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Interpretation of radiomics features-A pictorial review

Abbasian Ardakani A, Bureau NJ, Ciaccio EJ, Acharya UR

Comput Methods Programs Biomed, 2022 Mar;215:106609.
PMID: 34990929 DOI: 10.1016/j.cmpb.2021.106609

Radiomics is a newcomer field that has opened new windows for precision medicine. It is related to extraction of a large number of quantitative features from medical images, which may be difficult to detect visually. Underlying tumor biology can change physical properties of tissues, which affect patterns of image pixels and radiomics features. The main advantage of radiomics is that it can characterize the whole tumor non-invasively, even after a single sampling from an image. Therefore, it can be linked to a "digital biopsy". Physicians need to know about radiomics features to determine how their values correlate with the appearance of lesions and diseases. Indeed, physicians need practical references to conceive of basics and concepts of each radiomics feature without knowing their sophisticated mathematical formulas. In this review, commonly used radiomics features are illustrated with practical examples to help physicians in their routine diagnostic procedures.

Matched MeSH terms: Precision Medicine*
Chitosan-based nanoscale delivery systems in hepatocellular carcinoma: Versatile bio-platform with theranostic application

Karimi K, Mojtabavi S, Tehrany PM, Nejad MM, Rezaee A, Mohtashamian S, et al.

Int J Biol Macromol, 2023 Jul 01;242(Pt 3):124935.
PMID: 37230442 DOI: 10.1016/j.ijbiomac.2023.124935

The field of nanomedicine has provided a fresh approach to cancer treatment by addressing the limitations of current therapies and offering new perspectives on enhancing patients' prognoses and chances of survival. Chitosan (CS) is isolated from chitin that has been extensively utilized for surface modification and coating of nanocarriers to improve their biocompatibility, cytotoxicity against tumor cells, and stability. HCC is a prevalent kind of liver tumor that cannot be adequately treated with surgical resection in its advanced stages. Furthermore, the development of resistance to chemotherapy and radiotherapy has caused treatment failure. The targeted delivery of drugs and genes can be mediated by nanostructures in treatment of HCC. The current review focuses on the function of CS-based nanostructures in HCC therapy and discusses the newest advances of nanoparticle-mediated treatment of HCC. Nanostructures based on CS have the capacity to escalate the pharmacokinetic profile of both natural and synthetic drugs, thus improving the effectiveness of HCC therapy. Some experiments have displayed that CS nanoparticles can be deployed to co-deliver drugs to disrupt tumorigenesis in a synergistic way. Moreover, the cationic nature of CS makes it a favorable nanocarrier for delivery of genes and plasmids. The use of CS-based nanostructures can be harnessed for phototherapy. Additionally, the incur poration of ligands including arginylglycylaspartic acid (RGD) into CS can elevate the targeted delivery of drugs to HCC cells. Interestingly, smart CS-based nanostructures, including ROS- and pH-sensitive nanoparticles, have been designed to provide cargo release at the tumor site and enhance the potential for HCC suppression.

Matched MeSH terms: Precision Medicine
Editorial for the Special Issue: Revolutionizing Regenerative Research Strategies Toward Precision Medicine-Asia-Pacific Region

Ramasamy R, Yahaya BH, Tan SL

Tissue Eng Part C Methods, 2022 10;28(10):499-500.
PMID: 36326783 DOI: 10.1089/ten.tec.2022.29035.editorial
Matched MeSH terms: Precision Medicine*
Abstracts of the International Congress of Pathology & Laboratory Medicine 2023: Precision Medicine: Revolutionizing Pathology in Genomic Era, organised by the College of Pathologists, Academy of Medicine of Malaysia and at World Trade Centre Kuala Lumpur on 20-22 September 2023

Malays J Pathol, 2023 Dec;45(3):481-566.
PMID: 38155390

No abstract available.

Matched MeSH terms: Precision Medicine*
Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Tobias DK, Merino J, Ahmad A, Aiken C, Benham JL, Bodhini D, et al.

Nat Med, 2023 Oct;29(10):2438-2457.
PMID: 37794253 DOI: 10.1038/s41591-023-02502-5

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Matched MeSH terms: Precision Medicine*
Fulltext Empowering Precision Medicine: Unlocking Revolutionary Insights through Blockchain-Enabled Federated Learning and Electronic Medical Records

Ali A, Al-Rimy BAS, Tin TT, Altamimi SN, Qasem SN, Saeed F

Sensors (Basel), 2023 Aug 28;23(17).
PMID: 37687931 DOI: 10.3390/s23177476

Precision medicine has emerged as a transformative approach to healthcare, aiming to deliver personalized treatments and therapies tailored to individual patients. However, the realization of precision medicine relies heavily on the availability of comprehensive and diverse medical data. In this context, blockchain-enabled federated learning, coupled with electronic medical records (EMRs), presents a groundbreaking solution to unlock revolutionary insights in precision medicine. This abstract explores the potential of blockchain technology to empower precision medicine by enabling secure and decentralized data sharing and analysis. By leveraging blockchain's immutability, transparency, and cryptographic protocols, federated learning can be conducted on distributed EMR datasets without compromising patient privacy. The integration of blockchain technology ensures data integrity, traceability, and consent management, thereby addressing critical concerns associated with data privacy and security. Through the federated learning paradigm, healthcare institutions and research organizations can collaboratively train machine learning models on locally stored EMR data, without the need for data centralization. The blockchain acts as a decentralized ledger, securely recording the training process and aggregating model updates while preserving data privacy at its source. This approach allows the discovery of patterns, correlations, and novel insights across a wide range of medical conditions and patient populations. By unlocking revolutionary insights through blockchain-enabled federated learning and EMRs, precision medicine can revolutionize healthcare delivery. This paradigm shift has the potential to improve diagnosis accuracy, optimize treatment plans, identify subpopulations for clinical trials, and expedite the development of novel therapies. Furthermore, the transparent and auditable nature of blockchain technology enhances trust among stakeholders, enabling greater collaboration, data sharing, and collective intelligence in the pursuit of advancing precision medicine. In conclusion, this abstract highlights the transformative potential of blockchain-enabled federated learning in empowering precision medicine. By unlocking revolutionary insights from diverse and distributed EMR datasets, this approach paves the way for a future where healthcare is personalized, efficient, and tailored to the unique needs of each patient.

Matched MeSH terms: Precision Medicine*
Advancing Precision Medicine with Gene and Cell Therapy in Malaysia: Ethical, Legal, and Social Implications

Kalidasan V, Theva Das K

Hum Gene Ther, 2024 Jan;35(1-2):9-25.
PMID: 38047523 DOI: 10.1089/hum.2023.139

A new era of gene and cell therapy for treating human diseases has been envisioned for several decades. However, given that the technology can alter any DNA/cell in human beings, it poses specific ethical, legal, and social difficulties in its application. In Malaysia, current bioethics and medical ethics guidelines tackle clinical trials and biomedical research, medical genetic services, and stem cell research/therapy. However, no comprehensive framework and policy is available to cater to ethical gene and cell therapy in the country. Incorporating ethical, legal, and social implications (ELSI) would be crucial to guide the appropriate use of human gene and cell therapy in conjunction with precision medicine. Policy experts, scientists, bioethicists, and public members must debate the associated ELSI and the professional code of conduct while preserving human rights.

Matched MeSH terms: Precision Medicine*
Fulltext Opportunities and challenges of 5G network technology toward precision medicine

Kang CC, Lee TY, Lim WF, Yeo WWY

Clin Transl Sci, 2023 Nov;16(11):2078-2094.
PMID: 37702288 DOI: 10.1111/cts.13640

Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.

Matched MeSH terms: Precision Medicine*
Fulltext Molecular Imaging - a way forward in translating disease behaviour in an era of personalized medicine

Fathinul Fikri AS

J Int Med Res, 2018 Feb;46(2):652-653.
PMID: 28718732 DOI: 10.1177/0300060517719211
Matched MeSH terms: Precision Medicine*
Fulltext Future Prospective of Radiopharmaceuticals from Natural Compounds Using Iodine Radioisotopes as Theranostic Agents

Nurhidayah W, Setyawati LU, Daruwati I, Gazzali AM, Subroto T, Muchtaridi M

Molecules, 2022 Nov 18;27(22).
PMID: 36432107 DOI: 10.3390/molecules27228009

Natural compounds provide precursors with various pharmacological activities and play an important role in discovering new chemical entities, including radiopharmaceuticals. In the development of new radiopharmaceuticals, iodine radioisotopes are widely used and interact with complex compounds including natural products. However, the development of radiopharmaceuticals from natural compounds with iodine radioisotopes has not been widely explored. This review summarizes the development of radiopharmaceuticals from natural compounds using iodine radioisotopes in the last 10 years, as well as discusses the challenges and strategies to improve future discovery of radiopharmaceuticals from natural resources. Literature research was conducted via PubMed, from which 32 research articles related to the development of natural compounds labeled with iodine radioisotopes were reported. From the literature, the challenges in developing radiopharmaceuticals from natural compounds were the purity and biodistribution. Despite the challenges, the development of radiopharmaceuticals from natural compounds is a golden opportunity for nuclear medicine advancement.

Matched MeSH terms: Precision Medicine
Incorporating Insulin Growth Factor-1 into Regenerative and Personalized Medicine for Cardiovascular Disease: A Systematic Review

Gan QF, Lim YT, Foo CN, Yu CW, Woon CK, Cheong SK, et al.

Curr Stem Cell Res Ther, 2023;18(2):202-215.
PMID: 35392790 DOI: 10.2174/1574888X17666220407085901

BACKGROUND: Cardiovascular disease (CVD) is one of the world's leading causes of increased morbidity and mortality. Current interventions for CVD, including percutaneous transluminal coronary angioplasty (PTCA) and coronary artery bypass grafting (CABG), carry certain risks and complications, which may also affect the patient's quality of life. It is important to minimize those risks and complications while speeding up the recovery. Insulin Growth Factor-1 (IGF-1) is a growth factor responsible for cellular migration, proliferation, differentiation, and angiogenesis, which supports cardiovascular regeneration.
METHODS: In light of the current trend of regenerative medicine, the present review aims to pool data relating to the incorporation of IGF-1 in regenerative medicine and provide input on the current research gaps and concerns arising on translating this approach from benchwork into clinical settings.
RESULTS: Using the keywords IGF-1 'OR' Insulin Growth Factor 1 'AND' Mesenchymal Stem Cells 'AND' Tissue Healing from 2009 to 2020, we identified 160 and 52 from Medline and PubMed, screening out 202 articles due to non-fulfilment of the inclusion criteria.
CONCLUSION: Incorporating IGF-1 into regenerative and personalized medicine may be promising for treating CVD; however, the concerns include the role of IGF-1 in inducing cancer growth and its ability to migrate to the specific site of injury, especially for those who present with multiple pathologies should be addressed prior to its translation from bench work into clinical settings.

Matched MeSH terms: Precision Medicine
Fulltext CRAC (Clinical Relevance of Alterations in Cancer): a Knowledge Base for the Selection of Molecularly Matched Therapy for Solid Tumors

Lebedeva AA, Kavun AI, Veselovsky EM, Mileyko VA, Ivanov MV

Sovrem Tekhnologii Med, 2022;14(6):15-23.
PMID: 37181287 DOI: 10.17691/stm2022.14.6.02

Multigene testing using NGS (next-generation sequencing) provides a large amount of information and can detect multiple molecular alterations. Subsequent clinical interpretation is a time-consuming process necessary to select a treatment strategy. Existing databases often contain inconsistent information and are not regularly updated. The use of ESCAT levels of evidence requires a deep understanding of the nature of alterations and does not answer the question of which therapy option to select when multiple biomarkers with the same level of evidence are detected. To address these issues, we created the Clinical Relevance of Alterations in Cancer (CRAC) database on the relevance of detected alterations in specific genes, which are often analyzed as part of NGS panels. The team of oncologists and biologists assigned a CRAC score from 1 to 10 to each biomarker (a type of genomic alteration characteristic of specific genes) for 15 malignancies; an average score was entered into the database. CRAC scores are a numerical reflection of the following factors: therapy availability and the prospects of drug treatment with experimental drugs for patients with a particular type of tumor. A total of 134 genes and 15 of the most common tumor types have been selected for CRAC. The biomarker-nosology associations with CRAC scores in the range of 1-3 are the most frequent (n=2719 out of 3495; 77.8%), the least frequent ones (n=52 out of 3495; 1.5%) are with the highest CRAC scores 9 and 10. To estimate the practical effectiveness of the CRAC database, 208 reports on comprehensive molecular profiling were retrospectively analyzed; the applicability of CRAC was compared with the ESCAT level of evidence system. The highest CRAC scores corresponded to the ESCAT maximum levels of evidence: the range of scores 8-10 corresponded to evidence levels I and II. No biomarker within the same level of evidence was represented by the same CRAC score; the largest range of CRAC scores was observed for biomarkers of levels evidence IIIA and IV - from 2 to 10 and from 1 to 9, respectively. The use of CRAC scores allowed to identify additional 95 alterations with CRAC scores of 1-5 in the studied patients. The developed database is available at: https://crac.oncoatlas.ru/.

Matched MeSH terms: Precision Medicine
Fulltext Precision Medicine for Sepsis Management in Low- and Middle-Income Countries-Melioidosis as a Model?

Wen YT, Cheng AC, Podin Y

Am J Respir Crit Care Med, 2024 Feb 01;209(3):237-238.
PMID: 38055331 DOI: 10.1164/rccm.202311-2056ED
Matched MeSH terms: Precision Medicine
Photonics-powered augmented reality skin electronics for proactive healthcare: multifaceted opportunities

Taha BA, Addie AJ, Kadhim AC, Azzahran AS, Haider AJ, Chaudhary V, et al.

Mikrochim Acta, 2024 Apr 08;191(5):250.
PMID: 38587660 DOI: 10.1007/s00604-024-06314-3

Rapid technological advancements have created opportunities for new solutions in various industries, including healthcare. One exciting new direction in this field of innovation is the combination of skin-based technologies and augmented reality (AR). These dermatological devices allow for the continuous and non-invasive measurement of vital signs and biomarkers, enabling the real-time diagnosis of anomalies, which have applications in telemedicine, oncology, dermatology, and early diagnostics. Despite its many potential benefits, there is a substantial information vacuum regarding using flexible photonics in conjunction with augmented reality for medical purposes. This review explores the current state of dermal augmented reality and flexible optics in skin-conforming sensing platforms by examining the obstacles faced thus far, including technical hurdles, demanding clinical validation standards, and problems with user acceptance. Our main areas of interest are skills, chiroptical properties, and health platform applications, such as optogenetic pixels, spectroscopic imagers, and optical biosensors. My skin-enhanced spherical dichroism and powerful spherically polarized light enable thorough physical inspection with these augmented reality devices: diabetic tracking, skin cancer diagnosis, and cardiovascular illness: preventative medicine, namely blood pressure screening. We demonstrate how to accomplish early prevention using case studies and emergency detection. Finally, it addresses real-world obstacles that hinder fully realizing these materials' extraordinary potential in advancing proactive and preventative personalized medicine, including technical constraints, clinical validation gaps, and barriers to widespread adoption.

Matched MeSH terms: Precision Medicine
Application and Importance of Theranostics in the Diagnosis and Treatment of Cancer

Jeyamogan S, Khan NA, Siddiqui R

Arch Med Res, 2021 02;52(2):131-142.
PMID: 33423803 DOI: 10.1016/j.arcmed.2020.10.016

The number of cancer cases worldwide in terms of morbidity and mortality is a serious concern, despite the presence of therapeutic interventions and supportive care. Limitations in the current available diagnosis methods and treatments methods may contribute to the increase in cancer mortality. Theranostics, is a novel approach that has opened avenues for the simultaneous precise diagnosis and treatment for cancer patients. Although still in the early development stage, theranostic agents such as quantum dots, radioisotopes, liposomes and plasmonic nanobubbles can be bound to anticancer drugs, cancer cell markers and imaging agents, with the support of available imaging techniques, provide the potential to facilitate diagnosis, treatment and management of cancer patients. Herein, we discuss the potential benefits of several theranostic tools for the management of cancer. Specifically, quantum dots, radio-labelled isotopes, liposomes and plasmonic nanobubbles coupled with targeting agents and/or anticancer molecules and imaging agents as theranostic agents are deliberated upon in this review. Overall, the use of theranostic agents shows promise in cancer management. Nevertheless, intensive research is required to realize these expectations.

Matched MeSH terms: Precision Medicine/methods*
Single-cell RNA sequencing in human lung cancer: Applications, challenges, and pathway towards personalized therapy

Chong ZX, Ho WY, Yeap SK, Wang ML, Chien Y, Verusingam ND, et al.

J Chin Med Assoc, 2021 Jun 01;84(6):563-576.
PMID: 33883467 DOI: 10.1097/JCMA.0000000000000535

Lung cancer is one of the most prevalent human cancers, and single-cell RNA sequencing (scRNA-seq) has been widely used to study human lung cancer at the cellular, genetic, and molecular level. Even though there are published reviews, which summarized the applications of scRNA-seq in human cancers like breast cancer, there is lack of a comprehensive review, which could effectively highlight the broad use of scRNA-seq in studying lung cancer. This review, therefore, was aimed to summarize the various applications of scRNA-seq in human lung cancer research based on the findings from different published in vitro, in vivo, and clinical studies. The review would first briefly outline the concept and principle of scRNA-seq, followed by the discussion on the applications of scRNA-seq in studying human lung cancer. Finally, the challenges faced when using scRNA-seq to study human lung cancer would be discussed, and the potential applications and challenges of scRNA-seq to facilitate the development of personalized cancer therapy in the future would be explored.

Matched MeSH terms: Precision Medicine*
Fulltext Current landscape of personalized medicine adoption and implementation in Southeast Asia

Chong HY, Allotey PA, Chaiyakunapruk N

BMC Med Genomics, 2018 Oct 26;11(1):94.
PMID: 30367635 DOI: 10.1186/s12920-018-0420-4

BACKGROUND: The emergence of personalized medicine (PM) has raised some tensions in healthcare systems. PM is expensive and health budgets are constrained - efficient healthcare delivery is therefore critical. Notwithstanding the cost, many countries have started to adopt this novel technology, including resource-limited Southeast Asia (SEA) countries. This study aimed to describe the status of PM adoption in SEA, highlight the challenges and to propose strategies for future development.
METHODS: The study included scoping review and key stakeholder interviews in four focus countries - Indonesia, Malaysia, Singapore, and Thailand. The current landscape of PM adoption was evaluated based on an assessment framework of six key themes - healthcare system, governance, access, awareness, implementation, and data. Six PM programs were evaluated for their financing and implementation mechanisms.
RESULTS: The findings revealed SEA has progressed in adopting PM especially Singapore and Thailand. A regional pharmacogenomics research network has been established. However, PM policies and programs vary significantly. As most PM programs are champion-driven and the available funding is limited, the current PM distribution has the potential to widen existing health disparities. Low PM awareness in the society and the absence of political support with financial investment are fundamental barriers. There is a clear need to broaden opportunities for critical discourse about PM especially for policymakers. Multi-stakeholder, multi-country strategies need to be prioritized in order to leverage resources and expertise.
CONCLUSIONS: Adopting PM remains in its infancy in SEA. To achieve an effective PM adoption, it is imperative to balance equity issues across diverse populations while improving efficiency in healthcare.

Matched MeSH terms: Precision Medicine*
Fulltext Next-generation, personalised, model-based critical care medicine: a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them

Chase JG, Preiser JC, Dickson JL, Pironet A, Chiew YS, Pretty CG, et al.

Biomed Eng Online, 2018 Feb 20;17(1):24.
PMID: 29463246 DOI: 10.1186/s12938-018-0455-y

Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Matched MeSH terms: Precision Medicine/methods*
Fulltext Exploring Ayurvedic Knowledge on Food and Health for Providing Innovative Solutions to Contemporary Healthcare

Payyappallimana U, Venkatasubramanian P

Front Public Health, 2016;4:57.
PMID: 27066472 DOI: 10.3389/fpubh.2016.00057

Ayurveda, a traditional system of medicine that originated over three millennia ago in the South Asian region, offers extensive insights about food and health based on certain unique conceptual as well as theoretical positions. Health is defined as a state of equilibrium with one's self (svasthya) but which is inextricably linked to the environment. Ayurvedic principles, such as the tridosa (three humors) theory, provide the relationship between the microcosm and the macrocosm that can be applied in day-to-day practice. Classical Ayurveda texts cover an array of themes on food ranging from diversity of natural sources, their properties in relation to seasons and places and to their specific function both in physiological and pathological states. The epistemic perspective on health and nutrition in Ayurveda is very different from that of biomedicine and modern nutrition. However, contemporary knowledge is reinventing and advancing several of these concepts in an era of systems biology, personalized medicine, and the broader context of a more holistic transition in sciences in general. Trans-disciplinary research could be important not only for pushing the boundaries of food and health sciences but also for providing practical solutions for contemporary health conditions. This article briefly reviews the parallels in Ayurveda and biomedicine and draws attention to the need for a deeper engagement with traditional knowledge systems, such as Ayurveda. It points out that recreation of the methodologies that enabled the holistic view point about health in Ayurveda may unravel some of the complex connections with Nature.

Matched MeSH terms: Precision Medicine
Fulltext Advancing Personalized Medicine Through the Application of Whole Exome Sequencing and Big Data Analytics

Suwinski P, Ong C, Ling MHT, Poh YM, Khan AM, Ong HS

Front Genet, 2019;10:49.
PMID: 30809243 DOI: 10.3389/fgene.2019.00049

There is a growing attention toward personalized medicine. This is led by a fundamental shift from the 'one size fits all' paradigm for treatment of patients with conditions or predisposition to diseases, to one that embraces novel approaches, such as tailored target therapies, to achieve the best possible outcomes. Driven by these, several national and international genome projects have been initiated to reap the benefits of personalized medicine. Exome and targeted sequencing provide a balance between cost and benefit, in contrast to whole genome sequencing (WGS). Whole exome sequencing (WES) targets approximately 3% of the whole genome, which is the basis for protein-coding genes. Nonetheless, it has the characteristics of big data in large deployment. Herein, the application of WES and its relevance in advancing personalized medicine is reviewed. WES is mapped to Big Data "10 Vs" and the resulting challenges discussed. Application of existing biological databases and bioinformatics tools to address the bottleneck in data processing and analysis are presented, including the need for new generation big data analytics for the multi-omics challenges of personalized medicine. This includes the incorporation of artificial intelligence (AI) in the clinical utility landscape of genomic information, and future consideration to create a new frontier toward advancing the field of personalized medicine.

Matched MeSH terms: Precision Medicine

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