scholarly journals Mutant allele specific imbalance in oncogenes with copy number alterations: Occurrence, mechanisms, and potential clinical implications

2017 ◽  
Vol 384 ◽  
pp. 86-93 ◽  
Author(s):  
Chih-Chieh Yu ◽  
Wanglong Qiu ◽  
Caroline S. Juang ◽  
Mahesh M. Mansukhani ◽  
Balazs Halmos ◽  
...  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Xinping Fan ◽  
Guanghao Luo ◽  
Yu S. Huang

Abstract Background Copy number alterations (CNAs), due to their large impact on the genome, have been an important contributing factor to oncogenesis and metastasis. Detecting genomic alterations from the shallow-sequencing data of a low-purity tumor sample remains a challenging task. Results We introduce Accucopy, a method to infer total copy numbers (TCNs) and allele-specific copy numbers (ASCNs) from challenging low-purity and low-coverage tumor samples. Accucopy adopts many robust statistical techniques such as kernel smoothing of coverage differentiation information to discern signals from noise and combines ideas from time-series analysis and the signal-processing field to derive a range of estimates for the period in a histogram of coverage differentiation information. Statistical learning models such as the tiered Gaussian mixture model, the expectation–maximization algorithm, and sparse Bayesian learning were customized and built into the model. Accucopy is implemented in C++ /Rust, packaged in a docker image, and supports non-human samples, more at http://www.yfish.org/software/. Conclusions We describe Accucopy, a method that can predict both TCNs and ASCNs from low-coverage low-purity tumor sequencing data. Through comparative analyses in both simulated and real-sequencing samples, we demonstrate that Accucopy is more accurate than Sclust, ABSOLUTE, and Sequenza.


2010 ◽  
Vol 8 (5) ◽  
pp. 207
Author(s):  
F. Kaveh ◽  
H. Edvardsen ◽  
A.L. Børresen-Dale ◽  
V.N. Kristensen ◽  
H.K. Solvang

2018 ◽  
Author(s):  
Sebastià Franch-Expósito ◽  
Laia Bassaganyas ◽  
Maria Vila-Casadesús ◽  
Eva Hernández-Illán ◽  
Roger Esteban-Fabró ◽  
...  

ABSTRACTSomatic copy number alterations (CNAs) are a hallmark of cancer. Although CNA profiles have been established for most human tumor types, their precise role in tumorigenesis as well as their clinical and therapeutic relevance remain largely unclear. Thus, computational and statistical approaches are required to thoroughly define the interplay between CNAs and tumor phenotypes. Here we developed CNApp, a user-friendly web tool that offers sample- and cohort-level computational analyses, allowing a comprehensive and integrative exploration of CNAs with clinical and molecular variables. By using purity-corrected segmented data from multiple genomic platforms, CNApp generates genome-wide profiles, computes CNA scores for broad, focal and global CNA burdens, and uses machine learning-based predictions to classify samples. We applied CNApp to a pan-cancer dataset of 10,635 genomes from TCGA showing that CNA patterns classify cancer types according to their tissue-of-origin, and that broad and focal CNA scores positively correlate in samples with low amounts of whole-chromosome and chromosomal arm-level imbalances. Moreover, using the hepatocellular carcinoma cohort from the TCGA repository, we demonstrate the reliability of the tool in identifying recurrent CNAs, confirming previous results. Finally, we establish machine learning-based models to predict colon cancer molecular subtypes and microsatellite instability based on broad CNA scores and specific genomic imbalances. In summary, CNApp facilitates data-driven research and provides a unique framework for the first time to comprehensively assess CNAs and perform integrative analyses that enable the identification of relevant clinical implications. CNApp is hosted at http://cnapp.bsc.es.


2017 ◽  
Author(s):  
Edith M. Ross ◽  
Kerstin Haase ◽  
Peter Van Loo ◽  
Florian Markowetz

AbstractMotivationAllele-specific copy number alterations are commonly used to trace the evolution of tumours. A key step of the analysis is to segment genomic data into regions of constant copy number. For precise phylogenetic inference, breakpoints shared between samples need to be aligned to each other.ResultsHere we present asmultipcf, an algorithm for allele-specific segmentation of multiple samples that infers private and shared segment boundaries of phylogenetically related samples. The output of this algorithm can directly be used for allele-specific copy number calling using ASCAT.Availabilityasmultipcf is available as part of the ASCAT R package (version 2.5) from github.com/Crick-CancerGenomics/ascat


2020 ◽  
Author(s):  
Xinping Fan ◽  
Guanghao Luo ◽  
Yu S. Huang

AbstractBackgroundCopy number alterations (CNAs), due to their large impact on the genome, have been an important contributing factor to oncogenesis and metastasis. Detecting genomic alterations from the shallow-sequencing data of a low-purity tumor sample remains a challenging task.ResultsWe introduce Accucopy, a method to infer total copy numbers (TCNs) and allele-specific copy numbers (ASCNs) from challenging low-purity and low-coverage tumor samples. Accucopy adopts many robust statistical techniques such as kernel smoothing of coverage differentiation information to discern signals from noise and combines ideas from time-series analysis and the signal-processing field to derive a range of estimates for the period in a histogram of coverage differentiation information. Statistical learning models such as the tiered Gaussian mixture model, the Expectation-Maximization (EM) algorithm, and Sparse Bayesian Learning (SBL) were customized and built into the model. Accucopy is implemented in C++/Rust, packaged in a docker image, and supports non-human samples, more at http://www.yfish.org/software/.ConclusionsWe describe Accucopy, a method that can predict both TCNs and ASCNs from low-coverage low-purity tumor sequencing data. Through comparative analyses in both simulated and real-sequencing samples, we demonstrate that Accucopy is more accurate than Sclust, ABSOLUTE, and Sequenza.


Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 420-420
Author(s):  
Kumi Nakazaki ◽  
Yasuhito Nannya ◽  
Masashi Sanada ◽  
Go Yamamoto ◽  
Chiaki Aoyama ◽  
...  

Abstract Non-Hodgkin lymphomas (NHL) are hematopoietic malignancies originated from diversity of peripheral lymphoid organs. During the past two decades, there have been significant advances in the pathogenesis of NHL including identification of a number of genes associated with the disease-specific translocations and other genetic alterations. In view of cytogenetics, however, NHL frequently shows complex chromosomal abnormalities involving copy number alterations as well as other unbalanced translocations, many of which have not been unveiled at the molecular levels. Affymetrix® 100K/500K mapping arrays were originally developed for large-scale SNP typing required for genome-wide association studies, but the quantitative nature of the whole-genome amplification and hybridization used in these platforms also makes them powerful tools for genome-wide analysis of cancer genomes with use of uniformly distributed 116,204/520,000 SNP-specific probes. Moreover the use of SNP specific probes enables allele-specific copy number analysis that is totally impossible with other platforms. Here we developed the robust algorithms (Copy number analyzer for Affymetrix® GeneChip®; CNAG) for high-quality processing of 100K/500K data and analyzed a total of 72 NHL samples (61 primary samples including 34 diffuse large B-cell lymphoma, 18 follicular lymphoma and 11 cell lines including 3 adult T cell leukemia/ lymphoma) for genome-wide copy number alterations, LOH, and allelic imbalances at the resolutions of 23.6/5.4 kb. In 100K analysis, 34 homozygous deletions and 42 high-grade amplifications and other numerous copy number alternations and/or LOH, were identified together with possible gene targets as for some regions. 500K analysis disclosed even more subtle changes. Common overlapping alternations included deletions in 1p31.1 and 9p21.3, and 19p13.32 and high-grade amplifications in 3p14.2–p14.1,7q21.13–q21.3, and 20q11.21. Of particular importance is, however, the finding of otherwise undetected copy number neutral LOHs, which are revealed only by allele-specific copy-number analysis. In fact the copy number neutral LOHs represented a novel type of genetic abnormality in NHL because they were very frequent and found in more than 87% (20/23) of NHL cases examined with allele-specific copy number analysis, making a stark contrast to ALL, in which these abnormalities were rare. They typically involved chromosomal ends, indicating somatic recombinations are the potential mechanism of generating these abnormalities. Notably, there was a clear predisposition of the copy number neutral LOH to specific chromosomal loci including 1p, 1q, 6p, 9p, 17q, and 19p suggesting existence of relevant genes to NHL pathogenesis within these common regions. In conclusion, Affymetrix® SNP-genotyping microarrays and our CNAG algorithms provide a powerful platform of dissecting NHL genomes and could facilitate identification of the novel molecular mechanisms for lymphomagenesis.


PLoS ONE ◽  
2009 ◽  
Vol 4 (10) ◽  
pp. e7464 ◽  
Author(s):  
Junichi Soh ◽  
Naoki Okumura ◽  
William W. Lockwood ◽  
Hiromasa Yamamoto ◽  
Hisayuki Shigematsu ◽  
...  

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