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

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.

Addition of immune checkpoint inhibitors to chemotherapy versus chemotherapy alone in patients with triple‐negative breast cancer: A systematic review and meta‐analysis

“…In our meta‐analysis, PD‐L1 positive patients had significantly better PFS and OS than PD‐L1 negative patients. Similar conclusions were obtained in some of the included studies, such as IMpassion130, Keynote‐355, and Keynote‐522, which may be because PD‐L1 positive patients are more sensitive to ICI drugs 37 . However, IMpassion131 results were inconsistent which might be attributed to differing CT drugs and CT regiments 38 or might be related to varying proportions of random allocation and experimental errors.…”

“…Similar conclusions were obtained in some of the included studies, such as IMpassion130, Keynote‐355, and Keynote‐522, which may be because PD‐L1 positive patients are more sensitive to ICI drugs. 37 However, IMpassion131 results were inconsistent which might be attributed to differing CT drugs and CT regiments 38 or might be related to varying proportions of random allocation and experimental errors. Nonetheless, our analysis confirmed that ICI plus CT can improve the short‐term pCR rate of patients with TNBC compared with CT alone, as well as improve the ORR of ITT patients and PD‐L1 positive patients.…”

The miR-199a-5p/PD-L1 axis regulates cell proliferation, migration and invasion in follicular thyroid carcinoma

“…PD-L1 is a member of the B7 superfamily, which can be expressed in various types of tumors including lung cancer [ 7 ], melanoma [ 8 ] and thyroid cancer [ 9 ]; it plays a crucial role in the maintenance of immunological tolerance and is associated with poor prognosis and anti-tumor treatment resistance [ 10 ]. The expression of PD-L1 is an established prerequisite for immune checkpoint inhibitors in several tumor types [ 11 ].…”

Explainable artificial intelligence for patient stratification and drug repositioning

“…Cancer is a group of diseases that is the second leading cause of death in the United Pan-cancer represents a comprehensive heterogeneity analysis required to solve the intra-heterogeneity problem which is the major barrier to classifying patients into potential benefited groups [234]. This type of analysis has been used for a variety of research questions including studying genes' effect on cancer in general instead of studying their effect on each cancer type [235,236].…”

“…These molecular features were represented by RNA signatures, Tumor Mutational Burden (TMB), Copy-Number Alteration (CNA), and genes expression using network analysis [234,[239][240][241]. These methods represent an improvement on previous methods that depend on a limited set of genes, but they only focus on genotypic features without taking into account heterogeneity on the phenotypic level.…”