Relationship between SP142 PD-L1 Expression and 18F-FDG Uptake 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.

PET/CT molecular imaging in the era of immune-checkpoint inhibitors therapy

Cancer immunotherapy, especially immune-checkpoint inhibitors (ICIs), has paved a new way for the treatment of many types of malignancies, particularly advanced-stage cancers. Accumulating evidence suggests that as a molecular imaging modality, positron emission tomography/computed tomography (PET/CT) can play a vital role in the management of ICIs therapy by using different molecular probes and metabolic parameters. In this review, we will provide a comprehensive overview of the clinical data to support the importance of 18F-fluorodeoxyglucose PET/CT (18F-FDG PET/CT) imaging in the treatment of ICIs, including the evaluation of the tumor microenvironment, discovery of immune-related adverse events, evaluation of therapeutic efficacy, and prediction of therapeutic prognosis. We also discuss perspectives on the development direction of 18F-FDG PET/CT imaging, with a particular emphasis on possible challenges in the future. In addition, we summarize the researches on novel PET molecular probes that are expected to potentially promote the precise application of ICIs.

Correlation of epidermal growth factor receptor mutation status and PD-L1 expression with [18F]FDG PET using volume-based parameters in non-small cell lung cancer

OBJECTIVE

We investigated the relationship between 2-deoxy-2-[18F]fluoro-D-glucose (FDG) PET using volume-based parameters and epidermal growth factor receptor (EGFR) mutation status, programmed death-ligand-1 (PD-L1) expression level, and their combination, in pretreated non-small cell lung cancer (NSCLC).

METHODS

FDG PET findings and EGFR mutation status and PD-L1 expression level were investigated retrospectively in 93 patients with newly diagnosed NSCLC (77 adenocarcinomas, 16 squamous cell carcinomas). Tumors were divided into six groups: EGFR mutant/negative PD-L1, EGFR mutant/low PD-L1, EGFR mutant/high PD-L1, EGFR wild/negative PD-L1, EGFR wild/low PD-L1, and EGFR wild/high PD-L1. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for primary tumor were measured from PET images. The EGFR mutation status and PD-L1 expression level were estimated in tumor tissue specimens and compared with the PET parameters.

RESULTS

None of the PET parameters differed significantly between EGFR-mutated and wild-type EGFR. According to the PD-L1 level, significant differences were detected in SUVmax (P = 0.001) and TLG (P = 0.016), but not MTV. Comparing all six groups, significant difference was detected in only SUVmax (P = 0.011).

CONCLUSION

Based on the preliminary results of this study, FDG PET may help in the prediction of PD-L1 expression level, but not EGFR mutation status, in patients with newly diagnosed NSCLC. The SUVmax rather than MTV or TLG, may be of value in predicting the six groups according to the combination of EGFR mutation status and PD-L1 expression level.

Molecular Imaging and the PD-L1 Pathway: From Bench to Clinic

Programmed death-1 (PD-1) and programmed death ligand 1 (PD-L1) inhibitors target the important molecular interplay between PD-1 and PD-L1, a key pathway contributing to immune evasion in the tumor microenvironment (TME). Long-term clinical benefit has been observed in patients receiving PD-(L)1 inhibitors, alone and in combination with other treatments, across multiple tumor types. PD-L1 expression has been associated with response to immune checkpoint inhibitors, and treatment strategies are often guided by immunohistochemistry-based diagnostic tests assessing expression of PD-L1. However, challenges related to the implementation, interpretation, and clinical utility of PD-L1 diagnostic tests have led to an increasing number of preclinical and clinical studies exploring interrogation of the TME by real-time imaging of PD-(L)1 expression by positron emission tomography (PET). PET imaging utilizes radiolabeled molecules to non-invasively assess PD-(L)1 expression spatially and temporally. Several PD-(L)1 PET tracers have been tested in preclinical and clinical studies, with clinical trials in progress to assess their use in a number of cancer types. This review will showcase the development of PD-(L)1 PET tracers from preclinical studies through to clinical use, and will explore the opportunities in drug development and possible future clinical implementation.

Association between 18F-FDG metabolic activity and programmed death ligand-1 (PD-L1) expression using 22C3 immunohistochemistry assays in non-small cell lung cancer (NSCLC) resection specimens

OBJECTIVE

This study sought to investigate the association between ¹⁸F-fludeoxyglucose (¹⁸F-FDG) uptake in positron emission tomography/CT (PET/CT) scans and different programmed death ligand-1 (PD-L1) expression conditions in non-small cell lung cancer (NSCLC).

METHODS

From October 2017 to December 2019, NSCLC was retrospectively identified in 419 consecutive patients who underwent ¹⁸F-FDG PET/CT scans and PD-L1 expression tests using the PD-L1 22C3 assay. The association between clinicopathological characteristics and PD-L1 expression was assessed.

RESULTS

The frequency of PD-L1-positive tumours was 38.2% (160/419) in NSCLC. In NSCLC, the multivariate analysis showed a high maximum standardised uptake value (SUVmax) (p < 0.0001) and an EGFR wild type genotype (p = 0.027) was significantly associated with PD-L1-positivity. In adenocarcinoma (ADC), the multivariate analysis showed that a high SUVmax (p < 0.0001) was significantly associated with PD-L1-positivity. In NSCLC and ADC, a Mann-Whitney U test showed significant differences between groups with PD-L1 high expression and PD-L1 low expression levels in terms of SUVmax (p = 0.011 and p = 0.013, respectively). The results of the receiver operating characteristic curve analysis showed that the area under the curve of the SUVmax was 0.767 (95% CI, 0.720-0.814; p < 0.0001) and 0.712 (95% CI, 0.651-0.774; p < 0.0001) in NSCLC and ADC, respectively.

CONCLUSION

The study demonstrates that the SUVmax was significantly associated with PD-L1 expression in NSCLC and ADC. The SUVmax was significantly different between the PD-L1 high and low expression conditions, as quantified using a PD-L1 22C3 assay.

ADVANCES IN KNOWLEDGE

This study provides direct evidence that SUVmax as a metabolic biomarker may help select patients with NSCLC likely to benefit from pembrolizumab.