Computing and AI in Genomics-Driven Precision Oncology: Methods, Trials, and Translation

Abstract

Precision oncology has driven a paradigm shift in cancer care, from histology-guided regimens to genomics-informed, biomarker-driven therapies. This paper reviews major milestones in basic translational research in cancer genomics, advances in clinical trial design, and translational activities led by top Universities. This paper describes the major translational breakthroughs, such as TCGA, CRISPR-enabled functional genomics, and real-time trial-matching platforms (e.g., MatchMiner), and underscores the need for their collective advancement in stratifying patients and personalizing treatments. It also showcases how case studies from major institutions have aided the integration of multi-omics, adaptive clinical trials, and ethical AI approaches in research and clinical practice. Digital Twin models at MIT, GenOMICC in Oxford, and Spatial Omics in Cambridge demonstrate diversified, mutually supportive approaches in the realm of translational precision medicine. Emerging approaches such as Single-Cell Sequencing, Spatial Omics, and AI-enriched clinical trials demonstrate an imminent future marked by learning health platforms. Despite major scientific advances, critical challenges remain in equity, data harmonization, and variant interpretation. Disparities in clinical trials and a lack of representation in diverse populations might accentuate global health inequities. Precision oncology brings together pioneering breakthroughs across technological, governance, political, and ethical domains. This paper also offers an inclusive blueprint for demarcating the limitations of Precision Oncology, intersecting with Genomic Understanding, innovative clinical trials, and Socio-politics, prying into pioneering breakthroughs in treatments from renowned institutions to universally practical approaches in global health ecosystems.

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