Multi-organ immune-related adverse events from immune checkpoint inhibitors and their downstream implications: a retrospective multicohort study

Abstract

Background: 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.

Methods: 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.

Findings: 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).

Interpretation: 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.

Funding: US National Institutes of Health.

Figure 1.. Pairwise co-occurrence patterns of irAEs in multiple organs

A. Adjusted Risk Ratio (RR) on the MGBD cohort; B. Number of cases who developed co-occurring irAEs on the MGBD cohort. C. Adjusted Risk Ratio (RR) on the TriNetX cohort; D. Number of cases who developed co-occurring irAEs on the TriNetX cohort. N/A (not applicable) indicates that no patients developed irAEs affecting the two specific organ systems (e.g., irAEs affecting ocular and rheumatologic organs in the TriNetX cohort).

Figure 2.. Co-occurrence patterns using NMF and hierarchical clustering

NMF decomposed the irAE count matrix into two low-rank matrices, representing organ-level irAE factors (referred to as ‘basis’) and the weights of irAE factors for each patient (referred to as ‘weight’). For the basis matrix, rows correspond to organ systems, and columns correspond to irAE factors, each named by the predominant organ system (e.g., F: Endocrine represents the factor predominated by endocrine irAEs). A and B show the two basis matrices for the MGBD and TriNetX cohorts, respectively. For the weight matrix, rows are irAE factors; columns are patients. C and D present how patients were clustered with the weight matrices. Patients from both cohorts were grouped into seven clusters, each predominantly characterized by a single factor. Each cluster was named by the dominant factor. For example, the leftmost cluster was dominated by “F: Endocrine” and, thus, named as “Cluster: Endocrine”.

Figure 3.. Survival outcomes of patient clusters using landmark analyses

The results include Hazard Ratios, 95% Confidence Intervals, and significance levels, measured at various landmark times from five to twelve months following the initiation of immune checkpoint inhibitor (ICI) therapy. Separate multivariable Cox Proportional Hazards models, adjusted for sex, race, ethnicity, age at ICI, Charlson comorbidity index, cancer type, cancer stage, non-ICI treatment, ICI type, and ICI interruption, were used at different landmark times. In each analysis, the reference group corresponded to patients who did not experience an irAE within the landmark time. The reference group was the same across analyses for different clusters at a specific landmark time. A and B show results by each cluster for the MGBD and TriNetX cohorts, respectively, demonstrating that endocrine- and cutaneous-predominant irAE clusters are consistently associated with more favorable survival, whereas respiratory- and neurologic-predominant clusters are associated with unfavorable survival by comparison to patients without irAEs.