Journal of Carcinogenesis & Mutagenesis

The Emerging Role of Artificial Intelligence in Mapping Cancer Mutations and Developing Precision Therapies

Anna Valentine^* Department of Molecular Genetics, The Ohio State University, Columbus, USA
^*Correspondence: Anna Valentine, Department of Molecular Genetics, The Ohio State University, Columbus, USA, Email:

Received: 18-Nov-2024, Manuscript No. JCM-24-27857; Editor assigned: 20-Nov-2024, Pre QC No. JCM-24-27857; Reviewed: 04-Dec-2024, QC No. JCM-24-27857; Revised: 11-Dec-2024, Manuscript No. JCM-24-27857; Published: 19-Dec-2024, DOI: 10.35248/2157-2518.24.S47.001

Description

Cancer remains one of the leading causes of death worldwide, with its complexity and heterogeneity posing significant challenges for diagnosis, treatment and outcomes. Over the years, advances in genomic research have revealed that cancer is driven by a variety of genetic mutations that result in abnormal cell behavior. In recent years, Artificial Intelligence (AI) has emerged as a transformative tool in cancer research, enabling scientists to better understand the genetic underpinnings of cancer and to develop more precise, personalized therapies. This article explores the growing role of AI in mapping cancer mutations and the development of precision therapies [1-3].

The human genome contains billions of base pairs of Deoxy Ribo Nucleic Acid (DNA) and every tumor contains a unique set of mutations. Identifying these mutations is essential for understanding cancer biology and for the development of targeted therapies. Traditionally, the process of identifying cancer-related mutations involved labour-intensive and time consuming methods such as DNA sequencing, which often required large amounts of computational power and expertise to interpret. However, with the rise of AI, researchers can now accelerate the discovery and interpretation of genetic mutations in cancer [4].

One of the most powerful applications of AI in cancer research is its ability to map cancer mutations across different cancer types and patient populations. With the wealth of genomic data now available through projects like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), AI has the potential to reveal new insights into the genetic architecture of cancer [5-7].

AI-powered tools are already being used to identify recurrent mutations in specific genes across different tumor types. For example, AI algorithms can analyse tumor samples from patients with different forms of cancer and highlight mutations that are common among specific cancer types. This allows researchers to pinpoint potential biomarkers for early detection and prognosis, as well as targets for treatment [8-10].

AI also plays an important role in identifying structural variations within the genome that contribute to cancer development. These structural variations, such as gene fusions and amplifications, can be difficult to detect using traditional methods. However, with AI’s ability to process complex genomic data, researchers can now uncover these mutations more efficiently, expanding the scope of precision oncology.

The ultimate goal of understanding cancer mutations is to develop therapies that specifically target the genetic alterations driving the cancer. Traditionally, cancer treatments have been one-size-fits-all, often resulting in significant side effects due to the lack of precision. However, AI is transforming the landscape of drug development by enabling the creation of therapies tailored to an individual’s unique genetic profile.

AI models can be trained on large datasets of clinical and genomic information to predict how different mutations respond to specific treatments. By analysing the relationship between genetic mutations and drug efficacy, AI can help identify which treatments are most likely to be effective for a particular patient. This approach is at the heart of precision medicine.

Conclusion

The integration of AI into cancer research is revolutionizing the way we understand cancer mutations and develop precision therapies. By accelerating the identification of genetic mutations, mapping cancer genomes and predicting treatment responses, AI is poised to play a key role in the future of oncology. As AI continues to evolve, it is likely to bring the further advancements in personalized cancer care, ultimately improving the outcomes for patients and prepare for more effective and less toxic treatments. Which aims to offer customized treatment plans based on the specific genetic makeup of both the patient and the tumor.

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