From the Immune Profile to the Immunoscore: Signatures for Improving Postsurgical Prognostic Prediction of Pancreatic Neuroendocrine Tumors - Wei, Miaoyan; Xu, Jin; Hua, Jie; Meng, Qingcai; Liang, Chen; Liu, Jiang; Zhang, Bo; Wang, Wei; Yu, Xianjun; Shi, Si (Frontiers in Immunology, 2021)
Objective: Immune infiltration plays an important role in tumor development and progression and shows promising prognostic value in numerous tumors. In this study, we aimed to identify the role of immune infiltration in pancreatic neuroendocrine tumors (Pan-NETs) and establish an Immunoscore system to improve the prediction of postsurgical recurrence-free survival. Methods: In order to derive transcriptional signatures and deconvolute the specific immune populations, two GEO datasets containing 158 Pan-NET patients were reanalyzed to summarize the immune infiltration landscape and identify immune-related signatures. Using real-time reverse transcription-polymerase chain reaction, immunofluorescence and immunochemistry methods, candidate signatures were further detected. The least absolute shrinkage and selection operator (LASSO) logistic regression model used statically significant survival predicators in the training cohort (n=125) to build an Immunoscore system. The prognostic and predictive accuracy was validated in an external independent cohort of 77 patients. Results: The immune infiltration profile in Pan-NETs showed significant heterogeneity, among which accumulated immune cells, T lymphocytes and macrophages predominated. Fourteen statistically significant immune-related signatures were further identified in the screening cohort. The Immunoscore system for Pan-NETs (ISpnet) consisting of six immune features (CCL19, IL-16, CD163, IRF4, CD8PT and CD8IT) was constructed to classify patients as high and low risk in the training cohort (cut-off value = 2.14). Low-risk patients demonstrated longer 5-year recurrence-free survival (HR 0.061, 95% CI 0.026 to 0.14; p < 0.0001), with fewer recurrences and better prognoses. To predict the individual risk of recurrence, a nomogram incorporating both immune signature and clinicopathological characteristics was developed. Conclusion: Our model, terms ISpnet, captures the immune features associated prognostic indicators in PNETs and represents the first immune feature-based score for the postsurgical prognostic prediction of Pan-NETs. The nomogram based on the ISpnet and independent clinical risk factors might facilitate decision-making regarding early recurrence risk monitoring, identify high-risk patients in need of adjuvant therapy, and provide auxiliary guidance for patients with Pan-NETs that may benefit from immunotherapy in clinical trials.