A workflow to study mechanistic indicators for driver gene prediction with Moonlight

⁺ These authors contributed equally to the paper as first authors.

Mona Nourbakhsh^+^ , Astrid Saksager^+^, Nikola Tom, Xi Steven Chen, Antonio Colaprico, Catharina Olsen, Matteo Tiberti, Elena Papaleo

Contents

Abstract

In order to make light of cancer development, it is crucial to understand which genes play a role in the mechanisms linked to this disease and moreover which role that is. Commonly biological processes such as proliferation and apoptosis have been linked to cancer progression. We have developed the Moonlight framework that allows for prediction of cancer driver genes through multi-omics data integration. Based on expression data we perform functional enrichment analysis, infer gene regulatory networks and upstream regulator analysis to score the importance of well-known biological processes with respect to the studied cancer. We then use these scores to predict oncogenic mediators with two specific roles: genes that potentially act as tumor suppressor genes (TSGs) and genes that potentially act as oncogenes (OCGs). This constitutes Moonlight’s primary layer. As gene expression data alone does not explain the cancer phenotypes, a second layer of evidence is needed. We have automated the integration of a secondary mutational layer that predicts driver mutations in the oncogenic mediators and thereby allows for the prediction of cancer driver genes using the driver mutation prediction tool CScape-somatic. These new functionalities are provided in the updated version of Moonlight, namely Moonlight2. Overall, this methodology not only allows us to identify genes with dual role (TSG in one cancer type and OCG in another) but also to elucidate the underlying biological processes.

Introduction

Cancer development is influenced by mutations in two distinctly different categories of genes, known as tumor suppressor genes (TSG) and oncogenes (OCG). The occurrence of mutations in genes of the first category leads to faster cell proliferation while mutations in genes of second category increases or changes their function. In 2020, we developed the Moonlight framework that allows for prediction of cancer driver genes (Colaprico, Antonio and Olsen, Catharina and Bailey, Matthew H. and Odom, Gabriel J. and Terkelsen, Thilde and Silva, Tiago C. and Olsen, André V. and Cantini, Laura and Zinovyev, Andrei and Barillot, Emmanuel and Noushmehr, Houtan and Bertoli, Gloria and Castiglioni, Isabella and Cava, Claudia and Bontempi, Gianluca and Chen, Xi Steven and Papaleo, Elena 2020). Here, gene expression data are integrated together with biological processes and gene regulatory networks to score the importance of well-known biological processes with respect to the studied cancer. These scores are used to predict oncogenic mediators: putative TSGs and putative OCGs. As gene expression data alone is not enough to explain the deregulation of the genes, a second layer of evidence is needed. For this reason, we automated the integration of a secondary mutational layer which predicts driver mutations and passenger mutations in the oncogenic mediators. These new functionalities are released in the updated version of Moonlight to produce Moonlight2R. The prediction of the driver mutations are carried out using the CScape-somatic driver mutation prediction tool. Moreover, the new functionalities estimate the potential effect of a mutation on the transcriptional, translational, or protein structure/function level. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes (Nourbakhsh, Mona and Saksager, Astrid and Tom, Nikola and Chen, Xi Steven and Colaprico, Antonio and Olsen, Catharina and Tiberti, Matteo and Papaleo, Elena 2023).

Moonlight’s pipeline

Moonlight’s pipeline is shown below:

Moonlight’s proposed workflow

The proposed pipeline consists of following eight steps:

1. The input to Moonlight is a set of differentially expressed genes between two biological conditions such as cancer and healthy samples or two cancer subtyoes. Besides differentially expressed genes, gene expression and mutation data are also needed.
2. FEA Functional Enrichment Analysis, using Fisher’s test, to identify gene sets (with biological functions linked to cancer) significantly enriched by regulated genes (RG).
3. GRN Gene Regulatory Network inferred between each single DEG (sDEG) and all genes by means of mutual information, obtaining for each DEG a list of RG.
4. URA Upstream Regulator Analysis for DEGs in each enriched gene set, we applied z-score being the ratio between the sum of all predicted effects for all the gene involved in the specific function and the square-root of the number of all genes.
5. PRA Pattern Recognition Analysis identifies putative TSGs (down) and OCGs (up). We either use user defined biological processes or random forests.
6. DMA Driver Mutation Analysis identifies driver mutations in the putative TSGs and OCGs through the driver mutation prediction tool, CScape-somatic.
7. We applied the above procedure to multiple cancer types to obtain cancer-specific lists of TSGs and OCGs. We compared the lists for each cancer: if a sDEG was TSG in a cancer and OCG in another we defined it as dual-role TSG-OCG. Otherwise if we found a sDEG defined as OCG or TSG only in one tissue we defined it as tissue specific biomarker.
8. We use the COSMIC database to define a list of gold standard TSG and OCGs to assess the accuracy of the proposed method.

Installation

To install Moonlight2R use the code below.

Installation from BioConductor

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("Moonlight2R")

Installation from GitHub

First, install devtools or if you already have it installed, load it.

install.packages("devtools")
library(devtools)

Install Moonlight2R from GitHub:

devtools::install_github(repo = "ELELAB/Moonlight2R")

Installation from GitHub with accompanying vignette

First, install the BiocStyle Bioconductor package.

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("BiocStyle")

Then install Moonlight2R with its accompanying vignette.

devtools::install_github(repo = "ELELAB/Moonlight2R", build_vignettes = TRUE)

You can view the vignette in the following way.

vignette( "Moonlight2R", package="Moonlight2R")

Load libraries

library(Moonlight2R)

## Loading required package: doParallel

## Loading required package: foreach

## Loading required package: iterators

## Loading required package: parallel

## 

## 

## Setting options('download.file.method.GEOquery'='auto')

## Setting options('GEOquery.inmemory.gpl'=FALSE)

library(magrittr)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

Obtain Input

The input to Moonlight is a set of differentially expressed genes and gene expression and mutation data are also needed. Gene expression data, mutation data and differentially expressed genes can for example be obtained from TCGA using the R package TCGAbiolinks. Help documents on how to use TCGAbiolinks are available here. To find other examples of usage of TCGAbiolinks on TCGA cancer types see our GitHub repository. Example data of the input (differentially expressed genes, gene expression data, and mutation data) are stored in the Moonlight2R package:

data(DEGsmatrix)
data(dataFilt)
data(dataMAF)
data(GEO_TCGAtab)
data(LOC_transcription)
data(LOC_translation)
data(LOC_protein)
data(Oncogenic_mediators_mutation_summary)
data(DEG_Mutations_Annotations)

Download: Get GEO data

You can search GEO data using the getDataGEO function.

GEO_TCGAtab: a 18x12 matrix that provides the GEO data set we matched to one of the 18 given TCGA cancer types

knitr::kable(GEO_TCGAtab, digits = 2,
       caption = "Table with GEO data set matched to one
       of the 18 given TCGA cancer types ",
       row.names = TRUE)

Table 1: Table with GEO data set matched to one
of the 18 given TCGA cancer types

	Cancer	TP	NT	DEG.	Dataset	TP.1	NT.1	Platform	DEG..	Common	GEO_Normal	GEO_Tumor
1	BLCA	408	19	2937	GSE13507	165	10	GPL65000	2099	896	control	cancer
2	BRCA	1097	114	3390	GSE39004	61	47	GPL6244	2449	1248	normal	Tumor
3	CHOL	36	9	5015	GSE26566	104	59	GPL6104	3983	2587	Surrounding	Tumor
4	COAD	286	41	3788	GSE41657	25	12	GPL6480	3523	1367	N	A
5	ESCA	184	11	2525	GSE20347	17	17	GPL571	1316	406	normal	carcinoma
6	GBM	156	5	4828	GSE50161	34	13	GPL570	4504	2660	normal	GBM
7	HNSC	520	44	2973	GSE6631	22	22	GPL8300	142	129	normal	cancer
8	KICH	66	25	4355	GSE15641	6	23	GPL96	1789	680	normal	chromophobe
9	KIRC	533	72	3618	GSE15641	32	23	GPL96	2911	939	normal	clear cell RCC
10	KIRP	290	32	3748	GSE15641	11	23	GPL96	2020	756	normal	papillary RCC
11	LIHC	371	50	3043	GSE45267	46	41	GPL570	1583	860	normal liver	HCC sample
12	LUAD	515	59	3498	GSE10072	58	49	GPL96	666	555	normal	tumor
13	LUSC	503	51	4984	GSE33479	14	27	GPL6480	3729	1706	normal	squamous cell carcinoma
14	PRAD	497	52	1860	GSE6919	81	90	GPL8300	246	149	normal prostate	tumor samples
15	READ	94	10	3628	GSE20842	65	65	GPL4133	2172	1261	M	T
16	STAD	415	35	2622	GSE2685	10	10	GPL80	487	164	N	T
17	THCA	505	59	1994	GSE33630	60	45	GPL570	1451	781	N	T
18	UCEC	176	24	4183	GSE17025			GPL570			tp	lcm

getDataGEO: Search by cancer type and data type [Gene Expression]

The user can query and download the cancer types supported by GEO, using the function getDataGEO:

dataFilt_GEO <- getDataGEO(GEOobject = "GSE20347", platform = "GPL571")

dataFilt_GEO <- getDataGEO(TCGAtumor = "ESCA")

FEA: Functional Enrichment Analysis

The user can perform a functional enrichment analysis using the function FEA. For each DEG in the gene set a z-score is calculated. This score indicates how the genes act in the gene set.

data(DEGsmatrix)
data(DiseaseList)
data(EAGenes)
dataFEA <- FEA(DEGsmatrix = DEGsmatrix)

The output can be visualized with a FEA plot.

FEAplot: Functional Enrichment Analysis Plot

The user can plot the result of a functional enrichment analysis using the function plotFEA. A negative z-score indicates that the process’ activity is decreased. A positive z-score indicates that the process’ activity is increased.

plotFEA(dataFEA = dataFEA, additionalFilename = "_exampleVignette", height = 10, width = 20)

The figure generated by the above code is shown below:

GRN: Gene Regulatory Network

The user can perform a gene regulatory network analysis using the function GRN which infers the network using the parmigene package. For illustrative purposes and to decrease runtime, we have set nGenesPerm = 5 and nBoot = 5 in the example below, however, we recommend setting these parameters to nGenesPerm = 2000 and nBoot = 400 to achieve optimal results, as they are set by default in the function arguments.

data(DEGsmatrix)
data(dataFilt)
dataGRN <- GRN(DEGsmatrix = DEGsmatrix, TFs = sample(rownames(DEGsmatrix), 100), normCounts = dataFilt,
                   nGenesPerm = 5, kNearest = 3, nBoot = 5, DiffGenes = TRUE)

URA: Upstream Regulator Analysis

The user can perform upstream regulator analysis using the function URA. This function is applied to each DEG in the enriched gene set and its neighbors in the GRN.

data(dataGRN)
data(DEGsmatrix)
data(DiseaseList)
data(EAGenes)

dataFEA <- FEA(DEGsmatrix = DEGsmatrix)

BPselected <- dataFEA$Diseases.or.Functions.Annotation[1:5]
dataURA <- URA(dataGRN = dataGRN,
               DEGsmatrix = DEGsmatrix,
               BPname = BPselected, 
               nCores=1)

PRA: Pattern Regognition Analysis

The user can retrieve TSG/OCG candidates using either selected biological processes or a random forest classifier trained on known COSMIC OCGs/TSGs.

data(dataURA)
data(tabGrowBlock)
data(knownDriverGenes)
dataPRA <- PRA(dataURA = dataURA,
               BPname = c("apoptosis","proliferation of cells"),
               thres.role = 0)

DMA: Driver Mutation Analysis

The user can identify driver mutations with DMA in the oncogenic mediators established by PRA. The passenger or driver status is estimated with CScape-somatic.
This function will further generate three files: DEG_Mutations_Annotations.rda, Oncogenic_mediators_mutation_summary.rda and cscape_somatic_output.rda. These will be placed in the specified results-folder. The user needs to download two CScape-somatic files in order to run DMA named css_coding.vcf.gz and css_noncoding.vcf.gz, respectively. These two files can be downloaded at http://cscape-somatic.biocompute.org.uk/#download. The corresponding .tbi files (css_coding.vcf.gz.tbi and css_noncoding.vcf.gz.tbi) must also be downloaded and be placed in the same folder.

data(dataPRA)
data(dataMAF)
data(DEGsmatrix)
data(LOC_transcription)
data(LOC_translation)
data(LOC_protein)
data(NCG)
data(EncodePromoters)
dataDMA <- DMA(dataMAF = dataMAF,
               dataDEGs = DEGsmatrix, 
               dataPRA = dataPRA,
               results_folder = "DMAresults",
               coding_file = "css_coding.vcf.gz",
               noncoding_file = "css_noncoding.vcf.gz")

GLS: Gene Literature Search

The user can perform a literature search on driver genes predicted from DMA using the GLS function. This function takes as input driver genes, a query and maximum number of records to retrieve from PubMed. Standard PubMed syntax can be used in the query. For example, Boolean operators AND, OR, NOT can be applied and tags such as [AU], [TITLE/ABSTRACT], [Affiliation] can be used. GLS fetches data of PubMed records matching the specified query and outputs PubMed IDs matching the query along with doi, title, abstract, year of publication, keywords, and total number of PubMed publications. This is done for each of the genes supplied in the input.

data(dataDMA)
genes_query <- Reduce(c, dataDMA)
dataGLS <- GLS(genes = genes_query, 
           query_string = "AND cancer AND driver",
           max_records = 20)

## Processing PubMed data .............. done!

head(dataGLS)

## # A tibble: 6 × 8
##   pmid     gene  doi                  title abstract year  keywords pubmed_count
##   <chr>    <chr> <chr>                <chr> <chr>    <chr> <chr>           <dbl>
## 1 36831452 ABCG2 10.3390/cancers1504… Hypo… Epithel… 2023  4-hydro…           14
## 2 35956408 ABCG2 10.3390/nu14153232   Sele… Cisplat… 2022  AMPK; c…           14
## 3 35427871 ABCG2 10.1016/j.drup.2022… Clin… Small-m… 2022  NSCLC; …           14
## 4 34255797 ABCG2 10.1371/journal.pon… Meta… Abcg2/B… 2021  ATP Bin…           14
## 5 34065402 ABCG2 10.3390/ijms22105384 KRAS… Lung ca… 2021  ABC dru…           14
## 6 33455278 ABCG2 10.1021/acsbiomater… SOX2… Certain… 2021  fibroge…           14

Level of consequence: Effect of mutations on three different levels

The user can investigate the predicted effect of different mutation types on the transcriptional level through the table LOC_transcription:

knitr::kable(LOC_transcription)

Variant_Classification	SNP	INS	DEL	DNP	TNP	ONP
5’Flank	1	1	1	1	1	1
5’UTR	1	1	1	1	1	1
Translation_Start_Site	0	0	0	0	0	0
Missense_Mutation	0	NA	NA	0	0	0
Nonsense_Mutation	1	1	1	1	1	1
Nonstop_Mutation	0	0	0	0	0	0
Splice_Site	1	1	1	1	1	1
Splice_Region	1	1	1	1	1	1
Silent	0	NA	NA	0	0	0
In_Frame_Del	NA	0	0	NA	NA	NA
In_Frame_Ins	NA	0	0	NA	NA	NA
Frame_Shift_Del	NA	0	0	NA	NA	NA
Frame_Shift_Ins	NA	0	0	NA	NA	NA
Intron	1	1	1	1	1	1
3’UTR	1	1	1	1	1	1
3’Flank	1	1	1	1	1	1
RNA	1	1	1	1	1	1
IGR	1	1	1	1	1	1

The user can investigate the predicted effect of different mutation types on the translational level through the table LOC_translation:

knitr::kable(LOC_translation)

Variant_Classification	SNP	INS	DEL	DNP	TNP	ONP
5’Flank	1	1	1	1	1	1
5’UTR	1	1	1	1	1	1
Translation_Start_Site	1	1	1	1	1	1
Missense_Mutation	0	NA	NA	0	0	0
Nonsense_Mutation	1	1	1	1	1	1
Nonstop_Mutation	1	1	1	1	1	1
Splice_Site	1	1	1	1	1	1
Splice_Region	1	1	1	1	1	1
Silent	1	NA	NA	1	1	1
In_Frame_Del	NA	0	0	NA	NA	NA
In_Frame_Ins	NA	0	0	NA	NA	NA
Frame_Shift_Del	NA	0	0	NA	NA	NA
Frame_shift_Ins	NA	0	0	NA	NA	NA
Intron	1	1	1	1	1	1
3’UTR	1	1	1	1	1	1
3’Flank	1	1	1	1	1	1
IGR	1	1	1	1	1	1
RNA	1	1	1	1	1	1

The user can investigate the predicted effect of different mutation types on the protein level through the table LOC_protein:

knitr::kable(LOC_protein)

Variant_Classification	SNP	INS	DEL	DNP	TNP	ONP
5’Flank	0	0	0	0	0	0
5’UTR	0	0	0	0	0	0
Translation_Start_Site	1	1	1	1	1	1
Missense_Mutation	1	NA	NA	1	1	1
Nonsense_Mutation	1	1	1	1	1	1
Nonstop_Mutation	1	1	1	1	1	1
Splice_Site	1	1	1	1	1	1
Splice_Region	1	1	1	1	1	1
Silent	0	NA	NA	0	0	0
In_Frame_Del	NA	1	1	NA	NA	NA
In_Frame_Ins	NA	1	1	NA	NA	NA
Frame_Shift_Del	NA	1	1	NA	NA	NA
Frame_Shift_Ins	NA	1	1	NA	NA	NA
Intron	1	1	1	1	1	1
3’UTR	0	0	0	0	0	0
3’Flank	0	0	0	0	0	0
RNA	0	0	0	0	0	0
IGR	0	0	0	0	0	0

plotNetworkHive: GRN hive visualization taking into account COSMIC cancer genes

In the following plot the nodes are separated into three groups: known tumor suppressor genes (yellow), known oncogenes (green) and the rest (gray).

data(knownDriverGenes)
data(dataGRN)
plotNetworkHive(dataGRN, knownDriverGenes, 0.55)

plotDMA: Heatmap of the driver/passenger status of mutations in TSGs/OCGs

In the following plot the driver genes with driver mutations are shown.

data(dataDMA)
data(DEG_Mutations_Annotations)
data(Oncogenic_mediators_mutation_summary)
plotDMA(DEG_Mutations_Annotations, 
        Oncogenic_mediators_mutation_summary, 
        type = 'complete', additionalFilename = "")

plotMoonlight: Heatmap of Moonlight gene z-score for the TSGs/OCGs

In the following plot the top 50 genes with the most driver mutations are visualised. The values are the moonlight gene z-score for the two biological processes

data(DEG_Mutations_Annotations)
data(Oncogenic_mediators_mutation_summary)
data(dataURA_plot)
plotMoonlight(DEG_Mutations_Annotations,
        Oncogenic_mediators_mutation_summary, 
        dataURA_plot, gene_type = "drivers", n = 50)

Moonlight Analysis: Case Studies

Introduction

This vignette shows a complete workflow of the ‘Moonlight2R’ package. The code is divided into three case studies:

• 1. Predicting oncogenic mediators using Moonlight’s primary layer
• 2. Moonlight pipeline in one function
• 3. Moonlight with driver mutation analysis

Case study n. 1: Predicting oncogenic mediators using Moonlight’s primary layer

For illustrative purposes and to decrease runtime, we have set nGenesPerm = 5 and nBoot = 5 in the call of GRN in the following code block, however, we recommend setting these parameters to nGenesPerm = 2000 and nBoot = 400 to achieve optimal results, as they are set by default in the function arguments.

data(DEGsmatrix)
data(dataFilt)
data(DiseaseList)
data(EAGenes)
data(tabGrowBlock)
data(knownDriverGenes)

dataFEA <- FEA(DEGsmatrix = DEGsmatrix)

dataGRN <- GRN(TFs = sample(rownames(DEGsmatrix), 100), 
               DEGsmatrix = DEGsmatrix,
               DiffGenes = TRUE,
               normCounts = dataFilt,
               nGenesPerm = 5,
               nBoot = 5,
           kNearest = 3)

dataURA <- URA(dataGRN = dataGRN, 
              DEGsmatrix = DEGsmatrix, 
              BPname = c("apoptosis",
                         "proliferation of cells"))

dataDual <- PRA(dataURA = dataURA, 
               BPname = c("apoptosis",
                          "proliferation of cells"),
               thres.role = 0)

oncogenic_mediators <- list("TSG"=names(dataDual$TSG), "OCG"=names(dataDual$OCG))

plotURA: Upstream regulatory analysis plot

The user can plot the result of the upstream regulatory analysis using the function plotURA.

data(dataURA) 
plotURA(dataURA = dataURA, additionalFilename = "_exampleVignette")

The figure resulted from the code above is shown below:

Case study n. 2: Moonlight pipeline in one function

For illustrative purposes and to decrease runtime, we have set nGenesPerm = 5 and nBoot = 5 in the example below, however, we recommend setting these parameters to nGenesPerm = 2000 and nBoot = 400 to achieve optimal results, as they are set by default in the function arguments.

data(dataFilt)
data(DEGsmatrix)
data(dataMAF)
data(DiseaseList)
data(EAGenes)
data(tabGrowBlock)
data(knownDriverGenes)
data(LOC_transcription)
data(LOC_translation)
data(LOC_protein)
data(NCG)
data(EncodePromoters)

listMoonlight <- moonlight(dataDEGs = DEGsmatrix,
                           dataFilt = dataFilt,
                           nTF = 100,
                           DiffGenes = TRUE,
               nGenesPerm = 5,
                           nBoot = 5,
                           BPname = c("apoptosis","proliferation of cells"),
                           dataMAF = dataMAF,
                           path_cscape_coding = "css_coding.vcf.gz", 
                           path_cscape_noncoding = "css_noncoding.vcf.gz")
save(listMoonlight, file = paste0("listMoonlight_ncancer4.Rdata"))

plotCircos: Moonlight Circos Plot

An example of running Moonlight on five cancer types is visualized below in a circos plot. Outer ring: color by cancer type, Inner ring: OCGs and TSGs, Inner connections: green: common OCGs yellow: common TSGs red: possible dual role

data(listMoonlight)
plotCircos(listMoonlight = listMoonlight, additionalFilename = "_ncancer5")

The figure generated by the code above is shown below:

Case study n. 3: Moonlight with driver mutation analysis

For illustrative purposes and to decrease runtime, we have set nGenesPerm = 5 and nBoot = 5 in the example below, however, we recommend setting these parameters to nGenesPerm = 2000 and nBoot = 400 to achieve optimal results, as they are set by default in the function arguments.

data(DEGsmatrix)
data(dataFilt)
data(dataMAF)
data(DiseaseList)
data(EAGenes)
data(tabGrowBlock)
data(knownDriverGenes)
data(LOC_transcription)
data(LOC_translation)
data(LOC_protein)
data(NCG)
data(EncodePromoters)

# Perform gene regulatory network analysis
dataGRN <- GRN(TFs = rownames(DEGsmatrix), 
           DEGsmatrix = DEGsmatrix,
           DiffGenes = TRUE,
               normCounts = dataFilt,
               nGenesPerm = 5, 
               kNearest = 3, 
               nBoot = 5)

# Perform upstream regulatory analysis
# As example, we use apoptosis and proliferation of cells as the biological processes
dataURA <- URA(dataGRN = dataGRN,
               DEGsmatrix = DEGsmatrix,
               BPname = c("apoptosis", 
                          "proliferation of cells"), 
               nCores = 1)

# Perform pattern recognition analysis
dataPRA <- PRA(dataURA = dataURA,
               BPname = c("apoptosis", 
                          "proliferation of cells"),
               thres.role = 0)

# Perform driver mutation analysis
dataDMA <- DMA(dataMAF = dataMAF,
               dataDEGs = DEGsmatrix, 
               dataPRA = dataPRA,
               results_folder = "DMAresults",
               coding_file = "css_coding.vcf.gz",
               noncoding_file = "css_noncoding.vcf.gz")

Next, we analyze the predicted driver genes and their mutations.

data(Oncogenic_mediators_mutation_summary)
data(DEG_Mutations_Annotations)

# Extract oncogenic mediators that contain at least one driver mutation
# These are the driver genes
knitr::kable(Oncogenic_mediators_mutation_summary %>%
  filter(!is.na(CScape_Driver)))

Hugo_Symbol	Moonlight_Oncogenic_Mediator	CScape_Passenger	CScape_Driver	CScape_Unclassified	Transcription_mut_sum	Translation_mut_sum	Protein_mut_sum	Total_Mutations	NCG_driver	NCG_cgc_annotation	NCG_vogelstein_annotation	NCG_saito_annotation	NCG_pubmed_id	NCG_cancer_type
ABCG2	OCG	NA	1	NA	0	0	1	1	Candidate	NA	NA	NA	30718927	esophageal_adenocarcinoma

# Extract mutation annotations of the predicted driver genes
driver_mut <- DEG_Mutations_Annotations %>% 
  filter(!is.na(Moonlight_Oncogenic_Mediator), 
         CScape_Mut_Class == "Driver")

# Extract driver genes with a predicted effect on the transcriptional level
transcription_mut <- Oncogenic_mediators_mutation_summary %>%
  filter(!is.na(CScape_Driver)) %>%
  filter(Transcription_mut_sum > 0)

# Extract mutation annotations of predicted driver genes that have a driver mutation
# in its promoter region with a potential effect on the transcriptional level 
promoters <- DEG_Mutations_Annotations %>% 
  filter(!is.na(Moonlight_Oncogenic_Mediator), 
         CScape_Mut_Class == "Driver",
         Potential_Effect_on_Transcription == 1,
         Annotation == 'Promoter')

Citation

Please cite the MoonlightR and Moonlight2R packages:

• “Interpreting pathways to discover cancer driver genes with Moonlight.” Nature Communications (2020): 10.1038/s41467-019-13803-0. (Colaprico, Antonio and Olsen, Catharina and Bailey, Matthew H. and Odom, Gabriel J. and Terkelsen, Thilde and Silva, Tiago C. and Olsen, André V. and Cantini, Laura and Zinovyev, Andrei and Barillot, Emmanuel and Noushmehr, Houtan and Bertoli, Gloria and Castiglioni, Isabella and Cava, Claudia and Bontempi, Gianluca and Chen, Xi Steven and Papaleo, Elena 2020)

• “A workflow to study mechanistic indicators for driver gene prediction with Moonlight.” Briefings in Bioinformatics (2023): 10.1093/bib/bbad274. (Nourbakhsh, Mona and Saksager, Astrid and Tom, Nikola and Chen, Xi Steven and Colaprico, Antonio and Olsen, Catharina and Tiberti, Matteo and Papaleo, Elena 2023)

Related publications to this vignette:

• “TCGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data.” Nucleic acids research (2015): gkv1507. (Colaprico, Antonio and Silva, Tiago C. and Olsen, Catharina and Garofano, Luciano and Cava, Claudia and Garolini, Davide and Sabedot, Thais S. and Malta, Tathiane M. and Pagnotta, Stefano M. and Castiglioni, Isabella and Ceccarelli, Michele and Bontempi, Gianluca and Noushmehr, Houtan 2016)

• “TCGA Workflow: Analyze cancer genomics and epigenomics data using Bioconductor packages”. F1000Research 10.12688/f1000research.8923.1 (Silva, TC and Colaprico, A and Olsen, C and D’Angelo, F and Bontempi, G and Ceccarelli, M and Noushmehr, H 2016)

---

Session Information ******

sessionInfo()

## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] parallel  stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] dplyr_1.1.3       magrittr_2.0.3    Moonlight2R_1.0.0 doParallel_1.0.17
## [5] iterators_1.0.14  foreach_1.5.2     BiocStyle_2.30.0 
## 
## loaded via a namespace (and not attached):
##   [1] fs_1.6.3                      matrixStats_1.0.0            
##   [3] bitops_1.0-7                  HiveR_0.3.63                 
##   [5] enrichplot_1.22.0             HDO.db_0.99.1                
##   [7] httr_1.4.7                    RColorBrewer_1.1-3           
##   [9] tools_4.3.1                   utf8_1.2.4                   
##  [11] R6_2.5.1                      lazyeval_0.2.2               
##  [13] GetoptLong_1.0.5              withr_2.5.1                  
##  [15] gridExtra_2.3                 RISmed_2.3.0                 
##  [17] cli_3.6.1                     Biobase_2.62.0               
##  [19] Cairo_1.6-1                   scatterpie_0.2.1             
##  [21] sass_0.4.7                    readr_2.1.4                  
##  [23] randomForest_4.7-1.1          askpass_1.2.0                
##  [25] Rsamtools_2.18.0              yulab.utils_0.1.0            
##  [27] gson_0.1.0                    DOSE_3.28.0                  
##  [29] R.utils_2.12.2                limma_3.58.0                 
##  [31] RSQLite_2.3.1                 generics_0.1.3               
##  [33] gridGraphics_0.5-1            shape_1.4.6                  
##  [35] BiocIO_1.12.0                 gtools_3.9.4                 
##  [37] dendextend_1.17.1             GO.db_3.18.0                 
##  [39] Matrix_1.6-1.1                fansi_1.0.5                  
##  [41] S4Vectors_0.40.0              abind_1.4-5                  
##  [43] R.methodsS3_1.8.2             lifecycle_1.0.3              
##  [45] yaml_2.3.7                    SummarizedExperiment_1.32.0  
##  [47] gplots_3.1.3                  qvalue_2.34.0                
##  [49] SparseArray_1.2.0             BiocFileCache_2.10.0         
##  [51] grid_4.3.1                    blob_1.2.4                   
##  [53] promises_1.2.1                crayon_1.5.2                 
##  [55] lattice_0.22-5                cowplot_1.1.1                
##  [57] KEGGREST_1.42.0               magick_2.8.1                 
##  [59] pillar_1.9.0                  knitr_1.44                   
##  [61] ComplexHeatmap_2.18.0         fgsea_1.28.0                 
##  [63] GenomicRanges_1.54.0          tcltk_4.3.1                  
##  [65] rjson_0.2.21                  codetools_0.2-19             
##  [67] fastmatch_1.1-4               glue_1.6.2                   
##  [69] ggfun_0.1.3                   qpdf_1.3.2                   
##  [71] data.table_1.14.8             parmigene_1.1.0              
##  [73] vctrs_0.6.4                   png_0.1-8                    
##  [75] treeio_1.26.0                 gtable_0.3.4                 
##  [77] cachem_1.0.8                  xfun_0.40                    
##  [79] S4Arrays_1.2.0                mime_0.12                    
##  [81] tidygraph_1.2.3               statmod_1.5.0                
##  [83] rgl_1.2.1                     interactiveDisplayBase_1.40.0
##  [85] ellipsis_0.3.2                nlme_3.1-163                 
##  [87] ggtree_3.10.0                 bit64_4.0.5                  
##  [89] filelock_1.0.2                GenomeInfoDb_1.38.0          
##  [91] bslib_0.5.1                   KernSmooth_2.23-22           
##  [93] colorspace_2.1-0              BiocGenerics_0.48.0          
##  [95] DBI_1.1.3                     tidyselect_1.2.0             
##  [97] bit_4.0.5                     compiler_4.3.1               
##  [99] extrafontdb_1.0               curl_5.1.0                   
## [101] tidyHeatmap_1.8.1             xml2_1.3.5                   
## [103] DelayedArray_0.28.0           bookdown_0.36                
## [105] shadowtext_0.1.2              rtracklayer_1.62.0           
## [107] scales_1.2.1                  caTools_1.18.2               
## [109] fuzzyjoin_0.1.6               rappdirs_0.3.3               
## [111] stringr_1.5.0                 digest_0.6.33                
## [113] rmarkdown_2.25                GEOquery_2.70.0              
## [115] XVector_0.42.0                htmltools_0.5.6.1            
## [117] pkgconfig_2.0.3               jpeg_0.1-10                  
## [119] base64enc_0.1-3               extrafont_0.19               
## [121] MatrixGenerics_1.14.0         dbplyr_2.3.4                 
## [123] fastmap_1.1.1                 rlang_1.1.1                  
## [125] GlobalOptions_0.1.2           htmlwidgets_1.6.2            
## [127] shiny_1.7.5.1                 farver_2.1.1                 
## [129] jquerylib_0.1.4               jsonlite_1.8.7               
## [131] BiocParallel_1.36.0           GOSemSim_2.28.0              
## [133] R.oo_1.25.0                   RCurl_1.98-1.12              
## [135] GenomeInfoDbData_1.2.11       ggplotify_0.1.2              
## [137] patchwork_1.1.3               munsell_0.5.0                
## [139] Rcpp_1.0.11                   ape_5.7-1                    
## [141] viridis_0.6.4                 stringi_1.7.12               
## [143] ggraph_2.1.0                  zlibbioc_1.48.0              
## [145] MASS_7.3-60                   AnnotationHub_3.10.0         
## [147] plyr_1.8.9                    org.Hs.eg.db_3.18.0          
## [149] HPO.db_0.99.2                 ggrepel_0.9.4                
## [151] Biostrings_2.70.0             graphlayouts_1.0.1           
## [153] splines_4.3.1                 hms_1.1.3                    
## [155] circlize_0.4.15               seqminer_9.1                 
## [157] igraph_1.5.1                  reshape2_1.4.4               
## [159] stats4_4.3.1                  BiocVersion_3.18.0           
## [161] XML_3.99-0.14                 evaluate_0.22                
## [163] BiocManager_1.30.22           tzdb_0.4.0                   
## [165] tweenr_2.0.2                  httpuv_1.6.12                
## [167] Rttf2pt1_1.3.12               tidyr_1.3.0                  
## [169] purrr_1.0.2                   polyclip_1.10-6              
## [171] clue_0.3-65                   ggplot2_3.4.4                
## [173] ggforce_0.4.1                 xtable_1.8-4                 
## [175] restfulr_0.0.15               easyPubMed_2.13              
## [177] tidytree_0.4.5                MPO.db_0.99.7                
## [179] later_1.3.1                   viridisLite_0.4.2            
## [181] tibble_3.2.1                  clusterProfiler_4.10.0       
## [183] aplot_0.2.2                   memoise_2.0.1                
## [185] AnnotationDbi_1.64.0          GenomicAlignments_1.38.0     
## [187] IRanges_2.36.0                cluster_2.1.4

References

Colaprico, Antonio and Olsen, Catharina and Bailey, Matthew H. and Odom, Gabriel J. and Terkelsen, Thilde and Silva, Tiago C. and Olsen, André V. and Cantini, Laura and Zinovyev, Andrei and Barillot, Emmanuel and Noushmehr, Houtan and Bertoli, Gloria and Castiglioni, Isabella and Cava, Claudia and Bontempi, Gianluca and Chen, Xi Steven and Papaleo, Elena. 2020. “Interpreting Pathways to Discover Cancer Driver Genes with Moonlight.” https://doi.org/10.1038/s41467-019-13803-0.

Colaprico, Antonio and Silva, Tiago C. and Olsen, Catharina and Garofano, Luciano and Cava, Claudia and Garolini, Davide and Sabedot, Thais S. and Malta, Tathiane M. and Pagnotta, Stefano M. and Castiglioni, Isabella and Ceccarelli, Michele and Bontempi, Gianluca and Noushmehr, Houtan. 2016. “TCGAbiolinks: An R/Bioconductor Package for Integrative Analysis of TCGA Data.” https://doi.org/10.1093/nar/gkv1507.

Nourbakhsh, Mona and Saksager, Astrid and Tom, Nikola and Chen, Xi Steven and Colaprico, Antonio and Olsen, Catharina and Tiberti, Matteo and Papaleo, Elena. 2023. “A Workflow to Study Mechanistic Indicators for Driver Gene Prediction with Moonlight.” https://doi.org/10.1093/bib/bbad274.

Silva, TC and Colaprico, A and Olsen, C and D’Angelo, F and Bontempi, G and Ceccarelli, M and Noushmehr, H. 2016. “TCGA Workflow: Analyze Cancer Genomics and Epigenomics Data Using Bioconductor Packages.” https://doi.org/10.12688/f1000research.8923.1.