Precision Oncology: Targeting Genomic Alterations and Cancer Signaling with Integrative Multi-Omics, Deep Learning and Network Biology in Medical Oncology

TL;DR

This paper reviews how integrating multi-omics data, deep learning, and network biology advances precision oncology by enabling personalized cancer treatment through detailed molecular profiling and analysis.

Contribution

It highlights recent developments in computational tools and multi-omics integration that enhance understanding and targeting of cancer-specific molecular alterations.

Findings

Multi-omics data reveal critical molecular changes in tumors.

Deep learning improves pattern recognition in complex biological data.

Network biology aids in understanding cancer signaling pathways.

Abstract

Cancer is a complex genetic disease involving uncontrolled cell growth and proliferation, and necessitates effective targeting of dysregulated cellular pathways underlying cancer progression. Multiple genetic and epigenetic alterations characterize tumor progression and define hallmarks of cancer. Importantly, patients with the same cancer type respond differently to available cancer treatments, likely due to tumor-specific DNA, RNA, and proteins, indicating the need for patient-specific treatment options. Precision oncology has evolved as a form of cancer therapy that is focused on genetic and molecular profiling of tumors to identify specific molecular alterations involved in carcinogenesis for tailored individualized cancer treatment. Advances in high-throughput sequencing technologies have enabled gene expression profiling, providing multiomics data for detailed molecular characterization of various tumors. Integration and analysis of various multiomic sequencing data are crucial in this regard, as they can reveal critical molecular changes, such as cancer-driving mutations, post-translational modifications, gene fusions, amplifications, and alterations in signaling networks within tumors. Furthermore, the role of computational techniques such as artificial intelligence and deep learning, in analyzing complex data and identifying patterns of disease development for better outcomes is now well established in precision medicine. Additionally, AI-powered multi-omics and network biology have been harnessed to integrate and analyze biological data through networks, which may prove crucial in solving key problems facing precision oncology. This article aims to briefly explain the foundations and frontiers of precision oncology in the context of cutting-edge developments in tools and techniques associated with it, and try to assess its scope and importance in achieving the intended goals.