Open AccessPrediction of PIK3CA mutations from cancer gene expression data
Author(s) -
Jun Kang,
Ahwon Lee,
Youn Soo Lee
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0241514
Subject(s) - tensin , pten , logistic regression , receiver operating characteristic , regression , gene expression , cancer , computational biology , gene , biology , bioinformatics , oncology , computer science , genetics , medicine , statistics , machine learning , mathematics , pi3k/akt/mtor pathway , apoptosis
Breast cancers with PIK3CA mutations can be treated with PIK3CA inhibitors in hormone receptor-positive HER2 negative subtypes. We applied a supervised elastic net penalized logistic regression model to predict PIK3CA mutations from gene expression data. This regression approach was applied to predict modeling using the TCGA pan-cancer dataset. Approximately 10,000 cases were available for PIK3CA mutation and mRNA expression data. In 10-fold cross-validation, the model with λ = 0.01 and α = 1.0 (ridge regression) showed the best performance, in terms of area under the receiver operating characteristic (AUROC). The final model was developed with selected hyper-parameters using the entire training set. The training set AUROC was 0.93, and the test set AUROC was 0.84. The area under the precision-recall (AUPR) of the training set was 0.66, and the test set AUPR was 0.39. Cancer types were the most important predictors. Both insulin like growth factor 1 receptor ( IGF1R ) and the phosphatase and tensin homolog ( PTEN ) were the most significant genes in gene expression predictors. Our study suggests that predicting genomic alterations using gene expression data is possible, with good outcomes.