README.md
In PANACEA: Personalized Network-Based Anti-Cancer Therapy Evaluation

PANACEA: Personalized Network-based Anti-Cancer Therapy Evaluation

Identification of the most appropriate pharmacotherapy for each patient based on genomic alterations is a major challenge in personalized oncology. PANACEA is a collection of personalized anti-cancer drug prioritization approaches utilizing network methods. The methods utilize personalized “driverness” scores from driveR to rank drugs, mapping these onto a protein-protein interaction network. The “distance-based” method scores each drug based on these scores and distances between drugs and genes to rank given drugs. The “RWR” method propagates these scores via a random-walk with restart framework to rank the drugs.

The method is described in detail in Ulgen E, Ozisik O, Sezerman OU. PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology. Bioinformatics. 2023 Jan 1;39(1):btad022. https://doi.org/10.1093/bioinformatics/btad022

PANACEA workflow

You can install the latest release version of PANACEA from CRAN via:

install.packages("PANACEA")

You can install the development version of PANACEA from GitHub with:

# install.packages("devtools") @ if you don't have devtools installed
devtools::install_github("egeulgen/PANACEA", build_vignettes = TRUE)

The wrapper function score_drugs() can be used to score and rank drugs for an individual tumor sample via the “distance-based” or “RWR” method. The required inputs are:

driveR_res: data frame of driveR results. Details on how to obtain driveR output are provided in this vignette
drug_interactions_df: data frame of drug-gene interactions (defaults to interactions from DGIdb expert-curated sources)
W_mat: (symmetric) adjacency matrix for the protein interaction network (defaults to STRING v11.5 interactions with combined score > .4)
method: scoring method (one of “distance-based” or “RWR”)

For detailed information on how to use PANACEA, please see the vignette “How to use PANACEA” via vignette("how_to_use") or visit this link