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Personalized medicine

Personalized medicine
Literature review current through: Sep 2023.
This topic last updated: Sep 06, 2023.

INTRODUCTION — Personalized medicine (also termed personalized genomics, genomic medicine, or precision medicine) refers to the application of patient-specific profiles, incorporating genetic and genomic data as well as clinical and environmental factors, to assess individual risks and tailor prevention and disease-management strategies.

This topic reviews concepts in personalized medicine, including the use of genetic testing marketed directly to consumers (DTC testing).

Principles of genetic testing, genetic counseling, and pharmacogenomics are discussed separately.

Testing – (See "Genetic testing" and "Next-generation DNA sequencing (NGS): Principles and clinical applications".)

Counseling – (See "Genetic counseling: Family history interpretation and risk assessment".)

Pharmacogenomics – (See "Overview of pharmacogenomics".)

TERMINOLOGY — Personalized medicine involves the use of an individual's clinical information or genetic profile to guide decisions regarding prevention, diagnosis, and treatment of disease [1]. The definition encompasses a broad range of clinical practices in which genetic test results are used to guide patient care.

Additional terms related to genetic testing are defined in the table (table 1) and in a separate topic review. (See "Genetics: Glossary of terms".)

BENEFITS AND LIMITATIONS

Potential benefits — Advocates for personalized medicine cite its potential to yield significant health benefits for patients, practitioners, and society, including:

Improved medical decision-making

Delivery of appropriate therapies that are tailored to a patient's sequence variants or genotype rather than the general population

Optimized disease prevention strategies, including lifestyle and behavioral modification, as well as pharmaco-prevention

Avoiding the use of race as a surrogate for genetic markers

Avoiding administering medications of lower efficacy

Reduced exposure to medications that have the potential for greater toxicity, with resulting lower incidence of treatment-related side-effects and complications

In turn, this could lead to enhanced patient satisfaction with the treatment process, improved adherence to therapy, and reduced health care costs.

Several studies have demonstrated that the availability of genetic information enhances patient adherence to behavior modification and other disease prevention strategies [2-6].

Smoking cessation – Alpha-1 antitrypsin (AAT) deficiency is known to cause severe lung disease in smokers. In a study of 199 smokers, individuals who were homozygous for an AAT null allele with severe deficiency of the AAT protein, were significantly more likely to attempt to quit smoking (59 percent) than either mutation carriers (heterozygotes; 34 percent) or individuals with a normal genotype (26 percent) [2].

In a study of 261 smokers who were asked to consider one of two randomly assigned hypothetical scenarios, individuals who were informed of having genetic risk factors for heart disease were more likely to report an inclination to quit smoking than individuals in a high-risk (but without genetic risk factors) group [5]. A substantial proportion of respondents reported that their decisions were motivated by the belief that smoking cessation would translate to reductions in heart disease, suggesting that genetic risk factors would not negatively influence the ability to modify behavior and improve risk factor avoidance.

Cholesterol lowering – In a study of 781 individuals who were carriers of pathogenic gene variants that cause familial hypercholesterolemia (ascertained through an affected relative), cholesterol-lowering medication use in at-risk individuals increased from 51 to 81 percent two years after learning genotype status [6]. Significant reductions in low-density lipoprotein C levels were noted, although normal levels were not achieved in most subjects.

What is not clear is whether genetic information for individuals who have no known increased risk prior to genetic testing will lead to change in lifestyle behaviors. In a sample of over 2000 patients who completed genome-wide testing through a direct-to-consumer (DTC) vendor, testing did not result in any short-term change in diet or exercise behavior [7].

Momentum for the implementation of personalized medicine in clinical practice is increasing. The price of exome sequencing and genome sequencing continues to fall, and the catalog of disease-associated copy number variants or deleterious sequence variants, produced from genome-wide association studies, array comparative genomic hybridization (array CGH), and next-generation sequencing (NGS; including exome sequencing and genome sequencing), is rapidly increasing [4].

Limitations to widespread use — Skeptics argue that, while there are selected examples where specific biomarkers or genetic tests can help guide medical decision-making, more widely implemented profiling remains challenging in clinical practice. Factors cited to support this viewpoint include the high costs of genetic testing (which may decline), the lack of reliable predictive biomarkers for most conditions, the lack of clear therapeutic alternatives (based on genetic differences) for many conditions, and the lack of knowledge and expertise among most clinicians regarding genetics, risk prediction, and genetic counseling [8-10].

Additional barriers to implementation apply to cancer therapy. These issues are discussed in more detail below. (See 'Obstacles for implementation' below.)

Despite obstacles, there is strong support for the development of personal genomics among health care policy makers and research funding agencies [5,6]. Multi-omic methods such as transcriptomics, epigenomics, proteomics, and metabolomics have been considered as part of an integrated approach for precision medicine [11]. It is also likely that precision medicine will be influenced by artificial intelligence (AI) technologies in the future [12].

Need for greater diversity in genomic databases — As stated in an editorial about the field of genomic medicine, participants (and investigators) in genomic databases are predominantly White people from European or North American countries [13].

This lack of representation of individuals with other genetic ancestry can adversely impact care and potentially cause harm, such as when a variant common in African Americans was misclassified as disease-causing for hypertrophic cardiomyopathy when it is in fact a benign variant. Increasing recognition of this situation has resulted in some efforts to include underserved and underrepresented minorities in genetic studies. (See "Secondary findings from genetic testing", section on 'Underrepresented ethnicities'.)

PERSONALIZED MEDICINE INITIATIVES — Biomarker assays are impacting the practice of several medical specialties, most notably in oncology.

The use of a personalized medicine approach to other diseases has also been advanced by some federal initiatives. As examples:

In 2007, the Department of Health and Human Services (HHS) in the United States launched the Personalized Health Care Initiative (PHCI) [14]. This initiative proposed a set of goals "for achieving gene-based medical care combined with health information technology." The PHCI aims to accelerate the development of personalized treatment strategies and transform the practice of medicine towards individualized patient care.

Components of the program include translational research initiatives to develop high-throughput sequencing technologies (ie, next-generation sequencing [NGS]), enhanced mapping of the genetic determinants of disease and drug responsiveness, development of an informatics infrastructure to promote electronic medical records (EMRs), and broad implementation of genomic data.

In 2015, a precision medicine initiative was proposed in the United States, which would promote enhanced use of existing and new databases of genomic information to improve diagnosis and therapy [15]. In the near-term, the initiative will focus on cancer; a longer-term aim is to generate knowledge related to a broader range of diseases. This initiative provided the framework for the "All of Us" initiative, which aims to sequence one million individuals in order to build a broad and diverse database of genetic variants to better inform risks and treatments for different diseases.

In 2023, the National Institutes of Health has several initiatives as part of the Precision Medicine program. These include [16]:

Precision oncology – Includes identifying genetic changes in different types of cancer and basing treatments on genetic variants rather than cancer type.

Cancer immunotherapy – Uses the patient's immune system in cancer therapy.

Pharmacogenomics – Uses patient genotype data to guide medication dosing and determine risks of adverse events.

Rare Diseases – Improves and accelerates development of new therapies.

Cancer detection — The concept of a biomarker test to detect cancer in asymptomatic individuals is especially appealing because it could lead to early diagnosis; earlier detection may also reduce the need to use more aggressive, more toxic, more costly therapy; and potentially result in a greater number of individuals being cured of their disease.

CancerSEEK is a multi-analyte blood test that detects common, cancer-associated DNA variants using a panel of 61 amplicons for cancer "driver" mutations (for which only one abnormal copy would be sufficient to cause cancer) as well as 41 protein biomarkers associated with common cancers [17]. In a series of 1005 individuals already known to have nonmetastatic cancer of one of eight common cancer types (ovary, liver, stomach, pancreas, esophagus, colon/rectum, lung, or breast), the median sensitivity for detecting cancer was 70 percent. Among individual tumor types, sensitivity was highest for ovarian cancer (98 percent) and lowest for breast cancer (33 percent). Sensitivity increased with more advanced-stage cancers (43 percent for stage I, 73 percent for stage II, and 78 percent for stage III). Specificity was over 99 percent; only 7 of 812 healthy individuals without known cancer tested positive, and it is possible that these individuals had early cancer that was not clinically detectable. The test was also able to determine the type of cancer in the majority of individuals (median, 63 percent). Limitations of the study included the presence of more advanced cancers in case patients than would be expected in the general population, and an absence of known chronic inflammatory conditions in the controls that might lead to more false positive tests. Additional validation is needed before this test is incorporated into clinical practice.

Cancer treatment — In some cases, gene expression profiling may help to stratify the need for therapy or the type of therapy in patients with early-stage cancer. Examples include:

Breast cancer – (See "Deciding when to use adjuvant chemotherapy for hormone receptor-positive, HER2-negative breast cancer" and "Adjuvant endocrine and targeted therapy for postmenopausal women with hormone receptor-positive breast cancer".)

Lung cancer – (See "Systemic therapy in resectable non-small cell lung cancer" and "Subsequent line therapy in non-small cell lung cancer lacking a driver mutation".)

Colon cancer – (See "Adjuvant therapy for resected stage III (node-positive) colon cancer".)

Hematologic cancers – Gene expression profiling has been especially useful in classifying lymphomas, as the profiling enables distinction among a number of lymphoma subtypes that cannot be reliably distinguished histologically, which may lead to differences in management. (See "Pathobiology of diffuse large B cell lymphoma and primary mediastinal large B cell lymphoma" and "Prognosis of diffuse large B cell lymphoma", section on 'Gene expression profiling' and "Clinical manifestations, pathologic features, and diagnosis of peripheral T cell lymphoma, not otherwise specified", section on 'Genetic features'.)

Several cancer treatments have been approved based on molecular markers, independent of the type of cancer, referred to as "tissue-agnostic" therapy. These markers include high microsatellite instability, high tumor mutation burden, NTRK fusions, BRAF V600E mutations, and RET fusions [18]. (See "Tissue-agnostic cancer therapy: DNA mismatch repair deficiency, tumor mutational burden, and response to immune checkpoint blockade in solid tumors" and "TRK fusion-positive cancers and TRK inhibitor therapy".)

The 2015 SHIVA trial was an early randomized trial applying personalized medicine to cancer treatments [19]. This trial randomly assigned 195 patients with metastatic solid tumors, for which standard treatments were ineffective, to receive a molecularly-targeted agent (based on the molecular profile of the tumor) versus standard of care. The median progression-free survival was similar between the two groups (2.3 versus 2.0 months). One conclusion from this study was that further research into personalized medicine is needed. Additional research may focus on combining molecularly targeted agents to overcome resistance or incorporating information about tumor evolution using circulating tumor DNA. Additional studies involving matching therapies to tumor profiles are ongoing [20-24].

The role of genetic testing in solid tumors and gene expression profiling in cancer of unknown primary site is discussed separately. (See "Next-generation DNA sequencing (NGS): Principles and clinical applications", section on 'Cancer screening and management' and "Poorly differentiated cancer from an unknown primary site", section on 'Molecular cancer classifier assays'.)

Pharmacogenetic testing — The earliest clinical implementations of genetic profiling have been in the area of pharmacogenetics, also referred to as pharmacogenomics. Pharmacogenetics is the study of variability in drug response due to genetic factors and includes the prediction of a patient's response to a specific therapy and susceptibility to toxicity and adverse events. Pharmacogenetic data may inform both the selection of a particular treatment and the individualized dose and dosing schedule for that treatment. This subject is described in more detail separately. (See "Overview of pharmacogenomics".)

Drug labels for numerous drugs include information regarding pharmacogenetic biomarkers that can be tested. Though most notably impacting dosing of medications used to treat hematologic malignancies and solid tumors, pharmacogenetic markers are also available for medications used in the treatment of infectious, cardiac, rheumatologic, and pulmonary diseases [25]. Label content includes warnings regarding genotype-specific contraindications or toxicities, dosing recommendations, or information regarding the availability of genetic tests (without specific recommendations for testing). An updated listing of pharmacogenetic biomarkers cited in drug labels in the United States is available on the US Food and Drug Administration (FDA) website [26].

The most extensively studied pharmacogenetic variants are those of the cytochrome P450 drug metabolizing liver enzymes (CYPs). Fifty-eight CYPs have been characterized in humans, and functional single nucleotide polymorphisms (SNPs) that alter functional activity have been identified for many CYPs. These variants influence the metabolism of a wide range of commonly prescribed medications, including 33 with pharmacogenetic biomarker labels. (See "Overview of pharmacogenomics", section on 'Online resources for clinicians'.)

Prenatal testing — With the recognition that a sufficient quantity of fetal DNA is present in the maternal circulation for clinical testing, it is becoming increasingly easier and safer to accurately assess the structural integrity and sequence variation of fetal genes. Consequently, prenatal genetic testing is playing an increasing role in obstetric care, with a move towards implementation of personalized approaches in fetal medicine [27]. (See "Prenatal screening for common aneuploidies using cell-free DNA".)

An example is the implementation of a noninvasive prenatal testing (NIPT) of cell-free fetal DNA (cf-DNA, also called free fetal DNA, ff-DNA) sequences from maternal blood samples. (See "Prenatal screening for common aneuploidies using cell-free DNA" and "Cell-free DNA screening for fetal conditions other than the common aneuploidies".)  

Other applications of cf-DNA in testing (eg, for trisomy 21) are presented separately. (See "Prenatal screening for common aneuploidies using cell-free DNA".)

DIRECT-TO-CONSUMER TESTING

Evolution of DTC testing — The availability and scope of direct-to-consumer (DTC) genetic testing services is evolving, and various legislative and regulatory bodies are actively developing policies regarding these services. Some companies are seeking approval for testing without physician involvement, whereas others are obtaining a physician order based on an online survey. Personal genome testing has been available to the general public in the United Kingdom and Canada since late 2014 [28].

Although DTC testing is marketed directly to patients and performed without an ordering physician, results are typically shared with the health care team and/or genetic counselors [29].

In the Impact of Personal Genomics Study survey, 54 of 961 respondents (5.6 percent) who had recently undergone DTC testing reported either starting a new medication or changing a medication they were already taking based on the results of DTC tests. Most of these patients (83.3 percent) reported discussing the change with a health care provider. Importantly, the vast majority of respondents (875 people, 91.2 percent) had at least one pharmacogenetic variant indicated on their report, and there was a direct association between the number of positive pharmacogenetic tests received and the likelihood of a change in medication prescription (1.57-fold increase in the odds of a change for each positive test result, 95% CI 1.17-2.11) [30].

The following illustrates the history of the availability of genetic testing services by the company 23andMe, which has been the most active in seeking regulatory approval for DTC genetic testing from the US Food and Drug Administration (FDA):

Initial marketing – In approximately 2006, 23andMe began directly marketing genetic testing services to the general public; this included testing for risk variants associated with certain medical conditions, as well as genealogy/ancestry.

FDA ruling – In late 2013, the FDA requested that the company discontinue marketing of their health-related personal genome service (PGS; ie, disease-risk prediction) in the United States because the clinical validity of the service had not been demonstrated by the company [31]. The FDA considered the PGS kit to be a device and hence under their regulatory purview. The company suspended its clinical services in the United States but continued to provide services related to raw genetic data, information about carrier status, and ancestry testing, as well as testing services in the United Kingdom.

FDA authorization for carrier states – In February 2015, the FDA authorized for marketing DTC carrier testing by 23andMe in the United States for Bloom syndrome (an autosomal recessive disorder) [32]. Simultaneously, the FDA classified all carrier-screening tests as class II medical devices, subject to general controls such as misbranding. The FDA subsequently exempted these tests from premarket review. With these regulatory changes, 23andMe and several other companies have begun offering DTC carrier screening for a variety of conditions. As an example, in early 2017, 23andMe began providing DTC testing for increased risk of a predetermined set of 10 conditions that include celiac disease, factor XI deficiency, G6PD deficiency, and late-onset Alzheimer disease; results of this testing might result in lifestyle modifications and/or discussions with a clinician [33]. It should be noted that these tests improve patient access to information about carrier status, but it is unclear how well the results will be used, particularly in the absence of pre- or post-test genetic counseling. Results are returned to patients using reporting formats approved by the FDA.

FDA authorization for cancer risk genes – In March 2018, the FDA authorized marketing DTC testing for three pathogenic variants in BRCA1 and BRCA2 that are common in individuals of Ashkenazi Jewish descent [34]. These represent a very small subset of possible variants, which number over 1000, and they are not common in the general population. Additional BRCA1 and BRCA2 variants were added in 2023 [35]. Customers can elect to receive or not to receive these results when they register for nonclinical (eg, ancestry) testing. The FDA advised that information from this cancer risk testing "should not be used…to determine any treatments."

In January 2019, the FDA authorized marketing for two MUTYH mutations. Biallelic, deleterious variants in MUTYH are associated with adenomatous polyps and an increased risk of early-onset colon cancer; individuals with one deleterious variant in MUTYH have a twofold to threefold increase in their risk for colorectal cancer at the same age as individuals in the general population without pathogenic variants in MUTYH [36,37]. Similar caveats for this testing apply as for BRCA testing, including the much larger number of colon cancer risk genes that are not being tested, and concerns about validity of the results, possible misinterpretation by patients (eg, assuming their colon cancer risk is low based on analysis of only two variants in a single gene), and unclear implications for management.

DTC testing commonly uses a kit for saliva-based testing that can be sent through the mail.

Concerns about value, accuracy, and interpretation — Results of DTC testing should not be used to make a new diagnosis or initiate a new treatment without first confirming the results of testing in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory with a proper "chain of evidence" that ensures the result applies to the tested individual [38,39]. Conceptually, this is analogous to a home pregnancy test performed soon after a missed menstrual period; the results may be correct, but standard practice is to obtain a confirmatory test before initiating a new pregnancy-related medication or instituting additional prenatal care [40,41].

Answers to frequently asked questions about DTC testing are also provided on a website of the National Human Genome Research Institute (NHGRI FAQ page) [42].

It is imperative that physicians are aware of this caveat and are familiar with the potential limitations of DTC, which are outlined below. There is evidence, however, that some practitioners are making clinical decisions based on DTC results without first confirming the results in a CLIA-certified laboratory; this practice should be avoided.

A number of concerns have been raised about the accuracy, interpretation, and value of DTC genetic testing [43]. The following examples illustrate some of these concerns:

Reliability and reproducibility – Given the heterogeneity among DTC vendors in the genetic variants tested and the predictive models used to develop risk estimates, questions have been raised regarding the reliability and reproducibility of these services. The accuracy of identifying pathogenic or likely pathogenic variants is one concern; the accuracy of using the resulting information to predict the risk of disease is a separate concern of equal or greater importance.

Two studies have evaluated these issues; both have found high concordance between companies in determining genotype, but significant variability in interpretation of disease risk, despite agreement on genotype:

One study compared genotyping panels from two DTC companies: 23andMe and Navigenics [44]. DNA from five individuals was sent to each company for testing, and risk predictions for 13 diseases were compared. Genotype calls (ie, the genotypes reported at each locus based on single nucleotide polymorphisms [SNP] analysis) were in excellent agreement (>99 percent concordance). In contrast, risk prediction for diseases was discordant in about one-third of cases. Disease risk predictions from the two companies disagreed more than one-half of the time for seven conditions (eg, systemic lupus erythematosus, heart disease, Crohn disease, type 2 diabetes); risk predictions were concordant in four conditions. Conditions demonstrating strong agreement were those in which an identical SNP with a very strong genetic effect was tested by both companies (for example, the SNP for celiac disease that confers a sevenfold increase in risk). SNPs with such strong effects are rarely observed in common complex diseases.

A second study evaluated the concordance across three companies (23andMe, Navigenics, and deCODE), although only one sample was tested [45]. Genotype concordance was high (99.6 percent), but significant variability in risk prediction was noted, with undetermined clinical validity and utility. The variability in risk prediction was influenced by the SNPs that were genotyped for each condition and the reference population used [45]. Diseases for which genotyped SNPs have strong predictive value were more likely to receive similar risk estimates from different DTC companies [46]. This study also pointed out the need for risk data that is based upon client ethnicity, as the majority of genome-wide association studies have focused on populations with European ancestry, with uncertain applications to other ethnicities, including people with ancestry from Asia, Africa, and other continents [45,46].

These reports suggest that risk prediction remains variable, and interpretation of results for most SNPs should be approached with caution. CLIA-certified laboratories also have variability in interpretation, but they are probably more concordant than DTC laboratories.

Implications for predicting genetic risk – In most cases, data about genetic risk have been obtained from testing individuals with a known personal or family history of disease. Data from testing a healthy population have not yet been obtained for most conditions, and it has not been demonstrated that risk is similar when a variant is identified in an individual with a negative personal and family history of disease.

Implications for management – Results from genetic testing, both typical and atypical, may have major implications for management. In their 2013 notification to the 23andMe company regarding screening for medical risk variants, the US FDA expressed concerns regarding the potential misuse of reported genetic information leading to inappropriate management. As an example, patients might make treatment decisions regarding prophylactic mastectomy, chemoprevention, or aggressive surveillance based on false-positive or false-negative BRCA1 genotypes. An editorialist suggested that the FDA may not have intervened if the genome service had been available only to an ordering physician, rather than marketed directly to consumers [47]. The implications of results should be discussed with a clinical geneticist, genetic counselor, or clinician with expertise in the condition in question, but as the amount of DTC testing increases, resources to provide such counseling may become strained.

Role in lifestyle modification — To the extent that results of DTC testing promote healthy lifestyle and health maintenance interventions such as exercise, healthy diet, and recommended screenings (eg, cancer screening, risk assessment for cardiovascular disease), they may be likely to be of value to the individual. For disorders in which other interventions may be appropriate (eg, medication, surgery), the value of speaking with a clinical geneticist, genetic counselor, or expert clinician should be emphasized regardless of the results of DTC testing. These experts may help with interpretation of results, additional testing not provided in the DTC product, and interventions most likely to be helpful. (See "Genetic counseling: Family history interpretation and risk assessment".)

In addition, patients should be made aware that:

For all but a limited number of SNPs of large effect, the majority of SNPs tested in DTC test kits provide only incremental changes in a patient’s risk profile.

Prospective studies of the predictive accuracy of these products have not been performed, thus precluding the provision of effective counseling or reliable decision-making for most results.

There is substantial intercompany variability in the risk estimates reported [45,46].

The influence of non-genetic factors, environmental exposures, and lifestyle choices on the interpretation of these results is unknown for many of the SNPs tested.

These caveats make the point that for most conditions, a negative test result (ie, susceptibility variant not identified) does not guarantee low risk for disease development, and positive lifestyle modifications should be encouraged regardless of DTC testing results.

OTHER PERSONALIZED MEDICINE PLATFORMS — The early and intuitive focus for personalized medicine has been the development of genetic-based tests. Other "omic" approaches are being developed that will provide a more complete characterization of risk that includes variation between individuals in gene regulation, epigenetics, and cellular metabolism [48]. Such approaches, which are under development as part of research and rarely provided clinically, include:

Gene expression profiling (also referred to as transcriptomics) – The analysis of mRNA (of either individual genes or panels of gene targets) representing gene expression patterns. This profiling often uses microarray technology. Whole transcriptome analysis (also known as RNA-Seq) can also be used as an aid for variant interpretation. Gene expression is dynamic and influenced by a range of cellular, genetic and environmental factors, which makes gene expression a particularly attractive method for profiling malignant cells. (See "Tools for genetics and genomics: Gene expression profiling".)

Proteomics – Qualitative and quantitative analysis of the collection of protein constituents in a biological sample. Typically performed using modification of polyacrylamide gel electrophoresis (PAGE) or matrix-assisted laser desorption/ionization (MALDI) approaches, these methods provide measures of the types and abundance of proteins in a biological sample. Proteomics assays are under investigation in certain tumors.

Metabolomics – The characterization of metabolic profiles; typically consists of a collection of assays that characterize panels of metabolites related to specific pathways. These studies can be static (cross-sectional profiling at a given time-point) or dynamic (assessing the change in profile patterns following a specific metabolic challenge) [49]. In combination with separation methods such as high-performance liquid chromatography or gas chromatography, metabolites are typically characterized either by mass spectrometry (MS) or nuclear magnetic resonance spectroscopy (NMR).

Lipidomics – Characterization of the complete collection of lipids. Lipid structures, like metabolites, can be differentiated by MS or NMR [50,51]. These methods are being applied towards the development of diagnostic tests that assess the lipid composition of cell membranes [52].

Epigenomics – Profile of the epigenetic modifications to DNA (often, methylation) that control gene expression. Unlike genomic changes, epigenomic changes can be affected by the environment. (See "Principles of epigenetics".)

Exposomics – The sum of exposures an individual incurs over a period of time. These may include nutrients, foods, toxins, stresses, exercise, vaccinations, medications, and other exposures. The exposome is highly dynamic and malleable over an individual’s life.

Microbiomics – Characterization of the microbes (typically, bacteria) that reside in or on an individual. A common example is the gut microbiome, which might influence adiposity and/or immunity.

These applications are being increasingly applied to clinical practice, and development of reliable, clinically adaptable assays for these platforms is actively being pursued. One example is in the field of oncology, where gene expression profiling of malignant cells or tissue is becoming an important diagnostic and prognostic tool. Some of the earliest and most successful implementations of gene expression profiling in oncology focused on single genes, such as determining estrogen receptor expression status in breast cancer for informing prognosis and chemotherapeutic options [53]. Subsequently, whole transcriptome expression profiling technologies have been applied to tumor samples, with notable success [54,55]. (See "Tools for genetics and genomics: Gene expression profiling", section on 'Clinical use'.)

OBSTACLES FOR IMPLEMENTATION — Despite early successes in the clinical introduction of a limited number of pharmacogenetic assays, multiple barriers preclude the widespread implementation of personalized medicine as standard clinical practice across medicine.

Limited predictive value of most tests – The development of validated biomarkers and genetic assays represents an important bottleneck. There are a limited number of examples where genetic testing provides substantial gains in guiding therapeutic recommendations for healthy individuals, although diagnostic genetic testing may be very useful for patient management and recurrence risk estimation. However, the number of genetic variants associated with disease susceptibility and pharmacogenetic response continues to increase, especially in oncology.

Lack of physician knowledge – The lack of general knowledge regarding genetics among many medical practitioners is one of the most pressing challenges preventing broad implementation of personalized medicine.

Many clinicians without genetic training do not feel adequately prepared to provide counseling for genetic testing [56]; the number of genetic counselors and clinical geneticists in North America is considered to be lower than required to meet expected demands.

However, medical schools are actively developing curricula content in the areas of personalized medicine to prepare the next generation of physicians, including several programs that have developed case studies of genome sequence data [57,58]. Furthermore, there is still little infrastructure in place to educate clinicians who are already in practice, and the best approaches for educating nongeneticist health care professionals in precision medicine remain uncertain. Approaches include incorporation of teaching into daily work activities, continuing education, courses, interactive workshops, telemedicine, and participation in patient medical homes [59-62].

Several online pharmacogenetics resources are available to assist with clinical decision-making, as listed in a separate topic review. (See "Overview of pharmacogenomics", section on 'Use of genomic biomarkers to guide therapy'.)

Inadequate informatics infrastructure – The recordkeeping for clinical laboratory data resides with the health care providers (and their affiliated institutions) who originally requested the tests.

This model is inadequate to accommodate next-generation sequencing (NGS) data, and newer approaches are under development for modifying the electronic medical record (EMR) for easier access to genetic testing results.

Information provenance and patient privacy issues – Many questions arise when considering how personal genomic information should be managed.

Who would be responsible for ordering a pharmacogenetic genotyping panel (ie, the major CYP loci) that profiles responsiveness for a wide array of drugs?

How would such information be transmitted to other clinicians and pharmacists?

When should genomic profiling be ordered? If personal genomic profiling is performed as part of a neonatal screening program, who will be the purveyors of such data?

Would pediatricians be responsible for interpreting data and facilitating the development of patient management plans related to conditions of adult onset?

Many geneticists do not recommend testing for adult-onset disorders in childhood unless there are diagnostic or management implications relevant to the child at the age of testing. (See "Genetic testing", section on 'Ethical, legal, and psychosocial issues' and "Genetic testing", section on 'Which relatives should be tested?' and "Genetic testing", section on 'Obtaining informed consent'.)

At what age and how would children be informed of their specific liabilities?

Information is typically not provided for asymptomatic or presymptomatic conditions for which there is no known cure or treatment.

Inconsistent standardization and oversight of testing – As illustrated by the preliminary concordance studies of direct-to-consumer (DTC) genetic tests (see 'Direct-to-consumer testing' above), there is considerable inconsistency in the prediction of genetic risk [44,45]. These inconsistencies are largely due to differences in the risk-estimation models used and in the single nucleotide polymorphisms (SNP) content of the array platforms. Standardization of these models and the SNP content will help improve this problem. The US Food and Drug Administration (FDA) is developing policies regarding standardization and oversight of these tests, and policies have been discussed in Australia and the United Kingdom; some have advocated for international certification of quality standards [63].

Reimbursement issues – Changes in the reimbursement policies will be needed to promote a personalized medicine initiative. Several attempts have been made to modify the Medicare clinical laboratory fee schedule to accommodate the reimbursement for complex genetic tests.

Genetic discrimination – Discrimination based on the results of genetic testing or even on the basis of having obtained a test is a potential concern (eg, denial of insurance coverage or other services). Many jurisdictions have tried to put protections in place, such as the Genetic Insurance Non-discrimination Act (GINA) in the United States, which provides some protections (eg, for health insurance, but not for life insurance). (See "Genetic testing", section on 'Genetic discrimination'.)

Societal issues and misconceptions – Numerous societal challenges must be addressed prior to widespread implementation of personalized medicine [64,65].

Acceptance of genetically-based treatment recommendations may be low, particularly among certain groups [54].

Concern that misconception of genetic risk could result in a medicalization of society, whereby healthy individuals become preoccupied with disease prevention based on their profile or undergo unnecessary tests or procedures. One study of individuals who underwent DTC genome profiling found no measurable evidence for increased anxiety, comparing baseline and post-testing anxiety scores [7].

Genome sequencing will likely identify a variety of "risk" alleles in all individuals. With the limited available understanding of risk concepts or penetrance of pathogenic/likely pathogenic variants and risk alleles, the identification of risk alleles or carrier status for autosomal recessive disorders could inappropriately influence reproductive decisions.

Individuals may be adversely impacted when advised of increased risk for sensitive issues, such as psychiatric disorders or behavioral traits.

The desirability of learning if an individual has an increased risk for conditions for which there is no available treatment or prevention is debatable. In one study, more than 80 percent of at-risk individuals who had no symptoms chose not to pursue testing for Huntington disease [66].

Family or caregiver dynamics might be affected by disclosure, or failure to disclose, genetic information.

Oncology-specific considerations – Several considerations were raised in a 2022 article, including [18]:

Need to obtain a pathologic diagnosis before ordering biomarker tests.

Slow turnaround time may delay cancer therapy.

Supporting evidence for biomarker-based treatment may lag behind biomarker identification.

Appropriate biomarker-based therapies may be challenging to obtain.

INFORMATION FOR PATIENTS — UpToDate offers two types of patient education materials, "The Basics" and "Beyond the Basics." The Basics patient education pieces are written in plain language, at the 5th to 6th grade reading level, and they answer the four or five key questions a patient might have about a given condition. These articles are best for patients who want a general overview and who prefer short, easy-to-read materials. Beyond the Basics patient education pieces are longer, more sophisticated, and more detailed. These articles are written at the 10th to 12th grade reading level and are best for patients who want in-depth information and are comfortable with some medical jargon.

Here are the patient education articles that are relevant to this topic. We encourage you to print or e-mail these topics to your patients. (You can also locate patient education articles on a variety of subjects by searching on "patient info" and the keyword(s) of interest.)

Basics topics (see "Patient education: Genetic testing (The Basics)")

SUMMARY

Definition – Personalized medicine (also called precision medicine) involves the use of an individual's genetic profile to guide decisions made with regards to the prevention, diagnosis, and treatment of disease. Other genetics terminology is presented separately. (See 'Terminology' above and "Genetics: Glossary of terms".)

Rationale – Potential benefits of personalized medicine include customized treatment plans that can include targeted pharmacotherapy to improve drug response and reduce toxicity and expense, targeted recommendations for lifestyle modifications and other disease-prevention strategies, and enhanced patient satisfaction with health care. Genetic information can enhance patient compliance with behavior-modification recommendations. (See 'Personalized medicine initiatives' above.)

Drug dosing – The earliest implementation of genetic profiling into clinical practice has been in the area of pharmacogenetic testing. Pharmacogenetic biomarkers are available for over 70 drugs; many of these markers involve polymorphisms in cytochrome drug metabolizing activity. (See 'Pharmacogenetic testing' above and "Overview of pharmacogenomics".)

DTC testing – The use of direct-to-consumer (DTC) genetic testing services is evolving, and various legislative and regulatory bodies are actively developing policies regarding these services. Genetic testing for carrier status is considered a class II medical device and does not require premarket approval by the US Food and Drug Administration (FDA). Clinicians and patients should be aware of the concerns that have been raised about the accuracy, interpretation, and value of DTC genetic testing. Answers to frequently asked questions about DTC testing are provided by the National Human Genome Research Institute (NHGRI FAQ page). (See 'Direct-to-consumer testing' above.)

Other "omics" initiatives – Analyses based upon gene expression profiling, proteomics, metabolomics, or lipidomics may enhance the predictive value of testing used for personalized medicine. Gene expression profiling is used for some tumors, reflecting cellular, genetic, and environmental factors. (See 'Other personalized medicine platforms' above.)

Challenges to implementation – Multiple issues will need to be addressed to successfully implement personalized medicine. Areas of concern include clinicians' limited knowledge base of genetics and risk interpretation if not trained in genetics; lack of infrastructure to store and confidentially retrieve an individual's genetic data; timing of testing; issues related to which clinicians are responsible for ordering, transmitting, and maintaining information; test standardization and quality; reimbursement; and overcoming public misperceptions related to genetic data. (See 'Obstacles for implementation' above.)

Related considerations – Comprehensive reviews of genetic testing and counseling are provided separately. (See "Genetic testing" and "Genetic counseling: Family history interpretation and risk assessment".)

  1. Genetics Home Reference http://ghr.nlm.nih.gov/glossary=personalizedmedicine (Accessed on February 17, 2015).
  2. Carpenter MJ, Strange C, Jones Y, et al. Does genetic testing result in behavioral health change? Changes in smoking behavior following testing for alpha-1 antitrypsin deficiency. Ann Behav Med 2007; 33:22.
  3. Collins RE, Wright AJ, Marteau TM. Impact of communicating personalized genetic risk information on perceived control over the risk: a systematic review. Genet Med 2011; 13:273.
  4. Wright AJ, French DP, Weinman J, Marteau TM. Can genetic risk information enhance motivation for smoking cessation? An analogue study. Health Psychol 2006; 25:740.
  5. Sanderson SC, Michie S. Genetic testing for heart disease susceptibility: potential impact on motivation to quit smoking. Clin Genet 2007; 71:501.
  6. Huijgen R, Kindt I, Verhoeven SB, et al. Two years after molecular diagnosis of familial hypercholesterolemia: majority on cholesterol-lowering treatment but a minority reaches treatment goal. PLoS One 2010; 5:e9220.
  7. Bloss CS, Schork NJ, Topol EJ. Effect of direct-to-consumer genomewide profiling to assess disease risk. N Engl J Med 2011; 364:524.
  8. Li C. Personalized medicine - the promised land: are we there yet? Clin Genet 2011; 79:403.
  9. Vogenberg FR, Barash CI, Pursel M. Personalized medicine: part 3: challenges facing health care plans in implementing coverage policies for pharmacogenomic and genetic testing. P T 2010; 35:670.
  10. Coote JH, Joyner MJ. Is precision medicine the route to a healthy world? Lancet 2015; 385:1617.
  11. Babu M, Snyder M. Multi-Omics Profiling for Health. Mol Cell Proteomics 2023; 22:100561.
  12. Ahmed Z. Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis. Prog Mol Biol Transl Sci 2022; 190:101.
  13. Collins FS, Doudna JA, Lander ES, Rotimi CN. Human Molecular Genetics and Genomics - Important Advances and Exciting Possibilities. N Engl J Med 2021; 384:1.
  14. US Department of Health and Human Services. www.hhs.gov. (Accessed on May 03, 2011).
  15. Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med 2015; 372:793.
  16. National Institutes of Health (NIH). The Promise of Precision Medicine, 2023. Available at: https://www.nih.gov/about-nih/what-we-do/nih-turning-discovery-into-health/promise-precision-medicine (Accessed on June 13, 2023).
  17. Cohen JD, Li L, Wang Y, et al. Detection and localization of surgically resectable cancers with a multi-analyte blood test. Science 2018; 359:926.
  18. Schilsky RL, Longo DL. Closing the Gap in Cancer Genomic Testing. N Engl J Med 2022; 387:2107.
  19. Le Tourneau C, Delord JP, Gonçalves A, et al. Molecularly targeted therapy based on tumour molecular profiling versus conventional therapy for advanced cancer (SHIVA): a multicentre, open-label, proof-of-concept, randomised, controlled phase 2 trial. Lancet Oncol 2015; 16:1324.
  20. https://am.asco.org/ascos-targeted-agent-and-profiling-utilization-registry-tapur-study.
  21. http://www.mdanderson.org/publications/oncolog/previous-issues/2015-january/impact2-study-tests-benefits-of-personalized-cancer-treatment-based-on-molecular-profiling.html.
  22. https://www.clinicaltrials.gov/ct2/show/NCT02029001.
  23. https://www.clinicaltrials.gov/ct2/show/NCT02534675.
  24. https://www.clinicaltrials.gov/ct2/show/NCT02508662.
  25. US Food and Drug Administration. http://www.fda.gov (Accessed on May 03, 2011).
  26. https://www.fda.gov/drugs/science-research-drugs/table-pharmacogenomic-biomarkers-drug-labeling (Accessed on May 13, 2019).
  27. Bianchi DW. From prenatal genomic diagnosis to fetal personalized medicine: progress and challenges. Nat Med 2012; 18:1041.
  28. Wise J. Personal genome service launches in UK. BMJ 2014; 349:g7435.
  29. Makhnoon S, Yu R, Peterson SK, Shete S. Clinical Cancer and Direct-to-Consumer Genetic Test Result-Sharing Behavior: Findings from HINTS 2020. J Pers Med 2022; 13.
  30. Carere DA, VanderWeele TJ, Vassy JL, et al. Prescription medication changes following direct-to-consumer personal genomic testing: findings from the Impact of Personal Genomics (PGen) Study. Genet Med 2017; 19:537.
  31. http://www.fda.gov/ICECI/EnforcementActions/WarningLetters/2013/ucm376296.htm (Accessed on December 02, 2013).
  32. https://www.fda.gov/newsevents/newsroom/pressannouncements/ucm435003.htm (Accessed on August 24, 2017).
  33. https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm551185.htm?source=govdelivery&utm_medium=email&utm_source=govdelivery (Accessed on April 07, 2017).
  34. https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm599560.htm (Accessed on March 07, 2018).
  35. https://www.fda.gov/news-events/press-announcements/fda-roundup-september-1-2023 (Accessed on September 01, 2023).
  36. Nielsen M, Lynch H, Infante E, Brand R. MUTYH-associated polyposis. In: GeneReviews, Adam MP, Ardinger HH, Pagon RA, et al (Eds), University of Seattle, Seattle 2019.
  37. https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfivd/Detail.cfm?ID=6778 (Accessed on February 14, 2019).
  38. Nguyen Dolphyn TT, Ormond KE, Weissman SM, et al. Patient experiences with clinical confirmatory genetic testing after using direct-to-consumer raw DNA and third-party genetic interpretation services. Transl Behav Med 2023; 13:104.
  39. Horton R, Crawford G, Freeman L, et al. Direct-to-consumer genetic testing with third party interpretation: beware of spurious results. Emerg Top Life Sci 2019; 3:669.
  40. Horton R, Crawford G, Freeman L, et al. Direct-to-consumer genetic testing. BMJ 2019; 367:l5688.
  41. Kilbride MK, Domchek SM, Bradbury AR. How Should Patients and Providers Interpret the US Food and Drug Administration's Regulatory Language for Direct-to-Consumer Genetic Tests? J Clin Oncol 2019; 37:2514.
  42. Direct-to-Consumer Genetic Testing FAQ For Healthcare Professionals. National Genome Research Institutes. Available at: https://www.genome.gov/For-Health-Professionals/Provider-Genomics-Education-Resources/Healthcare-Provider-Direct-to-Consumer-Genetic-Testing-FAQ (Accessed on May 18, 2023).
  43. Wright CF, Hall A, Zimmern RL. Regulating direct-to-consumer genetic tests: what is all the fuss about? Genet Med 2011; 13:295.
  44. Ng PC, Murray SS, Levy S, Venter JC. An agenda for personalized medicine. Nature 2009; 461:724.
  45. Imai K, Kricka LJ, Fortina P. Concordance study of 3 direct-to-consumer genetic-testing services. Clin Chem 2011; 57:518.
  46. Zehnbauer B. Direct-to-consumer genetics testing--fair comparisons? Clin Chem 2011; 57:369.
  47. Zettler PJ, Sherkow JS, Greely HT. 23andMe, the Food and Drug Administration, and the future of genetic testing. JAMA Intern Med 2014; 174:493.
  48. Coughlin SS. Toward a road map for global -omics: a primer on -omic technologies. Am J Epidemiol 2014; 180:1188.
  49. Holmes E, Wilson ID, Nicholson JK. Metabolic phenotyping in health and disease. Cell 2008; 134:714.
  50. Gross RW, Han X. Lipidomics at the interface of structure and function in systems biology. Chem Biol 2011; 18:284.
  51. Puri R, Duong M, Uno K, et al. The emerging role of plasma lipidomics in cardiovascular drug discovery. Expert Opin Drug Discov 2012; 7:63.
  52. Quinn PJ, Rainteau D, Wolf C. Lipidomics of the red cell in diagnosis of human disorders. Methods Mol Biol 2009; 579:127.
  53. Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol 2006; 24:3726.
  54. Paik S, Shak S, Tang G, et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 2004; 351:2817.
  55. Friedman DR, Weinberg JB, Barry WT, et al. A genomic approach to improve prognosis and predict therapeutic response in chronic lymphocytic leukemia. Clin Cancer Res 2009; 15:6947.
  56. Scheuner MT, Sieverding P, Shekelle PG. Delivery of genomic medicine for common chronic adult diseases: a systematic review. JAMA 2008; 299:1320.
  57. Walt DR, Kuhlik A, Epstein SK, et al. Lessons learned from the introduction of personalized genotyping into a medical school curriculum. Genet Med 2011; 13:63.
  58. http://deansnewsletter.stanford.edu/archive/09_28_09.html (Accessed on September 28, 2009).
  59. Nisselle A, King EA, McClaren B, et al. Measuring physician practice, preparedness and preferences for genomic medicine: a national survey. BMJ Open 2021; 11:e044408.
  60. Calabrò GE, Tognetto A, Mazzaccara A, et al. Capacity Building of Health Professionals on Genetics and Genomics Practice: Evaluation of the Effectiveness of a Distance Learning Training Course for Italian Physicians. Front Genet 2021; 12:626685.
  61. Carroll JC, Rideout AL, Wilson BJ, et al. Genetic education for primary care providers: improving attitudes, knowledge, and confidence. Can Fam Physician 2009; 55:e92.
  62. Wilkes MS, Day FC, Fancher TL, et al. Increasing confidence and changing behaviors in primary care providers engaged in genetic counselling. BMC Med Educ 2017; 17:163.
  63. Hauskeller C. Direct to consumer genetic testing. BMJ 2011; 342:d2317.
  64. Ormond KE, Wheeler MT, Hudgins L, et al. Challenges in the clinical application of whole-genome sequencing. Lancet 2010; 375:1749.
  65. Samani NJ, Tomaszewski M, Schunkert H. The personal genome--the future of personalised medicine? Lancet 2010; 375:1497.
  66. Creighton S, Almqvist EW, MacGregor D, et al. Predictive, pre-natal and diagnostic genetic testing for Huntington's disease: the experience in Canada from 1987 to 2000. Clin Genet 2003; 63:462.
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References

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