ArticleFrontiers in endocrinology2026
Clinical outcomes of cancer patients with pre-existing autoimmune thyroid disease treated with PD-(L)-1 inhibitors: a propensity score methodology with inverse probability of treatment weighting retrospective study.
Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, these therapies are associated with immune-related adverse events (irAEs), with thyroid dysfunction as one of the most common endocrine irAEs. The role of preexisting thyroid autoantibodies (Abs) in predicting endocrine irAEs and their impact on patient survival remains unclear. Objective: This study explored the relationship between preexisting thyroid antibodies and the development of endocrine irAEs, as well as their association with overall survival (OS) in patients treated with ICIs. Methods: We retrospectively reviewed patients with malignancy treated with one or more PD-(L)1 inhibitor between July 2018 and March 2022 at The First Affiliated Hospital of Nanjing Medical University, and 4,119 patients were initially included in this study. Ultimately, 585 patients whose anti-TPO and anti-Tg levels were measured before ICI treatment were analyzed. The patients were stratified based on their pre-existing thyroid antibody status. Multivariate Cox regression models were used to analyze the risk of thyroid irAEs and the differences in OS. Propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were employed to validate the robustness of the results. Results: Patients with pre-existing thyroid Ab (N = 80, 13.7%) had a significantly higher incidence of thyroid irAEs compared to the antibody-negative group (N = 505) (HR = 4.13, 95% CI: 2.69-6.35, p < 0.001). PSM and IPTW analyses further confirmed that patients with pre-existing thyroid Ab had a markedly increased risk of grade≥2 irAEs (HR = 7.54, 95% CI: 2.74-20.75, p < 0.001). No significant difference in OS was observed between the two groups (log-rank p = 0.5). Notably, patients with pre-existing thyroid Abs and irAEs had better survival outcomes (log-rank p = 0.027). A landmark analysis at 12 months indicated that patients who developed thyroid irAEs between 0 and 12 months of age had relatively low mortality rates over this time period. However, from 12 months onwards, the survival curves of the two groups tended to overlap. Conclusion: Preexisting thyroid antibodies provide a significant risk factor for thyroid irAEs in patients treated with ICIs. The development of thyroid irAEs has been associated with improved survival, thus suggesting a potential link between immune activation and clinical benefits.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.