CNApp, a tool for the quantification of copy number alterations and integrative analysis revealing clinical implications
- Institut D'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Hospital Clínic de Barcelona, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Universitat de Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Spain
Abstract
Somatic copy number alterations (CNAs) are a hallmark of cancer, but their role in tumorigenesis and clinical relevance remain largely unclear. Here we developed CNApp, a web-based tool that allows a comprehensive exploration of CNAs 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 the TCGA pan-cancer dataset of 10,635 genomes showing that CNAs classify cancer types according to their tissue-of-origin, and that each cancer type shows specific ranges of broad and focal CNA scores. Moreover, CNApp reproduces recurrent CNAs in hepatocellular carcinoma, and predicts colon cancer molecular subtypes and microsatellite instability based on broad CNA scores and discrete genomic imbalances. In summary, CNApp facilitates CNA-driven research by providing a unique framework to identify relevant clinical implications. CNApp is hosted at https://tools.idibaps.org/CNApp/.
Data availability
Data and plots presented in the submission were generated by using our CNApp tool. Source code and additional files can be found at GitHub (https://github.com/ait5/CNApp).
Article and author information
Author details
Sebastià Franch-Expósito
Funding
CIBEREHD
- Sebastià Franch-Expósito
Fundacion Cientifica de la Asociacion Espanola Contra el Cancer (GCB13131592CAST)
- Juan José Lozano
- Antoni Castells
- Sergi Castellvi-Bel
- Jordi Camps
European Commission /Horizon 2020 Program (HEPCAR Ref. 667273-2)
- Josep Maria Llovet
U.S. Department of Defense (CA150272P3)
- Josep Maria Llovet
National Cancer Institute (P30-CA196521)
- Josep Maria Llovet
Samuel Waxman Cancer Research Foundation
- Josep Maria Llovet
Spanish National Health Institute (SAF2016-76390)
- Josep Maria Llovet
Generalitat de Catalunya/AGAUR (SGR-1162)
- Josep Maria Llovet
Generalitat de Catalunya/AGAUR (SGR-1358)
- Josep Maria Llovet
European Regional Development Fund (PI14/00783)
- Marcos Díaz-Gay
- Juan José Lozano
- Antoni Castells
- Sergi Castellvi-Bel
- Jordi Camps
European Regional Development Fund (PI17/01304)
- Marcos Díaz-Gay
- Juan José Lozano
- Antoni Castells
- Sergi Castellvi-Bel
- Jordi Camps
Generalitat de Catalunya (AGAUR 2016BP00161)
- Laia Bassaganyas
European Regional Development Fund (PI17/00878)
- Marcos Díaz-Gay
- Juan José Lozano
- Antoni Castells
- Sergi Castellvi-Bel
- Jordi Camps
Generalitat de Catalunya (2017 SGR 21)
- Juan José Lozano
- Antoni Castells
- Sergi Castellvi-Bel
- Jordi Camps
Generalitat de Catalunya (2017 SGR 653)
- Juan José Lozano
- Antoni Castells
- Sergi Castellvi-Bel
- Jordi Camps
Generalitat de Catalunya (AGAUR 2018FI B1_00213)
- Marcos Díaz-Gay
Spanish National Health Institute (FPI BES-2017-081286)
- Roger Esteban-Fabró
European Comission (PCIG11-GA-2012-321937)
- Juan José Lozano
- Antoni Castells
- Sergi Castellvi-Bel
- Jordi Camps
European Regional Development Fund (CP13/00160)
- Marcos Díaz-Gay
- Juan José Lozano
- Antoni Castells
- Sergi Castellvi-Bel
- Jordi Camps
CERCA Program
- Juan José Lozano
- Antoni Castells
- Josep Maria Llovet
- Sergi Castellvi-Bel
- Jordi Camps
Generalitat de Catalunya (2017 SGR 1035)
- Juan José Lozano
- Antoni Castells
- Sergi Castellvi-Bel
- Jordi Camps
PERIS Generalitat de Catalunya (SLT002/16/00398)
- Juan José Lozano
- Antoni Castells
- Sergi Castellvi-Bel
- Jordi Camps
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- C Daniela Robles-Espinoza, International Laboratory for Human Genome Research, Mexico
Version history
- Received: July 17, 2019
- Accepted: January 14, 2020
- Accepted Manuscript published: January 15, 2020 (version 1)
- Version of Record published: February 10, 2020 (version 2)
Copyright
© 2020, Franch-Expósito et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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CNApp, a tool for the quantification of copy number alterations and integrative analysis revealing clinical implications
eLife 9:e50267.
https://doi.org/10.7554/eLife.50267
Further reading
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- Computational and Systems Biology
- Genetics and Genomics
Runs-of-homozygosity (ROH) segments, contiguous homozygous regions in a genome were traditionally linked to families and inbred populations. However, a growing literature suggests that ROHs are ubiquitous in outbred populations. Still, most existing genetic studies of ROH in populations are limited to aggregated ROH content across the genome, which does not offer the resolution for mapping causal loci. This limitation is mainly due to a lack of methods for the efficient identification of shared ROH diplotypes. Here, we present a new method, ROH-DICE (runs-of-homozygous diplotype cluster enumerator), to find large ROH diplotype clusters, sufficiently long ROHs shared by a sufficient number of individuals, in large cohorts. ROH-DICE identified over 1 million ROH diplotypes that span over 100 single nucleotide polymorphisms (SNPs) and are shared by more than 100 UK Biobank participants. Moreover, we found significant associations of clustered ROH diplotypes across the genome with various self-reported diseases, with the strongest associations found between the extended human leukocyte antigen (HLA) region and autoimmune disorders. We found an association between a diplotype covering the homeostatic iron regulator (HFE) gene and hemochromatosis, even though the well-known causal SNP was not directly genotyped or imputed. Using a genome-wide scan, we identified a putative association between carriers of an ROH diplotype in chromosome 4 and an increase in mortality among COVID-19 patients (p-value = 1.82 × 10−11). In summary, our ROH-DICE method, by calling out large ROH diplotypes in a large outbred population, enables further population genetics into the demographic history of large populations. More importantly, our method enables a new genome-wide mapping approach for finding disease-causing loci with multi-marker recessive effects at a population scale.
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- Biochemistry and Chemical Biology
- Computational and Systems Biology
Previously, Tuller et al. found that the first 30–50 codons of the genes of yeast and other eukaryotes are slightly enriched for rare codons. They argued that this slowed translation, and was adaptive because it queued ribosomes to prevent collisions. Today, the translational speeds of different codons are known, and indeed rare codons are translated slowly. We re-examined this 5’ slow translation ‘ramp.’ We confirm that 5’ regions are slightly enriched for rare codons; in addition, they are depleted for downstream Start codons (which are fast), with both effects contributing to slow 5’ translation. However, we also find that the 5’ (and 3’) ends of yeast genes are poorly conserved in evolution, suggesting that they are unstable and turnover relatively rapidly. When a new 5’ end forms de novo, it is likely to include codons that would otherwise be rare. Because evolution has had a relatively short time to select against these codons, 5’ ends are typically slightly enriched for rare, slow codons. Opposite to the expectation of Tuller et al., we show by direct experiment that genes with slowly translated codons at the 5’ end are expressed relatively poorly, and that substituting faster synonymous codons improves expression. Direct experiment shows that slow codons do not prevent downstream ribosome collisions. Further informatic studies suggest that for natural genes, slow 5’ ends are correlated with poor gene expression, opposite to the expectation of Tuller et al. Thus, we conclude that slow 5’ translation is a ‘spandrel’--a non-adaptive consequence of something else, in this case, the turnover of 5’ ends in evolution, and it does not improve translation.
