Identification of high-dimensional omics-derived predictors for tumor growth dynamics using machine learning and pharmacometric modeling

Abstract

Pharmacometric modeling can capture tumor growth inhibition (TGI) dynamics and variability. These approaches do not usually consider covariates in high-dimensional settings, whereas high-dimensional molecular profiling technologies ("omics") are being increasingly considered for prediction of anticancer drug treatment response. Machine learning (ML) approaches have been applied to identify high-dimensional omics predictors for treatment outcome. Here, we aimed to combine TGI modeling and ML approaches for two distinct aims: omics-based prediction of tumor growth profiles and identification of pathways associated with treatment response and resistance. We propose a two-step approach combining ML using least absolute shrinkage and selection operator (LASSO) regression with pharmacometric modeling. We demonstrate our workflow using a previously published dataset consisting of 4706 tumor growth profiles of patient-derived xenograft (PDX) models treated with a variety of mono- and combination regimens. Pharmacometric TGI models were fit to the tumor growth profiles. The obtained empirical Bayes estimates-derived TGI parameter values were regressed using the LASSO on high-dimensional genomic copy number variation data, which contained over 20,000 variables. The predictive model was able to decrease median prediction error by 4% as compared with a model without any genomic information. A total of 74 pathways were identified as related to treatment response or resistance development by LASSO, of which part was verified by literature. In conclusion, we demonstrate how the combined use of ML and pharmacometric modeling can be used to gain pharmacological understanding in genomic factors driving variation in treatment response.

FIGURE 1

A schematic visualization of the proposed two‐step approach. First, the tumor growth curves were modeled to obtain tumor growth parameter estimates, second, the individual estimates tumor growth parameter estimates were regressed on copy number variations (genomics) by different least absolute shrinkage and selection operator (LASSO) techniques. The group LASSO was applied to obtain biological pathways. The multivariate LASSO was applied to predict the tumor growth parameter values, which were then inserted into the tumor growth inhibition model equations to obtain predictions of the tumor growth curves

FIGURE 2

Results of the tumor growth inhibition (TGI) model estimation. (a) The distributions of the individual, estimated TGI parameters. (b) Selected tumor growth profiles showing how $k_d$ and $k_r$ vary for different treatments, with from left to right a very ineffective treatment, a slightly effective treatment, a very effective treatment and a very effective treatment with time‐dependent resistance development

FIGURE 3

Predicted curves from the multivariate least absolute shrinkage and selection operator. (a) Tumor growth curves visualized with the area under the estimated curve (orange) and between the estimated and predicted curves (grey) and the error (in scaled area between the predicted and the estimated curves [sABC]). From left to right show a very good prediction to a very poor prediction. (b) The distributions of the individual patient‐derived xenograft sABCs for the different treatments, given by the interquartile range. Outliers are not included in the plot

FIGURE 4

Selected pathways obtained by the group least absolute shrinkage and selection operator for the treatment efficacy ($k_d$ = black), time‐dependent resistance development ($k_r$ = orange) or both (blue) over the different treatments. The distribution of pathways found for different treatments (top)

FIGURE 5

Overview of overlapping pathways between the different treatments. The nodes are the treatments (white background) and pathways (orange background) and the edges indicate which tumor growth inhibition parameter links the pathway to the treatment