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Does artificial intelligence need companionship to assist in drug discovery? The Kirsten rat sarcoma virus study
Mourad Stitou1, John M Koomen2, Denis J Imbody3
1Department of Machine Learning, Moffitt Cancer Center, Tampa, FL 33612, United States.
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.
Insights
Artificial intelligence (AI) can help target and understand resistance to cancer therapies, particularly for Kirsten rat sarcoma virus guanosine triphosphate hydrolases (KRAS) mutations. Understanding KRAS is key to improving treatment strategies for various cancers.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Kirsten rat sarcoma virus guanosine triphosphate hydrolases (KRAS) is the most frequently mutated oncoprotein family in human cancers.
- Aberrant KRAS activation drives tumorigenesis, aggressive disease, and poor prognosis in cancers like lung, colorectal, and pancreatic cancer.
- KRAS mutations significantly influence cancer cell behavior and treatment response.
Purpose of the Study:
- To explore the application of artificial intelligence (AI) in understanding and overcoming resistance to targeted therapies.
- To use Kirsten RAS (KRAS) mutations as a model for investigating AI's role in cancer treatment resistance.
- To highlight the clinical significance of KRAS mutations in determining treatment sensitivities.
Main Methods:
- Review of preclinical and clinical studies on KRAS mutations and treatment resistance.
- Discussion of the potential of AI in analyzing complex cancer genomic data.
- Integration of knowledge regarding KRAS over-expression and radiotherapy resistance.
Main Results:
- KRAS mutations are established drivers in multiple cancer types, impacting treatment outcomes.
- Tumors with KRAS mutations exhibit distinct sensitivities to therapies compared to wild-type KRAS tumors.
- Over-expression of KRAS family members is linked to resistance to radiotherapy.
Conclusions:
- AI offers promising avenues for deciphering resistance mechanisms in targeted cancer therapy.
- Targeting KRAS mutations and understanding resistance are critical for advancing cancer treatment.
- Further research integrating AI can optimize therapeutic strategies for KRAS-mutated cancers.
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