title: Adaptive Estimation of Personalized Maximum Tolerated Dose in Cancer Phase I Clinical Trials Based on All Toxicities and Individual Genomic Profile.
abstract: BACKGROUND
  Many biomarkers have been shown to be associated with the efficacy of cancer therapy. Estimation of personalized maximum tolerated doses (pMTDs) is a critical step toward personalized medicine, which aims to maximize the therapeutic effect of a treatment for individual patients. In this study, we have established a Bayesian adaptive Phase I design which can estimate pMTDs by utilizing patient biomarkers that can predict susceptibility to specific adverse events and response as covariates.
  
  
  METHODS
  Based on a cutting-edge cancer Phase I clinical trial design called escalation with overdose control using normalized equivalent toxicity score (EWOC-NETS), which fully utilizes all toxicities, we propose new models to incorporate patient biomarker information in the estimation of pMTDs for novel cancer therapeutic agents. The methodology is fully elaborated and the design operating characteristics are evaluated with extensive simulations.
  
  
  RESULTS
  Simulation studies demonstrate that the utilization of biomarkers in EWOC-NETS can estimate pMTDs while maintaining the original merits of this Phase I trial design, such as ethical constraint of overdose control and full utilization of all toxicity information, to improve the accuracy and efficiency of the pMTD estimation.
  
  
  CONCLUSIONS
  Our novel cancer Phase I designs with inclusion of covariate(s) in the EWOC-NETS model are useful to estimate a personalized MTD and have substantial potential to improve the therapeutic effect of drug treatment.
authors.affiliation: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America.|Biostatistics and Bioinformatics Shared Resource, Winship Cancer Institute, Emory University, Atlanta, Georgia, United States of America.
authors.forename: Zhengjia
authors.initials: Z
authors.lastname: Chen
authors.affiliation: Division of Biostatistics and Bioinformatics, Pennsylvania State University, Hershey, Pennsylvania, United States of America.
authors.forename: Zheng
authors.initials: Z
authors.lastname: Li
authors.affiliation: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America.
authors.forename: Run
authors.initials: R
authors.lastname: Zhuang
authors.affiliation: Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.
authors.forename: Ying
authors.initials: Y
authors.lastname: Yuan
authors.affiliation: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America.
authors.forename: Michael
authors.initials: M
authors.lastname: Kutner
authors.affiliation: Department of Hematology and Medical Oncology, Emory University, Atlanta, Georgia, United States of America.
authors.forename: Taofeek
authors.initials: T
authors.lastname: Owonikoko
authors.affiliation: Department of Radiation Oncology, Emory University, Atlanta, Georgia, United States of America.
authors.forename: Walter J
authors.initials: WJ
authors.lastname: Curran
authors.affiliation: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, United States of America.|Biostatistics and Bioinformatics Shared Resource, Winship Cancer Institute, Emory University, Atlanta, Georgia, United States of America.
authors.forename: Jeanne
authors.initials: J
authors.lastname: Kowalski
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D003198:Computer Simulation; D004305:Dose-Response Relationship, Drug; D023281:Genomics; D006801:Humans; D020714:Maximum Tolerated Dose; D009369:Neoplasms / Q000188:drug therapy* / Q000235:genetics / Q000473:pathology; D057285:Precision Medicine
chemical_list: D014408:Biomarkers, Tumor