title: Predicting survival in head and neck squamous cell carcinoma from TP53 mutation.
abstract: For TP53-mutated head and neck squamous cell carcinomas (HNSCCs), the codon and specific amino acid sequence change resulting from a patient's mutation can be prognostic. Thus, developing a framework to predict patient survival for specific mutations in TP53 would be valuable. There are many bioinformatics and functional methods for predicting the phenotypic impact of genetic variation, but their overall clinical value remains unclear. Here, we assess the ability of 15 different methods to predict HNSCC patient survival from TP53 mutation, using TP53 mutation and clinical data from patients enrolled in E4393 by the Eastern Cooperative Oncology Group (ECOG), which investigated whether TP53 mutations in surgical margins were predictive of disease recurrence. These methods include: server-based computational tools SIFT, PolyPhen-2, and Align-GVGD; our in-house POSE and VEST algorithms; the rules devised in Poeta et al. with and without considerations for splice-site mutations; location of mutation in the DNA-bound TP53 protein structure; and a functional assay measuring WAF1 transactivation in TP53-mutated yeast. We assessed method performance using overall survival (OS) and progression-free survival (PFS) from 420 HNSCC patients, of whom 224 had TP53 mutations. Each mutation was categorized as "disruptive" or "non-disruptive". For each method, we compared the outcome between the disruptive group vs. the non-disruptive group. The rules devised by Poeta et al. with or without our splice-site modification were observed to be superior to others. While the differences in OS (disruptive vs. non-disruptive) appear to be marginally significant (Poeta rules + splice rules, P = 0.089; Poeta rules, P = 0.053), both algorithms identified the disruptive group as having significantly worse PFS outcome (Poeta rules + splice rules, P = 0.011; Poeta rules, P = 0.027). In general, prognostic performance was low among assessed methods. Further studies are required to develop and validate methods that can predict functional and clinical significance of TP53 mutations in HNSCC patients.
authors.affiliation: Department of Biomedical Engineering, Institute for Computational Medicine, The Johns Hopkins University, Baltimore, MD, USA, david.masica@gmail.com.
authors.forename: David L
authors.initials: DL
authors.lastname: Masica
authors.forename: Shuli
authors.initials: S
authors.lastname: Li
authors.forename: Christopher
authors.initials: C
authors.lastname: Douville
authors.forename: Judith
authors.initials: J
authors.lastname: Manola
authors.forename: Robert L
authors.initials: RL
authors.lastname: Ferris
authors.forename: Barbara
authors.initials: B
authors.lastname: Burtness
authors.forename: Arlene A
authors.initials: AA
authors.lastname: Forastiere
authors.forename: Wayne M
authors.initials: WM
authors.lastname: Koch
authors.forename: Christine H
authors.initials: CH
authors.lastname: Chung
authors.forename: Rachel
authors.initials: R
authors.lastname: Karchin
mesh_terms: D000465:Algorithms; D002294:Carcinoma, Squamous Cell / Q000235:genetics* / Q000503:physiopathology; D019295:Computational Biology / Q000379:methods*; D018450:Disease Progression; D005826:Genetics, Medical / Q000379:methods*; D006258:Head and Neck Neoplasms / Q000235:genetics* / Q000503:physiopathology; D006801:Humans; D009154:Mutation / Q000235:genetics*; D011379:Prognosis; D016019:Survival Analysis; D016159:Tumor Suppressor Protein p53 / Q000235:genetics*
chemical_list: C495901:TP53 protein, human; D016159:Tumor Suppressor Protein p53
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