Personalized targeted cancer therapies represent a transformative approach in oncology, leveraging individual tumors’ unique genetic and molecular characteristics to tailor treatment strategies.  Rapid advancements in genomic technologies, such as microarray and sequencing, facilitate this approach, identifying specific biomarkers associated with various cancers. Biomarkers are crucial in developing and implementing personalized therapies, indicating biological processes, disease states, or responses to therapeutic interventions. The complexity of molecular alterations in tumors is a critical factor influencing the prognosis and treatment response in cancer patients. Integrating multi-omics approaches, such as genomics, transcriptomics, proteomics, and metabolomics, is invaluable in personalized medicine, leading to identifying novel biomarkers that can be targeted therapeutically. However, challenges remain in validating and implementing biomarkers in clinical practice, such as the heterogeneity of tumors, the dynamic nature of cancer evolution, and the potential for resistance mechanisms.

Personalized targeted cancer therapies represent a significant advancement in oncology, focusing on tailoring treatment strategies based on individual patient characteristics, mainly genetic and molecular profiles. This approach is grounded in the understanding that cancer is not a uniform disease but a collection of heterogeneous conditions that can vary significantly from one patient to another. The emergence of personalized medicine has been facilitated by rapid advancements in genomic technologies, including microarray and sequencing technologies, which have identified specific biomarkers associated with various cancers [1,2]. Biomarkers play a crucial role in developing and implementing personalized, targeted therapies. They indicate biological processes, disease states, or responses to therapeutic interventions. Identifying actionable biomarkers allows clinicians to predict which patients are most likely to benefit from specific treatments, thereby enhancing the efficacy of therapeutic regimens while minimizing unnecessary side effects [3,4]. For instance, in lung cancer, mutations in the Epidermal Growth Factor Receptor (EGFR) gene can guide targeted therapies such as tyrosine kinase inhibitors, which have shown improved outcomes compared to traditional chemotherapy [5,6]. The complexity of molecular alterations in tumors is a critical factor influencing the prognosis and treatment response in cancer patients. Studies have demonstrated that the presence of specific genetic mutations or changes can significantly impact the effectiveness of targeted therapies. For example, in pancreatic cancer, the identification of gene mutations such as KRAS and TP53 has been linked to poor prognosis and resistance to standard treatments [3]. This highlights the necessity of comprehensive molecular profiling to inform treatment decisions and optimize patient outcomes.

Furthermore, integrating multi-omics approaches, encompassing genomics, transcriptomics, proteomics, and metabolomics, is invaluable in personalized medicine. These methodologies facilitate a more comprehensive understanding of the tumor microenvironment and the molecular underpinnings of cancer, ultimately leading to the identification of novel biomarkers that can be targeted therapeutically [7,8]. For instance, circulating tumor DNA (ctDNA) as a biomarker has emerged as a promising strategy for monitoring treatment response and detecting minimal residual disease in various cancers, including breast and colorectal [9]. The clinical application of personalized, targeted therapies is exemplified by initiatives such as the National Cancer Institute's Molecular Analysis for Therapy Choice (NCI-MATCH) trial, which utilizes genomic sequencing to match patients with appropriate targeted therapies based on their tumor's molecular profile. This trial has demonstrated that many patients with actionable mutations can benefit from personalized treatment strategies, underscoring the potential of biomarker-driven approaches in improving clinical outcomes [6,10].

Developing novel therapeutic agents, including monoclonal antibodies and immune checkpoint inhibitors, has expanded oncologists’ arsenal of targeted therapies [11,12]. Despite the promising advancements in personalized, targeted therapies, challenges remain in validating and implementing biomarkers in clinical practice. The heterogeneity of tumors, the dynamic nature of cancer evolution, and the potential for resistance mechanisms complicate the landscape of biomarker development [13,14]. For instance, while specific biomarkers may predict initial treatment responses, they may not account for subsequent resistance, necessitating ongoing research to identify additional predictive markers [15]. Moreover, integrating biomarkers into routine clinical practice requires robust clinical trial data to establish their efficacy and safety in diverse patient populations [16].