Correlation of circulating T lymphocytes with response to neoadjuvant chemoimmunotherapy in operable esophageal squamous cell carcinoma

Correlation of circulating T lymphocytes with response to neoadjuvant chemoimmunotherapy in operable esophageal squamous cell carcinoma | 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 Correlation of circulating T lymphocytes with response to neoadjuvant chemoimmunotherapy in operable esophageal squamous cell carcinoma Kunzhi Li, Kangning Wang, Yixuan Huang, Mu Yang, Xing Wei, Yongtao Han, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5361643/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 Purpose : This study aimed to investigate the correlation of the circulating T lymphocytes with response to neoadjuvant chemotherapy combined immune-oncology therapy (neoCTIO) in operable esophageal squamous cell carcinoma (ESCC) and explore the predictive markers. Methods : ESCC patients staged cT2N1-2M0 or cT3-4aN0-2M0 were enrolled. All patients received two cycles of neoCTIO of each 21-day cycle. Minimally invasive esophagectomy (MIE) was performed 4-8 weeks after neoCTIO. Peripheral blood lymphocytes subsets and effector cytokines were detected before and after neoCTIO by using flow cytometry. The primary endpoints were the advanced change of subsets, effector cytokines in T lymphocytes, and pathological complete response (pCR). The secondary endpoints included major pathological response (MPR). Results : A total of 33 patients with ESCC were enrolled. 96.7% (32/33) received MIE with R0 resection and 10 (10/32, 31.3%) achieved MPR, including 6 (6/32, 18.8%) patients with pCR. The ORR was 43.8% (14/32). The number of Effector Memory CD8+ T lymphocytes was elevated after neoadjuvant therapy (P = 0.002). In the responders, CD8+ T lymphocytes showed higher IFNγ and TNFα co-expression (P=0.010). Responders exhibited higher numbers of effector subsets (P = 0.029) and lower numbers of naive subsets (P = 0.006). No statistical difference was found in the cell frequency of CD4+T lymphocyte subsets between the responders and the non- responders. Conclusion : The baseline numbers of effector subsets and co-expression of IFN-γ and TNF-α in circulating CD8+ T lymphocytes were positive predictors while the baseline frequency of naive subsets was a negative predictive marker of the response to therapy. biomarker CD8+ T lymphocytes CD4+ T lymphocytes ESCC neoadjuvant chemoimmunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Esophageal squamous cell carcinoma (ESCC) is a common malignancy of the upper gastrointestinal tract, prevalent in East Asia ( Zheng, R et al. 2022; Abnet et al, C. 2018). Patients with locally advanced stage ESCC account for over half of the total number of cases. Encouragingly, neoadjuvant therapy followed by surgery has been shown to improve significantly the efficacy of treatment ( Yang, H et al. 2018; Shapiro, J et al. 2015; Yamasaki, M et al. 2017). Although the current National Comprehensive Cancer Network(NCCN) guidelines recommend neoadjuvant chemoradiotherapy combined with surgery as the standard treatment for operable esophageal squamous cell carcinoma, the incidence of distant metastasis and local recurrence in patients with this treatment modality remains unsatisfactory. The neoadjuvant treatment model for operable esophageal squamous cell carcinoma patients still needs to be continuously explored. In recent years, the use of PD-1 checkpoint inhibitors for ESCC therapy has produced an apparent clinical curative effect. There is increasing evidence that neoadjuvant chemotherapy combined with immune-oncology (neoCTIO) is an effective and safe treatment for resectable locally advanced ESCC ( Yang, W et al. 2022; Liu, J et al. 2022). This treatment approach may have more advantages in controlling distant metastasis, and has great potential for future development. However, the characteristics of the population of patients benefiting from this therapy remains unclear and is a major obstacle to facilitating its standard use in the clinic. According to current knowledge, although the expression of PD-L1 in tumor tissues is related to the efficacy of immunotherapy, it is not predictive enough to be a measure of the therapeutic response of a patient to immunotherapy ( Kelly, R.J et al. 2021; Larkin, J et al. 2015). Recent studies have shown that patients with non-small cell lung cancer can benefit from neoCTIO using PD-1 checkpoint inhibitors, regardless of the expression status of PD-L1 in tumor tissue (Shu CA et al. 2020). Current studies have confirmed that the tumor mutational burden (TMB), tumor microenvironment (TME) and microsatellite instability (MSI) can also predict the therapeutic effects of immunotherapy to some extent ( Cristescu, R et al. 2018; Dudley, J.C et al. 2016; Bagaev, A et al. 2021), but these findings have not been successfully translated into clinically acceptable prediction methods. Therefore, it will be of great significance to identify a valid and easy-to-measure predictive biomarker to facilitate the formulation of individualized treatment plans for particular subgroups of patients. It is very difficult, however, to anticipate the degree of the immune response from bioinformation about tumor tissue specimens and its biomarkers. In addition to the numbers of receptors expressed on tumor cells, the patients’ immune status is also crucial in establishing an appropriate immunotherapy regimen. A number of studies have confirmed that different immune statuses of cytotoxic T cells (CD8 + T cells), such as immune inflation and immune exhaustion, will directly affect the progression of tumors and hence the prognosis of patients ( Alexandrov, L.B et al. 2013; Wherry, E.J et al. 2011). PD-1 inhibitors activate CD8 + T cells and enhance their ability to recognize and kill tumor cells, by blocking the PD-L1/PD-1 pathway communication between tumor and immune cells ( van der Gracht, E.T et al. 2020; Giunta, E.F et al. 2020). Recent studies have found that circulating immune cells are associated with the curative effect of immunotherapy in melanoma ( von Andrian, U.H et al. 2000). A clinical study has shown that melanoma patients with a more stable circulating immune cell environment can achieve a better prognosis (Giunta, E.F et al. 2000). The circulating CD8 + T cells extravasate into tumor stroma under the guidance of chemokines, contact tumor tissue through interstitial migration, and exert cytotoxic effects(Salmon, H et al. 2012; Lee, J et al. 2021)。Like melanoma, ESCC has the biological characteristics of high immunogenicity ( Salmon, H et al. 2012). In our previous studies CD8 + tumor-infiltrating lymphocytes (TIL) density was significantly increased in tumor tissues after neoadjuvant therapy༈He, W et al. 2022༉。Therefore, we speculate that the status of circulating T cell is directly related to the immune response and may be a potential biomarker of immunotherapeutic effects. Although some studies have attempted to explore the mechanisms of immunotherapy in terms of gene, protein, and tumor cell antigen, the connection between the fine subsets of T lymphocytes, which serve as the working cells of the immune system, and esophageal cancer immunotherapy is currently unclear. Thus, the aims of the present study were to investigate the relationship between subsets of circulating CD8 + and CD4 + T cells as well as the synthesis of cytotoxic factors and the response to neoCTIO in operable ESCC, with the objective of identifying clinically useful biomarkers. Material and Methods 1 ． Patients This study enrolled patients who were ready for undergoing an esophagectomy for esophageal cancer from September 2021 to December 2022. All patients had undergone contrast-enhanced CT scans of the neck, chest, upper and middle abdomen. Esophagogastroduodenoscopy and endoscopic ultrasonography were carried out, and cancer staging was performed according to the eighth edition of the Tumor-Node-Metastasis (TNM) classification published by Union for International Cancer Control (UICC). The criteria for enrollment in the study were: biopsy-confirmed thoracic ESCC, clinical stage cT2N1-2M0 or cT3-4aN0-2M0, without any anti-tumor treatment; aged 18 to 75 years; heart, lung, liver and kidney functions were normal; and an Eastern Cooperative Oncology Group (ECOG) score of 0-1. The exclusion criteria were: endoscopic biopsy confirmed small cell carcinoma; other anti-tumor therapies had been administered; a preoperative examination indicated that the carcinoma was unresectable at T4b or M1; corticosteroids or other immunosuppressive drugs were used within 14 days before enrollment; a history of active autoimmune disease or autoimmune disease that possibly recur; severe chronic or active infectious diseases; and a history of interstitial lung disease. The study protocols were approved by the Ethics Committee for Medical Research and New Medical Technology of Sichuan Cancer Hospital (SCCEC-02-2021-043). All patients provided written informed consent prior to been enrolled in the study. 2 ． Study Design This was a prospective exploratory study. All included patients received toripalimab immunotherapy 24 h after chemotherapy, with a regimen of paclitaxel (135 mg/m2, IV, D1) and carboplatin (area under the curve [AUC] = 5 [according to Calvert formula], IV, D1). The immunotherapy regimen was toripalimab 240 mg administered progressively by intravenous infusion over a 30 min period. Every patient received each treatment regimen in a 3-week cycle for 2 cycles. Minimally invasive (McKeown) esophagectomy (MIE) with two-field lymph node dissection was performed 4-8 weeks after 2 cycles of neoadjuvant therapy. Tumor tissue samples were collected for pathological examination. Peripheral blood samples were collected 1 day before treatment and 1 day before surgery. The primary endpoints of the study were changes in the CD8+ and CD4+ T cell subsets as well as cytotoxic factor concentrations in CD8+ T cell and the pathological response rate. The secondary endpoints were the major pathological response (MPR) and the overall response rate (ORR). Adverse events (AEs) were recorded according to the Common Terminology Criteria for AEs (ver. 4.3.2). CD8+ T cell subsets included Effector Memory (CD45RO+CCR7-), effector (CD45RO-CCR7+), naive (CD45RO-CCR7+), memory precursor (MP:CD127+KLRG1-), and short-lived effector (SLE:CD127-KLRG1+) cells; the cytotoxic factors measured were IFN-γ and TNF-α. CD4+ T cell subsets included Th1（IFN-γ+IL-4-）、Th2（IFN-γ-IL-4+）、Treg（CD25+CD127-）. 3. Response Assessment All patients who had underwent surgery had ypTNM staging (ver. 8) and all resected primary tumor as well as lymph nodes were examined pathologically. The pathological response assessment was evaluated by two pathologists in our hospital according to standard pathological assessment criteria following NCCN guidelines after neoadjuvant therapy. The patients were randomly assigned into a pathological response group or a non-response group. The pathological response group was defined as patients whose tumor regression grading (TRG) was 0-2. Patients whose TRG was 3 were defined as the non-response group. The clinical response was assessed by an expert imaging physician and an expert thoracic surgeon based on CT and esophagogastroduodenoscopy according to the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST), with efficacy evaluated as a complete response (CR), partial response (PR), stable disease (SD) or progressive disease (PD). 4 ． Multi-colour flow cytometry Peripheral blood T cell subsets were detected before the first CTIO and surgery. Before each test, 2 mL of fasting peripheral blood was drawn from patients and placed in a heparin anticoagulant tube. Single cell suspensions were prepared by lysis of red blood cells using red blood cell lysis buffer. The single cell suspension was then stimulated with coupling propidium monoazide (PMA) (1 µL) plus ionomycin (1.5 µL) plus Brefeldin A solution (1 µL) for 4 h. Then, cell surface staining of the suspension was carried out, followed by fixation/permeabilization and intracellular staining. The cytokines and immunophenotype of T cell fine subsets in the sorted single cell suspensions were determined by a multi-color flow cytometer (BD, FACSCantoTM Ⅱ, San Jose, USA). All flow data analysis was performed using FlowJo software (ver. 10.4.0). 5 ． Statistical Analysis The data of this experiment were statistically analyzed and plotted using Graph Pad 7.0. The measurement data following normal distribution were expressed as mean ± standard deviation, and the comparison between each other was analyzed by a two independent samples t-test. Measurement data that was not normally distributed are reported as the median and were analyzed by a two independent samples Mann-Whitney U test. For measurement data of paired samples, a two-paired-samples t-test or Wilcoxon rank-sum test was employed. A chi-squared test or Fisher's exact test was used for categorical variables. A P-value < 0.05 was considered to be statistically significant. Results 1. Demographics Characteristics Of the 150 esophagectomy patients enrolled, ultimately 33 were included. The average age of the entire patient group was 58.9 years, with a median age of 58.0 years. One patient experienced severe gastrointestinal bleeding after the first neoadjuvant treatment and discontinued treatment, without undergoing surgery (Figure 1). 2. Pathological and Clinical Response Assessment Of these, 32（32/33, 97.0%） received 2 cycles of neoadjuvant therapy and MIE with R0 resection. Sixteen patients (50%) exhibited different degrees of pathological remission (0-50% of the tumor remained). Ten patients (10/32, 31.2%) achieved MPR, including 6 (6/32, 18.8%) with pathological response rate (pCR) in the per-protocol population (Figure 2). 16 (50%) experienced no obvious tumor regression. All patients completed imaging and esophagogastroduodenoscopy examinations before and after treatment. There were 14 response cases and 18 no-response cases in the clinical evaluations, with an ORR of 43.8%. The agreement rate between the pathological and clinical evaluation of treatment response was 68.8%. There were no PD cases. 3. Comparation of characteristics between treatment response and non- response The mean age of the pathological response group was significantly older than that of the non-response group (P = 0.013). No significant difference in the interval to surgery was found between the two groups (P = 0.787). After neoadjuvant therapy, the ypT and ypN stages in the pathological response group were significantly lower than those in the pathological non-response group, with statistical significance. For all other characteristics of the two groups, no differences were found (Table 1). 4. Dynamic changes of circulating T lymphocytes before and after NeoCTIO treatment The Effector Memory (CD45RO+CCR7-) of CD8+T cells subset showed a significant increase after neoadjuvant therapy, with an average increase of 3.7% in cell frequency (31.4±12.7% vs. 35.1±12.6%), and the difference was statistically significant (P=0.002) (Figure 3a-b). However, there were no significant changes in naive (23.8±21.5% vs. 21.4±16.4%, P=0.247), SLE (20.3±12.1% vs.22.6±11.0%, P=0.319), MP (31.6±16.0% vs.30.2±16.0%, P=0.689), effector (39.4±18.9% vs. 39.6±16.1%, P=0.927). There was also no significant change in the subpopulation of CD8+T cells co-expressing IFNγ and TNFα (38.9±21.5% vs.34.0±19.5%, P=0.280) before and after treatment. No significant changes were observed in CD4+T cell subsets, including Th1 (21.9±11.6% vs.21.1±10.6%, P=0.202), Th2 (1.0±0.6% vs.1.2±0.8%, P=0.303), and Treg (4.9±1.6% vs.5.0±1.7%, P=0.701) before and after treatment. 5.Comparison of T lymphocytes between responders and non-responders during neoadjuvant therapy At baseline before the neoadjuvant therapy, the pathological response group had a significantly higher frequency of CD8+T cells in the effector subset (47.1±19.4% vs. 32.2±16.5%, P=0.029) compared to the non-response group (Figure 4 c-d). The naive subset was significantly lower in frequency in the pathological response group (13.0±12.3% vs. 33.1±23.4%, P=0.006) compared to the non-response group (Figure 4 e-f). The IFNγ and TNFα co-expression was higher in the pathological response group (49.9±17.3% vs. 30.7±21.3%, P=0.010) compared to the non-response group (Figure 4 a-b). Other Effector Memory (34.7±12.9% vs. 29.6±13.4%, P=0.287), MP (31.1±20.7% vs.32.6±11.2%, P=0.810), and SLE (21.1±16.2% vs.21.2±10.1%, P=0.980) did not show significant differences between the two groups. There were no significant differences in baseline levels of CD4+T cell subsets, including Th1 (22.4±11.4% vs. 20.5±9.7%, P=0.627), Th2 (1.1±0.7% vs. 1.0±0.5%, P=0.727), and Treg (5.0±1.5% vs. 4.9±1.7%, P=0.881) (P>0.05), between the two groups, and there were no significant differences after neoadjuvant therapy. Discussion For the treatment of locally resectable advanced ESCC, the main objective of neoadjuvant therapy combined with surgery is to increase the rate of radical resection (R0) and eliminate micro-metastases, thereby improving the survival rate. In the present study, the neoCTIO regimen for this group of patients was toripalimab combined with paclitaxel and carboplatin, and all patients underwent MIE with R0 resection. The tumor response to treatment was reached in 18.8% of patients with pCR, 31.2% with MPR, 43.3% with ORR and no patients with PD, which were similar to the results of previously published studies ( Yang, W et al. 2022; Liu, J et al. 2022; He, W et al. 2022; Liu, J et al. 2022). The results of the present study confirmed again that different PD-1 checkpoint inhibitor products combined with chemotherapy can achieve significant efficacy under the similar immunologic mechanism. However, only about 40% of patients benefited from this modality, therefore finding effective biomarkers will be critical for establishing a model neoCTIO treatment regimen. Such findings will greatly help to improve the total therapeutic efficacy for ESCC patients by preventing at least 60% of patients from receiving ineffective treatment and by diverting the patient subgroup to more effective alternative treatment modalities. The two main guidelines for predicting the treatment response depends on the clinical markers and biomarkers, respectively. In our study, we did not find statistically significant differences in the clinical characteristics between the pathological response and non-response groups, with the exception of age. The results of lower age in the pathological non-response group may be a statistical aberration due to the small number of patients enrolled. These insignificant results suggest again that it is difficult to screen out the population of patients who will benefit according to their general clinical characteristics. At present, studies of biomarkers have mainly focused on the expression of PD-L1 in tumor tissues. The results of a recent meta-analysis revealed that PD-L1 expression by the tumor proportion score (TPS) was the best predictor of immune checkpoint inhibitor benefits in patients with squamous cell carcinoma, with a high TPS (TPS ≥ 1) having a predictive value of 41.0% compared to 16.0% for other variables ( Yoon, H.H et al. 2022). Although the expression of PD-L1 in tumor tissues is the best single predictive marker, its prediction accuracy is < 50%, that is, at least 50% of patients having a good response only exhibit medium or low expression. These findings suggest that there is still a continued need to find better predictive or accompanying biomarkers. In one of our preliminary studies, baseline PD-L1 and TMB expression in tumor tissues did not effectively predict the tumor response to treatment ( He, W et al. 2022). In fact, the host's own cytotoxic immune status was also a key factor for determining the response to immunotherapy. Therefore, it will be necessary to analyze further bioinformation about a patient’s immune functions to identify effective biomarkers that jointly predict the accuracy of the clinical diagnosis. Biomarkers obtained from peripheral blood samples have the advantages of convenience, rapidity, safety and easy dynamic monitoring of treatment efficacy. The T cell mediated cellular immunity activation state may well represent the host's immune response ability and is closely related to tumor generation and progression(Kumar, B.V et al. 2018)。CD8 + T cells, when activated by tumor cell antigens, exert cytotoxic effects and directly kill tumor cells. During prolonged tumor progression, the PD-1 receptor on CD8 + T cells is specifically recognized and bound by the PD-L1 receptor on tumor cells to deplete their viability and function༈Hirano, F et al. 2005; Waeckerle-Men, Y et al. 2007༉。This negative regulatory mechanism can be blocked by PD-1 checkpoint inhibitors, enhancing the ability of CD8 + T cells to kill tumor cells༈Iwai, Y et al. 2002༉。Before and after intravenous infusion of PD-1 checkpoint inhibitors, we may find predictive biomarkers by detecting changes in the fine subsets and functions of CD8 + T cells in peripheral blood, and analyzing the correlation with the response to treatment. Recent studies with PD-1 checkpoint inhibitors in non-small cell lung cancer have shown that the dynamics change of PD-1 + CD8 + T cells in the peripheral blood can predict the response to immunotherapy (Kim, C.G et al. 2021)。In the classical immunological theory system, CD4 + T cells are often considered as immune helper cells, which maintain the stability of the immune internal environment and promote the comfortable progress of immune responses (Kumar, B.V et al. 2018). Most researchers focus on CD8 + T cells, and there is less research on CD4 + T cells at present. A study has found that CD4 + T cells may play a key role in bladder cancer immunotherapy (Oh DY et al. 2020), and stimulate functional cells to express anti-tumor effects through immune regulation and the release of immune cytotoxic factor. However, there is no high-level evidence or clinical research to explain the function and role of CD4 + T cells in the unique immune environment of ESCC patients with neoCTIO. The present study is the first to dynamically monitor changes in fine subsets and the cytotoxic factors in circulating T cells in a neoCTIO treatment model for ESCC. The results demonstrated that only Effector Memory subsets in peripheral blood were significantly increased in numbers after neoadjuvant therapy, while the other subsets were not significantly changed. The reason may be that PD-1 checkpoint inhibitors block the inhibitory pathway of PD-L1/PD-1 between tumor cells and T cells, and promote the activation and differentiation of naive cells into Effector Memory cells, which are a subset with significant antitumor effects。Effector Memory cells are widely distributed in the blood and tissues and have strong anti-tumor effects (Han, J et al. 2020; Chamoto, K et al. 2017; Klebanoff, C.A et al. 2005). When tumor antigens are exposed again, Effector Memory cells can respond more quickly and strongly. Higher levels of Effector Memory CD8 + T cells show stronger anti-tumor effects in the early stages of the tumor. An animal experiment using mice showed that CD8 + T cell subsets with memory function respond best to PD-1 checkpoint inhibitors (Henning, A.N et al. 2018; Kargl et al. 2017; Kim, J et al. 2015) found that Effector Memory CD8 + T cells are the main cells of lymphocyte infiltration in human lung cancer tissues. The immune inflation of tumor patients is mainly characterized by the high frequency and maintenance of Effector Memory CD8 + T cells (Kim, J et al. 2015; O'Hara, G.A et al. 2012). The increase and continuous high expression of Effector Memory may be one of the potential mechanisms for the sustained anti-tumor effect of T cell-mediated PD-1 checkpoint inhibitors. In this study, we detected the expression of various lymphocyte subsets and cytotoxic factors before surgery. Compared with the expression before immunotherapy, the changes in the expression of various lymphocyte subsets and cytotoxic factors were not significant, except for the Effector Memory subset. This may be related to the fact that the detection time after treatment in this study was more than 4 weeks from the last dose. A study of nivolumab in neoadjuvant therapy for lung cancer found that the increase in peripheral blood T cells peaked 1–2 weeks after treatment with a PD-1 inhibitor(Forde, P.M et al. 2018)。All patients who underwent surgery in this group did not experience severe postoperative complications. From the perspective of changes in lymphocyte immune subsets, the surgery time we selected was relatively appropriate and safe. However, this study did not continuously observe the dynamic changes of lymphocyte subsets postoperatively, and further research is needed to confirm this view. In order to explore the predictive value of the fine subsets of circulating T cells, we analyzed the differences in cell numbers for each subset according to pathological responses and changes within groups before and after treatment. In this study, we did not find significant relationship between the pathological responses and proportion and changes of CD4 + T cell subsets in the peripheral blood. We found more effector and less naive cells in the baseline CD8 + T lymphocytes in the pathological response group. This finding indicated that patients in the pathological response group had a more positive status of tumor immune response. stimulation of tumor antigens resulted in the conversion of naive cells into effector cells, which have significantly enhanced antitumor functions (Kumar, B.V et al. 2018; Lisci, M et al. 2021; Sommermeyer, D et al. 2016). In the present study, we found that naive subsets in circulating CD8 + T cells were significantly enriched in some patients, whose pathological response was often poor. The mechanism may be the absence of certain necessary immune response conditions. The main reason may be the incomplete immune response chain or the absence of certain necessary immune response conditions. It has been found that mutations in B2M, TAP1, TAP2 and other genes in tumor cells results in defects of signal transduction that can affect the expression of MHC class I and II antigens༈Kalbasi, A et al. 2020༉. In addition, inhibitory receptors such as TIGIT and CTLA-4 also exert a certain negative influence on T cell activation༈Johnston, R.J et al. 2014; Gangaev, A et al. 2021; Duraiswamy, J et al. 2013༉. These suppressions of immune pathways will greatly promote immune escape. In addition to the above factors, the loss of antigen presentation ability by dendritic cells and immune cell defects can also lead to the failure of the immune response to proceed normally. The relationship between the expression of cytotoxic factors in circulating CD8 + T lymphocytes and the response to treatment was analyzed. The results showed that the pathological response group had a higher proportion of CD8 + T cells synthesizing IFN-γ and TNF-α before treatment. IFN-γ and TNF-α are important cytokines of immune cells that act to inhibit tumor growth(Liao, P et al. 2022; Wu, M.J et al. 2022; Hezaveh, K et al. 2022)。High concentrations of IFN-γ and TNF-α expression also suggest that patients had good immune response capacity, which provides a good immunotherapy background for the treatment of PD-1 inhibitors. Circulating peripheral blood mononuclear cells in the response group of melanoma patients have been shown to exhibit a higher baseline concentration of IFN-γ ( Giunta, E.F et al. 2020). High-throughput sequencing of lung adenocarcinoma has shown that the TNF family has a certain predictive value in immunotherapy༈Zhang, C et al. 2020༉。the low concentrations of IFN-γ and TNF-α in the non-pathological response group may indicate that the patients have late dysfunction due to a persistent antigenic response of tumor cells༈Wherry, E.J et al. 2011༉。In a mouse model of meningitis virus infection, CD8 + T cells became exhausted after brief reactivation by PD-1/PD-L1 pathway blockade when the antigen persisted. PD-1 inhibitors failed to reverse late dysfunctional cells༈Pauken, K.E et al. 2016༉, which may be a possible mechanism for the failure of immunotherapy in these patients. Therefore, the baseline IFN-γ and TNF-α co-expression by circulating CD8 + T cells is also a valuable biomarker for predicting the response to neoCTIO therapy. There were a number of limitations to the study. First, it was an exploratory study with a small sample size. Second, it did not measure the expression of PD-L1 in tumor tissues or tumor-infiltrating CD8 + T cells. Finally, the relationship between tumor-infiltrating CD8 + T cells and circulating CD8 + T cells was not further explored. The effective rate of neoadjuvant CTIO for resectable ESCC in this study was about 50%. The baseline numbers of effector subsets and the co-expression of IFN-γ and TNF-α in circulating CD8 + T lymphocytes had a positive predictive value, while the baseline numbers of naive subsets was a negative predictive biomarker. CD4 + T cell subsets have no correlation with the treatment response. As an important part of the predictive system in neoCTIO, these baseline characteristics of circulating CD8 + T cells can be used to improve further the prediction accuracy of the population of patients most likely to benefit combined with other effective biomarkers. Conclusion The baseline numbers of effector subsets and co-expression of IFN-γ and TNF-α in circulating CD8 + T lymphocytes were positive predictors while the baseline frequency of naive subsets was a negative predictive marker of the response to therapy. Abbreviations AEs: Adverse events AUC: area under the curve CR: complete response ECOG: Eastern Cooperative Oncology Group ESCC: esophageal squamous cell carcinoma MIE: Minimally invasive esophagectomy MPR: major pathological response MSI: microsatellite instability NCCN: National Comprehensive Cancer Network neoCTIO :neoadjuvant chemotherapy combined immune-oncology therapy ORR: overall response rate pCR: pathological complete response PD：progressive disease PMA：propidium monoazide PR：partial response R0: radical resection RECIST: Response Evaluation Criteria in Solid Tumors SD：stable disease TMB: tumor mutational burden TME: tumor microenvironment TNM: Tumor-Node-Metastasis TPS: tumor proportion score TRG: tumor regression grading UICC: Union for International Cancer Control Declarations Author contributions : Kunzhi Li: Conceptualization; Experiment; Formal analysis; Methodology; Visualization; Investigation; Writing—original draft; Writing—review & editing; Kangning Wang: Data curation; Writing—review & editing; Yixuan Huang: Conceptualization; Experiment; Formal analysis; Visualization; Writing—review & editing; Mu Yang: Conceptualization; Experiment; Formal analysis; Visualization; Xing Wei: Conceptualization; Methodology; Writing—review & editing; Yongtao Han: Data curation; Writing—review & editing; Yan Miao: Data curation; Writing—review & editing; Qiang Fang: Conceptualization; Formal analysis; Funding acquisition; Methodology; Project administration; Supervision; Visualization; Writing—original draft; Writing—review & editing. Funding: This research was supported by Sichuan Province Science and Technology Support Program (No. 2021YJ0118). Data availability : The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations Ethical approval: The study protocols were approved by the Ethics Committee for Medical Research and New Medical Technology of Sichuan Cancer Hospital (SCCEC-02-2021-043). The study design and conduct complied with all relevant guidelines and regulations regarding the use of human study participants and was conducted in accordance with the criteria set by the Declaration of Helsinki. All patients voluntarily participated and provided written informed consent before the initiation of any study-related treatment or procedures. Acknowledgments: The authors are grateful to all medical and nursing staff, enrolled patients and their families, and hospital committee members. 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Science (New York, N.Y.), 354(6316), 1160–1165. https://doi.org/10.1126/science.aaf2807 Table Table 1 Comparation of Characteristics between pathological responders and non- responders Characteristics Patients N=32 Pathological response group N=16 Pathological non- response group N=16 P Age 0.013 Mean ±SD 58.5±6.7 61.4±5.9 55.6±6.5 Median (range) 58.0(44.0-69.0) 61.5(50.0-69.0) 54.5(44.0-68.0) Sex 1.000 Female 2(6.3%) 1(6.3%) 1(6.3%) Male 30(93.8%) 15(93.8%) 15(93.8%) BMI 22.1±2.3 22.0±2.4 22.3±2.3 0.703 Location 0.187 Upper chest 3(9.4%) 3(18.8%) 0(0.0%) Middle chest 20(62.5%) 10(62.5%) 10(62.5%) Lower chest 9(28.1%) 3(18.8%) 6(37.5%) cT 1.000 2 1 (3.1%) 1 (6.3%) 0(0.0%) 3 29(90.6%) 14(87.5%) 15(93.8%) 4 2(6.3%) 1(6.3%) 1(6.3%) cN 0.073 0 14(43.8%) 6(37.5%) 8(50.0%) 1 10(31.3%) 8(50.0%) 2(12.5%) 2 8(25.0%) 2(12.5%) 6(37.5%) Stage 1.000 II 13(40.6%) 6(37.5%) 9(56.2%) III 19(59.4%) 10(62.5%) 7(43.8%) ypT 0.001 0 7(21.9%) 7(43.8%) 0(0.0%) 1 7(21.9%) 5(31.3%) 2(12.5%) 2 3(9.4%) 3(18.8%) 0(0.0%) 3 15(46.9%) 1(6.3%) 14(87.5%) ypN 0.024 0 21(65.6%) 11(68.8%) 10(62.5%) 1 4(12.5%) 4(25.0%) 0(0.0%) 2 7(21.9%) 1(6.3%) 6(37.5%) ECOG 0.473 0 19(59.4%) 11(68.8%) 8(50.0%) 1 13(40.6%) 5(31.2%) 8(50.0%) Interval to surgery a 33.5(25.0-82.0) 33.0(25.0-53.0) 37.0(25.0-82.0) 0.787 Lymphocyte（109/L） 1.6±0.5 1.5±0.6 1.8±0.4 0.062 Neutrophil（109/L） 4.4±1.4 4.5±1.4 4.3±1.4 0.666 Monocyte（109/L） 0.5±0.2 0.5±0.2 0.5±0.2 0.572 a Interval to surgery: Between end of neoadjuvant Chemoimmunotherapy and surgery Additional Declarations No competing interests reported. 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Patient 2, Patient 23, and Patient 25 are the images of CR, PR, and SD patients, upper gastrointestinal endoscopy, and pathological staining, respectively; b: Tumor regression waterfall plot after neoadjuvant chemotherapy combined with immunotherapy. Tumor regression ≥50% is defined as the treatment response group (TRG = 0-2 points); c: Evaluation of treatment response to neoadjuvant chemotherapy combined with immunotherapy. Red, yellow, purple, and black represent CR, PR, SD, and PD in clinical response assessment; Red, yellow, purple represent TRG=0, TRG=0-2, TRG=3 in pathological response assessment.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5361643/v1/474284fda8ed2bbe2ffd8498.png"},{"id":69443198,"identity":"94be5cf2-c771-4484-9e00-1d33683e153c","added_by":"auto","created_at":"2024-11-20 11:33:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94857,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChange of Effector Memory (CD45RO+CCR7-) subsets before and after neoadjuvant therapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a-b): Representative dot plot(a) and frequency of Effector Memory (CD45RO+CCR7-) cells as a percentage of total CD8+ T lymphocytes in before and after treatment.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5361643/v1/f58fb678a29b96c8f9706841.png"},{"id":69444563,"identity":"073d92a5-af58-4a6f-aa80-8632306be8ec","added_by":"auto","created_at":"2024-11-20 11:41:45","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":147566,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of baseline effector (CD45RO-CCR7-) and naive (CD45RO-CCR7+) subsets, and IFNγ+TNFα co-expression between pathological response group and non-pathological response group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a-b)Representative dot plot(a) and frequency(b) of IFNγ+TNFα co-expression as a percentage of total CD8+ T lymphocytes before treatment with PD-1 checkpoint inhibitors in patients with and without pathological response; (c-d) Representative dot plot(c) and frequency(d) of effector (CD45RO-CCR7-) cells as a percentage of total CD8+ T lymphocytes before treatment with PD-1 checkpoint inhibitors in patients with and without pathological response; (e-f) Representative dot plot(e) and frequency(f) of naive (CD45RO-CCR7+) cells as a percentage of total CD8+ T lymphocytes before treatment with PD-1 checkpoint inhibitors in patients with and without pathological response\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5361643/v1/9a568a296225a94954921b61.png"},{"id":84430836,"identity":"e120cd5a-0f84-4e01-b7a5-b443cf8b52fd","added_by":"auto","created_at":"2025-06-11 23:01:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1755839,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5361643/v1/05c26c66-c469-4aed-b07f-7f251f392602.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation of circulating T lymphocytes with response to neoadjuvant chemoimmunotherapy in operable esophageal squamous cell carcinoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEsophageal squamous cell carcinoma (ESCC) is a common malignancy of the upper gastrointestinal tract, prevalent in East Asia ( Zheng, R et al. 2022; Abnet et al, C. 2018). Patients with locally advanced stage ESCC account for over half of the total number of cases. Encouragingly, neoadjuvant therapy followed by surgery has been shown to improve significantly the efficacy of treatment ( Yang, H et al. 2018; Shapiro, J et al. 2015; Yamasaki, M et al. 2017). Although the current National Comprehensive Cancer Network(NCCN) guidelines recommend neoadjuvant chemoradiotherapy combined with surgery as the standard treatment for operable esophageal squamous cell carcinoma, the incidence of distant metastasis and local recurrence in patients with this treatment modality remains unsatisfactory. The neoadjuvant treatment model for operable esophageal squamous cell carcinoma patients still needs to be continuously explored. In recent years, the use of PD-1 checkpoint inhibitors for ESCC therapy has produced an apparent clinical curative effect. There is increasing evidence that neoadjuvant chemotherapy combined with immune-oncology (neoCTIO) is an effective and safe treatment for resectable locally advanced ESCC ( Yang, W et al. 2022; Liu, J et al. 2022). This treatment approach may have more advantages in controlling distant metastasis, and has great potential for future development. However, the characteristics of the population of patients benefiting from this therapy remains unclear and is a major obstacle to facilitating its standard use in the clinic. According to current knowledge, although the expression of PD-L1 in tumor tissues is related to the efficacy of immunotherapy, it is not predictive enough to be a measure of the therapeutic response of a patient to immunotherapy ( Kelly, R.J et al. 2021; Larkin, J et al. 2015). Recent studies have shown that patients with non-small cell lung cancer can benefit from neoCTIO using PD-1 checkpoint inhibitors, regardless of the expression status of PD-L1 in tumor tissue (Shu CA et al. 2020). Current studies have confirmed that the tumor mutational burden (TMB), tumor microenvironment (TME) and microsatellite instability (MSI) can also predict the therapeutic effects of immunotherapy to some extent ( Cristescu, R et al. 2018; Dudley, J.C et al. 2016; Bagaev, A et al. 2021), but these findings have not been successfully translated into clinically acceptable prediction methods. Therefore, it will be of great significance to identify a valid and easy-to-measure predictive biomarker to facilitate the formulation of individualized treatment plans for particular subgroups of patients.\u003c/p\u003e \u003cp\u003eIt is very difficult, however, to anticipate the degree of the immune response from bioinformation about tumor tissue specimens and its biomarkers. In addition to the numbers of receptors expressed on tumor cells, the patients\u0026rsquo; immune status is also crucial in establishing an appropriate immunotherapy regimen. A number of studies have confirmed that different immune statuses of cytotoxic T cells (CD8\u0026thinsp;+\u0026thinsp;T cells), such as immune inflation and immune exhaustion, will directly affect the progression of tumors and hence the prognosis of patients ( Alexandrov, L.B et al. 2013; Wherry, E.J et al. 2011). PD-1 inhibitors activate CD8\u0026thinsp;+\u0026thinsp;T cells and enhance their ability to recognize and kill tumor cells, by blocking the PD-L1/PD-1 pathway communication between tumor and immune cells ( van der Gracht, E.T et al. 2020; Giunta, E.F et al. 2020). Recent studies have found that circulating immune cells are associated with the curative effect of immunotherapy in melanoma ( von Andrian, U.H et al. 2000). A clinical study has shown that melanoma patients with a more stable circulating immune cell environment can achieve a better prognosis (Giunta, E.F et al. 2000). The circulating CD8\u0026thinsp;+\u0026thinsp;T cells extravasate into tumor stroma under the guidance of chemokines, contact tumor tissue through interstitial migration, and exert cytotoxic effects(Salmon, H et al. 2012; Lee, J et al. 2021)。Like melanoma, ESCC has the biological characteristics of high immunogenicity ( Salmon, H et al. 2012). In our previous studies CD8\u0026thinsp;+\u0026thinsp;tumor-infiltrating lymphocytes (TIL) density was significantly increased in tumor tissues after neoadjuvant therapy༈He, W et al. 2022༉。Therefore, we speculate that the status of circulating T cell is directly related to the immune response and may be a potential biomarker of immunotherapeutic effects. Although some studies have attempted to explore the mechanisms of immunotherapy in terms of gene, protein, and tumor cell antigen, the connection between the fine subsets of T lymphocytes, which serve as the working cells of the immune system, and esophageal cancer immunotherapy is currently unclear. Thus, the aims of the present study were to investigate the relationship between subsets of circulating CD8\u0026thinsp;+\u0026thinsp;and CD4\u0026thinsp;+\u0026thinsp;T cells as well as the synthesis of cytotoxic factors and the response to neoCTIO in operable ESCC, with the objective of identifying clinically useful biomarkers.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e．\u003c/strong\u003e\u003cstrong\u003ePatients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study enrolled patients who were ready for undergoing an esophagectomy for esophageal cancer from September 2021 to December 2022. All patients had undergone contrast-enhanced CT scans of the neck, chest, upper and middle abdomen. Esophagogastroduodenoscopy and endoscopic ultrasonography were carried out, and cancer staging was performed according to the eighth edition of the Tumor-Node-Metastasis (TNM) classification published by Union for International Cancer Control (UICC). The criteria for enrollment in the study were: biopsy-confirmed thoracic ESCC, clinical stage cT2N1-2M0 or cT3-4aN0-2M0, without any anti-tumor treatment; aged 18 to 75 years; heart, lung, liver and kidney functions were normal; and an Eastern Cooperative Oncology Group (ECOG) score of 0-1. The exclusion criteria were: endoscopic biopsy confirmed small cell carcinoma; other anti-tumor therapies had been administered; a preoperative examination indicated that the carcinoma was unresectable at T4b or M1; corticosteroids or other immunosuppressive drugs were used within 14 days before enrollment; a history of active autoimmune disease or autoimmune disease that possibly recur; severe chronic or active infectious diseases; and a history of interstitial lung disease. The study protocols were approved by the Ethics Committee for Medical Research and New Medical Technology of Sichuan Cancer Hospital (SCCEC-02-2021-043). All patients provided written informed consent prior to been enrolled in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003cstrong\u003e．\u003c/strong\u003e\u003cstrong\u003eStudy Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a prospective exploratory study. All included patients received toripalimab immunotherapy 24 h after chemotherapy, with a regimen of paclitaxel (135 mg/m2, IV, D1) and carboplatin (area under the curve [AUC] = 5 [according to Calvert formula], IV, D1).\u0026nbsp;The immunotherapy regimen was toripalimab 240 mg administered progressively by intravenous infusion over a 30 min period. Every patient received each treatment regimen in a 3-week cycle for 2 cycles. Minimally invasive (McKeown) esophagectomy (MIE) with two-field lymph node dissection was performed 4-8 weeks after 2 cycles of neoadjuvant therapy. Tumor tissue samples were collected for pathological examination. Peripheral blood samples were collected 1 day before treatment and 1 day before surgery. The primary endpoints of the study were changes in the CD8+ and CD4+ T cell subsets as well as cytotoxic factor concentrations in CD8+ T cell and the pathological response rate. The secondary endpoints were the major pathological response (MPR) and the overall response rate (ORR). Adverse events (AEs) were recorded according to the Common Terminology Criteria for AEs (ver. 4.3.2). CD8+ T cell subsets included Effector Memory (CD45RO+CCR7-), effector (CD45RO-CCR7+), naive (CD45RO-CCR7+), memory precursor (MP:CD127+KLRG1-), and short-lived effector (SLE:CD127-KLRG1+) cells; the cytotoxic factors measured were IFN-\u0026gamma; and TNF-\u0026alpha;. CD4+ T cell subsets included Th1（IFN-\u0026gamma;+IL-4-）、Th2（IFN-\u0026gamma;-IL-4+）、Treg（CD25+CD127-）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Response Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll patients who had underwent surgery had ypTNM staging (ver. 8) and all resected primary tumor as well as lymph nodes were examined pathologically. The pathological response assessment was evaluated by two pathologists in our hospital according to standard pathological assessment criteria following NCCN guidelines after neoadjuvant therapy. The patients were randomly assigned into a pathological response group or a non-response group. The pathological response group was defined as patients whose tumor regression grading (TRG) was 0-2. Patients whose TRG was 3 were defined as the non-response group. The clinical response was assessed by an expert imaging physician and an expert thoracic surgeon based on CT and esophagogastroduodenoscopy according to the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST), with efficacy evaluated as a complete response (CR), partial response (PR), stable disease (SD) or progressive disease (PD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003cstrong\u003e．\u003c/strong\u003e\u003cstrong\u003eMulti-colour flow cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePeripheral blood T cell subsets were detected before the first CTIO and surgery. Before each test, 2 mL of fasting peripheral blood was drawn from patients and placed in a heparin anticoagulant tube. Single cell suspensions were prepared by lysis of red blood cells using red blood cell lysis buffer. The single cell suspension was then stimulated with coupling propidium monoazide (PMA) (1 \u0026micro;L) plus ionomycin (1.5 \u0026micro;L) plus Brefeldin A solution (1 \u0026micro;L) for 4 h. Then, cell surface staining of the suspension was carried out, followed by fixation/permeabilization and intracellular staining. The cytokines and immunophenotype of T cell fine subsets in the sorted single cell suspensions were determined by a multi-color flow cytometer (BD, FACSCantoTM Ⅱ, San Jose, USA). All flow data analysis was performed using FlowJo software (ver. 10.4.0).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e．\u003c/strong\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of this experiment were statistically analyzed and plotted using Graph Pad 7.0. The measurement data following normal distribution were expressed as mean \u0026plusmn; standard deviation, and the comparison between each other was analyzed by a two independent samples t-test. Measurement data that was not normally distributed are reported as the median and were analyzed by a two independent samples Mann-Whitney U test. For measurement data of paired samples, a two-paired-samples t-test or Wilcoxon rank-sum test was employed. A chi-squared test or Fisher\u0026apos;s exact test was used for categorical variables. A P-value \u0026lt; 0.05 was considered to be statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. Demographics Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 150 esophagectomy patients enrolled, ultimately 33 were included. The average age of the entire patient group was 58.9 years, with a median age of 58.0 years. One patient experienced severe gastrointestinal bleeding after the first neoadjuvant treatment and discontinued treatment, without undergoing surgery (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Pathological and Clinical Response Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf these, 32（32/33, 97.0%）\u0026nbsp;received 2 cycles of neoadjuvant therapy and MIE with R0 resection. Sixteen patients (50%) exhibited different degrees of pathological remission (0-50% of the tumor remained). Ten patients (10/32, 31.2%) achieved MPR, including 6 (6/32, 18.8%) with pathological response rate (pCR) in the per-protocol population (Figure 2). 16 (50%) experienced no obvious tumor regression. All patients completed imaging and esophagogastroduodenoscopy examinations before and after treatment. There were 14 response cases and 18 no-response cases in the clinical evaluations, with an ORR of 43.8%. The agreement rate between the pathological and clinical evaluation of treatment response was 68.8%. There were no PD cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Comparation of characteristics between treatment response and non- response\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean age of the pathological response group was significantly older than that of the non-response group (P = 0.013). No significant difference in the interval to surgery was found between the two groups (P = 0.787). After neoadjuvant therapy, the ypT and ypN stages in the pathological response group were significantly lower than those in the pathological non-response group, with statistical significance.\u0026nbsp;For all other characteristics of the two groups, no differences were found\u0026nbsp;(Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Dynamic changes of circulating T lymphocytes before and after NeoCTIO treatment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Effector Memory (CD45RO+CCR7-) of CD8+T cells subset showed a significant increase after neoadjuvant therapy, with an average increase of 3.7% in cell frequency (31.4\u0026plusmn;12.7% vs. 35.1\u0026plusmn;12.6%), and the difference was statistically significant (P=0.002) (Figure 3a-b). However, there were no significant changes in naive (23.8\u0026plusmn;21.5% vs. 21.4\u0026plusmn;16.4%, P=0.247), SLE (20.3\u0026plusmn;12.1% vs.22.6\u0026plusmn;11.0%, P=0.319), MP (31.6\u0026plusmn;16.0% vs.30.2\u0026plusmn;16.0%, P=0.689), effector (39.4\u0026plusmn;18.9% vs. 39.6\u0026plusmn;16.1%, P=0.927). There was also no significant change in the subpopulation of CD8+T cells co-expressing IFN\u0026gamma; and TNF\u0026alpha; (38.9\u0026plusmn;21.5% vs.34.0\u0026plusmn;19.5%, P=0.280) before and after treatment.\u003c/p\u003e\n\u003cp\u003eNo significant changes were observed in CD4+T cell subsets, including Th1 (21.9\u0026plusmn;11.6% vs.21.1\u0026plusmn;10.6%, P=0.202), Th2 (1.0\u0026plusmn;0.6% vs.1.2\u0026plusmn;0.8%, P=0.303), and Treg (4.9\u0026plusmn;1.6% vs.5.0\u0026plusmn;1.7%, P=0.701) before and after treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.Comparison of T lymphocytes between responders and non-responders during neoadjuvant therapy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt baseline before the neoadjuvant therapy, the pathological response group had a significantly higher frequency of CD8+T cells in the effector subset (47.1\u0026plusmn;19.4% vs. 32.2\u0026plusmn;16.5%, P=0.029) compared to the non-response group (Figure 4 c-d). The naive subset was significantly lower in frequency in the pathological response group (13.0\u0026plusmn;12.3% vs. 33.1\u0026plusmn;23.4%, P=0.006) compared to the non-response group (Figure 4 e-f). The IFN\u0026gamma; and TNF\u0026alpha; co-expression was higher in the pathological response group (49.9\u0026plusmn;17.3% vs. 30.7\u0026plusmn;21.3%, P=0.010) compared to the non-response group (Figure 4 a-b). Other Effector Memory (34.7\u0026plusmn;12.9% vs. 29.6\u0026plusmn;13.4%, P=0.287), MP (31.1\u0026plusmn;20.7% vs.32.6\u0026plusmn;11.2%, P=0.810), and SLE (21.1\u0026plusmn;16.2% vs.21.2\u0026plusmn;10.1%, P=0.980) did not show significant differences between the two groups.\u003c/p\u003e\n\u003cp\u003eThere were no significant differences in baseline levels of CD4+T cell subsets, including Th1 (22.4\u0026plusmn;11.4% vs. 20.5\u0026plusmn;9.7%, P=0.627), Th2 (1.1\u0026plusmn;0.7% vs. 1.0\u0026plusmn;0.5%, P=0.727), and Treg (5.0\u0026plusmn;1.5% vs. 4.9\u0026plusmn;1.7%, P=0.881) (P\u0026gt;0.05), between the two groups, and there were no significant differences after neoadjuvant therapy.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFor the treatment of locally resectable advanced ESCC, the main objective of neoadjuvant therapy combined with surgery is to increase the rate of radical resection (R0) and eliminate micro-metastases, thereby improving the survival rate. In the present study, the neoCTIO regimen for this group of patients was toripalimab combined with paclitaxel and carboplatin, and all patients underwent MIE with R0 resection. The tumor response to treatment was reached in 18.8% of patients with pCR, 31.2% with MPR, 43.3% with ORR and no patients with PD, which were similar to the results of previously published studies ( Yang, W et al. 2022; Liu, J et al. 2022; He, W et al. 2022; Liu, J et al. 2022). The results of the present study confirmed again that different PD-1 checkpoint inhibitor products combined with chemotherapy can achieve significant efficacy under the similar immunologic mechanism. However, only about 40% of patients benefited from this modality, therefore finding effective biomarkers will be critical for establishing a model neoCTIO treatment regimen. Such findings will greatly help to improve the total therapeutic efficacy for ESCC patients by preventing at least 60% of patients from receiving ineffective treatment and by diverting the patient subgroup to more effective alternative treatment modalities.\u003c/p\u003e \u003cp\u003e The two main guidelines for predicting the treatment response depends on the clinical markers and biomarkers, respectively. In our study, we did not find statistically significant differences in the clinical characteristics between the pathological response and non-response groups, with the exception of age. The results of lower age in the pathological non-response group may be a statistical aberration due to the small number of patients enrolled. These insignificant results suggest again that it is difficult to screen out the population of patients who will benefit according to their general clinical characteristics. At present, studies of biomarkers have mainly focused on the expression of PD-L1 in tumor tissues. The results of a recent meta-analysis revealed that PD-L1 expression by the tumor proportion score (TPS) was the best predictor of immune checkpoint inhibitor benefits in patients with squamous cell carcinoma, with a high TPS (TPS ≥ 1) having a predictive value of 41.0% compared to 16.0% for other variables ( Yoon, H.H et al. 2022). Although the expression of PD-L1 in tumor tissues is the best single predictive marker, its prediction accuracy is \u0026lt; 50%, that is, at least 50% of patients having a good response only exhibit medium or low expression. These findings suggest that there is still a continued need to find better predictive or accompanying biomarkers. In one of our preliminary studies, baseline PD-L1 and TMB expression in tumor tissues did not effectively predict the tumor response to treatment ( He, W et al. 2022). In fact, the host's own cytotoxic immune status was also a key factor for determining the response to immunotherapy. Therefore, it will be necessary to analyze further bioinformation about a patient’s immune functions to identify effective biomarkers that jointly predict the accuracy of the clinical diagnosis.\u003c/p\u003e \u003cp\u003eBiomarkers obtained from peripheral blood samples have the advantages of convenience, rapidity, safety and easy dynamic monitoring of treatment efficacy. The T cell mediated cellular immunity activation state may well represent the host's immune response ability and is closely related to tumor generation and progression(Kumar, B.V et al. 2018)。CD8 + T cells, when activated by tumor cell antigens, exert cytotoxic effects and directly kill tumor cells. During prolonged tumor progression, the PD-1 receptor on CD8 + T cells is specifically recognized and bound by the PD-L1 receptor on tumor cells to deplete their viability and function༈Hirano, F et al. 2005; Waeckerle-Men, Y et al. 2007༉。This negative regulatory mechanism can be blocked by PD-1 checkpoint inhibitors, enhancing the ability of CD8 + T cells to kill tumor cells༈Iwai, Y et al. 2002༉。Before and after intravenous infusion of PD-1 checkpoint inhibitors, we may find predictive biomarkers by detecting changes in the fine subsets and functions of CD8 + T cells in peripheral blood, and analyzing the correlation with the response to treatment. Recent studies with PD-1 checkpoint inhibitors in non-small cell lung cancer have shown that the dynamics change of PD-1 + CD8 + T cells in the peripheral blood can predict the response to immunotherapy (Kim, C.G et al. 2021)。In the classical immunological theory system, CD4 + T cells are often considered as immune helper cells, which maintain the stability of the immune internal environment and promote the comfortable progress of immune responses (Kumar, B.V et al. 2018). Most researchers focus on CD8 + T cells, and there is less research on CD4 + T cells at present. A study has found that CD4 + T cells may play a key role in bladder cancer immunotherapy (Oh DY et al. 2020), and stimulate functional cells to express anti-tumor effects through immune regulation and the release of immune cytotoxic factor. However, there is no high-level evidence or clinical research to explain the function and role of CD4 + T cells in the unique immune environment of ESCC patients with neoCTIO.\u003c/p\u003e \u003cp\u003eThe present study is the first to dynamically monitor changes in fine subsets and the cytotoxic factors in circulating T cells in a neoCTIO treatment model for ESCC. The results demonstrated that only Effector Memory subsets in peripheral blood were significantly increased in numbers after neoadjuvant therapy, while the other subsets were not significantly changed. The reason may be that PD-1 checkpoint inhibitors block the inhibitory pathway of PD-L1/PD-1 between tumor cells and T cells, and promote the activation and differentiation of naive cells into Effector Memory cells, which are a subset with significant antitumor effects。Effector Memory cells are widely distributed in the blood and tissues and have strong anti-tumor effects (Han, J et al. 2020; Chamoto, K et al. 2017; Klebanoff, C.A et al. 2005). When tumor antigens are exposed again, Effector Memory cells can respond more quickly and strongly. Higher levels of Effector Memory CD8 + T cells show stronger anti-tumor effects in the early stages of the tumor. An animal experiment using mice showed that CD8 + T cell subsets with memory function respond best to PD-1 checkpoint inhibitors (Henning, A.N et al. 2018; Kargl et al. 2017; Kim, J et al. 2015) found that Effector Memory CD8 + T cells are the main cells of lymphocyte infiltration in human lung cancer tissues. The immune inflation of tumor patients is mainly characterized by the high frequency and maintenance of Effector Memory CD8 + T cells (Kim, J et al. 2015; O'Hara, G.A et al. 2012). The increase and continuous high expression of Effector Memory may be one of the potential mechanisms for the sustained anti-tumor effect of T cell-mediated PD-1 checkpoint inhibitors.\u003c/p\u003e \u003cp\u003eIn this study, we detected the expression of various lymphocyte subsets and cytotoxic factors before surgery. Compared with the expression before immunotherapy, the changes in the expression of various lymphocyte subsets and cytotoxic factors were not significant, except for the Effector Memory subset. This may be related to the fact that the detection time after treatment in this study was more than 4 weeks from the last dose. A study of nivolumab in neoadjuvant therapy for lung cancer found that the increase in peripheral blood T cells peaked 1–2 weeks after treatment with a PD-1 inhibitor(Forde, P.M et al. 2018)。All patients who underwent surgery in this group did not experience severe postoperative complications. From the perspective of changes in lymphocyte immune subsets, the surgery time we selected was relatively appropriate and safe. However, this study did not continuously observe the dynamic changes of lymphocyte subsets postoperatively, and further research is needed to confirm this view.\u003c/p\u003e \u003cp\u003eIn order to explore the predictive value of the fine subsets of circulating T cells, we analyzed the differences in cell numbers for each subset according to pathological responses and changes within groups before and after treatment. In this study, we did not find significant relationship between the pathological responses and proportion and changes of CD4 + T cell subsets in the peripheral blood. We found more effector and less naive cells in the baseline CD8 + T lymphocytes in the pathological response group. This finding indicated that patients in the pathological response group had a more positive status of tumor immune response. stimulation of tumor antigens resulted in the conversion of naive cells into effector cells, which have significantly enhanced antitumor functions (Kumar, B.V et al. 2018; Lisci, M et al. 2021; Sommermeyer, D et al. 2016). In the present study, we found that naive subsets in circulating CD8 + T cells were significantly enriched in some patients, whose pathological response was often poor. The mechanism may be the absence of certain necessary immune response conditions. The main reason may be the incomplete immune response chain or the absence of certain necessary immune response conditions. It has been found that mutations in B2M, TAP1, TAP2 and other genes in tumor cells results in defects of signal transduction that can affect the expression of MHC class I and II antigens༈Kalbasi, A et al. 2020༉. In addition, inhibitory receptors such as TIGIT and CTLA-4 also exert a certain negative influence on T cell activation༈Johnston, R.J et al. 2014; Gangaev, A et al. 2021; Duraiswamy, J et al. 2013༉. These suppressions of immune pathways will greatly promote immune escape. In addition to the above factors, the loss of antigen presentation ability by dendritic cells and immune cell defects can also lead to the failure of the immune response to proceed normally.\u003c/p\u003e \u003cp\u003eThe relationship between the expression of cytotoxic factors in circulating CD8 + T lymphocytes and the response to treatment was analyzed. The results showed that the pathological response group had a higher proportion of CD8 + T cells synthesizing IFN-γ and TNF-α before treatment. IFN-γ and TNF-α are important cytokines of immune cells that act to inhibit tumor growth(Liao, P et al. 2022; Wu, M.J et al. 2022; Hezaveh, K et al. 2022)。High concentrations of IFN-γ and TNF-α expression also suggest that patients had good immune response capacity, which provides a good immunotherapy background for the treatment of PD-1 inhibitors. Circulating peripheral blood mononuclear cells in the response group of melanoma patients have been shown to exhibit a higher baseline concentration of IFN-γ ( Giunta, E.F et al. 2020). High-throughput sequencing of lung adenocarcinoma has shown that the TNF family has a certain predictive value in immunotherapy༈Zhang, C et al. 2020༉。the low concentrations of IFN-γ and TNF-α in the non-pathological response group may indicate that the patients have late dysfunction due to a persistent antigenic response of tumor cells༈Wherry, E.J et al. 2011༉。In a mouse model of meningitis virus infection, CD8 + T cells became exhausted after brief reactivation by PD-1/PD-L1 pathway blockade when the antigen persisted. PD-1 inhibitors failed to reverse late dysfunctional cells༈Pauken, K.E et al. 2016༉, which may be a possible mechanism for the failure of immunotherapy in these patients. Therefore, the baseline IFN-γ and TNF-α co-expression by circulating CD8 + T cells is also a valuable biomarker for predicting the response to neoCTIO therapy.\u003c/p\u003e \u003cp\u003eThere were a number of limitations to the study. First, it was an exploratory study with a small sample size. Second, it did not measure the expression of PD-L1 in tumor tissues or tumor-infiltrating CD8 + T cells. Finally, the relationship between tumor-infiltrating CD8 + T cells and circulating CD8 + T cells was not further explored.\u003c/p\u003e \u003cp\u003eThe effective rate of neoadjuvant CTIO for resectable ESCC in this study was about 50%. The baseline numbers of effector subsets and the co-expression of IFN-γ and TNF-α in circulating CD8 + T lymphocytes had a positive predictive value, while the baseline numbers of naive subsets was a negative predictive biomarker. CD4 + T cell subsets have no correlation with the treatment response. As an important part of the predictive system in neoCTIO, these baseline characteristics of circulating CD8 + T cells can be used to improve further the prediction accuracy of the population of patients most likely to benefit combined with other effective biomarkers.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe baseline numbers of effector subsets and co-expression of IFN-γ and TNF-α in circulating CD8 + T lymphocytes were positive predictors while the baseline frequency of naive subsets was a negative predictive marker of the response to therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAEs: Adverse events\u003c/p\u003e\n\u003cp\u003eAUC: area under the curve\u003c/p\u003e\n\u003cp\u003eCR: complete response\u003c/p\u003e\n\u003cp\u003eECOG: Eastern Cooperative Oncology Group\u003c/p\u003e\n\u003cp\u003eESCC: esophageal squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eMIE: Minimally invasive esophagectomy\u003c/p\u003e\n\u003cp\u003eMPR: major pathological response\u003c/p\u003e\n\u003cp\u003eMSI: microsatellite instability\u003c/p\u003e\n\u003cp\u003eNCCN: National Comprehensive Cancer Network\u003c/p\u003e\n\u003cp\u003eneoCTIO\u0026nbsp;:neoadjuvant chemotherapy combined immune-oncology\u0026nbsp;therapy\u003c/p\u003e\n\u003cp\u003eORR: overall response rate\u003c/p\u003e\n\u003cp\u003epCR: pathological complete response\u003c/p\u003e\n\u003cp\u003ePD：progressive disease\u003c/p\u003e\n\u003cp\u003ePMA：propidium monoazide\u003c/p\u003e\n\u003cp\u003ePR：partial response\u003c/p\u003e\n\u003cp\u003eR0: radical resection\u003c/p\u003e\n\u003cp\u003eRECIST: Response Evaluation Criteria in Solid Tumors\u003c/p\u003e\n\u003cp\u003eSD：stable disease\u003c/p\u003e\n\u003cp\u003eTMB: tumor mutational burden\u003c/p\u003e\n\u003cp\u003eTME: tumor microenvironment\u003c/p\u003e\n\u003cp\u003eTNM: Tumor-Node-Metastasis\u003c/p\u003e\n\u003cp\u003eTPS: tumor proportion score\u003c/p\u003e\n\u003cp\u003eTRG: tumor regression grading\u003c/p\u003e\n\u003cp\u003eUICC: Union for International Cancer Control\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e: Kunzhi Li: Conceptualization; \u0026nbsp;Experiment; \u0026nbsp;Formal analysis; \u0026nbsp;Methodology; \u0026nbsp; Visualization; \u0026nbsp; Investigation; \u0026nbsp;Writing—original draft; \u0026nbsp;Writing—review \u0026amp; editing; Kangning Wang: Data curation; Writing—review \u0026amp; editing; Yixuan Huang: Conceptualization; \u0026nbsp;Experiment; \u0026nbsp;Formal analysis; \u0026nbsp;Visualization; \u0026nbsp;Writing—review \u0026amp; editing; Mu Yang: Conceptualization; \u0026nbsp;Experiment; \u0026nbsp;Formal analysis; \u0026nbsp;Visualization; Xing Wei: Conceptualization; \u0026nbsp;Methodology; Writing—review \u0026amp; editing; Yongtao Han: Data curation; Writing—review \u0026amp; editing; Yan Miao: Data curation; Writing—review \u0026amp; editing; Qiang Fang: Conceptualization; Formal analysis; Funding acquisition; Methodology; Project administration; Supervision; Visualization; Writing—original draft; Writing—review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research was supported by Sichuan Province Science and Technology Support Program (No. 2021YJ0118).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eThe study protocols were approved by the Ethics Committee for Medical Research and New Medical Technology of Sichuan Cancer Hospital (SCCEC-02-2021-043). The study design and conduct complied with all relevant guidelines and regulations regarding the use of human study participants and was conducted in accordance with the criteria set by the Declaration of Helsinki. \u0026nbsp;All patients voluntarily participated and provided written informed consent before the initiation of any study-related treatment or procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors are grateful to all medical and nursing staff, enrolled patients and their families, and hospital committee members.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicting interest:\u0026nbsp;\u003c/strong\u003eThe authors have no conflict of interest to declare.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZheng R, et al. Cancer incidence and mortality in China, 2016. 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Science (New York, N.Y.), 354(6316), 1160\u0026ndash;1165. https://doi.org/10.1126/science.aaf2807\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Comparation of Characteristics between pathological responders and non- responders\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"95%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003ePatients\u003c/p\u003e\n \u003cp\u003eN=32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003ePathological\u0026nbsp;response\u0026nbsp;group\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN=16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003ePathological non-\u0026nbsp;response\u0026nbsp;group\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eN=16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eMean \u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e58.5\u0026plusmn;6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e61.4\u0026plusmn;5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e55.6\u0026plusmn;6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eMedian (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e58.0(44.0-69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e61.5(50.0-69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e54.5(44.0-68.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e2(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e1(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e30(93.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e15(93.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e15(93.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e22.1\u0026plusmn;2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e22.0\u0026plusmn;2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e22.3\u0026plusmn;2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;Upper chest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e3(9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;Middle chest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e20(62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e10(62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e10(62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;Lower chest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e9(28.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003ecT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1 (3.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1 (6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e29(90.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e14(87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e15(93.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e2(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e1(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003ecN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e14(43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e8(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e10(31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e8(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e8(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;II\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e13(40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e9(56.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;III\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e19(59.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e10(62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e7(43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eypT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e7(21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e7(43.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e7(21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e5(31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e2(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e3(9.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e3(18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e15(46.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e14(87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eypN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e21(65.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e11(68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e10(62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e4(12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e4(25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e0(0.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e7(21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1(6.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e6(37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eECOG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e\u0026nbsp;0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e19(59.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e11(68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e8(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e13(40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e5(31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e8(50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eInterval to surgery \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e33.5(25.0-82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e33.0(25.0-53.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e37.0(25.0-82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.787\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eLymphocyte（109/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1.6\u0026plusmn;0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e1.5\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e1.8\u0026plusmn;0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eNeutrophil（109/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e4.4\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e4.5\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e4.3\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.5306%;\"\u003e\n \u003cp\u003eMonocyte（109/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e0.5\u0026plusmn;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3878%;\"\u003e\n \u003cp\u003e0.5\u0026plusmn;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18.3673%;\"\u003e\n \u003cp\u003e0.5\u0026plusmn;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.3265%;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Interval to surgery: Between end of neoadjuvant Chemoimmunotherapy and surgery\u003c/p\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":"biomarker, CD8+ T lymphocytes, CD4+ T lymphocytes, ESCC, neoadjuvant chemoimmunotherapy ","lastPublishedDoi":"10.21203/rs.3.rs-5361643/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5361643/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: This study aimed to investigate the correlation of the circulating T lymphocytes with response to neoadjuvant chemotherapy combined immune-oncology therapy (neoCTIO) in operable esophageal squamous cell carcinoma (ESCC) and explore the predictive markers.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: ESCC patients staged cT2N1-2M0 or cT3-4aN0-2M0 were enrolled. All patients received two cycles of neoCTIO of each 21-day cycle. Minimally invasive esophagectomy (MIE) was performed 4-8 weeks after neoCTIO. Peripheral blood lymphocytes subsets and effector cytokines were detected before and after neoCTIO by using flow cytometry. The primary endpoints were the advanced change of subsets, effector cytokines in T lymphocytes, and pathological complete response (pCR). The secondary endpoints included major pathological response (MPR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 33 patients with ESCC were enrolled. 96.7% (32/33) received MIE with R0 resection and 10 (10/32, 31.3%) achieved MPR, including 6 (6/32, 18.8%) patients with pCR. The ORR was 43.8% (14/32). The number of Effector Memory CD8+ T lymphocytes was elevated after neoadjuvant therapy (P = 0.002). In the responders, CD8+ T lymphocytes showed higher IFNγ and TNFα co-expression (P=0.010). Responders exhibited higher numbers of effector subsets (P = 0.029) and lower numbers of naive subsets (P = 0.006). No statistical difference was found in the cell frequency of CD4+T lymphocyte subsets between the responders and the non- responders.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: The baseline numbers of effector subsets and co-expression of IFN-γ and TNF-α in circulating CD8+ T lymphocytes were positive predictors while the baseline frequency of naive subsets was a negative predictive marker of the response to therapy.\u003c/p\u003e","manuscriptTitle":"Correlation of circulating T lymphocytes with response to neoadjuvant chemoimmunotherapy in operable esophageal squamous cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 11:33:40","doi":"10.21203/rs.3.rs-5361643/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":"dec12a26-c0f2-4615-b394-3864ad806a0e","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-11T22:53:20+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 11:33:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5361643","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5361643","identity":"rs-5361643","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}