Understanding co-occurrence patterns and prognostic implications of immune-related adverse events is crucial for immunotherapy management. However, previous studies have been limited by sample size and generalisability. In this study, we leveraged a multi-institutional cohort and a population-level database to investigate co-occurrence patterns of and survival outcomes after multi-organ immune-related adverse events among recipients of immune checkpoint inhibitors. In this retrospective study, we identified individuals who received immune checkpoint inhibitors between May 31, 2015, and June 29, 2022, from the Massachusetts General Hospital, Brigham and Women's Hospital, and Dana-Farber Cancer Institute (Boston, MA, USA; MGBD cohort), and between April 30, 2010, and Oct 11, 2021, from the independent US population-based TriNetX network. We identified recipients from all datasets using medication codes and names of seven common immune checkpoint inhibitors, and patients were excluded from our analysis if they had incomplete information (eg, diagnosis and medication records) or if they initiated immune checkpoint inhibitor therapy after Oct 11, 2021. Eligible patients from the MGBD cohort were then propensity score matched with recipients of immune checkpoint inhibitors from the TriNetX database (1:2) based on demographic, cancer, and immune checkpoint inhibitor characteristics to facilitate cohort comparability. We applied immune-related adverse event identification rules to identify patients who did and did not have immune-related adverse events in the matched cohorts. To reduce the likelihood of false positives, patients diagnosed with suspected immune-related adverse events within 3 months after chemotherapy were excluded. We performed pairwise correlation analyses, non-negative matrix factorisation, and hierarchical clustering to identify co-occurrence patterns in the MGBD cohort. We conducted landmark overall survival analyses for patient clusters based on predominant immune-related adverse event factors and calculated accompanying hazard ratios (HRs) and 95% CIs, focusing on the 6-month landmark time for primary analyses. We validated our findings using the TriNetX cohort. We identified 15 246 recipients of immune checkpoint inhibitors from MGBD and 50 503 from TriNetX, of whom 13 086 from MGBD and 26 172 from TriNetX were included in our propensity score-matched cohort. Median follow-up durations were 317 days (IQR 113–712) in patients from MGBD and 249 days (91–616) in patients from TriNetX. After applying immune-related adverse event identification rules, 8704 recipients of immune checkpoint inhibitors were retained from MGBD, of whom 3284 (37·7%) had and 5420 (62·3%) did not have immune-related adverse events, and 18 162 recipients were retained from TriNetX, of whom 5538 (30·5%) had and 12 624 (69·5%) did not have immune-related adverse events. In both cohorts, positive pairwise correlations of immune-related adverse events were commonly observed. Co-occurring immune-related adverse events were decomposed into seven factors across organs, revealing seven distinct patient clusters (endocrine, cutaneous, respiratory, gastrointestinal, hepatic, musculoskeletal, and neurological). In the MGBD cohort, the patient clusters that predominantly had endocrine (HR 0·53 [95% CI 0·40–0·70], p<0·0001) and cutaneous (0·61 [0·46–0·81], p=0·0007) immune-related adverse events had favourable overall survival outcomes at the 6-month landmark timepoint, while the other clusters either had unfavourable (respiratory: 1·60 [1·25–2·03], p=0·0001) or neutral survival outcomes (gastrointestinal: 0·86 [0·67–1·10], p=0·23; musculoskeletal: 0·97 [0·78–1·21], p=0·78; hepatic: 1·20 [0·91–1·59], p=0·19; and neurological: 1·30 [0·97–1·74], p=0·074). Similar results were found in the TriNetX cohort (endocrine: HR 0·75 [95% CI 0·60–0·93], p=0·0078; cutaneous: 0·62 [0·48–0·82], p=0·0007; respiratory: 1·21 [1·00–1·46], p=0·044), except for the neurological cluster having unfavourable (rather than neutral) survival outcomes (1·30 [1·06–1·59], p=0·013). Reliably identifying the immune-related adverse event cluster to which a patient belongs can provide valuable clinical information for prognosticating outcomes of immunotherapy. These insights can be leveraged to counsel patients on the clinical impact of their individual constellation of immune-related adverse events and ultimately develop more personalised surveillance and mitigation strategies. US National Institutes of Health.

中文翻译：

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免疫检查点抑制剂引起的多器官免疫相关不良事件及其下游影响：回顾性多队列研究


了解免疫相关不良事件的共现模式和预后影响对于免疫治疗管理至关重要。然而，先前的研究受到样本量和普遍性的限制。在这项研究中，我们利用多机构队列和人群水平数据库来研究免疫检查点抑制剂接受者中多器官免疫相关不良事件的共现模式和生存结果。在这项回顾性研究中，我们确定了在 2015 年 5 月 31 日至 2022 年 6 月 29 日期间接受过来自麻省总医院、布莱根妇女医院和达纳法伯癌症研究所（美国马萨诸塞州波士顿；MGBD）的免疫检查点抑制剂的个体。队列），以及2010年4月30日至2021年10月11日期间，来自独立的美国基于人口的TriNetX网络。我们使用七种常见免疫检查点抑制剂的药物代码和名称从所有数据集中识别了接受者，如果患者信息不完整（例如诊断和药物记录）或如果他们在 10 月 11 日之后开始免疫检查点抑制剂治疗，则他们被排除在我们的分析之外。 2021年。然后，根据人口统计学、癌症和免疫检查点抑制剂特征，将 MGBD 队列中符合条件的患者与 TriNetX 数据库中的免疫检查点抑制剂接受者进行倾向评分（1:2）匹配，以促进队列可比性。我们应用免疫相关不良事件识别规则来识别匹配队列中发生和未发生免疫相关不良事件的患者。为了减少假阳性的可能性，化疗后 3 个月内诊断出疑似免疫相关不良事件的患者被排除。 我们进行了成对相关分析、非负矩阵分解和层次聚类来识别 MGBD 队列中的共现模式。我们根据主要的免疫相关不良事件因素对患者群进行了具有里程碑意义的总体生存分析，并计算了伴随的风险比 (HR) 和 95% CI，重点关注初步分析的 6 个月里程碑时间。我们使用 TriNetX 队列验证了我们的发现。我们确定了来自 MGBD 的 15 246 名免疫检查点抑制剂接受者和来自 TriNetX 的 50 503 名免疫检查点抑制剂接受者，其中 13 086 名来自 MGBD 的接受者和 26 172 名来自 TriNetX 的接受者被纳入我们的倾向评分匹配队列中。 MGBD 患者的中位随访时间为 317 天（IQR 113-712），TriNetX 患者的中位随访时间为 249 天（91-616）。应用免疫相关不良事件识别规则后，8704名免疫检查点抑制剂接受者从MGBD中保留下来，其中3284名（37·7%）有免疫相关不良事件，5420名（62·3%）没有发生免疫相关不良事件，18名接受者没有发生免疫相关不良事件。 TriNetX 保留了 162 名接受者，其中 5538 名（30·5%）有免疫相关不良事件，12 624 名（69·5%）没有发生免疫相关不良事件。在这两个队列中，通常观察到免疫相关不良事件的正配对相关性。同时发生的免疫相关不良事件被分解为跨器官的七个因素，揭示了七个不同的患者群（内分泌、皮肤、呼吸、胃肠道、肝脏、肌肉骨骼和神经系统）。 在 MGBD 队列中，患者群主要有内分泌（HR 0·53 [95% CI 0·40–0·70]，p<0·0001）和皮肤（0·61 [0·46–0·81 ], p=0·0007) 免疫相关不良事件在 6 个月的里程碑时间点具有良好的总体生存结果，而其他集群则具有不利的结果（呼吸系统：1·60 [1·25–2·03]，p =0·0001）或中性生存结果（胃肠道：0·86 [0·67–1·10]，p=0·23；肌肉骨骼：0·97 [0·78–1·21]，p=0· 78；肝脏：1·20 [0·91–1·59]，p=0·19；神经系统：1·30 [0·97–1·74]，p=0·074）。 TriNetX 队列中也发现了类似的结果（内分泌：HR 0·75 [95% CI 0·60–0·93]，p=0·0078；皮肤：0·62 [0·48–0·82]，p =0·0007；呼吸：1·21 [1·00–1·46]，p=0·044），但神经系统簇具有不利（而不是中性）生存结果（1·30 [1·06– 1·59]，p=0·013）。可靠地识别患者所属的免疫相关不良事件群可以为预测免疫治疗的结果提供有价值的临床信息。这些见解可用于为患者提供关于其个体免疫相关不良事件的临床影响的咨询，并最终制定更个性化的监测和缓解策略。美国国立卫生研究院。