Detection Methods in Precision Medicine

Mengsu (Michael) Yang, Michael Thompson, Mengsu (Michael) Yang, Michael Thompson

• Editorial: Royal Society of Chemistry
• Año: 2020
• ISBN:
  9781788019965
• Edición: 1
• Categoría: Scienze fisiche
• Categoría: Chimica analitica

Precision medicine is a topical subject that attracts tremendous attention from scientific and medical communities, being set to transform health care in the future. This book will be among the first to cover the detection methods for precision medicine. The first section provides an overview of the biomarkers used for precision medicine, such as proteins, nucleic acids, and metabolites. The coverage then turns to sequencing techniques and their applications, and other bioanalytical techniques, including mass spectrometry for proteome and phosphoproteome analysis, immunological methods and droplet technologies. The final sections include biosensors applied to precision medicine and clinical applications.

This book provides a reference for researchers and students interested and working in the development of bioanalytical techniques for clinical applications. It provides a useful introduction for physicians and medical laboratory technologists to the recent advances in detection methods for precision medicine.

Part 1

Biomarkers for Precision Medicine

CHAPTER 1

Genome-wide Discovery of MicroRNA Biomarkers for Cancer Precision Medicine

ZHONGXU ZHU^†a, GUIYUAN HAN^†a, HAO HUANG^†a, LINGLI HE^a, YU CHEN^a, JIA KE^b, FENG GAO^b, LOUIS VERMEULEN^c AND XIN WANG^*a,d

^a Department of Biomedical Sciences, City University of Hong Kong, 31 To Yuen Street, Kowloon Tong, Hong Kong, China, ^b The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China ^c Amsterdam UMC, University of Amsterdam, Laboratory for Experimental Oncology and Radiobiology, Center for Experimental and Molecular Medicine, Cancer Center Amsterdam and Amsterdam Gastroenterology and Metabolism, Meibergdreef 9, 1105, Amsterdam, AZ, Netherlands ^d Shenzhen Research Institute, City University of Hong Kong, Shenzhen, China
^*E-mail: [email protected]

MicroRNAs (miRNAs) are an abundant class of small non-coding RNA molecules that regulate gene expression at the post-transcriptional level. MiRNAs are found frequently dysregulated during cancer initiation, development, and metastasis, and are present in a wide variety of clinical specimens such as blood, saliva, urine, and feces. These relatively abundant and stable molecules provide great potential to be exploited for cancer detection, prognosis, and therapy response prediction, as well as disease monitoring. Herein, we introduce state-of-the-art development of miRNA biomarkers with a particular focus on a genome-wide, data-driven methodology, which has demonstrated higher robustness and reproducibility compared to traditional methods. We will first review miRNA-based biomarkers for various clinical applications and discuss the potential limitations of traditional approaches. Next, we will summarize the major steps involved in a data-driven methodology for biomarker development. Finally, we will discuss the main advantages and challenges in real clinical applications, as well as possible solutions and emerging opportunities.

1.1 Introduction

Evidence-based medicine (EBM) has been considered the optimal practice for decision-making in health care for decades. Compared to traditional medicine, which relies more on clinical experience and pathophysiologic rationale, EBM emphasizes the use of best available evidence from well-designed and well-conducted research. Despite the wide adoption of EBM as the gold standard of clinical practice, it has a number of known limitations, such as limited usefulness when applied to individuals and potential bias in randomized controlled trials.^1,4 As a result, in clinical practice, patients suffering from the same disease are often provided with nearly the same therapeutic interventions (‘one-size-fits-all’ approach to therapy), which overlooks the difference between individuals. In fact, the therapeutic responses of individual patients may depart widely from the average treatment effect. For instance, patients with the same cancer type may have heterogeneous genetic makeup in the tumors associated with discrepant clinical outcomes, and therefore should be managed differently in the clinic.

Over the last decades, the rich knowledge about human diseases gained from extensive basic, preclinical, and clinical research and the rapid development of biotechnology have paved the way towards precision medicine. Different from EBM, precision medicine is a new paradigm of medicine empowered by comprehensive molecular and clinical profiling of patients.⁵ It tailors precise disease diagnosis, prognosis, and therapeutics to the individuals based on (epi-)genetic, phenotypic, and clinical characteristics and eventually aims to achieve the right treatment, to the right patient, at the right time.^5,6 Recent advances achieved in multiple related fields have collectively laid a strong foundation for the development of precision medicine in the coming decades. On the one hand, the rapid development of biotechnologies contributes significantly to reduction in the cost of high-throughput sequencing, enabling the generation of multi-omic profiles for patients on a large scale. On the other hand, the fast-growing computing power and development of analytical methods has made it possible to store, transfer, analyze, and interpret the ‘big’ biomedical data generated. More recently, artificial intelligence (AI) has demonstrated unprecedented performance in various preclinical and clinical studies,⁷ providing new opportunities to develop automated, intelligent systems in the clinic.

Among the broad spectrum of research areas in cancer precision medicine, biomarker development is a central mission. A biomarker is a biological substance that can be measured and evaluated as an indicator of normal or abnormal conditions or a sign of disease condition.⁸ It can be found in tissues, blood, or other body fluids. Molecules such as DNA, mRNA, miRNA, and proteins, as well as metabolites and microorganisms, can all be exploited as biomarkers. Numerous biomarkers have been developed for cancer screening, diagnosis, prognosis, and prediction of therapy response as well as disease monitoring. Importantly, tailored for specific clinical applications, molecular biomarkers can be customized based on a selection of different types of clinical specimens, diverse biomolecules to test, and various high- or low-throughput platforms for profiling.⁹

MicroRNAs (miRNAs) are small regulatory non-coding RNAs that can downregulate gene expression mainly via base pairing to 3′ untranslated regions of target mRNAs. These small molecules influence almost every cancer-related process involving cell proliferation, apoptosis, metastasis, and angiogenesis.^10,15 Compared to other molecules, miRNAs are relatively stable and abundant in various clinical specimens, presenting promising candidates for biomarker development. In the last decade, miRNAs have been exploited as valuable diagnostic, prognostic, and predictive biomarkers in various cancer types.¹⁶ Recently, the growing interest in ‘liquid biopsies’ has also put circulating miRNAs to the forefront of biomarker discovery and development.

In this chapter, we will first summarize miRNA-based biomarkers for various clinical applications based on a substantial literature review and discuss the potential limitations of traditional approaches. Recent years have seen large-scale cohort studies generating vast amounts of omics data, which will continue to grow exponentially in the following decades.¹⁷ These ‘big’ multi-omic datasets, together with histopathological reports and medical records, provide tremendous opportunities for biomarker discovery and in silico validations. Next, we will introduce a data-driven methodology for biomarker development and the major steps involved. Furthermore, we will summarize the major advantages of the data-driven methodology and discuss the main challenges limiting its implementation, possible solutions, and emerging opportunities.

1.2 A Review of miRNA-based Cancer Biomarkers in Various Clinical Applications

Cancer-associated miRNAs were discovered in 2008, followed by hundreds of miRNA studies thereafter.¹⁸ Notably, miRNA is relatively stable in body fluids such as plasma and serum, and in exosomes and tissue samples, which increases their potential to be exploited as biomarkers.^19,20 Indeed, a number of studies have already demonstrated the versatile clinical value of miRNAs for diagnosis, prognosis, therapy response prediction, and disease monitoring, which will be reviewed in detail in this section.

1.2.1 Diagnosis

Cancer diagnosis, especially at early stages, is crucial for more effective disease prevention. Early detection of cancer significantly increases the chances for successful treatment, leading to the reduction of mortality and improvement of long-term survival of patients. However, due to the general low sensitivity and specificity, early detection of cancer has been a bottleneck. Recent studies on miRNA-based biomarkers have shown a great potential in employing circulating miRNAs for early detection of cancers. For instance, miR-29a, miR-92, and their combined signatures achieved more than 80% sensitivity and 70% specificity in differentiating colorectal cancer (CRC) patients from healthy controls.^21,22 Circulating miR-210 could also well distinguish cance...