In cit-bioinfo/BLCAsubtyping: Predicting bladder cancer molecular subtypes for different classification systems based on expression profiles

Overview

This package presents a tool to classify bladder tumours according to six published molecular classifications : Baylor[1], UNC[2], MDA[3], Lund[4], CIT-Curie[5], TCGA[6]

Citation

For now, you can cite the following bioRxiv preprint: bioRxiv 488460; doi: https://doi.org/10.1101/488460 https://github.com/cit-bioinfo/BLCAsubtyping

Install

You may install this package with devtools:

require(devtools)
devtools::install_github("cit-bioinfo/BLCAsubtyping")

Usage

This package provides a main function named classify which labels a batch of RNA transcriptomic profiles according to one or several of the 6 classifications implemented. classify requires the following main arguments :
- expMat : A data.frame or matrix of normalized expression data with genes in row and samples in column. RNA-seq data should be log-transformed. - gpl : A data.frame with gene/probeset annotation with at least one column with HGNC gene symbols and row names corresponding to the row names of expMat.
- symbol : A character specifying the column name of gpl containing HGNC gene symbols.
- classification.systems : A character vector with the names of the classifications to be run on the expMat data, among "Baylor"([1]), "UNC"([2]), "MDA"([3]), "Lund"([4]), "CIT"([5]), "TCGA"([6]).

Example

The package includes an example dataset [5] to illustrate the use of the main function.

library(BLCAsubtyping)
data(cit) 

cit contains 50 samples from the CIT dataset. It is a matrix with gene symbols as rownames and sample IDs as colnames. If rownames are not HGNC gene symbols, an annotation data.frame can be given to the gpl argument, where the rownames of the annotation are the rownames of the gene expression matrix and one of the columns is HGNC gene symbols. The name of the column with the gene symbols can be passed to the symbol argument.

In the following call to classify, the samples will be classified according to all 6 classification systems.

cl <- classify(expMat = cit, classification.systems = c("Baylor", "UNC", "MDA", "CIT", "Lund", "TCGA"))

The function returns a dataframe with subtyping results from each classification system for all samples.

head(cl)

kableExtra::kable_styling(knitr::kable(head(cl), digits = 3, format = "html"))

References

[1] Mo, Q. et al. Prognostic Power of a Tumor Differentiation Gene Signature for Bladder Urothelial Carcinomas. J. Natl. Cancer Inst. (2018).

[2] Damrauer, J. S. et al. Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology. Proc. Natl. Acad. Sci. U.S.A. 111, 3110–3115 (2014).

[3] Choi, W. et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell 25, 152–165 (2014).

[4] Marzouka, N. et al. A validation and extended description of the Lund taxonomy for urothelial carcinoma using the TCGA cohort. Scientific Reports 8, 3737 (2018).

[5] Rebouissou, S. et al. EGFR as a potential therapeutic target for a subset of muscle-invasive bladder cancers presenting a basal-like phenotype. Sci Transl Med 6, 244ra91 (2014).

[6] Robertson, A. G. et al. Comprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer. Cell 171, 540-556.e25 (2017).

cit-bioinfo/BLCAsubtyping documentation built on Dec. 31, 2022, 2:22 p.m.