Next-generation sequencing technology in cancer
DOI:
https://doi.org/10.22376/ijtos.2024.2.3.23-31Keywords:
Next-generation sequencing (NGS),, Cancer genomics, Personalized treatment, Bioinformatic analysis, Solid cancer diagnosis, Targeted therapiesAbstract
Next-generation sequencing (NGS) technology has revolutionized cancer research and treatment by enabling comprehensive analysis of genetic mutations, alterations, and expression profiles. It allows for the identification of cancer-driving mutations, helping in the development of targeted therapies. NGS provides detailed insights into tumor heterogeneity, resistance mechanisms, and clonal evolution. Its high-throughput capacity facilitates large-scale studies, improving our understanding of cancer genomics. By enabling personalized treatment plans based on individual genetic profiles, NGS holds promise for more effective and tailored cancer therapies. Early reviews on cancer genomics often lacked comprehensive coverage of emerging technologies. They missed in-depth analysis of NGS advancements, their impact on cancer research, and clinical applications. The review addresses this gap by reviving a thorough examination of NGS methods, their role in identifying genetic mutations, and their potential in personalized cancer treatment, thus providing essential insights into the evolving landscape of cancer genomics. The article covers the advancements in technology and bioinformatic approaches for NGS data analysis. It delves into NGS applications in research and diagnostics, particularly for solid cancer diagnosis. The review highlights specific cancer types, including hereditary breast cancer, melanoma, prostate cancer, thyroid cancer, lung cancer, and colorectal cancer. It explores NGS contribution in understanding the genetic basis of these cancers and its potential for enhancing personalized diagnosis and treatment strategies. This review rectifies early lacunas by providing a comprehensive and updated examination of NGS technology, addressing gaps in previous analyses and emphasizes bioinformatic approaches for NGS data analysis, crucial for interpreting vast genomic data accurately. The review meets the current need for a thorough understanding of NGS’s role in personalized cancer treatment and research.
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