Spatial intratumor heterogeneity of programmed death-ligand 1 expression predicts poor prognosis in resected non-small cell lung cancer
Menée au Japon à partir d'un modèle mathématique et de 239 échantillons de carcinome du poumon non à petites cellules de stade IIA-IIIB puis validée sur 70 patients, cette étude met en évidence une association entre l'hétérogénéité spatiale intratumorale de l'expression du ligand PD-L1 et un pronostic défavorable
Objective : We quantified the pathological spatial intratumor heterogeneity (ITH) of programmed death-ligand 1 (PD-L1) expression and investigated its relevance to patient outcomes in surgically resected non-small cell lung carcinoma (NSCLC).
Materials and methods : This study enrolled 239 consecutive surgically resected NSCLC specimens of pathological stage IIA–IIIB. To characterize the spatial ITH of PD-L1 expression in NSCLC tissues, we developed a mathematical model based on texture image analysis and determined the spatial heterogeneity index of PD-L1 (SHIP) for each tumor. The correlation between the SHIP values and clinicopathological characteristics, including prognosis, was analyzed. Furthermore, an independent cohort of 70 cases was analyzed for model validation.
Results : Clinicopathological analysis showed correlations between high SHIP values and histological subtype (squamous cell carcinoma, p < .001) and vascular invasion (p = .004). Survival analysis revealed that patients with high SHIP values presented a significantly worse recurrence-free rate than those with low SHIP values (5-year RFS 26.3% vs 47.1%, p < .005). The impact of SHIP on cancer survival rates was verified through validation in an independent cohort. Moreover, high SHIP values were significantly associated with tumor recurrence in squamous cell carcinoma (5-year RFS 29.2% vs 52.8%, p < .05) and adenocarcinoma (5-year RFS 19.6% vs 43.0%, p < .01). Moreover, we demonstrated that a high SHIP value was an independent risk factor for tumor recurrence.
Conclusions : We presented an image analysis model to quantify the spatial ITH of protein expression in tumor tissues. This model demonstrated that the spatial ITH of PD-L1 expression in surgically resected NSCLC predicts poor patient outcomes.
Journal of the National Cancer Institute , résumé, 2023