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A reliable method to determine which candidate chemotherapeutic drugs effectively inhibit tumor growth in patient-derived xenografts (PDX) in single mouse trials

  • Derek Gordon
  • David E. AxelrodEmail author
Original Article
  • 32 Downloads

Abstract

Purpose

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.

Methods

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.

Results

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%.

Conclusion

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.

Keywords

Drug screening Chemotherapeutic drugs Statistical classification Finite mixture model Antitumor 

Notes

Acknowledgements

We thank Gao et al. for making their extensive PDX data publically available, and Payal Patel and Lisheng Zhou for programming assistance.

Authors’ contributions

DEA and DG equally conceived of all study designs, methods, and statistical analyses. Both authors wrote and approved of the manuscript.

Funding

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.

Ethical approval

This article does not contain any studies with animals performed by any of the authors.

Supplementary material

280_2019_3942_MOESM1_ESM.docx (38 kb)
Supplementary material 1 (DOCX 38 kb)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Genetics and Human Genetics InstituteRutgers, The State University of New JerseyPiscatawayUSA
  2. 2.Department of Genetics and Rutgers Cancer Institute of New JerseyRutgers, The State University of New JerseyPiscatawayUSA

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