In egeulgen/PANACEA: Personalized Network-Based Anti-Cancer Therapy Evaluation

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Overview

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 (PIN). 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 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 PIN (defaults to STRING v11.5 interactions with combined score > .4)
method: scoring method (one of "distance-based" or "RWR")

library(PANACEA)

Example Input

In this vignette, driveR results for a lung adenocarcinoma case, example_driveR_res, is used as the example input dataset. Details on how to obtain driveR output are provided in this vignette.

head(example_driveR_res)

"Distance-based" Method

For this method, the score between a drug, d, and an altered gene, g, is defined as:

$$score(g, d) = \frac{1}{(dist(g,d) + 1)^2} driver_prob_g$$ where $dist(g,d)$ is the distance between g and d within the PIN, and $driver_prob_g$ is the driverness probability obtained from driveR.

The final score for a drug is calculated as the average of the scores between each altered gene and d:

$$score(d) = \sum_{g \in G} \frac{1}{|G|} score(g,d)$$ where G is the set of all altered genes.

Scoring of drugs for the example lung adenocarcinoma case example_driveR_res via this "distance-based" method can be performed as follows:

example_scores_dist <- score_drugs(example_driveR_res, method = "distance-based")

usethis::use_data(example_scores_dist)

This scores and ranks drugs via the "distance-based" method using drug-gene interactions from DGIdb expert-curated sources and the STRING v11.5 PIN with combined score > 700. Below, top 10 drugs are printed:

head(example_scores_dist, 10)

"RWR" Method

For this method, a random-walk with restart framework is used to propagate the driverness probabilities. A drug's final score is its final propagation score.

Scoring of drugs for the example lung adenocarcinoma case example_driveR_res via this "RWR" method can be performed as follows:

example_scores_RWR <- score_drugs(example_driveR_res, method = "RWR")

usethis::use_data(example_scores_RWR)

This scores and ranks drugs via the "RWR" method using drug-gene interactions from DGIdb expert-curated sources and the STRING v11.5 PIN with combined score > 700. Below, top 10 drugs are printed: