Peripheral Blood Biomarkers Predicting the Efficacy of Immune Checkpoint Inhibitors in Non-small Cell Lung Cancer: A Retrospective Study

Peripheral Blood Biomarkers Predicting the Efficacy of Immune Checkpoint Inhibitors in Non-small Cell Lung Cancer: A Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Peripheral Blood Biomarkers Predicting the Efficacy of Immune Checkpoint Inhibitors in Non-small Cell Lung Cancer: A Retrospective Study Jinshan Yan, Xin Li, Hong Xiao, Lu Xu, Pan Wang, Lutong Cai, Ruotong Liu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4545921/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: Non-small cell lung cancer (NSCLC) leads to substantial challenges in cancer treatment owing to its diverse histological and molecular characteristics. Immune checkpoint inhibitors (ICIs) have revolutionized the management of NSCLC. Nevertheless, there exist limitations in utilizing biomarkers, like PD-L1 expression for predicting the efficacy of ICIs, necessitating novel biomarkers. Methods We investigated the relationship between peripheral blood T cell subsets, cytokines, and efficacy of ICIs in patients who received ICIs as their first-line treatment for pathologically confirmed locally advanced or metastatic NSCLCs. Propensity score matching (PSM) was employed to match individuals between the response and non-response groups. Subsequently, peripheral blood T lymphocyte profiles and cytokine subsets were measured using flow cytometry. Mann-Whitney and Kruskal-Wallis tests were used for intergroup analysis before, after, and during treatment. Log-rank regression and Cox regression models were used to analyze survival and conduct multivariate analysis, respectively. Results Between July 1, 2021, and December 31, 2023, there were 470 patients with clinical stage IIIB to IV NSCLC. After applying the inclusion criteria, a post-propensity score-matching analysis was performed on 102 patients. The median progression-free survival (PFS) was 14.30 months. These subsets included activated CD4 + T cells (HLA-DR + )/CD4% (P = 0.0170), memory CD8 + T cells/CD8% (P = 0.0115), activated CD8 + T cells (CD38+)/CD8% (P = 0.0020), and activated CD8 + T cells (HLA-DR+)/CD8% (P < 0.0001). Changes in cytokine levels before and after treatment with ICIs indicated that IL-6 levels showed a downward trend in the responder group. Additionally, our analysis revealed that an increased ratio of activated CD8 + T cells (CD38 + )/CD8% (average PFS: 22.207m vs. 15.474m) and a decreased ratio of activated CD8 + T cells (HLA-DR + )/CD8% after treatment (mean PFS: 17.729m vs. 25.662m) are associated with longer PFS. Multivariate analysis unveiled that alterations in the abundance of activated CD8 + T cells were independent prognostic factors for PFS in patients with advanced NSCLC. Conclusions This study emphasizes the significance of peripheral blood biomarkers in predicting the efficacy of ICIs in NSCLC. Activated CD8 + T cells (CD38 + ) represent a promising biomarker for response to ICIs, providing insights into personalized treatment strategies. Further prospective studies are warranted to validate findings and improve the outcome of NSCLC. Immune checkpoint inhibitors (ICIs) Non-small cell lung cancer (NSCLC) Peripheral blood biomarker T cell subsets Cytokines Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Non-small cell lung cancer (NSCLC) presents a significant challenge in cancer treatment due to its heterogeneous histological and molecular profiles, leading to complex clinical manifestations and making it a leading cause of cancer-related deaths 1 . Immune checkpoint inhibitors (ICIs) have been a game-changer, significantly improving survival rates for patients with NSCLC 2 . Specifically, compared to traditional chemotherapy, atezolizumab showed promising results in a phase III clinical trial conducted by IPSOS, improving survival rates and quality of life, with a favorable safety profile when used as a first-line treatment 3 . However, available biomarkers, such as PD-L1 expression and tumor mutational burden (TMB), cannot adequately predict the efficacy of ICIs 4 . These markers do not provide a full landscape of a patient's immune status, which is critical for the effective application of immunotherapy. Regardless of PD-L1 expression status, studies like KEYNOTE-189 and RATIONALE-307 have demonstrated that the combination of ICIs and chemotherapy can significantly enhance survival benefits over chemotherapy alone 5 . This finding underscores the urgent need to identify and incorporate new biomarkers into treatment protocols to predict outcomes and more accurately tailor immunotherapy regimens for patients with NSCLC, highlighting a significant gap in the current approach. ICIs primarily activate lymphocytes 6 and deliver lymphocytes into the tumor microenvironment 7 . Tumor-infiltrating immune cells in the tumor immune microenvironment may be related to the efficacy of immunotherapy for NSCLC 8 . T lymphocytes serve as primary effector cells in anti-tumor immune responses 9 . T cell surface biomarkers, including CD45RO, CD45RA, CD38,HLA-DR, and CD28, in the tumor microenvironment can help subdivide these cells into naive, memory, activated, and senescent subgroups 10 . Different subgroups play different roles in immunotherapy 11 . Previous studies have demonstrated that ICIs induce the tumor infiltration of naive and memory T cells from tumor-draining lymph nodes (tdLN) 12 . Additionally, both CD38 and HLA-DR are expressed in T cells, serving as markers of T cell activation 13 . Consequently, CD38 + T cells and HLA-DR + T cells significantly contribute to the anti-tumor response 14 . Nevertheless, senescent T cells express PD-L1, leading to immune evasion 15 . However, the intricate functions of T cell subtypes in the TME remain largely unknown 16 . Similarly, cytokines play a pivotal role in signal transmission in the tumor microenvironment 17 . For instance, TNF 18 and IL-6 19 promote tumor cell survival, proliferation, and angiogenesis, whereas IFN-γ 20 , IL-2 21 , and IL-15 22 enhance lymphocyte cytotoxicity and exert anti-tumor effects. High-dose recombinant IL-2 was granted FDA approval for metastatic renal cell carcinoma and melanoma in 1992 and 1998 23 . Furthermore, ongoing clinical trials are exploring additional cytokines 24 . Thus, cytokines are pivotal in regulating the interplay between tumor cells and immune cells in the tumor microenvironment. Although cytokines play a pivotal role in cancer immunotherapy 25 , the efficacy of cytokines and ICIs in patients with NSCLC remains unclear. Thus, it is imperative to substantiate our claims with real-world medical data. In this retrospective study, we explored the relationship between the detailed profiles of peripheral blood T lymphocytes, cytokine subsets, and the efficacy of ICIs in patients with NSCLC. We aimed to identify novel biomarkers to identify patients more efficiently with NSCLC who will significantly benefit from ICIs, advancing the field of personalized cancer therapy. 2. Material and methods 2.1 Clinical Data Data from 470 patients with pathologically confirmed locally advanced or metastatic NSCLC were retrospectively collected in this study. From July 1, 2021, to December 31, 2023, patients received anti-PD-1/PD-L1-based systemic therapy as their first-line treatment at the Medical Oncology Department, the First Hospital of China Medical University. Patients were screened based on the following criteria: ( 1 ) patients were diagnosed with NSCLC based on cytological or histological evidence of lung adenocarcinoma or lung squamous cell carcinoma. All patients with lung adenocarcinoma were negative for EGFR, ALK, and ROS1 driver genes; ( 2 ) patients were diagnosed with locally advanced or metastatic NSCLC (stage IIIB, IIIC, or IV) according to the eighth edition of the American Joint Committee on Cancer Classification lung cancer staging system; ( 3 ) patients had complete baseline and imaging assessment data; ( 4 ) patients received first-line treatment with ICIs, either as a single drug or in combination therapy; and ( 5 ) patients' physical status was good with adequate organ function, and the ECOG performance status was ≤ 2. All patients included in this study received regular follow-ups for more than 6 months. Patients diagnosed with neuroendocrine carcinoma, sarcomatoid carcinoma, and those ineligible for immunotherapy were excluded from this study (Table 1 ). This study was conducted following the Declaration of Helsinki and was approved by the Ethics Committee of the First Hospital of China Medical University. Informed consent was obtained from all participants (IRB protocol: 2022-272-2). Table 1 Baseline Characteristics Patient Characteristics N (%) Age ≤ 75 > 75 80(90.9%) 8(9.1%) Sex Male Female 74(84.1%) 14(15.9%) ECOG PS 0–1 2 86(97.7%) 2(2.3%) Smoking history Yes No 68(77.3%) 20(22.7%) Histology Squamous carcinoma Adenocarcinoma 50(56.8%) 38(43.2%) PD-L1expression TPS < 1% TPS ≥ 50% Not available 57(64.8%) 16(18.2%) 15(17.0%) 2.2 Baseline information and treatment strategy Next-generation sequencing (NGS) was used to measure the mutation status of driver genes. Relevant information was extracted from patient's medical records. Using the 22C3 antibody, the PD-L1 tumor proportion score (TPS) was determined based on immunohistochemical staining. In addition, data on age, gender, smoking history, pathology, and ECOG performance status were also included. Physicians guided critical decisions, including the formulation of treatment plans, and determined to either continue, modify, or discontinue treatment. 2.3 Analysis of blood samples Peripheral blood samples were collected at baseline and when assessing imaging response before administering ICIs and disease progression. All blood samples were acquired through peripheral blood venipuncture and kept in anticoagulant tubes containing EDTA. The test was completed in the clinical laboratory department of the First Hospital of China Medical University. Flow cytometry was carried out to analyze the T lymphocyte subsets and cytokines. Briefly, fluorescent antibodies CD3-Percp, CD4-APC-cy7, CD8-PE-cy7, CD38-PE and HLA-DR-APC were, respectively, added into No.1 tube. In addition, fluorescent antibodies CD3-Percp, CD4-PacficBlue, CD8-APC-cy7, CD28-PE, CD57-FITC, CD45RA-PE-cy7, CD45RO-APC (Table S1 ) were, respectively, added into No.2 tube. 100 ul peripheral blood was added into the No.1 and No.2 tubes, and then, the tubes were mixed by eddy oscillation and incubated for 15 mins in the dark at room temperature. 2 mL hemolysin was added, and tubes were incubated for 15 mins in the dark at room temperature, followed by centrifugation at 350 g for 5 mins. After centrifugation and washing with PBS, samples were analyzed on the Navios flow cytometer(Beckman Coulter, USA). We measured different subtypes of CD4 + T cells and CD8 + T cells, including naive T cells (CD4 + CD45RA + , CD8 + CD45RA + ), memory T cell (CD4 + CD45RO + , CD8 + CD45RO + ), activated T cells (CD4 + CD38 + , CD4 + HLA-DR + , CD8 + CD38 + , CD8 + HLA-DR + DR + ) and Senescent T cells (CD4 + CD28 − CD57 + , CD8 + CD28 − CD57 + ). Moreover, cytokine levels in the patients' peripheral blood were detected by ELISA Kits (SAIJI Biotechology Co.,Ltd, Jiangxi,China). 2.4 Outcomes The primary endpoint of this study was therapeutic response and progression-free survival (PFS), and the secondary endpoint was overall survival (OS). All enrolled patients underwent efficacy evaluation every 6–8 weeks. The Response Evaluation Criteria in Solid Tumors Version 1.1 (RECIST 1.1) was employed to assess tumor response, including complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). PFS is defined as the time interval from the initial administration of immunotherapy to either disease progression or death before progressive disease (PD). OS, on the other hand, is defined as the time interval from patient enrollment to death, irrespective of the cause. Response group was defined as a CR, PR, or SD lasting longer than 6 months 26 . The non-response group was defined as a PD less than 6 months 26 . 2.5 Statistical analysis Propensity score matching (PSM) was employed to minimize the bias of retrospective analysis. To reduce the effect of confounding factors, we used R (4.2) to perform propensity score matching on 3 covariates (smoking history, gender, and age) (Table 2 ). The matching ratio of the non-response group and the response group was 1:3, and the caliper value was 0.02. This study utilized Mann-Whitney and Kruskal-Wallis tests for inter-group analysis before, after, and during treatment. Log-rank regression was employed to analyze first-line PFS. All analyses were conducted using SPSS 26.0 (SPSS Inc., Chicago, IL). GraphPad Prism 9 (GraphPad Software, San Diego, CA) was used for data visualization. Two-sided P values < 0.05 were considered statistically significant (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001; NS = not significant). Table 2 Before and after PSM analysis between two groups Variable Before PSM After PSM Level Response Non response p Smd response Non response P Smd N 73 22 66 22 Smoking history Yes 22 (30.1%) 5 (22.7%) 0.685 0.169 15 (22.7%) 5 (22.7%) 1.000 < 0.001 No 51 (69.9%) 17 (77.3%) 51 (77.3%) 17 (77.3%) Sex Male 60 (82.2%) 18 (81.8%) 1.000 0.010 56 (84.8%) 18 (81.8%) 1.000 0.081 Female 13 (17.8%) 4 (18.2%) 10 (15.2%) 4 (18.2%) Age 66.51 (6.08%) 66.36 (7.29%) 0.927 0.021 66.83 (5.69%) 66.36 (7.29%) 0.756 0.072 3. Results 3.1 Patients’ characteristics and distribution of peripheral blood lymphocyte subsets This study retrospectively enrolled 470 patients, aged 18 years or older, with pathologically confirmed stage IIIB-IV advanced NSCLC who were treated at the Department of Medical Oncology of the First Affiliated Hospital of China Medical University. Patients received first-line immunotherapy, with or without other treatments between July 2021 and December 2023. Following the inclusion and exclusion criteria, 102 patients were finally included in the analysis. After propensity score matching (PSM) at a ratio of 1:3, 66 patients were assigned to the response group, and 22 patients were assigned to the non-response group (Fig. 1 ). The baseline characteristics of patients are detailed in Table 1 . The median age was 66 years (range, 52–79 years) at the time of diagnosis. Male patients accounted for 84.1% of all participants. Sixty-eight patients (77.3%) had a smoking history. Most patients exhibited an ECOG Performance Status (PS) of 0–1. In total, 38 patients (43.2%) were diagnosed with adenocarcinoma, whereas 50 patients (56.8%) were diagnosed with squamous cell carcinoma. Sixteen patients (28.4%) exhibited high PD-L1 expression, whereas 57 (55.9%) showed low PD-L1 expression. No PD-L1 expression data were collected from other patients. The median (95% confidence interval, CI) PFS after the initiation of ICIs was 14.30 months (8.95–19.65 months), and the objective response rate (ORR) was 56.82%. Moreover, there was a significant difference in PFS between the response group and the non-response group (21.63m vs. 4.00m) (Fig. 2 A). Similar results were obtained in subgroup analyses of patients with squamous cell carcinoma and patients with adenocarcinoma (Fig. 2 B-C). Next, we summarized the abundance of peripheral blood lymphocyte subsets at baseline and investigated the correlation between different T cell subsets and the efficacy of ICIs (Fig. 3 A). 3.2 Changes in peripheral blood T cell subset after treatment with ICIs To measure the effect of ICIs on T cells, we analyzed changes in peripheral blood T cell subsets after two cycles of treatment with ICIs. Compared to the non-response group, ICIs significantly altered the abundance of several T cell subsets in the response group, including activated CD4 + T cells (HLA-DR + )/CD4% (P = 0.0170), memory CD8 + T cells/CD8% (P = 0.0115), activated CD8 + T cells (CD38 + )/CD8% (P = 0.0020) and activated CD8 + T cells (HLA-DR + )/CD8% (P < 0.0001). Nevertheless, the abundance of T cell subsets did not significantly alter in the non-response group (Fig. 3 B, Fig. 4 ). Subsequently, CD4 + and CD8 + T cells were subdivided based on pathological types, showing statistically significant differences across subgroups. Most importantly, data from the squamous cell carcinoma subgroup was closely consistent with the results of the overall analysis (Fig. 5 ). Hence, we inferred that changes across various subpopulations in the responder group are intricately linked to the efficacy of immunotherapy. 3.3 Dynamic changes of T cell subsets after treatment with ICIs To investigate the relationship between the dynamic changes observed in the results and the efficacy of ICIs, we subsequently selected patients in the treatment response group who had already experienced disease progression. This exploration aimed to identify indicators closely associated with efficacy before treatment, at the point of optimal efficacy, and throughout the course of the patient's disease progression. We explored the dynamic changes of activated CD4 + T cells (HLA-DR + )/CD4%, memory CD8 + T cells /CD8%, activated CD8 + T cells (CD38 + )/CD8%, and activated CD8 + T cells (HLA-DR + )/CD8% in the response group. We collected baseline, best efficacy, and progression indicators from patients who had already experienced disease progression, indicating that the ratio of activated CD4 + T cells (HLA-DR + )/CD4% (P = 0.0300) and activated CD8 + T cells (CD38 + )/CD8% (P = 0.0298) was closely associated with disease development (Fig. 6 ). Hence, the dynamic alterations in T cell subsets ascertained a noteworthy correlation between activated T cells and the efficacy of ICIs. 3.4 Changes of T cell subsets predict the response to ICIs in NSCLC We categorized the changes in activated CD4 + T cells (HLA-DR + )/CD4%, memory CD8 + T cells/CD8%, activated CD8 + T cells (CD38 + )/CD8% and activated CD8 + T cells (HLA-DR + )/CD8% after treatment into increasing and decreasing groups to investigate the association between changes in markers closely related to the therapeutic response before and after treatment with ICIs. Our findings indicated that an increased proportion of activated CD8 + T cells (CD38 + )/CD8% (average PFS: 22.207m vs. 15.474m) and a decreased proportion of activated CD8 + T cells (HLA-DR + )/CD8% (average PFS: 17.729m vs. 25.662m) after treatment were correlated with longer first-line PFS (Fig. 7 ). These findings suggest a close correlation between alterations in the abundance of activated CD8 + T cells and PFS of patients during their first-line treatment. 3.5 Changes in peripheral blood cytokines after treatment with ICIs IL-6 levels exhibited a significant downward trend in the response group, while other indicators remained consistent or showed no significant difference. However, we did not find statistically significant changes in IL-6 levels among patients who progressed in the response group. Additionally, alterations in IL-6 levels after treatment did not affect the late first-line PFS of patients in the response group, potentially affected by the detection method (Fig. 8 ). 3.6 Effect of variables on the PFS of patients with NSCLC Our results showed that several factors affect the efficacy of immunotherapy in patients with NSCLC. Thus, we used changes in activated CD4 + T cells (HLA-DR + )/CD4%, memory CD8 + T cells/CD8%, activated CD8 + T cells (CD38 + )/CD8%, activated CD8 + T cells (HLA-DR + )/CD8%, and IL-6 levels for Cox univariate and multivariate analyses of PFS. Multivariate analysis showed that changes in memory CD8 + T cells/CD8%, and activated CD8 + T cells (CD38 + )/CD8% were independent prognostic factors for PFS in patients with advanced NSCLC receiving first-line ICIs (P < 0.05) (Table 3 ). Table 3 Univariate and multivariate analyses for PFS Univariate analysis Multivariate analysis Variable HR 95%CI P Value HR 95%CI P Value △Activated CD4 + T cells (HLA-DR + )% 0.835 0.434–1.607 0.589 △Memory CD8 + T cells% 2.143 1.159–3.965 0.015 0.516 0.276–0.966 0.006 △ Activated CD8 + T cells (CD38 + )% 0.609 0.330–1.122 0.111 2.413 1.286–4.527 0.039 △ Activated CD8 + T cells (HLA-DR + )% 1.395 0.664–2.927 0.379 △IL-6 0.958 0.504–1.820 0.895 3.7 We explored the correlation between therapeutic outcomes and activated CD8 + T cells (CD38 + )/CD8% in two cases Case 1 was an adult patient with 20-year history of smoking whose CT-guided biopsy confirmed stage IV lung adenocarcinoma (T3N3M1b, with lymph node and bone metastasis) on August 25, 2022. Genetic testing revealed TP53 mutations, high tumor mutational burden (19.73 Muts/Mb), and strong PD-L1 expression (> 90%). Given these findings, a regimen of reduced-dose anti-PD-1 therapy and platinum-based chemotherapy, comprising tislelizumab (100mg), pemetrexed (0.9g), and carboplatin (450mg) every three weeks for four cycles, was initiated to do not exacerbate patient's glomerulonephritis. A partial response (PR) was observed after 2 and 4 cycles; thus, platinum-based drugs were discontinued, and pemetrexed and tislelizumab were continued. PR was observed after 8 cycles, but disease progression (PD) was observed after 11 cycles on August 11, 2023. Notably, a positive correlation was observed between the therapeutic effect and changes in activated CD8 + T cells (CD38 + )/CD8%. (Fig. 9 A). Case 2 was an adult patient, a former smoker, diagnosed with stage IIIC (cT3N3M0) squamous cell carcinoma of the right upper lobe. The patient did not have driver mutations but showed baseline PD-L1 expression (22C3 TPS = 20%, CPS = 25). The patient received 4 cycles of immunotherapy as the first-line treatment, using a combination of pembrolizumab (100mg), albumin-bound paclitaxel (300mg), and carboplatin (400mg). Following two cycles, the disease remained stable (SD); however, after completing four cycles, disease progression was observed. This progression was associated with a significant reduction in activated CD8 + T cells (CD38 + )/CD8% ratio from 32.03 to 6.73. The observed change in activated CD8 + T cells (CD38 + )/CD8% suggested its potential as a biomarker for assessing treatment efficacy. (Fig. 9 B). 4. Discussion ICIs have made a huge breakthrough in the treatment of NSCLC 27 . They gradually became the primary treatment modality for advanced NSCLC, particularly among patients lacking targetable molecular alterations 28 . Studies have shown that ICIs not only directly kill tumor cells but also regulate the immune response by enhancing immunogenic cell death (ICD) 29 and overcoming immunosuppression 30 . The expression of PD-L1, a marker widely used in clinical practice, is correlated with the efficacy of ICIs 31 . However, because of tumor heterogeneity and variations in tumor biopsy sites, PD-L1 has become a controversial biomarker for predicting the response to ICIs. Therefore, it is necessary to screen for peripheral blood biomarkers predicting response to immunotherapy 32 . Biomarkers that are easily accessible, highly sensitive, and specific and can be indirectly measured, and dynamically monitored are needed to assess the efficacy of ICIs in NSCLC. Our study identified the critical involvement of T cell subsets in response to ICIs. Thus, changes in the abundance and function of T cell subsets can help understand their anti-tumor effects. T cell subsets hold a central role in the immune system. Each type of T cell has its own unique function in the immune response 33 . Naive T cells can recognize novel antigens. After encountering the corresponding antigen, naive T cells recognize the antigen presentation complex through the T cell receptor (TCR), leading to their activation and differentiation into either activated or memory T cells. Memory T cells exhibit rapid response and produce immune response after re-exposure to the same antigen, playing a crucial role in long-term immunity 34 . The widespread utilization of PD-1 immune checkpoint inhibitors necessitates enhancing their response rates and clinical efficacy. Increasingly, studies have demonstrated that the primordial differentiation of tumor-specific memory CD8 + T cells 35 and central memory T cells 36 are related to clinical response to ICIs and can serve as indicators for the efficacy of PD-1 inhibitors. T cells become activated by recognizing specific antigens, which can then directly kill infected cells or regulate the immune response by releasing cytokines. Activated T cells recognize specific antigens, enabling them to directly eliminate infected cells or modulate the immune response through cytokines. Levels and subtypes of activated T cells, such as CD4 + or CD8 + , hold promise for diagnosing and monitoring various diseases. For instance, they can be used to assess the response to immunotherapy in infectious diseases, autoimmune disorders, and cancer 37 . Senescent T cells may enter the aging state following repeated replication or long-term exposure to antigens. These cells show signs of decreased function, such as decreased proliferation and changes in cytokine release 38 . In the era of precision medicine, individualized diagnosis and treatment programs can help predict patients’ immune responses. T cell recognition, activation, exhaustion, and memory phenotype analysis can be used to determine immune function in different disease states, assist diagnosis, explore the pathogenesis, course, and prognosis of diseases, and monitor and guide clinical treatment 39 . In this study, we found that activated CD8 + T cells (CD38 + ) are closely associated with the efficacy of ICIs. CD38 serves as an activation marker for specific T cells. Activated CD8 + T cells (CD38 + ) are enriched within tumor sites, where they release IFN-γ and granzyme B, resulting in direct tumor cell killing and eliciting an anti-tumor response 40 . Recent studies have shown that activated CD8 + T cells (CD38 + ) are closely associated with the benefit of immunotherapy. A real-world study of ICIs and their effect on peripheral blood lymphocyte subsets in cancer patients reported that activated CD8 + T cells (CD38 + ) may serve as potential indicators of the response to immunotherapy 41 . Similarly, a study with patients with advanced renal cell carcinoma (RCC) demonstrated that patients who exhibited the highest increase in the abundance of activated CD8 + T cells (CD38 + ) after treatment displayed the best antitumor immune response and achieved the most clinical benefit 42 . Furthermore, a study evaluated the association between CD38 expression and NSCLC prognosis and immune infiltration and revealed that increased CD38 expression is correlated with improved clinical prognosis. Moreover, activated CD8 + T cells (CD38 + ) isolated from tumors were reactivated after treatment with anti-PD in vitro 40 . These studies support the credibility of our results. Our study collected clinical data from patients with advanced NSCLC receiving first-line ICIs, including age, gender, ECOG, smoking history, pathological type, PD-L1 expression status, lymphocyte subsets, and PFS. We investigated the correlation between dynamic changes in T cell subsets and ICIs and demonstrated a close association between activated CD8 + T cells (CD38 + ) and the efficacy of immunotherapy. The dynamic fluctuations in activated peripheral blood CD8 + T cells (CD38 + ) can serve as an independent prognostic indicator for immunotherapy among patients with advanced NSCLC receiving first-line ICIs. Consequently, activated peripheral blood CD8 + T cells (CD38 + ) represent a novel biomarker for response to immunotherapy. The pivotal role of CD38 in T cell activation and its effect on response to ICIs is undeniable; however, IL-6 is associated with immune evasion 43 and negatively correlated with the prognosis of tumor patients 44 . Additionally, our findings revealed a significant decrease in IL-6 levels after treatment in the responder group. Numerous studies have demonstrated elevated expression of IL-6 across various cancer types, highlighting its role as a significant prognostic marker. Among patients with metastatic melanoma undergoing treatment with ICIs, elevated IL-6 levels correlated with decreased survival rates, diminished treatment response, and increased susceptibility to immune-related adverse events 45 . Similar findings have been reported in studies on NSCLC 46 and liver cancer 47 . IL-6 stimulates tumor cell proliferation and promotes invasion via the IL-6/JAK/STAT3 pathway 48 . Additionally, IL-6 and JAK/STAT3 signaling pathways have been linked to the immunosuppressive microenvironment of tumor 49 . Nonetheless, the limitations of the detection method in this study did not allow detect cytokine levels below the critical threshold of 2.44 pg/ml. This study has several limitations. First, this study was retrospective and conducted at a single center, limiting the number of patients included in the analysis. Second, patients received various ICIs. Third, prolonging OS poses challenges in tumor treatment. Due to the lack of OS data, we assessed survival benefits solely based on PFS. In addition, a prospective study with a larger sample size is necessary to investigate whether peripheral blood immune cell subsets can serve as predictive biomarkers for survival after treatment with ICIs. 5. Conclusion In summary, peripheral blood biomarkers are convenient and non-invasive. Our study found that in the response group, the proportion of activated CD8 + T cells (CD38 + ) was closely related to the efficacy of ICIs, and an increased abundance of activated CD8 + T (CD38 + ) cells after treatment predicted a longer PFS after treatment with first-line ICIs. This study paves the way for further use of peripheral blood immune cells as non-invasive biomarkers for immunotherapy in patients with NSCLC. Abbreviations NSCLC Non-small cell lung cance ICIs Immune checkpoint inhibitors PD-L1 Programmed cell death ligand-1 PD-1 Programmed cell death-1 TMB Tumor mutational burden TNF Tumor necrosis factor TME Tumor microenvironment IFN Interferon FDA Food and Drug Administration EGFR Epidermal growth factor receptor ALK Anaplastic lymphoma kinase ROS1 ROS Proto-Oncogene 1 SMARCA4 SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A member 4 TP53 Tumor Protein 53 NGS Next-generation sequencing TPS Tumor proportion score EDTA Ethylene Diamine Tetraacetie Acid PBS Phosphate-buffered Saline CCR7 Chemokine receptor7 PFS Progression-free survival OS Overall survival CR Complete response PR Partial response SD Stable disease PD Progressive disease PSM Propensity score matching ORR Objective response rate CPS Combined Positive Score TPS Tumor cell Proportion Score ICD Immunogenic cell death TCR T cell receptor RCC Renal cell carcinoma tdLN Tumor-draining lymph nodes RECIST Response Evaluation Criteria in Solid Tumors CI Confidence Interval HR Hazard Ratio CEA Carcinoembryonic Antigen SCC Squamous Cell Carcinoma Antigen Declarations Ethics approval and consent to participate Our study was approved by the Ethics Committee of the First Hospital of China Medical University. The study was conducted in accordance with the declaration of Helsinki. Consent for publication Not applicable. Availability of data and materials Not applicable. Competing interests The authors declares that they have no competing interests. Funding This work was partially supported by the National Natural Science Foundation of China (Grant No. 82373413); National Key Research and Development Program of China (Grant No. 2023YFC2508602);Guangdong Association of Clinical Trials (GACT)/Chinese Thoracic Oncology Group (CTONG) and Guangdong Provincial Key Lab of Translational Medicine in Lung Cancer (Grant No. 2017B030314120, CTONG-YC20220111); Shenyang Science and technology project funding NO. 21-173-9-30; Shenyang supporting program for young and middle-aged technology innovation talents NO. RC220284. Authors’ contributions JY and XL was a major contributor in writing the manuscript. 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Wu J, Gao FX, Wang C, et al. IL-6 and IL-8 secreted by tumour cells impair the function of NK cells via the STAT3 pathway in oesophageal squamous cell carcinoma. J Exp Clin Cancer Res. 2019;38(1):321. 10.1186/s13046-019-1310-0 . Additional Declarations No competing interests reported. Supplementary Files ResearchReportingGuidelinechecklist.docx TableS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4545921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317931726,"identity":"61085e5a-d0bc-46b6-bb46-5c341b5c9e88","order_by":0,"name":"Jinshan Yan","email":"","orcid":"","institution":"the First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinshan","middleName":"","lastName":"Yan","suffix":""},{"id":317931728,"identity":"e34596be-0d7f-43f3-a8e0-cea90d281931","order_by":1,"name":"Xin Li","email":"","orcid":"","institution":"the First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Li","suffix":""},{"id":317931729,"identity":"320f1d5d-e301-4fc4-870a-9044d2edbaa2","order_by":2,"name":"Hong Xiao","email":"","orcid":"","institution":"the First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Xiao","suffix":""},{"id":317931731,"identity":"0c53870d-6950-4ccf-9713-d680a81e9de6","order_by":3,"name":"Lu Xu","email":"","orcid":"","institution":"the First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Xu","suffix":""},{"id":317931732,"identity":"a1144e02-5ace-40eb-855a-13cd741b82d4","order_by":4,"name":"Pan Wang","email":"","orcid":"","institution":"National Clinical Research Center for Laboratory Medicine","correspondingAuthor":false,"prefix":"","firstName":"Pan","middleName":"","lastName":"Wang","suffix":""},{"id":317931733,"identity":"2cc2aa04-11af-4afd-bda2-5bf9dc828a24","order_by":5,"name":"Lutong Cai","email":"","orcid":"","institution":"Shenyang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lutong","middleName":"","lastName":"Cai","suffix":""},{"id":317931734,"identity":"84c16235-9fbb-4cc0-8b4d-15061d2d28fc","order_by":6,"name":"Ruotong Liu","email":"","orcid":"","institution":"Shenyang Medical College","correspondingAuthor":false,"prefix":"","firstName":"Ruotong","middleName":"","lastName":"Liu","suffix":""},{"id":317931735,"identity":"bde20185-f018-41fc-9d38-1eef9f6360d5","order_by":7,"name":"Heming Li","email":"","orcid":"","institution":"the First Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Heming","middleName":"","lastName":"Li","suffix":""},{"id":317931736,"identity":"04786ec6-a63f-482d-bfda-892e1af49308","order_by":8,"name":"Mingfang Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDACCRBhY8PDz97Y+OAD8VrS0mQkew43G84gQcshG4Mb6W3SHMTokJ/d/PDRjYQDPJIzHzZIMzDYyek2ENDCOOeYsXFOwh0efunEBuMChmRjswMEtDBLJJhJ5/54xiM5O7EheQbDgcRthLSwSaR/k85JOMxjcPNgw2EeYrTwSOSYQbTcYGxsJkqLhEROMdAvaTySPYnNjDMMiPCL/Iz0jY9zEmzs+dmPP//xocJOjqAWNGBAmvJRMApGwSgYBTgAAEs3QhOcAe44AAAAAElFTkSuQmCC","orcid":"","institution":"the First Hospital of China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Mingfang","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-06-07 11:51:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4545921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4545921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59524758,"identity":"e90fbfc8-5c5e-4a4d-836f-908cd30a968a","added_by":"auto","created_at":"2024-07-02 20:48:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":161055,"visible":true,"origin":"","legend":"\u003cp\u003ePatient Selection and Exclusion Process. From an initial cohort of 470 individuals, application of inclusion and exclusion criteria resulted in a refined cohort of 102 patients for analysis. Utilizing the Response Evaluation Criteria in Solid Tumors (RECIST v1.1), patients were stratified into two cohorts: 80 in the response group and 22 in the non-response group. Subsequently, propensity score matching (PSM) was employed to balance three covariates: smoking history, sex, and age, achieving a 1:3 matching ratio between non-response group (22) and response group (66). Response group: defined as Complete Response (CR), Partial Response (PR), or Stable Disease (SD) with a duration of more than 6 months. Non-response group: defined as Progressive Disease (PD) within 6 months.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/69864f33322bb3dc6aec564b.png"},{"id":59524764,"identity":"44a94387-acca-4922-a416-d05d10e1f38b","added_by":"auto","created_at":"2024-07-02 20:48:36","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":362971,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA, \u003c/strong\u003eThe Kaplan-Meier survival curve depicts progression-free survival in patients with non-small cell lung cancer (NSCLC) undergoing ICI therapy. \u003cstrong\u003eB and C, \u003c/strong\u003eSubgroup analyses were conducted based on pathological type, with B representing patients with lung squamous cell carcinoma (LUSC) and C representing patients with lung adenocarcinoma (LUAD).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/c544206421a3e653528d4618.png"},{"id":59525850,"identity":"fd2adee8-5be4-4b18-917c-40bded9e73cf","added_by":"auto","created_at":"2024-07-02 20:56:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":257919,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA, \u003c/strong\u003ePeripheral blood CD3\u003csup\u003e+\u003c/sup\u003e, CD4\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, CD19\u003csup\u003e+\u003c/sup\u003e, CD16\u003csup\u003e+\u003c/sup\u003eCD56 distribution in peripheral blood. X is the average number of each lymphocyte subset. \u003cstrong\u003eB-F, \u003c/strong\u003ethe correlation between the dynamic changes in CD4\u003csup\u003e+\u003c/sup\u003e T cells in NSCLC patients before and after two cycles of ICIs and the clinical benefits observed in these patients. \u003cstrong\u003eB,\u003c/strong\u003e naive CD4\u003csup\u003e+\u003c/sup\u003e T cells/CD4%. \u003cstrong\u003eC,\u003c/strong\u003e memory CD4\u003csup\u003e+\u003c/sup\u003e T cell/CD4%. \u003cstrong\u003eD,\u003c/strong\u003e activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD4%. \u003cstrong\u003eE,\u003c/strong\u003e activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4%. \u003cstrong\u003eF,\u003c/strong\u003e senescent CD4\u003csup\u003e+\u003c/sup\u003e T cells/CD4%. (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001; NS = not significant.)\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/20da19afac601f6f05d1984f.png"},{"id":59526113,"identity":"b194affe-31af-46da-88e1-e6d17149bfee","added_by":"auto","created_at":"2024-07-02 21:04:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":250254,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between the dynamic changes in CD8\u003csup\u003e+\u003c/sup\u003e T cells in NSCLC patients before and after two cycles of ICIs treatment and the clinical benefits observed in these patients. \u003cstrong\u003eA,\u003c/strong\u003e naive CD8\u003csup\u003e+\u003c/sup\u003e T cells/CD8%. \u003cstrong\u003eB,\u003c/strong\u003e memory CD8\u003csup\u003e+\u003c/sup\u003e T cell/CD8%. \u003cstrong\u003eC,\u003c/strong\u003e activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8%. \u003cstrong\u003eD,\u003c/strong\u003e activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8%. \u003cstrong\u003eE,\u003c/strong\u003e Senescent CD8\u003csup\u003e+\u003c/sup\u003e T cells/CD8%. (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001; NS = not significant.)\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/b7958e67c74e70ea2dd0f95b.png"},{"id":59525852,"identity":"2ba22c03-a014-466a-8432-210313d0b590","added_by":"auto","created_at":"2024-07-02 20:56:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":213113,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis of statistically significant indicators of dynamic changes in T cell subsets in patients with NSCLC before treatment and after two cycles of ICIs. \u003cstrong\u003eA-D,\u003c/strong\u003e lung squamous cell carcinoma (LUSC) subgroup. \u003cstrong\u003eA\u003c/strong\u003e, LUSC activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4%. \u003cstrong\u003eB\u003c/strong\u003e, LUSC memory CD8\u003csup\u003e+\u003c/sup\u003eT cells /CD8%. \u003cstrong\u003eC,\u003c/strong\u003e LUSC activated CD8\u003csup\u003e+\u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8%. \u003cstrong\u003eD,\u003c/strong\u003e LUSC activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8%. E-H, lung adenocarcinoma (LUAD) subgroup. \u003cstrong\u003eE, \u003c/strong\u003eLUAD activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4%. \u003cstrong\u003eF\u003c/strong\u003e, LUAD memory CD8\u003csup\u003e+\u003c/sup\u003eT cells /CD8%. \u003cstrong\u003eG,\u003c/strong\u003e LUAD activated CD8\u003csup\u003e+\u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8%. \u003cstrong\u003eH,\u003c/strong\u003e LUAD activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8%. (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001; NS = not significant.)\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/3d9e53d4896ef85ca67df165.png"},{"id":59524761,"identity":"77ebb01d-a986-496d-9a79-7190abad0813","added_by":"auto","created_at":"2024-07-02 20:48:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":342629,"visible":true,"origin":"","legend":"\u003cp\u003eThe study explored the correlation between dynamic changes at baseline, optimal efficacy, and progression, and the efficacy of ICIs in the response group, focusing on activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4%, memory CD8\u003csup\u003e+\u003c/sup\u003e T cells/CD8%, activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8%, and activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8%. \u003cstrong\u003eA,\u003c/strong\u003e activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4%. \u003cstrong\u003eB,\u003c/strong\u003e memory CD8\u003csup\u003e+\u003c/sup\u003e T cells/CD8%. \u003cstrong\u003eC, \u003c/strong\u003eactivated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8%. \u003cstrong\u003eD,\u003c/strong\u003e activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8%. (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001; NS = not significant.)\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/43242aa90f4db44ac16f659e.png"},{"id":59525853,"identity":"1a0cf8ee-898e-410d-a91f-a07e48410ed6","added_by":"auto","created_at":"2024-07-02 20:56:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":225979,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier survival curves depict progression-free survival among patients with NSCLC who responded to treatment with ICIs. Time is quantified in months following the commencement of ICIs. Patients were stratified according to changes in biomarkers before and after treatment, including increasing and decreasing groups.\u003cstrong\u003e A,\u003c/strong\u003e activated CD4\u003csup\u003e+\u003c/sup\u003eT cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e) /CD4%. \u003cstrong\u003eB,\u003c/strong\u003e memory CD8\u003csup\u003e+\u003c/sup\u003e T cells/CD8%. \u003cstrong\u003eC, \u003c/strong\u003eactivated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8%. \u003cstrong\u003eD,\u003c/strong\u003e activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8%.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/ec99bc74674df59257394c2c.png"},{"id":59524765,"identity":"03c8848c-d3f1-47b9-ba87-e39d12d6d63b","added_by":"auto","created_at":"2024-07-02 20:48:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":313133,"visible":true,"origin":"","legend":"\u003cp\u003eExploring the relationship between cytokine and the efficacy of ICIs. A-G, the correlation between the dynamic changes in cytokine in NSCLC patients before and after two cycles of ICIs treatment and the clinical benefits observed in these patients. \u003cstrong\u003eA,\u003c/strong\u003e IL-2. \u003cstrong\u003eB,\u003c/strong\u003eIL-2 \u003cstrong\u003eC,\u003c/strong\u003e IL-10. \u003cstrong\u003eD,\u003c/strong\u003e IL-17. \u003cstrong\u003eE,\u003c/strong\u003e TNF-α \u003cstrong\u003eF,\u003c/strong\u003e IFN-γ. \u003cstrong\u003eG,\u003c/strong\u003eIL-6.\u003cstrong\u003eH,\u003c/strong\u003e the study explored the correlation between dynamic changes at baseline, optimal efficacy, and progression, and the efficacy of ICIs in the response group, focusing on IL-6. I, Kaplan-Meier survival curves depict progression-free survival among patients with NSCLC who responded to treatment with ICIs. Time is quantified in months following the commencement of ICIs. Patients were stratified according to changes in IL-6 before and after treatment, including increasing and decreasing groups. (*p ≤ 0.05; **p ≤ 0.01; ***p ≤ 0.001; ****p ≤ 0.0001; NS = not significant.)\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/b73e205a1957c5efbaa0b182.png"},{"id":59524768,"identity":"7df25d96-a239-4e97-bd4e-9248ad923096","added_by":"auto","created_at":"2024-07-02 20:48:36","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":5020374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e, for a lung adenocarcinoma (LUAD) patient who demonstrated clinical benefit from 11 months of first-line treatment with Tislelizumab (100mg), Pemetrexed (0.9g), and Carboplatin (450mg) with Carboplatin discontinued after the 4th cycle, a Partial Response (PR) was observed in the 2nd and 4th cycles. Notably, a confirmed PR was documented upon reaching a peak activated CD8\u003csup\u003e+ \u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% of 52.93% following the 8th cycle, with a subsequent decline to 22.82% correlating with disease progression. \u003cstrong\u003eB, \u003c/strong\u003ea lung squamous cell carcinoma (LUSC) patient, who experienced a mere 4 months of first-line therapy benefit, underwent treatment with platinum-based chemotherapy and Pembrolizumab. The disease was stable (SD) post the 2nd cycle, during which a decrease in the activated CD8\u003csup\u003e+ \u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% from 32.03% to 17.01% was recorded. As anticipated, disease progression (PD) was observed after the 4th cycle, with a further reduction in the activated CD8\u003csup\u003e+ \u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% to 6.73%.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/dfb9440330652b6ad8411204.png"},{"id":59526419,"identity":"5891f997-03ec-49b0-933d-6c11f044b259","added_by":"auto","created_at":"2024-07-02 21:12:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9211582,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/dea2ea1a-1dac-40a3-905d-3814739bc382.pdf"},{"id":59524767,"identity":"289aaf7f-79e8-40bf-97d3-c1cf9f029c81","added_by":"auto","created_at":"2024-07-02 20:48:36","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":22002,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchReportingGuidelinechecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/3d23069a8f7ac44ef417c188.docx"},{"id":59524762,"identity":"5be0e0ca-544f-4233-a664-1fd0f9301328","added_by":"auto","created_at":"2024-07-02 20:48:36","extension":"docx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":12287,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4545921/v1/33eeabb39fc6da01b3d7a9c5.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Peripheral Blood Biomarkers Predicting the Efficacy of Immune Checkpoint Inhibitors in Non-small Cell Lung Cancer: A Retrospective Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNon-small cell lung cancer (NSCLC) presents a significant challenge in cancer treatment due to its heterogeneous histological and molecular profiles, leading to complex clinical manifestations and making it a leading cause of cancer-related deaths \u003csup\u003e1\u003c/sup\u003e. Immune checkpoint inhibitors (ICIs) have been a game-changer, significantly improving survival rates for patients with NSCLC \u003csup\u003e2\u003c/sup\u003e. Specifically, compared to traditional chemotherapy, atezolizumab showed promising results in a phase III clinical trial conducted by IPSOS, improving survival rates and quality of life, with a favorable safety profile when used as a first-line treatment \u003csup\u003e3\u003c/sup\u003e. However, available biomarkers, such as PD-L1 expression and tumor mutational burden (TMB), cannot adequately predict the efficacy of ICIs \u003csup\u003e4\u003c/sup\u003e. These markers do not provide a full landscape of a patient's immune status, which is critical for the effective application of immunotherapy. Regardless of PD-L1 expression status, studies like KEYNOTE-189 and RATIONALE-307 have demonstrated that the combination of ICIs and chemotherapy can significantly enhance survival benefits over chemotherapy alone \u003csup\u003e5\u003c/sup\u003e. This finding underscores the urgent need to identify and incorporate new biomarkers into treatment protocols to predict outcomes and more accurately tailor immunotherapy regimens for patients with NSCLC, highlighting a significant gap in the current approach.\u003c/p\u003e \u003cp\u003eICIs primarily activate lymphocytes \u003csup\u003e6\u003c/sup\u003e and deliver lymphocytes into the tumor microenvironment \u003csup\u003e7\u003c/sup\u003e. Tumor-infiltrating immune cells in the tumor immune microenvironment may be related to the efficacy of immunotherapy for NSCLC \u003csup\u003e8\u003c/sup\u003e. T lymphocytes serve as primary effector cells in anti-tumor immune responses \u003csup\u003e9\u003c/sup\u003e. T cell surface biomarkers, including CD45RO, CD45RA, CD38,HLA-DR, and CD28, in the tumor microenvironment can help subdivide these cells into naive, memory, activated, and senescent subgroups \u003csup\u003e10\u003c/sup\u003e. Different subgroups play different roles in immunotherapy \u003csup\u003e11\u003c/sup\u003e. Previous studies have demonstrated that ICIs induce the tumor infiltration of naive and memory T cells from tumor-draining lymph nodes (tdLN) \u003csup\u003e12\u003c/sup\u003e. Additionally, both CD38 and HLA-DR are expressed in T cells, serving as markers of T cell activation \u003csup\u003e13\u003c/sup\u003e. Consequently, CD38\u003csup\u003e+\u003c/sup\u003e T cells and HLA-DR\u003csup\u003e+\u003c/sup\u003e T cells significantly contribute to the anti-tumor response \u003csup\u003e14\u003c/sup\u003e. Nevertheless, senescent T cells express PD-L1, leading to immune evasion \u003csup\u003e15\u003c/sup\u003e. However, the intricate functions of T cell subtypes in the TME remain largely unknown \u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimilarly, cytokines play a pivotal role in signal transmission in the tumor microenvironment \u003csup\u003e17\u003c/sup\u003e. For instance, TNF \u003csup\u003e18\u003c/sup\u003e and IL-6 \u003csup\u003e19\u003c/sup\u003e promote tumor cell survival, proliferation, and angiogenesis, whereas IFN-γ \u003csup\u003e20\u003c/sup\u003e, IL-2 \u003csup\u003e21\u003c/sup\u003e, and IL-15 \u003csup\u003e22\u003c/sup\u003e enhance lymphocyte cytotoxicity and exert anti-tumor effects. High-dose recombinant IL-2 was granted FDA approval for metastatic renal cell carcinoma and melanoma in 1992 and 1998 \u003csup\u003e23\u003c/sup\u003e. Furthermore, ongoing clinical trials are exploring additional cytokines \u003csup\u003e24\u003c/sup\u003e. Thus, cytokines are pivotal in regulating the interplay between tumor cells and immune cells in the tumor microenvironment. Although cytokines play a pivotal role in cancer immunotherapy \u003csup\u003e25\u003c/sup\u003e, the efficacy of cytokines and ICIs in patients with NSCLC remains unclear. Thus, it is imperative to substantiate our claims with real-world medical data.\u003c/p\u003e \u003cp\u003eIn this retrospective study, we explored the relationship between the detailed profiles of peripheral blood T lymphocytes, cytokine subsets, and the efficacy of ICIs in patients with NSCLC. We aimed to identify novel biomarkers to identify patients more efficiently with NSCLC who will significantly benefit from ICIs, advancing the field of personalized cancer therapy.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Clinical Data\u003c/h2\u003e\n \u003cp\u003eData from 470 patients with pathologically confirmed locally advanced or metastatic NSCLC were retrospectively collected in this study. From July 1, 2021, to December 31, 2023, patients received anti-PD-1/PD-L1-based systemic therapy as their first-line treatment at the Medical Oncology Department, the First Hospital of China Medical University. Patients were screened based on the following criteria: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) patients were diagnosed with NSCLC based on cytological or histological evidence of lung adenocarcinoma or lung squamous cell carcinoma. All patients with lung adenocarcinoma were negative for EGFR, ALK, and ROS1 driver genes; (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) patients were diagnosed with locally advanced or metastatic NSCLC (stage IIIB, IIIC, or IV) according to the eighth edition of the American Joint Committee on Cancer Classification lung cancer staging system; (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) patients had complete baseline and imaging assessment data; (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e) patients received first-line treatment with ICIs, either as a single drug or in combination therapy; and (\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e) patients\u0026apos; physical status was good with adequate organ function, and the ECOG performance status was \u0026le;\u0026thinsp;2. All patients included in this study received regular follow-ups for more than 6 months. Patients diagnosed with neuroendocrine carcinoma, sarcomatoid carcinoma, and those ineligible for immunotherapy were excluded from this study (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was conducted following the Declaration of Helsinki and was approved by the Ethics Committee of the First Hospital of China Medical University. Informed consent was obtained from all participants (IRB protocol: 2022-272-2).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline Characteristics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 67.9324%;\"\u003e\n \u003cp\u003ePatient Characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 31.2237%;\"\u003e\n \u003cp\u003eN (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 67.9324%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;75\u003c/p\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;75\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 31.2237%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e80(90.9%)\u003c/p\u003e\n \u003cp\u003e8(9.1%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 67.9324%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 31.2237%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e74(84.1%)\u003c/p\u003e\n \u003cp\u003e14(15.9%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 67.9324%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eECOG PS\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 31.2237%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e86(97.7%)\u003c/p\u003e\n \u003cp\u003e2(2.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 67.9324%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking history\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 31.2237%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e68(77.3%)\u003c/p\u003e\n \u003cp\u003e20(22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 67.9324%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistology\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSquamous carcinoma\u003c/p\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 31.2237%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e50(56.8%)\u003c/p\u003e\n \u003cp\u003e38(43.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 67.9324%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePD-L1expression\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTPS\u0026thinsp;\u0026lt;\u0026thinsp;1%\u003c/p\u003e\n \u003cp\u003eTPS\u0026thinsp;\u0026ge;\u0026thinsp;50%\u003c/p\u003e\n \u003cp\u003eNot available\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 31.2237%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e57(64.8%)\u003c/p\u003e\n \u003cp\u003e16(18.2%)\u003c/p\u003e\n \u003cp\u003e15(17.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Baseline information and treatment strategy\u003c/h2\u003e\n \u003cp\u003eNext-generation sequencing (NGS) was used to measure the mutation status of driver genes. Relevant information was extracted from patient\u0026apos;s medical records. Using the 22C3 antibody, the PD-L1 tumor proportion score (TPS) was determined based on immunohistochemical staining. In addition, data on age, gender, smoking history, pathology, and ECOG performance status were also included. Physicians guided critical decisions, including the formulation of treatment plans, and determined to either continue, modify, or discontinue treatment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Analysis of blood samples\u003c/h2\u003e\n \u003cp\u003ePeripheral blood samples were collected at baseline and when assessing imaging response before administering ICIs and disease progression. All blood samples were acquired through peripheral blood venipuncture and kept in anticoagulant tubes containing EDTA. The test was completed in the clinical laboratory department of the First Hospital of China Medical University. Flow cytometry was carried out to analyze the T lymphocyte subsets and cytokines. Briefly, fluorescent antibodies CD3-Percp, CD4-APC-cy7, CD8-PE-cy7, CD38-PE and HLA-DR-APC were, respectively, added into No.1 tube. In addition, fluorescent antibodies CD3-Percp, CD4-PacficBlue, CD8-APC-cy7, CD28-PE, CD57-FITC, CD45RA-PE-cy7, CD45RO-APC (Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e were, respectively, added into No.2 tube. 100 ul peripheral blood was added into the No.1 and No.2 tubes, and then, the tubes were mixed by eddy oscillation and incubated for 15 mins in the dark at room temperature. 2 mL hemolysin was added, and tubes were incubated for 15 mins in the dark at room temperature, followed by centrifugation at 350 g for 5 mins. After centrifugation and washing with PBS, samples were analyzed on the Navios flow cytometer(Beckman Coulter, USA). We measured different subtypes of CD4\u003csup\u003e+\u003c/sup\u003eT cells and CD8\u003csup\u003e+\u003c/sup\u003eT cells, including naive T cells (CD4\u003csup\u003e+\u003c/sup\u003eCD45RA\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003eCD45RA\u003csup\u003e+\u003c/sup\u003e), memory T cell (CD4\u003csup\u003e+\u003c/sup\u003eCD45RO\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003eCD45RO\u003csup\u003e+\u003c/sup\u003e), activated T cells (CD4\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e, CD4\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003eCD38\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003eHLA-DR\u003csup\u003e+\u003c/sup\u003eDR\u003csup\u003e+\u003c/sup\u003e) and Senescent T cells (CD4\u003csup\u003e+\u003c/sup\u003eCD28\u003csup\u003e\u0026minus;\u003c/sup\u003eCD57\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003eCD28\u003csup\u003e\u0026minus;\u003c/sup\u003eCD57\u003csup\u003e+\u003c/sup\u003e). Moreover, cytokine levels in the patients\u0026apos; peripheral blood were detected by ELISA Kits (SAIJI Biotechology Co.,Ltd, Jiangxi,China).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Outcomes\u003c/h2\u003e\n \u003cp\u003eThe primary endpoint of this study was therapeutic response and progression-free survival (PFS), and the secondary endpoint was overall survival (OS). All enrolled patients underwent efficacy evaluation every 6\u0026ndash;8 weeks. The Response Evaluation Criteria in Solid Tumors Version 1.1 (RECIST 1.1) was employed to assess tumor response, including complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). PFS is defined as the time interval from the initial administration of immunotherapy to either disease progression or death before progressive disease (PD). OS, on the other hand, is defined as the time interval from patient enrollment to death, irrespective of the cause. Response group was defined as a CR, PR, or SD lasting longer than 6 months \u003csup\u003e26\u003c/sup\u003e. The non-response group was defined as a PD less than 6 months \u003csup\u003e26\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e\n \u003cp\u003ePropensity score matching (PSM) was employed to minimize the bias of retrospective analysis. To reduce the effect of confounding factors, we used R (4.2) to perform propensity score matching on 3 covariates (smoking history, gender, and age) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The matching ratio of the non-response group and the response group was 1:3, and the caliper value was 0.02. This study utilized Mann-Whitney and Kruskal-Wallis tests for inter-group analysis before, after, and during treatment. Log-rank regression was employed to analyze first-line PFS. All analyses were conducted using SPSS 26.0 (SPSS Inc., Chicago, IL). GraphPad Prism 9 (GraphPad Software, San Diego, CA) was used for data visualization. Two-sided P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant (*p\u0026thinsp;\u0026le;\u0026thinsp;0.05; **p\u0026thinsp;\u0026le;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026le;\u0026thinsp;0.001; ****p\u0026thinsp;\u0026le;\u0026thinsp;0.0001; NS\u0026thinsp;=\u0026thinsp;not significant).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBefore and after PSM analysis between two groups\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eBefore PSM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eAfter PSM\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponse\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon response\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSmd\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eresponse\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon response\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSmd\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmoking history\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (30.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (22.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (69.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (77.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51 (77.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (77.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (82.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (81.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (84.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (81.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (17.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (18.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.51 (6.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.36 (7.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.83 (5.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.36 (7.29%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Patients\u0026rsquo; characteristics and distribution of peripheral blood lymphocyte subsets\u003c/h2\u003e\n \u003cp\u003eThis study retrospectively enrolled 470 patients, aged 18 years or older, with pathologically confirmed stage IIIB-IV advanced NSCLC who were treated at the Department of Medical Oncology of the First Affiliated Hospital of China Medical University. Patients received first-line immunotherapy, with or without other treatments between July 2021 and December 2023. Following the inclusion and exclusion criteria, 102 patients were finally included in the analysis. After propensity score matching (PSM) at a ratio of 1:3, 66 patients were assigned to the response group, and 22 patients were assigned to the non-response group (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The baseline characteristics of patients are detailed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 66 years (range, 52\u0026ndash;79 years) at the time of diagnosis. Male patients accounted for 84.1% of all participants. Sixty-eight patients (77.3%) had a smoking history. Most patients exhibited an ECOG Performance Status (PS) of 0\u0026ndash;1. In total, 38 patients (43.2%) were diagnosed with adenocarcinoma, whereas 50 patients (56.8%) were diagnosed with squamous cell carcinoma. Sixteen patients (28.4%) exhibited high PD-L1 expression, whereas 57 (55.9%) showed low PD-L1 expression. No PD-L1 expression data were collected from other patients. The median (95% confidence interval, CI) PFS after the initiation of ICIs was 14.30 months (8.95\u0026ndash;19.65 months), and the objective response rate (ORR) was 56.82%. Moreover, there was a significant difference in PFS between the response group and the non-response group (21.63m vs. 4.00m) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). Similar results were obtained in subgroup analyses of patients with squamous cell carcinoma and patients with adenocarcinoma (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB-C). Next, we summarized the abundance of peripheral blood lymphocyte subsets at baseline and investigated the correlation between different T cell subsets and the efficacy of ICIs (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Changes in peripheral blood T cell subset after treatment with ICIs\u003c/h2\u003e\n \u003cp\u003eTo measure the effect of ICIs on T cells, we analyzed changes in peripheral blood T cell subsets after two cycles of treatment with ICIs. Compared to the non-response group, ICIs significantly altered the abundance of several T cell subsets in the response group, including activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4% (P\u0026thinsp;=\u0026thinsp;0.0170), memory CD8\u003csup\u003e+\u003c/sup\u003e T cells/CD8% (P\u0026thinsp;=\u0026thinsp;0.0115), activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% (P\u0026thinsp;=\u0026thinsp;0.0020) and activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Nevertheless, the abundance of T cell subsets did not significantly alter in the non-response group (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Subsequently, CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells were subdivided based on pathological types, showing statistically significant differences across subgroups. Most importantly, data from the squamous cell carcinoma subgroup was closely consistent with the results of the overall analysis (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). Hence, we inferred that changes across various subpopulations in the responder group are intricately linked to the efficacy of immunotherapy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Dynamic changes of T cell subsets after treatment with ICIs\u003c/h2\u003e\n \u003cp\u003eTo investigate the relationship between the dynamic changes observed in the results and the efficacy of ICIs, we subsequently selected patients in the treatment response group who had already experienced disease progression. This exploration aimed to identify indicators closely associated with efficacy before treatment, at the point of optimal efficacy, and throughout the course of the patient\u0026apos;s disease progression. We explored the dynamic changes of activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4%, memory CD8\u003csup\u003e+\u003c/sup\u003e T cells /CD8%, activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8%, and activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8% in the response group. We collected baseline, best efficacy, and progression indicators from patients who had already experienced disease progression, indicating that the ratio of activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4% (P\u0026thinsp;=\u0026thinsp;0.0300) and activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% (P\u0026thinsp;=\u0026thinsp;0.0298) was closely associated with disease development (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Hence, the dynamic alterations in T cell subsets ascertained a noteworthy correlation between activated T cells and the efficacy of ICIs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Changes of T cell subsets predict the response to ICIs in NSCLC\u003c/h2\u003e\n \u003cp\u003eWe categorized the changes in activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4%, memory CD8\u003csup\u003e+\u003c/sup\u003eT cells/CD8%, activated CD8\u003csup\u003e+\u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% and activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8% after treatment into increasing and decreasing groups to investigate the association between changes in markers closely related to the therapeutic response before and after treatment with ICIs. Our findings indicated that an increased proportion of activated CD8\u003csup\u003e+\u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% (average PFS: 22.207m vs. 15.474m) and a decreased proportion of activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8% (average PFS: 17.729m vs. 25.662m) after treatment were correlated with longer first-line PFS (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). These findings suggest a close correlation between alterations in the abundance of activated CD8\u003csup\u003e+\u003c/sup\u003e T cells and PFS of patients during their first-line treatment.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Changes in peripheral blood cytokines after treatment with ICIs\u003c/h2\u003e\n \u003cp\u003eIL-6 levels exhibited a significant downward trend in the response group, while other indicators remained consistent or showed no significant difference. However, we did not find statistically significant changes in IL-6 levels among patients who progressed in the response group. Additionally, alterations in IL-6 levels after treatment did not affect the late first-line PFS of patients in the response group, potentially affected by the detection method (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Effect of variables on the PFS of patients with NSCLC\u003c/h2\u003e\n \u003cp\u003eOur results showed that several factors affect the efficacy of immunotherapy in patients with NSCLC. Thus, we used changes in activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4%, memory CD8\u003csup\u003e+\u003c/sup\u003e T cells/CD8%, activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8%, activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8%, and IL-6 levels for Cox univariate and multivariate analyses of PFS. Multivariate analysis showed that changes in memory CD8\u003csup\u003e+\u003c/sup\u003e T cells/CD8%, and activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% were independent prognostic factors for PFS in patients with advanced NSCLC receiving first-line ICIs (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and multivariate analyses for PFS\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e△Activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.434\u0026ndash;1.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e△Memory CD8\u003csup\u003e+\u003c/sup\u003e T cells%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.159\u0026ndash;3.965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.276\u0026ndash;0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e△ Activated CD8\u003csup\u003e+\u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.330\u0026ndash;1.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.286\u0026ndash;4.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e△ Activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.664\u0026ndash;2.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e△IL-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.504\u0026ndash;1.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003e3.7 We explored the correlation between therapeutic outcomes and activated CD8\u003c/strong\u003e \u003csup\u003e\u0026nbsp;\u003cstrong\u003e+\u003c/strong\u003e\u0026nbsp;\u003c/sup\u003e \u003cstrong\u003eT cells (CD38\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e)/CD8% in two cases\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ewas an adult patient with 20-year history of smoking whose CT-guided biopsy confirmed stage IV lung adenocarcinoma (T3N3M1b, with lymph node and bone metastasis) on August 25, 2022. Genetic testing revealed TP53 mutations, high tumor mutational burden (19.73 Muts/Mb), and strong PD-L1 expression (\u0026gt;\u0026thinsp;90%). Given these findings, a regimen of reduced-dose anti-PD-1 therapy and platinum-based chemotherapy, comprising tislelizumab (100mg), pemetrexed (0.9g), and carboplatin (450mg) every three weeks for four cycles, was initiated to do not exacerbate patient\u0026apos;s glomerulonephritis. A partial response (PR) was observed after 2 and 4 cycles; thus, platinum-based drugs were discontinued, and pemetrexed and tislelizumab were continued. PR was observed after 8 cycles, but disease progression (PD) was observed after 11 cycles on August 11, 2023. Notably, a positive correlation was observed between the therapeutic effect and changes in activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8%. (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCase 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ewas an adult patient, a former smoker, diagnosed with stage IIIC (cT3N3M0) squamous cell carcinoma of the right upper lobe. The patient did not have driver mutations but showed baseline PD-L1 expression (22C3 TPS\u0026thinsp;=\u0026thinsp;20%, CPS\u0026thinsp;=\u0026thinsp;25). The patient received 4 cycles of immunotherapy as the first-line treatment, using a combination of pembrolizumab (100mg), albumin-bound paclitaxel (300mg), and carboplatin (400mg). Following two cycles, the disease remained stable (SD); however, after completing four cycles, disease progression was observed. This progression was associated with a significant reduction in activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% ratio from 32.03 to 6.73. The observed change in activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% suggested its potential as a biomarker for assessing treatment efficacy. (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eB).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eICIs have made a huge breakthrough in the treatment of NSCLC \u003csup\u003e27\u003c/sup\u003e. They gradually became the primary treatment modality for advanced NSCLC, particularly among patients lacking targetable molecular alterations \u003csup\u003e28\u003c/sup\u003e. Studies have shown that ICIs not only directly kill tumor cells but also regulate the immune response by enhancing immunogenic cell death (ICD) \u003csup\u003e29\u003c/sup\u003e and overcoming immunosuppression \u003csup\u003e30\u003c/sup\u003e. The expression of PD-L1, a marker widely used in clinical practice, is correlated with the efficacy of ICIs \u003csup\u003e31\u003c/sup\u003e. However, because of tumor heterogeneity and variations in tumor biopsy sites, PD-L1 has become a controversial biomarker for predicting the response to ICIs. Therefore, it is necessary to screen for peripheral blood biomarkers predicting response to immunotherapy \u003csup\u003e32\u003c/sup\u003e. Biomarkers that are easily accessible, highly sensitive, and specific and can be indirectly measured, and dynamically monitored are needed to assess the efficacy of ICIs in NSCLC. Our study identified the critical involvement of T cell subsets in response to ICIs. Thus, changes in the abundance and function of T cell subsets can help understand their anti-tumor effects.\u003c/p\u003e \u003cp\u003eT cell subsets hold a central role in the immune system. Each type of T cell has its own unique function in the immune response \u003csup\u003e33\u003c/sup\u003e. Naive T cells can recognize novel antigens. After encountering the corresponding antigen, naive T cells recognize the antigen presentation complex through the T cell receptor (TCR), leading to their activation and differentiation into either activated or memory T cells. Memory T cells exhibit rapid response and produce immune response after re-exposure to the same antigen, playing a crucial role in long-term immunity \u003csup\u003e34\u003c/sup\u003e. The widespread utilization of PD-1 immune checkpoint inhibitors necessitates enhancing their response rates and clinical efficacy. Increasingly, studies have demonstrated that the primordial differentiation of tumor-specific memory CD8\u003csup\u003e+\u003c/sup\u003e T cells \u003csup\u003e35\u003c/sup\u003e and central memory T cells \u003csup\u003e36\u003c/sup\u003e are related to clinical response to ICIs and can serve as indicators for the efficacy of PD-1 inhibitors. T cells become activated by recognizing specific antigens, which can then directly kill infected cells or regulate the immune response by releasing cytokines. Activated T cells recognize specific antigens, enabling them to directly eliminate infected cells or modulate the immune response through cytokines. Levels and subtypes of activated T cells, such as CD4\u003csup\u003e+\u003c/sup\u003e or CD8\u003csup\u003e+\u003c/sup\u003e, hold promise for diagnosing and monitoring various diseases. For instance, they can be used to assess the response to immunotherapy in infectious diseases, autoimmune disorders, and cancer \u003csup\u003e37\u003c/sup\u003e. Senescent T cells may enter the aging state following repeated replication or long-term exposure to antigens. These cells show signs of decreased function, such as decreased proliferation and changes in cytokine release \u003csup\u003e38\u003c/sup\u003e. In the era of precision medicine, individualized diagnosis and treatment programs can help predict patients\u0026rsquo; immune responses. T cell recognition, activation, exhaustion, and memory phenotype analysis can be used to determine immune function in different disease states, assist diagnosis, explore the pathogenesis, course, and prognosis of diseases, and monitor and guide clinical treatment \u003csup\u003e39\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, we found that activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e) are closely associated with the efficacy of ICIs. CD38 serves as an activation marker for specific T cells. Activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e) are enriched within tumor sites, where they release IFN-γ and granzyme B, resulting in direct tumor cell killing and eliciting an anti-tumor response \u003csup\u003e40\u003c/sup\u003e. Recent studies have shown that activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e) are closely associated with the benefit of immunotherapy. A real-world study of ICIs and their effect on peripheral blood lymphocyte subsets in cancer patients reported that activated CD8\u0026thinsp;+\u0026thinsp;T cells (CD38\u003csup\u003e+\u003c/sup\u003e) may serve as potential indicators of the response to immunotherapy \u003csup\u003e41\u003c/sup\u003e. Similarly, a study with patients with advanced renal cell carcinoma (RCC) demonstrated that patients who exhibited the highest increase in the abundance of activated CD8\u003csup\u003e+\u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e) after treatment displayed the best antitumor immune response and achieved the most clinical benefit \u003csup\u003e42\u003c/sup\u003e. Furthermore, a study evaluated the association between CD38 expression and NSCLC prognosis and immune infiltration and revealed that increased CD38 expression is correlated with improved clinical prognosis. Moreover, activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e) isolated from tumors were reactivated after treatment with anti-PD in vitro\u003csup\u003e40\u003c/sup\u003e. These studies support the credibility of our results. Our study collected clinical data from patients with advanced NSCLC receiving first-line ICIs, including age, gender, ECOG, smoking history, pathological type, PD-L1 expression status, lymphocyte subsets, and PFS. We investigated the correlation between dynamic changes in T cell subsets and ICIs and demonstrated a close association between activated CD8\u003csup\u003e+\u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e) and the efficacy of immunotherapy. The dynamic fluctuations in activated peripheral blood CD8\u003csup\u003e+\u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e) can serve as an independent prognostic indicator for immunotherapy among patients with advanced NSCLC receiving first-line ICIs. Consequently, activated peripheral blood CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e) represent a novel biomarker for response to immunotherapy.\u003c/p\u003e \u003cp\u003eThe pivotal role of CD38 in T cell activation and its effect on response to ICIs is undeniable; however, IL-6 is associated with immune evasion \u003csup\u003e43\u003c/sup\u003e and negatively correlated with the prognosis of tumor patients \u003csup\u003e44\u003c/sup\u003e. Additionally, our findings revealed a significant decrease in IL-6 levels after treatment in the responder group. Numerous studies have demonstrated elevated expression of IL-6 across various cancer types, highlighting its role as a significant prognostic marker. Among patients with metastatic melanoma undergoing treatment with ICIs, elevated IL-6 levels correlated with decreased survival rates, diminished treatment response, and increased susceptibility to immune-related adverse events \u003csup\u003e45\u003c/sup\u003e. Similar findings have been reported in studies on NSCLC \u003csup\u003e46\u003c/sup\u003e and liver cancer \u003csup\u003e47\u003c/sup\u003e. IL-6 stimulates tumor cell proliferation and promotes invasion via the IL-6/JAK/STAT3 pathway \u003csup\u003e48\u003c/sup\u003e. Additionally, IL-6 and JAK/STAT3 signaling pathways have been linked to the immunosuppressive microenvironment of tumor \u003csup\u003e49\u003c/sup\u003e. Nonetheless, the limitations of the detection method in this study did not allow detect cytokine levels below the critical threshold of 2.44 pg/ml. This study has several limitations. First, this study was retrospective and conducted at a single center, limiting the number of patients included in the analysis. Second, patients received various ICIs. Third, prolonging OS poses challenges in tumor treatment. Due to the lack of OS data, we assessed survival benefits solely based on PFS. In addition, a prospective study with a larger sample size is necessary to investigate whether peripheral blood immune cell subsets can serve as predictive biomarkers for survival after treatment with ICIs.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn summary, peripheral blood biomarkers are convenient and non-invasive. Our study found that in the response group, the proportion of activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e) was closely related to the efficacy of ICIs, and an increased abundance of activated CD8\u003csup\u003e+\u003c/sup\u003e T (CD38\u003csup\u003e+\u003c/sup\u003e) cells after treatment predicted a longer PFS after treatment with first-line ICIs. This study paves the way for further use of peripheral blood immune cells as non-invasive biomarkers for immunotherapy in patients with NSCLC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eNSCLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eNon-small cell lung cance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eICIs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eImmune checkpoint inhibitors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003ePD-L1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eProgrammed cell death ligand-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003ePD-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eProgrammed cell death-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eTMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eTumor mutational burden\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eTNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eTumor necrosis factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eTME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eTumor microenvironment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eIFN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eInterferon\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eFDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eFood and Drug Administration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eEpidermal growth factor receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eALK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eAnaplastic lymphoma kinase\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eROS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eROS Proto-Oncogene 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eSMARCA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eSWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily A member 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eTP53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eTumor Protein 53\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eNGS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eNext-generation sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eTPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eTumor proportion score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eEDTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eEthylene Diamine Tetraacetie Acid\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003ePBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003ePhosphate-buffered\u0026nbsp;Saline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eCCR7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eChemokine receptor7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003ePFS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eProgression-free survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eOverall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eComplete response\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003ePR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003ePartial response\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eStable disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eProgressive disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003ePSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003ePropensity score matching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eORR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eObjective response rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eCPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eCombined Positive Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eTPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eTumor cell Proportion Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eICD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eImmunogenic cell death\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eTCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eT cell receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eRCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eRenal cell carcinoma\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003etdLN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eTumor-draining lymph nodes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eRECIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eResponse Evaluation Criteria in Solid Tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eHazard Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eCEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eCarcinoembryonic Antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.299638989169676%\" valign=\"top\"\u003e\n \u003cp\u003eSCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"78.70036101083032%\" valign=\"top\"\u003e\n \u003cp\u003eSquamous Cell Carcinoma Antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eOur study was approved by the Ethics Committee of the First Hospital of China Medical University. The study was conducted in accordance with the declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declares that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by the National Natural Science Foundation of China (Grant No. 82373413);\u0026nbsp;National Key Research and Development Program of China (Grant No. 2023YFC2508602);Guangdong Association of Clinical Trials (GACT)/Chinese Thoracic Oncology Group (CTONG) and Guangdong Provincial Key Lab of Translational Medicine in Lung Cancer (Grant No. 2017B030314120, CTONG-YC20220111); Shenyang Science and technology project funding NO. 21-173-9-30; Shenyang supporting program for young and middle-aged technology innovation talents NO. RC220284.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eJY and XL was a major contributor in writing the manuscript. XH, LX, WP, LC and RL collected the related references and took the charge of manuscript reviewing and editing. HL and MZ critically revises the manuscript. All authors read and approved the finial manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFriedlaender A, Perol M, Banna GL, Parikh K, Addeo A. Oncogenic alterations in advanced NSCLC: a molecular super-highway. 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J Exp Clin Cancer Res. 2019;38(1):321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13046-019-1310-0\u003c/span\u003e\u003cspan address=\"10.1186/s13046-019-1310-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Immune checkpoint inhibitors (ICIs), Non-small cell lung cancer (NSCLC), Peripheral blood biomarker, T cell subsets, Cytokines","lastPublishedDoi":"10.21203/rs.3.rs-4545921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4545921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eNon-small cell lung cancer (NSCLC) leads to substantial challenges in cancer treatment owing to its diverse histological and molecular characteristics. Immune checkpoint inhibitors (ICIs) have revolutionized the management of NSCLC. Nevertheless, there exist limitations in utilizing biomarkers, like PD-L1 expression for predicting the efficacy of ICIs, necessitating novel biomarkers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We investigated the relationship between peripheral blood T cell subsets, cytokines, and efficacy of ICIs in patients who received ICIs as their first-line treatment for pathologically confirmed locally advanced or metastatic NSCLCs. Propensity score matching (PSM) was employed to match individuals between the response and non-response groups. Subsequently, peripheral blood T lymphocyte profiles and cytokine subsets were measured using flow cytometry. Mann-Whitney and Kruskal-Wallis tests were used for intergroup analysis before, after, and during treatment. Log-rank regression and Cox regression models were used to analyze survival and conduct multivariate analysis, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBetween July 1, 2021, and December 31, 2023, there were 470 patients with clinical stage IIIB to IV NSCLC. After applying the inclusion criteria, a post-propensity score-matching analysis was performed on 102 patients. The median progression-free survival (PFS) was 14.30 months. These subsets included activated CD4\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD4% (P\u0026thinsp;=\u0026thinsp;0.0170), memory CD8\u003csup\u003e+\u003c/sup\u003e T cells/CD8% (P\u0026thinsp;=\u0026thinsp;0.0115), activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38+)/CD8% (P\u0026thinsp;=\u0026thinsp;0.0020), and activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (HLA-DR+)/CD8% (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Changes in cytokine levels before and after treatment with ICIs indicated that IL-6 levels showed a downward trend in the responder group. Additionally, our analysis revealed that an increased ratio of activated CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD38\u003csup\u003e+\u003c/sup\u003e)/CD8% (average PFS: 22.207m vs. 15.474m) and a decreased ratio of activated CD8\u0026thinsp;+\u0026thinsp;T cells (HLA-DR\u003csup\u003e+\u003c/sup\u003e)/CD8% after treatment (mean PFS: 17.729m vs. 25.662m) are associated with longer PFS. Multivariate analysis unveiled that alterations in the abundance of activated CD8\u003csup\u003e+\u003c/sup\u003e T cells were independent prognostic factors for PFS in patients with advanced NSCLC.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study emphasizes the significance of peripheral blood biomarkers in predicting the efficacy of ICIs in NSCLC. Activated CD8\u003csup\u003e+\u003c/sup\u003eT cells (CD38\u003csup\u003e+\u003c/sup\u003e) represent a promising biomarker for response to ICIs, providing insights into personalized treatment strategies. Further prospective studies are warranted to validate findings and improve the outcome of NSCLC.\u003c/p\u003e","manuscriptTitle":"Peripheral Blood Biomarkers Predicting the Efficacy of Immune Checkpoint Inhibitors in Non-small Cell Lung Cancer: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 20:48:30","doi":"10.21203/rs.3.rs-4545921/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"78359454-7703-4db9-b7f3-3e292e4b5c4d","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-02T20:48:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-02 20:48:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4545921","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4545921","identity":"rs-4545921","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}