背景

肺浸润性粘液腺癌（Lung Mucinous adenocarcinoma，LIMA）是非小细胞肺癌的一种独特病理亚型，在分子特征、病理特征及预后等方面与肺非粘液腺癌（Lung non-mucinous adenocarcinoma，LNMA）存在显著差异。然而，LIMA的治疗目前主要借鉴LNMA的诊疗框架，缺乏针对LIMA的个体化指导。LIMA诊疗中的关键问题包括分期与预后不匹配，以及手术切除策略、淋巴结清扫策略和围手术期治疗模式不明确等。本研究旨在通过多维度数据整合，构建一个全面的LIMA专属围手术期个体化诊疗体系，为LIMA患者的围手术期管理提供理论支持和实践指导。

方法

本研究基于多中心的LIMA专病队列中1589例患者的临床诊疗数据，结合监测、流行病学及最终结果（Surveillance, Epidemiology, and End Results，SEER）数据库中8829例LIMA患者和260130例LNMA患者的临床数据，以及癌症基因组图谱（The Cancer Genome Atlas，TCGA）数据库中20例LIMA患者与537例LNMA患者的基因组与转录组测序数据，采用多组学分析策略，探索LIMA与LNMA患者在分子突变图谱、肿瘤免疫微环境、淋巴结转移、远处器官转移以及远期预后模式的差异。研究旨在明确LIMA作为独立病理亚型的特征，并进一步构建适用于LIMA的个体化诊疗体系，包括LIMA专属的新型肿瘤-淋巴结-转移（Tumor node metastasis，TNM）分期系统构建与验证、手术切除与淋巴结清扫策略制定以及围手术期综合治疗模式决策体系。为控制组间差异并减小偏倚，研究采用倾向性评分匹配方法进行数据平衡。为保证结果的稳健性，采用限制性立方样条、局部加权散点平滑法拟合以及结构断点识别等多种分析方法。

结果

本研究揭示了LIMA与LNMA在分子特征与临床特征方面的显著差异。LIMA患者中最常见的突变基因为KRAS（45%），而LNMA中最常见的突变基因为TP53（50%）。转录组分析表明LIMA患者的免疫细胞尤其是CD8⁺ T细胞丰度显著低于LNMA，这一结果在LIMA患者的免疫组化及免疫荧光实验中得到验证。LIMA患者的免疫抑制性微环境（CD8⁺ T细胞浸润不足及低PD-L1表达）与免疫治疗反应较差密切相关。此外，LIMA的淋巴结转移率相对较低（4.20% vs 7.19%，p<0.001），更倾向于肺内播散（48.3% vs 31.4%，P<0.001）。相较于LNMA，LIMA患者的预后呈现“两极分化”模式，即早期患者预后更好而晚期患者则预后更差。手术是LIMA患者最有效的治疗手段，不加选择地辅助放疗或化疗可能对LIMA预后有不良影响。基于SEER数据库和多中心验证，本研究以LNMA患者的总生存期为基准，通过将LIMA患者与LNMA患者的总生存期进行对比，将预后相同的LIMA患者归类到LNMA对应的分期，构建了LIMA专属新型TNM分期系统。该系统在各个分期间差异显著，并在中国多中心验证队列中同样表现出较好的区分度。确定了影响手术切除范围选择的肿瘤直径最佳截断值为1.5cm。对于直径小于等于1.5cm的肿瘤可选择亚肺叶切除术；而对于超过1.5cm的肿瘤，肺叶切除术能有更好的远期预后。通过对LIMA患者的淋巴结转移概率进行分析，构建了LIMA的淋巴结转移图谱。研究发现，LIMA的淋巴结转移主要发生在外周区和上区（尤其是3P和14站），而下区（如8站和9站）的转移概率最低。淋巴结清扫数目与预后之间呈现“U”型关系，提示过度清扫与清扫不足均与预后不良相关。本研究构建了淋巴结转移概率预测模型（曲线下面积为0.837），并制定了个体化的淋巴结清扫策略：低风险患者（概率<0.1）清扫7枚淋巴结，中风险患者（概率0.1-0.5）清扫14枚淋巴结，高风险患者（概率>0.5）则清扫17枚淋巴结。基于临床TNM分期、影像学分型、肿瘤直径大小、淋巴结转移风险、N分期以及分子特征这六个关键决策因子，确定了LIMA围手术期综合治疗模式的临床决策模型。具体而言，对于IA1-IIB期肺癌患者，推荐直接手术治疗。IIIA-IIIB期患者需根据影像学特征评估，若表现为肺炎型，建议先进行新辅助治疗再手术。对于N0期或低风险患者，不推荐辅助治疗；而高风险患者可以从辅助治疗获益，依据分子特征选择治疗方案。对于KRAS突变患者，应避免辅助治疗；对于免疫表型为IV型（PD-L1+/CD8+）的患者，可考虑选择免疫治疗，存在EGFR突变者可选EGFR抑制剂，其他情况则考虑免疫与靶向等联合治疗方案。

结论

本研究基于多中心真实世界数据，解析了LIMA独特的临床与分子特征，构建了涵盖分期诊断、手术切除策略优化和围手术期最佳综合治疗模式的LIMA多维度诊疗体系。该体系为LIMA个体化治疗提供了新的理论依据，未来通过前瞻性研究验证该体系的有效性，有望改善LIMA患者生活质量与远期预后。

Background

Lung invasive mucinous adenocarcinoma (LIMA) is a unique pathological subtype of non-small cell lung cancer (NSCLC), which significantly differs from Lung non-mucinous adenocarcinoma (LNMA) in molecular characteristics, pathological features, and prognosis. However, LIMA treatment currently relies on the therapeutic framework established for LNMA, lacking individualized guidelines for LIMA. The important issues in LIMA diagnosis and treatment include mismatched staging and prognosis, as well as unclear strategies for surgical resection, lymph node dissection, and perioperative treatment model. This study aimed to integrate multidimensional data to establish a comprehensive, individualized perioperative management framwork for LIMA, providing both theoretical support and clinical guidance for the stratified management of LIMA patients.

Methods

This study utilized clinical data from a multicenter LIMA cohort consisting of 1,589 patients, alongside clinical data from the Surveillance, Epidemiology, and End Results (SEER) database, which includes 8,829 LIMA and 260,130 LNMA patients, as well as genomic and transcriptomic data from The Cancer Genome Atlas (TCGA) database, comprising 20 LIMA and 537 LNMA patients. Using a multi-omics analytical approach, the differences of molecular mutation profiles, tumor immune microenvironment, lymph node metastasis, distant organ metastasis, and long-term prognosis patterns between LIMA and LNMA patients were explored. The study aimed to define LIMA as an independent pathological subtype and establish an individualized treatment framework for LIMA, which includes the development and validation of a novel LIMA-specific TNM staging system, optimization of surgical resection and lymph node dissection strategies, and the establishment of a perioperative comprehensive treatment decision-making model. Propensity score matching was employed to control for inter-group differences and minimize bias, and various analytical methods, including restricted cubic spline, locally weighted scatterplot smoothing, and breakpoint analysis, were utilized to ensure robust results.

Results

This study revealed significant differences in molecular and clinical characteristics between LIMA and LNMA patients. The most common mutation in LIMA was KRAS (45%), whereas TP53 (50%) was the most prevalent mutation in LNMA. Transcriptomic analysis showed that LIMA patients had lower abundances of immune cells, particularly CD8⁺ T cells, than LNMA patients, a finding confirmed by immunohistochemistry and immunofluorescence experiments in LIMA patients. The immunosuppressive microenvironment in LIMA patients (insufficient CD8⁺ T cell infiltration and low PD-L1 expression) was closely associated with poor response to immunotherapy. Additionally, LIMA had a relatively lower lymph node metastasis rate (4.20% vs 7.19%, p<0.001) and a greater tendency for intrapulmonary dissemination (48.3% vs 31.4%, p<0.001). Compared to LNMA, LIMA patients exhibited a "bipolar" prognostic pattern, with better outcomes in early stages and worse outcomes in advanced stages. Surgery was identified as the most effective treatment for LIMA patients, and indiscriminate adjuvant radiotherapy or chemotherapy might adversely affect LIMA prognosis. Using the SEER database and multicenter validation, this study compared the overall survival of LIMA patients with that of LNMA patients and allocated LIMA patients with similar prognoses into corresponding LNMA stages. A novel TNM staging system specific to LIMA was constructed, which exhibited significant differences across stages and demonstrated good discriminative ability in a Chinese multicenter validation cohort. The optimal cutoff value for tumor size to determine the extent of surgical resection was determined to be 1.5 cm. Sub-lobectomy was recommended for tumors ≤1.5 cm, while lobectomy offered better long-term prognosis for tumors >1.5 cm. By analyzing lymph node metastasis probabilities in LIMA patients, a lymph node metastasis atlas for LIMA was constructed. LIMA lymph node metastasis was found to primarily occur in peripheral and upper zones (especially stations 3P and 14), with the lowest metastasis probabilities in lower zones (such as stations 8 and 9). A "U"-shaped relationship was observed between the number of resected lymph nodes and prognosis, indicating that both excessive and insufficient dissection were associated with poor outcomes. This study developed a lymph node metastasis probability prediction model (AUC=0.837) and established individualized lymph node dissection strategies: 7 lymph nodes for low-risk patients (probability <0.1), 14 for medium-risk patients (probability 0.1-0.5), and 17 for high-risk patients (probability >0.5). Based on six key decision-making factors—clinical TNM staging, radiological subtypes, tumor size, lymph node metastasis risk, N stage, and molecular characteristics—a clinical decision-making model for comprehensive perioperative treatment in LIMA was determined. Specifically, surgery was recommended for patients with stage IA1-IIB lung cancer. For stage IIIA-IIIB patients, neoadjuvant therapy followed by surgery was suggested if the imaging characteristics were pneumonic. Adjuvant therapy was not recommended for N0 or low-risk patients, while high-risk patients could benefit from it, with treatment plans selected based on molecular characteristics. For KRAS-mutated patients, adjuvant therapy should be avoided; for patients with immunophenotype IV (PD-L1+/CD8+), immunotherapy could be considered, EGFR inhibitors for those with EGFR mutations, and combination therapies for others.

Conclusion

Based on multicenter real-world data, this study analyzed the unique clinical and molecular characteristics of LIMA and establishes a multidimensional diagnostic and treatment framework for LIMA, which includes staging, surgical resection strategy optimization, and the development of an optimal perioperative treatment model. This framework provides a theoretical foundation for individualized LIMA treatment and, through prospective studies, could improve the quality of life and long-term prognosis for LIMA patients.