Artificial intelligence (AI) is a scientific field that focuses on developing algorithms and machines to conduct high-level functions. AI takes various applications, such as natural language processing, speech recognition, and machine vision, to simulate human intelligence. These processes run by computers are then used to analyze and organize mass amounts of data. The rapid development of AI allows for easy integration in various aspects of daily life. For example, the most recent form of AI available to individuals includes ChatGPT, which can understand and generate responses to various prompts, including writing papers. One significant advancement to the field of science and medicine is the application of AI in personalized medicine. AI has transformed the way healthcare is provided, including the improvement of diagnostics, data analysis, and personalized medicine. It also has the potential to more accurately detect cancer risks in patients, thereby enabling early diagnosis and treatment planning. Another application that has recently been published includes using AI to generate personalized cancer vaccines.
A recent article in Nature Biotechnology, by Dr. Michal Bassani-Sternberg and others developed an AI pipeline that considers multiple factors of each patient to generate personalized cancer vaccines. The algorithm inputs molecular analyses of tumors and specific targets of immune cells to create a vaccine based on their tumor and immune markers. Bassani-Sternberg is an Assistant Member of the Ludwig Institute for Cancer Research. Her work has helped pioneer the field of ‘immunopeptidomics,’ which uses advanced mass spectrometry and computational analysis to predict which patients will respond better to specific therapies. Bassani-Sternberg’s lab uses computational analyses to design, validate, and compare patient immunotherapeutic outcomes.
The AI-generated pipeline, NeoDisc, developed by Bassani-Sternberg and others, helps detect different immunological mechanisms within the tumor microenvironment. More specifically, it provides insight into how the tumor evades the immune system. This pipeline is invaluable and can improve personalized medicine by detecting biomarkers specific to a patient’s tumor. Many different cancers contain mutational burdens that can correlate to tumor aggression. However, not everyone has the same mutations. Additionally, proteins expressed by tumors on their surface or released into the nearby environment can be used as targets for immunotherapy, including cancer vaccines.
The diversity of these protein targets is a significant reason not every immunotherapy is as successful in targeting the tumor. Scientists can take the dominant or heavily expressed targets and make personalized cancer vaccines. In this case, most of the protein markers will be susceptible to treatment, and the tumor will shrink. However, the increased diversity of protein markers makes it difficult to find the best marker to target. NeoDisc can improve the development of effective vaccines by calculating which marker will elicit the strongest immune response.
NeoDisc is a pioneering tool that allows for sophisticated analyses of large-scale data. It informs scientists about which markers should be targeted in each patient, a feature previously unseen in computational tumor marker analysis. With NeoDisc, different subtypes of markers are detected and predicted to elicit the best response to treat cancer patients. This unique capability of NeoDisc is set to significantly improve the development of effective vaccines, thereby enhancing patient outcomes and transforming the field of medicine.
Article, Nature Biotechnology, Michal Bassani-Sternberg, Ludwig Institute for Cancer Research
