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Title:

Prioritizing Cancer Driver Genes Using Genomics Data

Version:

0.4.1

Maintainer:

Ege Ulgen <egeulgen@gmail.com>

Description:

Cancer genomes contain large numbers of somatic alterations but few genes drive tumor development. Identifying cancer driver genes is critical for precision oncology. Most of current approaches either identify driver genes based on mutational recurrence or using estimated scores predicting the functional consequences of mutations. 'driveR' is a tool for personalized or batch analysis of genomic data for driver gene prioritization by combining genomic information and prior biological knowledge. As features, 'driveR' uses coding impact metaprediction scores, non-coding impact scores, somatic copy number alteration scores, hotspot gene/double-hit gene condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG pathways. It uses these features to estimate cancer-type-specific probability for each gene of being a cancer driver using the related task of a multi-task learning classification model. The method is described in detail in Ulgen E, Sezerman OU. 2021. driveR: driveR: a novel method for prioritizing cancer driver genes using somatic genomics data. BMC Bioinformatics <doi:10.1186/s12859-021-04203-7>.

License:

MIT + file LICENSE

Encoding:

UTF-8

LazyData:

true

RoxygenNote:

7.2.3

URL:

https://egeulgen.github.io/driveR/,https://github.com/egeulgen/driveR/

BugReports:

https://github.com/egeulgen/driveR/issues

Imports:

caret, randomForest, GenomicRanges, GenomeInfoDb,GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene, S4Vectors, org.Hs.eg.db,rlang,

Depends:

R (≥ 4.0)

Suggests:

testthat, covr, knitr, rmarkdown

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2023-08-19 13:44:30 UTC; egeulgen

Author:

Ege Ulgen

[aut, cre, cph]

Repository:

CRAN

Date/Publication:

2023-08-19 14:02:36 UTC

driveR: An R Package for Prioritizing Cancer Driver Genes Using Genomics Data

Description

Details

driveR is a tool for personalized or batch analysis of genomic data fordriver gene prioritization by combining genomic information and priorbiological knowledge. As features, driveR uses coding impact metapredictionscores, non-coding impact scores, somatic copy number alteration scores,hotspot gene/double-hit gene condition, 'phenolyzer' gene scores andmemberships to cancer-related KEGG pathways. It uses these features toestimate cancer-type-specific probabilities for each gene of being a cancerdriver using the related task of a multi-task learning classification model.

Author(s)

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

KEGG "Pathways in cancer"-related Pathways - Descriptions

Description

A data frame containing descriptions for KEGG "Pathways in cancer"(hsa05200)-related pathways.Generated on Nov 17, 2020.

Usage

KEGG_cancer_pathways_descriptions

Format

A data frame with 21 rows and 2 variables:

id: KEGG pathway ID
description: KEGG pathway description

MTL Sub-model Descriptions

Description

A data frame containing descriptions for all sub-models of the MTL model.

Usage

MTL_submodel_descriptions

Format

A data frame with 21 rows and 2 variables:

short_name: short name for the cancer type
description: description of the cancer type

Create SCNA Score Data Frame

Description

Create SCNA Score Data Frame

Usage

create_SCNA_score_df(  gene_SCNA_df,  build = "GRCh37",  log2_ratio_threshold = 0.25,  MCR_overlap_threshold = 25)

Arguments

gene_SCNA_df

data frame of gene-level SCNAs (output ofcreate_gene_level_scna_df)

build

genome build for the SCNA segments data frame (default = "GRCh37")

log2_ratio_threshold

the log₂ratio threshold for keeping high-confidence SCNA events (default = 0.25)

MCR_overlap_threshold

the percentage threshold for the overlap betweena gene and an MCR region (default = 25). This means that if only a geneoverlaps an MCR region more than this threshold, the gene is assigned theSCNA density of the MCR

Details

The function first aggregates SCNA log₂ ratioon gene-level (by keeping the ratio with the maximal |log₂|ratio over all the SCNA segments overlapping a gene). Next, it identifies theminimal common regions (MCRs) that the genes overlap and finally assigns theSCNA density (SCNA/Mb) values as proxy SCNA scores.

Value

data frame of SCNA proxy scores containing 2 columns:

gene_symbol: HGNC gene symbol
SCNA_density: SCNA proxy score. SCNA density (SCNA/Mb) of the minimal common region (MCR) in which the gene is located.

Create Data Frame of Features for Driver Gene Prioritization

Description

Create Data Frame of Features for Driver Gene Prioritization

Usage

create_features_df(  annovar_csv_path,  scna_df,  phenolyzer_annotated_gene_list_path,  batch_analysis = FALSE,  prep_phenolyzer_input = FALSE,  build = "GRCh37",  log2_ratio_threshold = 0.25,  gene_overlap_threshold = 25,  MCR_overlap_threshold = 25,  hotspot_threshold = 5L,  log2_hom_loss_threshold = -1,  verbose = TRUE,  na.string = ".")

Arguments

annovar_csv_path

path to 'ANNOVAR' csv output file

scna_df

the SCNA segments data frame. Must contain:

chr: chromosome the segment is located in
start: start position of the segment
end: end position of the segment
log2ratio: log₂
ratio of the segment

phenolyzer_annotated_gene_list_path

path to 'phenolyzer'"annotated_gene_list" file

batch_analysis

boolean to indicate whether to perform batch analysis(TRUE, default) or personalized analysis (FALSE). IfTRUE,a column named 'tumor_id' should be present in both the ANNOVAR csv and the SCNAtable.

prep_phenolyzer_input

boolean to indicate whether or not to createa vector of genes for use as the input of 'phenolyzer' (default =FALSE).IfTRUE, the features data frame is not created and instead the vectorof gene symbols (union of all genes for which scores are available) isreturned.

build

genome build for the SCNA segments data frame (default = "GRCh37")

log2_ratio_threshold

the log₂ratio threshold for keeping high-confidence SCNA events (default = 0.25)

gene_overlap_threshold

the percentage threshold for the overlap betweena segment and a transcript (default = 25). This means that if only a segmentoverlaps a transcript more than this threshold, the transcript is assignedthe segment's SCNA event.

MCR_overlap_threshold

hotspot_threshold

to determine hotspot genes, the (integer) thresholdfor the minimum number of cases with certain mutation in COSMIC (default = 5)

log2_hom_loss_threshold

to determine double-hit events, thelog₂ threshold for identifyinghomozygous loss events (default = -1).

verbose

boolean controlling verbosity (default =TRUE)

na.string

string that was used to indicate when a score is not availableduring annotation with ANNOVAR (default = ".")

Value

Ifprep_phenolyzer_input=FALSE (default), a data frame offeatures for prioritizing cancer driver genes (gene_symbol asthe first column and 26 other columns containing features). Ifprep_phenolyzer_input=TRUE, the functions returns a vector gene symbols(union of all gene symbols for which scores are available) to be used as theinput for performing 'phenolyzer' analysis.

The features data frame contains the following columns:

gene_symbol: HGNC gene symbol
metaprediction_score: the maximum metapredictor (coding) impact score for the gene
noncoding_score: the maximum non-coding PHRED-scaled CADD score for the gene
scna_score: SCNA proxy score. SCNA density (SCNA/Mb) of the minimal common region (MCR) in which the gene is located
hotspot_double_hit: boolean indicating whether the gene is a hotspot gene (indication of oncogenes) or subject to double-hit (indication of tumor-suppressor genes)
phenolyzer_score: 'phenolyzer' score for the gene
hsa03320: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04010: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04020: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04024: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04060: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04066: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04110: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04115: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04150: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04151: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04210: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04310: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04330: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04340: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04350: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04370: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04510: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04512: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04520: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04630: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04915: boolean indicating whether or not the gene takes part in this KEGG pathway

Examples

path2annovar_csv <- system.file("extdata/example.hg19_multianno.csv",                                package = "driveR")path2phenolyzer_out <- system.file("extdata/example.annotated_gene_list",                                   package = "driveR")features_df <- create_features_df(annovar_csv_path = path2annovar_csv,                                  scna_df = example_scna_table,                                  phenolyzer_annotated_gene_list_path = path2phenolyzer_out)

Create Gene-level SCNA Data Frame

Description

Create Gene-level SCNA Data Frame

Usage

create_gene_level_scna_df(  scna_df,  build = "GRCh37",  gene_overlap_threshold = 25)

Arguments

scna_df

the SCNA segments data frame. Must contain:

chr: chromosome the segment is located in
start: start position of the segment
end: end position of the segment
log2ratio: log₂
ratio of the segment

build

genome build for the SCNA segments data frame (default = "GRCh37")

gene_overlap_threshold

Value

data frame of gene-level SCNA events, i.e. table of genes overlappedby SCNA segments.

Create Non-coding Impact Score Data Frame

Description

Create Non-coding Impact Score Data Frame

Usage

create_noncoding_impact_score_df(annovar_csv_path, na.string = ".")

Arguments

annovar_csv_path

path to 'ANNOVAR' csv output file

na.string

string that was used to indicate when a score is not availableduring annotation with ANNOVAR (default = ".")

Value

data frame of meta-prediction scores containing 2 columns:

gene_symbol: HGNC gene symbol
CADD_phred: PHRED-scaled CADD score

Determine Double-Hit Genes

Description

Determine Double-Hit Genes

Usage

determine_double_hit_genes(  annovar_csv_path,  gene_SCNA_df,  log2_hom_loss_threshold = -1,  batch_analysis = FALSE)

Arguments

annovar_csv_path

path to 'ANNOVAR' csv output file

gene_SCNA_df

data frame of gene-level SCNAs (output ofcreate_gene_level_scna_df)

log2_hom_loss_threshold

to determine double-hit events, thelog₂ threshold for identifyinghomozygous loss events (default = -1).

batch_analysis

Value

vector of gene symbols that are subject to double-hit event(s), i.e.non-synonymous mutation + homozygous copy-number loss

Determine Hotspot Containing Genes

Description

Determine Hotspot Containing Genes

Usage

determine_hotspot_genes(annovar_csv_path, hotspot_threshold = 5L)

Arguments

annovar_csv_path

path to 'ANNOVAR' csv output file

hotspot_threshold

to determine hotspot genes, the (integer) thresholdfor the minimum number of cases with certain mutation in COSMIC (default = 5)

Value

vector of gene symbols of genes containing hotspot mutation(s)

Example Cohort-level Features Table for Driver Prioritization

Description

The example dataset containing features for prioritizing cancer driver genes for 10 randomlyselected samples from TCGA's LAML (Acute Myeloid Leukemia) cohort

Usage

example_cohort_features_table

Format

A data frame with 349 rows and 27 variables:

gene_symbol: HGNC gene symbol
metaprediction_score: the maximum metapredictor (coding) impact score for the gene
noncoding_score: the maximum non-coding PHRED-scaled CADD score for the gene
scna_score: SCNA proxy score. SCNA density (SCNA/Mb) of the minimal common region (MCR) in which the gene is located
hotspot_double_hit: boolean indicating whether the gene is a hotspot gene (indication of oncogenes) or subject to double-hit (indication of tumor-suppressor genes)
phenolyzer_score: 'phenolyzer' score for the gene
hsa03320: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04010: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04020: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04024: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04060: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04066: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04110: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04115: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04150: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04151: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04210: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04310: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04330: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04340: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04350: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04370: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04510: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04512: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04520: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04630: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04915: boolean indicating whether or not the gene takes part in this KEGG pathway

Example Cohort-level Somatic Copy Number Alteration Table

Description

A data set containing the somatic copy number alteration data for 10 randomlyselected samples from TCGA's LAML (Acute Myeloid Leukemia) cohort

Usage

example_cohort_scna_table

Format

A data frame with 126147 rows and 5 variables:

chr: chromosome the segment is located in
start: start position of the segment
end: end position of the segment
log2ratio: log₂
ratio ofthe segment
tumor_id: ID for the tumor containing the SCNA segment

Source

https://dcc.icgc.org/releases/release_28

Example Features Table for Driver Prioritization

Description

The example dataset containing features for prioritizing cancer driver genes forthe lung adenocarcinoma patient studied in Imielinski M, Greulich H, Kaplan B, et al.Oncogenic and sorafenib-sensitive ARAF mutations in lung adenocarcinoma.J Clin Invest. 2014;124(4):1582-6.

Usage

example_features_table

Format

A data frame with 4901 rows and 27 variables:

gene_symbol: HGNC gene symbol
metaprediction_score: the maximum metapredictor (coding) impact score for the gene
noncoding_score: the maximum non-coding PHRED-scaled CADD score for the gene
scna_score: SCNA proxy score. SCNA density (SCNA/Mb) of the minimal common region (MCR) in which the gene is located
hotspot_double_hit: boolean indicating whether the gene is a hotspot gene (indication of oncogenes) or subject to double-hit (indication of tumor-suppressor genes)
phenolyzer_score: 'phenolyzer' score for the gene
hsa03320: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04010: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04020: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04024: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04060: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04066: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04110: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04115: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04150: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04151: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04210: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04310: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04330: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04340: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04350: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04370: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04510: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04512: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04520: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04630: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04915: boolean indicating whether or not the gene takes part in this KEGG pathway

Example Somatic Copy Number Alteration Table

Description

A data set containing the somatic copy number alteration data for the lungadenocarcinoma patient studied in Imielinski M, Greulich H, Kaplan B, et al.Oncogenic and sorafenib-sensitive ARAF mutations in lung adenocarcinoma.J Clin Invest. 2014;124(4):1582-6.

Usage

example_scna_table

Format

A data frame with 3160 rows and 4 variables:

chr: chromosome the segment is located in
start: start position of the segment
end: end position of the segment
log2ratio: log₂
ratio ofthe segment

Source

https://pubmed.ncbi.nlm.nih.gov/24569458/

Create Coding Impact Meta-prediction Score Data Frame

Description

Create Coding Impact Meta-prediction Score Data Frame

Usage

predict_coding_impact(  annovar_csv_path,  keep_highest_score = TRUE,  keep_single_symbol = TRUE,  na.string = ".")

Arguments

annovar_csv_path

path to 'ANNOVAR' csv output file

keep_highest_score

boolean to indicate whether to keep only the maximalimpact score per gene (default =TRUE). IfFALSE, all scoresper each gene are returned

keep_single_symbol

in ANNOVAR outputs, a variant may be annotated asexonic in multiple genes. This boolean argument controls whether or not tokeep only the first encountered symbol for a variant (default =TRUE)

na.string

string that was used to indicate when a score is not availableduring annotation with ANNOVAR (default = ".")

Value

data frame of meta-prediction scores containing 2 columns:

gene_symbol: HGNC gene symbol
metaprediction_score: metapredictor impact score

Examples

path2annovar_csv <- system.file("extdata/example.hg19_multianno.csv",                                package = "driveR")metapred_df <- predict_coding_impact(path2annovar_csv)

Prioritize Cancer Driver Genes

Description

Prioritize Cancer Driver Genes

Usage

prioritize_driver_genes(features_df, cancer_type)

Arguments

features_df

the features data frame for all genes, containing the following columns:

gene_symbol: HGNC gene symbol
metaprediction_score: the maximum metapredictor (coding) impact score for the gene
noncoding_score: the maximum non-coding PHRED-scaled CADD score for the gene
scna_score: SCNA proxy score. SCNA density (SCNA/Mb) of the minimal common region (MCR) in which the gene is located
hotspot_double_hit: boolean indicating whether the gene is a hotspot gene (indication of oncogenes) or subject to double-hit (indication of tumor-suppressor genes)
phenolyzer_score: 'phenolyzer' score for the gene
hsa03320: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04010: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04020: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04024: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04060: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04066: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04110: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04115: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04150: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04151: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04210: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04310: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04330: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04340: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04350: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04370: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04510: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04512: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04520: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04630: boolean indicating whether or not the gene takes part in this KEGG pathway
hsa04915: boolean indicating whether or not the gene takes part in this KEGG pathway

cancer_type

short name of the cancer type. All available cancer typesare listed inMTL_submodel_descriptions

Value

data frame with 3 columns:

gene_symbol: HGNC gene symbol
driverness_prob: estimated probability for each gene infeatures_df of being acancer driver. The probabilities are calculated using the selected (viacancer_type) cancer type's sub-model.
prediction: prediction based on the cancer-type-specific threshold (either "driver" or "non-driver")

Examples

drivers_df <- prioritize_driver_genes(example_features_table, "LUAD")

Tumor type specific probability thresholds

Description

Driver gene probability thresholds for all 21 cancer types (submodels).

Usage

specific_thresholds

Format

vector with 21 elements

Movatterモバイル変換

driveR: An R Package for Prioritizing Cancer Driver Genes Using Genomics Data

Description

Details

Author(s)

See Also

KEGG "Pathways in cancer"-related Pathways - Descriptions

Description

Usage

Format

MTL Sub-model Descriptions

Description

Usage

Format

Create SCNA Score Data Frame

Description

Usage

Arguments

Details

Value

Create Data Frame of Features for Driver Gene Prioritization

Description

Usage

Arguments

Value

See Also

Examples

Create Gene-level SCNA Data Frame

Description

Usage

Arguments

Value

Create Non-coding Impact Score Data Frame

Description

Usage

Arguments

Value

Determine Double-Hit Genes

Description

Usage

Arguments

Value

Determine Hotspot Containing Genes

Description

Usage

Arguments

Value

Example Cohort-level Features Table for Driver Prioritization

Description

Usage

Format

See Also

Example Cohort-level Somatic Copy Number Alteration Table

Description

Usage

Format

Source

Example Features Table for Driver Prioritization

Description

Usage

Format

See Also

Example Somatic Copy Number Alteration Table

Description

Usage

Format

Source

Create Coding Impact Meta-prediction Score Data Frame

Description

Usage

Arguments

Value

Examples

Prioritize Cancer Driver Genes

Description

Usage

Arguments

Value

See Also

Examples