Next Generation Sequencing and Machine Learning Technologies Are Painting the Epigenetic Portrait of Glioblastoma.

Ivana Jovčevska

doi:10.3389/fonc.2020.00798

Abstract

Even with a rare occurrence of only 1.35% of cancer cases in the United States of America, brain tumors are considered as one of the most lethal malignancies. The most aggressive and invasive type of brain tumor, glioblastoma, accounts for 60–70% of all gliomas and presents with life expectancy of only 12–18 months. Despite trimodal treatment and advances in diagnostic and therapeutic methods, there are no significant changes in patient outcome. Our understanding of glioblastoma was significantly improved with the introduction of next generation sequencing technologies. This led to the identification of different genetic and molecular subtypes, which greatly improve glioblastoma diagnosis. Still, because of the poor life expectancy, novel diagnostic, and treatment methods are broadly explored. Epigenetic modifications like methylation and changes in histone acetylation are such examples. Recently, in addition to genetic and molecular characteristics, epigenetic profiling of glioblastomas is also used for sample classification. Further advancement of next generation sequencing technologies is expected to identify in detail the epigenetic signature of glioblastoma that can open up new therapeutic opportunities for glioblastoma patients. This should be complemented with the use of computational power i.e., machine and deep learning algorithms for objective diagnostics and design of individualized therapies. Using a combination of phenotypic, genotypic, and epigenetic parameters in glioblastoma diagnostics will bring us closer to precision medicine where therapies will be tailored to suit the genetic profile and epigenetic signature of the tumor, which will grant longer life expectancy and better quality of life. Still, a number of obstacles including potential bias, availability of data for minorities in heterogeneous populations, data protection, and validation and independent testing of the learning algorithms have to be overcome on the way.

Highlights

This review starts with outlining the complex nature of glioblastoma by providing brief information about its occurrence, mortality rate, molecular features, and heterogeneity
DNA Methylation in Glioblastoma Cancers in general are characterized by global hypomethylation which is associated with gene expression, activation, and chromosomal rearrangements of oncogenes which leads to genomic instability, oncogene activation, and tumor progression, as well as locus-specific hypermethylation which results in heritable transcriptional silencing of tumor suppressor genes [82, 89]
Adding molecular characteristics into the histological diagnosis of glioblastoma is beneficial for precise diagnosis, single-gene tests based on DNA methylation analysis like methylguanine-DNA methyltransferase (MGMT) methylation status, fluorescence in situ hybridization [1p/19q codeletion, EGFR, proto-oncogene C-Myc (MYC), class E basic helix-loop-helix protein 37 (MYCN), platelet derived growth factor receptor alpha (PDGFRA) and 19q13.42), or immunohistochemistry (catenin beta-1 (CTNNB1) and Lin-28 homolog A (LIN28A)], have proven difficult to be standardized [128]

Summary

Introduction

This review starts with outlining the complex nature of glioblastoma by providing brief information about its occurrence, mortality rate, molecular features, and heterogeneity. Development of machine learning and deep learning algorithms for glioblastoma patient care may improve objective disease diagnosis and can contribute to tailoring the most effective treatment based on patient molecular profile i.e., precision medicine. By understanding how the disease works at different molecular levels (transcriptomic, genetic, epigenetic, and protein), new more powerful drugs can be designed that will be of a great benefit for the patients.

Results

Conclusion