How to use PANACEA

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:

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)
#>   gene_symbol driverness_prob prediction
#> 1       IFNA7      0.18945527 non-driver
#> 2        TJP1      0.13935021 non-driver
#> 3      IL20RA      0.09266710 non-driver
#> 4        SOST      0.08017132 non-driver
#> 5       NCOA2      0.06734062 non-driver
#> 6        ARAF      0.06232757 non-driver

“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")

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)
#>    EVEROLIMUS PEMBROLIZUMAB     CISPLATIN     LETROZOLE     CETUXIMAB 
#>   0.009405251   0.008960906   0.008960906   0.008900282   0.008755715 
#>  BICALUTAMIDE     SORAFENIB   ENCORAFENIB   REGORAFENIB    TRAMETINIB 
#>   0.008755715   0.008523904   0.008523904   0.008523904   0.008523904

“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")

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:

head(example_scores_RWR, 10)
#>               BLOSOZUMAB;ROMOSOZUMAB              TIAGABINE HYDROCHLORIDE 
#>                         0.0008476627                         0.0004770740 
#>                              KRN-330                            SERELAXIN 
#>                         0.0004326723                         0.0003804546 
#>                            TIAGABINE           LY-3009120;MLN-2480;XL-281 
#>                         0.0003554924                         0.0002520392 
#>                          OCRIPLASMIN                          ENCORAFENIB 
#>                         0.0002386693                         0.0002312123 
#> COLLAGENASE CLOSTRIDIUM HISTOLYTICUM                              COCAINE 
#>                         0.0002245975                         0.0002227435