Integration of Omics Approaches and Systems Biology for Clinical Applications

Antonia Vlahou, Fulvio Magni, Harald Mischak, Jerome Zoidakis, Antonia Vlahou, Fulvio Magni, Harald Mischak, Jerome Zoidakis

• Publisher: Wiley
• Year: 2018
• ISBN:
  9781119183969
• Edition: 1
• Topic: Ciencias físicas
• Subtopic: Espectroscopia y análisis de espectro

Introduces readers to the state of the art of omics platforms and all aspects of omics approaches for clinical applications

This book presents different high throughput omics platforms used to analyze tissue, plasma, and urine. The reader is introduced to state of the art analytical approaches (sample preparation and instrumentation) related to proteomics, peptidomics, transcriptomics, and metabolomics. In addition, the book highlights innovative approaches using bioinformatics, urine miRNAs, and MALDI tissue imaging in the context of clinical applications. Particular emphasis is put on integration of data generated from these different platforms in order to uncover the molecular landscape of diseases. The relevance of each approach to the clinical setting is explained and future applications for patient monitoring or treatment are discussed.

Integration of omics Approaches and Systems Biology for Clinical Applications presents an overview of state of the art omics techniques. These methods are employed in order to obtain the comprehensive molecular profile of biological specimens. In addition, computational tools are used for organizing and integrating these multi-source data towards developing molecular models that reflect the pathophysiology of diseases. Investigation of chronic kidney disease (CKD) and bladder cancer are used as test cases. These represent multi-factorial, highly heterogeneous diseases, and are among the most significant health issues in developed countries with a rapidly aging population. The book presents novel insights on CKD and bladder cancer obtained by omics data integration as an example of the application of systems biology in the clinical setting.

• Describes a range of state of the art omics analytical platforms
• Covers all aspects of the systems biology approach—from sample preparation to data integration and bioinformatics analysis
• Contains specific examples of omics methods applied in the investigation of human diseases (Chronic Kidney Disease, Bladder Cancer)

Integration of omics Approaches and Systems Biology for Clinical Applications will appeal to a wide spectrum of scientists including biologists, biotechnologists, biochemists, biophysicists, and bioinformaticians working on the different molecular platforms. It is also an excellent text for students interested in these fields.

Part I
Platforms for Molecular Data Acquisition and Analysis

1
Clinical Data Collection and Patient Phenotyping

Katerina Markoska¹ and Goce Spasovski²

¹ Medical Faculty, University “Ss. Cyril and Methodius” of Skopje, Skopje, Republic of Macedonia

² Department of Nephrology, University “Ss. Cyril and Methodius,” Medical Faculty, Skopje, Republic of Macedonia

1.1 Clinical Data Collection

1.1.1 Data Collection for Clinical Research

The goal of clinical studies is the evaluation of interventions on clinically relevant parameters [1]. Conducting a clinical study is a major undertaking accompanied with heavy and extensive responsibilities. Good primary research calls for constant dedication by practicing physicians and patients willing to participate for the sake of knowledge and better treatment of future patients [2].

The study design is the investigator’s map from which data collection follows and which enables the investigators to thoughtfully produce the necessary data forms. The formulation of a good research question, up front, informs the clinician or researcher about the most appropriate data elements to be collected [2]. Investigators often believe that collecting more data is better and that it is important to collect information on as many scientifically “interesting” factors as possible. Therefore, it is imperative to distinguish between those data elements that are essential and those that are academically “interesting” but may not be considered of interest to the key study hypothesis. This should greatly assist in narrowing down one’s study questions and collecting data more efficiently [3].

1.1.2 Clinical Data Management

Clinical data management (CDM) is the process of collection, cleaning, and management of subject data in compliance with regulatory standards. The primary objective of CDM processes is to provide high‐quality data by keeping the number of errors and missing data as low as possible and gather the maximum amount of data for analysis [4].

High‐quality data should be absolutely accurate, have minimal or no missing points, and should be suitable for statistical analysis. The data should meet the applicable regulatory requirements specified for data quality and comply with the protocol requirements. In case of a deviation or not meeting the protocol specifications, we may think of excluding the patient from the final database [5].

Current technological developments have accelerated the rate of data collection and positively impacted the CDM process and systems by improving their quality. From the regulatory perspective, the biggest challenge would be the standardization of data management processes across organizations and development of regulations to define the procedures that has to be followed. From the industry perspective, the challenge would be the planning and implementation of data management systems in a changing operational environment. CDM is evolving to become a standard‐based clinical research entity, balancing between the expectations from and constraints in the existing systems, driven by technological developments and business demands [5].

The Society for Clinical Data Management (SCDM) publishes Good Clinical Data Management Practices (GCDMP) guidelines that highlight the minimum standards and best practices, providing assistance to clinical data managers in their implementation of high‐quality CDM [5]. If data have to be submitted to regulatory authorities, it should be entered and processed in accordance with the Code of Federal Regulations (CFR), Title 21, Volume 1 of Part 11, Food and Drug Administration (FDA) regulations on electronic records and electronic signatures (ERES), cited as 21CFR11.10 [6].

Many clinical data management systems (CDMS) are available for data management. Most of the CDM systems available meet these criteria, and pharmaceutical companies as well as contract research organizations ensure this compliance. In multicentric trials, a CDMS has become essential to handle the huge amount of data. These CDM tools ensure the audit trail and help in the management of discrepancies [5].

One should leave sufficient time for planning and development of system and study database for the follow‐up and tracking of patients throughout the study. The following issues should be defined in advance: determination of the mechanism and processes for data collection if a patient misses a scheduled appointment, implementation of quality checks, preparation and collection of patient informed consent, and institutional review board (IRB) approval. Inclusion and exclusion criteria should be defined as well as data collection elements [2].

1.1.3 Creating Data Forms

The limited focus of disease‐specific consortia makes comprehensive coverage of individual areas more likely. Researchers should benefit from a clear understanding of the extensive overlap of various clinical terminologies, as well as advice regarding which standards are appropriate for a particular research context. They should also be able to address relationships between clinical research data collection standards and electronic health records (EHR) specifications, as well as the broad issue of secondary use of clinical data for research. Additional tasks could include the review of standards and their scope and relating them to needs of clinical research [7].

Item repositories can reduce the burden on new investigators to create their own items, because existing validated items or sets of items can be reused [8]. Pilot testing of data forms completed by patients allows investigators to react to suggestions from patients as well as from staff and personnel and provides more realistic estimates of data collection times [2].

1.1.3.1 Different Data Forms According to the Type of Study

Data form development is a collaborative effort among the investigators and often takes months of planning and preparation. It should be undertaken by investigators and/or stakeholders experienced in form construction and familiar with the methods of data collection, data processing, and content necessary for the study [2]. It is facilitated by review of the literature for instruments used in similar studies, also including the Clinical Data Acquisition Standards Harmonization (CDASH) recommendations, which give useful general guidance on constructing yes/no questions, scale direction, date/time formats, scope of CRF data collection, pre‐populated data, and collection of calculated or derived data. Certain items (especially questionnaire‐based ones) have a discrete set of permissible values (also called “responses” or “answers”), for example, the use of cigarettes (never/former/current), amount (less than 3 per day/3–10 per day/more than 10 per day), and fasting (no/yes) [7].

Study details like objectives, intervals, visits, investigators, sites, and patients should be defined in the database, and CRF layouts have to be designed for data entry [5]. In order to simplify the data collection, some answers can be coded. For example, 1 = yes and 2 = no, but these codes should be consistent throughout the CRF booklet (Table 1.1) [9].

Table 1.1 Coding on the case report form module.

Demography
Date of birth (DD/MM/YYYY)	□□/□□/□□□□
Gender	Male □ 1 Female □ 2
Height (cm)	□□□.□
Weight (kg)	□□□.□□
Smoker	Yes □ 1 No □ 2
Family history	Yes □ 1 No □ 2

The forms should be well designed in order to avoid variation in the responses and the site personnel can understand the...