Perspectives on the evolving state of the art management of gastrointestinal stromal tumours

LIX1 Controls MAPK Signaling Reactivation and Contributes to GIST-T1 Cell Resistance to Imatinib

Gastrointestinal stromal tumor (GIST), the most common sarcoma, is mainly caused by an oncogenic mutation in the KIT receptor tyrosine kinase. Targeting KIT using tyrosine kinase inhibitors, such as imatinib and sunitinib, provides substantial benefit; however, in most patients, the disease will eventually progress due to KIT secondary mutations leading to treatment failure. Understanding how GIST cells initially adapt to KIT inhibition should guide the selection of appropriate therapies to overcome the emergence of resistance. Several mechanisms have been broadly implicated in the resistance to imatinib anti-tumoral effects, including the reactivation of MAPK signaling upon KIT/PDGFRA targeted inhibition. This study provides evidence that LImb eXpression 1 (LIX1), a protein we identified as a regulator of the Hippo transducers YAP1 and TAZ, is upregulated upon imatinib or sunitinib treatment. LIX1 silencing in GIST-T1 cells impaired imatinib-induced MAPK signaling reactivation and enhanced imatinib anti-tumor effect. Our findings identified LIX1 as a key regulator of the early adaptative response of GIST cells to targeted therapies.

Establishment and Verification of Prognostic Nomograms for Patients with Gastrointestinal Stromal Tumors: A SEER-Based Study

With gastrointestinal tract as the origin, gastrointestinal stromal tumor (GIST) is recognized as the very widespread mesenchymal tumor. A precise prognostic model of survival is required to guide the treatment options of patients with GIST. This study was designed to map the overall survival (OS) and cancer-specific survival (CSS) of GIST patients. According to the Surveillance, Epidemiology, and End Results (SEER) program database, we acquired the data of 6,713 patients with GIST who were diagnosed between 2004 and 2014. We randomly separated the patients into training (n = 4,699) and validation (n = 2,014) groups. To assess the prognostic impact of multiple clinical parameters, the Kaplan-Meier approach and the Cox proportional hazards regression model were adopted, where essential prognostic variables were combined to create nomograms. The consistency index and curve of calibration had been adopted to assess nomogram discrimination ability and prediction accuracy. A multifactor analysis of the training cohort showed that age, gender, size of tumor, location, and primary surgery were remarkably related to survival, and these variables were applied to create nomograms. The nomogram demonstrated excellent accuracy in estimating 2-, 3-, and 5-year OS and CSS, with a C-index of 0.740 (95% confidence interval [CI], 0.723-0.757) for OS and 0.743 (95% CI, 0.718-0.768) for CSS. In the validation cohort, the nomogram-predicted C-index was 0.741 for OS (95%CI, 0.717-0.765) and 0.746 (95%CI, 0.713-0.779) for CSS. All calibration curves showed good consistency between predicted and actual survival. A new nomogram was created and verified to predict the OS and CSS of patients with GIST. These new prognostic models can help enhance the accuracy of survival outcome predictions, thus facilitating to provide constructive therapeutic suggestions.