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

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.

authors

Gordon D,Axelrod DE

doi

10.1007/s00280-019-03942-y

subject

Has Abstract

pub_date

2019-12-01 00:00:00

pages

1167-1178

issue

6

eissn

0344-5704

issn

1432-0843

pii

10.1007/s00280-019-03942-y

journal_volume

84

pub_type

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