Metabolomic and transcriptomic response to imatinib treatment of gastrointestinal stromal tumour in xenograft-bearing mice

Abstract

Background: Although imatinib is a well-established first-line drug for treating a vast majority of gastrointestinal stromal tumours (GIST), GISTs acquire secondary resistance during therapy. Multi-omics approaches provide an integrated perspective to empower the development of personalised therapies through a better understanding of functional biology underlying the disease and molecular-driven selection of the best-targeted individualised therapy. In this study, we applied integrative metabolomic and transcriptomic analyses to elucidate tumour biochemical processes affected by imatinib treatment.

Materials and methods: A GIST xenograft mouse model was used in the study, including 10 mice treated with imatinib and 10 non-treated controls. Metabolites in tumour extracts were analysed using gas chromatography coupled with mass spectrometry (GC-MS). RNA sequencing was also performed on the samples subset (n=6).

Results: Metabolomic analysis revealed 21 differentiating metabolites, whereas next-generation RNA sequencing data analysis resulted in 531 differentially expressed genes. Imatinib significantly changed the profile of metabolites associated mainly with purine and pyrimidine metabolism, butanoate metabolism, as well as alanine, aspartate, and glutamate metabolism. The related changes in transcriptomic profiles included genes involved in kinase activity and immune responses, as well as supported its impact on the purine biosynthesis pathway.

Conclusions: Our multi-omics study confirmed previously known pathways involved in imatinib anticancer activity as well as correlated imatinib-relevant downregulation of expression of purine biosynthesis pathway genes with the reduction of respectful metabolites. Furthermore, considering the importance of the purine biosynthesis pathway for cancer proliferation, we identified a potentially novel mechanism for the anti-tumour activity of imatinib. Based on the results, we hypothesise metabolic modulations aiming at the reduction in purine and pyrimidine pool may ensure higher imatinib efficacy or re-sensitise imatinib-resistant tumours.

Keywords: GIST; Imatinib; Metabolomics; Multi-omics; Transcriptomics; purines.

Fig. 1

Flow chart illustrating the main steps of the study.

Fig. 2

Multivariate statistical analysis of metabolomic data from imatinib-treated (n=10) and non-treated (n=10) GIST samples. (A) Clustering of QCs on a PCA score plot built on data acquired during GC-MS analysis of GIST tissue extracts. The grey dots represent the experimental samples, and the purple dots correspond to quality controls (QCs). The red dot indicates a detected outlier. Panel A also shows Hotelling's T2 plot with one critical outlier sample (T2Crit 99%). (B) OPLS-DA scatter score plot, R2=0.88, Q2=0.80, CV-ANOVA p-value 5.35 E-05. (C) Permutation test, the number of permutations 100; intercepts R2=0.0, 0.69, Q2=0.0, -0.466. Green colour represents non-treated controls while blue corresponds to imatinib-treated samples. (D) Volcano plot for the OPLS-DA model. Metabolites with highest VIP values and highest negative or positive pcorr highlight the metabolomic signature of GIST treatment.

Fig. 3

Imatinib-related effects on biological processes. (A) and molecular functions (B) based on the gene ontology assignment of transcriptomic changes. The mRNA levels in IT-GIST that were changed significantly (p value 0.05) by at least 2 folds were assigned with the WebGestalt database to cellular signalling pathways and selected based on FDR ˂ 0.05. (C) The changes in the expression of genes assigned to the identified signalling pathways are presented as the hierarchically clustered heatmap. The heat map generation and hierarchical clustering were performed with the Morpheus web server (https://software.broadinstitute.org/morpheus/). The assigned gene expression changes are provided in Table S1.

Fig. 4

The model of imatinib effects on the purine pathway. Red boxes represent downregulated metabolites, while green colour indicates upregulation.