Multi-Omics Perspective Reveals the Different Patterns of Tumor Immune Microenvironment Based on Programmed Death Ligand 1 (PD-L1) Expression and Predictor of Responses to Immune Checkpoint Blockade across Pan-Cancer.

Jinfen Wei,Meiling Hu,Jingxin Qin,Jiayun Chen,Shudai Lin,Kaitang Huang,Hongli Du

doi:10.3390/ijms22105158

Abstract

Immune checkpoint inhibitor (ICI) therapies have shown great promise in cancer treatment. However, the intra-heterogeneity is a major barrier to reasonably classifying the potential benefited patients. Comprehensive heterogeneity analysis is needed to solve these clinical issues. In this study, the samples from pan-cancer and independent breast cancer datasets were divided into four tumor immune microenvironment (TIME) subtypes based on tumor programmed death ligand 1 (PD-L1) expression level and tumor-infiltrating lymphocyte (TIL) state. As the combination of the TIL Z score and PD-L1 expression showed superior prediction of response to ICI in multiple data sets compared to other methods, we used the TIL Z score and PD-L1 to classify samples. Therefore, samples were divided by combined TIL Z score and PD-L1 to identify four TIME subtypes, including type I (3.24%), type II (43.24%), type III (6.76%), and type IV (46.76%). Type I was associated with favorable prognosis with more T and DC cells, while type III had the poorest condition and composed a higher level of activated mast cells. Furthermore, TIME subtypes exhibited a distinct genetic and transcriptional feature: type III was observed to have the highest mutation rate (77.92%), while co-mutations patterns were characteristic in type I, and the PD-L1 positive subgroup showed higher carbohydrates, lipids, and xenobiotics metabolism compared to others. Overall, we developed a robust method to classify TIME and analyze the divergence of prognosis, immune cell composition, genomics, and transcriptomics patterns among TIME subtypes, which potentially provides insight for classification of TIME and a referrable theoretical basis for the screening benefited groups in the ICI immunotherapy.

Highlights

For the past few years, clinical results revealed that immune checkpoint inhibitor (ICI) treatment, such as programmed death-1 (PD-1) and its ligand 1 (PD-L1) checkpoint blockade, have shown an exhilaratingly long-term effect in a variety of cancer patients and have become a research focus in current tumor immunotherapy [1,2,3]
We used the receiver operating characteristic (ROC) curve to measure the true-positive rates against the false-positive rates at various thresholds of the tumor-infiltrating lymphocyte (TIL) Z score and CD8A and
The results showed that the predictive power of the TIL Z

Summary

Introduction

For the past few years, clinical results revealed that immune checkpoint inhibitor (ICI) treatment, such as programmed death-1 (PD-1) and its ligand 1 (PD-L1) checkpoint blockade, have shown an exhilaratingly long-term effect in a variety of cancer patients and have become a research focus in current tumor immunotherapy [1,2,3]. It has been reported that a number of patients showed a low response rate or treatment resistance against the anti-PD-1/PD-L1 checkpoint blockade [4,5,6]. It has been reported that the TIL status in the tumor immune microenvironment (TIME) is positively related to good clinical prognosis and could better predict the response to anti-PD-1/PDL1 therapies [11,12,13,14]. Owing to the divergence of TIL status and PD-L1 expression, the immunologic effects of different TIME subtypes can be various, and the corresponding immunotherapeutic strategies can be different. Recent research has described four different subtypes of TIME based on the positive or negative status of TIL and PD-L1 expression, including type I (PD-L1+/TIL+: adaptive immune resistance), type II (PD-L1−/TIL−: immunological ignorance), type III (PD-L1+/TIL−: intrinsic PD-L1 induction), and type

Methods

Results

Discussion

Conclusion