$40M UNC Lineberger center grant to support cancer vaccine development
Key Takeaways
- Complex disease: Cancer is the 2nd-leading cause of death in the U.S.
- 200 types, plus mutations: Personalized cancer-treatment vaccines have been difficult to develop
- Developing a vaccine: UNC’s Lineberger Cancer Center has received a $40 million grant to support vaccine development using AI and machine learning
Biotech and pharma companies are able to create and manufacture large supplies of effective vaccines to prevent common illnesses such as influenza and COVID. However, progress on developing a vaccine to treat cancer — the 2nd-leading cause of death in the U.S., after cardiovascular disease — has lagged, due in large part to cancer’s complexity: 200 different diseases, with unique and varied mutations within a specific tumor that may or may not offer targets for a vaccine to attack.
Cancer-vaccine research progress took a big step forward in August when Merck and Moderna, both with a North Carolina presence, announced successful Phase 3 data in melanoma, a skin cancer. “Personalized mRNA cancer vaccines just cleared their biggest scientific hurdle,” Drug Development News declared. “But the possible implications of the findings are only as promising as the industry's ability to materialize them,” the article added.
With a new $40 million grant from the OpenAI Foundation, researchers at the UNC Lineberger Comprehensive Cancer Center will contribute to that effort by gathering data to train artificial intelligence models to identify stronger cancer-cell targets and help develop more precise and effective vaccines. The foundation is the non-profit parent organization that governs the for-profit company OpenAI Group PBC.
UNC immunologist Benjamin Vincent, M.D., and computational biologist Alex Rubinsteyn, Ph.D., lead the research team that will use AI to speed development of personalized cancer vaccines and share findings with cancer researchers and oncologists worldwide.
“Personalized cancer vaccines are finally starting to show signs of clinical efficacy, but many fail in the development process because they are too arbitrary,” said Rubinsteyn, an expert in machine learning and computational medicine at the UNC School of Medicine. “Our goal is to help the world design personalized and more effective cancer vaccines, using the power of rich data and artificial intelligence.”
Personalized cancer vaccines are finally starting to show signs of clinical efficacy, but many fail in the development process because they are too arbitrary. Our goal is to help the world design personalized and more effective cancer vaccines, using the power of rich data and artificial intelligence.
— Alex Rubinsteyn, Ph.D., UNC Lineberger Comprehensive Cancer Center
’The announcement comes as the OpenAI Foundation launches its second science program, Public Data for Health. The initiative supports high-quality, open-access scientific datasets that researchers and AI tools worldwide can use to advance life science and disease research for the public good.
“AI has enormous potential to help researchers design better cancer treatments, but that progress depends on having the right biological data to learn from,” said Jacob Trefethen, head of life sciences and curing diseases at the OpenAI Foundation.
“UNC’s work will generate data that researchers don’t have at the scale they need today and make those findings broadly available so scientists around the world can build on them. By giving the broader scientific community a stronger foundation to work from, we hope to open up new possibilities for cancer research and ultimately help more patients benefit from advances that once felt out of reach.”
Cancer vaccines are designed using tumor cells and immune cells derived from a patient. Proteins on the surface of tumors are of particular interest to researchers. Vaccines can be used to train the immune response to recognize these proteins and create a highly coordinated attack on cancer cells.
But performing such a task for any given cancer patient — each with their own specific cancer type and immune responses — requires complex scientific machinery, time, and, of course, data analysis. Vincent and Rubinsteyn aim to expedite this process by analyzing hundreds of de-identified tumor tissue and immune cells from three biobanks.
“Selecting tumor antigens that are actually good targets, and understanding the relative potency of vaccine formulations for personalized therapy, are big problems in the field which our work will address,” said Vincent, who is a member of the immunology and immunotherapy research program at UNC Lineberger Comprehensive Cancer Center.
If successful, the research could serve as the basis for future vaccine candidates and speed up the rate at which personalized cancer vaccines can get into the hands of oncologists. The global need is urgent: Statistics from the World Health Organization project between 2025 and 2050, the cancer incidence rate among men and women will grow from a total of 21 million to an estimated 35 million people.