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



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.
Genomics and Computational Biology Graduate Program, University of Pennsylvania, Philadelphia, PA, USA.|Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, 10-131 SCTR 34th and Civic Center Blvd, Philadelphia, PA, 19104, USA.
Gregory P
GP
Way
Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Dartmouth College, HB 7650, Hanover, NH, 03755, USA.
Robert J
RJ
Allaway
Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Dartmouth College, HB 7650, Hanover, NH, 03755, USA.
Stephanie J
SJ
Bouley
Department of Neurology, University of Virginia, Charlottesville, VA, USA.
Camilo E
CE
Fadul
Department of Molecular and Systems Biology, Geisel School of Medicine at Dartmouth, Dartmouth College, HB 7650, Hanover, NH, 03755, USA. Yolanda.Sanchez@dartmouth.edu.|Norris Cotton Cancer Center, Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA. Yolanda.Sanchez@dartmouth.edu.
Yolanda
Y
Sanchez
Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, 10-131 SCTR 34th and Civic Center Blvd, Philadelphia, PA, 19104, USA. csgreene@mail.med.upenn.edu.
Casey S
CS
Greene
D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D020868:Gene Silencing; D005909:Glioblastoma / Q000473:pathology*; D006801:Humans; D000069550:Machine Learning; D025542:Neurofibromin 1 / Q000235:genetics*; D059467:Transcriptome
D025542:Neurofibromin 1
Cancer; Classifier; Glioblastoma; Machine Learning; NF1 Inactivation; Neurofibromatosis Type I