A reliable method to determine which candidate chemotherapeutic drugs effectively inhibit tumor growth in patient-derived xenografts (PDX) in single mouse trials
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We report on a statistical method for grouping anti-cancer drugs (GRAD) in single mouse trials (SMT). The method assigns candidate drugs into groups that inhibit or do not inhibit tumor growth in patient-derived xenografts (PDX). It determines the statistical significance of the group assignments without replicate trials of each drug.
The GRAD method applies a longitudinal finite mixture model, implemented in the statistical package PROC TRAJ, to analyze a mixture of tumor growth curves for portions of the same tumor in different mice, each single mouse exposed to a different drug. Each drug is classified into an inhibitory or non-inhibitory group. There are several advantages to the GRAD method for SMT. It determines that probability that the grouping is correct, uses the entire longitudinal tumor growth curve data for each drug treatment, can fit different shape growth curves, accounts for missing growth curve data, and accommodates growth curves of different time periods.
We analyzed data for 22 drugs for 18 human colorectal tumors provided by researchers in a previous publication. The GRAD method identified 18 drugs that were inhibitory against at least one tumor, and 10 tumors for which there was at least one inhibitory drug. Analysis of simulated data indicated that the GRAD method has a sensitivity of 84% and a specificity of 98%.
A statistical method, GRAD, can group anti-cancer drugs into those that are inhibitory and those that are non-inhibitory in single mouse trials and provide probabilities that the grouping is correct.
KeywordsDrug screening Chemotherapeutic drugs Statistical classification Finite mixture model Antitumor
We thank Gao et al. for making their extensive PDX data publically available, and Payal Patel and Lisheng Zhou for programming assistance.
DEA and DG equally conceived of all study designs, methods, and statistical analyses. Both authors wrote and approved of the manuscript.
D.E.A. was supported by the Human Genetics Institute of New Jersey, the New Jersey Breast Cancer Research Fund, and Rutgers Cancer Institute of New Jersey (PA30CA072720).
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with animals performed by any of the authors.
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