The T Cell Immunoscore as a Reference for Biomarker Development Utilizing Real-World Data from Patients with Advanced...

Area of Science:

• Immunology
• Oncology
• Bioinformatics

Background:

• Assessing prognostic biomarkers is crucial for advanced malignancies treated with immune checkpoint inhibitors (ICIs).
• Real-world transcriptomic data offers a valuable resource for biomarker discovery.
• Developing reliable biomarkers is essential for guiding treatment decisions and improving patient outcomes.

Purpose of the Study:

• To determine the prognostic value of an immunoscore derived from CD3+ and CD8+ T cell density.
• To estimate overall survival (OS) in patients with advanced cancers receiving ICIs.
• To validate a reference for future machine learning-based biomarker development.

Main Methods:

• Transcriptomic data from 522 patients with advanced malignancies was analyzed.
• An immunoscore was calculated using CIBERSORTx to estimate CD3+ and CD8+ T cell densities.
• Cox regression, Kaplan-Meier curves, and Harrell's concordance index were used to assess OS association and prediction.

Main Results:

• The transcriptomics-based immunoscore significantly predicted patient risk of death (p < 0.001).
• Patients with intermediate-high immunoscores demonstrated better overall survival (OS) compared to those with low immunoscores.
• Prognostic value was significant in melanoma and head and neck cancer, but not in NSCLC or RCC.

Conclusions:

• An immunoscore calculated from real-world transcriptomic data is a promising signature for estimating OS in patients receiving ICIs.
• This transcriptomic immunoscore can serve as a valuable reference for developing advanced machine learning biomarkers.
• Further research can refine this approach for personalized cancer therapy.