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

View ORCID ProfileGregory P. Way, View ORCID ProfileRobert J. Allaway, View ORCID ProfileStephanie J. Bouley, Camilo E. Fadul, Yolanda Sanchez, View ORCID ProfileCasey S. Greene
doi: https://doi.org/10.1101/075382
Gregory P. Way
aGenomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA, USA
bDepartment of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
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Robert J. Allaway
cDepartment of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH, USA
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Stephanie J. Bouley
cDepartment of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH, USA
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Camilo E. Fadul
dDepartment of Neurology, University of Virginia, Charlottesville, VA, USA
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Yolanda Sanchez
cDepartment of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH, USA
eNorris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA
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Casey S. Greene
bDepartment of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA
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ABSTRACT

Background: 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, photocopies 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.

Results: 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). On TCGA data, the classifier detected NF1 mutated tumors (test set area under the receiver operating characteristic curve (AUROC) mean = 0.77, 95% quantile = 0.53 – 0.95) over 50 random initializations. On RNA-Seq data transformed into the space of gene expression microarrays, this method produced a classifier with similar performance (test set AUROC mean = 0.77, 95% quantile = 0.53 – 0.96). We applied our ensemble classifier trained on the transformed TCGA data to a microarray validation set of 12 samples with matched RNA and NF1 protein-level measurements. The classifier’s NF1 score was associated with NF1 protein concentration in these samples.

Conclusions: We demonstrate that TCGA can be used to train accurate predictors of NF1 inactivation in GBM. The ensemble classifier performed well for samples with very high or very low NF1 protein concentrations but had mixed performance in samples with intermediate NF1 concentrations. Nevertheless, high-performing and validated predictors have the potential to be paired with targeted therapies and personalized medicine.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted December 13, 2016.
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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
bioRxiv 075382; doi: https://doi.org/10.1101/075382
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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
bioRxiv 075382; doi: https://doi.org/10.1101/075382

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