Association of tumor-infiltrating lymphocyte subtypes with clinical characteristics and prognosis in young women with hormone receptor-positive breast cancer.
Academic Article
Overview
abstract
BACKGROUND: The role of tumor-infiltrating lymphocytes (TILs) remains unclear in hormone receptor (HR)-positive/HER2-negative breast cancer, particularly in young patients, whose immune microenvironment could be altered by age-related host and tumor differences. PATIENTS AND METHODS: Patients with stage I-III HR-positive/HER2-negative tumors were identified from a prospective cohort study of breast cancer patients diagnosed at age ≤ 40 years. Multiplexed immunofluorescence and semiautomated quantitative software measured cytotoxic T, non-CD8 T, T regulatory, exhausted T, and PDL1+ cells in stroma and tumor. Univariate analyses assessed differences in clinicopathologic characteristics by high versus low immune infiltration, divided based on median. TIL subtypes were evaluated as a continuous variable per 10% increase in Cox regression analyses for invasive breast cancer-free survival (iBCFS), distant disease-free survival (DDFS), and overall survival (OS), adjusted for clinicopathologic parameters. RESULTS: Among 390 patients, high immune infiltration was associated with increasing age, Black race, grade 3 tumors, and metaplastic or micropapillary histological subtypes. Over a median follow-up of 8 years, higher stromal and intratumoral non-CD8 T cell infiltration, T regulatory cell infiltration, and PDL1 expression was associated with improved iBCFS. Higher intratumoral non-CD8 T cell infiltration, T regulatory cell infiltration, and PDL1 expression was associated with improved DDFS; higher stromal PDL1 expression was also associated with improved DDFS. Higher intratumoral cytotoxic T cell infiltration and PDL1 expression was associated with improved OS. CONCLUSIONS: Characterization of immune subpopulations could help refine the prognostic value of TILs in young patients with HR-positive breast cancer, who may benefit from risk stratification for treatment individualization.
