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Genetics

Advancing Personalized Medicine: From Genetic Testing to Multi-Omic Integration

October 20, 2025

Key Takeaways:

  • Personalized medicine can leverage genetic and clinical data to tailor treatments, but its widespread clinical use is still limited outside of oncology and rare diseases.
  • Established testing methods support diagnostic accuracy and treatment outcomes, while emerging research innovations continue to demonstrate potential in select settings.
  • Technical, ethical, and regulatory challenges must be addressed to support broader implementation of personalized medicine.

Personalized medicine offers an alternative to the traditional “one-size-fits-all” approach to healthcare, aiming to tailor therapies based on individual genetic and clinical profiles. Advancements in sequencing and computational tools have made it possible to analyze complex genetic and phenotypic data. However, clinical adoption remains limited. Outside of oncology and certain rare disease programs, omic and multi-omic integration is largely confined to research settings. Realizing its clinical potential depends on whether these methods can be made practical for real-world clinical workflows.

Genetic Approaches Utilized in Clinical Practice

There are several genetic approaches currently utilized in clinical practice, including but not limited to identifying genetic variants for personalized medicine. 

  • Oncology-based molecular profiling. One of the more established applications of personalized medicine, molecular testing in cancers such as non-small cell lung and breast cancer helps guide treatment decisions from early through advanced and recurrent stages. Companion diagnostic tests have been approved by the U.S. Food and Drug Administration and other international agencies to identify biomarkers (for example EGFR, HER2, BRAF, etc.) in tissue or whole blood/plasma (liquid biopsy) of patients with cancer that will benefit from a targeted therapy.1
  • Pharmacogenomics. Pharmacogenomic testing has made incremental inroads into routine care for specific use cases. Clinical drug metabolism tests (for example, genotyping of CYP2C19 to guide clopidogrel prescribing2) can help inform drug selection and dosage. However, broader clinical adoption remains inconsistent, driven in part by expectations for high evidence thresholds and the absence of widely endorsed guidance in professional society guidelines.3
  • Clinical genetic testing. Genetic testing is well-established in rare disease settings, where polymerase chain reaction-based assays are routinely used to identify inherited disorders such as Huntington’s disease, cystic fibrosis, or sickle cell anemia.4,5 More advanced approaches, including exome sequencing, genome sequencing, and chromosomal microarray analysis, are increasingly used to improve diagnostic yield of other complex or undiagnosed conditions.6 However, inconsistent reimbursement policies and lingering uncertainty around clinical utility often limit their accessibility in broader patient populations.7

Emerging Innovations in Genetic Research

Advancements in biomedical research, like in the eMERGE network (https://emerge-network.org/), have refined methodologies for interpreting complex genetic and clinical datasets. Integrating genetic testing with electronic health records can enhance early disease detection and support more effective patient management.8 However, these approaches remain largely exploratory and are not yet widely adopted in routine clinical workflows. 

  • Multi-omic integration. The integration of clinical phenotyping with transcriptomic, proteomic, metabolomic, and epigenomic data offers a more holistic understanding of disease biology. As these approaches continue to demonstrate clinical utility and become cost-effective to implement, their availability is expected to expand across a broader range of disease contexts.
  • Artificial intelligence (AI). AI-driven approaches have been demonstrated to enhance the identification and classification of pathogenic variants9, as well as extend the analysis of splicing and structural mutations10. These tools are increasingly integrated into research pipelines and are finding some adoption in the interpretation of variants in clinical genetic sequencing. Their potential lies in accelerating the discovery of therapeutic targets and supporting future clinical applications once validated.
  • Risk prediction. Polygenic risk scores are being used to improve chronic disease prediction, especially when combined with multi-omic data and clinical screening tools. Early studies show promise for identifying individuals at elevated risk for conditions like heart disease, stroke, and diabetes.11 However, clinical implementation remains limited.

Challenges and Considerations

While the multi-omic integration of genetic and clinical data has demonstrated substantial utility in research settings, its translation into clinical practice is hindered by technical, ethical, and logistical barriers. Addressing these challenges is critical to enabling broader clinical adoption and ensuring equitable access to precision medicine.

  • Precise guidelines are needed to determine appropriate timing and candidacy for genetic testing.7
  • Patient privacy and informed consent protocols are essential when sharing genomic data across platforms and institutions.12
  • Scalability challenges arise from the need for interoperable data systems and the costs associated with generating and analyzing multi-omic datasets.
  • Regulatory and reimbursement frameworks must evolve to support the integration of personalized therapies into routine clinical care.13

Personalized medicine is positioned to offer more precise, individualized treatment strategies. While promising technologies like multi-omic integration are advancing rapidly in research, their routine clinical impact remains constrained by practical and systemic barriers like clear guidelines, cost/reimbursement, and test availability. Bridging the gap between research and real-world application will require coordinated efforts across technology, policy, and clinical practice to broader and more consistent implementation.

References

  1. FDA. List of Cleared or Approved Companion Diagnostic Devices (In Vitro and Imaging Tools). September 23, 2024. Accessed October 3, 2025. https://www.fda.gov/medical-devices/in-vitro-diagnostics/list-cleared-or-approved-companion-diagnostic-devices-in-vitro-and-imaging-tools
  2. Dean L, Kane M. Clopidogrel Therapy and CYP2C19 Genotype. In: Pratt VM, Scott SA, Pirmohamed M, Esquivel B, Kattman BL, Malheiro AJ, eds. Medical Genetics Summaries. National Center for Biotechnology Information (US); 2012.
  3. Smith DM, Douglas MP, Aquilante CL, et al. Progress in Pharmacogenomics Implementation in the United States: Barrier Erosion and Remaining Challenges. Clinical Pharmacology & Therapeutics. 2025;118(4):778-789. doi:https://doi.org/10.1002/cpt.3736
  4. Castellani C, Duff AJA, Bell SC, et al. ECFS best practice guidelines: the 2018 revision. J Cyst Fibros. 2018;17(2):153-178. doi:10.1016/j.jcf.2018.02.006
  5. Maestri S, Scalzo D, Damaggio G, Zobel M, Besusso D, Cattaneo E. Navigating triplet repeats sequencing: concepts, methodological challenges and perspective for Huntington’s disease. Nucleic Acids Res. 2025;53(1). doi:10.1093/nar/gkae1155
  6. Wojcik MH, Lemire G, Berger E, et al. Genome Sequencing for Diagnosing Rare Diseases. N Engl J Med. 2024;390(21):1985-1997. doi:10.1056/NEJMoa2314761
  7. Exploring Logistical Barriers to Genetic Testing. In: Realizing the Potential of Genomics across the Continuum of Precision Health Care: Proceedings of a Workshop. National Academies Press (US); 2023. Accessed October 3, 2025. https://www.ncbi.nlm.nih.gov/books/NBK592649/
  8. Zeng C, Bastarache LA, Tao R, et al. Association of Pathogenic Variants in Hereditary Cancer Genes With Multiple Diseases. JAMA Oncol. 2022;8(6):835-844. doi:10.1001/jamaoncol.2022.0373
  9. Jaganathan K, Kyriazopoulou Panagiotopoulou S, McRae JF, et al. Predicting Splicing from Primary Sequence with Deep Learning. Cell. 2019;176(3):535-548.e24. doi:10.1016/j.cell.2018.12.015
  10. Poplin R, Chang PC, Alexander D, et al. A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol. 2018;36(10):983-987. doi:10.1038/nbt.4235
  11. Polygenic Risk Score Task Force of the International Common Disease Alliance. Responsible use of polygenic risk scores in the clinic: potential benefits, risks and gaps. Nat Med. 2021;27(11):1876-1884. doi:10.1038/s41591-021-01549-6
  12. Bonomi L, Huang Y, Ohno-Machado L. Privacy challenges and research opportunities for genomic data sharing. Nat Genet. 2020;52(7):646-654. doi:10.1038/s41588-020-0651-0
  13. Dharani S, Kamaraj R. A Review of the Regulatory Challenges of Personalized Medicine. Cureus. 2024;16(8):e67891. doi:10.7759/cureus.67891
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