This MVA-DNF analysis pipeline and code was concieved, designed and developed by Deena M.A. Gendoo for the following publication:

Computational pharmacogenomics screen identifies synergistic statin-compound combinations as anti-breast cancer therapies

Further Bioinformatics Analysis on Drug combinations is also developed by Wail B-Alawi and can be found in the subfolder Wail_ComboAnalysis

Questions or Comments: Please email [email protected] or [email protected]

Please cite: Jenna van Leeuwen, Wail Ba-Alawi, Emily Branchard, Joseph Longo, Jennifer Silvester, David W. Cescon, Benjamin Haibe-Kains, Linda Z. Penn, Deena M.A. Gendoo. bioRxiv 2020.09.07.286922; doi: https://doi.org/10.1101/2020.09.07.286922

This repository hosts code for the MVA-DNF algorithm, using a pathway-centric approach to identify top drug agents, like dipyridamole, that potentiate statin-induced tumor cell death by targeting the mevalonate pathway.

An integrative pharmacogenomics pipeline has been developed to identify agents that were similar to dipyridamole at the level of drug structure, in vitro sensitivity and molecular perturbation, while enriching for compounds expected to target the mevalonate pathway. This resulted in the MVA-DNF (mevalonate drug network fusion).

Further code assesses the synergistic ability of the top DP-like compounds with Fluvastatin, and their effect on the mevalonate pathway.

The LINCS-L1000 dataset containing profiles of drug-treated cancer cell lines can be downloaded from NCBI GEO (GSE70138 and GSE92742, which contains Level2 data for epsilon probes/features that represent raw gene expression/GEX, rendered as a GCTX file). NCI-60 compound sensitivity data (with average z-score) can be downloaded as ‘DTP_NCI60_ZSCORE.xlsx’ from the Cellminer website using the link: < https://discover.nci.nih.gov/cellminer/loadDownload.do> and selecting for ‘Compound activity: DTP NCI-60 ’. RPPA data is available from http://neellab.github.io/bfg and RNAseq data is available from Orcestra portal using the link: https://www.orcestra.ca/pset/10.5281/zenodo.3905460. Gene set collections for GSEA analysis can be downloaded from MSigDB < http://www.gsea-msigdb.org/gsea/msigdb/index.jsp>. Processed versions of these data are available on the 'data' folders of this repository and incorporatated into the running scripts.

We describe how to reproduce the statistical analysis as reported in the manuscript. To do this, please proceed to:

1. Set up the software environment
2. Run the R scripts

We developed and tested our analysis pipeline using R running on Mac OS X platforms.

To mimic our software environment the following R packages should be installed. All these packages are available on CRAN or Bioconductor.

R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] RColorBrewer_1.1-2   gplots_3.0.1.1       ggplot2_3.3.2        GSVA_1.32.0          GSA_1.03.1          
 [6] piano_2.0.2          snowfall_1.84-6.1    snow_0.4-3           proxy_0.4-24         reshape2_1.4.3      
[11] survcomp_1.34.0      prodlim_2019.10.13   survival_3.1-7       ROCR_1.0-11          SNFtool_2.3.0       
[16] org.Hs.eg.db_3.8.2   annotate_1.62.0      XML_3.98-1.20        AnnotationDbi_1.46.1 IRanges_2.18.3      
[21] S4Vectors_0.22.1     Biobase_2.44.0       BiocGenerics_0.30.0  fingerprint_3.5.7    rcdk_3.5.0          
[26] rcdklibs_2.3         rJava_0.9-13         apcluster_1.4.8      PharmacoGx_1.14.2    fmsb_0.6.3          
[31] pheatmap_1.0.12     

loaded via a namespace (and not attached):
  [1] fgsea_1.10.1         colorspace_1.4-1     ellipsis_0.3.0       lsa_0.73.1           rstudioapi_0.10     
  [6] SnowballC_0.6.0      DT_0.10              bit64_0.9-7          splines_3.6.0        geneplotter_1.62.0  
 [11] shinythemes_1.1.2    SuppDists_1.1-9.4    heatmap.plus_1.3     itertools_0.1-3      jsonlite_1.6        
 [16] magicaxis_2.0.7      alluvial_0.1-2       cluster_2.1.0        png_0.1-7            graph_1.62.0        
 [21] shinydashboard_0.7.1 shiny_1.4.0          ExPosition_2.8.23    mapproj_1.2.6        compiler_3.6.0      
 [26] prettyGraphs_2.1.6   assertthat_0.2.1     Matrix_1.2-17        fastmap_1.0.1        limma_3.40.6        
 [31] later_1.0.0          visNetwork_2.0.8     htmltools_0.4.0      tools_3.6.0          igraph_1.2.4.1      
 [36] gtable_0.3.0         glue_1.3.1           RANN_2.6.1           dplyr_0.8.3          maps_3.3.0          
 [41] fastmatch_1.1-0      Rcpp_1.0.3           slam_0.1-46          vctrs_0.3.1          gdata_2.18.0        
 [46] iterators_1.0.12     stringr_1.4.0        mime_0.7             lifecycle_0.2.0      gtools_3.8.1        
 [51] MASS_7.3-51.4        scales_1.1.1         promises_1.1.0       relations_0.6-9      sets_1.0-18         
 [56] memoise_1.1.0        gridExtra_2.3        downloader_0.4       rmeta_3.0            stringi_1.4.3       
 [61] RSQLite_2.1.2        NISTunits_1.0.1      plotrix_3.7-6        caTools_1.17.1.2     BiocParallel_1.18.1 
 [66] lava_1.6.6           rlang_0.4.6          pkgconfig_2.0.3      bitops_1.0-6         pracma_2.2.5        
 [71] lattice_0.20-38      purrr_0.3.3          survivalROC_1.0.3    htmlwidgets_1.5.1    bit_1.1-14          
 [76] tidyselect_1.1.0     GSEABase_1.46.0      plyr_1.8.6.9000      magrittr_1.5         R6_2.4.0            
 [81] bootstrap_2019.6     DBI_1.0.0            sm_2.2-5.6           withr_2.1.2          pillar_1.4.4        
 [86] RCurl_1.95-4.12      tibble_3.0.1         crayon_1.3.4         KernSmooth_2.23-16   grid_3.6.0          
 [91] data.table_1.12.6    marray_1.62.0        blob_1.2.0           digest_0.6.22        xtable_1.8-4        
 [96] httpuv_1.5.2         munsell_0.5.0        celestial_1.4.6      tcltk_3.6.0          shinyjs_1.0           

Once the packages are installed, please download this github repository to run the code.

The main folder contains scripts to run the MVA-DNF algorithm and associated output of the manuscript for:
Figure 1
Supplementary Figure S1
Supplementary Table1

1. The scripts DeenaGendoo_Generate_MVA_DNF.R and DeenaGendoo_PermutationTestAndFiltering.R are used to generate the MVA-DNF matrix, and then identify top drug agents to Dipryidamole, using permutation testing
2. The script DeenaGendoo_Heatmap_DrugPertSigs.R generate heatmaps to show up/down regulated genes due to drug treatment (drug perturbation signatures) for Dipryidamole (DP) and DP-like drugs
3. The script DeenaGendoo_CompareLayerContributions_Dec2021.R is used to compare for any two drugs, whether the strength of the drug-drug relationships is a reflection of perturbation, sensitivity, or structural similarity.
4. The script DeenaGendoo_MvaDNF_DifferentPrototypeInputs.R assess whether using Nelfinavir or Honokiol as the input drug into the algorithm, instead of Dipyridamole, converges to the same set of drugs.
5. The script DeenaGendoo_TimeMemory_Calculations.R shows the barplots for the time and memory consumed by running the MVA-DNF algorithm & permutation pipeline, across tested at n=5 iterations using different sizes of input genes.
6. Specific files (Cytoscape files for Figure 1B) are also found in the Data folder.

The Wail_ComboAnalysis subfolder is used to generate associated output of the manuscript for:
Figure 4
Supplementary Figure S6
Supplementary Figure S7
Supplementary Figure S8

1. The script combo_analysis.R contains the analysis of the synergy between the identified DP-like drugs and Fluvastatin.