Exploring the Landscape of Immune Checkpoint Inhibitor-Induced Adverse Events Through Big Data Mining of Pan-Cancer Clinical Trials.
Exploring the Landscape of Immune Checkpoint Inhibitor-Induced Adverse Events Through Big Data Mining of Pan-Cancer Clinical Trials.
复制标题
通过泛癌症临床试验的大数据挖掘探索免疫检查点抑制剂引起的不良事件的情况。
DOI：
10.1093/oncolo/oyae012
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发表时间：
2024
期刊：
影响因子：
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通讯作者：
Tan,AikChoon
中科院分区：
文献类型：
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作者：
Fadlullah,MuhammadZakiHidayatullah;Lin,Ching-Nung;Coleman,Samuel;Young,Arabella;Naqash,AbdulRafeh;Hu-Lieskovan,Siwen;Tan,AikChoon
目的免疫检查点抑制剂(ICIS)显著提高了癌症患者的存活率，并提供了长期持久的益处。然而，接受ICI治疗的患者会出现一系列称为免疫相关不良事件(IrAEs)的毒性反应，这可能会损害这些治疗的临床益处。由于irAEs的发病率和谱系因癌症类型和ICI制剂的不同而不同，因此有必要在泛癌队列中表征irAEs的发病率和谱系，以辅助临床管理。设计：我们查询了在ClinicalTrials.gov注册的40万项试验，并检索了来自19种癌症类型和7种ICI制剂的71087名ICI治疗参与者的全面泛癌数据库。结果我们开发了irAExplorer(https://irae.tanlab.org)，)，这是一个从大数据挖掘中获得的关注ICIS患者不良事件的交互式数据库。IrAExplorer包括来自19种癌症类型的343项临床试验的71087名不同的临床试验参与者，这些试验具有良好的ICI治疗方案和协调的不良事件类别。我们通过irAExplorer演示了几个IRE分析，并强调了治疗或癌症特异性irAE之间的一些关联。结论irAExplorer是一个用户友好的资源，提供了跨泛癌症队列的治疗或癌症特异性irAE的探索、验证和发现。我们设想irAExplorer可以作为一个有价值的资源来交叉验证用户的内部数据集，以增加他们发现的健壮性。
PurposeImmune checkpoint inhibitors (ICIs) have significantly improved the survival of patients with cancer and provided long-term durable benefit. However, ICI-treated patients develop a range of toxicities known as immune-related adverse events (irAEs), which could compromise clinical benefits from these treatments. As the incidence and spectrum of irAEs differs across cancer types and ICI agents, it is imperative to characterize the incidence and spectrum of irAEs in a pan-cancer cohort to aid clinical management.DesignWe queried >400 000 trials registered at ClinicalTrials.gov and retrieved a comprehensive pan-cancer database of 71 087 ICI-treated participants from 19 cancer types and 7 ICI agents. We performed data harmonization and cleaning of these trial results into 293 harmonized adverse event categories using Medical Dictionary for Regulatory Activities.ResultsWe developed irAExplorer (https://irae.tanlab.org), an interactive database that focuses on adverse events in patients administered with ICIs from big data mining. irAExplorer encompasses 71 087 distinct clinical trial participants from 343 clinical trials across 19 cancer types with well-annotated ICI treatment regimens and harmonized adverse event categories. We demonstrated a few of the irAE analyses through irAExplorer and highlighted some associations between treatment- or cancer-specific irAEs.ConclusionThe irAExplorer is a user-friendly resource that offers exploration, validation, and discovery of treatment- or cancer-specific irAEs across pan-cancer cohorts. We envision that irAExplorer can serve as a valuable resource to cross-validate users’ internal datasets to increase the robustness of their findings.