{"paper_id":"2de7398f-6525-4f8a-8aab-473acd67a9b3","body_text":"Clinical efficacy and biomarkers of neoadjuvant chemoimmunotherapy in locally advanced 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 Clinical efficacy and biomarkers of neoadjuvant chemoimmunotherapy in locally advanced esophageal squamous cell carcinoma Siyou Deng, Qi Wang, Yueping Li, Ruijie Zhang, Jinjie Li, Yujie Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6357846/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Jun, 2025 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted 9 You are reading this latest preprint version Abstract Background: Neoadjuvant immunotherapy has emerged as a promising strategy for esophageal squamous cell carcinoma (ESCC). This study evaluates the therapeutic efficacy and safety of neoadjuvant immunochemotherapy (nICT) in ESCC and explores potential biomarkers associated with treatment outcomes. Methods: Patients with locally advanced ESCC were enrolled and received two cycles of nICT followed by surgical resection. The primary endpoint was the pathological complete response (pCR) rate, while secondary endpoints included overall survival (OS), event-free survival (EFS), safety, and the identification of predictive biomarkers. Results: A total of 47 patients were enrolled in the study, with 42 undergoing surgical 40 resection, all of whom achieved R0 resection. The rates of complete and partial pathological responses were 28.5% and 16.7%, respectively. The 1-year and 2-year EFS rates were 82% and 37.3%, while OS rates reached 100% and 71.4%, respectively. The majority of treatment-related adverse events (TRAEs) were Grade 1–2, and no surgical delays were observed. RNA sequencing analysis revealed epithelial-mesenchymal transition (EMT) as the most significantly enriched pathway in non-responders. Notably, higher infiltration of normal fibroblasts (NF) correlated with improved pathological response and enhanced long-term survival, whereas myofibroblastic cancer-associated fibroblasts (myCAF) negatively influenced treatment efficacy and clinical outcomes. Conclusions: Neoadjuvant PD-1 inhibitors combined with chemotherapy demonstrate encouraging potential for patients with locally advanced ESCC, inducing a robust immune response that correlates with clinical outcomes. The infiltration of myCAF emerges as a potential predictive biomarker for treatment response and disease progression, highlighting the need for further mechanistic exploration and validation in larger cohorts. Trial registration: NCT, NCT05028231. Registered 24 August 2021 Neoadjuvant therapy PD-1 blockade immunochemotherapy biomarkers ESCC Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Esophageal cancer (EC) is the sixth leading cause of cancer-related mortality and the seventh most frequently diagnosed malignancy worldwide [ 1 ]. Among its histological subtypes, esophageal squamous cell carcinoma (ESCC) predominates, particularly in high-incidence regions such as East Asia [ 2 ]. Despite significant advancements in treatment, the prognosis for locally advanced ESCC remains poor, highlighting an urgent need for more effective treatment strategies to enhance clinical outcomes. Neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection has been established as the standard treatment approach for patients with locally advanced ESCC. The CROSS and NEOCRTEC5010 trial have demonstrated better 5- and 10-year overall survival rates with preoperative chemoradiotherapy compared to surgery alone [ 3 , 4 ]. However, despite achieving high rates of R0 resection, approximately 15% of patients experienced locoregional recurrence within five years, while the incidence of distant metastasis remains as high as 30% [ 4 , 5 ]. Moreover, the use of radiotherapy is associated with increased perioperative complications and mortality and does not confer a survival advantage over neoadjuvant chemotherapy (nCT), primarily due to the severe toxicity associated with radiation therapy [ 6 , 7 ]. These limitations underscore the urgent need for novel therapeutic approaches that enhance both safety and long-term clinical outcomes. The advent of immune checkpoint inhibitors (ICIs) has transformed the treatment landscape of multiple malignancies, offering survival benefits across various cancer types [ 8 – 13 ]. Encouraged by the promising efficacy of immunotherapy combined with chemotherapy in advanced ESCC, there has been growing interest in extending its application to the neoadjuvant setting. Emerging clinical evidence have indicated that neoadjuvant immunochemotherapy (nICT) can induce favorable pathologic responses with manageable safety profile in patients with locally advanced ESCC [ 14 – 19 ]. However, limited data are available on long-term clinical outcomes. Pathological complete response (pCR) following neoadjuvant therapy has been consistently linked to improved long-term outcomes across multiple cancers [ 4 , 20 , 21 ]. However, not all patients exhibit a favorable response to immune checkpoint blockade, raising the urgent need for reliable pre-treatment biomarkers that can predict immune response and guide patient selection for nICT. Furthermore, emerging evidence suggests that postoperative adjuvant therapy may improve outcomes in patients at high risk of recurrence following neoadjuvant treatment [ 22 ]. Hence, identifying post-treatment biomarkers to assess recurrence risk and inform adjuvant therapeutic decisions becomes equally important. The tumor immune microenvironment (TIME) critically influences tumor progression, immune escape, and therapeutic efficacy [ 23 ]. The crosstalk between tumor and the TIME, both pre- and post-treatment, not only drives treatment resistance but also contributes to tumor progression [ 24 ]. A deeper understanding of these dynamic interactions holds the potential to refine patient stratification and optimize personalized treatment strategies. However, the predictive biomarkers within the neoadjuvant context, particularly for early-stage ESCC, remains insufficiently explored and warrants further investigation. In this research, we carried out a single-arm, phase II prospective clinical trial to evaluate the efficacy and safety of nICT. Additionally, we explored the pre-treatment and post-treatment biomarkers that could predicting the efficacy of this regimen in resectable ESCC. Materials and Methods Participants This investigator-initiated, single-arm, phase II prospective clinical trial was conducted at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. The trial was registered at ClinicalTrials.gov (NCT05028231) and adhered to the principles of the Declaration of Helsinki. Inclusion criteria: Eligible patients were aged 18–75 years and diagnosed with locally advanced, resectable ESCC classified as stage II–IVA based on the 8th edition of the AJCC staging system. Resectability was determined by a thoracic surgeon. Additional eligibility requirements included an Eastern Cooperative Oncology Group (ECOG) performance status of 0–1 and adequate organ function. Exclusion criteria: Patients with concurrent malignancies, pregnancy, lactation, autoimmune disorders, documented allergies to study drugs, or those unable to tolerate esophagectomy based on preoperative pulmonary and cardiovascular assessments were excluded. Procedure Participants underwent two cycles of nICT, administered at three-week intervals. Each cycle comprised an intravenous infusion of either Pembrolizumab (240 mg, Day 1; Merck, USA) or Tislelizumab (240 mg, Day 1; BeiGene, China), in combination with Paclitaxel (75 mg/m², Day 1; Hengrui, China) and either Carboplatin (AUC 5 mg/mL/min, Day 1; Qilu Pharma, China) or Cisplatin (25 mg/m², Days 1–3; Haosun Pharma, China). The selection of the PD-1 inhibitor was determined by the treating physician rather than through randomized allocation. Baseline assessments, including contrast-enhanced thoracic and abdominal computed tomography (CT), endoscopic ultrasonography (EUS), and cervical/subclavicular ultrasonography, were performed before the initiation of neoadjuvant therapy. These imaging evaluations were repeated after the completion of each of the two neoadjuvant treatment cycles to assess tumor response and resectability. Tumor response was assessed based on Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) [25]. Treatment-related adverse events (TRAEs) were assessed and documented at each visit, following the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE) version 5.0 [26]. TRAEs occurring within 30 days post-surgery or within 90 days of the first neoadjuvant dose were documented. Approximately 6–8 weeks after the final cycle of nICT, patients who remained surgically eligible underwent esophagectomy. The choice of surgical approach (McKeown or Ivor Lewis) was based on tumor location and resectability. All patients underwent two-field (abdominal and thoracic) lymph node dissection, with cervical lymph node dissection performed selectively for the patients with suspected cervical metastasis. Pathologic response was independently evaluated by two pathologists and categorized as follows: Grade 1 (pathologic complete response, pCR; no residual tumor), Grade 2 (pathologic partial response, pPR; ≤50% residual tumor), and Grade 3 (pathologic non-response, pNR; >50% residual tumor or new lesions). Following surgical resection, patients underwent routine follow-up assessments every three months during the first year and every six months in the second and third years. Overall survival (OS) was defined as the time from surgery to death from any cause or the last follow-up. Event-free survival (EFS) was measured from the date of surgery to the first occurrence of tumor recurrence, disease progression, or death. Patients who received adjuvant therapy after surgery were documented accordingly. Outcome The primary endpoint of the study was pCR rate. Secondary endpoints included OS, EFS, treatment safety, and the identification of biomarkers associated with treatment response. Exploratory Analysis Pretreatment endoscopic biopsy specimens and posttreatment surgical samples were collected for biomarker analysis, including RNA sequencing, cell-type composition analysis, and tumor microenvironment profiling. RNA sequencing was performed using the Illumina Novaseq 6000 platform (Shanghai Biotechnology Corp., China). Differential gene expression was conducted using the DESeq2 package (v1.44.0) in R, with a significance threshold set at |fold change (FC)| > 1.0 and p < 0.05. Enrichment analysis of differentially expressed genes (DEGs) was conducted using the clusterProfiler R package (v4.12.6), focusing on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Hallmark gene sets. Data visualization was performed using ggplot2 in R. To assess the cellular composition of tumors, single-cell RNA sequencing (scRNA-seq) data from 60 ESCC tumor samples were obtained from the Gene Expression Omnibus (GEO) database (accession number GSE160269) [27]. The deconvolution of bulk RNA sequencing data was performed using Scaden, a machine-learning algorithm trained on scRNA-seq datasets to estimate the relative abundance of distinct cell types [28]. Multiplex immunofluorescence (mIF) analysis was performed to assess distribution and composition in post-treatment tumor tissues. To quantify the abundance of myofibroblastic cancer-associated fibroblasts (myCAF) and map their localization within the tumor microenvironment, co-localization analysis was conducted using four myCAF-related markers— MMP11 , COL3A1 , α-SMA , and FAP . Statistical Analysis This study was designed as a single-arm phase II trial with pCR as the primary endpoint. To demonstrate superiority, a pCR rate of 25% was expected with neoadjuvant immunochemotherapy. Considering an anticipated dropout rate of 10%, a total of 47 patients were required to achieve 90% power at a one-sided alpha level of 10%. For continuous variables, the median and range were reported, while categorical variables were summarized as frequencies and percentages. Intergroup comparisons were performed using the Wilcoxon test to assess statistical differences. Kaplan–Meier survival curves were constructed to estimate OS and EFS, with intergroup differences assessed via the log-rank test. The 95% confidence intervals (CIs) were calculated using the Clopper–Pearson method. A P-value < 0.05 was considered statistically significant. Quantitative analysis of mIF was conducted using ImageJ software (NIH, Bethesda, MD, USA). All statistical analyses and data visualization were conducted using SPSS version 23.0 and R version 4.3.1. Results Patient characteristics Between September 2021 and May 2023, 55 patients were screened for eligibility. Of these, 47 met the inclusion criteria and were enrolled in the research. Ultimately, 42 patients (89.3%) who successfully completed two cycles of neoadjuvant therapy followed by surgical resection were included in the final analysis (Fig. 1a, b). The treatment course and follow-up status are depicted in the swimmer’s plot of every patients (Fig. 1c). The median age of the cohort was 59.5 years, with a predominance of male patients (81.0%). The majority of patients (73.8%) presented with T3-stage tumors, and lymph node involvement (N+) was identified in 83.3% of cases. Based on the AJCC 8th edition staging system, 59.5% of patients were classified as stage III. Tumor localization was predominantly in the middle (59.5%) and lower (28.6%) esophagus, collectively comprising 88.1% of cases. Additional baseline characteristics are detailed in Table 1. Table 1 Baseline characteristics of the patients Characteristic Patients (n=42) Age, years a 59 (41, 75) Sex Male 34 (81.0) Female 8 (19.0) ECOG performance status 0 32 (76.2) 1 10 (23.8) Smoking status Never 19 (45.2) Former or current 23 (54.8) Alcohol consumption Never 14 (33.3) Former or current 28 (66.7) Tumor location Upper 5 (11.9) Middle 25 (59.5) Lower 12 (28.6) Tumor length(cm) 6 (1, 16) Clinical T stage b T2 7 (16.7) T3 31 (73.8) T4 4 (9.5) Clinical N stage N0 7 (16.7) N1 28 (66.7) N2 7 (16.7) Clinical stage II 13 (31.0) III 25 (59.5) IVa 4 (9.5) Data are presented as n (%), unless otherwise specified. Abbreviations: ECOG, Eastern Cooperative Oncology Group; TNM, tumor-node-metastasis. a. The results of continuous variables are presented as median (range). b. Clinical disease stage was assessed according to the criteria of the American Joint Committee on Cancer, Eighth Edition. Surgical Outcomes Among the 42 patients who completed two cycles of nICT followed by surgical resection, the mean interval from the first dose of treatment to surgery was 2.02 ± 0.16 months. All surgeries achieved R0 resection, with a mean operative duration of 346.75 ± 76.13 minutes. A median of 18.65 ± 10.45 lymph nodes were dissected per patient, with 13 patients (30.9%) exhibiting pathological lymph node involvement. The median operative blood loss was 121.25 ± 275.28 mL. Postoperative complications included anastomotic leakage in 2 patients (4.76%) and pulmonary infection in 3 patients (7.14%). The median duration of postoperative hospitalization was 14 (range, 7–30 days). Notably, no immune-related complications or deaths occurred within 90 days following surgery (Table 2). Table 2 Summary of surgery-related adverse events Complication Patients (n=42) Anastomotic leakage 2 (4.76) Pulmonary infection 3 (7.14) Bleeding 0 Immune-related myocarditis 0 Immune-related nephritis 0 Immune-related hepatitis 0 Immune-related pneumonia 0 Immune-related hyperthyroidism 0 Immune-related hypothyroidism 0 Death within 90 days 0 Data are presented as n (%), unless otherwise specified. Clinical efficacy After two cycles of nICT, twelve patients achieved pathological complete response, 7 achieved a pathological partial response, and 23 had pathological non-response, as assessed by RECIST 1.1. The distribution of primary tumor pathologic responses and corresponding clinical characteristics are depicted in Fig. 2a. Notably, no cases of disease progression were observed during the neoadjuvant phase. As of the data cutoff on December 1, 2024, with a median follow-up of 29.95 months (range, 15.83–44.9 months), the 1-year and 2-year EFS rates were 82% (95% CI, 70.7–90.5) and 37.3% (95% CI, 23.0–60.3), respectively (Fig. 2b). The OS rates at the same time points were 100% (95% CI, 100.0–100.0), 71.4% (95% CI, 59.0–86.5) as depicted in Fig. 2c. We further analyzed the 24-month relapse rates in responders (pCR/pPR) versus non-responders (pNR). The relapse rates were 32.6% in the responders versus 82.6% in the non-responders. Radiology and pathology assessments, including CT scans, endoscopy, and histopathologic reports, confirmed significant tumor shrinkage in the responders, further validating the efficacy of the neoadjuvant regimen (Fig. 2d). The responders exhibited a pronounced trend toward improved EFS and OS compared to non-responders (Fig. 2e). Safety In our study, nICT was generally well tolerated, with no previously unreported treatment-related adverse events (TRAEs) (Table 3). Adverse events of any grade were observed in 95.2% (40/42) of patients, the majority (88.8%) of whom experienced Grades 1–2 adverse effects. The most commonly reported Grade 3–4 adverse events (AEs) were leukopenia and thrombocytopenia, which occurred in 11.9% of patients. Other common TRAEs included alopecia (n = 25), hepatotoxicity (n = 23), decreased appetite (n = 17), constipation (n = 17), and BNP elevation (n = 16), which were generally mild and manageable. The overall adverse event rates were comparable between groups (89.5% responders vs. 95.6% non-responders), however, grade 3–4 TRAEs were observed exclusively in the non-responder cohort. Notably, no patients experienced treatment-related mortality, and all Grade 3–4 TRAEs resolved with appropriate medical management. No Grade 5 TRAEs and surgical delays were reported, indicating that the treatment was well tolerated overall. Table 3 Treatment-related adverse events in all patients (n=42) TRAEs a All patients(n=42) Grade1-2 Grade3 Grade4 Neutropenia 12 (28.5) 3 (7.1) 1 (2.4) Thrombocytopenia 7 (16.7) 2 (4.8) 0 Decreased Appetite 15 (35.7) 2 (4.8) 0 Nausea 15 (35.7) 0 0 Vomiting 11 (26.2) 0 0 Alopecia 24 (57.1) 1 (2.4) 0 Constipation 17 (40.4) 0 0 Hepatic Toxicity 23 (54.8) 0 0 Dizziness 4 (9.5) 0 0 Fever 3 (7.1) 0 0 Cardiotoxicity 0 0 0 BNP Elevation 16 (38.1) 0 0 Rash 7 (16.7) 0 0 Pneumonitis 2 (4.7) 0 0 Data are presented as n (%), unless otherwise specified. Abbreviations: TRAE, treatment related adverse event. a.TRAEs were assessed during treatment and for up to 30 days after the last dose of neoadjuvant treatment according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 5.0. RNA-seq Analysis of Tumor Samples RNA sequencing was performed on tumor samples collected before and after nICT treatment. Patients were classified into responder (pCR/pPR) and non-responder (pNR) based on the pathologic response (Fig. 3a). In the non-response group, pathways related to epithelial-mesenchymal transition (EMT) and focal adhesion were significantly enriched, whereas in the responder group, immune-related pathways—including cytokine signaling, interferon-gamma response, and antigen presentation—were notably activated (Fig. 3b). Moreover, ten fibroblast-related genes exhibited differential expression between responders and non-responders, involving genes related to fibroblast activation and tumor microenvironment modulation ( CTHRC1, MFAP2, POSTN ), extracellular matrix (ECM) synthesis and remodeling ( COL1A1, COL1A2, COL3A1, SPARC ), ECM degradation ( MMP1, MMP11, SERPINH1 ) (Fig. 3 c ). According to the two-year relapse outcomes, the patients were divided into relapse (early relapse within two years) and non-relapse groups (Fig. 3 d ). In the relapse group, pathways related to cell cycle progression, metabolic reprogramming, genomic instability and carcinogenesis were enriched, while in the non-relapse group, the immune-related pathways showed upregulation (Fig. 3 d ). Additionally, Differential expression trends of fibroblast-related genes between relapse and non-relapse groups were observed but did not reach statistical significance (Fig. 3 f ). Fibroblast Subpopulation Dynamics A deep learning-based model was employed for cell-type deconvolution and composition analysis, revealing dynamic shifts in tumor microenvironment (TME) composition. The analysis displaying the relative proportions of epithelial cells, fibroblasts, endothelial cells, T cells, B cells, and myeloid cells in both the response and non-response groups, at baseline and post-treatment (Table S1). Notably, post-treatment samples exhibited a decrease in epithelial cell dominance, accompanied by increased immune cell infiltration, particularly in the response group (Fig. 4a). Further analysis of fibroblast subpopulations demonstrated changes in proportions of normal fibroblasts (NF) and myCAF between pre- and post-treatment samples (Fig. 4b and Table S2). A significant association between the proportions of NF and myCAF and pathological response was observed in both pre- and post-treatment TME (Fig. 4c). Moreover, the proportions of NF and myCAF in post-treatment samples were significantly different between the relapse (early relapse within two years) and non-relapse groups, suggesting a potential role of fibroblast subpopulations in tumor recurrence (Fig. 4d). Additionally, the proportion of NF and myCAF were strongly correlated with both EFS and OS (Fig. 4e). The patients with a higher NF proportion had significantly longer EFS (log-rank P = 0.0077) and a trend toward improved OS (log-rank P = 0.07). Conversely, patients with a higher myCAF proportion demonstrated significantly worse EFS (log-rank P = 0.03) and OS (log-rank P = 0.035). To further validate these findings, mIF analysis was performed on operative tumor samples. MyCAF subpopulations were identified through the co-expression of COL3A1 , FAP , MMP11 , and α-SMA . The mIF analysis revealed a higher proportion of myCAF in the non-responders compared to responders, and notably, the myCAF proportion was higher in the patients in the relapse group. Discussion Consistent with previous studies, our findings demonstrate that neoadjuvant chemoimmunotherapy is both effective and safe in the treatment of ESCC. Our trial met the primary endpoint, with 12 out of 42 (28.5%) surgically treated patients achieving pCR. Notably, there were no cases of postoperative mortality or an increased risk of surgical complications, indicating the manageable safety profile of nICT. Given the critical role of immune response prediction in neoadjuvant treatment and organ preservation strategies, our observation that myCAF proportion is significantly associated with pNR is particularly noteworthy. The pCR rate (28.5%) and pPR rate (16.7%) observed in our study represent a substantial improvement over the 3.8%–4% pCR rates reported in trials of neoadjuvant chemotherapy alone [29, 30]. Furthermore, survival analysis revealed 1-year and 2-year EFS rates of 82% and 37.3%, respectively, alongside OS rates of 100% and 71.4% at the same time points. These outcomes reinforce the therapeutic potential of this regimen. Notably, the treatment exhibited a manageable safety profile, with TRAEs occurring in only 11.9% of patients, significantly lower than the 32%–49% AEs rates seen in traditional neoadjuvant regimens. These results suggest that nICT provides a promising approach for tumor regression while maintaining a tolerable safety profile. Although nCRT remains the standard for treating locally advanced ESCC due to its ability to achieve high pCR rates, its widespread clinical adoption is constrained by radiation-induced toxicities, including esophagitis, anastomotic leakage, and long-term complications. Furthermore, postoperative recurrence and distant metastasis continue to pose significant challenges. Our study achieves a 28.5% pCR rate, which, while lower than that observed with nCRT, offers comparable therapeutic benefits while mitigating radiotherapy-related complications. This suggests that nICT may serve as a viable alternative in organ preservation strategies, particularly for patients ineligible for chemoradiotherapy due to toxicity concerns. Although PD-1 blockade has not yet been established as a standard neoadjuvant strategy, its emerging therapeutic potential warrants further exploration. Multiple ongoing clinical trials, including KEYNOTE-002, are currently investigating the role of postoperative immunotherapy in reducing recurrence and metastasis [31]. Our analysis revealed a significant survival advantage among responders compared to non-responders. Patients achieving pCR or pPR demonstrated superior OS and EFS, with relapse rates of 32.6% in responders vs. 82.6% in non-responders, underscoring the profound impact of immune-mediated tumor regression on long-term survival. Interestingly, all Grade 3–4 TRAEs occurred in non-responders, suggesting that immune activation may mitigate treatment-related toxicity. While the underlying mechanisms remain unclear, these findings highlight the importance of identifying predictive biomarkers for immune response to refine patient stratification and personalize neoadjuvant therapy. To explore potential biomarkers, RNA sequencing was conducted to identify molecular pathways associated with treatment response. Responders showed activation of pathways related to interferon-γ response and antigen presentation, which are critical for T cell activation and immune surveillance [32]. Conversely, non-responders exhibited upregulated EMT and hypoxia-related pathways, which plays a critical role in immune evasion and resistance to immunotherapy [33, 34]. Among the pathway, EMT emerged as the most strongly correlated pathway with therapy response, potentially promoting tumor invasion, metastasis, and evasion of immune cells, thereby diminishing treatment efficacy [35, 36]. Furthermore, recurrent tumors showed activation of cell cycle progression and metabolic reprogramming pathways, suggesting that combining immunotherapy with CDK4/6 inhibitors or glycolysis inhibitors may enhance long-term treatment outcomes [37, 38]. Notably, fibroblast-related gene upregulation was predominantly observed in non-responders, indicating that fibroblast-mediated ECM remodeling may act as a barrier to immune infiltration, a phenomenon previously reported in pancreatic cancer models [39]. Our study further explored the role of cancer-associated fibroblasts (CAFs) in predicting treatment response. We identified distinct fibroblast subtypes, NF and myCAF, finding their strong correlation with treatment response and prognosis in ESCC. Responders and non-recurrent patients exhibited a higher proportion of NF, a feature that may enhance CD8+ T cell infiltration and promote an immune-permissive tumor microenvironment. Conversely, myCAF cells, which contribute to a fibrotic and immunosuppressive TME, were more abundant in non-responders and recurrent patients. Our findings highlighting the critical role of fibroblast subpopulations in influencing the immune response and indicate that targeting the fibrotic microenvironment improve immunotherapeutic outcomes. Mechanistically, myCAF cells mediated ECM remodeling and activate EMT through the TGF-β signaling pathway, potentially creating physical barriers that limit immune cell infiltration and promote T cell exhaustion, thereby impairing the efficacy of immunotherapy [40, 41]. Moreover, the presence of myCAF was associated with increased tumor cell proliferation, invasion, and metastatic potential, which may negatively impact the prognosis of patients undergoing nICT [42]. Despite the vital role in tumor progression and treatment resistance, CAFs exhibit substantial cellular and functional heterogeneity, and no definitive markers have been established to precisely identify or characterize myCAF in ESCC [43]. To identify potential biomarkers, we selected four markers with specificity and high expression in myCAF for further investigation. The co-localization of these markers in postoperative tumor samples showed a higher expression level and proportion of myCAF in non-responders and recurrent patients, suggesting that myCAF abundance could serve as a prognostic indicator. Our findings discover the potential of myCAF as a predictive biomarker in ESCC, which may guide the rational use of adjuvant therapy to improve survival outcomes, particularly for patients at high risk of recurrence. Our study suggests that the myCAF proportion in pre-treatment samples may become a valuable prognostic marker for guiding the efficacy of nICT. Post-treatment patients with myCAF-dominant TME could also be used as a biomarker for deciding postoperative adjuvant therapy. These findings highlight the importance of understanding the dynamic fibroblasts in the TME as a means of optimizing treatment strategies. Given that myCAF cells contribute to immune suppression and limited drug delivery, while NF cells promote a more immune-permissive environment, evaluating the fibroblast proportion could offer a novel approach to predict the efficacy of neoadjuvant immunotherapy. Furthermore, this myCAF-driven microenvironment parallels resistance mechanisms observed in pancreatic and breast cancers, reinforcing its potential as a therapeutic target [44, 45]. Targeting CAFs in combination with immunotherapy could provide a promising strategy to overcome immune resistance and improve treatment efficacy and clinical outcomes in ESCC. Despite these promising results, our study has several limitations. As a single-arm trial, it lacked a control group, making it difficult to directly compare nICT with other treatment regimens. Moreover, the relatively small sample size constrains the generalizability of our results, highlighting the need for larger-scale clinical trials to validate the long-term efficacy and safety of this approach. Additionally, while our study identified distinct fibroblast subpopulations as potential biomarkers, further investigation is required to substantiate these findings and explore fibroblast-targeted therapeutic strategies to enhance the efficacy of nICT. Conclusions To summarize, neoadjuvant chemotherapy combined with PD-1 inhibitor represents a promising treatment option for locally advanced ESCC, demonstrating a favorable safety profile while inducing substantial pathological responses and tumor downstaging. The abundance of myCAF in tumor samples may serve as a predictive biomarker for immunotherapy efficacy and potentially guiding adjuvant therapy decisions. Future studies should focus on validating these findings and developing targeted interventions to modulate the fibrotic tumor microenvironment, thereby improving the efficacy of neoadjuvant immunotherapy. To further establish the clinical utility of this approach, randomized controlled trials with larger patient cohorts are warranted. Abbreviations AEs: Adverse Events AJCC: American Joint Committee On Cancer AUC: Area Under the Curve CAFs: Cancer-Associated Fibroblasts CIs: Confidence Intervals CT: Computed Tomography DEGs: Differentially Expressed Genes EC: Esophageal Cancer ECM: Extracellular Matrix ECOG: Eastern Cooperative Oncology Group EFS: Event-Free Survival EMT: Epithelial-Mesenchymal Transition ESCC: Esophageal Squamous Cell Carcinoma EUS: Endoscopic Ultrasonography GEO: Gene Expression Omnibus H&E: Hematoxylin and Eosin ICIs: Immune Checkpoint Inhibitors KEGG: Kyoto Encyclopedia of Genes and Genomes mIF: Multiplex Immunofluorescence myCAF: Myofibroblastic Cancer-Associated Fibroblasts NCI-CTCAE: National Cancer Institute Common Terminology Criteria For Adverse Events NF: Normal Fibroblasts nCT: Neoadjuvant Chemotherapy nCRT: Neoadjuvant Chemoradiotherapy nICT: Neoadjuvant Immunochemotherapy OS: Overall Survival pCR: Pathological Complete Response pPR: Pathological Partial Response pNR: Pathological Non Response RECIST: Response Evaluation Criteria In Solid Tumors scRNA-seq: Single-Cell RNA Sequencing TIME: Tumor Immune Microenvironment TME: Tumor Microenvironment TNM: Tumor-Node-Metastasis TRAEs: Treatment-Related Adverse Events Declarations Funding This research was supported by funding from the National Science Foundation of China (82172825). Competing interests The authors have declared that no conflict of interest exists. Author contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Siyou Deng, Qi Wang, Yueping Li, Ruijie Zhang, Jinjie Lin, Yujie Zhang, Yixin Cai, and Wei Sun. The first draft of the manuscript was written by Siyou Deng, and Qi Wang, Yueping Li, and Li Zhang contributed to subsequent revisions. Jiang Chang contributed to methodology and supervision. Ni Zhang contributed to conceptualization, investigation, and supervision. Li Zhang was responsible for conceptualization, funding acquisition, validation, resource management, and overall supervision. All authors read and approved the final manuscript. Data Availability Relevant data is provided within the manuscript or supplementary information files. Additional details and raw data can be made available upon reasonable request to the corresponding author. Ethics Approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Tongji Hospital, Huazhong University of Science and Technology (Approval No. 2019-S910). The trial was registered at ClinicalTrials.gov (Identifier: NCT05028231). Consent to Participate Written informed consent was obtained from all individual participants included in the study. Consent to Publish Not applicable. This manuscript does not contain any individual person’s data in any form (including images or videos). References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians Italic 71:209-49. 10.3322/caac.21660 Cao W, Chen HD, Yu YW, Li N, Chen WQ. 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European journal of cancer (Oxford, England : 1990) Italic 45:228-47. 10.1016/j.ejca.2008.10.026 Health UDo, Services H: Common Terminology Criteria for Adverse Events (CTCAE) version 5.0. Published November 27, 2017. 2021. Italic Zhang X, Peng L, Luo Y, Zhang S, Pu Y, Chen Y, et al. (2021) Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis. Nature communications Italic 12:5291. 10.1038/s41467-021-25539-x Menden K, Marouf M, Oller S, Dalmia A, Magruder DS, Kloiber K, et al. (2020) Deep learning-based cell composition analysis from tissue expression profiles. Science advances Italic 6:eaba2619. 10.1126/sciadv.aba2619 Surgical resection with or without preoperative chemotherapy in oesophageal cancer: a randomised controlled trial. Lancet (London, England) Italic 359:1727-33. 10.1016/s0140-6736(02)08651-8 Wang H, Tang H, Fang Y, Tan L, Yin J, Shen Y, et al. 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(2020) CDK4/6 inhibition enhances antitumor efficacy of chemotherapy and immune checkpoint inhibitor combinations in preclinical models and enhances T-cell activation in patients with SCLC receiving chemotherapy. Journal for immunotherapy of cancer Italic 8:10.1136/jitc-2020-000847 Finisguerra V, Dvorakova T, Formenti M, Van Meerbeeck P, Mignion L, Gallez B, et al. (2023) Metformin improves cancer immunotherapy by directly rescuing tumor-infiltrating CD8 T lymphocytes from hypoxia-induced immunosuppression. Journal for immunotherapy of cancer Italic 11:10.1136/jitc-2022-005719 Yuan Z, Li Y, Zhang S, Wang X, Dou H, Yu X, et al. (2023) Extracellular matrix remodeling in tumor progression and immune escape: from mechanisms to treatments. Molecular Cancer Italic 22:48. 10.1186/s12943-023-01744-8 Ge J, Jiang H, Chen J, Chen X, Zhang Y, Shi L, et al. (2025) TGF-β signaling orchestrates cancer-associated fibroblasts in the tumor microenvironment of human hepatocellular carcinoma: unveiling insights and clinical significance. BMC cancer Italic 25:113. 10.1186/s12885-025-13435-2 Milosevic V, Östman A. (2024) Interactions between cancer-associated fibroblasts and T-cells: functional crosstalk with targeting and biomarker potential. Upsala journal of medical sciences Italic 129:10.48101/ujms.v126.10710 10.48101/ujms.v129.10710 Galbo PM, Jr, Zang X, Zheng D. (2021) Molecular Features of Cancer-associated Fibroblast Subtypes and their Implication on Cancer Pathogenesis, Prognosis, and Immunotherapy Resistance. Clinical Cancer Research Italic 27:2636-47. 10.1158/1078-0432.CCR-20-4226 %J Clinical Cancer Research Dunbar KJ, Wong KK, Rustgi AK. (2024) Cancer-Associated Fibroblasts in Esophageal Cancer. Cellular and Molecular Gastroenterology and Hepatology Italic 17:687-95. https://doi.org/10.1016/j.jcmgh.2024.01.008 Kieffer Y, Hocine HR, Gentric G, Pelon F, Bernard C, Bourachot B, et al. (2020) Single-Cell Analysis Reveals Fibroblast Clusters Linked to Immunotherapy Resistance in Cancer. Cancer discovery Italic 10:1330-51. 10.1158/2159-8290.CD-19-1384 %J Cancer Discovery Datta J, Dai X, Bianchi A, De Castro Silva I, Mehra S, Garrido VT, et al. (2022) Combined MEK and STAT3 Inhibition Uncovers Stromal Plasticity by Enriching for Cancer-Associated Fibroblasts With Mesenchymal Stem Cell-Like Features to Overcome Immunotherapy Resistance in Pancreatic Cancer. Gastroenterology Italic 163:1593-612. 10.1053/j.gastro.2022.07.076 Additional Declarations No competing interests reported. Supplementary Files supplementarytable1.pdf supplementarytable2.pdf Cite Share Download PDF Status: Published Journal Publication published 18 Jun, 2025 Read the published version in Cancer Immunology, Immunotherapy → Version 1 posted Editorial decision: Revision requested 27 Apr, 2025 Reviews received at journal 27 Apr, 2025 Reviewers agreed at journal 26 Apr, 2025 Reviews received at journal 13 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers invited by journal 04 Apr, 2025 Editor assigned by journal 02 Apr, 2025 Submission checks completed at journal 02 Apr, 2025 First submitted to journal 02 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-6357846\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":445064753,\"identity\":\"05fb1b0e-2a77-4f3e-a4d3-eef49551d473\",\"order_by\":0,\"name\":\"Siyou Deng\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Huazhong University of Science and Technology\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Siyou\",\"middleName\":\"\",\"lastName\":\"Deng\",\"suffix\":\"\"},{\"id\":445064754,\"identity\":\"b4b914e4-79b7-4bb0-acad-0f2597b7a37e\",\"order_by\":1,\"name\":\"Qi 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Technology\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Li\",\"middleName\":\"\",\"lastName\":\"Zhang\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-04-02 05:53:09\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-6357846/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-6357846/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1007/s00262-025-04099-9\",\"type\":\"published\",\"date\":\"2025-06-18T15:57:07+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":82132347,\"identity\":\"a132e02d-1b7f-498a-abd8-d312384871a4\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 05:43:22\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":156868,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eThe study design\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ea: Flowchart of the patient screening process. pCR, pathological complete response (no viable tumor cells in the resected specimens, including primary tumors, tumor thrombosis, and lymph nodes); pPR, pathological partial response (≤50% viable tumor cells in the primary tumor); pNR, pathological non-response (\\u0026gt; 50% viable tumor cells in the primary tumor or emergence of new lesions.). b: Trial Schema. Eligible patients received two cycles of neoadjuvant therapy, consisting of Pembrolizumab (240 mg, Day 1; Merck, USA) or Tislelizumab (240 mg, Day 1; BeiGene, China), combined with Paclitaxel (75 mg/m², Day 1; Hengrui, China) and either Carboplatin (AUC 5 mg/mL/min, Day 1; Qilu Pharma, China) or Cisplatin (25 mg/m², Days 1–3; Haosun Pharma, China), followed by surgical resection. Radiologic assessments were conducted at baseline, two weeks after completion of the two neoadjuvant therapy cycles, and prior to surgery. Tumor samples were collected both at baseline and post-treatment for further analysis. ESCC: esophageal squamous cell carcinoma; AUC: area under the curve. c: Overview of treatment regimen in the neoadjuvant and adjuvant settings, and follow-up status per patient (n = 42). nICT, neoadjuvant immunochemotherapy, ICT, immunochemotherapy.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6357846/v1/36afe65b20b1dbccc60577bd.png\"},{\"id\":82132346,\"identity\":\"0a9575c5-9879-491b-a52a-2c3e21654e09\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 05:43:22\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":569503,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eClinical efficacy\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ea: Waterfall plot illustrating pathologic tumor regression in the surgical cohort (n = 42). Each bar represents an individual patient, with clinical response categories indicated at the top: pCR (pathological complete response), pPR (pathological partial response), and pNR (pathological non-response). b: Overall survival of the surgical population (n =42). c: Event-free survival of the surgical population (n =42). d: Representative computed tomography(CT) and endoscopy images, along with hematoxylin and eosin (H\\u0026amp;E)-stained tumor sections, obtained pre-neoadjuvant therapy and post-surgery, from a representative responder. e: Survival analysis of responder and non-responder according to clinical efficacy evaluation (pCR/pPR vs. pNR).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6357846/v1/cce7c877f69210281266bfd2.png\"},{\"id\":82132350,\"identity\":\"b3728e08-cbd6-46d1-931e-c9a78277252e\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 05:43:22\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":227119,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eRNA-seq analysis of tumor specimens at baseline and post-treatment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA: Differential gene expression between responders (pCR/pPR) and non-responders (pNR). Upregulated fibroblast-related genes are highlighted. A cut-off of gene expression fold change ≥1.0 and p \\u0026lt; 0.05 were applied to select differentially expressed genes (DEGs). B: Pathway enrichment analysis in responders and non-responders. C: Representative fibroblast-related genes in responders and non-responders. D: Differential gene expression between the relapse and non-relapse groups based on two-year relapse outcomes. E: Pathway enrichment analysis in the relapse and non-relapse groups. F: Expression of representative fibroblast-related genes in the relapse and non-relapse groups. Definitions: Responders were defined as patients achieving a complete or partial response based on RECIST 1.1, while non-responders were those with stable disease or progressive disease. Statistical comparisons between groups were conducted using the Wilcoxon test.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6357846/v1/5badb4cab5474b676d5128d2.png\"},{\"id\":82134896,\"identity\":\"80a30b8c-085b-4b20-9b79-96091f6b6b84\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 06:07:22\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1165296,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFibroblast Subpopulation of TME at baseline and post-treatment\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA: TME composition at baseline and post-treatment, showing the relative proportions of epithelial cells, fibroblasts, endothelial cells, immune cells (T cells, B cells, myeloid cells) in the response and non-response groups. B: Changes in fibroblast subpopulations pre- and post-treatment. C: Proportions of NF and myCAF in the response and non-response groups. D: Proportions of NF and myCAF in the relapse and non-relapse groups. E: Correlation of fibroblast subpopulation proportions with EFS and OS. F: mIF analysis showing higher expression of myCAF markers (\\u003cem\\u003eCOL3A1\\u003c/em\\u003e, \\u003cem\\u003eFAP\\u003c/em\\u003e, \\u003cem\\u003eMMP11\\u003c/em\\u003e, \\u003cem\\u003eα-SMA\\u003c/em\\u003e) in the patients from the non-response group and relapse group. \\u003cem\\u003eCOL3A1\\u003c/em\\u003e(green), \\u003cem\\u003eMMP11\\u003c/em\\u003e (yellow), \\u003cem\\u003eα-SMA\\u003c/em\\u003e (red) and\\u003cem\\u003e FAP\\u003c/em\\u003e (rose-red). The statistical difference of two groups was compared through the Wilcox test.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6357846/v1/b6846d01640fc6bcdc24f043.png\"},{\"id\":85231286,\"identity\":\"c3eda3c8-6484-4499-9ab0-355017362480\",\"added_by\":\"auto\",\"created_at\":\"2025-06-23 16:04:41\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2802408,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6357846/v1/4bbcf888-d721-4901-a1c6-582de9e83cf1.pdf\"},{\"id\":82132343,\"identity\":\"fc972015-f01e-4fdf-ab69-07e026093d39\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 05:43:22\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":66204,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"supplementarytable1.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6357846/v1/3476402c9db746512e0ead6b.pdf\"},{\"id\":82132344,\"identity\":\"d088cf18-6c8e-44d7-baa7-011d50b42938\",\"added_by\":\"auto\",\"created_at\":\"2025-05-07 05:43:22\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":56954,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"supplementarytable2.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-6357846/v1/5dcf30740255d9cb9cc97761.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Clinical efficacy and biomarkers of neoadjuvant chemoimmunotherapy in locally advanced esophageal squamous cell carcinoma\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eEsophageal cancer (EC) is the sixth leading cause of cancer-related mortality and the seventh most frequently diagnosed malignancy worldwide [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. Among its histological subtypes, esophageal squamous cell carcinoma (ESCC) predominates, particularly in high-incidence regions such as East Asia [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Despite significant advancements in treatment, the prognosis for locally advanced ESCC remains poor, highlighting an urgent need for more effective treatment strategies to enhance clinical outcomes.\\u003c/p\\u003e \\u003cp\\u003e Neoadjuvant chemoradiotherapy (nCRT) followed by surgical resection has been established as the standard treatment approach for patients with locally advanced ESCC. The CROSS and NEOCRTEC5010 trial have demonstrated better 5- and 10-year overall survival rates with preoperative chemoradiotherapy compared to surgery alone [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. However, despite achieving high rates of R0 resection, approximately 15% of patients experienced locoregional recurrence within five years, while the incidence of distant metastasis remains as high as 30% [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Moreover, the use of radiotherapy is associated with increased perioperative complications and mortality and does not confer a survival advantage over neoadjuvant chemotherapy (nCT), primarily due to the severe toxicity associated with radiation therapy [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. These limitations underscore the urgent need for novel therapeutic approaches that enhance both safety and long-term clinical outcomes.\\u003c/p\\u003e \\u003cp\\u003eThe advent of immune checkpoint inhibitors (ICIs) has transformed the treatment landscape of multiple malignancies, offering survival benefits across various cancer types [\\u003cspan additionalcitationids=\\\"CR9 CR10 CR11 CR12\\\" citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Encouraged by the promising efficacy of immunotherapy combined with chemotherapy in advanced ESCC, there has been growing interest in extending its application to the neoadjuvant setting. Emerging clinical evidence have indicated that neoadjuvant immunochemotherapy (nICT) can induce favorable pathologic responses with manageable safety profile in patients with locally advanced ESCC [\\u003cspan additionalcitationids=\\\"CR15 CR16 CR17 CR18\\\" citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]. However, limited data are available on long-term clinical outcomes.\\u003c/p\\u003e \\u003cp\\u003ePathological complete response (pCR) following neoadjuvant therapy has been consistently linked to improved long-term outcomes across multiple cancers [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]. However, not all patients exhibit a favorable response to immune checkpoint blockade, raising the urgent need for reliable pre-treatment biomarkers that can predict immune response and guide patient selection for nICT. Furthermore, emerging evidence suggests that postoperative adjuvant therapy may improve outcomes in patients at high risk of recurrence following neoadjuvant treatment [\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Hence, identifying post-treatment biomarkers to assess recurrence risk and inform adjuvant therapeutic decisions becomes equally important.\\u003c/p\\u003e \\u003cp\\u003eThe tumor immune microenvironment (TIME) critically influences tumor progression, immune escape, and therapeutic efficacy [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. The crosstalk between tumor and the TIME, both pre- and post-treatment, not only drives treatment resistance but also contributes to tumor progression [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. A deeper understanding of these dynamic interactions holds the potential to refine patient stratification and optimize personalized treatment strategies. However, the predictive biomarkers within the neoadjuvant context, particularly for early-stage ESCC, remains insufficiently explored and warrants further investigation.\\u003c/p\\u003e \\u003cp\\u003eIn this research, we carried out a single-arm, phase II prospective clinical trial to evaluate the efficacy and safety of nICT. Additionally, we explored the pre-treatment and post-treatment biomarkers that could predicting the efficacy of this regimen in resectable ESCC.\\u003c/p\\u003e\"},{\"header\":\"Materials and Methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eParticipants\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis investigator-initiated, single-arm, phase II prospective clinical trial was conducted at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. The trial was registered at ClinicalTrials.gov (NCT05028231) and adhered to the principles of the Declaration of Helsinki.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eInclusion criteria: Eligible patients were aged 18\\u0026ndash;75 years and diagnosed with locally advanced, resectable ESCC classified as stage II\\u0026ndash;IVA based on the 8th edition of the AJCC staging system. Resectability was determined by a thoracic surgeon. Additional eligibility requirements included an Eastern Cooperative Oncology Group (ECOG) performance status of 0\\u0026ndash;1 and adequate organ function.\\u003c/p\\u003e\\n\\u003cp\\u003eExclusion criteria: Patients with concurrent malignancies, pregnancy, lactation, autoimmune disorders, documented allergies to study drugs, or those unable to tolerate esophagectomy based on preoperative pulmonary and cardiovascular assessments were excluded.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eProcedure\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eParticipants underwent two cycles of nICT, administered at three-week intervals. Each cycle comprised an intravenous infusion of either Pembrolizumab (240 mg, Day 1; Merck, USA) or Tislelizumab (240 mg, Day 1; BeiGene, China), in combination with Paclitaxel (75 mg/m\\u0026sup2;, Day 1; Hengrui, China) and either Carboplatin (AUC 5 mg/mL/min, Day 1; Qilu Pharma, China) or Cisplatin (25 mg/m\\u0026sup2;, Days 1\\u0026ndash;3; Haosun Pharma, China). The selection of the PD-1 inhibitor was determined by the treating physician rather than through randomized allocation.\\u003c/p\\u003e\\n\\u003cp\\u003eBaseline assessments, including contrast-enhanced thoracic and abdominal computed tomography (CT), endoscopic ultrasonography (EUS), and cervical/subclavicular ultrasonography, were performed before the initiation of neoadjuvant therapy. These imaging evaluations were repeated after the completion of each of the two neoadjuvant treatment cycles to assess tumor response and resectability. Tumor response was assessed based on Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1)\\u0026nbsp;[25]. Treatment-related adverse events (TRAEs) were assessed and documented at each visit, following the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE) version 5.0\\u0026nbsp;[26]. TRAEs occurring within 30 days post-surgery or within 90 days of the first neoadjuvant dose were documented.\\u003c/p\\u003e\\n\\u003cp\\u003eApproximately 6\\u0026ndash;8 weeks after the final cycle of nICT, patients who remained surgically eligible underwent esophagectomy. The choice of surgical approach (McKeown or Ivor Lewis) was based on tumor location and resectability. All patients underwent two-field (abdominal and thoracic) lymph node dissection, with cervical lymph node dissection performed selectively for the patients with suspected cervical metastasis. Pathologic response was independently evaluated by two pathologists and categorized as follows: Grade 1 (pathologic complete response, pCR; no residual tumor), Grade 2 (pathologic partial response, pPR; \\u0026le;50% residual tumor), and Grade 3 (pathologic non-response, pNR; \\u0026gt;50% residual tumor or new lesions).\\u003c/p\\u003e\\n\\u003cp\\u003eFollowing surgical resection, patients underwent routine follow-up assessments every three months during the first year and every six months in the second and third years. Overall survival (OS) was defined as the time from surgery to death from any cause or the last follow-up. Event-free survival (EFS) was measured from the date of surgery to the first occurrence of tumor recurrence, disease progression, or death. Patients who received adjuvant therapy after surgery were documented accordingly.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eOutcome\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe primary endpoint of the study was pCR rate. Secondary endpoints included OS, EFS, treatment safety, and the identification of biomarkers associated with treatment response.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eExploratory Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePretreatment endoscopic biopsy specimens and posttreatment surgical samples were collected for biomarker analysis, including RNA sequencing, cell-type composition analysis, and tumor microenvironment profiling. RNA sequencing was performed using the Illumina Novaseq 6000 platform (Shanghai Biotechnology Corp., China). Differential gene expression was conducted using the DESeq2 package (v1.44.0) in R, with a significance threshold set at |fold change (FC)| \\u0026gt; 1.0 and p \\u0026lt; 0.05. Enrichment analysis of differentially expressed genes (DEGs) was conducted using the clusterProfiler R package (v4.12.6), focusing on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Hallmark gene sets. Data visualization was performed using ggplot2 in R. To assess the cellular composition of tumors, single-cell RNA sequencing (scRNA-seq) data from 60 ESCC tumor samples were obtained from the Gene Expression Omnibus (GEO) database (accession number GSE160269)\\u0026nbsp;[27]. The deconvolution of bulk RNA sequencing data was performed using Scaden, a machine-learning algorithm trained on scRNA-seq datasets to estimate the relative abundance of distinct cell types\\u0026nbsp;[28]. Multiplex immunofluorescence (mIF) analysis was performed to assess distribution and composition in post-treatment tumor tissues. To quantify the abundance of myofibroblastic\\u0026nbsp;cancer-associated\\u0026nbsp;fibroblasts (myCAF) and map their localization within the tumor microenvironment, co-localization analysis was conducted using four myCAF-related markers\\u0026mdash;\\u003cem\\u003eMMP11\\u003c/em\\u003e, \\u003cem\\u003eCOL3A1\\u003c/em\\u003e, \\u003cem\\u003e\\u0026alpha;-SMA\\u003c/em\\u003e, and \\u003cem\\u003eFAP\\u003c/em\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eStatistical Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was designed as a single-arm phase II trial with pCR as the primary endpoint. To demonstrate superiority, a pCR rate of 25% was expected with neoadjuvant immunochemotherapy. Considering an anticipated dropout rate of 10%, a total of 47 patients were required to achieve 90% power at a one-sided alpha level of 10%. For continuous variables, the median and range were reported, while categorical variables were summarized as frequencies and percentages. Intergroup comparisons were performed using the Wilcoxon test to assess statistical differences. Kaplan\\u0026ndash;Meier survival curves were constructed to estimate OS and EFS, with intergroup differences assessed via the log-rank test. The 95% confidence intervals (CIs) were calculated using the Clopper\\u0026ndash;Pearson method. A P-value \\u0026lt; 0.05 was considered statistically significant. Quantitative analysis of mIF was conducted using ImageJ software (NIH, Bethesda, MD, USA). All statistical analyses and data visualization were conducted using SPSS version 23.0 and R version 4.3.1.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003ePatient characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBetween September 2021 and May 2023, 55 patients were screened for eligibility. Of these, 47 met the inclusion criteria and were enrolled in the research. Ultimately, 42 patients (89.3%) who successfully completed two cycles of neoadjuvant therapy followed by surgical resection were included in the final analysis (Fig. 1a, b).\\u0026nbsp;The treatment course and follow-up status are depicted in the swimmer\\u0026rsquo;s plot of every patients (Fig. 1c). The median age of the cohort was 59.5 years, with a predominance of male patients (81.0%). The majority of patients (73.8%) presented with T3-stage tumors, and lymph node involvement (N+) was identified in 83.3% of cases. Based on the AJCC 8th edition staging system, 59.5% of patients were classified as stage III. Tumor localization was predominantly in the middle (59.5%) and lower (28.6%) esophagus, collectively comprising 88.1% of cases. Additional baseline characteristics are detailed in\\u0026nbsp;Table 1.\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003e1\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;Baseline characteristics of the patients\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" align=\\\"\\\" width=\\\"81%\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCharacteristic\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePatients (n=42)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eAge, years\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e59 (41, 75)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eSex\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eMale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e34 (81.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eFemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e8 (19.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eECOG performance status\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e32 (76.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003e1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e10 (23.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eSmoking status\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eNever\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e19 (45.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eFormer or current\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e23 (54.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eAlcohol consumption\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eNever\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e14 (33.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eFormer or current\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e28 (66.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eTumor location\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eUpper\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e5 (11.9)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eMiddle\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e25 (59.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eLower\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e12 (28.6)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eTumor length(cm)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e6 (1, 16)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eClinical T stage\\u003csup\\u003eb\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eT2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e7 (16.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eT3\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e31 (73.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eT4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e4 (9.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eClinical N stage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eN0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e7 (16.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eN1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e28 (66.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eN2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e7 (16.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eClinical stage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eII\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e13 (31.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eIII\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e25 (59.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 54px;\\\"\\u003e\\n \\u003cp\\u003eIVa\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 45px;\\\"\\u003e\\n \\u003cp\\u003e4 (9.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eData are presented as \\u003cem\\u003en\\u003c/em\\u003e (%), unless otherwise specified.\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: ECOG, Eastern Cooperative Oncology Group; TNM, tumor-node-metastasis.\\u003c/p\\u003e\\n\\u003cp\\u003ea. The results of continuous variables are presented as median (range).\\u003c/p\\u003e\\n\\u003cp\\u003eb. Clinical disease stage was assessed according to the criteria of the American Joint Committee on Cancer, Eighth Edition.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSurgical Outcomes\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAmong the 42 patients who completed two cycles of nICT followed by surgical resection, the mean interval from the first dose of treatment to surgery was 2.02 \\u0026plusmn; 0.16 months. All surgeries achieved R0 resection, with a mean operative duration of 346.75 \\u0026plusmn; 76.13 minutes. A median of 18.65 \\u0026plusmn; 10.45 lymph nodes were dissected per patient, with 13 patients (30.9%) exhibiting pathological lymph node involvement. The median operative blood loss was 121.25 \\u0026plusmn; 275.28 mL. Postoperative complications included anastomotic leakage in 2 patients (4.76%) and pulmonary infection in 3 patients (7.14%). The median duration of postoperative hospitalization was 14 (range, 7\\u0026ndash;30 days). Notably, no immune-related complications or deaths occurred within 90 days following surgery (Table 2).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003e2\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;Summary of surgery-related adverse events\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"73%\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eComplication\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003ePatients (n=42)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003eAnastomotic leakage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e2 (4.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003ePulmonary infection\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e3 (7.14)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003eBleeding\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003eImmune-related myocarditis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003eImmune-related nephritis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003eImmune-related hepatitis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003eImmune-related pneumonia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003eImmune-related hyperthyroidism\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003eImmune-related hypothyroidism\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 60px;\\\"\\u003e\\n \\u003cp\\u003eDeath within 90 days\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 39px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003eData are presented as \\u003cem\\u003en\\u003c/em\\u003e (%), unless otherwise specified.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical efficacy\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAfter two cycles of nICT, twelve patients achieved pathological complete response, 7 achieved a pathological partial response, and 23 had pathological non-response, as assessed by RECIST 1.1. The distribution of primary tumor pathologic responses and corresponding clinical characteristics are depicted in Fig. 2a. Notably, no cases of disease progression were observed during the neoadjuvant phase.\\u003c/p\\u003e\\n\\u003cp\\u003eAs of the data cutoff on December 1, 2024, with a median follow-up of 29.95 months (range, 15.83\\u0026ndash;44.9 months), the 1-year and 2-year EFS rates were 82% (95% CI, 70.7\\u0026ndash;90.5) and 37.3% (95% CI, 23.0\\u0026ndash;60.3), respectively (Fig. 2b). The OS rates at the same time points were 100% (95% CI, 100.0\\u0026ndash;100.0), 71.4% (95% CI, 59.0\\u0026ndash;86.5) as depicted in Fig. 2c. We further analyzed the 24-month relapse rates in responders (pCR/pPR) versus non-responders (pNR). The relapse rates were 32.6% in the responders versus 82.6% in the non-responders.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eRadiology and pathology assessments, including CT scans, endoscopy, and histopathologic reports, confirmed significant tumor shrinkage in the responders, further validating the efficacy of the neoadjuvant regimen (Fig. 2d). The responders exhibited a pronounced trend toward improved EFS and OS compared to non-responders (Fig. 2e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSafety\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eIn our study, nICT was generally well tolerated, with no previously unreported treatment-related adverse events (TRAEs) (Table 3). Adverse events of any grade were observed in 95.2% (40/42) of patients, the majority (88.8%) of whom experienced Grades 1\\u0026ndash;2 adverse effects. The most commonly reported Grade 3\\u0026ndash;4 adverse events (AEs) were leukopenia and thrombocytopenia, which occurred in 11.9% of patients. Other common TRAEs included alopecia (n = 25), hepatotoxicity (n = 23), decreased appetite (n = 17), constipation (n = 17), and BNP elevation (n = 16), which were generally mild and manageable. The overall adverse event rates were comparable between groups (89.5% responders vs. 95.6% non-responders), however, grade 3\\u0026ndash;4 TRAEs were observed exclusively in the non-responder cohort. Notably, no patients experienced treatment-related mortality, and all Grade 3\\u0026ndash;4 TRAEs resolved with appropriate medical management. No Grade 5 TRAEs and surgical delays were reported, indicating that the treatment was well tolerated overall.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003e3\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;Treatment-related adverse events in all patients (n=42)\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv align=\\\"center\\\"\\u003e\\n \\u003ctable border=\\\"0\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTRAEs\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAll patients(n=42)\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGrade1-2\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGrade3\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eGrade4\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eNeutropenia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e12 (28.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e3 (7.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e1 (2.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eThrombocytopenia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e7 (16.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e2 (4.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eDecreased Appetite\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e15 (35.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e2 (4.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eNausea\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e15 (35.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eVomiting\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e11 (26.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eAlopecia\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e24 (57.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e1 (2.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eConstipation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e17 (40.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eHepatic Toxicity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e23 (54.8)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eDizziness\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e4 (9.5)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eFever\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e3 (7.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eCardiotoxicity\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eBNP Elevation\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e16 (38.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003eRash\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e7 (16.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 34px;\\\"\\u003e\\n \\u003cp\\u003ePneumonitis\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 23px;\\\"\\u003e\\n \\u003cp\\u003e2 (4.7)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 20px;\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 21px;\\\"\\u003e\\n \\u003cp\\u003e0\\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\\u003eData are presented as \\u003cem\\u003en\\u003c/em\\u003e (%), unless otherwise specified.\\u003c/p\\u003e\\n\\u003cp\\u003eAbbreviations: TRAE, treatment related adverse event.\\u003c/p\\u003e\\n\\u003cp\\u003ea.TRAEs were assessed during treatment and for up to 30 days after the last dose of neoadjuvant treatment according to the National Cancer Institute Common Terminology Criteria for Adverse Events, version 5.0.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eRNA-seq Analysis of Tumor Samples\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eRNA sequencing was performed on tumor samples collected before and after nICT treatment. Patients were classified into responder (pCR/pPR) and non-responder (pNR) based on the pathologic response (Fig. 3a). In the non-response group, pathways related to epithelial-mesenchymal transition (EMT) and focal adhesion were significantly enriched, whereas in the responder group, immune-related pathways\\u0026mdash;including cytokine signaling, interferon-gamma response, and antigen presentation\\u0026mdash;were notably activated (Fig. 3b). Moreover, ten fibroblast-related genes exhibited differential expression between responders and non-responders, involving genes related to fibroblast activation and tumor microenvironment modulation (\\u003cem\\u003eCTHRC1, MFAP2, POSTN\\u003c/em\\u003e), extracellular matrix (ECM) synthesis and remodeling (\\u003cem\\u003eCOL1A1, COL1A2, COL3A1, SPARC\\u003c/em\\u003e), ECM degradation (\\u003cem\\u003eMMP1, MMP11, SERPINH1\\u003c/em\\u003e) (Fig. 3\\u003ca href=\\\"https://www.nature.com/articles/s41416-024-02805-5#Fig3\\\"\\u003ec\\u003c/a\\u003e).\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAccording to the two-year relapse outcomes, the patients were divided into relapse (early relapse within two years) and non-relapse groups (Fig. 3\\u003ca href=\\\"https://www.nature.com/articles/s41416-024-02805-5#Fig3\\\"\\u003ed\\u003c/a\\u003e). In the relapse group, pathways related to cell cycle progression, metabolic reprogramming, genomic instability and carcinogenesis were enriched, while in the non-relapse group, the immune-related pathways showed upregulation (Fig. 3\\u003ca href=\\\"https://www.nature.com/articles/s41416-024-02805-5#Fig3\\\"\\u003ed\\u003c/a\\u003e). Additionally, Differential expression trends of fibroblast-related genes between relapse and non-relapse groups were observed but did not reach statistical significance (Fig. 3\\u003ca href=\\\"https://www.nature.com/articles/s41416-024-02805-5#Fig3\\\"\\u003ef\\u003c/a\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFibroblast Subpopulation Dynamics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA deep learning-based model was employed for cell-type deconvolution and composition analysis, revealing dynamic shifts in tumor microenvironment (TME) composition. The analysis displaying the relative proportions of epithelial cells, fibroblasts, endothelial cells, T cells, B cells, and myeloid cells in both the response and non-response groups, at baseline and post-treatment (Table S1). Notably, post-treatment samples exhibited a decrease in epithelial cell dominance, accompanied by increased immune cell infiltration, particularly in the response group (Fig. 4a).\\u003c/p\\u003e\\n\\u003cp\\u003eFurther analysis of fibroblast subpopulations demonstrated changes in proportions of normal fibroblasts (NF) and myCAF between pre- and post-treatment samples (Fig. 4b and Table S2). A significant association between the proportions of NF and myCAF and pathological response was observed in both pre- and post-treatment TME (Fig. 4c). Moreover, the proportions of NF and myCAF in post-treatment samples were significantly different between the relapse (early relapse within two years) and non-relapse groups, suggesting a potential role of fibroblast subpopulations in tumor recurrence (Fig. 4d).\\u003c/p\\u003e\\n\\u003cp\\u003eAdditionally, the proportion of NF and myCAF were strongly correlated with both EFS and OS (Fig. 4e). The patients with a higher NF proportion had significantly longer EFS (log-rank P = 0.0077) and a trend toward improved OS (log-rank P = 0.07). Conversely, patients with a higher myCAF proportion demonstrated significantly worse EFS (log-rank P = 0.03) and OS (log-rank P = 0.035).\\u003c/p\\u003e\\n\\u003cp\\u003eTo further validate these findings, mIF analysis was performed on operative tumor samples. MyCAF subpopulations were identified through the co-expression of \\u003cem\\u003eCOL3A1\\u003c/em\\u003e, \\u003cem\\u003eFAP\\u003c/em\\u003e, \\u003cem\\u003eMMP11\\u003c/em\\u003e, and \\u003cem\\u003e\\u0026alpha;-SMA\\u003c/em\\u003e. The mIF analysis revealed a higher proportion of myCAF in the non-responders compared to responders, and notably, the myCAF proportion was higher in the patients in the relapse group.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eConsistent with previous studies, our findings demonstrate that neoadjuvant chemoimmunotherapy is both effective and safe in the treatment of ESCC. Our trial met the primary endpoint, with 12 out of 42 (28.5%) surgically treated patients achieving pCR. Notably, there were no cases of postoperative mortality or an increased risk of surgical complications, indicating the manageable safety profile of nICT. Given the critical role of immune response prediction in neoadjuvant treatment and organ preservation strategies, our observation that myCAF proportion is significantly associated with pNR is particularly noteworthy.\\u003c/p\\u003e\\n\\u003cp\\u003eThe pCR rate (28.5%) and pPR rate (16.7%) observed in our study represent a substantial improvement over the 3.8%\\u0026ndash;4% pCR rates reported in trials of neoadjuvant chemotherapy alone\\u0026nbsp;[29, 30]. Furthermore, survival analysis revealed 1-year and 2-year EFS rates of 82% and 37.3%, respectively, alongside OS rates of 100% and 71.4% at the same time points. These outcomes reinforce the therapeutic potential of this regimen. Notably, the treatment exhibited a manageable safety profile, with TRAEs occurring in only 11.9% of patients, significantly lower than the 32%\\u0026ndash;49% AEs rates seen in traditional neoadjuvant regimens. These results suggest that nICT provides a promising approach for tumor regression while maintaining a tolerable safety profile.\\u003c/p\\u003e\\n\\u003cp\\u003eAlthough nCRT remains the standard for treating locally advanced ESCC due to its ability to achieve high pCR rates, its widespread clinical adoption is constrained by \\u0026nbsp; radiation-induced toxicities, including esophagitis, anastomotic leakage, and long-term complications. Furthermore, postoperative recurrence and distant metastasis continue to pose significant challenges. Our study achieves a 28.5% pCR rate, which, while lower than that observed with nCRT, offers comparable therapeutic benefits while mitigating radiotherapy-related complications. This suggests that nICT may serve as a viable alternative in organ preservation strategies, particularly for patients ineligible for chemoradiotherapy due to toxicity concerns. Although PD-1 blockade has not yet been established as a standard neoadjuvant strategy, its emerging therapeutic potential warrants further exploration. Multiple ongoing clinical trials, including KEYNOTE-002, are currently investigating the role of postoperative immunotherapy in reducing recurrence and metastasis\\u0026nbsp;[31].\\u003c/p\\u003e\\n\\u003cp\\u003eOur analysis revealed a significant survival advantage among responders compared to non-responders. Patients achieving pCR or pPR demonstrated superior OS and EFS, with relapse rates of 32.6% in responders vs. 82.6% in non-responders, underscoring the profound impact of immune-mediated tumor regression on long-term survival. Interestingly, all Grade 3\\u0026ndash;4 TRAEs occurred in non-responders, suggesting that immune activation may mitigate treatment-related toxicity. While the underlying mechanisms remain unclear, these findings highlight the importance of identifying predictive biomarkers for immune response to refine patient stratification and personalize neoadjuvant therapy.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eTo explore potential biomarkers, RNA sequencing was conducted to identify molecular pathways associated with treatment response. Responders showed activation of pathways related to interferon-\\u0026gamma; response and antigen presentation, which are critical for T cell activation and immune surveillance\\u0026nbsp;[32]. Conversely, non-responders exhibited upregulated EMT and hypoxia-related pathways, which plays a critical role in immune evasion and resistance to immunotherapy\\u0026nbsp;[33, 34]. Among the pathway, EMT emerged as the most strongly correlated pathway with therapy response, potentially promoting tumor invasion, metastasis, and evasion of immune cells, thereby diminishing treatment efficacy [35, 36]. Furthermore, recurrent tumors showed activation of cell cycle progression and metabolic reprogramming pathways, suggesting that combining immunotherapy with CDK4/6 inhibitors or glycolysis inhibitors may enhance long-term treatment outcomes [37, 38]. Notably, fibroblast-related gene upregulation was predominantly observed in non-responders, indicating that fibroblast-mediated ECM remodeling may act as a barrier to immune infiltration, a phenomenon previously reported in pancreatic cancer models [39].\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eOur study further explored the role of cancer-associated fibroblasts (CAFs) in predicting treatment response. We identified distinct fibroblast subtypes, NF and myCAF, finding their strong correlation with treatment response and prognosis in ESCC. Responders and non-recurrent patients exhibited a higher proportion of NF, a feature that may enhance CD8+ T cell infiltration and promote an immune-permissive tumor microenvironment. Conversely, myCAF cells, which contribute to a fibrotic and immunosuppressive TME, were more abundant in non-responders and recurrent patients. Our findings highlighting the critical role of fibroblast subpopulations in influencing the immune response and indicate that targeting the fibrotic microenvironment improve immunotherapeutic outcomes.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eMechanistically, myCAF cells mediated ECM remodeling and activate EMT through the TGF-\\u0026beta; signaling pathway, potentially creating physical barriers that limit immune cell infiltration and promote T cell exhaustion, thereby impairing the efficacy of immunotherapy\\u0026nbsp;[40, 41]. Moreover, the presence of myCAF was associated with increased tumor cell proliferation, invasion, and metastatic potential, which may negatively impact the prognosis of patients undergoing nICT\\u0026nbsp;[42]. Despite the vital role in tumor progression and treatment resistance, CAFs exhibit substantial cellular and functional heterogeneity, and no definitive markers have been established to precisely identify or characterize myCAF in ESCC\\u0026nbsp;[43]. To identify potential biomarkers, we selected four markers with specificity and high expression in myCAF for further investigation. The co-localization of these markers in postoperative tumor samples showed a higher expression level and proportion of myCAF in non-responders and recurrent patients, suggesting that myCAF abundance could serve as a prognostic indicator. Our findings discover the potential of myCAF as a predictive biomarker in ESCC, which may guide the rational use of adjuvant therapy to improve survival outcomes, particularly for patients at high risk of recurrence.\\u003c/p\\u003e\\n\\u003cp\\u003eOur study suggests that the myCAF proportion in pre-treatment samples may become a valuable prognostic marker for guiding the efficacy of nICT. Post-treatment patients with myCAF-dominant TME could also be used as a biomarker for deciding postoperative adjuvant therapy. These findings highlight the importance of understanding the dynamic fibroblasts in the TME as a means of optimizing treatment strategies. Given that myCAF cells contribute to immune suppression and limited drug delivery, while NF cells promote a more immune-permissive environment, evaluating the fibroblast proportion could offer a novel approach to predict the efficacy of neoadjuvant immunotherapy.\\u0026nbsp;Furthermore, this myCAF-driven microenvironment parallels resistance mechanisms observed in pancreatic and breast cancers, reinforcing its potential as a therapeutic target\\u0026nbsp;[44, 45]. Targeting CAFs in combination with immunotherapy could provide a promising strategy to overcome immune resistance and improve treatment efficacy and clinical outcomes in ESCC.\\u003c/p\\u003e\\n\\u003cp\\u003eDespite these promising results, our study has several limitations. As a single-arm trial, it lacked a control group, making it difficult to directly compare nICT with other treatment regimens. Moreover, the relatively small sample size constrains the generalizability of our results, highlighting the need for larger-scale clinical trials to validate the long-term efficacy and safety of this approach. Additionally, while our study identified distinct fibroblast subpopulations as potential biomarkers, further investigation is required to substantiate these findings and explore fibroblast-targeted therapeutic strategies to enhance the efficacy of nICT.\\u0026nbsp;\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eTo summarize, neoadjuvant chemotherapy combined with PD-1 inhibitor represents a promising treatment option for locally advanced ESCC, demonstrating a favorable safety profile while inducing substantial pathological responses and tumor downstaging. The abundance of myCAF in tumor samples may serve as a predictive biomarker for immunotherapy efficacy and potentially guiding adjuvant therapy decisions. Future studies should focus on validating these findings and developing targeted interventions to modulate the fibrotic tumor microenvironment, thereby improving the efficacy of neoadjuvant immunotherapy. To further establish the clinical utility of this approach, randomized controlled trials with larger patient cohorts are warranted.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eAEs: Adverse Events\\u003c/p\\u003e\\n\\u003cp\\u003eAJCC: American Joint Committee On Cancer\\u003c/p\\u003e\\n\\u003cp\\u003eAUC: Area Under the Curve\\u003c/p\\u003e\\n\\u003cp\\u003eCAFs: Cancer-Associated Fibroblasts\\u003c/p\\u003e\\n\\u003cp\\u003eCIs: Confidence Intervals\\u003c/p\\u003e\\n\\u003cp\\u003eCT: Computed Tomography\\u003c/p\\u003e\\n\\u003cp\\u003eDEGs: Differentially Expressed Genes\\u003c/p\\u003e\\n\\u003cp\\u003eEC: Esophageal Cancer\\u003c/p\\u003e\\n\\u003cp\\u003eECM: Extracellular Matrix\\u003c/p\\u003e\\n\\u003cp\\u003eECOG: Eastern Cooperative Oncology Group\\u003c/p\\u003e\\n\\u003cp\\u003eEFS: Event-Free Survival\\u003c/p\\u003e\\n\\u003cp\\u003eEMT: Epithelial-Mesenchymal Transition\\u003c/p\\u003e\\n\\u003cp\\u003eESCC: Esophageal Squamous Cell Carcinoma\\u003c/p\\u003e\\n\\u003cp\\u003eEUS: Endoscopic Ultrasonography\\u003c/p\\u003e\\n\\u003cp\\u003eGEO: Gene Expression Omnibus\\u003c/p\\u003e\\n\\u003cp\\u003eH\\u0026amp;E: Hematoxylin and Eosin\\u003c/p\\u003e\\n\\u003cp\\u003eICIs: Immune Checkpoint Inhibitors\\u003c/p\\u003e\\n\\u003cp\\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes\\u003c/p\\u003e\\n\\u003cp\\u003emIF: Multiplex Immunofluorescence\\u003c/p\\u003e\\n\\u003cp\\u003emyCAF: Myofibroblastic Cancer-Associated Fibroblasts\\u003c/p\\u003e\\n\\u003cp\\u003eNCI-CTCAE: National Cancer Institute Common Terminology Criteria For Adverse Events\\u003c/p\\u003e\\n\\u003cp\\u003eNF: Normal Fibroblasts\\u003c/p\\u003e\\n\\u003cp\\u003enCT: Neoadjuvant Chemotherapy\\u003c/p\\u003e\\n\\u003cp\\u003enCRT: Neoadjuvant Chemoradiotherapy\\u003c/p\\u003e\\n\\u003cp\\u003enICT: Neoadjuvant Immunochemotherapy\\u003c/p\\u003e\\n\\u003cp\\u003eOS: Overall Survival\\u003c/p\\u003e\\n\\u003cp\\u003epCR: Pathological Complete Response\\u003c/p\\u003e\\n\\u003cp\\u003epPR: Pathological Partial Response\\u003c/p\\u003e\\n\\u003cp\\u003epNR: Pathological Non Response\\u003c/p\\u003e\\n\\u003cp\\u003eRECIST: Response Evaluation Criteria In Solid Tumors\\u003c/p\\u003e\\n\\u003cp\\u003escRNA-seq: Single-Cell RNA Sequencing\\u003c/p\\u003e\\n\\u003cp\\u003eTIME: Tumor Immune Microenvironment\\u003c/p\\u003e\\n\\u003cp\\u003eTME: Tumor Microenvironment\\u003c/p\\u003e\\n\\u003cp\\u003eTNM: Tumor-Node-Metastasis\\u003c/p\\u003e\\n\\u003cp\\u003eTRAEs: Treatment-Related Adverse Events\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research was supported by funding from the National Science Foundation of China (82172825). \\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors have declared that no conflict of interest exists.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Siyou Deng, Qi Wang, Yueping Li, Ruijie Zhang, Jinjie Lin, Yujie Zhang, Yixin Cai, and Wei Sun. The first draft of the manuscript was written by Siyou Deng, and Qi Wang, Yueping Li, and Li Zhang contributed to subsequent revisions. Jiang Chang contributed to methodology and supervision. Ni Zhang contributed to conceptualization, investigation, and supervision. Li Zhang was responsible for conceptualization, funding acquisition, validation, resource management, and overall supervision. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eRelevant data is provided within the manuscript or supplementary information files. Additional details and raw data can be made available upon reasonable request to the corresponding author.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics Approval\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Tongji Hospital, Huazhong University of Science and Technology (Approval No. 2019-S910). The trial was registered at ClinicalTrials.gov (Identifier: NCT05028231).\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWritten informed consent was obtained from all individual participants included in the study.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Publish\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable. This manuscript does not contain any individual person\\u0026rsquo;s data in any form (including images or videos).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians Italic 71:209-49. 10.3322/caac.21660\\u003c/li\\u003e\\n\\u003cli\\u003eCao W, Chen HD, Yu YW, Li N, Chen WQ. (2021) Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chinese medical journal Italic 134:783-91. 10.1097/cm9.0000000000001474\\u003c/li\\u003e\\n\\u003cli\\u003evan Hagen P, Hulshof MC, van Lanschot JJ, Steyerberg EW, van Berge Henegouwen MI, Wijnhoven BP, et al. (2012) Preoperative chemoradiotherapy for esophageal or junctional cancer. 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Published November 27, 2017. 2021. Italic \\u003c/li\\u003e\\n\\u003cli\\u003eZhang X, Peng L, Luo Y, Zhang S, Pu Y, Chen Y, et al. (2021) Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis. Nature communications Italic 12:5291. 10.1038/s41467-021-25539-x\\u003c/li\\u003e\\n\\u003cli\\u003eMenden K, Marouf M, Oller S, Dalmia A, Magruder DS, Kloiber K, et al. (2020) Deep learning-based cell composition analysis from tissue expression profiles. Science advances Italic 6:eaba2619. 10.1126/sciadv.aba2619\\u003c/li\\u003e\\n\\u003cli\\u003eSurgical resection with or without preoperative chemotherapy in oesophageal cancer: a randomised controlled trial. Lancet (London, England) Italic 359:1727-33. 10.1016/s0140-6736(02)08651-8\\u003c/li\\u003e\\n\\u003cli\\u003eWang H, Tang H, Fang Y, Tan L, Yin J, Shen Y, et al. 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Molecular Cancer Italic 22:48. 10.1186/s12943-023-01744-8\\u003c/li\\u003e\\n\\u003cli\\u003eGe J, Jiang H, Chen J, Chen X, Zhang Y, Shi L, et al. (2025) TGF-\\u0026beta; signaling orchestrates cancer-associated fibroblasts in the tumor microenvironment of human hepatocellular carcinoma: unveiling insights and clinical significance. BMC cancer Italic 25:113. 10.1186/s12885-025-13435-2\\u003c/li\\u003e\\n\\u003cli\\u003eMilosevic V, \\u0026Ouml;stman A. (2024) Interactions between cancer-associated fibroblasts and T-cells: functional crosstalk with targeting and biomarker potential. Upsala journal of medical sciences Italic 129:10.48101/ujms.v126.10710 10.48101/ujms.v129.10710\\u003c/li\\u003e\\n\\u003cli\\u003eGalbo PM, Jr, Zang X, Zheng D. (2021) Molecular Features of Cancer-associated Fibroblast Subtypes and their Implication on Cancer Pathogenesis, Prognosis, and Immunotherapy Resistance. Clinical Cancer Research Italic 27:2636-47. 10.1158/1078-0432.CCR-20-4226 %J Clinical Cancer Research\\u003c/li\\u003e\\n\\u003cli\\u003eDunbar KJ, Wong KK, Rustgi AK. (2024) Cancer-Associated Fibroblasts in Esophageal Cancer. Cellular and Molecular Gastroenterology and Hepatology Italic 17:687-95. https://doi.org/10.1016/j.jcmgh.2024.01.008\\u003c/li\\u003e\\n\\u003cli\\u003eKieffer Y, Hocine HR, Gentric G, Pelon F, Bernard C, Bourachot B, et al. (2020) Single-Cell Analysis Reveals Fibroblast Clusters Linked to Immunotherapy Resistance in Cancer. Cancer discovery Italic 10:1330-51. 10.1158/2159-8290.CD-19-1384 %J Cancer Discovery\\u003c/li\\u003e\\n\\u003cli\\u003eDatta J, Dai X, Bianchi A, De Castro Silva I, Mehra S, Garrido VT, et al. (2022) Combined MEK and STAT3 Inhibition Uncovers Stromal Plasticity by Enriching for Cancer-Associated Fibroblasts With Mesenchymal Stem Cell-Like Features to Overcome Immunotherapy Resistance in Pancreatic Cancer. Gastroenterology Italic 163:1593-612. 10.1053/j.gastro.2022.07.076\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"cancer-immunology-immunotherapy\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ciim\",\"sideBox\":\"Learn more about [Cancer Immunology, Immunotherapy](http://link.springer.com/journal/262)\",\"snPcode\":\"262\",\"submissionUrl\":\"https://submission.nature.com/new-submission/262/3\",\"title\":\"Cancer Immunology, Immunotherapy\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Neoadjuvant therapy, PD-1 blockade, immunochemotherapy, biomarkers, ESCC\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6357846/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6357846/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNeoadjuvant immunotherapy has emerged as a promising strategy for esophageal squamous cell carcinoma (ESCC). This study evaluates the therapeutic efficacy and safety of neoadjuvant immunochemotherapy (nICT) in ESCC and explores potential biomarkers associated with treatment outcomes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePatients with locally advanced ESCC were enrolled and received two cycles of nICT followed by surgical resection. The primary endpoint was the pathological complete response (pCR) rate, while secondary endpoints included overall survival (OS), event-free survival (EFS), safety, and the identification of predictive biomarkers.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 47 patients were enrolled in the study, with 42 undergoing surgical 40 resection, all of whom achieved R0 resection. The rates of complete and partial pathological responses were 28.5% and 16.7%, respectively. The 1-year and 2-year EFS rates were 82% and 37.3%, while OS rates reached 100% and 71.4%, respectively. The majority of treatment-related adverse events (TRAEs) were Grade 1–2, and no surgical delays were observed. RNA sequencing analysis revealed epithelial-mesenchymal transition (EMT) as the most significantly enriched pathway in non-responders. Notably, higher infiltration of normal fibroblasts (NF) correlated with improved pathological response and enhanced long-term survival, whereas myofibroblastic cancer-associated fibroblasts (myCAF) negatively influenced treatment efficacy and clinical outcomes.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusions:\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNeoadjuvant PD-1 inhibitors combined with chemotherapy demonstrate encouraging potential for patients with locally advanced ESCC, inducing a robust immune response that correlates with clinical outcomes. The infiltration of myCAF emerges as a potential predictive biomarker for treatment response and disease progression, highlighting the need for further mechanistic exploration and validation in larger cohorts.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTrial registration: \\u003c/strong\\u003eNCT, NCT05028231. 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