Precision medicine and molecular understanding of illness

By Justin Saeks

December 23, 2019 -- The current era of scientific research is seen by many as a golden age of discovery in genetics, due to rapid progress in numerous areas of science and technology. While healthcare and the pharmaceutical industry traditionally have directed their attention to symptoms rather than underlying causes, the new advances are creating opportunities to better exploit a rapidly expanding mechanistic understanding of disease. The challenges are significant and complex, and the current models of discovery and translation do not provide an obvious path toward an economically sustainable way to integrate data-intensive biology with medicine.

The promises of these advances include the capabilities to predict disease predisposition, better prevent disease, and more successfully diagnose and treat it. "Precision medicine" refers to the tailoring of medical decisions, treatment, practices, or products for the individual patient. In the most general sense, it is the focus on more accurately defining or classifying exact disease states and the body's response to treatment. This partly relates to the field of taxonomy, which is the science of classification. Molecular diagnostics is involved, alone or in combination with molecularly targeted drugs that are specific to certain disease subtypes. Given the quantity of diseases and their subtypes, and the differences between individuals, it is a gradual endeavor that will take decades.

Foundations of science live on

Looking back a century or two, the practice of describing and defining diseases was hyper-focused on physical signs and symptoms. A stark illustration of the difference is provided by Carolus Linnaeus, whose taxonomic system for classifying living organisms is still in use; in Genera Morborum (Linné, 1763), he described another system of categories for disease classification such as:

  • dolorous - painful
  • exanthematic - feverish with skin eruptions
  • phlogistic - feverish with heavy pulse and topical pain

Rabies was characterized as a psychiatric disorder, rather than infectious disease, because of the resulting brain dysfunction and the lack of understanding of microbes. While primitive on the surface, aspects of this approach remain to this day in many areas of healthcare, as researchers are still working out how to apply recent molecular advances. The problem that persists, which is now being addressed, is the difficulty of accounting for the various molecular pathways/ mutations/ modifications that drive disease or represent targets for treatment.

New technologies address current challenges

Subsequent to the completion of the Human Genome Project, there has been an acceleration in the number of genomes sequenced due to the Moore's Law phenomenon with sequencing costs and performance. The price has now approached $1,000 per genome in the most efficient labs. But the impact on healthcare has still only been incremental. There are many disease subtypes with different molecular causes that are still being classified as one disease; and similarly, there are multiple different diseases that share a common molecular cause. For example, the LMNA gene can have multiple possible mutations which result in a diverse set of diseases, including:

  • Charcot-Marie-Tooth axonal neuropathy
  • Emery-Dreyfus muscular dystrophy
  • lipodystrophy
  • premature aging disorders

Until recently, these were seen as distantly related despite the cellular, genetic, and molecular similarities.

The continued performance improvements in next-generation sequencing have driven major developments in many areas, particularly for rare genetic/ inherited diseases. These account for a much smaller population compared to cancer or cardiovascular diseases, but their impact is more compelling as about 1 in 4 patients have a causative gene identified on a first attempt. The success rate with the other 3 out of 4 is mixed, with a large amount of resources and expertise involved. Finding a causative gene is also just the first step, with the next question being how to treat each specific condition.

Similarities, different names

These different layers highlight the involvement of many different scientific disciplines that must be taken into account. While it sometimes can get into semantics, there is varying amount of overlap with these specific areas of research/ medicine and the tools being employed including:

  • biomarkers
  • companion diagnostics
  • immunogenetics
  • personalized medicine
  • pharmacogenomics

A few examples are described in further detail; it is impossible to do justice to the topic in one article. The large number of terms that define distinct but significantly overlapping areas highlights the complexity and the need for multidisciplinary research.

Companion diagnostics

Companion diagnostics are diagnostic tests used in conjunction with a therapeutic drug to determine its applicability to a specific person. The developments in companion diagnostics have largely related to oncology. The first were launched in the 1980s and the number of combinations has since grown 12-fold. The number of cancer drugs approved between the years 2010 and 2018 was higher than the previous twenty years. The progress has been somewhat narrow as the understanding of genetic changes in specific cancers improves gradually, along with the development of drugs targeting specific pathways. This has been especially strong for the detection of drug sensitivity and drug resistance in lung cancer, with the use of next-generation sequencing (NGS) to analyze panels of genes. The ultimate diagnostic test often uses PCR. Several NGS based diagnostics have achieved regulatory approval, and in early 2019, the FDA granted Breakthrough Device Designation for Illumina's pan-cancer NGS assay.

Pharmacogenomics/pharmacogenetics

There is another important dimension of precision medicine which relates to the metabolism of pharmaceuticals. For example, the variability between individuals in key cytochrome P450 genes for enzymes involved in liver metabolism can result in major differences in the correct therapeutic dose required to be effective and safe. Certain pharmaceuticals such as warfarin/coumadin have a small window that requires testing of genetics and/or coagulation in order to avoid a fatal reaction. Moreover, variants of the CYP2D6 gene have been associated with the discontinuation of the breast cancer drug tamoxifen due to the experience of severe side effects. There are some cases where the literature doesn't provide a clear consensus on recommendations; for instance, one study in the Netherlands found that a significant number of gene-drug pairs had different recommendations when comparing the two main national authorities on the subject, the Clinical Pharmacogenetics Implementation Consortium and the Dutch Pharmacogenetics Working Group.

Personalized medicine

This term represents a concept similar to precision medicine but has been falling out of favor somewhat for several reasons. According to the National Research Council (NRC) personalized medicine is an older term with a similar meaning as precision medicine. The word "personalized" could potentially be misinterpreted as suggesting that each patient will be treated differently than every other patient; nonetheless, in some areas where this is more accurate such as immunogenetics, it might still be applied. Another reason for the different wording is that it seems to emphasize the anecdotal aspect, which is inaccurate. The practice of personalized medicine may be used in the future, but in the current landscape there are still significant challenges to the approach including cost and reimbursement concerns, regulatory oversight and patient privacy.

Requirement for big data or large-scale studies

In 2011, the National Research Council highlighted specific recommendations in its report on precision medicine. Among other things, these include the development of an Information Commons which stores raw information about patients, and a Knowledge Network which provides a unifying framework for a range of endeavors including:

  • disease classification
  • disease detection and diagnosis
  • disease mechanism discovery
  • disease predisposition
  • disease treatment
  • drug development
  • health disparities

This would be built through the collection of data from millions of people, providing a viable alternative to the use of large prospective cohort studies which are prohibitively expensive and typically limited to hundreds of thousands of patients. Open data sharing will allow researchers to develop new treatments at a speed matching the current rate of scientific innovation. It remains to be seen whether physicians will adopt the new practices that are needed, and whether patients will contribute their data. There are an abundance of scientific, medical, technological, and ethical challenges that will require many changes in different aspects of the healthcare industry.

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