Prediction of distant metastatic recurrence by tumor-infiltrating lymphocytes in hormone receptor-positive breast cancer

Prognostic value and distribution pattern of tumor infiltrating lymphocytes and their subsets in distant metastases of advanced breast cancer

BACKGROUND

There are significant correlations between the levels of tumor infiltrating lymphocytes (TILs) and the prognosis of primary breast cancer. While little is known about immunological mechanisms in the distant metastasis of advanced breast cancer.

PATIENTS AND METHODS

A total of 106 patients with advanced metastatic breast cancer were enrolled in this study between 2016 and 2022. Hematoxylin and eosin staining and immunohistochemistry were used to assess the densities of stromal TILs (sTILs), intratumoral TILs (iTILs) and invasive marginal TILs (imTILs) and CD4+, CD8+, CD20+, FOXP3+ TILs in the primary tumor and metastasis (bone, lung, liver, and distant lymph node) of advanced breast cancer.

RESULTS

Higher levels of sTILs at metastatic sites were associated with better progression-free survival (PFS), postmetastasis survival (PMS) and overall survival (OS) (p = .026, .001 and .005, respectively). The levels of iTILs were significantly lower than those of sTILs and imTILs in both primary tumor (p< .001, both) and metastasis (p< .001, both). The level of CD4+ T cells was higher than those of CD8+ T cells and CD20+ B cells in both primary tumor (p < .001) and metastasis (p < .001). The levels of sTILs (p=0. 001) and imTILs (p< .001) in the primary tumor were generally higher than those in the metastasis.

CONCLUSION

The levels of TILs and their subsets can predict the survival and prognosis of patients with advanced breast cancer. The distributions of TILs and their subsets are similar between the primary tumor and metastasis. The metastases have a lower degree of lymphocytes infiltration than its corresponding primary tumor.

Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence

BACKGROUND

Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC.

METHODS

Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined.

RESULTS

A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis.

CONCLUSION

AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.

Identification of Prognostic Biomarkers for Breast Cancer Metastasis Using Penalized Additive Hazards Regression Model

Background:

Breast cancer (BC) has been reported as one of the most common cancers diagnosed in females throughout the world. Survival rate of BC patients is affected by metastasis. So, exploring its underlying mechanisms and identifying related biomarkers to monitor BC relapse/recurrence using new statistical methods is essential. This study investigated the high-dimensional gene-expression profiles of BC patients using penalized additive hazards regression models.

Methods:

A publicly available dataset related to the time to metastasis in BC patients (GSE2034) was used. There was information of 22 283 genes expression profiles related to 286 BC patients. Penalized additive hazards regression models with different penalties, including LASSO, SCAD, SICA, MCP and Elastic net were used to identify metastasis related genes.

Results:

Five regression models with penalties were applied in the additive hazards model and jointly found 9 genes including SNU13, CLINT1, MAPK9, ABCC5, NKX3-1, NCOR2, COL2A1, and ZNF219. According the median of the prognostic index calculated using the regression coefficients of the penalized additive hazards model, the patients were labeled as high/low risk groups. A significant difference was detected in the survival curves of the identified groups. The selected genes were examined using validation data and were significantly associated with the hazard of metastasis.

Conclusion:

This study showed that MAPK9, NKX3-1, NCOR1, ABCC5, and CD44 are the potential recurrence and metastatic predictors in breast cancer and can be taken into account as candidates for further research in tumorigenesis, invasion, metastasis, and epithelial-mesenchymal transition of breast cancer.

Phase I study of adjuvant immunotherapy with autologous tumor-infiltrating lymphocytes in locally advanced cervical cancer

BACKGROUND

Adoptive cell therapy (ACT) with tumor-infiltrating lymphocytes (TILs) has achieved remarkable clinical efficacy in metastatic cancers such as melanoma and cervical cancer (CC). Here we explored the safety, feasibility and preliminary tumor response and performed translational investigations of adjuvant immunotherapy using infusion of autogenous (auto)-TILs following concurrent chemoradiotherapy (CCRT) in CC patients with locally advanced disease.

METHODS

Twenty-seven CC patients with stage III to IV disease were recruited in this single-center, phase I study. TILs were isolated from lesions in the uterine cervix and generated under good manufacturing practices (GMP) conditions and then infused after CCRT plus intramuscular interleukin (IL)-2 injections.

RESTULTS

From 27 patients, TILs were successfully expanded from 20 patients, with a feasibility of 74.1%. Twelve patients received TILs following CCRT. Adverse events (AEs) were primarily attributable to CCRT. Only 1 (8.3%) patient experienced severe toxicity with a grade 3 hypersensitivity reaction after TIL infusion. No autoimmune AEs, such as pneumonitis, hepatitis, or myocarditis, occurred, and there was no treatment-related mortality. Nine of 12 patients (75.0%) attained complete response, with a disease control duration of 9 to 22 months. Translational investigation showed that the transcriptomic characteristics of the infused TIL products and some immune biomarkers in the tumor microenvironment and serum of CC patients at baseline were correlated with the clinical response.

CONCULSION

TIL-based ACT following CCRT was safe in an academic center setting, with potential effective responses in locally advanced CC patients. 'Hot' inflammatory immune environments are beneficial to the clinical efficacy of TIL-based ACT as adjuvant therapy.

TRIAL REGISTRATION

CLINICALTRIALS

gov NCT04443296.

FUNDING

Natinoal Key R&D Program: Sci-Tech Key Program of the Guangzhou City Science Foundation; the Guangdong Provinve Sci-Tech International Key Program; the National Natural Science Foundation of China.