Neoantigens are extremely small biomarkers forming from cancer mutations that demonstrate the presence of cancerous cells. Scientists say that expanding our knowledge of neoantigens could improve the way we develop anticancer treatments, unlocking more efficient immunotherapies with fewer side effects. New research from the Parker Institute for Cancer Immunotherapy (PICI) and the Cancer Research Institute encompasses an initiative named the Tumor Neoantigen Selection Alliance (TESLA) that aims to promote interdisciplinary work in this field.
The study, published in the journal Cell, highlights the exciting developments in personalized precision medicine. The TESLA initiative team says their findings demonstrate improved insights on how neoantigens present on the cancer cell and how they are recognized by the immune system.
"Until now, neoantigen prediction has been a black box. We had hints at what features might be important. The data model out of TESLA is the first to identify these five features as significant," said co-senior author on the paper and professor Robert D. Schreiber, Ph.D., at Washington University School of Medicine in St. Louis.
The data model from the initiative was developed through an advanced computational analysis that looked at which particular neoantigens were most likely to generate an immune response. The model proved capable of accurately predicting 75% of effective neoantigen targets and eliminating 98% of ineffective ones.
"Our aim is that data produced from TESLA becomes the reference standard when developing a new neoantigen-based treatment," said corresponding author Daniel Wells, Ph.D., principal data scientist at PICI. "If every method, old and new, used the data to benchmark their predictions, the whole field would be able to collaborate and iterate on new methods much more quickly."
TESLA has made the model and accompanying dataset free and open access to the research community with the intention of facilitating further investigation into personalized therapy development.
"This research has the potential to improve drug makers' and researchers' mathematical algorithms. It can prioritize antigens most likely to be present on each patient's cancer and most visible to the immune system while deprioritizing the ones that aren't. That means better individualized treatments for patients," said Lisa Butterfield, Ph.D., vice president of research and development at PICI. "We're excited to see where the field takes these findings."