Title: Personalized Network-Based Anti-Cancer Therapy Evaluation
Version: 1.0.1
Maintainer: Ege Ulgen <egeulgen@gmail.com>
Description: 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 methods are described in detail in Ulgen E, Ozisik O, Sezerman OU. 2023. PANACEA: network-based methods for pharmacotherapy prioritization in personalized oncology. Bioinformatics <doi:10.1093/bioinformatics/btad022>.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.2.3
URL: https://github.com/egeulgen/PANACEA, https://egeulgen.github.io/PANACEA/
BugReports: https://github.com/egeulgen/PANACEA/issues
Imports: org.Hs.eg.db, DBI, igraph, reshape2
Suggests: rmarkdown, knitr, testthat (≥ 3.0.0), covr
Config/testthat/edition: 3
Depends: R (≥ 4.0)
LazyData: true
LazyDataCompression: xz
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2023-08-19 12:57:02 UTC; egeulgen
Author: Ege Ulgen ORCID iD [aut, cre, cph]
Repository: CRAN
Date/Publication: 2023-08-19 13:22:36 UTC

PANACEA: Personalized Network-based Anti-Cancer Therapy Evaluation

Description

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.

Author(s)

Maintainer: Ege Ulgen egeulgen@gmail.com (ORCID) [copyright holder]

See Also

score_drugs for the wrapper function for scoring of drugs via network-based methods

DGIdb Interactions Expert-curated Sources

Description

Data frame containing drug-gene interactions from expert-curated sources (CancerCommons, CGI, ChemblInteractions, CIViC, ClearityFoundationBiomarkers, ClearityFoundationClinicalTrial, COSMIC, DoCM, MyCancerGenome, MyCancerGenomeClinicalTrial, TALC, TdgClinicalTrial, TEND) from DGIdb.

Usage

DGIdb_interactions_df

Format

a data frame containing 11323 rows and 2 variables:

drug_name

Drug name

gene_name

HGNC gene symbol for the interacting gene

Graph Laplacian Normalization

Description

Graph Laplacian Normalization

Usage

Laplacian.norm(W)

Arguments

W

square symmetric adjacency matrix

Value

normalized adjacency matrix

Adjacency List for STRING v11.5 - High Confidence Interactions

Description

Data frame of adjacency list for STRING v11.5 interactions with combined score > 700 (high confidence)

Usage

STRING_adj_df

Format

a data frame with 887797 rows and 3 variables:

protein1

Interactor 1

protein2

Interactor 2

value

edge weight(combined score)

Add Drugs as Nodes

Description

Add Drugs as Nodes

Usage

add_drugs_as_nodes(W_mat, drug_target_interactions, edge_weight = 1000)

Arguments

W_mat

adjacency matrix for the chosen PIN

drug_target_interactions

data frame containing (processed) drugs and target genes

edge_weight

edge weight for drug-target gene interaction (default = 1000)

Value

adjacency matrix with the drugs added as nodes

Turn Adjacency List into Adjacency Matrix

Description

Turn Adjacency List into Adjacency Matrix

Usage

adj_list2mat(adj_list)

Arguments

adj_list

Adjacency list

Value

Adjacency matrix

Convert Input Gene Symbols to Alias

Description

Convert Input Gene Symbols to Alias

Usage

convert2alias(input_genes, target_genes)

Arguments

input_genes

vector of input genes

target_genes

vector of target genes

Value

vector of converted gene symbols (if any alias in target genes)

Example driveR Result

Description

Data frame containing 'driveR' results for a lung adenocarcinoma case.

Usage

example_driveR_res

Format

a data frame containing 106 rows and 3 variables:

gene_symbol

HGNC gene symbol

driverness_prob

'driverness' probability

prediction

driveR's prediction for whether the gene is a 'driver' or 'non-driver'

Example PANACEA "RWR" Method Result

Description

Vector containing 'PANACEA' "RWR" results for a lung adenocarcinoma case. Names are drug names, values are scores

Usage

example_scores_RWR

Format

named vector of 1423 values

Example PANACEA "distance-based" Method Result

Description

Vector containing 'PANACEA' "distance-based" results for a lung adenocarcinoma case. Names are drug names, values are scores

Usage

example_scores_dist

Format

named vector of 1423 values

Network Propagation (Random-walk with Restart)

Description

Network Propagation (Random-walk with Restart)

Usage

network_propagation(prior_vec, W_prime, alpha, max.iter = 1000, eps = 1e-04)

Arguments

prior_vec

vector of prior knowledge on selected genes (names are gene symbols)

W_prime

(Laplacian-normalized, symmetric) adjacency matrix

alpha

restart parameter, controlling trade-off between prior information and network smoothing

max.iter

maximum allowed number of iterations (default = 1000)

eps

epsilon value to assess the L2 norm of the difference between iterations (default = 1e-4)

Details

Implementing RWR following the following publications: Cowen L, Ideker T, Raphael BJ, Sharan R. Network propagation: a universal amplifier of genetic associations. Nat Rev Genet. 2017 Sep;18(9):551–62. Shnaps O, Perry E, Silverbush D, Sharan R. Inference of personalized drug targets via network propagation. Pac Symp Biocomput. 2016;21:156–67.

Value

vector of propagation values

Process Drug-Target Interactions

Description

Process Drug-Target Interactions

Usage

process_drug_target_interactions(
  drug_target_interactions,
  PIN_genes,
  drug_name_col = "drug_name",
  target_col = "gene_name"
)

Arguments

drug_target_interactions

data frame containing drugs and target genes

PIN_genes

gene symbols for the chosen PIN

drug_name_col

name of the column containing drug names (default = "drug_name")

target_col

name of the column containing drug targets (default = "converted_target_gene")

Value

processed drug-target interactions. Processing involves converting symbols missing in the PIN, merging drugs that have the same target gene(s)

Scoring of Drugs via Network-based Methods

Description

Scoring of Drugs via Network-based Methods

Usage

score_drugs(driveR_res, drug_interactions_df, W_mat, method, ...)

Arguments

driveR_res

data frame of driveR results

drug_interactions_df

data frame of drug-gene interactions

W_mat

adjacency matrix for the PIN

method

scoring method (one of 'distance-based' or 'RWR')

...

additional arguments for score_drugs_distance_based or score_drugs_RWR_based

Details

This is the wrapper function for the two proposed methods for personalized scoring of drugs for individual cancer samples via network-based methods. The available methods are 'distance-based' and 'RWR'. For the 'distance-based' method, the score between a gene (g) and drug (d) is formulated as:

score(g, d) = driver(g) / (d(g, d) + 1)^2

where driver(g) is the driverness probability of gene g, as predicted by 'driveR' and d(g, d) is the distance withing the PIN between gene g and drug d. The final score of the drug d is then the average of the scores between each altered gene and d:

score(d) = \Sigma{score(g, d)} / |genes|

For the 'RWR' method, a random-walk with restart framework is used to propagate the driverness probabilities.

By default DGIdb_interactions_df is used as the drug_interactions_df.

If the W_mat argument is not supplied, the built-in STRNG data STRING_adj_df is used to generate W_mat.

Value

vector of scores per drug.

Examples

toy_data <- data.frame(
  gene_symbol = c("TP53", "EGFR", "KDR", "ATM"),
  driverness_prob = c(0.94, 0.92, 0.84, 0.72)
)
toy_interactions <- DGIdb_interactions_df[1:25, ]
res <- score_drugs(
  driveR_res = toy_data,
  drug_interactions_df = toy_interactions, # leave blank for default
  W_mat = toy_W_mat, # leave blank for default
  method = "distance-based",
  verbose = FALSE
)

RWR-based Scoring of Drugs

Description

RWR-based Scoring of Drugs

Usage

score_drugs_RWR_based(
  driveR_res,
  drug_interactions_df,
  W_mat,
  alpha = 0.05,
  max.iter = 1000,
  eps = 1e-04,
  drug_name_col = "drug_name",
  target_col = "gene_name",
  verbose = TRUE
)

Arguments

driveR_res

data frame of driveR results

drug_interactions_df

data frame of drug-gene interactions

W_mat

adjacency matrix for the PIN

alpha

restart parameter, controlling trade-off between prior information and network smoothing

max.iter

maximum allowed number of iterations (default = 1000)

eps

epsilon value to assess the L2 norm of the difference between iterations (default = 1e-4)

drug_name_col

for 'drug_interactions_df', the column name containing drug names/identifiers

target_col

for 'drug_interactions_df', the column name containing target gene symbols

verbose

boolean to control verbosity (default = TRUE)

Value

vector of scores per drug. Drugs with the same target gene(s) are merged (via process_drug_target_interactions)

Examples

toy_data <- data.frame(
  gene_symbol = c("TP53", "EGFR", "KDR", "ATM"),
  driverness_prob = c(0.94, 0.92, 0.84, 0.72)
)
toy_interactions <- DGIdb_interactions_df[1:100, ]
res <- score_drugs_RWR_based(
  driveR_res = toy_data,
  drug_interactions_df = toy_interactions,
  W_mat = toy_W_mat, verbose = FALSE
)

Distance-based Scoring of Drugs

Description

Distance-based Scoring of Drugs

Usage

score_drugs_distance_based(
  driveR_res,
  drug_interactions_df,
  W_mat,
  driver_prob_cutoff = 0.05,
  drug_name_col = "drug_name",
  target_col = "gene_name",
  verbose = TRUE
)

Arguments

driveR_res

data frame of driveR results

drug_interactions_df

data frame of drug-gene interactions

W_mat

adjacency matrix for the PIN

driver_prob_cutoff

cut-off value for 'driverness_prob' (default = 0.05)

drug_name_col

for 'drug_interactions_df', the column name containing drug names/identifiers

target_col

for 'drug_interactions_df', the column name containing target gene symbols

verbose

boolean to control verbosity (default = TRUE)

Value

vector of scores per drug. Drugs with the same target gene(s) are merged (via process_drug_target_interactions)

Examples

toy_data <- data.frame(
  gene_symbol = c("TP53", "EGFR", "KDR", "ATM"),
  driverness_prob = c(0.94, 0.92, 0.84, 0.72)
)
toy_interactions <- DGIdb_interactions_df[1:100, ]
res <- score_drugs_distance_based(
  driveR_res = toy_data,
  drug_interactions_df = toy_interactions,
  W_mat = toy_W_mat, verbose = FALSE
)

Toy Adjacency Matrix (for examples)

Description

Symmetric matrix containing example adjacency data

Usage

toy_W_mat

Format

matrix of 84 rows and 84 columns