Intelligent Predicting Response to Cancer Immunotherapy Through Systematic Modeling

Description

Immunotherapy has revolutionized cancer treatment, but predicting patient response remains challenging. Here, we presented Intelligent Predicting Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a novel network-based model that integrates multiple data types to predict immunotherapy outcomes. It incorporates gene expression, biological functional network, tumor microenvironment characteristics, immune-related pathways, and clinical data to provide a comprehensive view of factors influencing immunotherapy efficacy. By identifying key genetic and immunological factors, it provides an insight for more personalized treatment strategies and combination therapies to overcome resistance mechanisms.

Details

iPRISM

Examples

# if (!"devtools" %in% as.data.frame(installed.packages())$Package)
#   install.packages("devtools")
# devtools::install_github("hanjunwei-lab/iPRISM")

Enrichment Score Calculation

Description

Calculates the enrichment score (ES) for a given set of labels and correlation vector.

Usage

ESscore(labels.list, correl.vector = NULL)

Arguments

Value

The enrichment score (ES) for the given labels and correlation vector.

Weighted Enrichment Score Calculation

Description

Calculates the weighted enrichment score (ES) for a given set of labels and correlation vector.

Usage

ESscore_weighted(labels.list, correl.vector = NULL)

Arguments

Value

The weighted enrichment score (ES) for the given labels and correlation vector.

Seed Node Names

Description

A character vector with seed node names.

Usage

Seeds

Format

An object of class character of length 3.

Examples

library(iPRISM)
data(Seeds, package = "iPRISM")

Correlation Plot with Significance Points

Description

This function generates a correlation plot between two datasets, displaying correlation coefficients as a heatmap and significant correlations as scatter points.

Usage

cor_plot(
  data1,
  data2,
  sig.name1 = "value1",
  sig.name2 = "value2",
  cutoff.pvalue = 0.05,
  color = c("#62CCC9", "#FF9999")
)

Arguments

Details

The function computes correlation coefficients between corresponding columns in the two datasets and identifies significant correlations based on p-values.

Value

A ggplot object displaying the correlation heatmap and scatter points.

Examples

# Read all data into memory
data(data.path, package = "iPRISM")
data(data.cell, package = "iPRISM")
# Draw the plot
cor_plot(data1 = data.path,data2 = data.cell,sig.name1 = "path",sig.name2 = "cell")

data.cell

Description

The 'data.cell' represents the second type of feature matrix used for calculating correlations (in this case, cell abundances), where rows correspond to samples, and columns correspond to features.

Usage

data.cell

Format

An object of class matrix (inherits from array) with 121 rows and 21 columns.

Examples

library(iPRISM)
data(data.cell, package = "iPRISM")
dim(data.cell)

data.path

Description

The 'data.path' represents the first type of feature matrix used for calculating correlations (in this case, pathway expression levels), where rows correspond to samples, and columns correspond to features.

Usage

data.path

Format

An object of class matrix (inherits from array) with 121 rows and 17 columns.

Examples

library(iPRISM)
data(data.path, package = "iPRISM")
plot(data.path)

data_sig

Description

The 'data_sig' represents the sample feature matrix, where rows correspond to samples, and columns correspond to features.

Usage

data_sig

Format

An object of class matrix (inherits from array) with 121 rows and 31 columns.

Examples

library(iPRISM)
data(data_sig, package = "iPRISM")
plot(data_sig)

TME gene list after random walks

Description

This gene list includes genes from tumor microenvironment (TME). Random Walk with Restart (RWR) is applied to prioritize genes that are relevant to immunotherapy responses.

Usage

genelist_cp

Format

An object of class numeric of length 15867.

Examples

library(iPRISM)
data(genelist_cp, package = "iPRISM")

HLA gene list after random walks

Description

This gene list includes genes from human leukocyte antigen (HLA). Random Walk with Restart (RWR) is applied to prioritize genes that are relevant to immunotherapy responses.

Usage

genelist_hla

Format

An object of class numeric of length 15867.

Examples

library(iPRISM)
data(genelist_hla, package = "iPRISM")

ICI gene list after random walks

Description

This gene list includes genes from immune checkpoint inhibitors (ICI). Random Walk with Restart (RWR) is applied to prioritize genes that are relevant to immunotherapy responses.

Usage

genelist_imm

Format

An object of class numeric of length 15867.

Examples

library(iPRISM)
data(genelist_imm, package = "iPRISM")

Gene Set Enrichment Analysis (GSEA) using Multiplex Networks

Description

This function performs gene set enrichment analysis (GSEA) based on multiplex network data.

Usage

get_gsea_path(
  seed = seed,
  network = network,
  gamma = 0.7,
  pathlist = pathlist,
  gsea.weight = 1,
  gsea.nperm = 1000
)

Arguments

Details

The function constructs a multiplex network, performs random walk restart, and calculates gene scores. It then transforms the scores and applies GSEA using the provided gene sets.

Value

A GSEA result object.

Examples

data(Seeds, package = "iPRISM")
data(ppi, package = "iPRISM")
data(path_list, package = "iPRISM")


result <- get_gsea_path(seed = Seeds,
                       network = ppi,
                       pathlist = path_list[1:2],
                       gsea.nperm = 100)
print(result)

Fit Logistic Regression Model

Description

This function fits a logistic regression model to the given data.

Usage

get_logiModel(data.sig, pred.value, levels = c("R", "N"), step = TRUE)

Arguments

Details

The function converts the response variable to a factor with specified levels and fits a logistic regression model using the glm function.

Value

A fitted logistic regression model.

Examples

data(data_sig, package = "iPRISM")

b <- get_logiModel(data.sig = data_sig, pred.value = pred_value, step = TRUE)
summary(b)

Gene Set Enrichment Analysis (GSEA) Function

Description

This function performs gene set enrichment analysis using a gene list and a set of pathways.

Usage

gseafun(genelist, pathlist, nperm = 1000, weighted = 1)

Arguments

Value

A data frame with enrichment scores (ES), p-values, and adjusted p-values.

Examples

data(path_list, package = "iPRISM")
data(genelist_imm, package = "iPRISM")

res_gsea_imm <- gseafun(genelist = genelist_imm,
                        pathlist = path_list[1:2],
                        weighted = 1,
                        nperm = 1000)
print(res_gsea_imm)

path_list

Description

The 'path_list' contains the gene list associated with pathways.

Usage

path_list

Format

An object of class list of length 2656.

Examples

library(iPRISM)
data(path_list, package = "iPRISM")
length(path_list)

A protein-protein physical interaction network (PPI network)

Description

An igraph object containing a protein-protein physical interaction network.

Usage

ppi

Format

An object of class igraph of length 15867.

Examples

library(iPRISM)
data(ppi, package = "iPRISM")

library(igraph)
graph <- simplify(ppi)
graph_comp <- components(graph)$membership == which.max(components(graph)$csize)
graph <- induced_subgraph(graph, V(graph)[graph_comp])
plot(graph)

Original Class Labels for Samples

Description

A named vector where each element corresponds to a sample name and represents the original class label.

A named vector where each element corresponds to a sample name and represents the original class label.

Usage

pred_value

pred_value

Format

An object of class character of length 121.

An object of class character of length 121.

Examples

library(iPRISM)
data(pred_value, package = "iPRISM")
table(pred_value)

library(iPRISM)
data(pred_value, package = "iPRISM")
table(pred_value)