Introduction
The immune system was explored as a complex stable network. In healthy conditions, the immune checkpoints play vital roles in protecting from autoimmune diseases [
1]. In malignant conditions, tumors may exploit peripheral immune tolerance (especially against cytotoxic T cells) for tumorigenesis by orchestrating these immune checkpoints [
2]. As an important immune checkpoint axis, the Programmed Cell Death-1/ Programmed Death-ligand 1 (PD-1 / PD-L1) axis was first reported in autoimmune-induced inflammation, but now this axis was more famous for its role in suppressing anti-tumor immunity [
3].
Malignant cells usually acquire immune tolerance by following mechanisms: 1) Suppress immunogenicity by down-regulating tumor-specific or related antigen, and / or impairing antigen presentation capability (e.g., the dysfunction of major histocompatibility complex class-I antigen presentation system) [
4]. 2) Up-regulating the immunosuppressive ligand on the cell surface (e.g., Fas ligand, CD44, PD-L1, etc.) [
5,
6]. 3) Remodeling microenvironment secretome, which not only promotes host immune tolerance but also might enhance tumor stemness / proliferation (e.g., regulating granulocyte colony-stimulating factor, IL-10, and IL-6, etc.) [
7,
8]. 4) Recruiting immunosuppressive cells in the microenvironment (e.g., myeloid-derived suppressor cells, regulatory T cells, etc.) [
9,
10]. These mechanisms together undermine the balance between pro-and anti-tumor immune responses and contribute to tumor immune escape.
Currently, α-PD-1 / PD-L1 aiming at switching off immune checkpoint is the most popular immune checkpoint blockade strategy. PD-1 also known as CD279 is a receptor mainly expressed on the surface of T and pro-B cells, and two ligands could bind to this receptor, PD-L1 and PD-L2 [
11]. Originally, several lines of evidence suggested that the PD-1 / PD-L1 axis negatively regulates immune responses, in mice models PD-1 knockout lead to severe autoimmune diseases [
12,
13]. And recently, more evidence revealed its role in evading immune surveillance and suppressing anti-tumor immunity, highlighting this axis as a target for immunotherapy.
Clinically, α-PD-1 / PD-L1 cancer immunotherapy continues to progress at a fast speed, and therapeutic strategies and pharmaceutic development are evolving rapidly to maximize patient benefit. In several solid tumors, especially lung cancer, α-PD-1 / PD-L1 immunotherapy has already been adopted in the first-line approaches for late-stage, adjuvant, and neoadjuvant cancer treatments [
14‐
16]. But only a fraction of patients with solid tumors responds well to α-PD-1 / PD-L1 therapy (around 20–40%, depending on cancer types) [
2]. So, why some patients don’t respond to α-PD-1 / PD-L1 immunotherapy is one of the major questions in the field. Currently, biomarkers, such as neutrophil-to-lymphocyte ratio, gut microbiota, tumor-infiltrating lymphocytes, etc., are used for predicting immunotherapy’s efficacy in non-small cell lung cancer (NSCLC) [
17,
18], and PD-L1 and tumor mutation burden (TMB) remain the most widely used biomarkers approved by the Food and Drug Administration (FDA). Of note, recently concerns were raised about the adequacy of traditional markers / indicators for immune checkpoint blockade (ICB) treatment, such as microenvironment PD-L1 level and TMB [
19,
20]. Hence, discriminating potential α-PD-1 / PD-L1 immunotherapy beneficiaries with adequate biomarkers still remains an urgent priority [
21].
Considering α-PD-1 / PD-L1 immunotherapy targets immunocytes and is designed to shift the immune balance towards anti-tumor response, the attempt of monitoring dynamic differentiation changes of immunocytes for evaluating neo indicators for α-PD-1 / PD-L1 immunotherapy is reasonable. Of note, different from traditional tissue-based methods (for evaluating PD-L1 or TMB level), a milliliter level blood-based method evaluating the differentiation status of immunocytes provides a flexible alternative. We introduced flow cytometry as an appropriate method in current immune-related research. Flow cytometry (FCM) is a laser fluorescence-based technique used to detect and analyze the chemical / biological and optical characteristics of cells and particles. In basic research / clinical practice, compared with traditional protein detection approaches (such as immunohistochemistry and immunoblot), FCM featured multiplex and high sensitivity. In the medical laboratory, FCM had been wielded adopted as a powerful tool for immunology-related measurement in hematopoietic malignancies, autoimmune diseases, and allograft transplants [
22‐
24].
In the current study, we evaluated the potential of monitoring the differentiation of immunocytes in peripheral blood as predictors / indicators for α-PD-1 therapy. We reported several interesting lymphocytes’ differentiation pattern and clinical parameters correlates with ICB response / outcomes.
Discussion
Lymphocytes which are differentiated from lymphoblasts-HSC (hematopoietic stem cells) circulate in peripheral blood and primary / secondary lymphoid organs and master adaptive immune responses / surveillance [
27]. There are three major populations of lymphocytes, B, NK and T populations. And anti-tumor immunity is primarily conducted and regulated by several T subpopulations. In the thymus, T cells undergo positive and negative selection and differentiation into two major distinct subsets, CD4
+ T
H / Regulatory T cells and CD8
+ CTL cells [
28]. Mature lymphocytes encounter antigens in secondary lymphoid organs and eventually differentiate into subpopulations of cells with different effector functions, such as activated T cells (HLA-DR
+). And microenvironment PD-L1 (CD274) binds to PD-1 (CD279) which is mainly expressed on the surface of T cells and results in T cell exhausting (expressing CD39) [
29], α-PD-1 monoclonal antibody blocks PD-1 on T cell surface, avoids CTL exhausting, facilitates cytokines releasing (e.g., Interferons-γ, which may also influence cell differentiation), and remodels lymphocytes differentiation / activation [
30,
31].
In practice, classical biomarkers being examined before immunotherapy include TMB and PD-L1 [
17,
32]. Several pieces of evidence suggested the failure of using these markers as biomarkers for ICB responses [
19]. With the inadequacy of classical markers, more researchers focused on emerging biomarkers such as neoantigen patterns, gut microbiota, tertiary lymphoid structure, etc. [
33,
34]. In the current study, we explored the predictive value of lymphocytes differentiation (baseline and dynamic changes) for ICB treatment responses / outcomes.
We analyzed the training set variables using machine learning and found that baseline activated T cells, Δ12W total lymphocytes, and baseline CD4+/CD8 + T cells were essential predictors of ICB prognosis. Subsequently, we constructed a clinical prediction model and validated it with a validation set, aiming at a comprehensive assessment of the variables.
Systemic immune dysregulation and cytotoxic agents induced hematopoietic damage together leading to lower peripheral lymphocytes in cancer patients [
35], and people assume that low peripheral lymphocyte counts positively correlate with fewer tumor-infiltrating immunocytes and predict poor responses / outcomes [
36]. Wang et al. reported that total lymphocyte count was higher in the ICB benefit group [
37]. Different from previous reports indicating lymphocyte counts predict ICB responses / outcomes [
38,
39], we didn’t find any statistical difference in total lymphocyte counts between DCB and NDB patients or survival / progression benefit between high and low lymphocyte counts at baseline (Fig.
2A, Supplementary Tables
11 and
12). For dynamic changes, previous studies reported the importance of increased lymphocytes after ICB treatment, we also found that increased lymphocyte counts after ICB treatment predicts good responses / outcomes at a week of 12 (Figs.
2C and
3F, Supplementary Tables
6 and
8) [
37,
40]. In summary, we have showed the essential of monitoring lymphocyte counts during ICB treatment by flow cytometry.
As key players in immune surveillance and anti-tumor immunity, T cell activation featured with the expression of major histocompatibility complex class-II molecular (e.g., HLA-DR) requires both antigen-specific and costimulatory signals. And increased activated T cell (HLA-DR
+) counts during ICB treatment indicate the success of ICB treatment [
41,
42]. In current research, we also found that the elevation of activated T cells indicates better outcomes after a short period of treatment (6 weeks, Fig.
3J, Supplementary Table
9). But interestingly, at baseline, we found that higher activated T cells correlate with less clinical benefit (Fig.
2D, Supplementary Table
6), shorter PFS (Fig.
3C, Supplementary Table
8), or OS (Fig.
3E, Supplementary Table
9).
After that, we explored the distribution of T cells and their major subtypes, T
Hs and CTLs in peripheral blood. We found that patients with higher levels of CTLs count at baseline bear a poor prognosis (Fig.
3M, Supplementary Table
8), but an elevated CTLs level after 12 weeks of ICB treatment indicates favorable responses (Supplementary Table
5), our data complement previous knowledge indicating the importance of tumor-infiltrating CTLs level [
43]. But interestingly, an increased total T cell counts was associated with poor responses / outcomes from a week of 6 (Fig.
3H, Supplementary Tables
5 and
9). This counterintuitive phenomenon might be explained by CD4
+ / CD8
+ T cell ratio, after 12 weeks of ICB treatment a decreased ratio was associated with favorable responses, considering in peripheral blood the majority of T cells are CD4
+ T
Hs subpopulations which indicates the increased total T cell counts in NDB group might be explained by the elevation of T
Hs. And complement with previous data we also found that baseline CD4
+ / CD8
+ T cell ratio was positively associated with clinical benefits including responses and outcomes (Figs.
2G and
3A) [
44,
45].
Tumor markers were commonly used as auxiliary biomarkers for cancer diagnosis. Currently, CA125 is mainly considered as a specific tumor marker for ovarian cancer, but several studies showed that CA125 was elevated in about 46.6% of NSCLC patients, and predicts worse outcomes / aggressive phenotypes [
46,
47]. In the current study, we found that patients with higher level of CA125 at baseline have better outcomes, but an elevated CA125 level after 12 weeks of ICB treatment indicates worse ICB response and outcomes (Fig.
3B, G Supplementary Tables
6 and
8).
We constructed clinical prediction models for ICB treatment response and outcome in the training set, and validated the model with the validation set (Figs.
3,
4 and
5). Our model showed moderate prediction performance for immunotherapeutic responses and outcomes, and it can provide intuitive initial treatment expectation for clinicians.
Some limitations should be addressed for current research. Firstly, because PD-L1 immunohistochemistry staining is not a mandatory test for patients who will receive 2+-line therapy or in combination with platinum based first-line therapy, therefore no PD-L1 tumor proportion score (PD-L1 TPS) expression was recorded and reported in current study. Secondly, the patients enrolled in this study were treated in different clinical groups from our hospital, it’s difficult to fully record the immune-related adverse events (irAE). Thirdly, the retrospect study with fewer markers for flow cytometry panel limited the exploration of immunophenotype, a prospective study with more flow cytometry makers is required for fully understanding the relationship between ICB outcomes / responses and immunophenotypes.
In the current study, we focused on analyzing the dynamic changes of peripheral blood lymphocytes differentiation characteristics in patients receiving ICB treatment. We observed distinctive modification of immune status in certain groups of patients with favorable responses / outcomes after immunotherapy (e.g., elevated activated T cell counts after ICB treatment), which might help to select and identify novel therapeutic beneficiaries. Moreover, precise identification of more subpopulations using other lymphocyte markers might provide richer results, and further studies using larger cohorts of patients with control arms are warranted to validate these biomarkers.
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