Ratio of T-cell Subsets Predicts Prognosis in Triple-Negative Breast Cancer Patients
Paridhi Tyagi1*, Dongyeul Lee2, Bernett Lee PhD3, Siddhartha Mitra4, Jabed Iqbal2
1Millburn School, Millburn, NJ, 07078, USA.
2Division of Pathology, Singapore General Hospital, 20 College Road, Singapore, 169856, Singapore.
3Assistant Professor and Director, Centre for Biomedical Informatics, Lee Kong Chian School of Medicine, 11 Mandalay Road, Singapore.
4Bioinformatics Researcher, New York, NY, 10017, USA.
*Corresponding Author: Paridhi Tyagi, Millburn School, Millburn, NJ, 07078, USA.
https://doi.org/10.58624/SVOAMR.2025.03.008
Received: March 29, 2025
Published: May 06, 2025
Citation: Tyagi P, Lee D, Lee B, Mitra S, Iqbal J. Ratio of T-cell Subsets Predicts Prognosis in Triple-Negative Breast Cancer Patients. SVOA Medical Research 2025, 3:3, 70-79. doi: 10.58624/SVOAMR.2025.03.008
Abstract
Background: Triple-negative breast cancer (TNBC) is an aggressive form of cancer characterized by the absence of estrogen, progesterone, and HER2 receptors. Current therapies are ineffective for long-term control and have side effects. Recent research highlights the role of tumor-infiltrating lymphocytes (TILs), specifically various T-cell subsets, in influencing TNBC prognosis. Understanding the interactions between CD4+, CD8+, Treg, TH1, and TH2 cells within the tumor microenvironment could pave the way for personalized immunotherapies, improving the survival and quality of life of TNBC patients.
Methods: The dataset consisted of 204 chemotherapy-treated patients with TNBC from the METABRIC and TCGA datasets. T-cell subset ratios were calculated on the basis of gene expression data. Statistical tests, including logistic regression, Kaplan-Meier survival analysis, and principal component analysis, were conducted to assess the relationship between T-cell ratios and patient prognosis. Statistical validation was performed via confusion matrices, odds ratios, and the Mann-Whitney U test to confirm the model’s performance in predicting patient outcomes.
Results: Initial analyses revealed differences in T-cell subset ratios between living and deceased patients with TNBC. Logistic regression confirmed that higher ratios of CD4/CD8, CD4/Treg, CD8/Treg, and CD4/TH1 cells were linked to a better prognosis, whereas higher ratios of TH1/TH2, TH2/Treg, and CD8/TH1 cells were associated with worse outcomes. Statistical validation revealed that higher CD8+ T-cell ratios and certain T-cell subset ratios such as CD4+/ CD8+, and CD8+/TH1 ratios, were correlated with improved overall survival. These findings highlight the potential of T-cell subset ratios as prognostic biomarkers in TNBC.
Discussion: These findings support prior research linking immune system composition with TNBC prognosis. The heterogeneity of triple-negative breast cancer and the dynamic nature of the tumor microenvironment suggest that longitudinal studies with larger, more diverse cohorts are needed to refine these findings.
Conclusion: The model offers a promising approach to personalize treatment plans and optimize resource allocation for high-risk patients. Personalized treatment plans can result in higher survival rates by tailoring treatments to individual patient characteristics. Additionally, optimal resource allocation can lower costs and increase access to care for more patients.
Keywords: Triple-negative Breast Cancer; T-cells; CD4+ T cells; Tumor-Invading Lymphocytes; Principal component analysis