Abstract
PurposeDual-time-point 18F-fluorodeoxyglucose positron emission tomography (DTP 18F-FDG PET), which reflects the dynamics of tumor glucose metabolism, may also provide a novel approach to the characterization of both cancer cells and immune cells within the tumor immune microenvironment (TIME). We investigated the correlations between the metabolic parameters (MPs) of DTP 18F-FDG PET images and the tumor microenvironment immune types (TMITs) in patients with non-small cell lung cancer (NSCLC).MethodsA retrospective analysis was performed in 91 patients with NSCLC who underwent preoperative DTP 18F-FDG PET/CT scans. MPs in the early scan (eSUVmax, eSUVmean, eMTV, eTLG) and delayed scan (dSUVmax, dSUVmean, dMTV, dTLG) were calculated, respectively. The change in MPs (ΔSUVmax, ΔSUVmean, ΔMTV, ΔTLG) between the two time points were calculated. Tumor specimens were analyzed by immunohistochemistry for PD-1/PD-L1 expression and CD8+ tumor-infiltrating lymphocytes (TILs). TIME was classified into four immune types (TMIT I ~ IV) according to the expression of PD-L1 and CD8+ TILs. Correlations between MPs with TMITs and the immune-related biomarkers were analyzed. A composite metabolic signature (Meta-Sig) and a combined model of Meta-Sig and clinical factors were constructed to predict patients with TMIT I tumors.ResultseSUVmax, eSUVmean, dSUVmax, dSUVmean, ΔSUVmax, ΔSUVmean, and ΔTLG were significantly higher in PD-L1 positive patients (p = 0.0007, 0.0006, < 0.0001, < 0.0001, 0.0002, 0.0002, 0.0247, respectively), and in TMIT-I tumors (p = 0.0001, < 0.0001, < 0.0001, < 0.0001, 0.0009, 0.0009, 0.0144, respectively). Compared to stand-alone MP, the Meta-Sig and combined model displayed better performance for assessing TMIT-I tumors (Meta-sig: AUC = 0.818, sensitivity = 86.36%, specificity = 73.91%; Model: AUC = 0.869, sensitivity = 77.27%, specificity = 82.61%).ConclusionHigh glucose metabolism on DTP 18F-FDG PET correlated with the TMIT-I tumors, and the Meta-Sig and combined model based on clinical and metabolic information could improve the performance of identifying the patients who may respond to immunotherapy.
Highlights
Lung cancer is the leading cause of cancer death in China [1]
Positive programmed death ligand 1 (PD-L1) immunostaining was observed on the membranes and/or in the cytoplasm of tumor cells
The response of Nonsmall cell lung cancer (NSCLC) patients to immunotherapy is affected by the tumor immune microenvironment (TIME)
Summary
Lung cancer is the leading cause of cancer death in China [1]. Nonsmall cell lung cancer (NSCLC) accounts for more than 80% of all lung cancer cases. The expression of PD-L1 on tumor cells is considered as a predictive biomarker for the response to anti-PD-1/PD-L1 ICIs [2]. Not all patients with positive PD-L1 expression respond well to immunotherapy It suggests that other tumor immune microenvironment (TIME) factors may affect the response to the ICIs [3]. In addition to PD-L1 expression, CD8+ tumor-infiltrating lymphocytes (TILs) might play an important role in anti–PD-1/PD-L1 therapies [2]. Characterized by high infiltration of CD8+ cytotoxic lymphocytes, the infiltrated–inflamed TIME has significantly better responses to ICIs [5]. Tumors with high PD-L1 expression and the presence of CD8+ TILs are classified as tumor microenvironment immune type I (TMIT-I), a immunologically ‘hot’ subtype that would likely benefit from anti-PD-1/PD-L1 therapies [6]. There is no noninvasive method to identify TMIT I tumors, and up to now the overall and dynamic detection of TIME biomarkers is still challenging
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