There is growing interest, within the pharmaceutical industry, in using a patients’ own cancer material to screen the effect of numerous treatments within an animal model. The reason for shifting from standard xenografts, based on historical immortalised cell-lines, is that those models are considered to be very different to the patients’ tumours of today. Thus PDX models are considered to be a more relevant model as they are “closer” to the patient population in which you are about to test your new treatment.
Typically only a handful of PDX models are used, but recently there has been a shift in focus to perform population PDX studies which mimic small scale clinical trials. One of these studies by Gao et al. also published, as an excel file in the supplementary information, the raw data which included not only the treatment effect growth curves, but also genomic data consisting of DNA copy number, gene expression and mutation. Using this data it is possible to explore correlations between treatment response and genomic features.
We at Systems Forecasting are always appreciative of freely available data sets, and have designed an equally free and available PDXdata app to browse through this data.
The app can be used to read excel files in the same form as the Novartis file “nm.3954-S2.xlsx”. It translates volume measurements to diameter, and computes a linear fit to each tumour growth time series. The user can then plot time series, organised by ID or by treatment, or examine statistics for the entire data set. The aim is to explore how well linear models can be used to fit this type of data.
The “Diameters” page shown in the figure below is used to plot time series for data. First read in the excel data file; this may take a while, so a progress bar is included. Data can be grouped either by ID(s) or by treatment(s). Note the Novartis data has several treatments for the same ID, and the data is filtered to include only those IDs with an untreated case. If there is only one treatment per ID, one can group by treatment and then plot the data for the IDs with that treatment. In this case the untreated fit is computed from the untreated IDs.
As shown in the next figure, the “Copy Number” and “RNA” tabs allow the user to plot the correlations between copy number or RNA and treatment efficacy, as measured by change in the slope of linear growth, for individual treatments (provided data is available for the selected treatment).
Finally, the “Statistics” page plots a histogram of data derived from the linear models. These include intercept, slope, and sigma for the linear fit to each time series; the difference in slope between the treated and untreated cases (delslope); the growth from initial to final time of the linear fit to the untreated case (lingr); and the difference delgr=diamgr-lingr, which measures diameter loss due to drug.
This app is very much a work-in-progress, and at the moment is primarily a way to browse, view and plot the data. We will add more functionality as it becomes available.