Prediction of PIK3CA mutations from cancer gene expression data.

Jun Kang,Ahwon Lee,Youn Soo Lee,Nandini Dey

doi:10.1371/journal.pone.0241514

Jun Kang, Ahwon Lee + Show 2 more

Open Access

https://doi.org/10.1371/journal.pone.0241514

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Journal: PloS one	Publication Date: Nov 9, 2020
Citations: 5	License type: CC BY 4.0

Affiliation: Seoul St. Mary's Hospital, Catholic University of Korea

Abstract

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.

Highlights

Targeted therapy has become a standard treatment for many cancer patients, the approach requires a test for a specific cancer genomic alteration, to treat patients
10,845 cases were available for both PIK3CA mutation and mRNA expression data. 5,128 out of 20,502 genes were included in the modeling process, after filtering for median absolute deviation, as described in the modeling process method
Our model showed good performance in predicting PIK3CA mutations in various cancer types

Summary

Introduction

Targeted therapy has become a standard treatment for many cancer patients, the approach requires a test for a specific cancer genomic alteration, to treat patients. Several direct genomic alteration tests have been developed and proven for their clinical utility to treat patients [1, 2]. Machine learning approaches can be applied to detect genomic alterations. Machine learning algorithms can build prediction models from a large number of predictors, such as radiomic features [3], pathology image [4] or gene expression data [5]. Authors used data from The Cancer Genome Atlas (TCGA), with a supervised elastic net penalized logistic regression classifier, with stochastic gradient descent.

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