Advancing Cancer Care: Precision Medicine and Machine Learning for Patient Stratification

Abstract

Precision medicine holds promise for revolutionizing cancer care by tailoring treatments to individual patients based on their unique genetic and molecular profiles. However, realizing this potential requires effective patient stratification to identify subgroups that may benefit from specific therapies. Machine learning techniques offer a powerful approach for classifying cancer subtypes and predicting treatment responses. In this study, we explore the integration of precision medicine principles and machine learning algorithms for patient stratification in cancer care. We present a comprehensive review of recent advancements in this field, highlighting the role of genomic, transcriptomic, and proteomic data in characterizing cancer heterogeneity. Furthermore, we discuss various machine learning models, including supervised and unsupervised approaches, utilized for cancer subtype classification and patient stratification. Additionally, we examine the challenges associated with integrating multi-omics data and implementing machine learning algorithms in clinical practice, such as data heterogeneity, model interpretability, and scalability. Despite these challenges, the synergistic combination of precision medicine and machine learning holds great potential for improving patient outcomes in cancer care. By identifying molecularly distinct subtypes and predicting individual treatment responses, this integrated approach can facilitate the development of personalized treatment strategies and enhance therapeutic efficacy.