Personalizing public health in the medical laboratory: The interplay of microarrays and physiological genomics

Dec. 13, 2013

Microarrays and the medical laboratory

Microarray probes can serve as economical and ultrasensitive reporters of dynamic cellular phenomena and protein interactions, allowing precise physiological phenotypes to be coupled to genomics. Although single gene effects are the basis of genetic diseases, partial penetrance is the rule in common clinical care. The pathways of physiology in personalized medicine are multi-genetic, as they rely on networks of genes and not on single receptors and enzymes. With the advent of gene microarray-based arrays, parallel processing of gene variability and gene expression is practically possible at the level of physiological systems.

A revolution in our understanding of health and disease has been launched by next-generation sequencing of genomes of living organisms. To a large degree, this achievement represents the pinnacle of reductionist scientific thought, as having all genes dissected one could in principle allow reconstitution of the organism. In contrast, the classical discipline of physiology has been dealing with systems from its very outset. Although clinically extraordinarily relevant, physiology remained an engineering embodiment of scientific thought distant from the molecular basis of function. Physiological genomics (physiogenomics) bridges the gap between the systems approach and the reductionist approach by using human variability in physiological process, either in health or disease, to drive their understanding at the genome level. Physiogenomics and microarray capabilities will be critical in the medical laboratory for understanding of disease etiology and treatment and for the advancement of personalized medicine.

The role of microarrays for the medical laboratory will remain essential, despite the advances in large-scale sequencing. Microarrays are uniquely suitable to the stringent quality control needs of clinical applications. Their format is well suited to the reporting needs of healthcare providers. The well defined content of a microarray is more competitive in a draconian reimbursement environment, where payers are increasingly demanding clinical value for the assay. Large-scale sequencing is predominantly a research tool to provide curated content to microarrays. Finally, the analytical data measured by microarrays is most compatible with interpretative algorithms that will drive personalized medicine.

There is an urgent need to couple the engineering advances in highly parallel genomic screens with statistical tools to derive valid information from pattern-recognizing algorithms. The practical consequence is that by learning from variability, and not depending on means and standard deviation, we can expect reduced sample sizes in clinical studies and, most important, the ability to discover the markers and implement them in practice for prototyping and clinical validation.

Why physiological genomics?

Physiogenomics utilizes an integrated approach composed of genotypes and phenotypes and a population approach deriving signals from functional variability among individuals. In physiogenomics, genotype markers of gene variation or alleles (single nucleotide polymorphisms [SNPs], haplotypes, insertions/deletions) are analyzed to discover statistical associations to physiological characteristics (phenotypes). The phenotypes are measured in populations of individuals either at baseline or after they have been similarly exposed or challenged by environmental triggers. These environmental interactions span the gamut from exercise and diet to drugs and toxins and from extremes of temperature, pressure, and altitude to radiation. In complex diseases we are likely to find both baseline characteristics and response phenotypes to as yet undetermined environmental triggers. Variability in a genomic marker among individuals that tracks with the variability in physiological characteristics establishes associations and mechanistic links with specific genes.

Physiogenomics integrates the engineering systems approach with molecular probes stemming from microarray genomic markers that have altered the face of life sciences research. Physiogenomics marks the entry of genomics into systems biology and requires novel analytical platforms to integrate the data and derive the most robust associations. Once physiological systems are under scrutiny, the industrial tools of high-throughput genomics do not suffice, as fundamental processes such as signal amplification, functional reserve, and feedback loops of homeostasis must be incorporated. Physiogenomics comprises marker discovery and model building. We will describe each of these interrelated components in a generalized fashion.

Role of physiological genotypes

We term the diagnostic models derived from physiogenomic diagnostics “physiological genotypes.” Physiological genotypes have several unique features. They are predictive models incorporating haplotypes from various genes and any covariates (e.g., baseline levels). Physiological genotypes are multi-genetic in nature, and also include clinical information routinely gathered in medical care. They harness the combined power of genotypes (“nature”) and phenotypes (“nurture”) to predict drug responses and the responses to other environmental challenges. Physiological genotypes are multi-modular, and each individual molecule is derived from whether a significant association is found by univariate testing of the respective end point. Their overall operational features are specificity and sensitivity each of 80% or more. Since each component has individual characteristics, physiological genotypes reflect combined features of the various modules. Physiological genotypes provide answers to clinical management questions with high reliability and impact and can be used to address in a yes/no manner issues such as the risk of side effects from a medication.

Various specific genetic features of physiological genotypes are attractive for studying environmental interactions in prevention and treatment of disease. The genotype component does not change and is not confounded with environment. Some genotypes associated with a phenotype can become a surrogate marker for the actual measurement of the phenotype. This capability may be particularly useful when measurement of the phenotype is difficult, expensive, or confounded by environmental conditions. Most important, genotyping technologies are rapidly decreasing in cost and are becoming increasingly automated. To this end, multiple genotypes from different genes coding for proteins in interacting pathways allow sampling the genetic variability in entire physiological networks quite economically.

Each gene not associated with a particular outcome effectively serves as a negative control and demonstrates neutral segregation of non-related markers. The negative controls altogether constitute a genomic control for the positive associations where segregation of alleles tracks segregation of outcomes. By requiring the representation of the least common allele for each gene to be at least 5% of the population, one can ascertain associations clearly driven by statistical outliers. Negative results are particularly useful in physiogenomics, since one can still gain mechanistic understanding of complex systems from those, especially for sorting out the influences of the various candidate genes among the various phenotypes.

These analytical tools permit the extension of physiogenomics to several thousand additional genes with modern microarrays. Phenotypes could be added as well; for example, inflammatory and neuroendocrine markers are an area of intense interest in clinical medicine. The ability to measure changes in these markers for disease prevention strategies provides us a unique opportunity to examine genes determining a path to personalized health. The research can now utilize saved blood plasma and DNA for each patient to measure the appropriate genotypes and biochemical markers in blood, thus opening the possibility of retrospective analysis from archived clinical samples.

The goal: to personalize public health

Public health and personalized medicine share the common objective of prolonging life, but the means they employ to reach that goal have long represented very different perspectives and strategies. Medicine has focused on treating patients in need of restoration to health, while public health seeks to decrease the number of individuals acquiring illness in the first place. Personalized medicine focuses on the needs of individuals in order to deliver the best treatment available to that person, taking into account their personal and clinical characteristics. In contrast, public health focuses on the needs of the population as a whole, developing a health program that will serve the common good by reducing overall disease risk.

If it were possible to identify those individuals who would benefit most from a particular form of treatment or disease prevention, then it might be possible to optimize the program as a whole, by avoiding side effects and increasing efficiency. (Figure 1 represents this point in a schematic illustration.) This effort would bring together aspects of the medical and the public health perspectives by personalizing the public health strategies, thus providing a method by which individuals can optimize their own health, and in the process benefit the population at large.

Figure 1

Genomics has revolutionized biomedical science, and characterizing an individual’s genotype offers the possibility of learning a considerable amount of detail regarding his or her physiology. However, the efficacy of an individualized health program is not solely coded in the genes, but in other aspects of nature and environment as well. All of these features come together in a physiogenomic model that can help to identify the individual and his or her response to a particular health program.

As more environmental responses are characterized through physiogenomics, the chances will increase that all patients will be served with greater precision of intervention and with optimal outcome. That we may even contemplate this scenario is a testament to the combined power of physiogenomics and microarrays. The highly parallel genome probing possible with microarrays and the systems engineering approach underlying physiogenomics allow us to reconstitute the organism and its environmental response from the individual components. The specificity and individualization afforded by microarrays and physiogenomics are ushering medicine into the era of
personalized health.

Gualberto Ruaño, MD, PhD, is President and Medical Director, Genomas, Inc., and Laboratory of Personalized Health, and Director of Genetics Research, Hartford Hospital, Hartford, CT.

For Further Reading

  1. Ruaño G, Windemuth A, inventors;  Physiogenomic Method for Predicting Clinical Outcomes of Treatment in Patients. U.S. Patent 7,747,392. 2010.
  2. Ruaño G, Windemuth A, Holford T.  Physiogenomics: Integrating systems engineering and nanotechnology for personalized health. In: Bronzino JD, ed.The Biomedical Engineering Handbook, 3rd ed. CRC Press Taylor and Francis; 2006;28:1-9.
  3. Holford T, Windemuth A, Ruaño G. Personalizing public health. Personalized Medicine. 2005;2(3):239-249.