Training Distribution Matching (TDM) transformation of RNAseq results of The Cancer Genome Atlas Glioblastoma parameter sweep for stochastic gradient descent logistic classifier with elastic net penalty. (A) Training and testing area under the receiver operating characteristic curve (AUROC) is given for each parameter tested. All accuracies are presented following 5-fold cross validation after 100 random initializations. (B) The l1 mixing parameter with the optimal alpha and (C) the classifier performance across all random starts for the best hyperparameters. (PNG 724Â kb

Camilo Fadul (3721708)

Casey Greene (2880572)

Gregory Way (2880575)

Robert Allaway (3721711)

Stephanie Bouley (3721714)

Yolanda Sanchez (213896)

PubMed

We have identified molecules that exhibit synthetic lethality in cells with loss of the neurofibromin 1 (NF1) tumor suppressor gene. However, recognizing tumors that have inactivation of the NF1 tumor suppressor function is challenging because the loss may occur via mechanisms that do not involve mutation of the genomic locus. Degradation of the NF1 protein, independent of NF1 mutation status, phenocopies inactivating mutations to drive tumors in human glioma cell lines. NF1 inactivation may alter the transcriptional landscape of a tumor and allow a machine learning classifier to detect which tumors will benefit from synthetic lethal molecules. We developed a strategy to predict tumors with low NF1 activity and hence tumors that may respond to treatments that target cells lacking NF1. Using RNAseq data from The Cancer Genome Atlas (TCGA), we trained an ensemble of 500 logistic regression classifiers that integrates mutation status with whole transcriptomes to predict NF1 inactivation in glioblastoma (GBM)

Way, Gregory P

Allaway, Robert J

Bouley, Stephanie J. J

Fadul, Camilo E

Sanchez, Yolanda

Greene, Casey

Dartmouth Digital Commons (Dartmouth College)

English

A Machine Learning Classifier Trained on Cancer Transcriptomes Detects NF1 Inactivation Signal in Glioblastoma

Gregory P. Way

Robert J. Allaway

Stephanie J. Bouley

Camilo E. Fadul

Yolanda Sanchez

Casey S. Greene

Springer - Publisher Connector

A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma

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