Next-generation sequencing technologies have recently been used in pharmacogenomic studies to characterize large panels of cancer cell lines at the genomic and transcriptomic levels. Among these technologies, RNA-sequencing enable profiling of alternatively spliced transcripts. Given the high frequency of mRNA splicing in cancers, linking this feature to drug response will open new avenues of research in biomarker discovery. To identify robust transcriptomic biomarkers for drug response across studies, we develop a meta-analytical framework combining the pharmacological data from two large-scale drug screening datasets. We use an independent pan-cancer pharmacogenomic dataset to test the robustness of our candidate biomarkers across multiple cancer types. We further analyze two independent breast cancer datasets and find that specific isoforms of IGF2BP2, NECTIN4, ITGB6, and KLHDC9 are significantly associated with AZD6244, lapatinib, erlotinib, and paclitaxel, respectively. Our results support isoform expressions as a rich resource for biomarkers predictive of drug response.
To cite this work in publication, use
Safikhani, Zhaleh and Smirnov, Petr and Thu, Kelsie L. and Silvester, Jennifer and El-Hachem, Nehme and Quevedo, Rene and Lupien, Mathieu and Mak, Tak W. and Cescon, David and Haibe-Kains, Benjamin, Gene isoforms as expression-based biomarkers predictive of drug response in vitro, Nature Communications, 2017, Volume 8, Number 1, Pages 1126, Doi: 10.1038/s41467-017-01153-8
https://codeocean.com/capsule/012aaa90-ed73-41be-b47b-290815cf56e9/
All the data, intermediate results of the pipeline and the preditions are available via above link.
Analysis of this paper has been done in the following session environment. So to reproduce all the paper results the same session should be prepared by installing all the mentioned packages.
#sessionInfo()
R version 3.1.0 Patched (2014-06-08 r65888) Platform: x86_64-apple-darwin13.1.0 (64-bit)
locale: [1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
attached base packages: [1] parallel stats graphics grDevices utils datasets methods base
other attached packages:
[1] PharmacoGx_0.9.1 RCurl_1.95-4.8 bitops_1.0-6
[4] hgu133plus2.db_2.14.0 hgu133plus2frmavecs_1.3.0 hgu133afrmavecs_1.3.0
[7] hgu133a.db_2.14.0 hthgu133a.db_2.14.0 hthgu133acdf_2.14.0
[10] hthgu133afrmavecs_1.1.0 frma_1.16.0 affyio_1.32.0
[13] affxparser_1.36.0 affy_1.42.3 Hmisc_3.17-2
[16] ggplot2_2.1.0 Formula_1.2-1 lattice_0.20-29
[19] R.utils_2.2.0 R.oo_1.20.0 R.methodsS3_1.7.1
[22] gdata_2.17.0 sva_3.10.0 mgcv_1.7-29
[25] nlme_3.1-124 corpcor_1.6.8 MetaGx_0.9.10
[28] lsa_0.73.1 SnowballC_0.5.1 mRMRe_2.0.5
[31] igraph_1.0.1 genefu_1.14.0 biomaRt_2.20.0
[34] mclust_5.1 survcomp_1.14.0 prodlim_1.5.7
[37] survival_2.38-3 jetset_3.1.3 org.Hs.eg.db_2.14.0
[40] RSQLite_0.11.4 DBI_0.2-7 AnnotationDbi_1.26.1
[43] GenomeInfoDb_1.0.2 rcdk_3.3.2 fingerprint_3.5.2
[46] WriteXLS_4.0.0 magicaxis_1.9.4 sm_2.2-5.4
[49] plotrix_3.6-1 MASS_7.3-33 RColorBrewer_1.1-2
[52] downloader_0.4 piano_1.4.2 caTools_1.17.1
[55] Biobase_2.24.0 BiocGenerics_0.10.0
These pSets are required to be accessible for the scripts in a directory named data/PSets:
CCLE_hs.RData, GRAY_hs.RData, UHN.RData, GDSC.RData, gCSI_hs.RData
#You can contact the authors to get access to these datasets [email protected], [email protected]
#training.R:
Script is computing signature for the effect of each gene and its isoforms expression on the molecular profile of cell lines for each drug. It returns the estimated coefficient, the the p-values, statistics(mean, median, min, max and variance) of adjusted r squared for the association of each gene and its isoforms to each drug in a dataset named auc_recomputed_drug_association.RData.
*This script takes a long time to be completed. You can ask authors to provide you with the auc_recomputed_drug_association.RData to skip this long step
#training_results.R:
Script is computing the false discovery rate of the results and report the strongest breast cancer treatment biomarkers for all 15 drugs in common between CCLE and GDSC in a dataset named all.biomarkers.RData.
#Pre_Validation_gcsi.R:
Script is validating biomarkers against gCSI dataset and put the validation results in validated.biomarker.gcsi.RData
#Pre_Validation.R:
Script is validating biomarkers against GRAY dataset and put the validation results in validated.biomarker.gray.RData and breast.biomarkers.RData
#Final_Validation.R:
Script is validating pre validated biomarkers against UHN dataset and pute the final validation results in validated.biomarker.uhn.RData
#Figures_Tables.R:
Script is creating supplemntary figures, files and tables