Document Type
Article
Publication Date
2-6-2017
Publication Title
BioMed Central Genomics
Department
Geisel School of Medicine
Abstract
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).
DOI
10.1186/s12864-017-3519-7
Original Citation
Way GP, Allaway RJ, Bouley SJ, Fadul CE, Sanchez Y, Greene CS. A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastoma. BMC Genomics. 2017 Feb 6;18(1):127. doi: 10.1186/s12864-017-3519-7. PMID: 28166733; PMCID: PMC5292791.
Dartmouth Digital Commons Citation
Way, Gregory P.; Allaway, Robert J.; Bouley, Stephanie J. J.; Fadul, Camilo E.; Sanchez, Yolanda; and Greene, Casey, "A Machine Learning Classifier Trained on Cancer Transcriptomes Detects NF1 Inactivation Signal in Glioblastoma" (2017). Dartmouth Scholarship. 598.
https://digitalcommons.dartmouth.edu/facoa/598
