Cancer Genome Sequencing Data Research Articles

Cancer genome evolution

Cancer genome sequencing efforts have resulted in high amounts of diverse, conflicting genomic data, which ultimately challenge the gene mutation theory of cancer. Since each tumor is different, and the genomic landscape of cancer is highly dynamic, a new evolutionary framework and approaches are required to unify the field. While many recent publications of cancer genome sequencing data have addressed cancer genome evolution, there is no clear definition and a lack of systematic analyses, as the true evolutionary meaning of these massive genetic and epigenetic changes has been largely ignored in the search for specific drivers. Furthermore, there is a common confusion between traditional, Darwinian stepwise evolution and stochastic, punctuated cancer evolution. Herein, the genome theory, an emerging holistic theory that covers multiple levels of genetic and non-genetic alteration by shifting emphasis onto genome-defined (sequence and three-dimensional topology) system inheritance, is presented as a refreshing and promising new framework. Specifically, following introduction of the importance of the genetic information defined by the genome, the newly discovered pattern of cancer evolution (including the two phases of cancer evolution) and its genetic basis (including fuzzy inheritance) are described, which were recently confirmed by single-cell level DNA sequencing and explain how genome and gene/epigene level heterogeneity drive macro- and micro-cellular evolution. From these new understandings, both the limitations of current methodologies and new strategies are discussed. Finally, we suggest that understanding cancer evolution holds the key to understanding other complex diseases and evolutionary theory in general.

MixClone: a mixture model for inferring tumor subclonal populations.

BackgroundTumor genomes are often highly heterogeneous, consisting of genomes from multiple subclonal types. Complete characterization of all subclonal types is a fundamental need in tumor genome analysis. With the advancement of next-generation sequencing, computational methods have recently been developed to infer tumor subclonal populations directly from cancer genome sequencing data. Most of these methods are based on sequence information from somatic point mutations, However, the accuracy of these algorithms depends crucially on the quality of the somatic mutations returned by variant calling algorithms, and usually requires a deep coverage to achieve a reasonable level of accuracy.ResultsWe describe a novel probabilistic mixture model, MixClone, for inferring the cellular prevalences of subclonal populations directly from whole genome sequencing of paired normal-tumor samples. MixClone integrates sequence information of somatic copy number alterations and allele frequencies within a unified probabilistic framework. We demonstrate the utility of the method using both simulated and real cancer sequencing datasets, and show that it significantly outperforms existing methods for inferring tumor subclonal populations. The MixClone package is written in Python and is publicly available at https://github.com/uci-cbcl/MixClone.ConclusionsThe probabilistic mixture model proposed here provides a new framework for subclonal analysis based on cancer genome sequencing data. By applying the method to both simulated and real cancer sequencing data, we show that integrating sequence information from both somatic copy number alterations and allele frequencies can significantly improve the accuracy of inferring tumor subclonal populations.

Deconvolving tumor purity and ploidy by integrating copy number alterations and loss of heterozygosity

Next-generation sequencing (NGS) has revolutionized the study of cancer genomes. However, the reads obtained from NGS of tumor samples often consist of a mixture of normal and tumor cells, which themselves can be of multiple clonal types. A prominent problem in the analysis of cancer genome sequencing data is deconvolving the mixture to identify the reads associated with tumor cells or a particular subclone of tumor cells. Solving the problem is, however, challenging because of the so-called 'identifiability problem', where different combinations of tumor purity and ploidy often explain the sequencing data equally well. We propose a new model to resolve the identifiability problem by integrating two types of sequencing information-somatic copy number alterations and loss of heterozygosity-within a unified probabilistic framework. We derive algorithms to solve our model, and implement them in a software package called PyLOH. We benchmark the performance of PyLOH using both simulated data and 12 breast cancer sequencing datasets and show that PyLOH outperforms existing methods in disambiguating the identifiability problem and estimating tumor purity. The PyLOH package is written in Python and is publicly available at https://github.com/uci-cbcl/PyLOH. xhx@ics.uci.edu Supplementary data are available at Bioinformatics online.

Mining genome sequencing data to identify the genomic features linked to breast cancer histopathology

Background: Genetics and genomics have radically altered our understanding of breast cancer progression. However, the genomic basis of various histopathologic features of breast cancer is not yet well-defined. Materials and Methods: The Cancer Genome Atlas (TCGA) is an international database containing a large collection of human cancer genome sequencing data. cBioPortal is a web tool developed for mining these sequencing data. We performed mining of TCGA sequencing data in an attempt to characterize the genomic features correlated with breast cancer histopathology. We first assessed the quality of the TCGA data using a group of genes with known alterations in various cancers. Both genome-wide gene mutation and copy number changes as well as a group of genes with a high frequency of genetic changes were then correlated with various histopathologic features of invasive breast cancer. Results: Validation of TCGA data using a group of genes with known alterations in breast cancer suggests that the TCGA has accurately documented the genomic abnormalities of multiple malignancies. Further analysis of TCGA breast cancer sequencing data shows that accumulation of specific genomic defects is associated with higher tumor grade, larger tumor size and receptor negativity. Distinct groups of genomic changes were found to be associated with the different grades of invasive ductal carcinoma. The mutator role of the TP53 gene was validated by genomic sequencing data of invasive breast cancer and TP53 mutation was found to play a critical role in defining high tumor grade. Conclusions: Data mining of the TCGA genome sequencing data is an innovative and reliable method to help characterize the genomic abnormalities associated with histopathologic features of invasive breast cancer.

SomatiCA: Identifying, Characterizing and Quantifying Somatic Copy Number Aberrations from Cancer Genome Sequencing Data

Whole genome sequencing of matched tumor-normal sample pairs is becoming routine in cancer research. However, analysis of somatic copy-number changes from sequencing data is still challenging because of insufficient sequencing coverage, unknown tumor sample purity and subclonal heterogeneity. Here we describe a computational framework, named SomatiCA, which explicitly accounts for tumor purity and subclonality in the analysis of somatic copy-number profiles. Taking read depths (RD) and lesser allele frequencies (LAF) as input, SomatiCA will output 1) admixture rate for each tumor sample, 2) somatic allelic copy-number for each genomic segment, 3) fraction of tumor cells with subclonal change in each somatic copy number aberration (SCNA), and 4) a list of substantial genomic aberration events including gain, loss and LOH. SomatiCA is available as a Bioconductor R package at http://www.bioconductor.org/packages/2.13/bioc/html/SomatiCA.html.

Abstract A163: A diverse array of mutations in MTOR are hyperactivating and correlate with rapamycin sensitivity.

Abstract Components of the PI3K-Akt-mTOR signaling axis are frequently mutated in cancer but relatively few mutations have been observed in the MTOR gene itself, which encodes the mTOR kinase. We used cancer genome sequencing data to generate a catalogue of over 450 MTOR point mutations found in cancer and from these identified a set of over 25 that cause mTOR pathway hyperactivation in cells. Mutations occur in multiple cancer types and cluster in several distinct regions in the C-terminal half of mTOR. The distribution and diversity of hyperactivating mutations is distinct from those found in kinases such as AKT1 or BRAF but similar to the pattern seen in PIK3CA. The mutations do not affect the sub-cellular localization of mTOR or its capacity to nucleate the mTORC1 and mTORC2 complexes, but the mutants do bind less DEPTOR, an mTOR inhibitor. In kinetic analyses, several of the most frequently recurrent mutations demonstrate increased K-cat over wild-type mTOR, suggesting that these mutations alter the mTOR kinase itself. Lastly, cancer cell lines with naturally arising mTOR mutations that fall within the clusters exhibited hypersensitivity to mTOR inhibitors. Thus, a diverse set of mutations in mTOR can activate the mTOR pathway and may predict which patients are most likely to respond to rapamycin-based therapies. Citation Information: Mol Cancer Ther 2013;12(11 Suppl):A163. Citation Format: Brian Grabiner, David Sabatini. A diverse array of mutations in MTOR are hyperactivating and correlate with rapamycin sensitivity. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2013 Oct 19-23; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2013;12(11 Suppl):Abstract nr A163.

Prioritizing Potentially Druggable Mutations with dGene: An Annotation Tool for Cancer Genome Sequencing Data

A major goal of cancer genome sequencing is to identify mutations or other somatic alterations that can be targeted by selective and specific drugs. dGene is an annotation tool designed to rapidly identify genes belonging to one of ten druggable classes that are frequently targeted in cancer drug development. These classes were comprehensively populated by combining and manually curating data from multiple specialized and general databases. dGene was used by The Cancer Genome Atlas squamous cell lung cancer project, and here we further demonstrate its utility using recently released breast cancer genome sequencing data. dGene is designed to be usable by any cancer researcher without the need for support from a bioinformatics specialist. A full description of dGene and options for its implementation are provided here.

An empirical Bayesian framework for somatic mutation detection from cancer genome sequencing data

Recent advances in high-throughput sequencing technologies have enabled a comprehensive dissection of the cancer genome clarifying a large number of somatic mutations in a wide variety of cancer types. A number of methods have been proposed for mutation calling based on a large amount of sequencing data, which is accomplished in most cases by statistically evaluating the difference in the observed allele frequencies of possible single nucleotide variants between tumours and paired normal samples. However, an accurate detection of mutations remains a challenge under low sequencing depths or tumour contents. To overcome this problem, we propose a novel method, Empirical Bayesian mutation Calling (https://github.com/friend1ws/EBCall), for detecting somatic mutations. Unlike previous methods, the proposed method discriminates somatic mutations from sequencing errors based on an empirical Bayesian framework, where the model parameters are estimated using sequencing data from multiple non-paired normal samples. Using 13 whole-exome sequencing data with 87.5–206.3 mean sequencing depths, we demonstrate that our method not only outperforms several existing methods in the calling of mutations with moderate allele frequencies but also enables accurate calling of mutations with low allele frequencies (≤10%) harboured within a minor tumour subpopulation, thus allowing for the deciphering of fine substructures within a tumour specimen.

Detecting somatic point mutations in cancer genome sequencing data: a comparison of mutation callers

BackgroundDriven by high throughput next generation sequencing technologies and the pressing need to decipher cancer genomes, computational approaches for detecting somatic single nucleotide variants (sSNVs) have undergone dramatic improvements during the past 2 years. The recently developed tools typically compare a tumor sample directly with a matched normal sample at each variant locus in order to increase the accuracy of sSNV calling. These programs also address the detection of sSNVs at low allele frequencies, allowing for the study of tumor heterogeneity, cancer subclones, and mutation evolution in cancer development.MethodsWe used whole genome sequencing (Illumina Genome Analyzer IIx platform) of a melanoma sample and matched blood, whole exome sequencing (Illumina HiSeq 2000 platform) of 18 lung tumor-normal pairs and seven lung cancer cell lines to evaluate six tools for sSNV detection: EBCall, JointSNVMix, MuTect, SomaticSniper, Strelka, and VarScan 2, with a focus on MuTect and VarScan 2, two widely used publicly available software tools. Default/suggested parameters were used to run these tools. The missense sSNVs detected in these samples were validated through PCR and direct sequencing of genomic DNA from the samples. We also simulated 10 tumor-normal pairs to explore the ability of these programs to detect low allelic-frequency sSNVs.ResultsOut of the 237 sSNVs successfully validated in our cancer samples, VarScan 2 and MuTect detected the most of any tools (that is, 204 and 192, respectively). MuTect identified 11 more low-coverage validated sSNVs than VarScan 2, but missed 11 more sSNVs with alternate alleles in normal samples than VarScan 2. When examining the false calls of each tool using 169 invalidated sSNVs, we observed >63% false calls detected in the lung cancer cell lines had alternate alleles in normal samples. Additionally, from our simulation data, VarScan 2 identified more sSNVs than other tools, while MuTect characterized most low allelic-fraction sSNVs.ConclusionsOur study explored the typical false-positive and false-negative detections that arise from the use of sSNV-calling tools. Our results suggest that despite recent progress, these tools have significant room for improvement, especially in the discrimination of low coverage/allelic-frequency sSNVs and sSNVs with alternate alleles in normal samples.

A heuristic platform for clinical interpretation of cancer genome sequencing data.

10502 Background: The ability to identify and effectively sort the full spectrum of biologically and therapeutically relevant genetic alterations identified by massively parallel sequencing may improve cancer care. A major challenge involves rapid and rational categorization of data-intensive output, including somatic mutations, insertions/deletions, copy number alterations, and rearrangements into ranked categories for clinician review. Methods: A database of clinically actionable alterations was created, consisting of over 100 annotated genes known to undergo somatic genomic alterations in cancer that may impact clinical decision-making. A heuristic algorithm was developed, which selectively identifies somatic alterations based on the clinically actionable alterations database. Remaining variants are sorted based on additional heuristics, including high priority alterations based on presence in the Cancer Gene Census, biologically significant cancer genes based on presence in COSMIC or MSigDB, and low priority alterations in the same gene family as biologically significant cancer genes. The heuristic algorithm was applied to whole exome sequencing data of clinical samples and whole genome sequencing data from a cohort of prostate cancer samples processed using established Broad Institute pipelines. Results: Application of the heuristic algorithm to the prostate cancer whole genome rearrangement data identified 172 (out of 5978) rearrangements involving actionable genes (averaging 2-3 events per tumor). Furthermore, two clinical samples processed prospectively were analyzed, yielding three potentially actionable alterations for clinical review. Conclusions: The heuristic model for clinical interpretation of next generation sequencing data may facilitate rapid analysis of tumor genomic information for clinician review by identifying and prioritizing alterations that can directly impact care. Our platform can also be applied to research data to prospectively explore clinically relevant findings from existing cohorts. Future analytical approaches using heuristic or probabilistic algorithms should underpin a robust prospective assessment of clinical cancer genome data.

Reconstructing cancer genomes from paired-end sequencing data

BackgroundA cancer genome is derived from the germline genome through a series of somatic mutations. Somatic structural variants - including duplications, deletions, inversions, translocations, and other rearrangements - result in a cancer genome that is a scrambling of intervals, or "blocks" of the germline genome sequence. We present an efficient algorithm for reconstructing the block organization of a cancer genome from paired-end DNA sequencing data.ResultsBy aligning paired reads from a cancer genome - and a matched germline genome, if available - to the human reference genome, we derive: (i) a partition of the reference genome into intervals; (ii) adjacencies between these intervals in the cancer genome; (iii) an estimated copy number for each interval. We formulate the Copy Number and Adjacency Genome Reconstruction Problem of determining the cancer genome as a sequence of the derived intervals that is consistent with the measured adjacencies and copy numbers. We design an efficient algorithm, called Paired-end Reconstruction of Genome Organization (PREGO), to solve this problem by reducing it to an optimization problem on an interval-adjacency graph constructed from the data. The solution to the optimization problem results in an Eulerian graph, containing an alternating Eulerian tour that corresponds to a cancer genome that is consistent with the sequencing data. We apply our algorithm to five ovarian cancer genomes that were sequenced as part of The Cancer Genome Atlas. We identify numerous rearrangements, or structural variants, in these genomes, analyze reciprocal vs. non-reciprocal rearrangements, and identify rearrangements consistent with known mechanisms of duplication such as tandem duplications and breakage/fusion/bridge (B/F/B) cycles.ConclusionsWe demonstrate that PREGO efficiently identifies complex and biologically relevant rearrangements in cancer genome sequencing data. An implementation of the PREGO algorithm is available at http://compbio.cs.brown.edu/software/.

Non-redundancy within the RAS oncogene family: insights into mutational disparities in cancer.

The RAS family of oncoproteins has been studied extensively for almost three decades. While we know that activation of RAS represents a key feature of malignant transformation for many cancers, we are only now beginning to understand the complex underpinnings of RAS biology. Here, we will discuss emerging cancer genome sequencing data in the context of what is currently known about RAS function. Taken together, retrospective studies of primary human tissues and prospective studies of experimental models support the notion that the variable mutation frequencies exhibited by the RAS oncogenes reflect unique functions of the RAS oncoproteins.