DRAP: a toolbox for drug response analysis and visualization tailored for preclinical drug testing on patient-derived xenograft models

Abstract

BackgroundOne of the key reasons for the high failure rate of new agents and low therapeutic benefit of approved treatments is the lack of preclinical models that mirror the biology of human tumors. At present, the optimal cancer model for drug response study to date is patient-derived xenograft (PDX) models. PDX recaptures both inter- and intra-tumor heterogeneity inherent in human cancer, which represent a valuable platform for preclinical drug testing and personalized medicine applications. Building efficient drug response analysis tools is critical but far from adequate for the PDX platform.ResultsIn this work, we first classified the emerging PDX preclinical trial designs into four patterns based on the number of tumors, arms, and animal repeats in every arm. Then we developed an R package, DRAP, which implements Drug Response Analyses on PDX platform separately for the four patterns, involving data visualization, data analysis and conclusion presentation. The data analysis module offers statistical analysis methods to assess difference of tumor volume between arms, tumor growth inhibition (TGI) rate calculation to quantify drug response, and drug response level analysis to label the drug response at animal level. In the end, we applied DRAP in two case studies through which the functions and usage of DRAP were illustrated.ConclusionDRAP is the first integrated toolbox for drug response analysis and visualization tailored for PDX platform. It would greatly promote the application of PDXs in drug development and personalized cancer treatments.Electronic supplementary materialThe online version of this article (10.1186/s12967-019-1785-7) contains supplementary material, which is available to authorized users.

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“…Mean TGI rate was used to quantify the drug response of the different treatment arms compared with the control group at different time points. V T,t and V T,0 represent the mean tumor volume at the time t and time 0 of the treatment arm, while V C,t and V C,0 represent the mean tumor volume of the vehicle control arm (32,33).…”

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“…Tumor volume (TV) was measured twice a week using calipers and the formula: where L = length and W = width of tumor. Percent tumor growth inhibition (%TGI) was calculated as a readout of treatment efficacy as described in [ 34 ]: where V T,t, and V T,0 are the mean TV of the treatment group at day t and 0, respectively. V C,t, and V C,0 are the mean TV of the control group at day t and 0, respectively.…”

“…where L = length and W = width of tumor. Percent tumor growth inhibition (%TGI) was calculated as a readout of treatment efficacy as described in [34]:…”

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“… 144 Specially designed for PDX platforms, DRAP is a data analysis software designed that processes data separately according to different PDX preclinical trial designs. 145 As mentioned above, one of the most important missions of PDX models is to identify reliable biomarkers of drug response based on pharmacogenomic datasets, that is, the pharmacologic and high-throughput sequencing profiles of PDX. Several computational platforms have been constructed to analyze preclinical pharmacogenomic data and identify robust biomarkers that predict patient drug response and prognosis.…”