- Pre-treatment blood-based factors predict response to neoantigen + anti-PD-1 combination
- TCR repertoire clonality and stability correlate with improved clinical outcomes
- Baseline T and B cell memory phenotypes associate with improved clinical outcomes
- Combined baseline TCR repertoire and PBMC phenotypes predict immunotherapy response
Personalized neoantigen vaccines are designed based on patient and tumor-specific mutations that aim to drive effective anti-tumor immunity. This treatment modality has been combined with checkpoint inhibitors in the clinic. Unfortunately, as with many current immunotherapies, not all patients respond to this combined treatment. Identifying patients most likely to respond positively to treatment during clinical trial enrollment may assist with patient stratification and medical decision-making.
In this webinar, the BioNTech US team presents data on their study of peripheral blood cells from metastatic melanoma patients before, during, and after treatment with personalized neoantigen therapy plus anti-PD-1. By combining T cell receptor repertoire profiling and immunophenotyping, the team found that analyzing blood cells collected pre-treatment presents a strong predictor for response to treatment in a minimally invasive manner.
Kristen N. Balogh, PhD
Senior Scientist & Team Leader, Translational Immunology
I am a Senior Scientist and the Team Leader of the Translational Immunology team at BioNTech US. My work centers around characterizing the immune responses generated to various immunotherapeutic treatments in the clinical pipeline at BioNTech. I am also very interested in identifying biomarkers that predict or correlate with response to immunotherapeutic treatment modalities that can help improve patient care.
Asaf Poran, PhD
Group Leader, Systems Immunology
I am the Group Leader of the Systems Immunology team at BioNTech US. In my role, I work closely with our translational scientists to devise novel approaches for the evaluation of patient immune responses to immunotherapy. I assist in the design and analysis of data derived from experiments using cutting-edge technologies. I am passionate about harnessing integrative approaches to our multi-modal patient data to evaluate and improve treatment decisions for our patients.