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

Despite the significant progress in cancer genomics in America, there is still a noteworthy gap regarding genomic markers that predict drug sensitivity which presents a major obstacle to personalized oncology care. Tumors This research project aims to identify a set of genetic variations or mutations that influence the individual response of a cancer patient to certain drugs. This study also aims to develop machine learning models that can analyze a patient's genomic data to predict their likely response to different therapies. This study utilized the Genomic of Drug Sensitivity in Cancer (GDSC). The GDSC dataset is a very valued resource in therapeutic biomarker discovery in cancer research. This dataset combined drug response data with genomic profiles of cancer cell lines, enabling investigations into the relationship between genetic features and drug sensitivity. The main task associated with this dataset was to predict drug sensitivity, measured as IC50 values, from genomic features of cancer cell lines. Several accredited and proven Machine Learning algorithms were utilized in the study, particularly, Linear Regression, Ridge Regression, and SGD Regression. The most important regression model evaluation metrics deployed in a drug sensitivity prediction included the Mean Squared Error- MSE, Root Mean Squared Error- RMSE, and Mean Absolute Error- MAE. The Ridge Regression model outperformed the Linear Regression and the SGD algorithm, particularly, the Ridge Regression model captured excellently the hidden trends in the data much better compared to the other two models. Predictive analytics can significantly enhance clinical decision-making in the USA by providing health professionals with data-driven insights into the best available treatment options. As patient complexity and treatment options continue to grow, such models will help clinicians choose the most appropriate interventions for individual patients, informed by historical data on their disease course and other individual patient factors, including genetic profiling and comorbid status.