Model-based genotype-phenotype mapping used to investigate gene signatures of immune sensitivity and resistance in melanoma micrometastasis.

Guido Santos,Julio Vera,Gerold Schuler,Martin Eberhardt,Xin Lai,Sushmita Paul,Florian S Dreyer,Svetoslav Nikolov

doi:10.1038/srep24967

Abstract

In this paper, we combine kinetic modelling and patient gene expression data analysis to elucidate biological mechanisms by which melanoma becomes resistant to the immune system and to immunotherapy. To this end, we systematically perturbed the parameters in a kinetic model and performed a mathematical analysis of their impact, thereby obtaining signatures associated with the emergence of phenotypes of melanoma immune sensitivity and resistance. Our phenotypic signatures were compared with published clinical data on pretreatment tumor gene expression in patients subjected to immunotherapy against metastatic melanoma. To this end, the differentially expressed genes were annotated with standard gene ontology terms and aggregated into metagenes. Our method sheds light on putative mechanisms by which melanoma may develop immunoresistance. Precisely, our results and the clinical data point to the existence of a signature of intermediate expression levels for genes related to antigen presentation that constitutes an intriguing resistance mechanism, whereby micrometastases are able to minimize the combined anti-tumor activity of complementary responses mediated by cytotoxic T cells and natural killer cells, respectively. Finally, we computationally explored the efficacy of cytokines used as low-dose co-adjuvants for the therapeutic anticancer vaccine to overcome tumor immunoresistance.

Highlights

In this paper, we combine kinetic modelling and patient gene expression data analysis to elucidate biological mechanisms by which melanoma becomes resistant to the immune system and to immunotherapy
Equation [2], the first rate term includes a logistic equation accounting for the self-limiting growth of the melanoma cell population: we introduced this rate term under the assumption that the melanoma cells in the micrometastasis have not yet broken the blood vessel barrier and achieve a maximum size represented by MT20
The model accounts for the interaction between the local immune system and tumor cells during the growth of melanoma micrometastases

Summary

Introduction

We combine kinetic modelling and patient gene expression data analysis to elucidate biological mechanisms by which melanoma becomes resistant to the immune system and to immunotherapy. A number of published works suggest the use of a systemic approach combining quantitative experimental data and mathematical modeling to dissect the tumor-immune system interaction[2,3] Most of these modelling efforts focus on representing and simulating cell-to-cell processes and do not consider the intracellular networks controlling immune and tumor cells, thereby losing the chance to integrate and analyze omics data on the molecular events underlying the tumor-immunity interaction and the immune-based therapies. The interplay between the tumor, the immune system and different types of therapies has been modelled in the last decade[5,6,8,11], including a study that employed model simulations and patient data to predict the optimal timing and dosage for a therapeutic anticancer vaccination[12] These models in some cases incorporate detailed descriptions of the underlying cell-to-cell communication, they do not take into account the intracellular networks governing immune or tumor cells. Ulloa-Montoya et al.[15] analyzed biopsy samples from metastatic melanoma patients and identified a pretreatment gene expression signature that can be used to predict the response to immunotherapy

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