297 research outputs found
Surveys of Subtle Allelic Imbalance in Tissue
No full textSomatically-acquired allelic imbalance (AI) is an established factor in cancer initiation and has recently been implicated as a marker for cancer risk. While DNA microarrays and next-generation sequencing are effective for whole-genome profiling of AI, in typical settings their sensitivities become extremely limited when the aberrant cell fraction (or tumor purity) is below 10-20%. Yet, this range may be critical for early detection and diagnostics, since often for such applications the samples of interest will be comprised of heterogeneous mixtures of cells with a large component of DNA from normal (i.e. the germline) rather than aberrant (e.g. the tumor) sources. Here we introduce a powerful haplotype-based computational technique (Vattathil & Scheet, 2013, Gen Res) and use it to characterize AI in several difficult settings. We start with a reanalysis of a study of over 35,000 samples of healthy tissue from recent genome-wide association studies and find a 2-fold higher rate of somatic mosaicism (within-individual genomic heterogeneity), which may indicate a wider applicability for the use of mosaicism as a biomarker for cancer risk. We next examine premalignant tissue, profiling polyps from individuals at risk for colorectal cancer to show subtle levels of AI across critical loci; we also demonstrate extensive mosaicism in the lung field (normal-appearing tissue surrounding the tumor), consistent with recent studies of expression (Kadara et. al., 2014, JNCI).
Finally, we study lymph node tissue (of lung cancer patients), sampled via endobronchial ultrasound, and discover chromosomal aberrations in samples that were deemed negative by pathology review but that were ultimately determined to be positive following surgical extraction, thus demonstrating potential for molecular diagnostics.Non UBCUnreviewedAuthor affiliation: MD Anderson Cancer CenterFacult
Accounting for Decay of Linkage Disequilibrium in Haplotype Inference and Missing-Data Imputation
No full textAlthough many algorithms exist for estimating haplotypes from genotype data, none of them take full account of both the decay of linkage disequilibrium (LD) with distance and the order and spacing of genotyped markers. Here, we describe an algorithm that does take these factors into account, using a flexible model for the decay of LD with distance that can handle both “blocklike” and “nonblocklike” patterns of LD. We compare the accuracy of this approach with a range of other available algorithms in three ways: for reconstruction of randomly paired, molecularly determined male X chromosome haplotypes; for reconstruction of haplotypes obtained from trios in an autosomal region; and for estimation of missing genotypes in 50 autosomal genes that have been completely resequenced in 24 African Americans and 23 individuals of European descent. For the autosomal data sets, our new approach clearly outperforms the best available methods, whereas its accuracy in inferring the X chromosome haplotypes is only slightly superior. For estimation of missing genotypes, our method performed slightly better when the two subsamples were combined than when they were analyzed separately, which illustrates its robustness to population stratification. Our method is implemented in the software package PHASE (v2.1.1), available from the Stephens Lab Web site
A Fast and Flexible Statistical Model for Large-Scale Population Genotype Data: Applications to Inferring Missing Genotypes and Haplotypic Phase
No full textWe present a statistical model for patterns of genetic variation in samples of unrelated individuals from natural populations. This model is based on the idea that, over short regions, haplotypes in a population tend to cluster into groups of similar haplotypes. To capture the fact that, because of recombination, this clustering tends to be local in nature, our model allows cluster memberships to change continuously along the chromosome according to a hidden Markov model. This approach is flexible, allowing for both “block-like” patterns of linkage disequilibrium (LD) and gradual decline in LD with distance. The resulting model is also fast and, as a result, is practicable for large data sets (e.g., thousands of individuals typed at hundreds of thousands of markers). We illustrate the utility of the model by applying it to dense single-nucleotide–polymorphism genotype data for the tasks of imputing missing genotypes and estimating haplotypic phase. For imputing missing genotypes, methods based on this model are as accurate or more accurate than existing methods. For haplotype estimation, the point estimates are slightly less accurate than those from the best existing methods (e.g., for unrelated Centre d'Etude du Polymorphisme Humain individuals from the HapMap project, switch error was 0.055 for our method vs. 0.051 for PHASE) but require a small fraction of the computational cost. In addition, we demonstrate that the model accurately reflects uncertainty in its estimates, in that probabilities computed using the model are approximately well calibrated. The methods described in this article are implemented in a software package, fastPHASE, which is available from the Stephens Lab Web site
Linkage disequilibrium-based quality control for large-scale genetic studies.
No full textQuality control (QC) is a critical step in large-scale studies of genetic variation. While, on average, high-throughput single nucleotide polymorphism (SNP) genotyping assays are now very accurate, the errors that remain tend to cluster into a small percentage of "problem" SNPs, which exhibit unusually high error rates. Because most large-scale studies of genetic variation are searching for phenomena that are rare (e.g., SNPs associated with a phenotype), even this small percentage of problem SNPs can cause important practical problems. Here we describe and illustrate how patterns of linkage disequilibrium (LD) can be used to improve QC in large-scale, population-based studies. This approach has the advantage over existing filters (e.g., HWE or call rate) that it can actually reduce genotyping error rates by automatically correcting some genotyping errors. Applying this LD-based QC procedure to data from The International HapMap Project, we identify over 1,500 SNPs that likely have high error rates in the CHB and JPT samples and estimate corrected genotypes. Our method is implemented in the software package fastPHASE, available from the Stephens Lab website (http://stephenslab.uchicago.edu/software.html)
Abstract 2594: Optimizing the replication of cancer genomics workflows: case studies
No full textAbstract
Reproducing results is a major issue in cancer biology, whose “work bench” is dynamic and complex, with frequently updated algorithms and software. The better to manage our work in this environment we have developed SyQADA, a System for Quality-Assured Data Analysis – a workflow automation system designed to simplify common sequential analysis processes on the same or different data. SyQADA manages many of the details of procedural bookkeeping involved in bioinformatics workflows: What samples are we using? Where are the raw data? Were all the samples processed? Did every job complete satisfactorily? Is there as much output as expected? Where are the input files for the next step? How long does a typical job take to run? Which program versions did we use? Can we easily compare these results with the output of a different version of a program, or with different input data?
Using SyQADA, we have found ourselves better able to reproduce results while at the same time reducing the human effort required to manage our upstream data analyses. Here, we briefly describe how our lung cancer studies have benefitted from the use of SyQADA.
To understand the effect of different variant callers for Ion Torrent deep sequencing data in a lung cancer genomics study, we created a work protocol that allowed us to compare the different sets of variants called on 34 distinct somatic DNA samples from 4 patients. This complex processing framework involved running multiple variant callers, annotating variants, filtering germline variants using quality control metrics, and collating results across samples and callers. With SyQADA, we were able to re-run individual processes changing parameters with trivial changes to our configuration, yielding improved output. We then applied that unmodified protocol to the 500 samples from 48 individuals in our study, and rapidly produced data from which we could perform biological analysis. We then applied the protocol to a study of pre-malignant lesions in 25 lung cancer patients. In both studies, our workflow allowed us to generate comparable results in a matter of hours rather than days.
SyQADA has been used by individuals with backgrounds ranging from expert programmer to Unix novice, to perform and repeat dozens of diverse analytical workflows. Projects to which SyQADA has been applied include allelic imbalance studies of TCGA samples for cancers of the breast, pancreas, lung, and colon, processing roughly 6000 samples through a dozen steps.
A zipfile containing the SyQADA executable source code, documentation, tutorial examples, and workflows used in our lab will be available.
Citation Format: Jerry Fowler, F. Anthony San Lucas, Smruthy Sivakumar, Aditya Deshpande, Humam Kadara, Paul A. Scheet. Optimizing the replication of cancer genomics workflows: case studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2594. doi:10.1158/1538-7445.AM2017-2594</jats:p
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Linkage Disequilibrium-Based Quality Control for Large-Scale Genetic Studies
No full textQuality control (QC) is a critical step in large-scale studies of genetic variation. While, on average, high-throughput single nucleotide polymorphism (SNP) genotyping assays are now very accurate, the errors that remain tend to cluster into a small percentage of “problem” SNPs, which exhibit unusually high error rates. Because most large-scale studies of genetic variation are searching for phenomena that are rare (e.g., SNPs associated with a phenotype), even this small percentage of problem SNPs can cause important practical problems. Here we describe and illustrate how patterns of linkage disequilibrium (LD) can be used to improve QC in large-scale, population-based studies. This approach has the advantage over existing filters (e.g., HWE or call rate) that it can actually reduce genotyping error rates by automatically correcting some genotyping errors. Applying this LD-based QC procedure to data from The International HapMap Project, we identify over 1,500 SNPs that likely have high error rates in the CHB and JPT samples and estimate corrected genotypes. Our method is implemented in the software package fastPHASE, available from the Stephens Lab website (http://stephenslab.uchicago.edu/software.html).</p
Extensive Hidden Genomic Mosaicism Revealed in Normal Tissue
No full textGenomic mosaicism arising from post-zygotic mutation has recently been demonstrated to occur in normal tissue of individuals ascertained with varied phenotypes, indicating that detectable mosaicism may be less an exception than a rule in the general population. A challenge to comprehensive cataloging of mosaic mutations and their consequences is the presence of heterogeneous mixtures of cells, rendering low-frequency clones difficult to discern. Here we applied a computational method using estimated haplotypes to characterize mosaic megabase-scale structural mutations in 31,100 GWA study subjects. We provide in silico validation of 293 previously identified somatic mutations and identify an additional 794 novel mutations, most of which exist at lower aberrant cell fractions than have been demonstrated in previous surveys. These mutations occurred across the genome but in a nonrandom manner, and several chromosomes and loci showed unusual levels of mutation. Our analysis supports recent findings about the relationship between clonal mosaicism and old age. Finally, our results, in which we demonstrate a nearly 3-fold higher rate of clonal mosaicism, suggest that SNP-based population surveys of mosaic structural mutations should be conducted with haplotypes for optimal discovery
Abstract 3587: Intratumoral divergence of copy number alterations in NSCLC
No full textAbstract
Intratumoral heterogeneity has been increasingly established as a critical phenotype of cancer genomes. Its characterization at the DNA level is based on the identification of somatic mutations consisting of single nucleotide variants (SNVs) and copy number alterations (CNAs), which include deletions, amplifications, and copy neutral loss of heterozygosity. Currently, most methods for the detection of intratumoral heterogeneity use an implicit “infinite sites” model for both SNVs and CNAs. While this may often be appropriate for SNVs, we demonstrate its violation for CNAs in unstable cancer genomes. Here, we propose a novel method to identify CNAs that were created by independent mutational events but alter the same genomic region. Our method identifies regions where the germline heterozygous signals (allelic intensities for DNA arrays or frequencies for next-generation sequencing) shift toward different parental haplotypes between different samples from the same tumor, thus indicative of divergent tumor clones. In this context we define a divergent CNA as one found on multiple samples from the same tumor but with different chromosomal changes giving rise to the CNAs. We applied our method to data from core needle biopsies extracted from the tumors of 31 non-small cell lung cancer (NSCLC) patients and processed using Illumina SNP arrays. We overlapped CNA calls from the same tumor, and then tested whether overlapping segments showed divergent CNAs. We observe instances of divergent CNAs in 23 of the 31 patient tumors comprising 260 in total (median = 5 divergent CNAs per tumor). Strikingly, one tumor had 34. We then assessed whether the level of recurrent mutation correlated with clinical or genomic features. While there was no association with smoking or histology, we did observe a positive association between the rate of divergence and somatic mutations (including loss) in putative genome “gatekeeper” genes, p53 and CDKN2A (P = 0.001). We detected divergent CNAs that spanned shared genomic regions in three or more NSCLC tumors. These included large (&gt; 1Mb) events in chromosome 6 (q13-14, q21-22, q25) and chromosome 21 (q22), as well as smaller events, which included the integrin collagen receptor locus ITGA1-PELO-ITGA2, 8p23.1, 8q24.3, 18q11.2 (ZNF521 gene), and 21q21.3, which has bindings sites for GATA2, GATA3, and STAT3. Our observed divergent genomic alterations represent half of the total number expected since imbalances of the same haplotype will not be observable in such data. In summary, our approach allows for the detection of genomic regions that are divergently altered. This information may support methods to identify CNAs under positive or negative selection in the tumor microenvironment as well as regions of increased genomic instability. This provides an added dimension to intratumoral heterogeneity analysis for a more comprehensive characterization of cancer genomes.
Citation Format: Yasminka A. Jakubek, Smruthy Sivakumar, Louise C. Strong, Humam Kadara, Paul Scheet. Intratumoral divergence of copy number alterations in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3587. doi:10.1158/1538-7445.AM2017-3587</jats:p
Abstract 3572: Characterization of chromosomal allelic imbalances through RNA-seq
No full textAbstract
Transcriptome sequencing (mRNA-seq) is becoming a very versatile technique for profiling tumors, extending beyond its original intent of transcript quantification, identification of alternative transcripts, and detection of gene fusions. For instance, through recent advancements in RNA-seq data analyses, one can now computationally assess allele-specific gene expression and generate profiles of expressed somatic mutations. Here, we demonstrate the ability to identify chromosomal allelic imbalances (AI) through detection of haplotype-specific patterns in gene transcripts. This class of RNA-based observations may potentially reveal DNA-level chromosomal allelic imbalances or uncover large regions of transcription deregulation. From the TCGA Uterine Carcinosarcoma project, we downloaded exome and RNA sequencing data for 48 patients’ tumor/normal sample pairs in addition to their clinical annotations. We also downloaded Affymetrix SNP6-based DNA copy number event calls made by the TCGA for use as a gold standard when evaluating the AI calls in the exome and RNA-seq. AI calls were made in both the exome and RNA-seq data by: (1) calling 1000 Genomes genotypes in the sequencing data, (2) phasing haplotypes and then (3) characterizing haplotype imbalances using a tool that we developed called hapLOHseq. hapLOHseq applied to the exome and RNA-seq data both resulted in a 72% specificity for identifying the gold standard AI events. In RNA-seq data we detected 43% of the chromosomal AI events identified in the exome sequencing data. When considering AI events specifically detected in the RNA-seq and not the gold standard (RNA-specific AI), the data suggest that higher RNA-specific AI loads could be negatively associated with survival (p-val = 0.076), with higher RNA-specific AI load patients having a median survival of 771 days compared to 1526 days for those patients with lower loads of RNA-specific AI. In conclusion, our results suggest that analysis of chromosomal AI in RNA-seq has equal specificity for detecting DNA-level AI when compared to exome sequencing, although at lower sensitivities. Clinically, our analyses suggest that patients with higher RNA-specific AI load may have a worse overall survival prognosis. The AI we are identifying in the RNA-seq samples may reflect large-scale transcription defects, resulting in a negative impact on the survival of patients. One possible cause of RNA-specific allelic imbalance could be the presence of cis mutations that impact a large-region of the transcription of one of the two haplotypes. Currently, we are identifying areas for improvement in our analytical methods, while interrogating and characterizing exome and RNA-seq AI in additional data sets.
Citation Format: Francis A. San Lucas, Yihua Liu, Zachary Weber, Erik Ehli, Gareth Davies, Paul Scheet. Characterization of chromosomal allelic imbalances through RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3572. doi:10.1158/1538-7445.AM2017-3572</jats:p
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