第一章：新辅助治疗后胃癌患者临床和病理评估与预后的关系

背景与目的：新辅助治疗作为围手术期治疗的重要组成部分，其疗效评估对指导后续治疗具有重要意义。然而，临床实践中临床与病理评估结果常不一致，且二者在预后判断中的优劣尚无定论。本研究旨在比较两种评估方式对胃癌患者预后的影响，并探讨二者的综合评估价值。

方法与材料：回顾性分析2004年至2021年在中国医学科学院肿瘤医院接受新辅助治疗后行根治性手术的局部进展期胃癌患者的临床病理资料。新辅助治疗疗效评估采用实体瘤疗效评价标准1.1进行临床反应判定，Mandard肿瘤退缩分级系统进行病理反应评估。计算机断层扫描评估为完全缓解或部分缓解者定义为临床缓解，术后原发灶病理评估为Mandard TRG 1-3级者定义为病理缓解。根据两种评估结果构建“综合评估”变量，将患者分为四组：同时缓解、仅临床缓解、仅病理缓解、均无缓解。采用kappa统计检验评估一致性，并通过单因素和多因素Cox回归分析筛选影响总体生存的独立危险因素。

结果：共纳入238例接受新辅助治疗后行根治性手术的胃癌患者。术后病理缓解率和临床缓解率分别为50.84%(121/238)和39.92%(95/238)。154例患者的临床和病理评估结果一致(66例同时缓解，88例均无缓解)，其余84例患者评估结果不一致，kappa值为0.297(P<0.001)，提示二者一致性较差。临床评估为部分缓解、疾病稳定及疾病进展患者的中位总生存期分别为103.5、73.1及9.4个月，病理评估为Mandard TRG 1-3级及4-5级患者的中位总生存期分别为99.6和54.6个月。多因素Cox回归分析显示，综合评估(P=0.030)、临床N分期(P<0.001)、血管或淋巴管侵犯(HR: 2.745, P<0.001)和新辅助治疗前CA724(HR: 1.577, P=0.047)均为影响总体生存的独立预后因素。在综合评估的四组中，同时缓解组的生存情况(中位生存期：103.5个月)显著优于其他组(P=0.008)。

结论：新辅助治疗后临床和病理评估一致性较差，同时缓解患者的预后显著优于其他患者。因此，综合考虑临床及病理评估有助于提高预后判断准确性，为术后治疗决策提供参考。

第二章：新辅助治疗后胃癌临床和病理同时缓解预测模型的构建与验证

背景与目的：新辅助治疗的生存获益取决于肿瘤对治疗的反应。对治疗反应良好的患者，新辅助治疗可延长总生存期和无病生存期，而反应不佳的患者不仅难以获益，还可能因延误最佳手术时机、增加治疗相关不良反应及医疗成本而面临更大风险。在本研究的第一章，我们已证实新辅助治疗后临床和病理同时缓解的患者预后最佳。本章旨在构建并验证预测胃癌患者新辅助治疗后临床和病理同时缓解的列线图模型。

材料与方法：回顾性收集2006年1月至2021年12月于中国医学科学院肿瘤医院接受新辅助治疗后手术的局部进展期胃癌患者的临床病理资料。基于前期建立的综合评估方法重新评估患者的治疗反应，将临床和病理同时缓解定义为反应良好，其余归为反应不佳。将纳入的病例按照7:3的比例随机分配至训练队列和验证队列。采用多因素logistic回归分析筛选影响新辅助治疗疗效的关键因素，并基于筛选结果在R软件中构建列线图模型。随后，在验证队列中进行外部验证，通过计算一致性指数（C-index）和绘制受试者工作特征曲线评估模型的预测效能与区分度。

结果：本研究共纳入940例接受新辅助治疗后手术的胃癌患者。生存分析显示，反应良好组的中位总生存时间显著长于反应不佳组（未达到 vs. 89.3个月， P<0.001），且5年生存率更高（79.6% vs. 59.6%）。多因素logistic回归分析表明，肿瘤大小、肿瘤位置、Borrmann 分型、CEA水平和新辅助治疗模式是预测胃癌患者新辅助治疗疗效的独立影响因素（均 P<0.05）。基于上述关键临床病理因素，本研究构建了一项列线图模型，其在训练队列中的C-index 为0.700，验证集为0.687。此外，ROC曲线进一步验证了模型的良好鉴别能力，反映其在不同数据集上均具有较高的稳健性和临床适用性。

结论：本研究证实肿瘤大小、肿瘤位置、Borrmann 分型、CEA水平和新辅助治疗模式是影响新辅助治疗疗效的重要因素。基于这些变量构建的列线图模型在预测新辅助治疗反应方面表现优异，有助于胃癌个体化治疗决策的制定。

第三章：新辅助治疗后病理完全缓解胃癌患者的生存结局及其影响因素分析

背景与目的：胃癌患者新辅助治疗疗效与预后密切相关，在第二章内容中，我们发现临床与病理同时缓解患者的预后最佳并据此构建列线图预测模型。病理完全缓解(pathological complete response, pCR)作为疗效评估中特殊的存在意味着肿瘤细胞的完全消失，虽罕见却具有重要的预后意义。本研究旨在评估pCR患者的生存情况，并探讨影响其总生存期和无病生存期的关键因素。

材料与方法：回顾性纳入2004年1月至2023年1月于中国医学科学院肿瘤医院接受新辅助治疗并达到病理完全缓解的局部进展期胃癌患者。新辅助化疗均采用标准化疗方案，部分患者治疗期间联合免疫治疗，新辅助放化疗采用同步放疗(分25次给予45 Gy)联合口服S-1。病理完全缓解的定义为术后病理评估符合Mandard肿瘤退缩分级1级，即原发灶及区域淋巴结无肿瘤细胞残留(ypT0N0)。生存分析采用Kaplan-Meier法绘制生存曲线，并通过log-rank检验进行组间比较。Cox回归分析用于筛选影响总生存期和无病生存期的独立预后因素。

结果：本研究共纳入新辅助治疗后病理完全缓解的胃癌患者112例，pCR率为7.4%(112/1517)，中位随访时间为42个月(范围：5-117个月)。3年和5年总生存率分别为90.2%和83.3%，3年和5年无病生存率分别为86.8%和82.0%。在多因素Cox回归分析中，新辅助化疗是改善病理完全缓解患者总生存期(P=0.015)和无病生存期(P=0.021)的独立预后因素。术后是否接受辅助治疗在总生存期与无病生存期方面差异无统计学意义。此外，与单纯新辅助化疗相比，新辅助免疫治疗联合化疗显著提高了pCR率(P<0.001)。

结论：达到病理完全缓解的局部晚期胃癌患者具有良好的长期生存结局，术后辅助治疗并未带来额外的生存益处。新辅助免疫治疗可提高pCR率，但其对pCR患者长期预后的具体影响仍需进一步研究。

第四章：LRP1B相关免疫特征对胃癌患者免疫治疗反应与预后的影响

背景与目的：本研究在第三章中证实，新辅助化疗联合免疫治疗有助于提高局部进展期胃癌患者病理完全缓解率。然而，并非所有患者均能从免疫治疗中获益，仅部分人群可获得长期生存优势。近期研究指出，低密度脂蛋白受体相关蛋白1b(LRP1B）作为潜在的抑癌基因，与免疫治疗反应密切相关。尽管其在多种实体瘤中突变频发，LRP1B在胃癌中的生物学功能及免疫学意义尚不明确。因此，本研究旨在探讨LRP1B对胃癌免疫微环境及患者预后的影响。

方法与材料：本研究整合TCGA数据库中478例胃癌患者的基因突变、RNA测序及临床资料，并纳入中国医学科学院肿瘤医院117例胃癌患者的临床信息及二代测序数据。采用CIBERSORT算法评估肿瘤免疫微环境中免疫细胞浸润特征，利用STRING数据库及Cytoscape软件构建蛋白相互作用网络。差异表达基因分析通过DESeq2 R包完成，结合KEGG通路富集分析与GSEA方法探讨LRP1B突变的潜在生物学功能。通过Cox回归与LASSO回归筛选免疫相关差异表达基因构建风险评分，并结合临床特征绘制列线图，使用Kaplan-Meier生存分析及ROC曲线评估其预后预测能力。

结果：LRP1B是胃癌中较为常见的突变基因，在TCGA队列和NCC队列中的突变率分别为21.8%和25.6%，与肿瘤突变负荷显著相关。免疫细胞浸润分析显示，LRP1B突变型样本在CD4+T细胞、巨噬细胞和滤泡辅助T细胞的浸润程度方面与野生型存在差异。通路富集分析表明差异表达基因主要富集于类固醇激素合成、视黄醇代谢和细胞色素P450外源物质代谢通路。GSEA分析发现突变型样本显著富集于DNA复制相关通路，而T/B细胞受体信号通路及自然杀伤细胞功能等免疫相关过程未见显著富集，表明其局部免疫表型较弱。基于免疫风险评分和临床特征构建的列线图模型在预后预测中表现良好，1年、3年和5年ROC曲线下面积(AUC)分别为0.75、0.75和0.76。

结论：LRP1B突变与肿瘤突变负荷和多种代谢通路改变密切相关，伴随多个抗原呈递基因上调及免疫细胞浸润丰度变化，可能对免疫治疗具有一定的指导意义。基于LRP1B突变相关基因构建的免疫风险评分可作为胃癌患者预后预测的有效工具。

Part Ⅰ: Relationship between clinical and pathological evaluation and prognosis in gastric cancer patients after neoadjuvant therapy

Background and purpose As a critical component of perioperative management, neoadjuvant therapy plays an essential role in the treatment of resectable locally advanced gastric cancer. Accurate evaluation of its efficacy is crucial for guiding subsequent therapeutic strategies. However, discrepancies frequently exist between clinical and pathological assessments in real-world practice, and their respective prognostic value remains controversial. This study aimed to compare the prognostic significance of clinical and pathological responses following neoadjuvant therapy and to explore the value of a combined evaluation approach.

Materials and methods We retrospectively analyzed the clinicopathological data of patients with locally advanced gastric cancer who underwent neoadjuvant therapy followed by curative surgery at Cancer Hospital, Chinese Academy of Medical Sciences from 2004 to 2021. Clinical response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1, while pathological response was assessed using the Mandard Tumor Regression Grading (TRG) system. A complete or partial response on computed tomography was defined as clinical response, and Mandard TRG 1–3 was considered pathological response. Based on these two assessment methods, a composite variable—referred to as "comprehensive assessment"—was constructed, classifying patients into four groups: both response, only clinical response, only pathological response, and no response. Agreement between clinical and pathological assessments was analyzed using the kappa statistic. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors for overall survival (OS).

Results A total of 238 patients were included. The rates of pathological and clinical response were 50.84% (121/238) and 39.92% (95/238), respectively. Clinical and pathological evaluations were consistent in 154 patients (66 with dual response, 88 with no response), while 84 patients showed discordant results (κ = 0.297, P < 0.001), indicating poor agreement. Median OS was 103.5, 73.1, and 9.4 months in patients with partial response, stable disease, and progressive disease based on clinical evaluation, respectively; and 99.6 vs. 54.6 months in patients with TRG 1–3 versus TRG 4–5. Multivariate Cox regression identified combined response (P = 0.030), clinical N stage (P < 0.001), vascular or lymphatic invasion (HR: 2.745, P<0.001), and pre-treatment CA724 level (HR: 1.577, P=0.047) as independent prognostic factors. Among the four combined response groups, the dual response group had the most favorable survival (median OS: 103.5 months, P = 0.008).

Conclusion Clinical and pathological responses to neoadjuvant therapy showed limited concordance. Patients achieving both clinical and pathological responses had significantly better outcomes. Therefore, a combined evaluation approach may improve the accuracy of prognostic assessment and support postoperative treatment decisions.

Part Ⅱ: Construction and validation of a predictive model for concurrent clinical and pathological response in gastric cancer after neoadjuvant therapy

Background and purpose The survival benefits of neoadjuvant therapy are largely dependent on the tumor's response to treatment. For patients with a favorable response, neoadjuvant therapy can significantly prolong overall survival and disease-free survival. However, for those with a poor response, not only is the benefit limited, but they may also face increased risks due to delayed optimal surgical timing, heightened treatment-related adverse events, and additional medical costs. In the first chapter of this study, we have demonstrated that patients achieving both clinical and pathological remission after neoadjuvant therapy have the best prognosis. This chapter aims to develop and validate a nomogram model to predict the concurrent clinical and pathological remission after neoadjuvant therapy in gastric cancer patients.

Materials and methods A retrospective analysis was conducted on clinicopathological data from patients with locally advanced gastric cancer (LAGC) who underwent neoadjuvant therapy followed by surgery at Cancer Hospital, Chinese Academy of Medical Sciences between 2006 and 2021. Patients' treatment responses were re-evaluated based on a previously established comprehensive assessment method, with those achieving both clinical and pathological remission classified as good responders, while the remaining were categorized as poor responders. The included cases were randomly assigned to the training and validation cohorts in a 7:3 ratio. Multivariate logistic regression analysis was performed to identify key predictive factors influencing neoadjuvant treatment efficacy, and a nomogram model was constructed based on these factors using R software. External validation was conducted in the validation cohort, and the predictive performance and discrimination ability of the model were assessed by calculating the concordance index (C-index) and plotting the receiver operating characteristic (ROC) curve.

Results A total of 940 patients with gastric cancer who underwent neoadjuvant therapy followed by surgery were included in this study. Survival analysis revealed that the median OS was significantly longer in the favorable response group than in the unfavorable response group (not reached vs. 89.3 months, P<0.001), with a higher 5-year survival rate (79.6% vs. 59.6%). Multivariate logistic regression analysis identified tumor size, tumor location, Borrmann classification, carcinoembryonic antigen (CEA) level, and neoadjuvant treatment regimen as independent predictors of response to neoadjuvant therapy in gastric cancer (P<0.05 for all). Based on these key clinicopathological factors, we developed a nomogram model, which demonstrated good predictive performance, with a C-index of 0.700 in the training cohort and 0.687 in the validation cohort. Furthermore, ROC curve analysis confirmed the model's strong discriminatory ability, indicating its robustness and clinical applicability across different datasets.

Conclusion This study confirms that tumor size, tumor location, Borrmann classification, CEA level, and neoadjuvant treatment regimen are crucial factors influencing the response to neoadjuvant therapy. The developed nomogram model exhibits excellent predictive performance in evaluating neoadjuvant therapy response and may serve as a valuable tool for guiding individualized treatment decisions in gastric cancer patients.

Part Ⅲ: Survival outcomes and associated factors in gastric cancer patients with pathological complete response after neoadjuvant therapy

Background The efficacy of neoadjuvant therapy (NAT) is closely associated with the prognosis of gastric cancer patients. In Part Ⅱ, we observed that individuals achieving concurrent clinical and pathological response exhibited the most favorable outcomes, and a corresponding nomogram prediction model was developed. Pathological complete response (pCR), a unique indicator of treatment efficacy, signifies the complete eradication of tumor cells. Although pCR is rare, it holds important prognostic value. This study aims to assess the survival outcomes of pCR patients and investigate key factors influencing overall survival (OS) and disease-free survival (DFS).

Patients and Methods This retrospective study included patients with locally advanced gastric cancer who received neoadjuvant therapy and achieved pCR at Cancer Hospital, Chinese Academy of Medical Sciences between January 2004 and January 2023. All patients undergoing neoadjuvant chemotherapy received standard regimens, with some receiving additional immunotherapy during treatment. Patients undergoing neoadjuvant chemoradiotherapy received concurrent radiotherapy (45 Gy in 25 fractions) combined with oral S-1. pCR was defined as Mandard tumor regression grade 1, indicating no residual tumor cells in the primary lesion or regional lymph nodes (ypT0N0). Kaplan–Meier analysis was used to generate survival curves, with differences between groups compared using the log-rank test. Cox proportional hazards regression was conducted to identify independent prognostic factors for OS and DFS.

Results A total of 112 patients with gastric cancer who achieved pCR after neoadjuvant therapy were included, with a pCR rate of 7.4% (112/1517). The median follow-up time was 42 months (range: 5–117 months). The 3-year and 5-year OS rates were 90.2% and 83.3%, respectively, and the 3-year and 5-year DFS rates were 86.8% and 82.0%, respectively. Multivariate Cox regression analysis revealed that neoadjuvant chemotherapy was an independent prognostic factor for improved OS (P=0.015) and DFS (P=0.021) in patients with pCR. No statistically significant differences in OS or DFS were observed between patients who did and did not receive postoperative adjuvant therapy. Furthermore, compared with chemotherapy alone, neoadjuvant chemoimmunotherapy significantly increased the pCR rate (P<0.001).

Conclusions Patients with locally advanced gastric cancer who achieved pCR had favorable long-term survival outcomes. Postoperative adjuvant therapy did not confer additional survival benefits. Although neoadjuvant immunotherapy improved the pCR rate, its specific impact on the long-term prognosis of patients with pCR requires further investigation.

Part Ⅳ: Impact of LRP1B–associated immune signature on immunotherapy response and prognosis in gastric cancer patients

Background In Part Ⅲ, we demonstrated that neoadjuvant chemotherapy combined with immunotherapy contributes to an increased rate of pathological complete response (pCR) in patients with locally advanced gastric cancer (LAGC). However, not all patients benefit from immunotherapy, and long-term survival advantages are achieved only in a subset of individuals. Recent studies have indicated that low-density lipoprotein receptor-related protein 1B (LRP1B), a potential tumor suppressor gene, is closely associated with the response to immunotherapy. Although LRP1B mutations are frequently observed in various solid tumors, its biological function and immunological significance in gastric cancer remain unclear. Therefore, this study aimed to investigate the impact of LRP1B on the tumor immune microenvironment and clinical prognosis in gastric cancer patients.

Patients and Methods This study integrated genomic mutation profiles, RNA sequencing data, and clinical information from 478 gastric cancer patients in The Cancer Genome Atlas (TCGA) database, along with clinical data and next-generation sequencing (NGS) results from 117 gastric cancer patients treated at the Cancer Hospital, Chinese Academy of Medical Sciences. The CIBERSORT algorithm was applied to evaluate the immune cell infiltration characteristics within the tumor immune microenvironment. Protein–protein interaction networks were constructed using the STRING database and Cytoscape software. Differentially expressed gene (DEG) analysis was performed with the DESeq2 R package, and the potential biological functions of LRP1B mutations were explored through KEGG pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). Immune-related DEGs were identified through Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses to establish an immune risk score. A prognostic nomogram was constructed by integrating the immune risk score and clinical characteristics, and its predictive performance was assessed using Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curves.

Results LRP1B was identified as one of the most frequently mutated genes in gastric cancer, with mutation rates of 21.8% and 25.6% in the TCGA and NCC cohorts, respectively, and showed a significant association with tumor mutation burden (TMB). Immune cell infiltration analysis revealed that LRP1B-mutant samples exhibited distinct infiltration patterns compared with wild-type samples, particularly in activated CD4+ T cells, macrophages, and follicular helper T cells. Pathway enrichment analysis indicated that differentially expressed genes were mainly enriched in steroid hormone biosynthesis, retinol metabolism, and cytochrome P450-mediated xenobiotic metabolism pathways. GSEA demonstrated that mutant samples were significantly enriched in DNA replication-related pathways, while no significant enrichment was observed in immune-related pathways such as T/B cell receptor signaling or natural killer cell-mediated cytotoxicity, suggesting a relatively immunosuppressed local immune phenotype. The nomogram model incorporating the immune risk score and clinical parameters demonstrated good predictive performance for prognosis, with area under the ROC curve (AUC) values of 0.75, 0.75, and 0.76 for 1-year, 3-year, and 5-year survival, respectively.

Conclusions LRP1B mutation is closely associated with tumor mutation burden and alterations in several metabolic pathways in gastric cancer, accompanied by upregulation of multiple antigen presentation-related genes and changes in immune cell infiltration abundance, potentially providing valuable guidance for immunotherapy. The immune risk score model based on LRP1B mutation-associated genes may serve as an effective prognostic tool for gastric cancer patients.