How artificial intelligence / artificial intelligence will contribute to cancer patient care and vaccine design

Newswise – New Brunswick, NJ, December 7, 2021 – Artificial Intelligence / Machine Learning (AI / ML) is the development of computer systems capable of performing tasks that would normally require human intelligence. AI / ML is used by people every day, for example, when using smart home devices or digital voice assistants. The use of AI / ML is also growing rapidly in biomedical research and healthcare. In a recent Viewpoint article, researchers at the Rutgers Cancer Institute of New Jersey and Rutgers New Jersey Medical School (NJMS) explored how AI / ML will complement existing approaches focused on genome sequence information. protein, including the identification of mutations in human tumors.

Stephen K. Burley, MD, Doctor of Philosophy, Co-Head of the Cancer Pharmacology Research Program at the Rutgers Cancer Institute, and University Professor and President Henry Rutgers and Director of the Institute for Quantitative Biomedicine at Rutgers University, with Renata Pasqualini, PhD, Resident Fellow of the Rutgers Cancer Institute and Head of the Division of Cancer Biology, Department of Radiation Oncology at Rutgers NJMS, and Wadih Arap, MD, PhD, Director of the Rutgers Cancer Institute at University Hospital, Co-Head of the Clinical Investigations and Precision Therapeutics Research Program at the Rutgers Cancer Institute and Head of the Hematology / Oncology Division of the Rutgers NJMS Department of Medicine, share more information on the article, published online on December 2 in The New England Journal of Medicine (DOI: 10.1056 / NEJMcibr2113027).

What is the potential of AI / MI in cancer research and clinical practice?

We anticipate that the most immediate applications of computed structure modeling will focus on point mutations detected in human tumors (germline or somatic). Calculated structural models of frequently mutated oncoproteins (eg, epidermal growth factor receptor, EGFR, shown in Figure 2B of the article) are already being used to help identify genes responsible for cancer, enabling the discovery treatment, explain drug resistance and inform treatment plans.

What are the biggest challenges for AI / ML in healthcare?

In broad terms, the core challenges would likely include AI / ML research and development, technology validation, efficient / equitable deployment and cohesive integration into existing health systems, and issues inherent in the regulatory environment as well as the complex problems of reimbursement of medical care.

How will this technology impact vaccine design, especially with regards to SARS CoV2?

Going beyond knowledge of 3D structure through entire proteomes (parts lists for biology and biomedicine), precise computer modeling will allow analyzes of clinically significant genetic changes manifested in 3D by individual proteins. For example, the SARS-CoV-2 Delta Variant of Concern spike protein carries 13 amino changes. Experimentally determined 3D structures of advanced SARS-CoV-2 protein variants bound to various antibodies, all freely available from the Protein Data Bank (, can be used with calculated structural models from new cutting-edge Variant of Concern proteins to understand the potential impact of other amino acid changes. In currently ongoing work (not yet published), we used AI / ML approaches to understand the structure-function relationship of the SARS-CoV-2 spike protein Omicron Variant of Concern (with over 30 acid changes amino), illustrating the practical and immediate application of this emerging technology.

What’s the next step to better use AI / ML in cancer research?

Development and equitable dissemination of user-friendly tools that cancer biologists can use to understand the proteins of three-dimensional structures involved in human cancers and how somatic mutations affect structure and function leading to uncontrolled proliferation of tumor cells.


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