Machine learning can accelerate discovery and application of cyber-molecular cancer diagnostics.

Abstract

© Journal of Medical Artificial Intelligence. All rights reserved. J Med Artif Intell 2020;3:7 | http://dx.doi.org/10.21037/jmai.2020.01.01 Accurate and early cancer diagnosis is fundamental for clinical management and public health. Unfortunately, the biological complexity of cancer confounds the development of effective diagnostic approaches to its detection. Histological examination of tissue samples obtained by biopsy directly from solid tumors and imaging technologies remain as the mainstays of cancer diagnostics. The liquid biopsy concept aims to overcome the shortcomings of these onco-diagnostics by detecting tumor-derived biomarkers such as circulating tumor cells, extracellular vesicles, nucleosomes, proteins, antigens, and extracellular nucleic acids in blood (1). Among many, mitochondrial DNA (mtDNA) is one of the most promising biomarkers of liquid biopsy. Mitochondria are highly abundant in human body, exceeding the number of human cells by 100–10,000 times. They play an essential role in the whole-body physiology, being involved in bioenergetics, apoptosis, innate immunity, networks of communication with different cell types and metabolic coordination. Owing to such fundamental involvement of mitochondria in human physiology, mtDNA mutations in general have a highly detrimental effect on cell viability. Nevertheless, the astronomical mitochondrial population size, lack of genetic mechanisms for effective control of mutations, genetic complementation and vegetative segregation of mtDNA establish an environment that supports a significant intra-host mitochondrial genetic heterogeneity, known as heteroplasmy (2). Some health conditions, such as cancer are potentially conducive to maintaining heteroplasmy. The intra-host mitochondrial genetic diversity detected in blood is very dynamic and may change at the rate usually observed in intra-host viral populations, rapidly responding to, for example, progression of cancer or hepatitis C virus infection (3-5). The dynamic nature of mitochondrial genetic heterogeneity in blood offers potential diagnostic opportunities for the detection of cancer and other health conditions (3,6). The fluid biopsy concept takes advantage of such opportunities and provides guiding principles for diagnosing and managing cancer using blood rather than solid tumor tissue, with several molecular approaches being developed for the direct detection of circulating mtDNA variants associated with cancer (7,8). We recently showed that heterogeneity at specially selected polymorphic mtDNA sites can be efficiently associated with liver cancer by means of machine learning, suggesting a different research direction towards development of novel cyber-molecular diagnostics (6). Such assays are basically complex computational models capable of extracting diagnostically relevant information from molecular data obtained using Ultra-Deep Sequencing (UDS) technologies. Molecular wet-laboratory assays generate diagnostic information directly from blood by detection of circulating specific sequence variants. Performance of wet-laboratory assays can be greatly afflicted by a limited presentation of tumor-derived genetic markers in blood (9), owing either to a low level of specific variants or to abundance of different mutations associated with cancer, reflecting the complex biological nature of this disease. Assays based on the identification of patterns in molecular data are potentially less Letter to the Editor