Instructor(s) name(s) and contact information

Workshop Description

This workshop will introduce users to the CoreGx and PharmacoGx R packages, which are useful tools for pharmacogenomic modelling to discover biomarkers of treatment response in cancer model systems. PharmacoGx specifically focuses on drug sensitivity experiments in cancer cell lines, which will be the major focus of this workshop. Additional infrastructure from our lab includes ToxicoGx for toxicogenomics in healthy human cell-lines, RadioGx for radiogenomics in cancer cell-lines and Xeva for pharmacogenomics in patient derived xenograph (PDX) murine models.

Participants will learn the fundamentals of using CoreGx and PharmacoGx to create a PharmacoSet—an integrative container for the storage, analysis and visualization of pharmacogenomic experiments. Particular focus will be placed on newly developed support for storing, analyzing and visualizing drug combination sensitivity experiments and correlating results therefrom for with multi-omic molecular profiles to discover biomarkers of drug sensitivity or resistance.


  • Basic knowledge of R syntax
  • Knowledge of or interest in pharmacogenomics
  • Familiarity with core Bioconductor classes such as the SummarizedExperiment and MultiAssayExperiment
  • Curiosity about or experience with data.table an assest!

Useful publications:

  • Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D. M. A., Grossmann, P., Beck, A. H., Aerts, H. J. W. L., Lupien, M., Goldenberg, A., & Haibe-Kains, B. (2016). PharmacoGx: An R package for analysis of large pharmacogenomic datasets. Bioinformatics (Oxford, England), 32(8), 1244–1246.
  • Tonekaboni, M., Ali, S., Soltan Ghoraie, L., Manem, V. S. K. & Haibe-Kains, B. Predictive approaches for drug combination discovery in cancer. Brief Bioinform 19, 263–276 (2018).

Workshop Participation

Participants expected to have the following required packages installed on their machines to be able to run the commands along with the instructors. PharmacoGx, synergyfinder from Bioconductor, data.table from CRAN.

Time outline

For a 1.5-hr workshop:

Activity Time
Introduction to CoreGx and PharmacoGx 5m
Overview of Data Structures 15m
How TRE Support Drug Combinations Data Analysis 10m
Using Data Mapper to build a Drug Combo PharmacoSet. 10m
Dose Response Models and Drug Sensitivity measures 10m
Drug Combination Synergy Models 10m
Biomarker Discovery: 15m
Introduction to Resources for Biomarker Validation 5m

Workshop goals and objectives

Learning goals

  • Describe pharmacogenomic mono and combination datasets and usefulness in cancer research
  • Understand how experimental designs and research questions map onto data structures
  • Learn how to extract information from these datasets
  • Learn how to visualize experimental results from these datasets
  • Learn how to model dose-response for both monotherapy and combination small compound datasets
  • Learn measures to quantify response and synergy in cell line sensitivity screens

Learning objectives

  • List available standardized pharmacogenomic and radiogenomic datasets and download them
  • Access the molecular features, dose-response and metadata contained within the data structures defined in the packages
  • Fit Hill Slope models to dose-response experiments using small compound treatments in cell lines
  • Calculate the AAC, AUC, IC50 metrics for response quantification in cell lines
  • Predict in vitro univariate biomarkers for drug response and drug synergy using the PharmacoGx


  1. Introduction to CoreGx and PharmacoGx
  2. Overview of Data Structures
    1. CoreSet
    2. TreatmentResponseExperiment
  3. How TRE Support Drug Combinations Data Analysis
  4. Using Data Mapper to build a Drug Combo PSet
    1. Data Mapper
    2. Combining with Omics Data into a PharmacoSet
  5. Dose Response Models and Drug Sensitivity Measures
  6. Drug Combination Synergy Models
  7. Biomarker Discovery:
    1. Monotherapy response
    2. Combination Synergy
  8. Introduction to Resources for Biomarker Validation