Showing posts with label genomics. Show all posts
Showing posts with label genomics. Show all posts
Saturday, January 27, 2018
Precision Medicine and the Reinvention of Human Disease (not just the genome)
If you believe the lay press, Precision Medicine involves sequencing a patient's genome and determining the proper treatment based on the individual's unique genetic attributes. The NIH (National Institutes of Health) seems to be encouraging this interpretation of the field. From the US National Institutes of Health comes the following description: "Precision Medicine is an emerging approach for disease prevention and treatment that takes into account people's individual variations in genes, environment, and lifestyle. The Precision Medicine Initiative will generate the scientific evidence needed to move the concept of Precision Medicine into clinical practice". An Advisory Committee to the NIH Director would include, under the mantle of Precision Medicine, "providing individual side-effect profiles of drugs, and preventative health care check-ups that include specific recommendations developed from interpreting an individual's genetic risk profile".
Between the millions of inter-individual variations in our genomes, the highly personalized lifestyle choices, and the differences in our environments, there seems to be plenty of uniqueness to spread around. It is easy to forget that our uniqueness as individuals often has much less to do with our diseases than does our sameness as members of the same species. Our sameness goes a long way toward explaining why humans seem to suffer from the same list of textbook diseases, regardless of their individualized genes and geography. Someone had to put the brakes on this epidemic of uniqueness.
Much to their credit, the National Research Council of the US National Academies tacked on the following caveat to the definition of Precision Medicine: "It does not literally mean the creation of drugs or medical devices that are unique to a patient, but rather the ability to classify individuals into subpopulations that differ in their susceptibility to a particular disease, in the biology and/or prognosis of those diseases they may develop, or in their response to a specific treatment". The Research Council wisely distinguished Personalized Medicine from Precision Medicine, by adding, "Although the term 'Personalized Medicine' is also used to convey this meaning, that term is sometimes misinterpreted as implying that unique treatments can be designed for each individual. For this reason, the Committee thinks that the term 'Precision Medicine' is preferable to 'Personalized Medicine'"
The National Research Council pointed out what should have been obvious from the start. We cannot provide individualized treatments, because treatments must be tested for safety and efficacy on groups of people. The best we can ever do is to assign patients to a group that has been fitted to a preapproved treatment. So where does this leave us?
More to follow in tomorrow's blog.
- Jules Berman
key words: precision medicine, genomics, individualized treatments, definition, jules j berman, Jules Berman Ph.D., M.D.
Monday, January 22, 2018
Announcement: Precision Medicine and the Reinvention of Human Disease
In January, 2018, Academic Press is publishing my latest book, Precision Medicine and the Reinvention of Human Disease
Here is the book description, from the back cover:
Despite what you may have read in the popular press and in social media, Precision Medicine is not devoted to finding unique treatments for individuals, based on analyzing their DNA. To the contrary, the goal of Precision Medicine is to find general treatments that are highly effective for large numbers of individuals who fall into precisely diagnosed groups.
We now know that every disease develops over time, through a sequence of defined biological steps, and that these steps may differ among individuals, based on genetic and environmental conditions. We are currently developing rational therapies and preventive measures, based on our precise understanding of the steps leading to the clinical expression of diseases.
Precision Medicine and the Reinvention of Human Disease explains the scientific breakthroughs that have changed the way that we understand diseases, and reveals how medical scientists are using this new knowledge to launch a medical revolution.
Key Features
- Clarifies the foundational concepts of Precision Medicine, distinguishing this field from its predecessors such as genomics, pharmacogenetics, and personalized medicine.
- Gathers the chief conceptual advances in the fields of genetics, pathology, and bioinformatics, and synthesizes a coherent narrative for the field of Precision Medicine.
- Delivers its message in plain language, and in a relaxed, conversational writing style, making it easy to understand the complex subject matter.
- Guides the reader through a coherent and logical narrative, gradually providing expertise and skills along the way.
- Covers the importance of data sharing in Precision Medicine, and the many data-related challenges that confront this fragile new field.
Preface. Chapter 1. Introduction: Seriously, What is Precision Medicine? Glossary References Chapter 2. Redefining Disease Causality Section 2.1 Causality and Its Paradoxes Section 2.2 Why We Are Confident that Diseases Develop in Steps Section 2.3 Cause of Death Section 2.4 What Is a Disease Pathway? Section 2.5 Does Single Event Pathogenesis Ever Happen? Glossary References Chapter 3. Genetics: Clues, Not Answers, to the Mysteries of Precision Medicine Section 3.1 Inscrutable Genes Section 3.2 Inscrutable Diseases Section 3.3 Recursive Epigenomic/Genomic Diseases Section 3.4 Why a Gene-based Disease Classification Is a Bad Idea Glossary References Chapter 4. Disease Convergence Section 4.1 Mechanisms of Convergence Section 4.2 Phenocopy Diseases: Convergence Without Mutation Section 4.3 The Autoantibody Phenocopies Section 4.4 Pathway-Directed Treatments for Convergent Diseases Glossary References Chapter 5. The Precision of the Rare Diseases Section 5.1 The Biological Differences Between Rare Diseases and Common Diseases Section 5.2 Precision Medicine's First Benefit: Cures for Rare Diseases Section 5.3 What the Rare Diseases Tell Us About the Common Diseases Section 5.4 Treatments for Rare Diseases are Effective Against the Common Diseases Glossary References Chapter 6. Precision Organisms Section 6.1 Modern Taxonomy of Infectious Diseases Section 6.2 Our Genome Is a Book Titled "The History of Human Infections" Section 6.3 Inflammatory Diseases: Collateral Damage in the War on Human Infection Section 6.4 Revising Koch's Postulates in the Era of Precision Diagnostics Section 6.5 Diseases-in-waiting Section 6.6 Precision Taxonomy Glossary References Chapter 7. Reinventing Diagnosis Section 7.1 The Principles of Classification Section 7.2 Superclasses Section 7.3 Classifications Cannot Be Based on Similarities Section 7.4 The Horrible Consequences of Class Blending Section 7.5 What Is Precision Diagnosis? Glossary References Chapter 8. Precision Data Section 8.1 What Are the Minimal Necessary Properties of Good Data? Section 8.2 Data Identification and Data Deidentification Section 8.3 What Do We Do With Non-quantitative, Descriptive Data? Section 8.4 Incredibly Simple Methods to Understand Precision Medicine Data Section 8.5 Data Reanalysis: More important than the Original Data Analysis Section 8.6 What Is Data Sharing, and Why Don't We Do More of It? Glossary References Chapter 9. The Alternate Futures of Precision Medicine Section 9.1 Hypersurveillance Section 9.2 Do It Yourself Medicine Section 9.3 Eugenics Section 9.4 Public Health Section 9.5 The Data Analyst of Tomorrow Section 9.6 Fast, Cheap, Precise Clinical Trials Section 9.7 Animal Experimentation Glossary References
Sunday, July 5, 2015
Precision Medicine meets the Data Complexity Barrier
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
References
[1] Unreliable research: Trouble at the lab. The Economist October 19, 2013.
[2] Prasad V, Vandross A, Toomey C, Cheung M, Rho J, Quinn S, et al. A decade of reversal: an analysis of 146 contradicted medical practices. Mayo Clin Proc 88:790-8, 2013.
[3] Kolata G. Cancer fight: unclear tests for new drug. The New York Times April 19, 2010.
[4] Ioannidis JP. Why most published research findings are false. PLoS Med 2:e124, 2005.
[5] Baker M. Reproducibility crisis: Blame it on the antibodies. Nature 521:274-276, 2015.
[6] Naik G. Scientists' Elusive Goal: Reproducing Study Results. Wall Street Journal December 2, 2011.
[7] Zimmer C. A sharp rise in retractions prompts calls for reform. The New York Times April 16, 2012.
[8] Altman LK. Falsified data found in gene studies. The New York Times October 30, 1996.
[9] Weaver D, Albanese C, Costantini F, Baltimore D. Retraction: altered repertoire of endogenous immunoglobulin gene expression in transgenic mice containing a rearranged mu heavy chain gene. Cell 65:536 (inclusive), 1991.
[10] Chang K. Nobel winner in physiology retracts two papers. The New York Times September 23, 1010.
[11] Fourth paper retracted at Potti's request. The Chronicle March 3, 2011.
[12] Whoriskey P. Doubts about Johns Hopkins research have gone unanswered, scientist says. The Washington Post March 11, 2013.
[13] Lin YY, Kiihl S, Suhail Y, Liu SY, Chou YH, Kuang Z, et al. Retraction: Functional dissection of lysine deacetylases reveals that HDAC1 and p300 regulate AMPK. Nature 482:251-255, retracted November, 2013.
[14] Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. U.S. Department of Health and Human Services, Food and Drug Administration, 2004.
[15] Hurley D. Why Are So Few Blockbuster Drugs Invented Today? The New York Times November 13, 2014.
[16] Angell M. The Truth About the Drug Companies. The New York Review of Books Vol 51, July 15, 2004.
[17] Crossing the Quality Chasm: A New Health System for the 21st Century. Quality of Health Care in America Committee, editors. Institute of Medicine, Washington, DC., 2001.
[18] Wurtman RJ, Bettiker RL. The slowing of treatment discovery, 1965-1995. Nat Med 2:5-6, 1996.
[19] Ioannidis JP. Microarrays and molecular research: noise discovery? The Lancet 365:454-455, 2005.
[20] Weigelt B, Reis-Filho JS. Molecular profiling currently offers no more than tumour morphology and basic immunohistochemistry. Breast Cancer Research 12:S5, 2010.
[21] Personalised medicines: hopes and realities. The Royal Society, London, 2005.Available from: https://royalsociety.org/~/media/Royal_Society_Content/policy/publications/2005/9631.pdf, viewed Jan 1, 2015.
[22] Beizer B. Software Testing Techniques. Van Nostrand Reinhold; Hoboken, NJ 2 edition, 1990.
[23] Ecker JR, Bickmore WA, Barroso I, Pritchard JK, Gilad Y, Segal E. Genomics: ENCODE explained. Nature 489:52-55, 2012.
[24] Rosen JM, Jordan CT. The increasing complexity of the cancer stem cell paradigm. Science 324:1670-1673, 2009.
[25] Bandelt H, Salas A. Contamination and sample mix-up can best explain some patterns of mtDNA instabilities in buccal cells and oral squamous cell carcinoma. BMC Cancer 9:113, 2009.
[26] Knight, J. Agony for researchers as mix-up forces retraction of ecstasy study. Nature 425:109, September 11, 2003.
[27] Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883-892, 2012.
[28] Ioannidis JP. Is molecular profiling ready for use in clinical decision making? The Oncologist 12:301-311, 2007.
[29] Ioannidis JP. Some main problems eroding the credibility and relevance of randomized trials. Bull NYU Hosp Jt Dis 66:135-139, 2008.
[30] Ioannidis JP, Panagiotou OA. Comparison of effect sizes associated with biomarkers reported in highly cited individual articles and in subsequent meta-analyses. JAMA 305:2200-2210, 2011.
[31] Ioannidis JPA, Panagiotou OA. "Comparison of effect sizes associated with biomarkers reported in highly cited individual articles and in subsequent meta-analyses. JAMA 305:2200-2210, 2011.
[32] Ioannidis JP: Excess significance bias in the literature on brain volume abnormalities. Arch Gen Psychiatry 68:773-780, 2011.
[33] Harris G. Diabetes drug maker hid test data, files indicate. The New York Times July 12, 2010.
[34] Berman JJ. Machiavelli's Laboratory. Amazon Digital Services, Inc., 2010.
[35] Misconduct in science: an array of errors. The Economist. September 10, 2011.
[36] Begley S. In cancer science, many 'discoveries' don't hold up. Reuters Mar 28, 2012,
[37] Abu-Asab MS, Chaouchi M, Alesci S, Galli S, Laassri M, Cheema AK, et al. Biomarkers in the age of omics: time for a systems biology approach. OMICS 15:105-112, 2011.
[38] Moyer VA; on behalf of the U.S. Preventive Services Task Force. Screening for prostate cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med May 21, 2011
[39] How science goes wrong. The Economist Oct 19, 2013.
[40] Shafer SL. Letter: To our readers. Anesthesia and Analgesia. February 20, 2009.
[41] Pear R. Obama to Request Research Funding for Treatments Tailored to Patients’ DNA. The New York Times January 24, 2015.
[42] Munos B. 2014 New Drug Approvals Hit 18-Year High. Forbes Jan 2, 2015. Available at: http://www.forbes.com/sites/bernardmunos/2015/01/02/the-fda-approvals-of-2014, viewed June 20, 2015.
[43] Berman JJ. Rare diseases and orphan drugs: Keys to Understanding and Treating Common Diseases. Academic Press, in press, 2014.
[44] Holland Frei Cancer Medicine. Kufe D, Pollock R, Weichselbaum R, Bast R, Gansler T, Holland J, Frei E, eds. BC Decker, Ontario, Canada, 2003.
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
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
References
[1] Unreliable research: Trouble at the lab. The Economist October 19, 2013.
[2] Prasad V, Vandross A, Toomey C, Cheung M, Rho J, Quinn S, et al. A decade of reversal: an analysis of 146 contradicted medical practices. Mayo Clin Proc 88:790-8, 2013.
[3] Kolata G. Cancer fight: unclear tests for new drug. The New York Times April 19, 2010.
[4] Ioannidis JP. Why most published research findings are false. PLoS Med 2:e124, 2005.
[5] Baker M. Reproducibility crisis: Blame it on the antibodies. Nature 521:274-276, 2015.
[6] Naik G. Scientists' Elusive Goal: Reproducing Study Results. Wall Street Journal December 2, 2011.
[7] Zimmer C. A sharp rise in retractions prompts calls for reform. The New York Times April 16, 2012.
[8] Altman LK. Falsified data found in gene studies. The New York Times October 30, 1996.
[9] Weaver D, Albanese C, Costantini F, Baltimore D. Retraction: altered repertoire of endogenous immunoglobulin gene expression in transgenic mice containing a rearranged mu heavy chain gene. Cell 65:536 (inclusive), 1991.
[10] Chang K. Nobel winner in physiology retracts two papers. The New York Times September 23, 1010.
[11] Fourth paper retracted at Potti's request. The Chronicle March 3, 2011.
[12] Whoriskey P. Doubts about Johns Hopkins research have gone unanswered, scientist says. The Washington Post March 11, 2013.
[13] Lin YY, Kiihl S, Suhail Y, Liu SY, Chou YH, Kuang Z, et al. Retraction: Functional dissection of lysine deacetylases reveals that HDAC1 and p300 regulate AMPK. Nature 482:251-255, retracted November, 2013.
[14] Innovation or Stagnation: Challenge and Opportunity on the Critical Path to New Medical Products. U.S. Department of Health and Human Services, Food and Drug Administration, 2004.
[15] Hurley D. Why Are So Few Blockbuster Drugs Invented Today? The New York Times November 13, 2014.
[16] Angell M. The Truth About the Drug Companies. The New York Review of Books Vol 51, July 15, 2004.
[17] Crossing the Quality Chasm: A New Health System for the 21st Century. Quality of Health Care in America Committee, editors. Institute of Medicine, Washington, DC., 2001.
[18] Wurtman RJ, Bettiker RL. The slowing of treatment discovery, 1965-1995. Nat Med 2:5-6, 1996.
[19] Ioannidis JP. Microarrays and molecular research: noise discovery? The Lancet 365:454-455, 2005.
[20] Weigelt B, Reis-Filho JS. Molecular profiling currently offers no more than tumour morphology and basic immunohistochemistry. Breast Cancer Research 12:S5, 2010.
[21] Personalised medicines: hopes and realities. The Royal Society, London, 2005.Available from: https://royalsociety.org/~/media/Royal_Society_Content/policy/publications/2005/9631.pdf, viewed Jan 1, 2015.
[22] Beizer B. Software Testing Techniques. Van Nostrand Reinhold; Hoboken, NJ 2 edition, 1990.
[23] Ecker JR, Bickmore WA, Barroso I, Pritchard JK, Gilad Y, Segal E. Genomics: ENCODE explained. Nature 489:52-55, 2012.
[24] Rosen JM, Jordan CT. The increasing complexity of the cancer stem cell paradigm. Science 324:1670-1673, 2009.
[25] Bandelt H, Salas A. Contamination and sample mix-up can best explain some patterns of mtDNA instabilities in buccal cells and oral squamous cell carcinoma. BMC Cancer 9:113, 2009.
[26] Knight, J. Agony for researchers as mix-up forces retraction of ecstasy study. Nature 425:109, September 11, 2003.
[27] Gerlinger M, Rowan AJ, Horswell S, Larkin J, Endesfelder D, Gronroos E, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 366:883-892, 2012.
[28] Ioannidis JP. Is molecular profiling ready for use in clinical decision making? The Oncologist 12:301-311, 2007.
[29] Ioannidis JP. Some main problems eroding the credibility and relevance of randomized trials. Bull NYU Hosp Jt Dis 66:135-139, 2008.
[30] Ioannidis JP, Panagiotou OA. Comparison of effect sizes associated with biomarkers reported in highly cited individual articles and in subsequent meta-analyses. JAMA 305:2200-2210, 2011.
[31] Ioannidis JPA, Panagiotou OA. "Comparison of effect sizes associated with biomarkers reported in highly cited individual articles and in subsequent meta-analyses. JAMA 305:2200-2210, 2011.
[32] Ioannidis JP: Excess significance bias in the literature on brain volume abnormalities. Arch Gen Psychiatry 68:773-780, 2011.
[33] Harris G. Diabetes drug maker hid test data, files indicate. The New York Times July 12, 2010.
[34] Berman JJ. Machiavelli's Laboratory. Amazon Digital Services, Inc., 2010.
[35] Misconduct in science: an array of errors. The Economist. September 10, 2011.
[36] Begley S. In cancer science, many 'discoveries' don't hold up. Reuters Mar 28, 2012,
[37] Abu-Asab MS, Chaouchi M, Alesci S, Galli S, Laassri M, Cheema AK, et al. Biomarkers in the age of omics: time for a systems biology approach. OMICS 15:105-112, 2011.
[38] Moyer VA; on behalf of the U.S. Preventive Services Task Force. Screening for prostate cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med May 21, 2011
[39] How science goes wrong. The Economist Oct 19, 2013.
[40] Shafer SL. Letter: To our readers. Anesthesia and Analgesia. February 20, 2009.
[41] Pear R. Obama to Request Research Funding for Treatments Tailored to Patients’ DNA. The New York Times January 24, 2015.
[42] Munos B. 2014 New Drug Approvals Hit 18-Year High. Forbes Jan 2, 2015. Available at: http://www.forbes.com/sites/bernardmunos/2015/01/02/the-fda-approvals-of-2014, viewed June 20, 2015.
[43] Berman JJ. Rare diseases and orphan drugs: Keys to Understanding and Treating Common Diseases. Academic Press, in press, 2014.
[44] Holland Frei Cancer Medicine. Kufe D, Pollock R, Weichselbaum R, Bast R, Gansler T, Holland J, Frei E, eds. BC Decker, Ontario, Canada, 2003.
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
Wednesday, July 1, 2015
The Genome Complexity Barrier in Precision Medicine
Elsevier has just published an editorial of mine entitled, The Data Complexity Barrier and the Surprising Importance of Rare Diseases. The premise of the article is that the common diseases of humans are way too complex to understand. Money spent on President Obama's Precision Medicine initiative will be wasted if funds are primarily directed to the common diseases. As it happens, the rare diseases are genetically simple, compared with the common diseases. Money spent on rare disease research is money well spent. In most instances, breakthroughs in understanding the rare diseases have led to new treatments for the common diseases.
I urge you to read the full editorial. Unlike much of the newsfeed hype on the subject of Precision Medicine, my article is well-researched and includes 44 references to the supporting literature.
- Jules J. Berman, Ph.D., M.D.
key words: precision medicine, pharmacogenomics, pharmacogenetics, personalized medicine, genomics, bioinformatics, funding, President Obama's initiative, common diseases, rare diseases, complexity barrier
I urge you to read the full editorial. Unlike much of the newsfeed hype on the subject of Precision Medicine, my article is well-researched and includes 44 references to the supporting literature.
- Jules J. Berman, Ph.D., M.D.
key words: precision medicine, pharmacogenomics, pharmacogenetics, personalized medicine, genomics, bioinformatics, funding, President Obama's initiative, common diseases, rare diseases, complexity barrier
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