Precision medicine

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Precision medicine (PM) is a medical model that proposes the customization of healthcare, with medical decisions, treatments, practices, or products being tailored to a subgroup of patients, instead of a one‐drug‐fits‐all model.[1][2] In precision medicine, diagnostic testing is often employed for selecting appropriate and optimal therapies based on the context of a patient’s genetic content or other molecular or cellular analysis.[3] Tools employed in precision medicine can include molecular diagnostics, imaging, and analytics.[4][5]

Relationship to personalized medicine[edit]

In explaining the distinction from a similar common term of personalized medicine, the National Research Council explains:

Precision Medicine refers to the tailoring of medical treatment to the individual characteristics of each patient. It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease, in the biology or prognosis of those diseases they may develop, or in their response to a specific treatment. Preventive or therapeutic interventions can then be concentrated on those who will benefit, sparing expense and side effects for those who will not. Although the term 'personalized medicine' is also used to convey this meaning, that term is sometimes misinterpreted as implying that unique treatments can be designed for each individual.[4]

On the other hand, use of the term "precision medicine" can extend beyond treatment selection to also cover creating unique medical products for particular individuals—for example, "...patient-specific tissue or organs to tailor treatments for different people."[6] Hence, the term in practice has so much overlap with "personalized medicine" that they are often used interchangeably.[7]

Scientific basis[edit]

Precision medicine often involves the application of panomic analysis and systems biology to analyze the cause of an individual patient's disease at the molecular level and then to utilize targeted treatments (possibly in combination) to address that individual patient's disease process. The patient's response is then tracked as closely as possible, often using surrogate measures such as tumor load (versus true outcomes, such as five-year survival rate), and the treatment finely adapted to the patient's response.[8][9] The branch of precision medicine that addresses cancer is referred to as "precision oncology".[10][11] The field of precision medicine that is related to psychiatric disorders and mental health is called "precision psychiatry."[12][13]

Inter-personal difference of molecular pathology is diverse, so as inter-personal difference in the exposome, which influence disease processes through the interactome within the tissue microenvironment, differentially from person to person. As the theoretical basis of precision medicine, the "unique disease principle"[14] emerged to embrace the ubiquitous phenomenon of heterogeneity of disease etiology and pathogenesis. The unique disease principle was first described in neoplastic diseases as the unique tumor principle.[15] As the exposome is a common concept of epidemiology, precision medicine is intertwined with molecular pathological epidemiology, which is capable of identifying potential biomarkers for precision medicine.[16]


The ability to provide precision medicine to patients in routine clinical settings depends on the availability of molecular profiling tests, e.g. individual germline DNA sequencing.[17] While precision medicine currently individualizes treatment mainly on the basis of genomic tests (e.g. Oncotype DX[18]), several promising technology modalities are being developed, from techniques combining spectrometry and computational power to real-time imaging of drug effects in the body.[19] Many different aspects of precision medicine are tested in research settings (e.g., proteome, microbiome), but in routine practice not all available inputs are used. The ability to practice precision medicine is also dependent on the knowledge bases available to assist clinicians in taking action based on test results.[20][21][22] Early studies applying omics-based precision medicine to cohorts of individuals with undiagnosed disease has yielded a diagnosis rate ~35% with ~1 in 5 of newly diagnosed receiving recommendations regarding changes in therapy.[23]

On the treatment side, PM can involve the use of customized medical products such drug cocktails produced by pharmacy compounding[24] or customized devices.[25] It can also prevent harmful drug interactions, increase overall efficiency when prescribing medications, and reduce costs associated with healthcare.[26]

The question of who benefits from publicly funded genomics is an important public health consideration, and attention is needed to ensure that implementation of genomic medicine does not further entrench social‐equity concerns.[27]

Artificial intelligence in precision medicine[edit]

Artificial intelligence is a providing paradigm shift toward precision medicine.[28] Machine learning algorithms are used for genomic sequence and to analyze and draw inferences from the vast amounts of data patients and healthcare institutions recorded in every moment.[29] AI techniques are used in precision cardiovascular medicine to understand genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates.[30] A 2021 paper reported that machine learning was able to predict the outcomes of Phase III clinical trials (for treatment of prostate cancer) with 76% accuracy.[31] This suggests that clinical trial data could provide a practical source for machine learning-based tools for precision medicine.

Precision medicine may be susceptible to subtle forms of algorithmic bias. For example, the presence of multiple entry fields with values entered by multiple observers can create distortions in the ways data is understood and interpreted.[32]

Precision Medicine Initiative[edit]

In his 2015 State of the Union address, U.S. President Barack Obama stated his intention to fund an amount of $215 million[33] to the "Precision Medicine Initiative" of the United States National Institutes of Health.[34] A short-term goal of the Precision Medicine Initiative was to expand cancer genomics to develop better prevention and treatment methods.[35] In the long term, the Precision Medicine Initiative aimed to build a comprehensive scientific knowledge base by creating a national network of scientists and embarking on a national cohort study of one million Americans to expand our understanding of health and disease.[36] The Mission Statement of the Precision Medicine Initiative read: "To enable a new era of medicine through research, technology, and policies that empower patients, researchers, and providers to work together toward development of individualized treatments".[37] In 2016 this initiative was renamed "All of Us" and an initial pilot project had enrolled about 10,000 people by January 2018.[38]

Benefits of precision medicine[edit]

Precision medicine helps health care providers better understand the many things—including environment, lifestyle, and heredity—that play a role in a patient's health, disease, or condition. This information lets them more accurately predict which treatments will be most effective and safe, or possibly how to prevent the illness from starting in the first place. In addition, benefits are to:

  • shift the emphasis in medicine from reaction to prevention
  • predict susceptibility to disease
  • improve disease detection
  • preempt disease progression
  • customize disease-prevention strategies
  • prescribe more effective drugs
  • avoid prescribing drugs with predictable negative side effects
  • reduce the time, cost, and failure rate of pharmaceutical clinical trials
  • eliminate trial-and-error inefficiencies that inflate health care costs and undermine patient care

See also[edit]


  1. ^ National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease (16 December 2011). Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease. doi:10.17226/13284. hdl:2027.42/146502. ISBN 978-0-309-22222-8. PMID 22536618.[page needed]
  2. ^ Yau, Tung On (October 2019). "Precision treatment in colorectal cancer: Now and the future". JGH Open. 3 (5): 361–369. doi:10.1002/jgh3.12153. PMC 6788378. PMID 31633039.
  3. ^ Lu YF, Goldstein DB, Angrist M, Cavalleri G (July 2014). "Personalized medicine and human genetic diversity". Cold Spring Harbor Perspectives in Medicine (Essay). 4 (9): a008581. doi:10.1101/cshperspect.a008581. PMC 4143101. PMID 25059740.
  4. ^ a b Timmerman, Luke (4 February 2013). "What's in a Name? A Lot, When It Comes to 'Precision Medicine'". Xconomy. Boston.
  5. ^ Jones DT, Banito A, Grünewald TG, Haber M, Jäger N, Kool M, et al. (August 2019). "Molecular characteristics and therapeutic vulnerabilities across paediatric solid tumours". Nature Reviews Cancer (Review). 19 (8): 420–438. doi:10.1038/s41568-019-0169-x. PMID 31300807. S2CID 196350118.
  6. ^ Clerk J (23 February 2015). "Changing medicine with 3-D bioprinting, where organs can be synthesized by technology". Los Angeles Times.
  7. ^ "Tailored Clinical Molecular Test Interpretation". N-of-One. Archived from the original on 2015-03-09. Retrieved 2015-03-05.
  8. ^ Blau CA, Liakopoulou E (24 October 2012). "Can we deconstruct cancer, one patient at a time?". Opinion. Trends in Genetics. CellPress. 29 (1): 6–10. doi:10.1016/j.tig.2012.09.004. PMC 4221262. PMID 23102584.
  9. ^ Tavassoly I, Hu Y, Zhao S, Mariottini C, Boran A, Chen Y, et al. (August 2019). "Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses". Molecular Oncology. 13 (8): 1725–1743. doi:10.1002/1878-0261.12521. PMC 6670022. PMID 31116490.
  10. ^ Garraway LA, Verweij J, Ballman KV (May 2013). "Precision oncology: an overview". Journal of Clinical Oncology. 31 (15): 1803–5. doi:10.1200/jco.2013.49.4799. PMID 23589545. S2CID 42163820.
  11. ^ Shrager J, Tenenbaum JM (February 2014). "Rapid learning for precision oncology". Nature Reviews. Clinical Oncology. 11 (2): 109–18. doi:10.1038/nrclinonc.2013.244. PMID 24445514. S2CID 11225698.
  12. ^ Fernandes BS, Williams LM, Steiner J, Leboyer M, Carvalho AF, Berk M (April 2017). "The new field of 'precision psychiatry'". BMC Medicine. 15 (1): 80. doi:10.1186/s12916-017-0849-x. PMC 5390384. PMID 28403846.
  13. ^ Fernandes BS, Berk M (June 2017). "Staging in bipolar disorder: one step closer to precision psychiatry". Revista Brasileira de Psiquiatria. 39 (2): 88–89. doi:10.1590/1516-4446-2017-3902. PMC 7111440. PMID 28591270.
  14. ^ Ogino S, Lochhead P, Chan AT, Nishihara R, Cho E, Wolpin BM, et al. (April 2013). "Molecular pathological epidemiology of epigenetics: emerging integrative science to analyze environment, host, and disease". Modern Pathology. 26 (4): 465–84. doi:10.1038/modpathol.2012.214. PMC 3637979. PMID 23307060.
  15. ^ Ogino, Shuji; Fuchs, Charles S; Giovannucci, Edward (July 2012). "How many molecular subtypes? Implications of the unique tumor principle in personalized medicine". Expert Review of Molecular Diagnostics. 12 (6): 621–628. doi:10.1586/erm.12.46. PMC 3492839. PMID 22845482.
  16. ^ Ogino S, Lochhead P, Giovannucci E, Meyerhardt JA, Fuchs CS, Chan AT (June 2014). "Discovery of colorectal cancer PIK3CA mutation as potential predictive biomarker: power and promise of molecular pathological epidemiology". Oncogene. 33 (23): 2949–55. doi:10.1038/onc.2013.244. PMC 3818472. PMID 23792451.
  17. ^ Ashley EA, Butte AJ, Wheeler MT, Chen R, Klein TE, Dewey FE, et al. (May 2010). "Clinical assessment incorporating a personal genome". Lancet. 375 (9725): 1525–35. doi:10.1016/s0140-6736(10)60452-7. PMC 2937184. PMID 20435227.
  18. ^ "Oncotype DX: Genomic Test to Inform Breast Cancer Treatment". 2019-06-13.
  19. ^ Precision Medicine: Harnessing the Extraordinary Growth in Medical Data for Personalized Diagnosis and Treatment
  20. ^ Huser V, Sincan M, Cimino JJ (2014). "Developing genomic knowledge bases and databases to support clinical management: current perspectives". Pharmacogenomics and Personalized Medicine. 7: 275–83. doi:10.2147/PGPM.S49904. PMC 4175027. PMID 25276091.
  21. ^ Ashley EA (June 2015). "The precision medicine initiative: a new national effort". JAMA. 313 (21): 2119–20. doi:10.1001/jama.2015.3595. PMID 25928209.
  22. ^ Ashley EA (August 2016). "Towards precision medicine". Nature Reviews. Genetics. 17 (9): 507–22. doi:10.1038/nrg.2016.86. PMID 27528417. S2CID 2609065.
  23. ^ Splinter, Kimberly; Adams, David R.; Bacino, Carlos A.; Bellen, Hugo J.; Bernstein, Jonathan A.; Cheatle-Jarvela, Alys M.; Eng, Christine M.; Esteves, Cecilia; Gahl, William A.; Hamid, Rizwan; Jacob, Howard J.; Kikani, Bijal; Koeller, David M.; Kohane, Isaac S.; Lee, Brendan H.; Loscalzo, Joseph; Luo, Xi; McCray, Alexa T.; Metz, Thomas O.; Mulvihill, John J.; Nelson, Stanley F.; Palmer, Christina G.S.; Phillips, John A.; Pick, Leslie; Postlethwait, John H.; Reuter, Chloe; Shashi, Vandana; Sweetser, David A.; Tifft, Cynthia J.; Walley, Nicole M.; Wangler, Michael F.; Westerfield, Monte; Wheeler, Matthew T.; Wise, Anastasia L.; Worthey, Elizabeth A.; Yamamoto, Shinya; Ashley, Euan A.; Undiagnosed Diseases Network (29 November 2018). "Effect of Genetic Diagnosis on Patients with Previously Undiagnosed Disease". New England Journal of Medicine. 379 (22): 2131–2139. doi:10.1056/NEJMoa1714458. PMC 6481166. PMID 30304647.
  24. ^ "Divining your future in healthcare". 2013-10-18.
  25. ^ "3D-Printed Medical Devices Spark FDA Evaluation". 30 August 2013.
  26. ^ "Personalized Medicine Benefits - The Jackson Laboratory".
  27. ^ Belcher, Andrea; Mangelsdorf, Marie; McDonald, Fiona; Curtis, Caitlin; Waddell, Nicola; Hussey, Karen (June 2019). "What does Australia's investment in genomics mean for public health?". Australian and New Zealand Journal of Public Health. 43 (3): 204–206. doi:10.1111/1753-6405.12887. PMID 30830712.
  28. ^ Mesko B (2017). "Expert Review of Precision Medicine and Drug Development". Journal Expert Review of Precision Medicine and Drug Development. 2 (5): 239–241. doi:10.1080/23808993.2017.1380516.
  29. ^ Ray A. "Artificial Intelligence and Blockchain for Precision Medicine". Inner Light Publishers. Retrieved 21 May 2018.
  30. ^ Krittanawong C, Zhang H, Wang Z, Aydar M, Kitai T (May 2017). "Artificial Intelligence in Precision Cardiovascular Medicine". Journal of the American College of Cardiology. 69 (21): 2657–2664. doi:10.1016/j.jacc.2017.03.571. PMID 28545640.
  31. ^ Beacher, Felix D.; Mujica-Parodi, Lilianne R.; Gupta, Shreyash; Ancora, Leonardo A. (5 May 2021). "Machine Learning Predicts Outcomes of Phase III Clinical Trials for Prostate Cancer". Algorithms. 14 (5): 147. doi:10.3390/a14050147.
  32. ^ Ferryman, Kadija; Pitcan, Mikaela (2018-02-26). "Fairness in Precision Medicine". Data & Society. Retrieved 2021-10-27.
  33. ^ "The Impact of Precision Medicine on Cancer".
  34. ^ Neergard L (30 January 2015). "Obama Proposes 'Precision Medicine' to End One-Size-Fits-All". Drug Discovery & Development. Associated Press.
  35. ^ "Near-term Goals".
  36. ^ "Longer-term Goals".
  37. ^ "The White House Precision Medicine Initiative". – via National Archives.
  38. ^ Cunningham PW (2018-01-16). "The Health 202: NIH wants 1 million Americans to contribute to new pool of gene data". The Washington Post. ISSN 0190-8286. Retrieved 2018-01-20.

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