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Paul Scheet - One of the best experts on this subject based on the ideXlab platform.

  • pan cancer patterns of Allelic Imbalance from chromosomal alterations in 33 tumor types
    Genetics, 2021
    Co-Authors: Smruthy Sivakumar, Anthony San F Lucas, Jerry Fowler, Yasminka A Jakubek, Zuhal Ozcan, Paul Scheet
    Abstract:

    Somatic copy number alterations (SCNAs) serve as hallmarks of tumorigenesis and often result in deviations from one-to-one Allelic ratios at heterozygous loci, leading to Allelic Imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation algorithm, providing segment mean copy number estimates from single-nucleotide polymorphism DNA microarray total intensities (log R ratio), but not allele-specific intensities ("B allele" frequencies) that inform of AI. Our approach provides more sensitive identification of SCNAs by modeling the "B allele" frequencies jointly, thereby bolstering the catalog of chromosomal alterations in this widely utilized resource. Here we present AI summaries for all 33 tumor sites in TCGA, including those induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH). We identified AI in 94% of the tumors, higher than in previous reports. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p, as well as mixed event types on 8p and 13q. We also observed both site-specific and pan-cancer (spanning 17p) cnLOH, patterns which have not been comprehensively characterized. The identification of such cnLOH events elucidates tumor suppressors and multi-hit pathways to carcinogenesis. We also contrast the landscapes inferred from AI- and total intensity-derived SCNAs and propose an automated procedure to improve and adjust SCNAs in TCGA for cases where high levels of aneuploidy obscured baseline intensity identification. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.

  • directional Allelic Imbalance profiling and visualization from multi sample data with recur
    Bioinformatics, 2019
    Co-Authors: Yasminka A Jakubek, Anthony San F Lucas, Paul Scheet
    Abstract:

    MOTIVATION Genetic analysis of cancer regularly includes two or more samples from the same patient. Somatic copy number alterations leading to Allelic Imbalance (AI) play a critical role in cancer initiation and progression. Directional analysis and visualization of the alleles in Imbalance in multi-sample settings allow for inference of recurrent mutations, providing insights into mutation rates, clonality and the genomic architecture and etiology of cancer. RESULTS The REpeat Chromosomal changes Uncovered by Reflection (RECUR) is an R application for the comparative analysis of AI profiles derived from SNP array and next-generation sequencing data. The algorithm accepts genotype calls and 'B allele' frequencies (BAFs) from at least two samples derived from the same individual. For a predefined set of genomic regions with AI, RECUR compares BAF values among samples. In the presence of AI, the expected value of a BAF can shift in two possible directions, reflecting an increased or decreased abundance of the maternal haplotype, relative to the paternal. The phenomenon of opposite haplotype shifts, or 'mirrored subclonal Allelic Imbalance', is a form of heterogeneity, and has been linked to clinico-pathological features of cancer. RECUR detects such genomic segments of opposite haplotypes in Imbalance and plots BAF values for all samples, using a two-color scheme for intuitive visualization. AVAILABILITY AND IMPLEMENTATION RECUR is available as an R application. Source code and documentation are available at scheet.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

  • abstract 2476 recur algorithm for directional Allelic Imbalance profiling and visualization from multi sample data
    Cancer Research, 2019
    Co-Authors: Yasminka A Jakubek, Anthony San F Lucas, Paul Scheet
    Abstract:

    Somatic chromosomal alterations play an important role in the development and progression of cancer. Allelic Imbalance (AI) resulting from such changes (gain, loss, or copy-neutral loss-of-heterozygosity; cnLOH) is defined as a deviation from the 1:1 ratio of inherited parental haplotypes. Observed “B allele” frequencies (BAFs) at germline heterozygous loci, from SNP array or next-generation sequencing data, are used to detect AI. In addition, log R ratio or read-depth data, can be analyzed alone or jointly with BAFs to detect gains and losses. When AI is detected in multiple intra-individual samples, spanning similar loci, it is natural to assess whether these signals reflect the same underlying mutation. One way to do this would be to note if the observed AI differs in mutation type (gain, loss), an approach that falls short for events of the same type and subtle AI events that cannot be classified as gains, losses, or cnLOH. In such a case, or with two mutations of the same type, samples may differ in their maternal/paternal haplotype balance and consequently their alleles will shift in opposite directions. This phenomenon is indicative of a recurrent or independent mutation, or an error in chromosome segregation that generates two “mirrored” clones, one with a gain and the other with a loss. Recent studies have used such a directional AI analysis to yield important insights into cancer initiation and progression (Jakubek et al. Cancer Research 2016, Jamal-Hanjani et al. New England Journal of Medicine 2017, Turajlic et al. Cell 2018). We developed REpeat Chromosomal changes Uncovered by Reflection (RECUR) to explicitly test directionality of AI from multiple samples with overlapping AI segments through the comparative analysis of AI profiles derived from SNP array and next-generation sequencing data. The algorithm accepts genotype calls and BAFs from at least 2 samples derived from the same individual. For a predefined set of genomic regions with AI, RECUR compares BAF values among samples. In the presence of AI, the expected value of a BAF can shift in two possible directions, reflecting an increased or decreased abundance of the maternal haplotype, relative to the paternal. RECUR detects such genomic segments of opposite haplotypes in Imbalance and plots BAF values for all samples, using a two-color scheme for intuitive visualization. It can help identify genomic regions under selective pressure for example recurrent deletions/gains in the same genomic loci and/or regions with generalized genomic instability. Integration of directional AI, copy number, and somatic mutation data can help build more accurate phylogenetic trees and further illuminate the timing and distribution of somatic chromosomal aberrations to offer insights into cancer initiation and progression. RECUR is available at scheet.org Citation Format: Yasminka A. Jakubek, F. Anthony San Lucas, Paul Scheet. RECUR: Algorithm for directional Allelic Imbalance profiling and visualization from multi-sample data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2476.

  • pan cancer patterns of Allelic Imbalance from chromosomal aberrations in 33 tumor types
    bioRxiv, 2019
    Co-Authors: Smruthy Sivakumar, Anthony San F Lucas, Jerry Fowler, Yasminka A Jakubek, Paul Scheet
    Abstract:

    ABSTRACT Somatic copy number alterations (SCNAs), including deletions and duplications, serve as hallmarks of tumorigenesis. SCNAs may span entire chromosomes and typically result in deviations from an expected one-to-one ratio of alleles at heterozygous loci, leading to Allelic Imbalance (AI). The Cancer Genome Atlas (TCGA) reports SCNAs identified using a circular binary segmentation (CBS) algorithm, providing segment mean copy number estimates from Affymetrix single-nucleotide polymorphism DNA microarray total (log R ratio) intensities, but not allele-specific (“B allele”) intensities that inform of AI. Here we seek to provide a TCGA-wide description of AI in tumor genomes, including AI induced by SCNAs and copy-neutral loss-of-heterozygosity (cnLOH), using a powerful haplotype-based method applied to allele-specific intensities. We present AI summaries for all 33 tumor sites and propose an automated adjustment procedure to improve calibration of existing SCNA calls in TCGA for tumors with high levels of aneuploidy where baseline intensities were difficult to establish without annotation of AI. Overall, 94% of tumor samples exhibited AI. Recurrent events included deletions of 17p, 9q, 3p, amplifications of 8q, 1q, 7p as well as mixed event types on 8p and 13q. The AI-based approach identified frequent cnLOH on 17p across multiple tumor sites, with additional site-specific cnLOH patterns. Our findings support the exploration of additional methods for robust automated inference procedures and to aid empirical discoveries across TCGA.

  • rapid and powerful detection of subtle Allelic Imbalance from exome sequencing data with haplohseq
    Bioinformatics, 2016
    Co-Authors: Anthony San F Lucas, Selina Vattathil, Smruthy Sivakumar, Jerry Fowler, Eduardo Vilar, Paul Scheet
    Abstract:

    Motivation: The detection of subtle genomic Allelic Imbalance events has many potential applications. For example, identifying cancer-associated Allelic Imbalanced regions in low tumor-cellularity samples or in low-proportion tumor subclones can be used for early cancer detection, prognostic assessment and therapeutic selection in cancer patients. We developed hapLOHseq for the detection of subtle Allelic Imbalance events from next-generation sequencing data. Results: Our method identified events of 10 megabases or greater occurring in as little as 16% of the sample in exome sequencing data (at 80×) and 4% in whole genome sequencing data (at 30×), far exceeding the capabilities of existing software. We also found hapLOHseq to be superior at detecting large chromosomal changes across a series of pancreatic samples from TCGA. Availability and Implementation: hapLOHseq is available at scheet.org/software, distributed under an open source MIT license. Contact: ude.ltsuw.mula@teehcsp Supplementary information: Supplementary data are available at Bioinformatics online.

Rita M Graze - One of the best experts on this subject based on the ideXlab platform.

  • a flexible bayesian method for detecting Allelic Imbalance in rna seq data
    BMC Genomics, 2014
    Co-Authors: Luis G Leonnovelo, Lauren M Mcintyre, Justin M Fear, Rita M Graze
    Abstract:

    One method of identifying cis regulatory differences is to analyze allele-specific expression (ASE) and identify cases of Allelic Imbalance (AI). RNA-seq is the most common way to measure ASE and a binomial test is often applied to determine statistical significance of AI. This implicitly assumes that there is no bias in estimation of AI. However, bias has been found to result from multiple factors including: genome ambiguity, reference quality, the mapping algorithm, and biases in the sequencing process. Two alternative approaches have been developed to handle bias: adjusting for bias using a statistical model and filtering regions of the genome suspected of harboring bias. Existing statistical models which account for bias rely on information from DNA controls, which can be cost prohibitive for large intraspecific studies. In contrast, data filtering is inexpensive and straightforward, but necessarily involves sacrificing a portion of the data. Here we propose a flexible Bayesian model for analysis of AI, which accounts for bias and can be implemented without DNA controls. In lieu of DNA controls, this Poisson-Gamma (PG) model uses an estimate of bias from simulations. The proposed model always has a lower type I error rate compared to the binomial test. Consistent with prior studies, bias dramatically affects the type I error rate. All of the tested models are sensitive to misspecification of bias. The closer the estimate of bias is to the true underlying bias, the lower the type I error rate. Correct estimates of bias result in a level alpha test. To improve the assessment of AI, some forms of systematic error (e.g., map bias) can be identified using simulation. The resulting estimates of bias can be used to correct for bias in the PG model, without data filtering. Other sources of bias (e.g., unidentified variant calls) can be easily captured by DNA controls, but are missed by common filtering approaches. Consequently, as variant identification improves, the need for DNA controls will be reduced. Filtering does not significantly improve performance and is not recommended, as information is sacrificed without a measurable gain. The PG model developed here performs well when bias is known, or slightly misspecified. The model is flexible and can accommodate differences in experimental design and bias estimation.

  • Allelic Imbalance in drosophila hybrid heads exons isoforms and evolution
    Molecular Biology and Evolution, 2012
    Co-Authors: Rita M Graze, Luis Leon G Novelo, Victor A Amin, Justin M Fear, George Casella, Sergey V Nuzhdin, Lauren M Mcintyre
    Abstract:

    Unraveling how regulatory divergence contributes to species differences and adaptation requires identifying functional variants from among millions of genetic differences. Analysis of Allelic Imbalance (AI) reveals functional genetic differences in cis regulation and has demonstrated differences in cis regulation within and between species. Regulatory mechanisms are often highly conserved, yet differences between species in gene expression are extensive. What evolutionary forces explain widespread divergence in cis regulation? AI was assessed in Drosophila melanogaster‐Drosophila simulans hybrid female heads using RNA-seq technology. Mapping bias was virtually eliminated by using genotype-specific references. Allele representation in DNA sequencing was used as a prior in a novel Bayesian model for the estimation of AI in RNA. Cis regulatory divergence was common in the organs and tissues of the head with 41% of genes analyzed showing significant AI. Using existing population genomic data, the relationship between AI and patterns of sequence evolution was examined. Evidence of positive selection was found in 30% of cis regulatory divergent genes. Genes involved in defense, RNAi/RISC complex genes, and those that are sex regulated are enriched among adaptively evolving cis regulatory divergent genes. For genes in these groups, adaptive evolution may play a role in regulatory divergence between species. However, there is no evidence that adaptive evolution drives most of the cis regulatory divergence that is observed. The majority of genes showed patterns consistent with stabilizing selection and neutral evolutionary processes.

Emilia Wiechec - One of the best experts on this subject based on the ideXlab platform.

Christian Schlotterer - One of the best experts on this subject based on the ideXlab platform.

  • Allelic Imbalance metre allim a new tool for measuring allele specific gene expression with rna seq data
    Molecular Ecology Resources, 2013
    Co-Authors: Ram Vinay Pandey, Susanne U Franssen, Andreas Futschik, Christian Schlotterer
    Abstract:

    Estimating differences in gene expression among alleles is of high interest for many areas in biology and medicine. Here, we present a user-friendly software tool, Allim, to estimate allele-specific gene expression. Because mapping bias is a major problem for reliable estimates of allele-specific gene expression using RNA-seq, Allim combines two different strategies to account for the mapping biases. In order to reduce the mapping bias, Allim first generates a polymorphism-aware reference genome that accounts for the sequence variation between the alleles. Then, a sequence-specific simulation tool estimates the residual mapping bias. Statistical tests for Allelic Imbalance are provided that can be used with the bias corrected RNA-seq data.

Dymitr Komitowski - One of the best experts on this subject based on the ideXlab platform.

  • Allelic Imbalance in three distinct regions on chromosome 17q in sporadic breast cancer correlates with prognostic parameters
    The Breast, 1997
    Co-Authors: Ute Hamann, Erich-franz Solomayer, K Gotte, Jens Huober, G Bastertt, S D Costa, Manfred Kaufmann, Dymitr Komitowski
    Abstract:

    Abstract Genetic heterogeneity of breast cancer involves multiple alterations of oncogenes and tumour suppressor genes. These genetic alterations may be important for initiation and progression of this malignancy. In order to find a parameter useful for the prognosis of breast cancer, Allelic Imbalance at five loci on chromosome 17q was examined in 63 sporadic primary breast tumours. The loci are located in three distinct regions frequently deleted in sporadic breast tumours. Allelic Imbalance was found in 48–62% of cancers. A significant correlation between Allelic Imbalance and five parameters associated with aggressive tumour behaviour was observed. Allelic Imbalance was associated with aneuploidy, oestrogen and progesterone receptor negativity, positive lymph node status and high tumour grading. Our results support the evidence for the existence of several tumour suppressor genes on chromosome 17q and allow the presumption that Allelic Imbalance at chromosome 17q loci may be a useful prognostic parameter in breast cancer.

  • Allelic Imbalance on chromosome 13q evidence for the involvement of brca2 and rb1 in sporadic breast cancer
    Cancer Research, 1996
    Co-Authors: Ute Hamann, Christian Herbold, Serban D Costa, Erich-franz Solomayer, Hans Ulrich Ulmer, Hans Frenzel, Gunther Bastert, Manfred Kaufmann, Dymitr Komitowski
    Abstract:

    Recently, the breast cancer susceptibility gene BRCA2 has been identified in chromosome 13q, a region that also contains the retinoblastoma gene RB1 . To elucidate a possible role of BRCA2 and RB1 in sporadic breast tumorigenesis, Allelic Imbalance (AI) at 13q loci was examined in 78 primary sporadic breast tumors. AI was found in 52–63% of tumors. Nine tumors showed AI only in the BRCA2 region but not at RB1 . Six tumors showed AI at RB1 but not in the BRCA2 region. AI in the BRCA2 region correlated significantly with aneuploidy ( P = 0.032) and AI at RB1 with small tumor size ( P = 0.025). Our data suggest that BRCA2 and RB1 may be both distinct target loci for AI on chromosome 13 in sporadic breast cancer.