Abstract PS1-03-04: Predictive Factors of Thyroid Adverse Events in Neoadjuvant Immunotherapy for Triple-Negative Breast Cancer

Abstract Background: Thyroid dysfunction is among the most common immune-related events (irAEs) of immune checkpoint inhibitors (ICIs). This study aimed to investigate the prevalence, risk factors, and prognostic implications of thyroid irAEs in triple-negative breast cancer (TNBC) patients receiving neoadjuvant anti-PD-1 therapy combined with chemotherapy, based on data from the TREND phase II prospective clinical trial. Methods: The TREND study is a phase II prospective clinical trial involving TNBC patients treated with tislelizumab combined with nab-paclitaxel, followed by epirubicin-cyclophosphamide before surgery. Data on demographics, thyroid function, anti-thyroid autoantibodies were collected, and univariate and multivariate logistic regression models were used to identify risk factors. Peripheral immune analysis and whole-exome sequencing were conducted to explore underlying mechanisms associated with thyroid irAEs. Results: Among the 50 patients analyzed, 14 (28%) developed thyroid irAEs. Multivariate analyses revealed that baseline positivity of thyroglobulin antibody (OR 9.311, p=0.011) and lower levels of free triiodothyronine levels (OR 0.056, p=0.034) are independent predictors of thyroid irAEs. Peripheral immune analysis revealed that patients with thyroid irAEs exhibited elevated levels of CD4+ T cells, CD8+ T cells and CD16+CD56+ NK cells, along with higher levels of CD4+T cells before the onset of thyroid irAEs. Additionally, increased tumor mutational burden, and mutations in KLF17 and LRIG1 were observed in patients with thyroid irAEs. Conclusions: This study highlights the risk factors and underlying pathogenesis associated with thyroid irAEs in TNBC patients treated with neoadjuvant anti-PD-1 therapy combined with chemotherapy. These findings may assist clinicians in identifying patients at elevated risk for irAEs during ICI therapy, thereby improving patient management in clinical practice. Citation Format: Y. Li, Y. Cheng, L. Ji, Y. Wang, L. Yao, H. Zhang, H. Dong, M. Wang, X. Yingying. Predictive Factors of Thyroid Adverse Events in Neoadjuvant Immunotherapy for Triple-Negative Breast Cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS1-03-04.

Authors

Institutions

Publication Details

Journal
Clinical Cancer Research
Published
2026-02-17
DOI
https://doi.org/10.1158/1557-3265.sabcs25-ps1-03-04
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract PS1-03-04: Predictive Factors of Thyroid Adverse Events in Neoadjuvant Immunotherapy for Triple-Negative Breast Cancer

M. Wang, L. Yao, X. Yingying, H. Zhang et al.
Clinical Cancer Research
Cancer Immunotherapy and Biomarkers
article

Abstract PS1-03-04: Predictive Factors of Thyroid Adverse Events in Neoadjuvant Immunotherapy for Triple-Negative Breast Cancer

M. Wang, L. Yao, X. Yingying, H. Zhang, Y. Li, Y. Wang, H. Dong, Y. Cheng, L. Ji
article en

Abstract

Abstract Background: Thyroid dysfunction is among the most common immune-related events (irAEs) of immune checkpoint inhibitors (ICIs). This study aimed to investigate the prevalence, risk factors, and prognostic implications of thyroid irAEs in triple-negative breast cancer (TNBC) patients receiving neoadjuvant anti-PD-1 therapy combined with chemotherapy, based on data from the TREND phase II prospective clinical trial. Methods: The TREND study is a phase II prospective clinical trial involving TNBC patients treated with tislelizumab combined with nab-paclitaxel, followed by epirubicin-cyclophosphamide before surgery. Data on demographics, thyroid function, anti-thyroid autoantibodies were collected, and univariate and multivariate logistic regression models were used to identify risk factors. Peripheral immune analysis and whole-exome sequencing were conducted to explore underlying mechanisms associated with thyroid irAEs. Results: Among the 50 patients analyzed, 14 (28%) developed thyroid irAEs. Multivariate analyses revealed that baseline positivity of thyroglobulin antibody (OR 9.311, p=0.011) and lower levels of free triiodothyronine levels (OR 0.056, p=0.034) are independent predictors of thyroid irAEs. Peripheral immune analysis revealed that patients with thyroid irAEs exhibited elevated levels of CD4+ T cells, CD8+ T cells and CD16+CD56+ NK cells, along with higher levels of CD4+T cells before the onset of thyroid irAEs. Additionally, increased tumor mutational burden, and mutations in KLF17 and LRIG1 were observed in patients with thyroid irAEs. Conclusions: This study highlights the risk factors and underlying pathogenesis associated with thyroid irAEs in TNBC patients treated with neoadjuvant anti-PD-1 therapy combined with chemotherapy. These findings may assist clinicians in identifying patients at elevated risk for irAEs during ICI therapy, thereby improving patient management in clinical practice. Citation Format: Y. Li, Y. Cheng, L. Ji, Y. Wang, L. Yao, H. Zhang, H. Dong, M. Wang, X. Yingying. Predictive Factors of Thyroid Adverse Events in Neoadjuvant Immunotherapy for Triple-Negative Breast Cancer [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS1-03-04.

Clinical Cancer ResearchVol. 32(4_Supplement)
First Hospital of China Medical University (CN), China Medical University (CN)
Good health and well-being
Openalex Percentile: Top 53%
Cancer Immunotherapy and Biomarkers
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.