Non-Coding RNAs, a Novel Paradigm for the Management of Gastrointestinal Stromal Tumors

The multifaceted landscape behind imatinib resistance in gastrointestinal stromal tumors (GISTs): A lesson from ripretinib

Gastrointestinal stromal tumors (GISTs) are rare mesenchymal sarcomas and the gold-standard treatment is represented by tyrosine kinase inhibitors (TKIs). Unfortunately, first-line treatment with the TKI imatinib usually promotes partial response or stable disease rather than a complete response, and resistance appears in most patients. Adaptive mechanisms are immediately relevant at the beginning of imatinib therapy, and they may represent the reason behind the low complete response rates observed in GISTs. Concurrently, resistant subclones can silently continue to grow or emerge de novo, becoming the most representative populations. Therefore, a slow evolution of the primary tumor gradually occurs during imatinib treatment, enriching heterogeneous imatinib resistant clonal subpopulations. The identification of secondary KIT/PDGFRA mutations in resistant GISTs prompted the development of novel multi-targeted TKIs, leading to the approval of sunitinib, regorafenib, and ripretinib. Although ripretinib has broad anti-KIT and -PDGFRA activity, it failed to overcome sunitinib as second-line treatment, suggesting that imatinib resistance is more multifaceted than initially thought. The present review summarizes several biological aspects suggesting that heterogeneous adaptive and resistance mechanisms can also be driven by KIT or PDGFRA downstream mediators, alternative kinases, as well as ncRNAs, which are not targeted by any TKI, including ripretinib. This may explain the modest effect observed with ripretinib and all anti-GIST agents in patients.

Transcriptomic study of gastrointestinal stromal tumors with liver metastasis

Introduction: GIST (gastrointestinal stromal tumor) is the most prominent mesenchymal neoplasms of the gastrointestinal tract, and liver is the most common metastasis site for GIST. The molecular mechanism leading to liver metastasis of GIST is currently unclear. Methods: With the goal of revealing the underlying mechanism, we performed whole-genome gene expression profiling on 18 pairs of RNA samples comprised of GIST tissues (with liver metastasis) and corresponding non-tumor tissues. After identifying differentially expressed gene, functional annotation and signal pathway analyses were conducted. GSE13861, datasets that compare GIST (without liver metastasis) with adjacent tissues, served as a comparison. Results: A total of 492 up-regulated genes and 629 down-regulated genes were identified as differentially expressed genes between liver metastasis tissues and non-tumor tissues. We characterized expression patterns of DEGs identified from our cohort and GSE13861 that show signatures of enrichment for functionality. In subsequent gene set enrichment analysis, differentially expressed genes were mainly enriched in Epithelial Mesenchymal Transition in both datasets. 493 genes were overlapped among our whole-genome gene expression profiling results and GSE13861, consisting 188 up-regulated genes and 305 down-regulated genes. By using CytoHubba plugin of Cytoscape, CDH1, CD34, KIT, PROM1, SOX9, FGF2, CD24, ALDH1A1, JAG1 and NES were identified as top ten hub genes in tumorigenesis and liver metastasis of GIST. higher expression levels of FGF2, JAG1, CD34, ALDH1A1 and the lower expression level of CDH1 were respectively associated with unfavorable overall survival. Meanwhile higher expression levels of CD34, FGF2, KIT, JAG1, ALDH1A were correlated with worse disease-free survival. Discussion: The present study may help to provide candidate pathways and targets for treatment of GIST and prevention methods to liver metastasis.

miRNAs expression pattern and machine learning models elucidate risk for gastric GIST

BACKGROUND

Gatrointestinal stromal tumors (GISTs) are the main mesenchymal tumors found in the gastrointestinal system. GISTs clinical phenotypes differ significantly and their molecular basis is not yet completely known. microRNAs (miRNAs) have been involved in carcinogenesis pathways by regulating gene expression at post-transcriptional level.

OBJECTIVE

The aim of the present study was to elucidate the expression profiles of miRNAs relevant to gastric GIST carcinogenesis, and to identify miRNA signatures that can discriminate the GIST from normal cases.

METHODS

miRNA expression was tested by miScript™miRNA PCR Array Human Cancer PathwayFinder kit and then we used machine learning in order to find a miRNA profile that can predict the risk for GIST development.

RESULTS

A number of miRNAs were found to be differentially expressed in GIST cases compared to healthy controls. Among them the hsa-miR-218-5p was found to be the best predictor for GIST development in our cohort. Additionally, hsa-miR-146a-5p, hsa-miR-222-3p, and hsa-miR-126-3p exhibit significantly lower expression in GIST cases compared to controls and were among the top predictors in all our predictive models.

CONCLUSIONS

A machine learning classification approach may be accurate in determining the risk for GIST development in patients. Our findings indicate that a small number of miRNAs, with hsa-miR218-5p as a focus, may strongly affect the prognosis of GISTs.