1. Computational and Systems Biology
  2. Genetics and Genomics
Download icon

CNApp, a tool for the quantification of copy number alterations and integrative analysis revealing clinical implications

  1. Sebastià Franch-Expósito
  2. Laia Bassaganyas
  3. Maria Vila-Casadesús
  4. Eva Hernández-Illán
  5. Roger Esteban-Fabró
  6. Marcos Díaz-Gay
  7. Juan José Lozano
  8. Antoni Castells
  9. Josep Maria Llovet
  10. Sergi Castellvi-Bel  Is a corresponding author
  11. Jordi Camps  Is a corresponding author
  1. 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
  2. Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Spain
Tools and Resources
  • Cited 7
  • Views 5,001
  • Annotations
Cite this article as: eLife 2020;9:e50267 doi: 10.7554/eLife.50267

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).

The following previously published data sets were used

Article and author information

Author details

  1. Sebastià Franch-Expósito

    Gastrointestinal and Pancreatic Oncology Team, 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, Barcelona, Spain
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4542-1701

  2. Laia Bassaganyas

    Liver Cancer Translational Research Group, Liver Unit, 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, Barcelona, Spain
    Competing interests
    No competing interests declared.

  3. Maria Vila-Casadesús

    Bioinformatics Unit, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
    Competing interests
    No competing interests declared.

  4. Eva Hernández-Illán

    Gastrointestinal and Pancreatic Oncology Team, 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, Barcelona, Spain
    Competing interests
    No competing interests declared.

  5. Roger Esteban-Fabró

    Liver Cancer Translational Research Group, Liver Unit, 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, Barcelona, Spain
    Competing interests
    No competing interests declared.

  6. Marcos Díaz-Gay

    Gastrointestinal and Pancreatic Oncology Team, 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, Barcelona, Spain
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0658-0467

  7. Juan José Lozano

    Bioinformatics Unit, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), Barcelona, Spain
    Competing interests
    No competing interests declared.

  8. Antoni Castells

    Gastrointestinal and Pancreatic Oncology Team, 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, Barcelona, Spain
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8431-2033

  9. Josep Maria Llovet

    Liver Cancer Translational Research Group, Liver Unit, 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, Barcelona, Spain
    Competing interests
    Josep Maria Llovet, is receiving research support from Bayer HealthCare Pharmaceuticals, Eisai Inc, Bristol-Myers Squibb and Ipsen, and consulting fees from Eli Lilly, Bayer HealthCare Pharmaceuticals, Bristol-Myers Squibb, EISAI Inc, Celsion Corporation, Exelixis, Merck, Ipsen, Glycotest, Navigant, Leerink Swann LLC, Midatech Ltd, and Nucleix.

  10. Sergi Castellvi-Bel

    Gastrointestinal and Pancreatic Oncology Team, 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, Barcelona, Spain
    For correspondence
    SBEL@clinic.cat
    Competing interests
    No competing interests declared.

  11. Jordi Camps

    Gastrointestinal and Pancreatic Oncology Team, 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, Barcelona, Spain
    For correspondence
    JCAMPS@clinic.cat
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2929-4228

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

  1. C Daniela Robles-Espinoza, International Laboratory for Human Genome Research, Mexico

Publication history

  1. Received: July 17, 2019
  2. Accepted: January 14, 2020
  3. Accepted Manuscript published: January 15, 2020 (version 1)
  4. 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.

Metrics

  • 5,001
    Page views
  • 273
    Downloads
  • 7
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Categories and tags

Research organism

Further reading

    1. Computational and Systems Biology

    Inequalities in the distribution of National Institutes of Health research project grant funding

    Michael S Lauer, Deepshikha Roychowdhury
    Research Article Updated

    Previous reports have described worsening inequalities of National Institutes of Health (NIH) funding. We analyzed Research Project Grant data through the end of Fiscal Year 2020, confirming worsening inequalities beginning at the time of the NIH budget doubling (1998–2003), while finding that trends in recent years have reversed for both investigators and institutions, but only to a modest degree. We also find that career-stage trends have stabilized, with equivalent proportions of early-, mid-, and late-career investigators funded from 2017 to 2020. The fraction of women among funded PIs continues to increase, but they are still not at parity. Analyses of funding inequalities show that inequalities for investigators, and to a lesser degree for institutions, have consistently been greater within groups (i.e. within groups by career stage, gender, race, and degree) than between groups.

    1. Computational and Systems Biology
    2. Epidemiology and Global Health

    Characterizing human mobility patterns in rural settings of sub-Saharan Africa

    Hannah R Meredith et al.
    Research Article

    Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.

    1. Computational and Systems Biology

    PARROT is a flexible recurrent neural network framework for analysis of large protein datasets

    Daniel Griffith, Alex S Holehouse
    Tools and Resources

    The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to interpret this data. Among these tools, machine learning approaches are increasingly being utilized due to their ability to infer complex nonlinear patterns from high-dimensional data. Despite their effectiveness, machine learning (and in particular deep learning) approaches are not always accessible or easy to implement for those with limited computational expertise. Here we present PARROT, a general framework for training and applying deep learning-based predictors on large protein datasets. Using an internal recurrent neural network architecture, PARROT is capable of tackling both classification and regression tasks while only requiring raw protein sequences as input. We showcase the potential uses of PARROT on three diverse machine learning tasks: predicting phosphorylation sites, predicting transcriptional activation function of peptides generated by high-throughput reporter assays, and predicting the fibrillization propensity of amyloid beta with data generated by deep mutational scanning. Through these examples, we demonstrate that PARROT is easy to use, performs comparably to state-of-the-art computational tools, and is applicable for a wide array of biological problems.