Understanding the determinants of long-term liver metastasis (LM) outcomes in gastrointestinal stromal tumor (GIST) patients is crucial. We established the feature selection model of intratumoral microbiome at the surgery, achieving robust predictive accuracies of 0.953 and 0.897 AUCs in discovery (n = 74) and validation (n = 34) cohorts, respectively. Notably, despite the significant reduction in LM occurrence with adjuvant imatinib (AI) treatment, intratumoral microbiome exerted independently stronger effects on post-operative LM. Employing both 16S and full-length rRNA sequencing, we pinpoint intracellular Shewanella algae as a foremost LM risk factor in both AI- and non-AI-treated patients. Experimental validation confirmed S. algae's intratumoral presence in GIST, along with migration/invasion-promoting effects on GIST cells. Furthermore, S. algae promoted LM and impeded AI treatment in metastatic mouse models. Our findings advocate for incorporating intratumoral microbiome evaluation at surgery, and propose S. algae as a therapeutic target for LM suppression in GIST.
Keywords: Gastrointestinal stromal tumor; Imatinib; Intratumoral microbiome; Liver metastasis; Machine learning algorithm.
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