Does artificial intelligence need companionship to assist in drug discovery? The Kirsten rat sarcoma virus study

Author:

Stitou Mourad1,Koomen John M2,Imbody Denis J3,Liao Yi4,Monastyrskyi Andrii4,Rix Uwe4,Duckett Derek R4,Haura Eric B3,Karolak Aleksandra1ORCID

Affiliation:

1. Department of Machine Learning, Moffitt Cancer Center , Tampa, FL 33612, United States

2. Department of Molecular Oncology, Moffitt Cancer Center , Tampa, FL 33612, United States

3. Department of Thoracic Oncology, Moffitt Cancer Center , Tampa, FL 33612, United States

4. Department of Drug Discovery, Moffitt Cancer Center , Tampa, FL 33612, United States

Abstract

Abstract In this Opinion article, we confront the role of artificial intelligence (AI) in targeting and understanding resistance to targeted therapy using the most frequently mutated oncoprotein family in human cancer, rat sarcoma virus guanosine triphosphate hydrolases (RAS GTPases), here Kirsten RAS (KRAS), as an example. Aberrant regulation of the active GTP-bound state of KRAS is associated with tumourigenesis, aggressive disease, and poor prognosis. KRAS mutations (eg, G12C, G12D, G12V, G13D, inter al.) are drivers of numerous cancer types, including non-small cell lung, colorectal, and pancreatic cancers. These mutations have shown to play a significant role in cell behaviour and response to treatment. Since its discovery in the 1980s, it has been recognized that over-expression of KRAS and other RAS family members induces resistance to radiotherapy. Moreover, over the years preclinical and clinical studies showed that tumours with KRAS mutations exhibit different treatment sensitivities compared to tumours with wild-type KRAS.

Publisher

Oxford University Press (OUP)

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