Bayesian Phase I/II Biomarker-Based Dose Finding for Precision Medicine With Molecularly Targeted Agents

ABSTRACT The optimal dose for treating patients with a molecularly targeted agent may differ according to the patient's individual characteristics, such as biomarker status. In this article, we propose a Bayesian phase I/II dose-finding design to find the optimal dose that is personalized for each patient according to his/her biomarker status. To overcome the curse of dimensionality caused by the relatively large number of biomarkers and their interactions with the dose, we employ canonical partial least squares (CPLS) to extract a small number of components from the covariate matrix containing the dose, biomarkers, and dose-by-biomarker interactions. Using these components as the covariates, we model the ordinal toxicity and efficacy using the latent-variable approach. Our model accounts for important features of molecularly targeted agents. We quantify the desirability of the dose using a utility function and propose a two-stage dose-finding algorithm to find the personalized optimal dose according to each patient's individual biomarker profile. Simulation studies show that our proposed design has good operating characteristics, with a high probability of identifying the personalized optimal dose. Supplementary materials for this article are available online.

[1]  Danh V. Nguyen,et al.  Multi-class cancer classification via partial least squares with gene expression profiles , 2002, Bioinform..

[2]  Sarah Zohar,et al.  The Continual Reassessment Method , 2006 .

[3]  Ron Wehrens,et al.  The pls Package: Principal Component and Partial Least Squares Regression in R , 2007 .

[4]  R. Chappell,et al.  The Continual Reassessment Method for Multiple Toxicity Grades: A Bayesian Quasi‐Likelihood Approach , 2007, Biometrics.

[5]  P. Garthwaite.  An Interpretation of Partial Least Squares , 1994 .

[6]  A Rogatko,et al.  Patient specific dosing in a cancer phase I clinical trial , 2001, Statistics in medicine.

[7]  Peter F Thall,et al.  Patient‐Specific Dose Finding Based on Bivariate Outcomes and Covariates , 2008, Biometrics.

[8]  J. Friedman,et al.  A Statistical View of Some Chemometrics Regression Tools , 1993 .

[9]  Hicham Noçairi,et al.  Discrimination on latent components with respect to patterns. Application to multicollinear data , 2005, Comput. Stat. Data Anal..

[10]  Ying Yuan,et al.  Bayesian optimal interval designs for phase I clinical trials , 2015, Journal of the Royal Statistical Society: Series C (Applied Statistics).

[11]  J. Friedman,et al.  [A Statistical View of Some Chemometrics Regression Tools]: Response , 1993 .

[12]  J O'Quigley,et al.  Continual reassessment method: a practical design for phase 1 clinical trials in cancer. , 1990, Biometrics.

[13]  C. Cox,et al.  Location-scale cumulative odds models for ordinal data: a generalized non-linear model approach. , 1995, Statistics in medicine.

[14]  Ying Yuan,et al.  Using Data Augmentation to Facilitate Conduct of Phase I–II Clinical Trials With Delayed Outcomes , 2014, Journal of the American Statistical Association.

[15]  P. McCullagh.  Regression Models for Ordinal Data , 1980 .

[16]  Zhilong Yuan,et al.  Isotonic designs for phase I cancer clinical trials with multiple risk groups , 2004, Clinical trials.

[17]  I Judson,et al.  Clinical benefit in Phase-I trials of novel molecularly targeted agents: does dose matter? , 2009, British Journal of Cancer.

[18]  M. G. Pittau,et al.  A weakly informative default prior distribution for logistic and other regression models , 2008, 0901.4011.

[19]  Ying Yuan,et al.  A Bayesian dose finding design for clinical trials combining a cytotoxic agent with a molecularly targeted agent , 2015 .

[20]  John O'Quigley,et al.  Continual Reassessment Method for Ordered Groups , 2003, Biometrics.

[21]  S. Piantadosi,et al.  Improved designs for dose escalation studies using pharmacokinetic measurements. , 1996, Statistics in medicine.

[22]  T. Næs,et al.  Canonical partial least squares—a unified PLS approach to classification and regression problems , 2009 .

[23]  Ying Yuan,et al.  Robust EM Continual Reassessment Method in Oncology Dose Finding , 2011, Journal of the American Statistical Association.

[24]  S. Chib,et al.  Bayesian analysis of binary and polychotomous response data , 1993 .

[25]  Anastasia Ivanova,et al.  Bivariate isotonic design for dose‐finding with ordered groups , 2006, Statistics in medicine.

[26]  S. D. Jong.  SIMPLS: an alternative approach to partial least squares regression , 1993 .

[27]  Ying Yuan,et al.  Bayesian Optimal Interval Design: A Simple and Well-Performing Design for Phase I Oncology Trials , 2016, Clinical Cancer Research.

[28]  Alan Agresti,et al.  Categorical Data Analysis , 2003 .

[29]  Ying Yuan,et al.  Bayesian dose finding by jointly modelling toxicity and efficacy as time‐to‐event outcomes , 2009 .

[30]  Ying Yuan,et al.  Adaptive designs for identifying optimal biological dose for molecularly targeted agents , 2014, Clinical trials.

[31]  Alison J. Burnham,et al.  Frameworks for latent variable multivariate regression , 1996 .

[32]  M. Stone.  Continuum regression: Cross-validated sequentially constructed prediction embracing ordinary least s , 1990 .

[33]  B. Nebiyou Bekele,et al.  Dose-Finding Based on Multiple Toxicities in a Soft Tissue Sarcoma Trial , 2004 .

[34]  Peter F Thall,et al.  Utility‐Based Optimization of Combination Therapy Using Ordinal Toxicity and Efficacy in Phase I/II Trials , 2010, Biometrics.