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Tumor immune microenvironmental biomarkers and immunotherapy response
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Tumor immune microenvironmental biomarkers and immunotherapy response.
In the past decade, the advent of immunotherapy has indeed completely changed the model of cancer treatment.
Many therapeutic drugs that target programmed cell death protein-1 (PD-1) or its ligands (PD-L1) have been approved by regulatory agencies as monotherapy drugs, or used in combination with chemotherapy and other targeted drugs. In clinical research, PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) are the main predictive biomarkers discovered so far.
However, PD-L1 IHC is still used in clinical practice to inform immune checkpoints The main auxiliary diagnostic test for inhibitor (ICI) therapy.
PD-L1 is expressed on the surface of tumor cells and tumor infiltrating immune cells. Studies in multiple tumor types have determined the correlation between PD-L1 levels and the clinical benefits of single-agent anti-PD-1/PD-L1 treatment.
Although patients with high PD-L1 expression can usually get the greatest clinical benefit, patients with low or no PD-L1 expression can still benefit. This prompts us to further explore the evolving cancer genome changes and infiltrating immune cell subtypes. Causality between types.
Considering the limitations of PD-L1 as a single biomarker, we need to understand the complex tumor microenvironment (TME) in order to develop better biomarkers and diagnostic methods, and more accurately predict clinical benefits. It is essential , To produce an effective immune response to tumors more specifically.
Unlike the duality of mutations, PD-L1 is a linear biomarker, so it can be evaluated at multiple expression thresholds. Due to the complex biological characteristics of PD-L1 expression on tumor cells and immune cells, the approved PD-L1 IHC analysis not only uses different expression thresholds, but also uses different scoring methods.
The PD-L1 scoring algorithm has analysis specificity, indication-dependent, and changes according to the treatment route. Some tests only score the expression of tumor cells (ignore immune cells), some tests only evaluate PD-L1 of immune cells (ignore tumor cells), and other tests measure PD-L1 of tumor and immune cells at the same time.
The following table shows the cut-off values for the main PD-L1 IHC analysis used in non-small cell lung cancer (NSCLC). It should be noted that the approval and use of these validated cut-offs are country-specific.
The expression of PD-L1 is dynamic, showing significant differences in tumor types, tumor locations, and even in the same tumor specimen.
The expression of PD-L1 in tumor cells is regulated by genome, epigenetics and transcriptional mechanisms. The strong correlation between PD-L1 on tumor cells and the degree of CD8+ T cell infiltration in NSCLC is probably due to transcriptional regulation in response to inflammatory signals (such as IFN-γ), which are caused by active anti-tumor Produced by the immune response.
The non-uniform expression of PD-L1 (usually confined to the immune infiltrated area) indicates that PD-L1 is adaptively induced due to the high local concentration of related cytokines in the TME, and further highlights the heterogeneity within the tumor. The intra-tumor heterogeneity and plasticity of PD-L1 may affect patient management decisions.
In addition, due to sampling, a false negative PD-L1 IHC result may be produced. The location of the biopsy and the treatments that have been performed are important parameters for PD-L1 expression.
For clinical reasons, it may not be feasible to obtain a new biopsy at the beginning of treatment, but due to the plasticity of the immune response, especially PD-L1, archived tumor specimens may not necessarily reflect TME in recurrent or metastatic lesions.
A variety of immune cell types, mainly macrophages, dendritic cells (DC), as well as T cells, NK cells, and B cells, can express PD-L1. It is not clear which specific immune groups or spatial characteristics of immune cells are most relevant to the clinic.
It is necessary to use multiple IHC technology to further describe the spatial interaction between tumor cells and specific immune cell subgroups to determine whether it is necessary to continue to classify immune cells into a broader category, which requires more detailed and more complex analysis.
Clinical trials have shown that tumors with a large number of effector T cells are more sensitive to ICI treatment.
Studies have used IFN-γ-centered gene expression characteristics to show the correlation between the overall response rate (ORR) and progression-free survival (PFS) of melanoma patients treated with the anti-PD-1 antibody pembrolizumab.
Consistent with these findings, the IMpower150 trial in NSCLC reported a feature of T effector cell (Teff) gene expression, consisting of PD-L1, CXCL9, and IFN-γ, which was found when comparing atezolizumab combined with chemotherapy versus chemotherapy alone Good for PFS.
In addition, patients with tumors with high mutational burden have obtained greater clinical benefits from CI treatment.
The consistency study of TMB and PD-L1 IHC seems to indicate that these biomarkers are orthogonal and independently predict the benefit of CI treatment.
Therefore, pembrolizumab has recently received regulatory approval for the treatment of unresectable or metastatic high TMB solid tumors that have progressed after previous treatment and have no alternative treatment options.
As the research on more predictive biomarkers continues to deepen, it is necessary to get rid of a single analysis method and integrate the various biological processes responsible for the immune response.
Preliminary exploratory analysis shows that the combination of biomarkers can translate into clinical benefits.
In the Checkmate-026 and Checkmate-227 studies, patients whose tumors express high levels of PD-L1 and have high tissue TMB status get the most benefit from treatment with the anti-PD-1 antibody nivolumab.
The OAK study also reported similar results.
Phenotypic diversity of immune effector cells
Many studies have described the relationship between tumor-infiltrating lymphocytes and their spatial distribution and therapeutic benefits.
Recognizing the spatial distribution of various immune effector cells needs to be supplemented by functional analysis, such as activation markers and antigen reactivity.
By analyzing the inherent anti-tumor reactivity of CD8+ T cell TCR sequences in tumor samples, it shows that the ability to recognize autologous tumors is only Limited to a few tumors infiltrating CD8+ T cells.
More complicated is that there may be significant TCR heterogeneity in CD4+ and CD8+ T cell populations in different areas of the tumor (center and edge infiltration), and the degree of heterogeneity has prognostic significance.
Generally, three different CD8+ T cell populations can be identified based on a common transcription profile:
- Primitive or memory cells expressing CC-chemokine receptor 7 (CCR7) and transcription factor 7 (TCF7);
- Perforin 1 (PRF1), Granzyme A (GZMA) and GZMB positive cytotoxic cells; and
- The “dysfunctional” diverse cell population is characterized by markers related to the depletion state, such as PD-1 (PDCD1), LAG3, and TIM3 (HAVCR2).
The relative proportions of these three groups seem to be highly variable, not only in different tumor types, but also in tumors of the same histology.
Transcription profiles and TCR sequencing information have been used to determine whether the phenotype of CD8+ cells in tumors has changed. Preliminary models indicate that TME-induced phenotypic heterogeneous dysfunctional cell populations evolved from naive CD8+ cells, and the latter produced cytotoxic cells in a TME-independent manner.
The extent to which the cells in the “dysfunctional” cell bank can be transformed into a “cytotoxic” state is currently unclear, however, understanding this is very important for designing therapies that promote and trigger this transition.
In addition, in several studies, intratumoral CD8+ T cells specific to neoantigens expressed by tumors have been identified, but they are only a small part of the total population of phenotypic heterogeneity. Interestingly, CD39 seems to be able to distinguish between tumor-specific (high CD39) and bystander (low CD39) CD8+ T cells.
Other phenotypic and functional analysis showed that the CD39+ subgroup co-expressed CD103 as well as PD-1, TIM3, and CTLA-4, and these markers are usually associated with depletion status. The co-expression of CD39 and CD103 depends on long-term stimulation by TCR and TGF-β exposure.
Interestingly, high levels of CD39+CD103+ double positive cells seem to translate into survival benefits for patients with squamous cell head and neck cancer.
Spatial distribution of immune effector cells
The latest research analyzes the spatial relationship between CD8+ T cells and MHC class II expressing cells (the most likely antigen presenting cells).
In patients with renal cell carcinoma, TCF1+/CD8+ T cells preferentially co-localize in the area of MHC class II expressing cells, while TCF1−/CD8+ cells are diffusely distributed throughout the tumor.
The incidence of TCF1+/CD8+ cells is also related to the incidence of DC, while TCF1−/CD8+ cells do not show this correlation. Interestingly, tumors containing TCF1+/CD8+ T cells and high-density areas expressing MHC class II APCs have better clinical efficacy, and the dense areas of APCs may be stem cell-like CD8+ T cells that can differentiate to maintain the effect of anti-tumor immune response.
Cells provide the environment within the tumor.
Obviously, the tumor infiltration of mature, active dendritic cells into the tumor bed increases immune activation and the recruitment of active immune effector cells.
It is important to accurately identify and quantify DCs. However, markers such as CD11c, CD11b, CD163, and MHC-II are not DC-specific and are expressed on other cell types such as macrophages.
The observation that DC can promote or suppress the neonatal immune response adds to the complexity, because there is currently no effective marker to distinguish between the two DC states.
In the process of tumor progression, TME transforms into an environment that actively protects tumor cells from immune attack.
TME cells considered to have tumor protection functions include T regulatory cells (Treg), tumor-associated macrophages (TAMs), and tumor-related Fibroblasts (CAFs) and myeloid suppressor cells (MDSCs).
Tumor protection is usually mediated by secreted cytokines and chemokines.
These cytokines and chemokines can prevent immune effector T cells from invading the tumor bed or rendering these cells ineffective, for example, TGF-β.
The combination therapy of TGB-β inhibitor and anti-PD-L1 antibody can cause the intratumoral penetration of Teff, change the phenotype from “exclusion” to “inflammatory”, reduce metastasis and improve long-term survival.
MDSC is a heterogeneous population of neutrophils and monocyte-like myeloid cells, and is increasingly regarded as a key mediator of immunosuppression in various cancers. In cancer patients, an increase in the number of circulating MDSCs is associated with advanced clinical stages, an increase in the incidence of metastatic disease, and immunosuppression.
In addition to immunosuppressive function, MDSC can also actively shape the tumor microenvironment through complex crosstalk with tumor cells and surrounding matrix, thereby increasing angiogenesis, tumor invasion and metastasis.
Intrinsic resistance of tumor cells
At present, there are still many gaps in our understanding of the different responses and drug resistance mechanisms of tumor immunotherapy.
Although we hope to find a panacea, the possible fact is that there are many mechanisms that help to generate resistance.
Although mutations in tumor suppressor factors (such as p53) increase the mutation load, help amplify the number of neoantigens, and make tumors more sensitive to the immune system, these mutations can also drive mutations in the antigen presentation pathway and key cellular mechanisms, thereby Trigger transcription and metabolism changes, which in turn have indirect effects on tumor infiltrating immune cells.
A key component of the tumor antigen presentation mechanism is MHC class I molecules, which are composed of HLA class I (HLA-I) subunits and β2 microglobulin (β2M).
The down-regulation of MHC-I exists in a variety of tumor types and may occur through multiple mechanisms, including gene mutation, epigenetic silencing, transcriptional changes, and post-translational modifications.
How does the down-regulation of MHC-I affect TME? Studies have found that the positive expression of HLA-I is related to the density of immune infiltration and has nothing to do with PD-L1 status. All HLA-I+/PD-L1+ tumors have a high degree of CD8+ T cell infiltration, and HLA-I loss is associated with a decrease in the number of T lymphocytes in the tumor, and its space is limited to the matrix around the tumor.
The HLA-I-/PD-L1- tumors are larger, and the CD8+ T cell density is lower. This study shows that the tumor immune escape phenotype combines two independent immune escape mechanisms: loss of HLA-I and upregulation of PD-L1.
Gain experience in neoadjuvant therapy
The recent exploration of early disease immunotherapy provides new opportunities to understand TME and its changes in treatment response. Neoadjuvant research is particularly rich, because diagnostic biopsies are often collected before treatment and can be compared with surgical resection immediately after neoadjuvant treatment.
The ABACUS trial is a single-arm Phase 2 study to study the neoadjuvant treatment of atezolizumab before cystectomy. Elevated PD-L1 levels (SP142; IC≥5%), baseline Teff gene expression and intraepithelial CD8+ T cells are associated with clinical benefits.
Interestingly, compared with preoperatively, an increase in the number of intraepithelial CD8+ T cells and PD-L1+ immune cells was observed in tumor samples after treatment, and this increase was more pronounced in responders than in relapsed patients .
In samples after treatment, it was found that TGF-β, fibroblast activation protein, and cell cycle genes were elevated to be associated with drug resistance.
The PURE-01 study reported similar results. Patients with bladder epithelial cancer received three cycles of pembrolizumab before radical cystectomy. Compared with patients with lower PD-L1 status, patients with elevated PD-L1 IHC (22C3; CPS≥10%) achieved a higher pathological complete remission rate.
Compared with the pre-treatment biopsy, the level of CPS of PD-L1 resected after treatment increased, and the level of TMB decreased.
In addition, compared with samples before treatment, immune-related genes (including genes related to IFN-γ signaling, antigen presentation, and T cell function) in samples after treatment also increased.
The NABUCCO study evaluated the effects of ipilimumab and nivolumab before resection of stage III urothelial carcinoma.
Compared with the ABACUS and PURE-01 studies, the treatment results have nothing to do with the baseline status of CD8+ T cell infiltration or Teff signal in the tumor.
In contrast, the study observed that there was a trend between increased TMB and increased frequency of DDR gene changes in patients who achieved a complete response. Patients who are unable to obtain a complete response are rich in TGF-β gene expression characteristics in baseline samples, indicating that there may be a resistance mechanism.
In addition, the correlation between TIL density and response was also studied. Interestingly, there was no correlation between baseline TIL and efficacy, but TIL enrichment was observed in patients who achieved CR after treatment.
Overall, baseline PD-L1 seems to predict ICI response. TMB, TIL and DDR are also promising as biomarkers, but further evaluation and verification are needed.
Differences in treatment options and the heterogeneity of patient groups may lead to inconsistencies in the results of different trials.
As neoadjuvant immunotherapy and chemotherapy-immunotherapy combinations are approved by regulatory authorities, identifying validated biomarkers to predict immune response is essential to help make treatment decisions.
Summary: Tumor immune microenvironmental biomarkers and immunotherapy response.
With the continuous development of the transition from chemotherapy to targeted therapy and immunotherapy, researchers will adopt more complex clinical trial designs, such as the combination of targeted therapy and immunotherapy, the combination of different CI drugs, and pay more attention to the development of biomarkers.
By design, these biomarkers are more sensitive to predicting response or resistance to single-drug or multi-drug treatment regimens.
In the future, a multi-parameter method is likely to be required to determine the patients most likely to respond to ICI treatment. It is very important to develop powerful tissue-based composite technologies.
This helps to better describe the characteristics of TME and analyze the interactions between tumor and effector immune cells and between effector cells and APC. The expression profile of related markers may indicate the activation, inhibition or exhaustion status of effector cells, and indicate the mechanism of resistance.
Tumor immune microenvironmental biomarkers and immunotherapy response.
Tumor immune microenvironmental biomarkers and immunotherapy response.
(source:internet, reference only)