INTRODUCTION

Cancer is a genomic disorder where inherited germline aberrations or acquired epigenetic changes contribute to the persistence and proliferation of cancerous cells. According to the WHO, cancer is characterized by the rapid formation of abnormal cells that expand beyond their normal limits, potentially invading nearby tissues and spreading to other organs [1]. As per the Indian Council of Medical Research-National Cancer Registry Programme [2], the estimated number of cancer cases in India in the years 2020 and 2022 are shown in Fig. 1, and the estimated number of mortality cases due to cancer in India (2022) as per WHO is depicted in Fig. 2. Advancements in molecular diagnostic tests and the development of effective therapies have improved the identification and targeting of genomic abnormalities, including small insertions and deletions, single-nucleotide variants, and structural modifications. Complex biomarkers, such as Microsatellite Instability (MSI), homologous recombination deficiency, and Tumor Mutational Burden (TMB), play a crucial role in cancer diagnosis and treatment. With the growing number of clinically actionable and pan-cancer biomarkers, along with the increasing accessibility of sequencing technology, comprehensive molecular approaches such as Whole-Transcriptome Sequencing (WTS), collectively known as Whole-Genome Transcriptome Sequencing (WGTS), and Whole-Genome Sequencing (WGS), are being progressively incorporated into cancer diagnostics. Recent studies further validate the adoption of WGS and WGTS, supporting their potential to become a standard in routine oncology care [3-5]. The identification of DNA as the molecular basis of inheritance and the elucidation of its structure strengthened the link between genetics and cancer. This connection was further supported by demonstrating that agents causing DNA damage and mutations also induce cancer. This historical context underscores the crucial role of the genome in cancer development [6-8]. These discoveries have facilitated the development of highly specific and effective therapies, making certain hematologic malignancies manageable and curable. Despite the initial limitations of sequencing technology, efforts to identify cancer-specific mutations began with the use of high-throughput Polymerase Chain Reaction (PCR) amplifications, followed by sequencing of well-established cancer genes [9, 10]. Nowadays, pharmaceutical companies are conducting various clinical trials of drugs, targeting specific proteins thought to drive oncogenesis, to significantly reduce tumor burden in some advanced metastatic patients (Table 1). By identifying specific genetic alterations, known as oncogenic drivers, which act as a source for uncontrolled cell growth, researchers have developed targeted therapies for distinct subgroups of Non-Small Cell Lung Cancer (NSCLC) patients [11, 12].

We conducted a comprehensive literature search using electronic databases including PubMed, Scopus, Web of Science, and Google Scholar. The search included publications from January 2012 to June 2025, using keywords such as genomic profiling in cancer, next-generation drug delivery systems, personalized cancer therapy, and nanotechnology in cancer treatment. We have included peer-reviewed articles, systematic reviews, and relevant clinical trials that addressed advancements in genomics and drug delivery specific to cancer care.

However, differential patient responses prompted efforts to correlate specific mutations with treatment response, leading to the identification of mutations associated with response to targeted therapy in non-small cell lung adenocarcinomas. These advancements have significantly improved survival rates for certain patient groups, pushing median survival beyond two years. Many techniques exist to identify alterations in the cancer genome, epigenome, transcriptome, and proteome. Genomic technologies like Whole-Exome Sequencing (WES), WGS, gene panels, hotspot testing, and histopathology reveal distinct features of cancer [19-21]. Transformative sequencing technologies, such as massively parallel sequencing (MPS), have emerged, enabling the unbiased sequencing of tumor and normal genomes and the identification of somatic mutations [22]. These technologies have enabled large-scale identification of somatic alterations across various cancer genomes, providing insights into the diverse mechanisms by which cancer genomes evolve. Support Vector Machine (SVM), Convolutional Neural Network, language model, Transformer-based language model, and large-scale language model are some applications of AI techniques in genetics, which are one of the highly productive research fields [23]. Challenges in solid tissue malignancies, such as the difficulty of obtaining post-treatment biopsies, have limited studies on changes to the cancer genome. Despite these challenges, a fundamental understanding of the tumor genome landscape has been established for common tumor types, and the clinical application of genomics is now a clear next step. Major programs such as The Cancer Atlas (TCGA) and the Cancer Genome Consortium (ICGC) have been pivotal in identifying key mutations that give cancer cells a growth advantage and could serve as potential therapeutic targets [24, 25]. This review discusses recent advancements in cancer genomics, including tumor heterogeneity, liquid biopsy, immunogenomics, and the role of Machine Learning (ML) in genomic data analysis.