journal of biomedical informatics
All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.

Integration of Genomics and Clinical Data: Advancing Precision Medicine

Soo-jin Kim*
 
Department of Biomedical Informatics, Sungkyunkwan University, Korea, Email: skim@informatics.skku.kr
 
*Correspondence: Soo-jin Kim, Department of Biomedical Informatics, Sungkyunkwan University, Korea, Email: skim@informatics.skku.kr

Published: 05-Dec-2024, DOI: 10.24105/ejbi.2024. 20(4): 280-281

This open-access article is distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC) (http://creativecommons.org/licenses/by-nc/4.0/), which permits reuse, distribution and reproduction of the article, provided that the original work is properly cited and the reuse is restricted to noncommercial purposes. For commercial reuse, contact submissions@ejbi.org

European Journal of Biomedical Informatics

Abstract

   

Introduction

The integration of genomics and clinical data represents a transformative step in the evolution of precision medicine. By combining genetic information with clinical observations, healthcare providers can move from a generalized, one-size-fits-all approach to a more personalized method of diagnosing, treating, and preventing diseases [1]. This integration enables clinicians to identify unique genetic markers that influence an individual’s response to treatments and disease risks, paving the way for more effective and targeted interventions [2].

Genomics involves the study of an individual’s complete set of DNA, including all of their genes and variations. This field has grown rapidly in recent years due to advances in sequencing technologies, such as next-generation sequencing (NGS), which have made it feasible to analyze vast amounts of genetic information quickly and cost-effectively [3]. Clinical data, on the other hand, encompasses an individual’s medical history, lifestyle factors, laboratory test results, imaging data, and more. Integrating these two datasets allows for a comprehensive view of a patient's health profile [4].

One of the most impactful areas of this integration is in the field of oncology. Cancer, a disease characterized by genetic mutations, has been a primary focus of genomics research [5]. By analyzing the genetic makeup of tumors, clinicians can identify mutations that drive cancer progression and tailor therapies to target these mutations. For instance, the identification of HER2-positive breast cancer has led to the development of HER2-targeted therapies, such as trastuzumab, which significantly improve outcomes for patients with this specific genetic profile. The integration of clinical data, such as imaging studies and biomarker levels, further refines treatment plans by monitoring response to therapy in real time [6].

In addition to oncology, the integration of genomics and clinical data is proving invaluable in managing rare genetic disorders. Many rare diseases are caused by single-gene mutations, making them particularly amenable to genomic analysis [7]. By combining genomic sequencing with clinical phenotyping, healthcare providers can achieve earlier and more accurate diagnoses, which is critical for initiating timely interventions. This approach has been especially beneficial for pediatric patients, where delays in diagnosis can have long-lasting consequences [8].

Another area where genomics and clinical data integration is advancing precision medicine is in pharmacogenomics, the study of how genetic variations affect drug metabolism and efficacy. Variants in genes such as CYP2C19 and CYP2D6 can influence how individuals metabolize commonly prescribed medications, including antidepressants, anticoagulants, and pain relievers. By incorporating genomic data into electronic health records (EHRs), clinicians can access actionable insights that guide drug selection and dosage, minimizing adverse effects and optimizing therapeutic outcomes [9].

Despite its promise, the integration of genomics and clinical data poses significant challenges. Data interoperability, privacy concerns, and the sheer volume of information are major hurdles. Standardizing data formats across institutions and ensuring secure storage and sharing are critical to realizing the full potential of this integration. Additionally, advanced computational tools, such as artificial intelligence and machine learning, are essential for analyzing and interpreting the complex relationships between genetic and clinical data [10].

Conclusion

In conclusion, the integration of genomics and clinical data is revolutionizing precision medicine by enabling personalized healthcare. By leveraging this synergy, clinicians can better understand disease mechanisms, predict individual risks, and design tailored treatments. As technology continues to evolve and barriers are addressed, this approach promises to enhance patient outcomes and reduce healthcare costs, ultimately transforming the future of medicine.

References

  1. Kalkman S, Mostert M, Gerlinger C, van Delden J JM, van Thiel G JMW. Responsible data sharing in international health research: a systematic review of principles and norms. BMC Med Ethics. 2019;20(01):21.

    2. Indexed at, Google Scholar, Cross Ref

  2. Mascalzoni D, Bentzen H B, Budin-Ljøsne I, Bygrave L A, Bell J, Dove E S et al. Are Requirements to Deposit Data in Research Repositories Compatible With the European Union’s General Data Protection Regulation? Ann Intern Med. 2019;170(05):332–4.

    3. Indexed at, Google Scholar, Cross Ref

  3. Krutzinna J, Taddeo M, Floridi L. Enabling Posthumous Medical Data Donation: An Appeal for the Ethical Utilisation of Personal Health Data. Sci Eng Ethics. 2019;25(05):1357–87.

    4. Indexed at, Google Scholar, Cross Ref

  4. Parasidis E, Pike E, McGraw D. A Belmont Report for Health Data. N Engl J Med. 2019;380(16):1493–5.

    5. Indexed at, Google Scholar, Cross Ref

  5. Schairer C E, Cheung C, Kseniya Rubanovich C, Cho M, Cranor L F, Bloss C S. Disposition toward privacy and information disclosure in the context of emerging health technologies. J Am Med Inform Assoc. 2019;26(07):610–9.

    6. Indexed at, Google Scholar, Cross Ref

  6. Bentzen H B, Høstmælingen N. Balancing Protection and Free Movement of Personal Data: The New European Union General Data Protection Regulation. Ann Intern Med. 2019;170(05):335–7.

    7. Indexed at, Google Scholar, Cross Ref

  7. Oxman A D, Paulsen E J. Who can you trust? A review of free online sources of “trustworthy” information about treatment effects for patients and the public. BMC Med Inform Decis Mak. 2019;19(01):35.

    8. Indexed at, Google Scholar, Cross Ref

  8. Anderson M, Anderson S L. How Should AI Be Developed, Validated, and Implemented in Patient Care? AMA J Ethics. 2019;21(02):E125–130.

    9. Indexed at, Google Scholar

  9. Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inform Assoc. 2020;27(03):491–7.

    10. Indexed at, Google Scholar, Cross Ref

  10. Antonio M G, Petrovskaya O, Lau F.Is research on patient portals attuned to health equity? A scoping review J Am Med Inform Assoc 201926(8-9):871–83.

    11. Indexed at, Google Scholar, Cross Ref