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.
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.
Metrics
-
- 10,065
- views
-
- 509
- downloads
-
- 54
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
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
-
- Computational and Systems Biology
- Genetics and Genomics
Obesity is a major risk factor for type 2 diabetes, dyslipidemia, cardiovascular disease, and hypertension. Intriguingly, there is a subset of metabolically healthy obese (MHO) individuals who are seemingly able to maintain a healthy metabolic profile free of metabolic syndrome. The molecular underpinnings of MHO, however, are not well understood. Here, we report that CTRP10/C1QL2-deficient mice represent a unique female model of MHO. CTRP10 modulates weight gain in a striking and sexually dimorphic manner. Female, but not male, mice lacking CTRP10 develop obesity with age on a low-fat diet while maintaining an otherwise healthy metabolic profile. When fed an obesogenic diet, female Ctrp10 knockout (KO) mice show rapid weight gain. Despite pronounced obesity, Ctrp10 KO female mice do not develop steatosis, dyslipidemia, glucose intolerance, insulin resistance, oxidative stress, or low-grade inflammation. Obesity is largely uncoupled from metabolic dysregulation in female KO mice. Multi-tissue transcriptomic analyses highlighted gene expression changes and pathways associated with insulin-sensitive obesity. Transcriptional correlation of the differentially expressed gene (DEG) orthologs in humans also shows sex differences in gene connectivity within and across metabolic tissues, underscoring the conserved sex-dependent function of CTRP10. Collectively, our findings suggest that CTRP10 negatively regulates body weight in females, and that loss of CTRP10 results in benign obesity with largely preserved insulin sensitivity and metabolic health. This female MHO mouse model is valuable for understanding sex-biased mechanisms that uncouple obesity from metabolic dysfunction.
-
- Computational and Systems Biology
Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of metabolites across tissue cryosections. While software packages exist for pixel-by-pixel individual metabolite and limited target pairs of ratio imaging, the research community lacks an easy computing and application tool that images any metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs may contribute to the discovery of unanticipated molecules in shared metabolic pathways. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling, markedly enhances spatial image contrast, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent hypothesis generation tool to enhance knowledge obtained from current spatial metabolite profiling technologies.
