A novel combination therapy for ER+ breast cancer suppresses drug resistance via an evolutionary double-bind

Authors: Rena Emond, Jeffrey West, Vince K. Grolmusz, Patrick A. Cosgrove, Aritro Nath, Alexander R.A. Anderson, Andrea H. Bild

Link to preprint: here.

This repository provides the MATLAB and MUSYC code for the manuscript above.

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Practical Guide

1. Installation & Setup

MATLAB Requirements

• MATLAB Version: Recommended R2020b or later.
• Toolboxes:
  • Statistics and Machine Learning Toolbox (required for statistical functions like sem).
  • Optimization Toolbox (may be required for fitting).

Setting up the Environment

1. Clone or download this repository to your local machine.
2. Open MATLAB.
3. Navigate to the repository folder: ER-breast-cancer-evolutionary-double-bind.

2. Repository Organization

The repository is organized into five main directories, each corresponding to a specific part of the analysis pipeline:

EGA/ (Evolutionary Game Assay)

Contains code for the game theory analysis and fitness landscape modeling (Figures 4 and 5).

• Core Scripts:
  • EGA.m: The main script for running the Evolutionary Game Analysis. It processes data, calculates fitness, and generates state space plots.
  • runTrajectories.m: Plots cell count trajectories over time.
  • plotFitness.m: Visualizes the fitness landscapes.
• Figure Generation:
  • figure4ABC.m: Recreates the panels for Figure 4 (Game assay results).
  • figure5ABC.m: Recreates the panels for Figure 5 (Evolutionary double-bind simulations).
• Data: _CSV/ contains the raw time-course data for different cell lines (MCF7, T47D) and drugs (Doxorubicin, Paclitaxel).

Musyc/ (Synergy Analysis)

Contains code for analyzing drug combination synergy using the MUSYC framework.

• Core Scripts:
  • main_surface.m: The primary script for generating 3D surface plots of drug interactions (Figure 2). It iterates through drugs and cell lines to visualize synergy/antagonism.
  • plotBestSurface.m: Helper to plot the best-fit 3D surface.
• Data:
  • _MUSYC/: Input CSV files generated by the Musyc tool.
  • _MUSYC_Results/: Output files from the analysis. Figure Generation:
  • figure2AE.m: Recreates some panels for Figure 2 (dose response plots).
  • figure2BCDFGH.m: Recreates some panels for Figure 2 (surface plots).

Normalization/

Scripts for preprocessing experimental data, specifically converting raw fluorescence values into cell counts.

• Key Scripts: norm_MCF7_Doxo.m, norm_MCF7_Pacli.m, norm_T47D_Pacli.m. These use linear models to map fluorescence to cell numbers based on standard curves.
  • These scripts are called by functions in the other folders, including EGA and Validation.
• Data: Excel files containing the standard curve data.

Surface/

Contains the raw 2D dose-response data used for the surface analyses.

• Files: Excel files (.xlsx) with dose-response matrices for various drug combinations.

Validation/

Code for validating the evolutionary models against experimental outcomes.

• Key Scripts:
  • figure5DEF.m: Generates validation figures comparing model predictions to experimental results.

3. How to Run the Code

1. To run the Evolutionary Game Analysis:

   • Open EGA/EGA.m.
   • Adjust parameters if necessary (e.g., cell_line, drug).
   • Run the script. Output plots will be saved to the plots/ directory (created automatically or needs to be created).

2. To generate Surface Plots:

   • Open Musyc/main_surface.m.
   • Run the script. It will generate figure windows for each drug combination defined in the loops.

3. To reproduce specific figures:

   • Run EGA/figure4ABC.m or EGA/figure5ABC.m directly. Ensure you are in the project root or have added all folders to the path.

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Note: Some scripts perform file operations (reading/writing). Ensure you have write permissions in the directory.