Checkpoint inhibitor-based immunotherapy has exhibited unprecedented success in the treatment of advanced-stage cancer in recent years. Several therapeutic antibodies targeting programmed death-1 (PD-1) or its ligand (PD-L1) have received regulatory approvals for the treatment of multiple malignancies, including melanoma, non-small cell lung cancer, kidney cancer and Hodgkin's lymphoma. However, a substantial proportion of patients still do not benefit from these agents, let alone the risk of immune-associated toxicities and financial burden. Therefore, it is imperative to identify valid predictive biomarkers which can help optimize the selection of patients. In this review, a mechanism-based interpretation of tumor PD-L1 expression and other candidate biomarkers of response to antitumor PD-1/PD-L1 blockade was provided, particularly for the tumor microenvironment-derived ‘immunomes’, and the challenges faced in their clinical use was addressed. Directions for future biomarker development and the potential of combined biomarker strategies were also proposed.
Cancer immunotherapy, which aims to foster the host immune response against cancer to obtain durable anticancer responses, has achieved marked success in the past decade (
Despite significant progress of PD-1/PD-L1-directed immunotherapy, the efficacy and safety profiles of these agents varies greatly across individual patients and among different tumor types. Not all patients respond to PD-1/PD-L1 blockade (
Immune tolerance is considered one of the hallmarks of cancer that is exploited by tumor cells to evade immune surveillance and elimination (
PD-L1 expression in tumors has been hypothesized to be associated with response to PD-1/PD-L1 blockade. In a phase I trial, Topalian
In addition to PD-L1 expression on tumor cells or tumor-infiltrating immune cells, other forms of PD-L1 can also predict response to anti-PD-1/PD-L1 therapy. A recent study by Chen
In numerous tumors, PD-L1 expression can be induced either via oncogenic drivers and transcriptional factors, or via cytokines produced by tumor-infiltrating immune cells (
Although the results suggest PD-L1 expression as a predictive biomarker, several clinical trials have repeatedly demonstrated that there is a small but definite proportion of PD-L1-negative patients who can also derive clinical benefit from PD-1/PD-L1 blockade (
Notably, the application of PD-L1 testing via IHC as a predictive biomarker is associated with several issues. Technically, different PD-L1 IHC antibodies with different analysis systems and different cut-off values for PD-L1 positivity were employed in early clinical trials (
Owing to advances in DNA sequencing techniques, a large amount of information on cancer genetics and genomics has been gained in the past few decades. There is increasing evidence that the TMB can predict response to ICIs, including anti-PD-1/PD-L1 drugs. The first indication was from the combined result, revealing that tumors with a high TMB (melanoma and NSCLC) often have a higher response rate to anti-PD-1 or anti-PD-L1 therapy across multiple tumor types (
Notably, recent evidence suggests that mismatch repair deficiency (MMRD) may be associated with an increased response to PD-1/PD-L1 blockade (
The association between TMB and response to anti-PD-1/PD-L1 drugs is considered to be primarily due to the generation of neoantigens, as a result of somatic mutations in tumor cells. The theory is that tumors with a high mutational load often possess more neoantigens, which can be recognized as non-self epitopes by the immune system, thereby enhancing T cell responses against tumors, as well as killing tumor cells when the PD-1/PD-L1 axis is blocked (
TMB is a promising predictive biomarker, albeit with its own limitations. Although a number of studies correlate TMB with tumor response to anti-PD-1/PD-L1 drugs, there has been thus far, seldom numerical cut-off value of TMB that was formally defined (
The TME consists of non-malignant stromal cells, such as cancer-associated fibroblasts (CAFs), bone marrow-derived cells and tumor-infiltrating immune cells, extracellular matrix, and the blood and lymphatic vascular networks (
Firstly, the presence of tumor-infiltrating T cells was demonstrated to be associated with clinical benefit from anti-PD-1/PD-L1 therapy. Tumeh
Secondly, the immunosuppressive cell populations in TME could restrain the response to PD-1/PD-L1 blockade (
Thirdly, the molecular profiles of TME pre- or post-anti- PD-1/PD-L1 immunotherapy represent alternative biomarkers of response (
The close association between the aforementioned TME components and treatment efficacy in PD-1/PD-L1 blockade can be explained by the following theory. Immune recognition of tumors results in a host-immune response, which promotes tumor eradication through immune mechanisms, including antigen presentation, T cell priming and trafficking, cytokine production, cytotoxic activity and the expression of other immune genes. However, the antitumor Th1 and CD8+ T cell responses are negatively regulated by PD-1/PD-L1-mediated adaptive immune resistance (
Traditionally, the immune profiles of tumors can be classified into three main phenotypes: The immune-inflamed phenotype, the immune-excluded phenotype and the immune-desert phenotype; based on whether tumors harbor an inflammatory TME or not (
Despite the known predictive biomarkers, such as PD-L1 expression, TMB and TME profiles, additional research is still necessary to explore other reliable candidate biomarkers for predicting the response of patients to anti-PD-1/PD-L1 therapies. In fact, emerging data have indicated future directions for biomarker development.
Immunogenic cell death (ICD) in tumors has been implicated in the response to PD-1/PD-L1 blockade. Recently, Zhao
The diverse T-cell repertoire, corresponding to different antigenic peptides bounding to class I or II MHC, is generated by random recombination of discrete TCR-αβ gene segments (
Recently, intensive studies have been conducted to dissect the impact of the gut microbiome on response to anti-PD-1/PD-L1 immunotherapy in human malignancies, including melanoma, hepatocellular carcinoma, gastric cancer and NSCLC (
The evaluation of peripheral blood could be another interesting approach. It has been reported by Weide
Owing to advances in technology, medical imaging can not only assess macroscopic features, but also characterize the cellular and molecular properties of malignant lesions, which may serve as a novel approach to select patients for PD-1/PD-L1 blockade. It has recently been revealed that PD-L1 and PD-1 expression in NSCLC could be quantified non-invasively with PET-CT imaging using the radiotracers 18F-BMS-986192 and 89Zr-nivolumab, respectively (
The clinical use, applicable tumors, as well as predicted clinical outcomes of each biomarker discussed in this review are summarized in
Combined biomarker strategies may enrich responders to anti-PD-1/PD-L1 immunotherapy in future. Factors enabling response prediction, such as PD-L1 IHC testing, TMB and TME profiles, should be incorporated together, since it has been indicated that high tumor PD-L1 expression does not equate to a T cell-inflamed content, and that high TMB does not always indicate pre-existing antitumor immune activities (
Overall, anti-PD-1/PD-L1 immunotherapy is one of the predominant methods for cancer treatment. The improvement in the understanding of the interactions among multiple factors, as well as the dynamic changes of certain variables within different tumor types will certainly help identify more reliable and effective predictors for PD-1/PD-L1 blockade, thereby paving the way for a framework that allows treatment decisions to be made on a personalized basis.
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The present study was supported in part by grant nos. 81902325 and 81903633 from the National Natural Science Foundation of China, grant no. 2017WS026 from the Health and Family Planning Commission of Shandong Province (China), and grant nos. 2019GSF107042 and 2019GSF107051 from the Key Research and Development Plan of Shandong Province (China).
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LS and SJ devised the conceptual idea. WY, BS and JS performed the literature search. WY prepared the figures. WY, XL, LS and SJ wrote, reviewed and revised the manuscript. All authors read and approved the final manuscript.
Not applicable.
Not applicable.
The authors declare that they have no competing interests.
PD-1/PD-L1-maintains immune tolerance in tumors. In lymphoid tissue, APCs can dispose neoantigens, and then activate naive T cells through MHC-II/TCR interaction and B7.1/B7.2/CD28 co-stimulatory pathways. The CD4+ T helper cells can also contribute to the priming of CD8+ T cytotoxic cells via various cytokines. In early stages of T cell activation, the T-cell response can be downregulated by B7.1/B7.2/CTLA-4 checkpoint pathways. The effector T cells can proliferate and migrate to TME, leading to tumor eradication via MHC-I/TCR interaction. The PD-1/PD-L1 checkpoint pathway can maintain immune resistance of tumor cells to T-cell attack. The mechanism of action is that PD-L1 results in the tyrosine phosphorylation of PD-1 cytoplasmic ITIM and ITSM in effector T cells, which recruit phosphatases, particularly SHP-2. This leads to the dephosphorylation of TCR proximal signaling molecules, attenuating TCR and CD28 signals, which promotes T-cell apoptosis, anergy and functional exhaustion. PD-1, programmed death-1; PD-L1, PD ligand-1; APCs, antigen presenting cells; MHC, major histocompatibility complex; TCR, T cell receptor; CTLA-4, cytotoxic T lymphocyte-associated antigen-4; TME, tumor microenvironment; ITIM, immunoreceptor tyrosine-based inhibition motif; ITSM, immunoreceptor tyrosine-based switch motif; SHP-2, Src homology region 2 domain-containing phosphatase-2.
TME-derived predictive biomarkers for PD-1/PD-L1 blockade. The TME consists of non-malignant stromal cells (cancer-associated fibroblasts, MDSCs, effector T helper cells, cytotoxic T cells, Treg cells and macrophages), extracellular matrix, and the blood and lymphatic vascular networks. Those stromal cells can release growth factors, matrix-degrading enzymes, cytokines and chemokines. The components of TME, including exhausted CD8+ T cells, MDSCs, Treg cells, IDO, IFN-γ and IFN-related genes (CXCL9, CXCL11, and IFN receptor-associated Jak1 and Jak2), and other immune genes (BACH2 and CCL3), proposed as biomarkers of response to anti-PD-1/PD-L1 therapy were categorized. TME, tumor microenvironment; PD-1, programmed death-1; PD-L1, PD ligand-1; MDSCs, myeloid-derived suppressor cells; Treg cells, regulatory T cells; IDO, indoleamine 2,3-dioxygenase; IFN, interferon; CXCL, C-X-C motif chemokine; Jak, Janus kinase; BACH2, BTB domain and CNC homolog 2; CCL, C-C motif chemokine.
Predictive biomarkers for antitumor PD-1/PD-L1 blockade.
Biomarkers | Details of clinical use | Tumors | Clinical outcomes | (Refs.) |
---|---|---|---|---|
PD-L1 | IHC analysis of tumor cells, tumor-infiltrating immune cells, or both | NSCLC, melanoma, RCC, squamous cell carcinoma of the head and neck, and urothelial carcinoma | ORR, PFS, OS, DoR and DCR | ( |
ELISA examination of circulating exosomal PD-L1 | Melanoma | ORR, PFS and OS | ( |
|
ELISA examination of plasma soluble PD-L1 | NSCLC | ORR and OS. | ( |
|
TMB | Whole-exome sequencing to identify somatic non-synonymous mutations in tumor or plasma samples | Melanoma and NSCLC | ORR, PFS and pathologic response | ( |
MMRD | PCR analysis of microsatellite sequences or IHC analysis of MMR proteins | Colorectal, ampulla of water, chol-angiocarcinoma, endometrial, pancreas, gastroesophegeal, neuroendocrine, osteosarcoma, prostate, small intestine and thyroid cancer | ORR, PFS and radiographic response | ( |
TME | ||||
Tumor-infiltrating T cells | Multiplex IHC, immunofluorescence or flow cytometric analysis of tumor samples | Melanoma, lung cancer, colorect-al cancer and mammary cancer | ORR, radiographic response and PFS | ( |
Tumor-infiltrating myeloid cells and Treg cells | Flow cytometric analysis of tumor samples | Colorectal cancer and mammary cancer | ORR | ( |
IDO | RT-PCR analysis of tumor-infiltrating T cells | Colorectal cancer | ORR | ( |
IFN-γ and IFN-related genes | Fluidigm BioMark HD RT-PCR platform to detect IFN-γ and IFN-related genes in tumor samples | NSCLC and melanoma | ORR and PFS | ( |
Other immune genes | Whole genome microarray, multiplex quantitative RT-PCR or NanoString platform to analyze gene expression in tumor samples | RCC and melanoma | ORR and radiographic response | ( |
ICD | Flow cytometry, ELISA, ATPLite bioluminescence and Fluorescence microscopy to detect ICD markers | Pancreatic cancer, melanoma and NSCLC | OS, radiographic response and cure rate | ( |
TCR clonality | Next generation sequencing of TCRβ CDR3 region | Melanoma | ORR and radiographic response | ( |
Gut microbiome | 16S ribosome RNA gene sequencing, metagenomic sequencing or exon sequencing to evaluate gut bacterial in fecal samples | Hepatocellular carcinoma, gastric cancer, melanoma and NSCLC | ORR, radiographic response and PFS | ( |
Peripheral blood biomarkers | Peripheral blood routine and biochemical examination, and flow cytometric examination | Melanoma and NSCLC | ORR, DCR, DoR, PFS and OS | ( |
Imaging biomarkers | Positron-emission tomography imaging with radio-labeled antibodies or radiomic feature analysis of targeted molecule | Lung, urothelial, kidney, gynaeco-logical, liver, breast, colorectal, head and neck, gastric, oesopha-geal, thyroid, prostate cancer, mel-anoma, sarcoma and lymphoma | ORR, PFS and OS | ( |
PD-L1, PD ligand-1; IHC, immunohistochemistry; NSCLC, non-small cell lung cancer; RCC, renal cell carcinoma; ELISA, enzyme-linked immunosorbent assay; TMB, tumor mutational burden; MMR, mismatch repair; MMRD, mismatch repair deficiency; PCR, polymerase chain reaction; RT-PCR, real-time polymerase chain reaction; TME, tumor microenvironment; Treg cells, regulatory T cells; IDO, indoleamine 2,3-dioxygenase; IFN, interferon; ICD, immunogenic cell death; TCR, T cell receptor; ORR, objective response rate; PFS, progression-free survival; OS, overall survival; DoR, duration of response; DCR, disease control rate including CR, PR, SD ≥6 weeks; CR, complete response; PR, partial response; SD, stable disease.