A new molecular signature method for prediction of driver cancer pathways from transcriptional data.
Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways.
Icahn Institute for Genomics and Multiscale Biology, New York, 10029, USA.
Dmitry
D
Rykunov
Icahn Institute for Genomics and Multiscale Biology, New York, 10029, USA.
Noam D
ND
Beckmann
Icahn Institute for Genomics and Multiscale Biology, New York, 10029, USA.
Hui
H
Li
Icahn Institute for Genomics and Multiscale Biology, New York, 10029, USA.
Andrew
A
Uzilov
Icahn Institute for Genomics and Multiscale Biology, New York, 10029, USA.
Eric E
EE
Schadt
Icahn Institute for Genomics and Multiscale Biology, New York, 10029, USA boris.reva@mssm.edu.
Boris
B
Reva
D000465:Algorithms; D014408:Biomarkers, Tumor; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D005819:Genetic Markers; D023281:Genomics / Q000379:methods; D006801:Humans; D000069550:Machine Learning; D008297:Male; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D015398:Signal Transduction; D059467:Transcriptome
D014408:Biomarkers, Tumor; D005819:Genetic Markers