A pyroptosis-associated gene risk model for predicting the prognosis of triple-negative breast cancer

Novel molecular insights into pyroptosis in triple-negative breast cancer prognosis and immunotherapy

BACKGROUND

Patients with triple-negative breast cancer (TNBC) often have a poor prognostic outcome. Current treatment strategies cannot benefit all TNBC patients. Previous findings suggested pyroptosis as a novel target for suppressing cancer development, although the relationship between TNBC and pyroptosis-related genes (PRGs) was still unclear.

METHODS

Gene expression data and clinical follow-up of TNBC patients were collected from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO). PRGs were screened using weighted gene co-expression network analysis. Cox regression analysis and the least absolute shrinkage and selection operator (i.e. LASSO) technique were applied to construct a pyroptosis-related prognostic risk score (PPRS) model, which was further combined with the clinicopathological characteristics of TNBC patients to develop a survival decision tree and a nomogram. The model was used to calculate the PPRS, and then the overall survival, immune infiltration, immunotherapy response and drug sensitivity of TNBC patients were analyzed based on the PPRS.

RESULTS

The PPRS model was closely related to clinicopathological features and can independently and accurately predict the prognosis of TNBC. According to normalized PPRS, patients in different cohorts were divided into two groups. Compared with the high-PPRS group, the low-PPRS group had significantly higher ESTIMATE (i.e. Estimation of STromal and Immune cells in MAlignantTumours using Expression data) score, immune score and stromal score, and it also had overexpressed immune checkpoints and significantly reduced Tumor Immune Dysfunction and Exclusion (TIDE) score, as well as higher sensitivity to paclitaxel, veliparib, olaparib and talazoparib. A decision tree and nomogram based on PPRS and clinical characteristics can improve the prognosis stratification and survival prediction for TNBC patients.

CONCLUSIONS

A PPRS model was developed to predict TNBC patients' immune characteristics and response to immunotherapy, chemotherapy and targeted therapy, as well as their survival outcomes.

Characterization of immune checkpoints expression and lymphocyte densities of iranian breast cancer patients; the co-expression status and clinicopathological associates

BACKGROUND

Breast malignancies are now the most common and deadliest type of neoplasms among women worldwide. Novel therapeutic approaches are needed to combat advanced stages of breast cancer. In this study, we aimed to investigate the expression and co-expression status of three immune checkpoints (PD-1, PD-L1, and LAG-3), as well as tumor-infiltrating lymphocytes (TIL) scores, and to further establish their potential correlations with clinicopathologic features.

METHODS

We performed a retrospective study on 361 pathologic samples of breast cancer. Immunohistochemistry was performed to assess the status of the immune checkpoint markers, and H&E staining was used to score TILs. The correlations of the immune checkpoint markers of tumor cells and tumor-associated immune cells and TIL scores with clinicopathological characteristics were analyzed.

RESULTS

Out of 361 assessed samples, LAG-3 was positive in 51%, while IC PD-L1 and TC PD-L1 were detectable in 36% and 8.9%, respectively. Moreover, both IC PD-L1 and LAG-3 stained positively in 24.4% of samples. IC PD-L1 expression was significantly higher in tumors with higher nuclear, mitotic, and overall grades and tubule formation. In addition, TC PD-L1 and LAG-3 exhibited a similar trend for higher overall grading. Tumors with positive estrogen- and progesterone-receptor (ER and PR) expression had significantly lower IC PD-L1 and TC PD-L1 staining, while LAG-3 positivity was more prevalent in HER2 positive samples. Tumors that were positive for these biomarkers had significantly higher Ki-67 scores. LAG-3 expression showed significant correlations with PD-1 and IC PD-L1 expression. Besides, the co-expression of LAG-3 and IC PD-L1 was significantly more encountered in luminal B and triple-negative subtypes, compared to the luminal A subtype. Regarding TILs, their scoring was significantly higher in ER and PR negative and HER2 positive samples. Intriguingly, samples with positive staining for LAG-3, IC PD-L1, and TC PD-L1 had significantly higher TIL scorings.

CONCLUSIONS

Immune checkpoints show differentially different levels of expression in certain molecular subtypes of breast cancer. Moreover, they reveal a meaningful correlation with each other, proliferation indices, and histologic grades. Finally, a sizable proportion of breast cancers co-express PD-L1 and LAG-3, which will make them appropriate targets for future combined ICIs.