Department of Medical Oncology, Sun Yat-sen University Cancer Center.
State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou.
Int J Surg. 2024 Aug 1;110(8):4660-4671. doi: 10.1097/JS9.0000000000001479.
Neoadjuvant and adjuvant immunotherapies for cancer have evolved through a series of remarkable and critical research advances; however, addressing their similarities and differences is imperative in clinical practice. Therefore, this study aimed to examine their similarities and differences from the perspective of informatics analysis.
This cross-sectional study retrospectively analyzed extensive relevant studies published between 2014 and 2023 using stringent search criteria, excluding nonpeer-reviewed and non-English documents. The main outcome variables are publication volume, citation volume, connection strength, occurrence frequency, relevance percentage, and development percentage. Furthermore, an integrated comparative analysis was conducted using unsupervised hierarchical clustering, spatiotemporal analysis, regression statistics, and Walktrap algorithm analysis.
This analysis included 1373 relevant studies. Advancements in neoadjuvant and adjuvant immunotherapies have been promising over the last decade, with an annual growth rate of 25.18 vs. 6.52% and global collaboration (International Co-authorships) of 19.93 vs. 19.84%. Respectively, five dominant research clusters were identified through unsupervised hierarchical clustering based on machine learning, among which Cluster 4 (Balance of neoadjuvant immunotherapy efficacy and safety) and Cluster 2 (Adjuvant immunotherapy clinical trials) [Average Publication Year (APY): 2021.70±0.70 vs. 2017.54±4.59] are emerging research populations. Burst and regression curve analyses uncovered domain pivotal research signatures, including microsatellite instability (R 2 =0.7500, P =0.0025) and biomarkers (R 2 =0.6505, P =0.0086) in neoadjuvant scenarios, and the tumor microenvironment (R 2 =0.5571, P =0.0209) in adjuvant scenarios. The Walktrap algorithm further revealed that 'neoadjuvant immunotherapy, nonsmall cell lung cancer (NSCLC), immune checkpoint inhibitors, melanoma' and 'adjuvant immunotherapy, melanoma, hepatocellular carcinoma, dendritic cells' (Relevance Percentage: 100 vs. 100%, Development Percentage: 37.5 vs. 17.1%) are extremely relevant to this field but remain underdeveloped, highlighting the need for further investigation.
This study identified pivotal research signatures and provided substantial predictions for neoadjuvant and adjuvant cancer immunotherapies. In addition, comprehensive quantitative comparisons revealed a notable shift in focus within this field, with neoadjuvant immunotherapy taking precedence over adjuvant immunotherapy after 2020; such a qualitative finding facilitate proper decision-making for subsequent research and mitigate the wastage of healthcare resources.
癌症的新辅助和辅助免疫疗法经历了一系列显著而关键的研究进展；然而，在临床实践中，必须解决它们的相似之处和不同之处。因此，本研究旨在从信息学分析的角度研究它们的异同。
本横断面研究使用严格的搜索标准，回顾性分析了 2014 年至 2023 年期间发表的大量相关研究，排除了非同行评审和非英文文献。主要观察变量是发表量、引用量、连接强度、出现频率、相关百分比和发展百分比。此外，还使用无监督层次聚类、时空分析、回归统计和 Walktrap 算法分析进行综合比较分析。
本分析包括 1373 项相关研究。在过去十年中，新辅助和辅助免疫疗法的进展令人鼓舞，年增长率分别为 25.18%和 6.52%，全球合作（国际合著）分别为 19.93%和 19.84%。分别，基于机器学习的无监督层次聚类确定了五个主要的研究群，其中群 4（新辅助免疫疗法疗效和安全性的平衡）和群 2（辅助免疫疗法临床试验）[平均发表年份（APY）：2021.70±0.70 与 2017.54±4.59]是新兴的研究人群。突发和回归曲线分析揭示了领域关键研究特征，包括新辅助情况下的微卫星不稳定性（R 2 =0.7500，P =0.0025）和生物标志物（R 2 =0.6505，P =0.0086），以及辅助情况下的肿瘤微环境（R 2 =0.5571，P =0.0209）。Walktrap 算法进一步显示，“新辅助免疫疗法、非小细胞肺癌 (NSCLC)、免疫检查点抑制剂、黑色素瘤”和“辅助免疫疗法、黑色素瘤、肝细胞癌、树突状细胞”（相关性百分比：100%与 100%，发展百分比：37.5%与 17.1%）与该领域极其相关，但仍未得到充分发展，这表明需要进一步研究。
本研究确定了关键的研究特征，并为新辅助和辅助癌症免疫疗法提供了大量预测。此外，全面的定量比较揭示了该领域的一个显著变化，即新辅助免疫疗法在 2020 年后优先于辅助免疫疗法；这种定性发现有助于为后续研究做出适当决策，并减少医疗资源的浪费。
