Theranostics (web application, frontend)

Galaxy and MEAN stack to create a user-friendly workflow for the rational optimization of cancer chemotherapy

See: https://www.frontiersin.org/articles/10.3389/fgene.2021.624259/full

By: Jorge Guerra Pires1,2*, Gilberto Ferreira da Silva1,2, Thomas Weyssow3, Alessandra Jordano Conforte2,4, Dante Pagnoncelli5, Fabricio Alves Barbosa da Silva4, Nicolas Carels1,2,*

Emails: Jorge Guerra Pires - [email protected]; Gilberto Ferreira da Silva - [email protected]; Thomas Weyssow - [email protected]; Alessandra Jordano Conforte - [email protected]; Dante Pagnoncelli - [email protected]; Fabricio Alves Barbosa da Silva - [email protected]; Nicolas Carels - [email protected]; [email protected]

1 these authors contributed with most of the research. 2 Plataforma de Modelagem de Sistemas Biológicos, Center for Technology Development in Health (CDTS), Oswaldo Cruz Foundation (FIOCRUZ), Rio de Janeiro, Brazil. 3 Informatic Department, Free University of Brussels (ULB), Brussels, Belgium. 4 Laboratório de Modelagem Computacional de Sistemas Biológicos, Scientific Computing Program, FIOCRUZ, Rio de Janeiro, Brazil. 5 Centro de Oncologia Integrado (COI), Botafogo, Rio de Janeiro, Brazil.

*Corresponding author for the backend: e-mails: [email protected] ; [email protected] *Corresponding author for the frontend: [email protected]

One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to sub-network entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, in future releases of the application, the most relevant targets identified by the pipeline shall be stored using MongoDB and shall be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.

Keywords: Systems biology, translational oncology, personalized medicine, Galaxy, MEAN stack, Angular, network, Shannon entropy.

This matter is under review in Frontiers in Genetics and the full paper will be uploaded here once it is accepted. You can find a Provisionally accepted copy here

Working prototype

One can find a working version on the following link: http://teranostico.herokuapp.com/. Credentials: i) username: [email protected]; ii) password: Reviewer. Keep in mind that since we are using a free Heroku account, the first access may be slow.

Backend (computational biology)

For the backend: https://github.com/BiologicalSystemModeling/Theranostics