Omics for personalized medicine: defining the current we swim in

KEYWORDS:

• Precision medicine
• molecular analysis
• Omics
• tumor markers
• direct-to-consumer testing
• overtreatment
• SNPs
• next generation sequencing

Introduction

Approaching a new epoch of personalized medicine, recently there has been a growing trend of Omics projects with the premise of utilizing high-throughput genomic analyses to achieve personalized health care, especially through prediction of disease risk and early intervention for a potentially better outcome. The spectrum of Omics is broad and encompasses genomics, transcriptomics, epigenomics, immunology, proteomics, in addition to metabolomics. In this Editorial, we will focus on genomics as it represents the majority of commercially available testing.

Examples of these projects include the ‘Baseline Study’ by Google Inc. which will utilize extensive genomic analysis to identify ‘biomarker patterns’ for detection of disease risk, under the assumption that being proactive with earlier intervention will lead to a significantly better health outcome [1]. Another project with a similar concept is the ‘Hundred Person Wellness Project’ by the Institute for Systems Biology. This project analyzes the detailed genomic, proteomic, epigenetic, metabolomic, and phenotypic measurements for 100,000 individuals through frequent examination of blood, saliva, and stools as well as other physiological and psychological parameters. A similar project is carried out by the ‘Lake Nona Life Project’. Another interesting and very promising field is the pediatric applications of personalized medicine including prenatal diagnosis, neonatal screening, and diagnosis of genetic diseases and others, as covered in more detail in a recent publication [2]. Another area that is gaining attention recently is the use of Omics to achieve a new horizon of precision medicine in therapy. Recently, the National Cancer Institute of the US launched the NCI-MATCH (Molecular Analysis for Targeted Therapy) including patients with a number of solid tumors or lymphomas who have genomic abnormalities known to drive cancer. Patients will be matched with targeted therapies as appropriate and this will represent a new revolution for cancer treatment [3].

Along with the same trend, there is a growing number of ‘direct-to-consumer (DTC) testing’. The foundation of DTC testing is to make actionable health information (obtained from high-throughput genomic data) accessible to the public, to enable them to be active participants in their health care decisions, thus approaching the new era of P4 medicine (Predictive, Preventive, Personalized, and Participatory) [4]. There is a number of DTC companies currently in the market including ‘Theranos’, ‘23 and me’[5], Decode genetics, Navigenetics, ‘ATLAS Sports genes’, and others.

Although these initiatives represent valuable steps toward understanding the underlying biology of diseases, thereby improving health care, there are a number of issues that have to be addressed carefully so that we do not give false hope to the public [6], as outlined below.

Translational Omics: a double-edged sword

Promises of P4 medicine

There are a number of potential benefits of Omics testing that hold the promise of significantly revolutionizing health care and moving into a new epoch of improved health and lifestyle. Advocates of translational Omics anticipate a new age of participatory medicine; DTC testing will make health information more accessible to the public and to the medical community at large, which is predicted to have favorable impact on patient care [4]. The benefits include more thorough understanding of the pathogenesis of common health problems such as mental health, obesity, diabetes, and cancer predisposition. Also, proponents of DTC testing base their support on the argument that it helps patients to quickly obtain broad-based health information, and to contextualize their own medical experience [7]. Additionally, marketing of these tests will be positively reflected on doctor–patient relationships through encouraging bidirectional dialogue, thus, getting patients to be active participants in managing their own health issues. Easy accessibility of important health-care parameters is another advantage.

In a recent survey of DTC test customers, 66% of participants felt the need for DTC tests to be available without governmental oversight [8]. Most of these customers, however, favored an organization operating alongside DTC companies to ensure the scientific legitimacy of the claims made by these companies. It is also important to emphasize that the most commonly cited reason to purchase a DTC genetic test is curiosity and the desire of knowledge; this can be a transforming experience leading to better health and life choices. It has also been expected that DTC tests will stimulate health-care professionals to keep up with genomic advances. Professional organizations are now increasingly offering additional training to bridge this knowledge gap.

Challenges of the Omics era

There are a number of challenges and limitations of translational Omics that need to be carefully addressed, especially when it comes to predictive ability (screening for the risk of cancer and other diseases) [9]. One such important issue is the relationship between association and causation. A recent study analyzed 406 published severe disease mutations and found that 27% of these were either common polymorphisms or lack direct evidence for pathogenicity. In addition, numerous alleged severe disease-causing variants were identified in the genome of the population control [4]. The study concluded that there is an urgent need for clear guidelines for distinguishing disease-causing from non-disease-causing variants. Without regular standards, we will be accelerating the risk of ‘false-positive’ reports of causality which can significantly impede the translation of genomic research findings and hinder the biological understanding of disease. In this regard, it is also important to distinguish between two types of mutations: the inherited germ-line variations/mutations and somatic mutations that accumulate in certain organ/tissue. These two types of mutations are investigated differently using different strategies for sequencing. They have distinct, however, complementary messages.

The predictive ability of genetic testing can be tricky. A study assessed the capacity of whole genome sequencing to identify individuals at clinically significant risk of 24 different diseases [10]. For 23 of these diseases, majority of individuals received negative test results. About 90% of the tested individuals were found, however, to be altered to a clinically significant predisposition to at least one disease.

False-positive results are another critical issue. A recent article by Cohane et al. showed that if the disease prevalence is 1:100,000 and we screened 10 million individuals with a test that has 99.9% sensitivity and specificity, we will be likely to get 10,000 false-positive results [11]. Another great risk to genomic medicine is the risk of the ‘incidentalome’. These are the unexpected positive findings that will be encountered during large-scale genomic analysis. Recent analyses showed that large-scale genomic analyses are likely to yield unexpected incidental findings for almost everyone [4]. Recently, the American College of Medical Genetics policy statement suggested only a minimal list of gene mutations that should be reported by the laboratory to the ordering physicians regardless of the indication for sequencing [12]. They also emphasized the importance of comprehensive pretest and posttest counseling to patients. As outlined in recent literature, Omics approaches are being tested for their clinical utility in Mendelian diseases, common multifactorial diseases like diabetes, hypertension, pharmacogenomics, and neonatal screening for pediatric disorders. Challenges that face most of these applications include the need for technology and computational analysis development, accuracy of prediction, training of medical personnel, and cost-effectiveness. As detailed elsewhere, ethical issues and issues related to gene patenting are also important.

In a recent landmark review by Diamandis et al. [13], the authors highlighted three major side effects of translational Omics: overtesting, overdiagnosis, and overtreatment. The article highlighted the important fact that if the disease is rare, the positive predictive value of the test will be low even if the test has excellent sensitivity and specificity. False-positive results may require subsequent invasive procedures to delineate. Furthermore, screening might not be always useful. An interesting example is the screening for prostate cancer. After the introduction of prostate specific antigen testing a couple of decades ago, we ran into the problem over diagnosis and consequently over treatment of patients with prostate cancer. It is now clear that many of these patients have an indolent form of the disease that is not lethal and these patients will benefit more from conservative approaches like active surveillance. It should be also noted that genomics cannot be directly translated into a phenotypic disease outcome. The relationship between genotype and phenotype is multidimensional that includes other factors including the microbiome, type of lifestyle, and other physiological parameters, as detailed elsewhere [4].

Another serious obstacle is the technical issues related to genomic testing, including different results obtained by different platforms, the need to standardize sample acquisition, and preparation and interpretation of what should be considered as a positive finding. Issues related to intra-tumor heterogeneity should be also considered.

It should be also noted that most of the currently available tests are based on single-nucleotide polymorphism (SNP) analyses that are not validated [14]. It is also very important to note that DTC tests, which are not associated with a qualified professional guidance to interpret their complex nature, can do more harm than good by giving an individual a false impression that a ‘full screen’ was performed for a specific disease, whereas in reality, only a couple of familial or common SNPs were assessed opposed to a full spectrum of disease predisposition testing. Obvious ethical-related issues (specially obtaining an informative consent), legal issues, and social issues have to be considered [15]. Security and privacy are not to be taken lightly [4].

Moving forward: strategies to improve the value of ‘Omics’

There are a number of strategies that can be implemented in order to help high-throughput genomics and DTC testing to be more efficient, reliable, and credible.

Cancer screening: back to the basics

Since a landmark of the genomic era is early detection of disease risk that will allow early intervention leading to better health outcome, it is useful to go back to the basics. The original WHO criteria for screening, published in 1968, are still valid in this regard [16]. It states that there should be a recognizable latent or early symptomatic stage. It also emphasizes that the overall benefit of screening should outweigh the harms. Unfortunately, most of these are not fulfilled for genomic testing [4].

Another interesting aspect of screening is the need to have an actionable item. The value of most DTC testing and many of the upcoming large-scale genomics remains a subject of vibrant debate. A recent study highlighted that in many situations, test results led to general recommendations of lifestyle changes, including exercise and healthy eating habits. Side effects of DTC tests included no significant behavioral changes and surprisingly no anxiety. ‘Fatalism’ was also among the side effects. Another recent systematic review of European guidelines indicates that professional societies and associations are currently more suggestive of disadvantages of DTC genetic testing [17]. Also, in many cases, the health benefit claims of testing are hypothetical and not supported by evidence. Until such evidence is provided, self-testing of healthy population should be discouraged. When individuals were provided with full detailed information regarding the limitation and risks of online DTC tests, they became less willing to perform this test.

It is not thus surprising that the emerging concept of ‘escape from cure’ is gaining attention [13]. Also, ‘the less is more’ movement is considered the next big thing in medicine; it aims to reduce screening and the utilization of low-value diagnostic testing and low-value treatment.

Strategies to increase efficiency of genetic testing

The journey from discovery to clinical application is a long one that requires appropriate technology and proper strategy [18]. As recently suggested, strategies to increase the cost-effectiveness of genomic medicine include targeted screening for high-risk population, concentrating on chronic diseases, and focused screening on diseases with known interventions. Another useful tactic is to identify molecular changes that are directly related to short-term benefits [9]. It is worth mentioning that the standard dogma that early treatment will lead to better outcome does not always hold true. In breast cancer, a recent study showed that the reduction in cancer-related mortality was not much improved by screening [19]. Independent validations are very important. Another important issue that will improve the value of genetic testing is the initiation of mechanistic studies to establish genotypic–phenotypic associations and understand the pathophysiology behind these observed Omics alterations. This will be also an important step toward the utilizations of Omics for treatment purposes. In addition, there is no accepted standard for genomic testing. There is a need for Clinical Laboratory Improvement Amendment program published standards for reporting genomic testing [20]. There is also a need for curated compendium database of known pathogenic variants. Finally, in order to improve the efficiency of genetic testing, multiple dimensions should be considered including sounds clinical validity, clinical utility, cost-effectiveness, health service impact analysis, in addition to a number of ethical and social factors such as psychological issues, and patient preferences, as recently summarized [21].

Transparency, open access, and partnerships

In an editorial published in 2010, Francis Collins identified a number of key lessons for the future of personalized medicine [22]. Free and open access to genomic data can have profoundly positive effect on the progress of genomic medicine. Due to the huge variation in technology, there is a need to develop a uniform framework for external quality assurance, ongoing validation, and accreditation [21]. Furthermore, achieving the promise of new target therapy requires a new paradigm of public–private partnership. Solid study design with careful attention to sample size and statistical consideration is also of prime importance. Linking genomic testing to health strategy that connects the research bench to bedside is also important and this requires a dialogue between the research community and those who are involved in health decisions at community level. Also, transparent reporting of negative or statistically insignificant results should be encouraged. Guidelines are also needed on reporting incidental findings, especially those where clinical intervention is available. Finally, there is a need to develop high-quality evidence on the value and limitations of genetic testing when it comes to patient management.

Future perspective

Although significant steps have been taken in the last decades to achieve personalized medicine, it seems like the ‘once and for all solution’ for disease risk prediction is far from being achievable at least in the foreseen future. It should also be mentioned here that most of these products are not ready for clinical practice. In addition, the medical community is not ready yet to adapt these testings.

Despite the great enthusiasm about the Human Genome Project, it turns out that it led to little useful predictive power over traditional prediction algorithms [23]. We need to have a more realistic understanding of the potential ability of risk prediction through genomic medicine. Proponents of personalized medicine should communicate a more realistic set of expectations to the public [24]; the genome alone is simply not enough to explain the complex phenotype of cancer and other diseases. The complexity of disease phenotype stems from the interaction with environmental and dietary factors (e.g. the microbiome) which contribute to disease susceptibility which is not projected in genomic analysis. Validation remains a key issue for the success of Omics.

Declaration of interest

This work is supported by grants from the Canadian Institute of Health Research (MOP 119606), Kidney Foundation of Canada (KFOC130030), the Kidney Cancer Research Network of Canada and Prostate Cancer Canada Movember Discovery Grants (D2013-39). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.