Tumor Associated Antigen Burden Predicts Immune Checkpoint Blockade Benefit in Tumors with Low T Cell Exhaustion Signature and Low Neoantigen Burden
This technology employs a TAB algorithm that quantitatively assesses both known and putative tumor-associated antigens and integrates this measure with T cell exhaustion signatures. It leverages clinical data from studies in urothelial and head and neck cancers to determine which patients may benefit from immune checkpoint blockade therapy, even when traditional biomarkers such as tumor mutation burden and PD-L1 levels are low. By capturing the dynamic interplay between antigen burden and T cell activity, the algorithm highlights patients who retain an active T cell repertoire despite reduced CD8 T cell exhaustion.
Description
What differentiates this system is its challenge to the conventional biomarker paradigm. Instead of relying solely on established markers like TMB and PD-L1, it provides a nuanced, data-driven approach for patient selection, showing significant predictive value in tumors with low conventional biomarkers. This innovative integration of antigen quantification with immune status metrics not only broadens the eligible patient population for immunotherapy but also promises a more tailored treatment strategy by identifying individuals overlooked by traditional screening methods.Applications
- Immunotherapy companion diagnostics- ICB patient stratification tool
- Personalized treatment selection
- Cancer biomarker development
Advantages
- Identifies immunotherapy candidates who have low traditional biomarkers but may still benefit from treatment.- Expands the eligible patient population for immune checkpoint blockade by leveraging tumor associated antigen burden.
- Enhances prediction accuracy by integrating T cell exhaustion signatures with TAB measurements.
- Challenges and improves upon current clinical paradigms based on TMB and PD-L1, offering a data-driven patient selection strategy.