Evidence map›Paper›PMID 39025103›Full record

ArticleThe Lancet. Oncology2024

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

Guihong Wan, Wenxin Chen, Sara Khattab, Katie Roster, Nga Nguyen, Boshen Yan, Ahmad Rajeh, Jayhyun Seo, Hannah Rashdan, Leyre Zubiri and 9 more

Abstract readMulticenter Study
In one paragraph

Article in The Lancet. Oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
83citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

83 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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23 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Guihong WanDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Wenxin ChenDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Sara KhattabDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Katie RosterDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Nga NguyenDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Boshen YanDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Ahmad RajehDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Jayhyun SeoDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Hannah RashdanDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Leyre ZubiriDepartment of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Matthew J HadfieldLegorreta Cancer Center, Division of Hematology/Oncology, Department of Medicine, Warren Alpert Medical School, Brown University, Providence, RI, USA.
Shadmehr DemehriDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Kun-Hsing YuDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
William LotterDepartment of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA.
Alexander GusevDepartment of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA.
Nicole R LeBoeufDepartment of Dermatology, Center for Cutaneous Oncology, Brigham and Women's Hospital and Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
Kerry L ReynoldsDepartment of Medicine, Division of Hematology-Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Shawn G KwatraDepartment of Dermatology, University of Maryland School of Medicine, Baltimore, MD, USA.
Yevgeniy R SemenovDepartment of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: ysemenov@mgh.harvard.edu.

Funding

Robust, Generalizable, and Fair Machine Learning Models for BiomedicineR35GM142879 · NIGMS · HARVARD MEDICAL SCHOOL · PI YU, KUN-HSING · 2021 to 2025
$2.4M
Mitigating Hematologic Adverse Events in Patients with Myeloid Malignancies: A Novel Causal Artificial Intelligence ApproachR01HL174679 · NHLBI · HARVARD MEDICAL SCHOOL · PI Kun-Hsing Yu · 2024 to 2026
$2.3M
Predictive modeling of cutaneous immune checkpoint inhibitor toxicitiesK23AR080791 · NIAMS · MASSACHUSETTS GENERAL HOSPITAL · PI Yevgeniy R Semenov · 2023 to 2026
$704k
Explainable Artificial Intelligence for Melanoma Recurrence Prediction via Integrative Modeling of Multiplexed Imaging with Transcriptomics and HistopathologyK99CA286966 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI WAN, GUIHONG · 2024 to 2025
$352k
NCI NIH HHS K99 CA286966NCI NIH HHS L30 CA264747NHLBI NIH HHS R01 HL174679NIAMS NIH HHS K23 AR080791NIGMS NIH HHS R35 GM142879
6 · The paper itself

Abstract

backgroundUnderstanding 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.

methodsIn 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.

findingsWe 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).

interpretationReliably 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.

fundingUS National Institutes of Health.

Indexed as

Immune Checkpoint InhibitorsNeoplasmsAgedFemaleHumansMaleMiddle AgedRetrospective StudiesImmune Checkpoint Inhibitors

Identifiers

PMID39025103
PMCPMC11316445

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.