15,766 research outputs found
Comparison of TCGA and GENIE genomic datasets for the detection of clinically actionable alterations in breast cancer.
Whole exome sequencing (WES), targeted gene panel sequencing and single nucleotide polymorphism (SNP) arrays are increasingly used for the identification of actionable alterations that are critical to cancer care. Here, we compared The Cancer Genome Atlas (TCGA) and the Genomics Evidence Neoplasia Information Exchange (GENIE) breast cancer genomic datasets (array and next generation sequencing (NGS) data) in detecting genomic alterations in clinically relevant genes. We performed an in silico analysis to determine the concordance in the frequencies of actionable mutations and copy number alterations/aberrations (CNAs) in the two most common breast cancer histologies, invasive lobular and invasive ductal carcinoma. We found that targeted sequencing identified a larger number of mutational hotspots and clinically significant amplifications that would have been missed by WES and SNP arrays in many actionable genes such as PIK3CA, EGFR, AKT3, FGFR1, ERBB2, ERBB3 and ESR1. The striking differences between the number of mutational hotspots and CNAs generated from these platforms highlight a number of factors that should be considered in the interpretation of array and NGS-based genomic data for precision medicine. Targeted panel sequencing was preferable to WES to define the full spectrum of somatic mutations present in a tumor
Accurate estimation of homologue-specific DNA concentration-ratios in cancer samples allows long-range haplotyping
Interpretation of allelic copy measurements at polymorphic markers in cancer samples presents distinctive challenges and opportunities. Due to frequent gross chromosomal alterations occurring in cancer (aneuploidy), many genomic regions are present at homologous-allele imbalance. Within such regions, the unequal contribution of alleles at heterozygous markers allows for direct phasing of the haplotype derived from each individual parent. In addition, genome-wide estimates of homologue specific copy- ratios (HSCRs) are important for interpretation of the cancer genome in terms of fixed integral copy-numbers. We describe HAPSEG, a probabilistic method to interpret bi- allelic marker data in cancer samples. HAPSEG operates by partitioning the genome into segments of distinct copy number and modeling the four distinct genotypes in each segment. We describe general methods for fitting these models to data which are suit- able for both SNP microarrays and massively parallel sequencing data. In addition, we demonstrate a specially tailored error-model for interpretation of systematic variations arising in microarray platforms. The ability to directly determine haplotypes from cancer samples represents an opportunity to expand reference panels of phased chromosomes, which may have general interest in various population genetic applications. In addition, this property may be exploited to interrogate the relationship between germline risk and cancer phenotype with greater sensitivity than is possible using unphased genotype. Finally, we exploit the statistical dependency of phased genotypes to enable the fitting of more elaborate sample-level error-model parameters, allowing more accurate estimation of HSCRs in cancer samples
A decision-theoretic approach for segmental classification
This paper is concerned with statistical methods for the segmental
classification of linear sequence data where the task is to segment and
classify the data according to an underlying hidden discrete state sequence.
Such analysis is commonplace in the empirical sciences including genomics,
finance and speech processing. In particular, we are interested in answering
the following question: given data and a statistical model of
the hidden states , what should we report as the prediction under
the posterior distribution ? That is, how should you make a
prediction of the underlying states? We demonstrate that traditional approaches
such as reporting the most probable state sequence or most probable set of
marginal predictions can give undesirable classification artefacts and offer
limited control over the properties of the prediction. We propose a decision
theoretic approach using a novel class of Markov loss functions and report
via the principle of minimum expected loss (maximum expected
utility). We demonstrate that the sequence of minimum expected loss under the
Markov loss function can be enumerated exactly using dynamic programming
methods and that it offers flexibility and performance improvements over
existing techniques. The result is generic and applicable to any probabilistic
model on a sequence, such as Hidden Markov models, change point or product
partition models.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS657 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Change-point model on nonhomogeneous Poisson processes with application in copy number profiling by next-generation DNA sequencing
We propose a flexible change-point model for inhomogeneous Poisson Processes,
which arise naturally from next-generation DNA sequencing, and derive score and
generalized likelihood statistics for shifts in intensity functions. We
construct a modified Bayesian information criterion (mBIC) to guide model
selection, and point-wise approximate Bayesian confidence intervals for
assessing the confidence in the segmentation. The model is applied to DNA Copy
Number profiling with sequencing data and evaluated on simulated spike-in and
real data sets.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS517 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Joint segmentation of many aCGH profiles using fast group LARS
Array-Based Comparative Genomic Hybridization (aCGH) is a method used to
search for genomic regions with copy numbers variations. For a given aCGH
profile, one challenge is to accurately segment it into regions of constant
copy number. Subjects sharing the same disease status, for example a type of
cancer, often have aCGH profiles with similar copy number variations, due to
duplications and deletions relevant to that particular disease. We introduce a
constrained optimization algorithm that jointly segments aCGH profiles of many
subjects. It simultaneously penalizes the amount of freedom the set of profiles
have to jump from one level of constant copy number to another, at genomic
locations known as breakpoints. We show that breakpoints shared by many
different profiles tend to be found first by the algorithm, even in the
presence of significant amounts of noise. The algorithm can be formulated as a
group LARS problem. We propose an extremely fast way to find the solution path,
i.e., a sequence of shared breakpoints in order of importance. For no extra
cost the algorithm smoothes all of the aCGH profiles into piecewise-constant
regions of equal copy number, giving low-dimensional versions of the original
data. These can be shown for all profiles on a single graph, allowing for
intuitive visual interpretation. Simulations and an implementation of the
algorithm on bladder cancer aCGH profiles are provided
Concordance of copy number abnormality detection using SNP arrays and Multiplex Ligation-dependent Probe Amplification (MLPA) in acute lymphoblastic leukaemia
In acute lymphoblastic leukaemia, MLPA has been used in research studies to identify clinically relevant copy number abnormality (CNA) profiles. However, in diagnostic settings other techniques are often employed. We assess whether equivalent CNA profiles are called using SNP arrays, ensuring platform independence. We demonstrate concordance between SNP6.0 and MLPA CNA calling on 143 leukaemia samples from two UK trials; comparing 1,287 calls within eight genes and a region. The techniques are 99% concordant using manually augmented calling, and 98% concordant using an automated pipeline. We classify these discordant calls and examine reasons for discordance. In nine cases the circular binary segmentation (CBS) algorithm failed to detect focal abnormalities or those flanking gaps in IKZF1 probe coverage. Eight cases were discordant due to probe design differences, with focal abnormalities detectable using one technique not observable by the other. Risk classification using manually augmented array calling resulted in four out of 143 patients being assigned to a different CNA risk group and eight patients using the automated pipeline. We conclude that MLPA defined CNA profiles can be accurately mirrored by SNP6.0 or similar array platforms. Automated calling using the CBS algorithm proved successful, except for IKZF1 which should be manually inspected
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