Refining mutanome-based individualised immunotherapy of melanoma using artificial intelligence

Autor: Farida Zakariya, Fatma K. Salem, Abdulwhhab Abu Alamrain, Vivek Sanker, Zainab G. Abdelazeem, Mohamed Hosameldin, Joecelyn Kirani Tan, Rachel Howard, Helen Huang, Wireko Andrew Awuah
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: European Journal of Medical Research, Vol 29, Iss 1, Pp 1-17 (2024)
Druh dokumentu: article
ISSN: 2047-783X
34232095
DOI: 10.1186/s40001-023-01625-2
Popis: Abstract Using the particular nature of melanoma mutanomes to develop medicines that activate the immune system against specific mutations is a game changer in immunotherapy individualisation. It offers a viable solution to the recent rise in resistance to accessible immunotherapy alternatives, with some patients demonstrating innate resistance to these drugs despite past sensitisation to these agents. However, various obstacles stand in the way of this method, most notably the practicality of sequencing each patient's mutanome, selecting immunotherapy targets, and manufacturing specific medications on a large scale. With the robustness and advancement in research techniques, artificial intelligence (AI) is a potential tool that can help refine the mutanome-based immunotherapy for melanoma. Mutanome-based techniques are being employed in the development of immune-stimulating vaccines, improving current options such as adoptive cell treatment, and simplifying immunotherapy responses. Although the use of AI in these approaches is limited by data paucity, cost implications, flaws in AI inference capabilities, and the incapacity of AI to apply data to a broad population, its potential for improving immunotherapy is limitless. Thus, in-depth research on how AI might help the individualisation of immunotherapy utilising knowledge of mutanomes is critical, and this should be at the forefront of melanoma management.
Databáze: Directory of Open Access Journals
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