title: Nonlinear quantitative radiation sensitivity prediction model based on NCI-60 cancer cell lines.
abstract: We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM). Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables. Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gy γ-ray) values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.
authors.affiliation: School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
authors.forename: Chunying
authors.initials: C
authors.lastname: Zhang
authors.affiliation: Hamon Center for Therapeutic Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA ; Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
authors.forename: Luc
authors.initials: L
authors.lastname: Girard
authors.affiliation: Hamon Center for Therapeutic Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA ; Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
authors.forename: Amit
authors.initials: A
authors.lastname: Das
authors.affiliation: School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
authors.forename: Sun
authors.initials: S
authors.lastname: Chen
authors.affiliation: School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China.
authors.forename: Guangqiang
authors.initials: G
authors.lastname: Zheng
authors.affiliation: School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China ; Hamon Center for Therapeutic Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA ; Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
authors.forename: Kai
authors.identifier: 0000-0001-9195-6160
authors.initials: K
authors.lastname: Song
mesh_terms: D000328:Adult; D000368:Aged; D045744:Cell Line, Tumor; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics* / Q000532:radiotherapy; D011836:Radiation Tolerance / Q000235:genetics*