Personalization of patient-specific treatments based on logical models (PROFILE_v2)

This is a repository of code and analyses related to the paper "Patient-specific Boolean models of signaling networks guide personalized treatments".

The paper can accessed here: https://elifesciences.org/articles/72626 and the preprint here: https://www.biorxiv.org/content/10.1101/2021.07.28.454126v1.

Present code is an extension to use the PROFILE tool, paper available here, to simulate patient-specific drug inhibitions to find patient-specific treatments.

Releases

v2.0

Code repository at the time of publication of the eLife paper "Patient-specific Boolean models of signalling networks guide personalised treatments" (https://doi.org/10.7554/eLife.72626).

Added some minor changes from the published paper:

• added Analysis of drug sensitivities across cell lines/data_plot_CL.Rdata
• corrected bug in Gradient inhibition of nodes/data_analysis.R

More information: https://github.com/ArnauMontagud/PROFILE_v2/releases/tag/v2.0

v.2.1

Corrigendum for several files since v2.0.

• Corrigendum for Appendix file:
  • Figures S5, S6 and S7 were updated to match the results of the Jupyter notebook.
  • References in the legend of Figures 36 and 38 were modified.
• Corrigendum for Montagud2022_interactions_sources.xlsx file:
  • Line 131, now correctly depicts SPOP as an inhibitor of DNA_Damage

Brief tutorial on performing the gradient inhibition of nodes

Requirements

• Python version 3.0 or greater
• Python's package numpy
• Perl
• R
• MaBoSS requires: flex, bison, gcc and g++
• A MaBoSS Boolean model

You need to have a MaBoSS Boolean model to be able to use this tool, such as the one provided (LNCAP_mutRNA_EGF BND and CFG files). Additionally, you may want to have a personalised model by using the PROFILE tool. A tutorial is available here.

Steps

A. In the Gradient inhibition of nodes folder, first prepare the files for the gradual inhibition of the nodes of interest:

1. ./drugs_loop_single.sh

Script that builds run_single.sh that has one line for the gradual inhibition of each single node of interest.

2. ./drugs_loop_double.sh

Script that builds run_double.sh that has one line for the gradual inhibition of each combination of nodes of interest.

B. Launch the simulations

1. run_single.sh
2. run_double.sh

C. Gather the simulation results

1. data_gathering_single.R
2. data_gathering_double.R

D. Analyse and plot results

1. data_analysis.R

A processed dataframe is available to perform the analysis

Scripts to reproduce figures of the paper

• Figure 2: Available in Appendix 2.
• Figure 3: Available in Appendix 3.
• Figure 4: Available in the TCGA_plot.Rmd script in Analysis of TCGA patients' simulations folder.
• Figure 5: Available in the data_analysis.R script in Gradient inhibition of nodes folder.
• Figure 6: Available in the CL_plot.Rmd script in Analysis of drug sensitivities across cell lines folder.
• Figures 7, 8 and 9: Available in the drug_assays.R script in Analysis of experimental validation folder.

Processed datasets are available to obtain figures 4 through 9.