalker fits generalized additive models for each sample based on sample-specific mutation profiles and builds on the joint frequency of both mutation genes and their close interactors. These interactors are selected and optimized using the Random Walk with Restart algorithm in a protein-protein interaction network. We applied the method in >300 tumor genomes in two large-scale NGS benchmark datasets: 183 lung adenocarcinoma samples and 121 melanoma samples. In each cancer, we derived a consensus mutation subnetwork containing significantly enriched consensus cancer genes and cancer-related functional pathways. These cancer-specific mutation networks were then validated using independent datasets for each cancer. Importantly, VarWalker prioritizes well-known, infrequently mutated genes, which are shown to interact with highly recurrently mutated genes yet have been ignored by conventional single-gene-based approaches. Utilizing VarWalker, we demonstrated that network-assisted approaches can be effectively adapted to facilitate the detection of cancer driver genes in NGS data.