Krittika Aggarwal, Priya Jindal, Akash Vikal, Preeti Patel, Balak Das Kurmi
Cancer genetics plays a revolutionary role in the field of oncology by providing novel insights into tumor evolution, heterogeneity, and treatment targets. Recent advances in sequencing technology, such as whole-genome and transcriptome sequencing, have enabled the precise molecular profiling of cancers, leading to the development of targeted and individualized treatment approaches. Liquid biopsy has emerged as a non-invasive approach for collecting samples, utilizing blood or other body fluids to identify tumor cells, molecular changes, and metabolites. Liquid biopsy helps to detect various biological markers, including ctDNA, CTCs, and exosomes. Immunogenomics has revolutionized cancer management through the use of immune checkpoint inhibitors and personalized neoantigen vaccines. Furthermore, the integration of machine learning facilitates the examination of extensive genomic datasets, enabling the detection of patterns, the prediction of treatment responses, and the identification of novel therapeutic targets. Innovative drug delivery technologies, such as nanoparticle-based and CRISPR-mediated genome editing, provide promise for more effective and less harmful treatments. This review discusses recent advancements in cancer genomics, including liquid biopsy, tumor heterogeneity, immunogenomics, and the function of machine learning in the analysis of genomic data. By understanding ethical and social issues, successful implementation can be possible. The combination of genomics, immunogenomics, and liquid biopsy holds promise for novel, personalized cancer treatments, indicating a new era in cancer drug development.