The Role(s) of Biomarkers in Personalized/Precision Medicine

Introduction

It is becoming more widely recognized that in order to effectively prevent and treat any health condition a systems biology approach needs to be adopted (Carini et al., 2016). The focus of systems biology is to analyze the complex relationships between the components of biological systems in response to molecular to environmental influences. A systems biology approach embraces the complexity of human physiological and pathological processes and helps prevent any critical information from being left out of analyses (Carini et al., 2016).

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A better understanding of the complex relationships within biological systems will provide higher quality data that can be translated more effectively into clinical practice. Personalized/precision medicine takes into account a patient’s genetic variability, lifestyle factors, and environment when determining a course of treatment (NIH, 2019).  Instead of a “one-size-fits-all” approach when it comes to treating a patient with a particular condition, these factors can be used to determine which treatment will be most effective for the individual patient, and which will likely not be effective or harmful. These variables will also allow for identification of those at risk for a disease who have not yet been diagnosed and in turn put more of a focus on prevention and early detection rather than a reactive approach (Landeck et al., 2016). 

Biomarkers are a key component in the development of personalized/precision medicine. A biomarker was defined by the National Institutes of Health Biomarkers Definitions Working Group in 1998 as “a characteristic that can be objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to a therapeutic intervention” (Biomarkers Definitions Working Group, 2001). Identifying valid biomarkers will allow for more precise determination of what disease a patient is suffering from, their prognosis, and what treatment and dose they will respond best to (Landeck et al., 2016).

This topic is extremely important to explore because it holds potential for greatly improving the lives of those that are at risk of developing a disease and those that are already suffering from disease. As well, it will greatly increase knowledge about living systems. In this mini-review, the current state of knowledge on the role of biomarkers in personalized medicine will be evaluated and what resources are available to the public and privately will be explored. As well, the legal implications in precision medicine will be discussed. 

Current State of Knowledge

In the last decades, advances in “omics” technologies have led to the discovery of many molecular biomarkers that can be used to identify a disease state or the ability to respond to a particular treatment. As well it has led to a greater understanding about the pathogenesis of many important diseases (Seyhan et al., 2019). Omics technologies are part of a systems biology approach and include high throughput techniques that make it possible to gather large amounts of data about specific molecules in a single experiment (Quezada et al., 2017). Examples of omics fields include: genomics, the study of the whole genome of an organism or a subset of it; transcriptomics, a field of study that looks at the full set of transcripts derived from a cell, tissue type or organism; and proteomics, the study of the universe of proteins in a cell or tissue type (Quezada et al., 2017). As mentioned, omics technologies are critical to first identify possible biomarkers by gathering large amounts of data on a particular molecule (e.g. DNA, mRNA, and proteins) in an individual with a particular disease. This plethora of data can then be further analyzed to determine if certain molecules/biomarkers are associated with a disease state, prognosis or response to therapeutic intervention. Upon discovery, these biomarkers are analytically and clinically validated in trials usually using techniques like PCR and ELISA (Quezada et al., 2017).

A myriad of biomarkers has been discovered over the years for many different diseases. Of high interest has been investigating genomic, epigenomic alterations and miRNA expression as biomarkers for diseases like cancer, diabetes, and inflammatory diseases. For example, a study by Rica et al. (2013), performed molecular profiling for DNA methylation and miRNA expression in Rheumatoid Arthritis (RA) synovial fibroblasts and compared the results with those of osteoarthritis patients who have a normal phenotype. The researchers found changes in key genes involved in RA pathogenesis, such as ILR6, CAPN8, DPP4, and several HOX genes. As well, the majority of genes modified by DNA methylation were inversely correlated with miRNA expression. The study concluded that genes that are regulated by DNA methylation and controlled by miRNAs, and miRNAs that are controlled by DNA methylation have the potential to be used as clinical biomarkers (Rico et al., 2013). However, it should be noted that although there is a surge in biomarker discovery, very few biomarkers are making it to clinical practice. Reasons for this include discovery of biomarkers from archived samples that do not properly represent the population in which the biomarker is intended to be used, poor study design and confounding control, and not following standardized protocols when it comes to sample collection, processing, and storage. Validated and properly calibrated techniques are crucial for correct and reproducible analyses (Quezada et al., 2017). 

Furthermore, valid biomarkers have the potential to lead to an easier and shorter drug development process for the subset of patients who will benefit from the treatment. Traditionally, patients are randomly selected for studies under the assumption that they are fairly homogenous. Many inclusion and exclusion criteria are normally in place to help ensure homogeneity in the patient population, however despite these efforts the patients often end up being quite heterogeneous in regards to lifestyle factors, drug metabolizing abilities, previous exposure to medications and genetic make-up (Seyhan et al., 2019). Prospective use of patient stratification based on the presence of absence of particular biomarkers will for the separation of probable responders from non-responders and speeding up the drug approval process for patients that will benefit while further study can then be done for the greater population. Without patient stratification, a drug that is beneficial for a subset of patients could just be disregarded and lost in the noise caused by non-responders. As well, patient stratification based on biomarkers and understanding underlying molecular mechanisms responsible for disease and drug response will allow for safer and more efficacious matching of patients to a suitable therapy. Additionally, patient stratification will reduce drug development costs by preventing non-responders by being improperly treated (Seyhan et al., 2019).

Next, advancements in technology over the years have led to the development of digital biomarkers. Digital biomarkers can be defined as physiological or behavioural data that is objective and quantifiable and can be measured via digital devices such as wearable’s or embedded environmental sensors (Piau et al., 2019). Digital biomarkers make it possible for easier and continuous data collection that then can be analyzed and used to explain and/or predict health outcomes. A variety of smartphone apps have already been created to monitor users’ health. Besides giving consumers the ability to monitor their health characteristics on a daily basis, digital biomarkers may also be potentially useful for clinical trials and physicians by providing large amounts of data on an individual patient’s health status over time (Seyhan et al., 2019). Limitations of digital biomarkers include having to retrain health care workers and the risks of interfering with individual privacy and handling data securely, which will be discussed in this paper further later on.

Personalized Medicine and Biomarkers in Universal and Private Health Care

Due to the Canada Health Act of 1984 health care in Canada is publicly funded by provincial and territorial governments and is universal (Begin, 1988). Almost all hospital and physician services are provided without user charges. For examples, there are limitations when it comes to psychiatric care and sex reassignment surgeries based on province (Lewis et al., 2001). However, about 30% of Canadian health care is privately funded. This includes services that are not covered, or only partially covered by the public sector, such as prescription drugs, dentistry and optometry (Hutchinson et al., 2011).

In Ontario, the use of precision medicine and biomarkers in the public health sector is related to medically necessary diagnostic and laboratory tests, and prescribed medicine for hospital patients. For example, standard of care biomarker testing in Canada for those diagnosed with advanced lung cancer is now used to determine whether patients have particular gene mutations and are eligible for targeted therapy (Melosky et al., 2018). This includes, testing for epidermal growth factor receptor (EGFR) gene mutations usually using polymerase chain reaction (PCR) with the patient’s biopsy sample. The EGFR gene encodes a receptor tyrosine kinase and these mutations are of the first targetable mutations to be discovered in lung cancer. They have a prevalence of about 20% in patients diagnosed with non-small cell lung cancer. Some, but not all EGFR gene mutations are sensitive to EGFR tyrosine kinase inhibitors (Melsky et al., 2018). Determining whether a patient has these mutations is considered standard of care because patients that have EGFR-sensitive mutations respond well to EGFR inhibitors like erlotinib, which is a Health-Canada approved treatment (Melosky et al., 2018).

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The use of precision medicine and biomarkers in the private health sector is related to prescription drugs provided in a non-hospital setting. An example of a prescription medication that targets a particular biomarker is ivacaftor for treating children with cystic fibrosis. This drug was approved by the FDA in 2012 and is designed to target alterations to chloride channels caused by a rare G551D mutation. The G551D mutation in cystic fibrosis causes chloride channels to be locked close, which prevents the flow of chloride and fluid. Ivacaftor prolongs the open state of chloride channels and in doing so increases BMI, quality of life and decreases sweat chloride concentration (Rafeeq et al., 2017). G551D mutation is only present in 2.3% of patients with cystic fibrosis and highlights the importance of biomarkers as therapeutic targets because without biomarker understanding and testing, ivacaftor would likely get dismissed since it is not very effective for those with more common cystic fibrosis mutations (Rafeeq et al., 2017). 

Ethical Implications of Precision Medicine and Biomarkers

Before a new approach in precision medicine can be implemented, it needs to be established that the benefits of the intervention outweigh the risks and that strategies are in place to ensure effective use in the clinical setting. One specific ethical challenge is determining when evidence surrounding a new treatment or intervention is sufficient to warrant introduction into clinical practice and trials (Korngiebel et al., 2016). The extent of the estimated benefit, the existence of alternative treatments, extent of estimated harm and the overall quality of the evidence are all important factors that are considered in this process. When judging the existing evidence needs to also consider whether they align with the views and preferences of all stakeholders and whether the intervention follows established trustworthy clinical guidelines (Korngiebel et al., 2016). In another aspect of precision medicine, ethical challenges of digital biomarkers need to be considered. Just as new drugs need to be critically evaluated for safety and effectiveness, digital health tools do as well. One of the biggest concerns with self-managed digital biomarkers is ensuring privacy and autonomy since when patients directly interact with the devices themselves it is not protected under the Health Insurance Portability and Accountability Act (Coravos et al., 2019). Clear informed consent and transparency surrounding data-sharing rights and privacy policies is essential. An approach to tracking privacy vulnerabilities and performance transparently is potentially having software manufacturers provide the FDA with a “Software Bill of Materials” before marketing the device. The challenge however is that many of these algorithms are patented or trade secrets, making the possibility of this level of transparency difficult (Coravos et al., 2019).

Concluding Remarks

In conclusion, biomarkers in personalized medicine have the potential to drastically improve the quality and cost effectiveness of health care for many patients by shifting treatment strategies from a “one size fits all” to being more considerate of heterogeneity in the population.

Developments in “omics” technologies has led to the discovery of many novel biomarkers, and although few biomarkers are used in both the private and public health sectors, more rigorous analytical and clinical trials are still needed in order to implement many more potential biomarkers into clinical practice. Additionally, the surge of digital biomarkers is beginning to automate the way patients are monitored and generate large amounts of data on a single patient continuously. However, through this process device validation and data security need to be of high priority.

Future Directions

 In order to more efficiently identify and validate biomarkers so that they can be integrated into clinical practice research needs to be done more rigorously upfront and take a top-down approach. If research heads in this direction there should be an increase seen in the amount of biomarkers entering the Canadian Public Health System in the future. Validating and integrating biomarkers has been a slow process so it may not be likely that very many biomarkers will make it into the clinic over the next 5 years. It seems more practical that this may take 10-20 years.

References

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