Background: Lung neuroendocrine tumors (LNETs) span well-differentiated typical/atypical carcinoids (TC/AC) to poorly differentiated large-cell neuroendocrine carcinoma (LCNEC). Robust biomarkers for grading and prognostication are lacking. We hypothesized that differential metabolic pathways activation, reflected by protein expression, holds prognostic relevance. We conducted a monocentric translational study on resected LNETs to characterize biomarkers involved in glycolysis, fatty acid, and amino acid pathways across different grades of LNETs. Secondary endpoints included assessing clinical outcomes and correlating biomarker expression with patient prognosis. Methods: Digital FFPE sections underwent standardized immunohistochemistry (IHC) and quantitative image analysis. Biomarkers included glycolysis (MCT1, MCT4, CD147), amino-acid metabolism (SLC1A5, SLC7A5, GLS), and fatty-acid synthesis (FAS, ACC). Expression was summarized by H-score and dichotomized. Associations with clinicopathologic variables, recurrence-free survival (RFS), and overall survival (OS) were tested using median and maximally selected rank statistics. Results: Overall, 49 LNETs were included: 11 TC, 19 AC; 19 LCNEC. LCNEC showed marked upregulation of glycolytic and amino-acid transport markers versus TC/AC. Fatty-acid markers were generally low across subtypes. High MCT1 and SLC7A5 predicted shorter OS; MCT1 and CD147 predicted shorter RFS. In multivariable analysis, MCT1 remained independently associated with RFS. Notably, a subset of ACs with elevated glycolysis/amino-acid markers showed LCNEC-like outcomes, independent of Ki-67. GLS peaked in AC, suggesting divergent glutamine utilization along the spectrum. Conclusion: Quantitative digital pathology reveals distinct metabolic signatures in LNETs. MCT1 and SLC7A5 emerge as prognostic biomarkers, with MCT1 independently predicting RFS. Integrating metabolic immunophenotyping with histopathology refines risk stratification-especially for AC-and highlights potentially actionable metabolic axes for future therapeutic interventions in LNETs.
Dissecting the prognostic role of metabolic markers in lung neuroendocrine Tumors: The MONET study
Pasello, Giulia
;Pigato, Giulia;Pezzuto, Federica;Scattolin, Daniela;Maso, Alessandro Dal;Bonanno, Laura;Calabrese, Fiorella;Dell'Amore, Andrea;Guarneri, Valentina;Indraccolo, Stefano
2026
Abstract
Background: Lung neuroendocrine tumors (LNETs) span well-differentiated typical/atypical carcinoids (TC/AC) to poorly differentiated large-cell neuroendocrine carcinoma (LCNEC). Robust biomarkers for grading and prognostication are lacking. We hypothesized that differential metabolic pathways activation, reflected by protein expression, holds prognostic relevance. We conducted a monocentric translational study on resected LNETs to characterize biomarkers involved in glycolysis, fatty acid, and amino acid pathways across different grades of LNETs. Secondary endpoints included assessing clinical outcomes and correlating biomarker expression with patient prognosis. Methods: Digital FFPE sections underwent standardized immunohistochemistry (IHC) and quantitative image analysis. Biomarkers included glycolysis (MCT1, MCT4, CD147), amino-acid metabolism (SLC1A5, SLC7A5, GLS), and fatty-acid synthesis (FAS, ACC). Expression was summarized by H-score and dichotomized. Associations with clinicopathologic variables, recurrence-free survival (RFS), and overall survival (OS) were tested using median and maximally selected rank statistics. Results: Overall, 49 LNETs were included: 11 TC, 19 AC; 19 LCNEC. LCNEC showed marked upregulation of glycolytic and amino-acid transport markers versus TC/AC. Fatty-acid markers were generally low across subtypes. High MCT1 and SLC7A5 predicted shorter OS; MCT1 and CD147 predicted shorter RFS. In multivariable analysis, MCT1 remained independently associated with RFS. Notably, a subset of ACs with elevated glycolysis/amino-acid markers showed LCNEC-like outcomes, independent of Ki-67. GLS peaked in AC, suggesting divergent glutamine utilization along the spectrum. Conclusion: Quantitative digital pathology reveals distinct metabolic signatures in LNETs. MCT1 and SLC7A5 emerge as prognostic biomarkers, with MCT1 independently predicting RFS. Integrating metabolic immunophenotyping with histopathology refines risk stratification-especially for AC-and highlights potentially actionable metabolic axes for future therapeutic interventions in LNETs.
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