Integrated Analysis of Whole Genome and Epigenome Data Using Machine Learning Technology: Toward the Establishment of Precision Oncology

Abstract

With the completion of the International Human Genome Project, we have entered what is known as the post-genome era, and efforts to apply genomic information to medicine have become more active. In particular, with the announcement of the Precision Medicine Initiative by U.S. President Barack Obama in his State of the Union address at the beginning of 2015, “precision medicine,” which aims to divide patients and potential patients into subgroups with respect to disease susceptibility, has become the focus of worldwide attention. The field of oncology is also actively adopting the precision oncology approach, which is based on molecular profiling, such as genomic information, to select the appropriate treatment. However, the current precision oncology is dominated by a method called targeted-gene panel (TGP), which uses next-generation sequencing (NGS) to analyze a limited number of specific cancer-related genes and suggest optimal treatments, but this method causes the problem that the number of patients who benefit from it is limited. In order to steadily develop precision oncology, it is necessary to integrate and analyze more detailed omics data, such as whole genome data and epigenome data. On the other hand, with the advancement of analysis technologies such as NGS, the amount of data obtained by omics analysis has become enormous, and artificial intelligence (AI) technologies, mainly machine learning (ML) technologies, are being actively used to make more efficient and accurate predictions. In this review, we will focus on whole genome sequencing (WGS) analysis and epigenome analysis, introduce the latest results of omics analysis using ML technologies for the development of precision oncology, and discuss the future prospects.

Targeting DNA Damage Response and Repair to Enhance Therapeutic Index in Cisplatin-Based Cancer Treatment

“…The concept of precision oncology attempts to select cancer treatment for patients by utilizing diverse techniques to choose more targeted therapies. The use of biomarker testing can help predict prognosis or even direct treatment selection based on variations in single genes or proteins, or a genomic pattern [ 77 ]. As a result, the use of molecular predictive markers and genomic profiling have improved the ability to predict whether patients will benefit from various chemotherapies, including cisplatin [ 78 ].…”

Epigenetic Mechanisms Underlying COVID-19 Pathogenesis

“…Crick in 1953 [ 144 ], molecular biology has emerged and various life mechanisms have been elucidated at the genetic level. In addition, with the completion of the Human Genome Project in 2003 and the elucidation of the human whole genome, there has been a growing momentum for the application of genomic information to medicine, known as genomic medicine [ 15 , 27 , 28 ]. U.S. President Barack Obama announced the Precision Medicine Initiative in 2015, leading to a worldwide push for precision medicine, wherein patients are selected for optimal treatment based on genomic mutations [ 145 ].…”

“…The Greek prefix epi-(ἐπι) in epigenetics implies a function "above" or "in addition to" the traditional genetic base [15]. Over the past two decades, epigenetic regulators have been implicated as critical factors in many pathways related to the development and progression of cancer and other diseases, including cell cycle regulation, invasiveness, signaling pathways, chemotherapy resistance, and immune evasion [16][17][18][19][20][21][22][23][24][25][26][27]. The three basic systems of epigenetic regulation are DNA methylation of gene regulatory regions; histone protein modifications such as methylation, acetylation, phosphorylation, and sumoylation; and non-coding RNAs [15].…”

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“…In recent years, artificial intelligence (AI), which includes machine learning and deep learning, has been developing rapidly, and AI is increasingly being adopted in medical research and applications [6][7][8][9][10][11][12][13][14][15][16]. Deep learning is a leading subset of machine learning, which is defined by non-programmed learning from a large amount of data with convolutional neural networks (CNNs) [17].…”