Workshop 27-28 March 2017

When the term personalized medicine was coined by Leroy Hood and became part of our daily jargon less than a decade ago, the realization of this goal appeared to be the next inevitable milestone on the road of medical discovery. The journey began with the sequencing of the first human genome that lasted for several years, cost several hundred million dollars, and was completed in 2000. It continued with the sequencing of genomes of patients afflicted with particular diseases in numbers sufficient to unravel the mutations that underlie their pathogenesis. Once this vital information was available, the structure of the mutated proteins could be used to develop specific, individualized, mechanism-based drugs to modulate the activity of overly active proteins or replace the gene or gene product resulting from loss-of-function mutations. While some of the promise of this “simple” road-map view remains, the path has become more convoluted, and major road blocks have emerged. The promise of personalized medicine was initially painted with rosy colors in large part due to naiveté in the scientific community with respect to the complexity of the problem. In addition, some scientists were eager to convince the public and funding agencies that a defined roadmap toward new therapies for many diseases was around the corner, lacking only adequate funding.

We argue that while the goals of personalized medicine can still be achieved, a more realistic view of the obstacles and pitfalls is needed. Obstacles reside in each and every level of the road to this revolution – from scientific discovery to drug development by pharmaceutical companies, and from legal to administrative concerns to political, religious and ethical issues. Overcoming these obstacles will require a larger and more broadly focused research investment that employs both traditional and novel approaches. For example, at the level of identifying genomic mutations in DNA, we naively thought that the initial road blocks would be solved with rapid sequencing methodologies and better bioinformatic analysis of coding DNA. However, the recent ENCODE (Encyclopedia of DNA elements) project has shown how wrong we were even at this basic starting point. Most of what was regarded as “junk DNA” – which constitutes a large proportion of the genomic DNA – has turned out to be functional. Genome-wide association studies have recently shown that many noncoding sequences are associated with common pathologies. Furthermore, some of these regions are DNAse-sensitive and are active during fetal development where they may play a role in the development of disease states during adulthood. The resulting amount of DNA that must be incorporated into the ongoing analysis of the coding DNA will require not only more complex sequence and bioinformatics analyses but also the development of novel methodologies to analyze its significance. Additional obstacles include the need to examine regulatory pathways that act upstream and downstream from the genome – the RNAs, miRNAs, the proteome and the metabolome, each of which represents thousands of additional molecules. Development of proteomic analyses including methodologies that monitor dynamic changes in large populations of proteins and their numerous post-translational modifications (phosphorylation, acetylation, amidation, hydroxylation, ubiquitination and modification by ubiquitin-like proteins, nitrosylation, and others) have so far proven to be much more difficult than the analysis of nucleic acid sequence. Furthermore, technologies to dynamically analyzed small molecules – sugars, lipids, and other metabolic intermediates are in their infancy but will be required for the detailed view needed to truly understand disease causality.

One can argue that “personalized medicine” has been part of the medical profession from its inception. Physicians throughout history have applied different therapies to treat similar ailments through a process that involves careful patient observation and the selected use of ancillary data. While progress is uneven, this evolution of medicine has improved the quality of life and led to extended life and extended life in most societies. For example, excavations from Egyptian and pre-Columbian periods suggest that the average life expectancy did not exceed ~30 years and almost 4,000 years passed before the beginning of the 20th century when average life expectancy reached ~50 years. The last century marked the shortest span in history to increase life expectancy by almost 30 years in developed countries. Most of the improvements in mortality were attributable to reductions in infectious disease mortality resulting from safer sources of food and drinking water, improved understanding of the principles of hygiene, the discovery of antibiotics and vaccinations, the development of medical technologies such as imaging and surgery and an improved understanding of disease pathophysiology. This increased longevity has been beneficial for society but has also been associated with the emergence of diseases of aging, including chronic and ischemic heart disease, cancers, COPD, and degenerative diseases, including Parkinson’s and Alzheimer’s. These diseases represent a major challenge to the affected individual, the medical-scientific community, and society at large.

The treatment of diseases has evolved greatly, and currently we still use medications that were discovered by astute observers and practitioners of medicine during a time we call the era of incidental discoveries. Among those we can include the discovery of salicylic acid by Johann Buchner, Henri Leroux, Raffaele Piria, and Charles Gerhardt, which was commercialized by Bayer in the 20 century, after reformulation by Felix Hoffmann. Also, the discovery of insulin initiated by the observations of Paul Langherhans stimulated studies by Oscar Minkowski, which in turn led to its isolation by Fredrick Banting and Charles Best in 1921. In partnership with Ely Lilly, this led to the purification and mass production of insulin. Similarly, the era of antibiotics was heralded by the discovery and production of penicillin for which Alexander Fleming, Howard Florey, and Ernst Chain were awarded the Nobel Prize in 1945. In 1975, Akira Endo initiated the era of biochemical discovery using high throughput technology, with his work that culminated in discovery of the blockbuster statin drugs.

In many ways, the application of genomic data represents the next logical step in this tradition. The paradigm of P4 Medicine, proposes a personalized, predictive, preventive, and participatory practice of medicine based on a systems approach. An important challenge to the predictive part of this paradigm using genomics sequence information is that many highly prevalent diseases including asthma, COPD, mental health and metabolic disorders, are multigenic, and the phenotypic pathology depends on the penetrance of the different genes involved and their modulation by environmental factors. For example, Bert Vogelstein and colleagues used sequencing data from monozygotic twin pairs to estimate the attributable risk for 24 types of cancer that might be identified from whole genome sequencing. They found that only a small fraction of the risk could be identified based on genetic factors and that this paled in comparison with the risks associated with environmental factors including smoking and obesity (The Predictive Capacity of Personal Genome Sequencing Science Translational 2012). Even in patients in whom cancer has developed, genomic instability might cause driver mutation(s) to be masked or absent in the advanced stages of the disease. This elusive behavior of tumors has elicited a fierce debate on the therapeutic approach to cancer – whether for example in lung cancer, to target the specific mutations, which accumulate and become resistant to therapy, or to target major upper stream “switches” such as evasion of cell death, immune surveillance, growth suppressors, and deregulation of cellular energetics.

From the standpoint of drug development, a major concern is that personalized medicine will mark the end of the blockbuster era, where one or a few competing drugs can be used to treat an entire population with a disease. In this setting, current models of drug development become prohibitively expensive. For example, even the now familiar classification of patients with breast cancer based on expression of HER/Neu2, estrogen receptor mutation, progesterone receptor mutation is likely an oversimplification of this complex disease. In the future, an array of genomic, RNA, proteomic and metabolic data will likely be used to classify patients with cancer and to identify potential therapeutic approaches. A similar process is likely in other diseases including pulmonary fibrosis, COPD, pulmonary hypertension and other complex common diseases. Pharmaceutical companies are already reluctant to participate in the development of certain drugs (antibiotics for example), and maybe even less interested in the development of drugs targeted to a smaller number of patients. To respond to this problem, investment is needed to develop improved preclinical disease models that can be used to predict drug efficacy and toxicity.  This process might be facilitated by using personalized medicine approaches to identify factors that might make individuals more or less sensitive to certain drugs.

Most difficult to resolve are the bioethical issues raised by personalized medicine. For example, genomic analysis of a blood or a tissue sample for clinical, research or even personal purposes might have multiple, unforeseen implications. Some “simple” questions relate to privacy and confidentiality with respect to the use of the information by employers, governments, or insurance companies to make decisions unrelated to health care. More complicated is the problem of how to address incidental information referring to a potential or evolving pathology of which the patient is unaware and for which the patient might not have consented, particularly, the discovery of predisposition of a disease that cannot be treated or prevented. This problem is even more complex when the information is discovered as part of prenuptial testing or in utero examinations of embryos. This information has the possibility to affect the physician-patient relationship, social networks, family structure, and parenthood in ways that are difficult to predict. These rapidly evolving ethical challenges will require continuously updated guidelines and legislation. The scientific community needs to proactively and clearly communicate the recent discoveries generated in laboratories to engage the political, philosophic, clerical, and judicial members of society to meet the challenges of ethical utilization of data and new technologies.

In conclusion, the road to personalized medicine is longer and much more tortuous than anyone imagined a decade ago. We find ourselves in the midst of an exciting era in medicine in which we can see that the promise of individualized prevention, early detection, and efficient treatment of diseases is possible. Realizing this goal will require innovative multidisciplinary approaches to address the scientific, commercial and ethical challenges posed by these new technologies and techniques. Like many endeavors in research, the next milestone in the road may not be around the corner and might come from an unexpected source. Continued investments in high quality research using both traditional and novel approaches in a wide field of study will be required to achieve these goals. As we do this, it is important to remember what our patients might think of when they hear the term “personalized medicine”. For example, Carolyn Bucksbaum recently provided $42 million (A $42 Million Gift Aims at Improving Bedside Manner. New York Times September 22, 2011) to establish a center to teach doctors “bedside manners” and to “preserve kindness and personalize” the patient-doctor relationship.  Providing both the personalized medicine described by Dr. Hood and Ms. Bucksbaum represents an exciting challenge in the practice of medicine in the 21st Century.

by Aaron Ciechanover(see more about author below)

Source: Personalized Medicine