A funny thing happened on the way to "Precision Medicine". It seems that many of the fundamental studies in the field of pharmacogenomics and personalized medicine are yielding irreproducible results. We find that we can not depend on the data that we depend on. If you don't believe me, consider these shocking headlines:
1. "Unreliable research: Trouble at the lab." (1) The Economist, in 2013 ran an article examining flawed biomedical research. The magazine article referred to an NIH official who indicated that "researchers would find it hard to reproduce at least three-quarters of all published biomedical findings." The article also described a study conducted at the pharmaceutical company Amgen, wherein 53 landmark studies were repeated. The Amgen scientists were successful at reproducing the results of only 6 of the 53 studies. Another group, at Bayer HealthCare, repeated 63 studies. The Bayer group succeeded in reproducing the results of only one-fourth of the original studies.
2. "A decade of reversal: an analysis of 146 contradicted medical practices." (2) The authors reviewed 363 journal articles, reexamining established standards of medical care. Among these articles were 146 manuscripts (40.2%) claiming that an existing standard of care had no clinical value.
3."Cancer fight: unclear tests for new drug." (3). This New York Times article examined whether a common test performed on breast cancer tissue (Her2) was repeatable. It was shown that for patients who tested positive for Her2, a repeat test indicated that 20% of the original positive assays were actually negative (i.e., falsely positive on the initial test). (3).
4. "Why most published research findings are false." (4). Modern scientists often search for small effect sizes, using a wide range of available analytic techniques, and a flexible interpretation of outcome results. Under such conditions, the manuscript author found that research conclusions are more likely to be false than true (4).
5. "Reproducibility crisis: Blame it on the antibodies" (5). Biomarker developers are finding that they cannot rely on different batches of a reagent to react in a consistent manner, from test to test. Hence, laboratory analytic methods, developed using a controlled set of reagents, may not have any diagnostic value when applied by other laboratories, using different sets of the same analytes (5).
Anyone who tries to stay current in biomedical research understands that much of the published literature is irreproducible (6); and that almost anything published today might be retracted tomorrow. This appalling truth applies to some of the most respected laboratories in the world (7), (8), (9), (10), (11), (12), (13). Those of us who have been involved in assessing the rate of progress in disease research are painfully aware of the numerous reports indicating a general slowdown in medical progress (14), (15), (16), (17), (18), (19), (20), (21).
For the optimists, it is tempting to assume that any problems that we may be experiencing today are par for the course, and temporary. It is the nature of science to stall for a while and lurch forwards in fits. Errors and retractions will always be with us so long as humans are involved in the scientific process.
For the pessimists, such as myself, there seems to be something going on that is really new and different; a game changer. This game changer is the "complexity barrier", a term credited to Boris Beizer, who used it to describe the impossibility of managing increasingly complex software products (22). The "complexity barrier" applies equally well to biomedical research. Recent studies have shown that inherited behavior is not fully determined by the genetic sequence of DNA. There are many different elements that modify and control genetic expression, and these elements do not have simple functionality (23). When complex cells are perturbed from their normal, steady-state activities, the rules that define cellular behavior become complex, and impossible to predict (24).
Modern biomedical data is high-volume (e.g., gigabytes and larger), heterogeneous (i.e., derived from diverse sources), private (i.e., measured on human subjects), and multi-dimensional (e.g., containing thousands of different measurements for each data record). The complexities of handling such data are daunting. When we closely examine the kinds of systemic flaws that cropping up in the recent biomedical literature, complexity always seems to play a supporting role. Here are a few examples:
1. Errors in sample selection, labeling, and measurement (25), (26), (27)
3. Misinterpretation of the data (28), (4), (29), (19), (30), (31), (32)
4. Data hiding and data obfuscation (33), (34)
5. Unverified and unvalidated data (35), (36), (37), (38), (30), (39)
6. Outright fraud (34), (40)
As biomedical data becomes increasingly complex, the complexity barrier will become impenetrable. It is ironic that just as we are coming to accept the limits of analyzing complex data, we are given President Obama's gift of a new funding initiative in support of precision medicine (41). How can we use this money effectively when we know that human diseases reside on the far side of the complexity barrier?
As it happens, not all diseases are genetically complex. The rare genetic diseases of humans, with very few exceptions, involve a single mutation in a single gene. In the past decade, we have made remarkable advances in understanding the rare diseases. For example, FDA approved a total of 44 drugs in 2014 (42). Of those 44 drugs, 21 (47%) were approved for the treatment of rare diseases, including: non-24-hour sleep-wake disorder, Morquio A syndrome, neurogenic orthostatic hypotension, generalized lipodystrophy, psoriatic arthritis, hemophilia B, multicentric Castleman's disease, hemophilia A (two drugs), Non-Hodgkin lymphoma, hereditary angioedema, leukemia (two drugs), multiple sclerosis, Gaucher disease, idiopathic pulmonary fibrosis (two drugs), gastric cancer, melanoma (two drugs), and ovarian cancer. In fact, most of the medical advances in the past two decades have occurred in the rare diseases; not the common diseases (43).
The rare diseases, being genetically simple, can be successfully treated with drugs targeted to a specific pathway. As it happens all of the cancers that we can cure in an advanced stage of growth (i.e., with metastases) are rare cancers (43), (44): choriocarcinoma, acute lymphocytic leukemia of childhood, Burkitt lymphoma, Hodgkin lymphoma, acute promyelocytic leukemia, large follicular center cell (diffuse histiocytic)lymphoma, embryonal carcinoma of testis, hairy cell leukemia, and seminoma.
Too often, the rare diseases are casually dismissed as outliers, not representative of metabolic pathways that drive the common diseases. Bad decision. Historically, the most important advances in common diseases have come from studying the rare diseases (43). Pathways that are studied, understood, and treated in the rare cancers will likely apply to common (i.e., genetically complex) cancers that share some of the same pathogenic pathways. Hence, the rare diseases are not the exceptions to the general rules that apply to common diseases; the rare diseases are the exceptions upon which the general rules of common diseases are based (43).
In this new, highly-funded era of precision medicine research, funding should flow to the rare diseases. If the common diseases are the genetic puzzles that modern medical researchers are mandated to solve, then the rare diseases are the pieces of the puzzles (43).
- © 2015 Jules J. Berman
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key words: precision medicine, personal medicine, pharmacogenomics, pharmacogenetics, personalized medicine, genomics, bioinformatics, funding, President Obama's initiative, common diseases, rare diseases, complexity barrier, complexity ceiling, scientific error