6533b82bfe1ef96bd128e2c3

RESEARCH PRODUCT

Predictive Factors of Response to Sunitinib in Imatinib-Resistant Gastrointestinal Stromal Tumors (GISTs): A Multi-Institutional Study

Lidia GattoIda De LucaAntonio RussoGiovanni GrignaniMaria Abbondanza PantaleoLorenzo D'ambrosioNello GrassiLorena IncorvaiaOronzo BrunettiNicola SilvestrisAntonio GalvanoDaniele FanaleBruno VincenziGiuseppe BadalamentiMargherita NanniniGianni PantusoNadia BarracoViviana Bazan

subject

GiSTSunitinibbusiness.industryImatinibmedicine.diseaseurologic and male genital diseasesImatinib resistantdigestive system diseasesfemale genital diseases and pregnancy complicationsmedicineCancer researchGastrointestinal stromal tumors (GISTs)businessneoplasmsmedicine.drugoncology_oncogenics

description

Imatinib 400 mg is the standard of care for medical treatment of advanced GISTs. In the majority of cases, however, GISTs eventually develop resistance to imatinib. The optimal second line treatment has not been established yet and imatinib dose escalation (800 mg) or sunitinib represent two feasible options. The objective of this retrospective, multi-institutional, study is to analyze the validity of several parameters as possible predictive factors of response to sunitinib after imatinib failure. We reviewed 128 metastatic GISTs treated with sunitinib between January 2007 to June 2017. Primary tumour site, metastatic site, c-KIT/PDGFR-α mutational status, PET-FDG status and type of disease progression to sunitinib were assessed as possible predictive factors of response. This study identifies the gastric site of primary tumor as a predictive factor to sunitinib efficacy in second line setting. The mutational status (GIST WT), the site of metastasis (peritoneum) and the FDG-PET status (negative), although not statistically significant, seem to be elements of increased activity for sunitinib treatment. These results provide the rationale to drive physician for sunitinib choice in second line setting for metastatic GISTs, to improve patients selection and to maximize the benefit from the treatment, on the basis of possible predictive factors of response.

10.20944/preprints201906.0033.v1