CANCER DRUG SENSITIVITY THROUGH GENOMIC DATA: INTEGRATING INSIGHTS FOR PERSONALIZED MEDICINE IN THE USA HEALTHCARE SYSTEM

Аннотация

INTRODUCTION Background Bhomik et al. (2024), asserted that while these promises have been made, the prediction of drug Fawaz et al. (2023), reported that personalized sensitivity in cancer remains a formidable medicine in oncology portrays a paradigm shift challenge. Tumors are highly heterogeneous, not from a one-size-fits-all dimension to one that only between different patients but also within the considers personal genetic variability in drug same patient, both intertumoral and intratumoral response in the US. Recent advances in next- heterogeneity. The drug response is further generation sequencing and bioinformatics have complicated because of the complex interplay been the gateway to understanding genetic among genomic alterations, tumor mutations, copy number variations, and microenvironment, and immune system. Many expression changes that determine tumor current predictive models have small datasets, behavior and therapeutic response. Alam et al. capture less real-world complexity, and lack (2023), indicated that precision medicine offers generalizability across diverse populations. enhanced efficacy, reduced toxicity, and improved Moreover, the US healthcare system faces the survival rates by tailoring treatment plans to the additional challenge of integrating these genomic unique molecular profile of the patient. In the USA, insights into routine clinical practice in the where cancer is one of the leading causes of presence of disparities in access and affordability morbidity and mortality, such advances could go a (Dutta et al., 2024). long way in easing the burden on the healthcare system. Genomic data have illuminated the key pathways https://www.theamericanjournals.com/index.php/tajmspr 37 THE USA JOURNALS THE AMERICAN JOURNAL OF MEDICAL SCIENCES AND PHARMACEUTICAL RESEARCH (ISSN – 2689-1026) VOLUME 06 ISSUE12 to drug sensitivity and resistance. For instance, in insights fully into clinical practice remains the treatment of NSCLC, mutations in the EGFR incompletely achieved (Dutta et al., 2023). gene predict the response to tyrosine kinase Overcoming such gaps will be pivotal toward the inhibitors, while BRCA1/2 mutations confer optimization of treatment outcomes as well as sensitivity to PARP inhibitors in breast and ovarian furthering science regarding the field of precision cancers (Chafai et al. 2024). These markers will oncology. guide drug selection and point to mechanisms of Research Questions resistance so that second-line therapies may be RQ1: What genomic markers are associated with devised. Bortty et al. (2023), argued that with the drug sensitivity in cancer? ever-increasing use of whole-genome sequencing of tumors, transcriptomic and even epigenetic This research question aims to identify a set of data, these advances certainly fuel the cataloging of genetic variations or mutations that influence the even more actionable biomarkers that impress individual response of a cancer patient to certain centrality on solitary genomic material data in the drugs. In doing so, the research question will precision of oncology. provide some significant predictions regarding who would benefit from a specific treatment and Problem Statement who would have undesirable side effects. Hossain et al. (2023), posited that despite the RQ2: How can predictive models be developed to considerable progress in cancer genomics in the guide personalized treatment plans? USA, there is still a noteworthy gap regarding genomic markers that predict drug sensitivity To develop machine learning models that can which presents a major obstacle to personalized analyze a patient's genomic data to predict their oncology care. Tumors are heterogeneous both at likely response to different therapies. These genetic and phenotypic levels; this gives rise to models could help clinicians select the most their different responses to the same therapeutic effective treatment options for each patient, agents among patients. Islam et al. (2023), stated optimizing their chances of successful treatment. that although some biomarkers, like EGFR RQ3: How can integrating genomic data improve mutations in lung cancer and BRCA mutations in patient outcomes in oncology? breast and ovarian cancers, have been highly The objective of this research question is to get the useful for target therapy, the genomic landscape general ramifications of the use of genomic that dictates drug sensitivity is much more information in clinical decision-making in complex and is still poorly characterized in many oncology. This may be based on a review of the malignancies. Moreover, current research often clinical utility of genomic testing, a review of the covers common cancers and lacks comprehensive cost-effectiveness of personalized medicine, representation of rare cancers and diverse patient and/or a review of the potential barriers to the populations, including ethnic minorities, which widespread adoption of genomic-based could limit generalization. Eve