Potential Predictive Immune and Metabolic Biomarkers of Tumor Microenvironment Regarding Pathological and Clinical Response in Esophageal Cancer After Neoadjuvant Chemoradiotherapy: A Systematic Review

Abstract

Introduction The tumor microenvironment (TME) plays a crucial role in therapy response and modulation of immunologic surveillance. Adjuvant immunotherapy has recently been introduced in post-surgery treatment of locally advanced esophageal cancer (EC) with residual pathological disease after neoadjuvant chemoradiotherapy (nCRT). F-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) remains a valuable imaging tool to assess therapy response and to visualize metabolic TME; however, there is still a paucity in understanding the interaction between the TME and nCRT response. This systematic review investigated the potential of TME biomarkers and 18F-FDG-PET/CT features to predict pathological and clinical response (CR) after nCRT in EC. Methods A literature search of the Medline and Embase electronic databases identified 4190 studies. Studies regarding immune and metabolic TME biomarkers and 18F-FDG-PET/CT features were included for predicting pathological response (PR) and/or CR after nCRT. Separate analyses were performed for 18F-FDG-PET/CT markers and these TME biomarkers. Results The final analysis included 21 studies—10 about immune and metabolic markers alone and 11 with additional 18F-FDG-PET/CT features. High CD8 infiltration before and after nCRT, and CD3 and CD4 infiltration after nCRT, generally correlated with better PR. A high expression of tumoral or stromal programmed death-ligand 1 (PD-L1) after nCRT was generally associated with poor PR. Moreover, total lesion glycolysis (TLG) and metabolic tumor volume (MTV) of the primary tumor were potentially predictive for clinical and PR. Conclusion CD8, CD4, CD3, and PD-L1 are promising immune markers in predicting PR, whereas TLG and MTV are potential 18F-FDG-PET/CT features to predict clinical and PR after nCRT in EC.

A machine learning radiomics based on enhanced computed tomography to predict neoadjuvant immunotherapy for resectable esophageal squamous cell carcinoma

“…Certainly, to promote clinical translation of radiomics, standardized image acquisition, normalized data processing and analysis, and large sample size from multi-centers are indispensable. In addition, the combination of genomics, proteomics, metabolomics, or other omics with radiomics further enhances robust and comprehensive predictive ability providing detailed information for decision making and precision medicine ( 41 , 42 ).…”

“…The application of radiomics in the neoadjuvant setting might be reasonable and accurate, since other treatment phases may give rise to a controversy about the optimal imaging time considering altered tumor heterogeneity due to treatment (39). In a meta-analysis integrating 16 studies, the median AUC was 0.84 (0.81-0.87) to predict neoadjuvant chemoradiotherapy for EC patients suggesting the feasibility of radiomics (40).To our knowledge, this is the first radiomics for ESCC patients treated with NIT, with similar predictive effectiveness with other cancers (17,18). Such a predictive tool could have an impact on the early identification of non-responders so that patients seek alternative treatment and save cost.…”

ASO Author Reflections: A Systematic Review on Predictive Immune and Metabolic Biomarkers to Predict Clinical and Pathological Response in Esophageal Cancer

“…In a systematic review, we explored potential metabolic and immune TME biomarkers and their predictive role in pathological (PR) and/or clinical response (CR) after nCRT in EC. 4 We also provided some research perspectives on metabolic and immune TME biomarkers that might be associated with 18 F-FDG positron emission tomography/computed tomography (PET/CT) features. Included were 21 studies, 10 about immune and metabolic markers alone and 11 with additional 18 F-FDG PET/CT features.…”

Deciphering Tumor Response: The Role of Fluoro-18-d-Glucose Uptake in Evaluating Targeted Therapies with Tyrosine Kinase Inhibitors

“…Positron emission tomography (PET) imaging using 2-deoxy-2-[ 18 F] fluoro-d-glucose ([ 18 F] FDG) as an imaging agent plays a pivotal role in oncology, e.g., cancer diagnosis and staging. [ 18 F] FDG is a marker of energy metabolism and cell proliferation, and it is routinely used in the clinic to evaluate patient responses to chemotherapy [ 3 , 4 , 5 , 6 ], and it can be used to monitor tumor growth. Treatment with some tyrosine kinase inhibitors (TKIs) may cause changes in blood glucose levels [ 2 , 7 , 8 ].…”