Distribution and maturity of tertiary lymphoid structures predict recurrence-free survival in cervical cancer.
[OBJECTIVE] Reliable biomarkers are needed to predict outcomes in patients with cervical cancer treated with immune checkpoint inhibitors.
APA
Peng Y, Chen M, et al. (2026). Distribution and maturity of tertiary lymphoid structures predict recurrence-free survival in cervical cancer.. International journal of gynecological cancer : official journal of the International Gynecological Cancer Society, 36(1), 102718. https://doi.org/10.1016/j.ijgc.2025.102718
MLA
Peng Y, et al.. "Distribution and maturity of tertiary lymphoid structures predict recurrence-free survival in cervical cancer.." International journal of gynecological cancer : official journal of the International Gynecological Cancer Society, vol. 36, no. 1, 2026, pp. 102718.
PMID
41259848
Abstract
[OBJECTIVE] Reliable biomarkers are needed to predict outcomes in patients with cervical cancer treated with immune checkpoint inhibitors. This study aimed to develop a novel immune classification system based on tertiary lymphoid structure maturation to stratify prognosis and PD-1 inhibitor response.
[METHODS] Surgical specimens from 451 patients with cervical cancer were analyzed to evaluate tertiary lymphoid structure spatial distribution (tumor region vs invasive margin) and maturity. Using machine learning, 4 parameters-tumor region score, invasive margin score, and tertiary lymphoid structure maturity in both regions-were integrated to establish an Immune Score-based classification (immune class I, immune class II, and immune class III). The model was validated in an external cohort of 58 PD-1 inhibitor-treated patients and compared with the International Federation of Gynecology and Obstetrics staging and combined positive score.
[RESULTS] Tertiary lymphoid structure positivity was more frequent in the invasive margin (58.2%) than in the tumor region (44.6%). All 4 tertiary lymphoid structure parameters independently predicted recurrence-free survival. The immune classification categorized patients into 3 groups with distinct 5-year recurrence-free survival rates (immune class I: 52.4%; immune class II: 78.1%; immune class III: 91.3%), outperforming International Federation of Gynecology and Obstetrics staging. In the PD-1 inhibitor cohort, higher immune class correlated with improved objective response rates (immune class I: 26.3%; immune class II: 56.3%; immune class III: 87.5%) and showed better predictive accuracy than the combined positive score. Immune class remained the only independent prognostic factor across all patient cohorts.
[CONCLUSIONS] This first tertiary lymphoid structure-based immune classification system effectively stratifies recurrence-free survival and PD-1 inhibitor response in cervical cancer, surpassing conventional staging methods. It underscores the clinical relevance of tertiary lymphoid structure organization and maturation, providing a practical tool for personalizing immunotherapy strategies.
[METHODS] Surgical specimens from 451 patients with cervical cancer were analyzed to evaluate tertiary lymphoid structure spatial distribution (tumor region vs invasive margin) and maturity. Using machine learning, 4 parameters-tumor region score, invasive margin score, and tertiary lymphoid structure maturity in both regions-were integrated to establish an Immune Score-based classification (immune class I, immune class II, and immune class III). The model was validated in an external cohort of 58 PD-1 inhibitor-treated patients and compared with the International Federation of Gynecology and Obstetrics staging and combined positive score.
[RESULTS] Tertiary lymphoid structure positivity was more frequent in the invasive margin (58.2%) than in the tumor region (44.6%). All 4 tertiary lymphoid structure parameters independently predicted recurrence-free survival. The immune classification categorized patients into 3 groups with distinct 5-year recurrence-free survival rates (immune class I: 52.4%; immune class II: 78.1%; immune class III: 91.3%), outperforming International Federation of Gynecology and Obstetrics staging. In the PD-1 inhibitor cohort, higher immune class correlated with improved objective response rates (immune class I: 26.3%; immune class II: 56.3%; immune class III: 87.5%) and showed better predictive accuracy than the combined positive score. Immune class remained the only independent prognostic factor across all patient cohorts.
[CONCLUSIONS] This first tertiary lymphoid structure-based immune classification system effectively stratifies recurrence-free survival and PD-1 inhibitor response in cervical cancer, surpassing conventional staging methods. It underscores the clinical relevance of tertiary lymphoid structure organization and maturation, providing a practical tool for personalizing immunotherapy strategies.
MeSH Terms
Humans; Female; Uterine Cervical Neoplasms; Middle Aged; Tertiary Lymphoid Structures; Adult; Aged; Prognosis; Immune Checkpoint Inhibitors; Neoplasm Recurrence, Local; Retrospective Studies; Disease-Free Survival
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