Johns Hopkins engineers develop deep-learning technology that may aid personalized cancer therapy
Source: scitechdaily.com Author: by Johns Hopkins Medicine Cytotoxic CD8+ T-cells recognizing cancer cells by receptor binding neoantigens. Credit: Image generated by DALL-E 2 from OpenAI A team of engineers and cancer researchers from Johns Hopkins has developed a deep-learning technology capable of accurately predicting protein fragments linked to cancer, which might trigger an immune system response. Should this technology prove successful in clinical tests, it could address a significant challenge in the creation of personalized immunotherapies and vaccines. In a study published July 20 in the journal Nature Machine Intelligence, investigators from Johns Hopkins Biomedical Engineering, the Johns Hopkins Institute for Computational Medicine, the Johns Hopkins Kimmel Cancer Center, and the Bloomberg~Kimmel Institute for Cancer Immunotherapy show that their deep-learning method, called BigMHC, can identify protein fragments on cancer cells that elicit a tumor cell-killing immune response, an essential step in understanding response to immunotherapy and in developing personalized cancer therapies. “Cancer immunotherapy is designed to activate a patient’s immune system to destroy cancer cells,” says Rachel Karchin, Ph.D., professor of biomedical engineering, oncology, and computer science, and a core member of the Institute for Computational Medicine. “A critical step in the process is immune system recognition of cancer cells through T cell binding to cancer-specific protein fragments on the cell surface.” The cancer protein fragments that elicit this tumor-killing immune response may originate from changes in the genetic makeup of cancer cells (or mutations), called mutation-associated neoantigens. Each patient’s tumor has a unique set of such neoantigens [...]