Programmed death ligand-1 expression and its association with the degree of differentiation and the presence of necrosis in non-small cell lung carcinoma

Blichárová, Alžbeta; Tancoš, Vladimír; Benetinová, Zuzana; Verbóová, Ľudmila; Grendár, Marian; Mazuráková, Alena; Mechírová, Eva

doi:10.1016/j.prp.2022.154296

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…The cervical cancer tissue microarray sample immunohistochemistry analysis performed by Saglam et al showed that PD-L1 expression was higher in poorly differentiated tumors than in moderately differentiated tumors [ 27 ]. The expression of PD-L1 was usually higher in poorly differentiated tumors; this finding has been reported for several other cancers, such as non-small cell lung carcinoma [ 28 , 29 ], thyroid cancer [ 30 ] and gallbladder cancer [ 31 ]. In our study, the degree of tumor differentiation was a negative independent predictor for PD-L1 status in the binary multivariate logistic regression analysis.…”

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confidence: 64%

Retrospective Analysis of the Predictive Value of 18F-FDG PET/CT Metabolic Parameters for PD-L1 Expression in Cervical Cancer

Pang

Song

et al. 2023

Diagnostics

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Background: Immunotherapy targeting PD-1/PD-L1 has been proven to be effective for cervical cancer treatment. To explore non-invasive examinations for assessing the PD-L1 status in cervical cancer, we performed a retrospective study to investigate the predictive value of 18F-FDG PET/CT. Methods: The correlations between PD-L1 expression, clinicopathological characteristics and 18F-FDG PET/CT metabolic parameters were evaluated in 74 cervical cancer patients. The clinicopathological characteristics included age, histologic type, tumor differentiation, FIGO stage and tumor size. The metabolic parameters included maximum standard uptake (SUVmax), mean standard uptake (SUVmean), total lesion glycolysis (TLG) and tumor metabolic volume (MTV). Results: In univariate analysis, SUVmax, SUVmean, TLG, tumor size and tumor differentiation were obviously associated with PD-L1 status. SUVmax (rs = 0.42) and SUVmean (rs = 0.40) were moderately positively correlated with the combined positive score (CPS) for PD-L1 in Spearman correlation analysis. The results of multivariable analysis showed that the higher SUVmax (odds ratio = 2.849) and the lower degree of differentiation (Odds Ratio = 0.168), the greater probability of being PD-L1 positive. The ROC curve analysis demonstrated that when the cut-off values of SUVmax, SUVmean and TLG were 10.45, 6.75 and 143.4, respectively, the highest accuracy for predicting PD-L1 expression was 77.0%, 71.6% and 62.2%, respectively. The comprehensive predictive ability of PD-L1 expression, assessed by combining SUVmax with tumor differentiation, showed that the PD-L1-negative rate was 100% in the low probability group, whereas the PD-L1-positive rate was 84.6% in the high probability group. In addition, we also found that the H-score of HIF-1α was moderately positively correlated with PD-L1 CPS (rs = 0.51). Conclusions: The SUVmax and differentiation of the primary lesion were the optimum predictors for PD-L1 expression in cervical cancer. There was a great potential for 18F-FDG PET/CT in predicting PD-L1 status and selecting cervical cancer candidates for PD1/PD-L1 immune checkpoint therapy.

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