[Progress on the Study of PD-L1 Detection Methods in Non-small Cell Lung Cancer]

[Construction of A Nomogram Prediction Model for PD-L1 Expression in Non-small Cell Lung Cancer Based on 18F-FDG PET/CT Metabolic Parameters]

BACKGROUND

In recent years, immunotherapy represented by programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunosuppressants has greatly changed the status of non-small cell lung cancer (NSCLC) treatment. PD-L1 has become an important biomarker for screening NSCLC immunotherapy beneficiaries, but how to easily and accurately detect whether PD-L1 is expressed in NSCLC patients is a difficult problem for clinicians. The aim of this study was to construct a Nomogram prediction model of PD-L1 expression in NSCLC patients based on 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography/conputed tomography (PET/CT) metabolic parameters and to evaluate its predictive value.

METHODS

Retrospective collection of 18F-FDG PET/CT metabolic parameters, clinicopathological information and PD-L1 test results of 155 NSCLC patients from Inner Mongolia People's Hospital between September 2016 and July 2021. The patients were divided into the training group (n=117) and the internal validation group (n=38), and another 51 cases of NSCLC patients in our hospital between August 2021 and July 2022 were collected as the external validation group according to the same criteria. Then all of them were categorized according to the results of PD-L1 assay into PD-L1+ group and PD-L1- group. The metabolic parameters and clinicopathological information of patients in the training group were analyzed by univariate and binary Logistic regression, and a Nomogram prediction model was constructed based on the screened independent influencing factors. The effect of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) in both the training group and the internal and external validation groups.

RESULTS

Binary Logistic regression analysis showed that metabolic tumor volume (MTV), gender and tumor diameter were independent influences on PD-L1 expression. Then a Nomogram prediction model was constructed based on the above independent influences. The ROC curve for the model in the training group shows an area under the curve (AUC) of 0.769 (95%CI: 0.683-0.856) with an optimal cutoff value of 0.538. The AUC was 0.775 (95%CI: 0.614-0.936) in the internal validation group and 0.752 (95%CI: 0.612-0.893) in the external validation group. The calibration curves were tested by the Hosmer-Lemeshow test and showed that the training group (χ2=0.040, P=0.979), the internal validation group (χ2=2.605, P=0.271), and the external validation group (χ2=0.396, P=0.820) were well calibrated. The DCA curves show that the model provides clinical benefit to patients over a wide range of thresholds (training group: 0.00-0.72, internal validation group: 0.00-0.87, external validation group: 0.00-0.66).

CONCLUSIONS

The Nomogram prediction model constructed on the basis of 18F-FDG PET/CT metabolic parameters has greater application value in predicting PD-L1 expression in NSCLC patients.

Study on PD-L1 Expression in NSCLC Patients and Related Influencing Factors in the Real World

PD-L1 is one of the current biomarkers for immune checkpoint inhibitor (ICI) therapy in patients with non-small-cell lung cancer. However, the expression of PD-L1 in the real world and its related influencing factors remain unclear. We want to observe the expression of PD-L1 in the real world and study the related influencing factors through the collection and analysis of clinical data. R software (version 4.0) was used to perform data analysis and the “corplot” package for correlation analysis. A total of 296 individuals (mean [SD] age, 67 [9] years; 23%female) were assessed. According to the expression amount of PD-L1, the cohort was divided into low nonexpression group (PD‐L1 < 1%, 26.7%), low-expression group (1% ≤ PD‐L1 < 50%, 49.3%), and high-expression group (PD‐L1 ≥ 50%, 23.5%). Age, gender, underlying diseases, smoking status, and PD-L1 expression level were not statistically significant. We found that the expression of PD-L1 was correlated with serum albumin (P < 0.05) and pathological type (P < 0.05) and had a negative correlation with EGFR mutation but did not correlate with gender, age, smoking status, combined with underlying diseases, tumor stage, whether it was initially treated or not, sampling site, specimen type, specimen storage time, R-IFN, CD4, CD8, NLR, CRP, and LDH. The present findings indicated that serum albumin, pathological type, and EGFR mutations are associated with PD-L1 expression in patients with NSCLC, which may provide a new basis for individualized immunotherapy and need further study to confirm. The results of this study help to further reveal the actual expression of PD-L1 in non-small-cell lung cancer patients with real events.