Season 3, Episode 2: What boosts immune boosters? with Kevin Litchfield
First Author: Kevin Litchfield
Episode Summary: Novel drugs that boost the immune system to fight cancer have become pharma darlings in the few short years since their approval. These drugs, known as immunotherapies, have so far focused on improving T cell responses and can be used to cure a multitude of different cancer types. Yet more often than not, immunotherapies have no effect on a patient, leaving doctors guessing on whether to prescribe the drug. To find the reason why some people respond while others don’t, Kevin and his team create a huge database of sequences derived from immunotherapy-treated patients. With it, he discovers biomarkers, mutational signatures, and immune profiles that correlate to response with the hopes that one day, these measurements form a diagnostic to ensure we treat the right patients.
About the Author
Kevin is a group leader at University College London and performed this work in the lab of Charles Swanton at the Francis Crick Institute. Dr. Swanton and his group are experts in studying the genome instability and evolution of cancer.
Kevin started his career as a mathematician but was always driven to apply his skills to improving medicine.
Key Takeaways
Immunotherapies aim to cure cancer by “taking the breaks off” your immune system, supercharging it to attack tumors.
Two immunotherapies known as checkpoint inhibitors (CPI), anti-CTLA-4 and anti-PD-1, work by enhancing T cells and have recently become blockbuster drugs for the treatment of multiple different cancer types.
These immunotherapies don’t work in many patients and medicine has yet to understand why.
Kevin aggregated DNA and RNA sequencing data across multiple studies to generate a dataset that contained over 1,000 CPI treated patients who did and did not benefit from treatment.
With this data, Kevin discovers mutational signatures, biomarkers, and immune profiles that correlate to whether a patient will respond to treatment.
Translation
Kevin finds measurable signatures of a patient’s cancer that could be used to determine whether a patient should receive CPIs.
This retrospective analysis will need to be validated as a prospective study to determine whether Kevin’s findings actually predict response.
More tumor data as well as information about the patient’s genetics is being brought in to improve the accuracy of this prediction.
Collaborations between academics, medical centers, non-profits, and industry partners will enable the findings to make an impact on patient outcomes.
Paper: Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition
