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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)1167-1178
Number of pages12
JournalCancer chemotherapy and pharmacology
Volume84
Issue number6
DOIs
StatePublished - Dec 1 2019

All Science Journal Classification (ASJC) codes

  • Oncology
  • Toxicology
  • Pharmacology
  • Cancer Research
  • Pharmacology (medical)

Keywords

  • Antitumor
  • Chemotherapeutic drugs
  • Drug screening
  • Finite mixture model
  • Statistical classification

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