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

Monday, April 13, 2015

English is a sloppy language

English has many examples of words that are spelled quite differently from one another but that have a similar sound when spoken, and that mean nearly the same thing.

Here are some examples:

bang and clang
bind and bond
blare and glare
burble, bubble, gurgle, and gargle
chock and chuck
cinch and clinch
clap and clop
click and clack
dandle and dangle
dank and damp
dribble and drizzle
elegant and eloquent
flip, flap, flop, plop, and glop
foundering and floundering
gabbing, gabbling, and babbling
hank and hunk
harping and carping
hiccough and hiccup
jabber and jibber, and gibberish and blabber
ketchup and catsup
persnickety and pernickety
pratfall and pitfall
prick up your ears, perk up your ears, and pick up your ears
pronking and pronging
sassy and saucey
sled and sledge
slob and slop
sniff and snuff
snip and snap
snooty, snotty, and snouty

Jules Berman

tags: synonyms, synonymy, language, spelling, orthography, pleionyms, plesionymy, onomatopoeia, wordplay, writing

Saturday, January 31, 2015

President Obama's Precision Medicine Initiative: A Suggestion

Yesterday, Reuter's published the numbers

Total budget: $215 million in his 2016 budget for the initiative.

$130 million would go to the NIH to fund the research cohort

$70 million to NIH's National Cancer Institute

$10 million would go to the Food and Drug Administration to develop databases

$5 million would go to the Office of the National Coordinator for Health Information Technology to develop privacy standards and ensure the secure exchange of data.

I couldn't understand, from reading the Reuter's article, whether this budget covers expenses to be incurred in 2016, or whether the budget is spread over multiple years. My guess is that it's the former, and that the initiative will draw additional funds for each year that it's active. It appears that the initiative will not be confined to genomics. Other high-throughput 'omics data will also be captured (e.g., epigenomics, metabolomics).

If we've learned anything in the post-genomics era, it's that biological systems are incredibly complex. Assembling terabytes of 'omics data will probably teach us this same, frustrating, lesson AGAIN; but it's not obvious how this effort will help us to substantially prevent, diagnose and treat the common diseases.

Believe it or not, most of the clinical advances in molecular medicine have come from studying rare diseases and rare variants of common disease. Unlike the common diseases, the rare diseases are often simple (one gene -> one protein -> one pathway -> one disease). It has been a surprise to some, but drugs developed for rare diseases often have applicability to common conditions. This happens because common diseases employ disease pathways that are also found in rare diseases. In my opinion, the best way to conquer the [complex] common diseases is to start by understanding the pathways operative in the [simple] rare diseases. So, if we want to invest $250 million in 'omics research, please let's focus on disease pathways discovered in rare diseases and shared by the common diseases.

- Ⓒ 2015 Jules J. Berman

tags: rare disease, orphan disease, personalized medicine, individualized medicine, genetic testing, gene testing, molecular diagnostics, biomarkers, pharmacogenetics, pharmacogenomics, future of medicine, state of the union address, funding initiative, NIH, FDA, big pharma, lobbyists, lobbying, scientific politics, scientific ethics, disease genetics, common diseases, complex diseases, rare variants, gene variants, whole genome sequencing

Monday, January 26, 2015

President Obama's Precision Medicine Initiative: Counter-arguments

In his 2015 State of the Union Address, President Obama launched his new Precision Medicine Initiative to “bring us closer to curing diseases like cancer and diabetes -- and to give all of us access to the personalized information we need to keep ourselves and our families healthier.” The initiative will be described in greater detail in the administration's budget proposal, but the following are widely assumed to be true: 1) requested funding will be in the hundreds of millions of dollars; 2) budgeted items will received considerable bipartisan support in congress; 3) the initiative will be championed by scientific and regulatory agencies of government (e.g., NIH and FDA) and by the pharmacy industry; and 4) the goals of the initiative will be similar to the goals of earlier legislation (The Genomics and Personalized Medicine Act) co-sponspored by President Obama when he was a Senator.

This blog lists six reasons why I am very skeptical of this initiative. Before I start, I'd like to clarify that I am a huge fan of President Obama. I believe he is the greatest U.S. president since FDR, and I will be eternally grateful for all that he has done for this country and for the world. Also, I am a great believer in the importance of genomics research. Hence, when I criticize this initiative, so early in the game, I do so with a great deal of ambivalence.

Nonetheless, here are my counter-arguments to the Precision Medicine Initiative.

1. Genomics research, this past decade, has taught us that genetics is a lot more complex than we had imagined. Human traits (e.g., height, weight), common diseases (e.g., diabetes, obesity, heart disease, and cancer), may involve hundreds of gene variants, non-coding regulatory sequences, competing epigenomic influences, and so on. Extrapolating from what we know about the complexity of gene expression, it seems that we are a very long way from understanding how disease genes are controlled. If we don't know how disease genes are controlled, then, with a few exceptions [see point 3], we can't practice genomic-based medicine.

2. It has long been promised that gene sequencing will lead, rapidly, to important advances in personalized medicine (also known as pharmacogenomics, and most recently renamed “precision medicine”). As it happens, simply knowing a sequence of nucleotides has not proven to be particularly helpful for understanding diseases that involve hundreds of genes, and an unspecified number of poorly characterized regulatory modifiers. Hence, personalized medicine, has not appreciably reduced the burden of morbidity and mortality produced by common diseases [see point 3].

3. Most of genomics-based medical progress has come in the realm of rare diseases and rare subsets of common diseases. As a generalization, rare diseases tend to be caused by single gene errors, while common diseases are caused by environmental agents (e.g., infections, toxins, carcinogens) or by multi-gene errors, or by some combination of the environment and multiple genes. The reason for this genetic dichotomy between rare diseases and common diseases is discussed, in depth, in my recently published book Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases. So yes, there are examples wherein abnormalities of one gene can account for a specific disease, but this phenomenon applies exclusively to rare diseases, to rare variants of common diseases, and to a few common genetic disorders associated with a relatively benign clinical course. In aggregate, single gene disorders account for a miniscule portion of the life-threatening disease burden in the U.S. and the world.

As an aside, I am an advocate for funding research into the genetics of rare diseases. It is the premise of my book that we have been astoundingly successful in rare disease research, and that treatments developed for the rare diseases are applicable to the common diseases [because rare and common diseases use the same cellular pathways]. Hence, a good way to conquer the common diseases is to steer the NIH research budget towards funding rare disease research.

4. It is conceivable that genomic tests will prove useful, notwithstanding the aforementioned complexities in gene controls. How so? It could be that a common disease is driven by a particular biological pathway that is dominated by one particular protein. If that protein were blocked [or stimulated in the case of an inhibitor protein], then it would be great to have a test for the pathway, or its dominant protein, or for gene variants that influence the activity of the expressed protein. Hence, precision medicine would apply in these instances. Nonetheless, the approach to precision medicine has been focused on looking for sequence variations, and I don't believe that sequence variations are bringing us much closer to finding the key proteins that drive the cellular pathways that account for common diseases. Again, the best way of finding key pathways operative in common diseases is to study the rare diseases [further explained in my book].

5. Clinical practice, based on candidate molecular tests requires lots of clinical trials and huge outcomes databases (i.e., validation data). Clinical trials are hugely expensive, and can require more than a decade to accrue patients, collect and analyze the data, and draw conclusions. Experience suggests that in most cases, the final conclusions are disappointing. Population databases, also hugely expensive, pose problems related to patient confidentiality and privacy, accuracy of data, analytic methodology, and validity of the conclusions drawn from the data. At present, we are nowhere near having the kinds of databases that we will need to confirm the value of new molecular tests.

Historically, the molecular biomarker field has been an embarrassment for the clinical research community. There have been a few successful exceptions (e.g., herceptin), but for the most part, biomarkers have been a bust. Should we really have much faith in the future of precision biomarkers, when history suggests that the odds of success are low?

6. Do we have any trustworthy authorities who can objectively claim that precision medicine is a good idea? Let's look at who's backing funding for the precision medicine initiative.

First, there's congress. Many of the members of congress, perhaps the majority, do not believe in global warming, or evolution, or the benefit of vaccinations. An appreciable number of congressmen believe that the earth is 5,000 years old, that pollution is not harmful, and that there's good evidence indicating that the end times are close at hand. Does anyone seriously believe that Congress can distinguish good science from bad science?

Of course, big pharma loves precision medicine because it generates expensive new tests and treatments that will be paid by insurers, no matter how great the cost or how small the benefit.

Then there is the matter of the federal agencies that fund or conduct science. It is the responsibility of the leaders of federal agencies to be responsive to Congress. If legislators need to justify, to their constituents, a new research initiative, then legislators will simply ask their agency heads to invent a credible scientific justification.
The question that never seems to be discussed is, “Is this the best way to improve the health of the nation, or are there alternative research initiatives that would give us a better outcome?”

- Ⓒ 2015 Jules J. Berman

tags: rare disease, orphan disease, personalized medicine, individualized medicine, genetic testing, gene testing, molecular diagnostics, biomarkers, pharmacogenetics, pharmacogenomics, future of medicine, state of the union address, funding initiative, NIH, FDA, big pharma, lobbyists, lobbying, scientific politics, scientific ethics, disease genetics, common diseases, complex diseases, rare variants, gene variants, whole genome sequencing

Friday, January 9, 2015

More on Luck and Cause

Earlier this week, I posted a blog criticizing the conclusions reached in a highly publicized paper written by a group of scientists at Johns Hopkins Medical Center. The authors conclude that "bad luck", rather than environmental or genetic causes, is responsible for the bulk of human cancers. My prior blog post explained why Hopkins is wrong.

After the blog was written, I received some very interesting feedback from a LinkedIn group (Science writers), much of which centered on the different ways that people use the words "luck" and "cause". Though mathematicians will despair, the word "good luck" is routinely applied to just about anything that has desirable outcome. So if a high school student gets a perfect score on the SAT exam, he or she was very very lucky. If you would interject to say that luck had nothing to do with it ("It was all due to student's high intelligence!"), you would be informed that the intelligence was a matter of luck, being as the student had done nothing to earn his or her intelligence. If you were to suggest that the "cause" of the high score was hard work, you would be told that "hard work" was just one of many conditions that led to the high score (e.g., "lucky" intelligence, a good night's sleep the night before, growing up in a stable living environment where current events, history and literature are discussed). There being many different "causes," it wouldn't make much sense to think in terms of any specific cause, and you might as well chalk it up to just plain good luck.

Getting back to biology and disease, consider these hypotheticals:

If you have 5 people living with an Ebola patient, and three of the five come down with the disease, would you say that these three came down with Ebola because they were "unlucky"? Or would you say that these three came down with Ebola because they were infected with the virus [and the other two were not]?

Would you say that the Ebola virus caused the infection in these three individuals? Or would you say that many factors, such as "luck", the environment, low innate viral resistance, poor nutrition, all set the stage for their infections, and that the Ebola virus was just one of many ingredients in the brew?

There's a real danger with using "luck" to describe events that we do not understand or cannot predict. Likewise, causation can be deceptive when dealing with a multi-step process that plays out over years or decades (like cancer).

When I think about "cause" I'm usually applying the "but-for" criteria ("but-for" this, that would not have happened). So, for me, Ebola virus causes Ebola hemorrhagic fever, and infections are not a matter of luck. Likewise, for me, there are "but-for" causes of cancer (e.g., chemicals, viruses, predisposing genes), and many important modifying factors that probably don't rise to the level of "but-for" causes (e.g., cell proliferation, DNA repair, genomic and epigenomic influences, regression-causing events, immune status); and cancer is not caused by bad luck.

Today, cancer has become the quintessential "bad luck" disease. In a prior blog, I described examples of "bad luck" cancers that transformed into "specific cause" cancers, when we studied the data. My personal opinion is that most cases of cancer are associated with known "but-for" causes. As we learn more and more about the different types of cancers, particularly the huge variety of rare cancers, we continue to find specific causes for specific cancers. I just assume, perhaps incorrectly, that every cancer has a cause.

I urge everyone reading this blog to also read my prior blog, which provides a full rebuttal to the Johns Hopkins "bad luck" cancer hypothesis.

- Jules Berman

tags: johns hopkins, press release, cancer news, bad luck, data repurposing, opinion, criticism, carcinogenesis, rare cancer, rare diseases, cancer incidence, comparative carcinogenesis, Jules J. Berman, Ph.D., M.D., cancer research, new findings, mutation rate, rebuttal, stem cell renewal, probabilistic models, data modeling, randomness, chance, misfortune, accident, unpredictable, causation, causative role, but-for, but for, sine qua non

Monday, January 5, 2015

Human diseases are not caused by bad luck

Earlier this week, I posted a blog criticizing the conclusions reached in a highly publicized paper written by a group of scientists at Johns Hopkins Medical Center. The authors conclude that "bad luck", rather than environmental or genetic causes, is responsible for the bulk of human cancers. My prior blog post explained why Hopkins is wrong.

After the blog was written, I got some very interesting feedback from a LinkedIn group (Science writers). Much of the discussion centered on the meaning of "luck", as it applies to biological processes.

Terms such as "luck", "accident", "misfortune", and "unpredictable" are often used, inappropriately, to describe complex events that we do not fully understand. For example, we speak in terms of "motor vehicle accidents" to describe vehicular crashes, even when we have discovered non-accidental causes (e.g., driving while intoxicated, driving on the wrong side of the road, failure to yield). We use the term "cerebrovascular accident" to describe strokes, even in individuals who have abundant risk factors (e.g., high blood pressure, e.g., occluded carotid artery). When we come down with a cold, we often say that it was our bad luck or our misfortune to get sick, even when we know that the cold was caused by a virus.

When we flip a coin, we like to think that the outcome occurs randomly, because there is a 50% chance of heads or of tails. But we all know, at some level, that the outcome of the toss is predetermined at the moment that the coin flips into the air. The laws of physics come into play, with a complexity that defies human prediction. Coin tosses, and roulette spins, are examples of processes that can be modeled, mathematically and intuitively, as probabilistic events. But we shouldn't confuse a probabilistic model with a physical reality.

Biology and medicine are replete with examples of phenomena that were attributed to "bad luck" until we finally determined their causes. For example, until the dawn of the twentieth century, the cause of malaria was unknown. There must have been something in the air (mala aria = bad air in medieval Italian). In 1880, Laveran identified the causative agent, a protozoan, in the blood of affected patients, for which he was awarded the Nobel prize in 1907. Through the centuries, people suffering from infectious diseases, vitamin deficiencies, and environmental toxins were considered "unfortunate", meaning "without luck."

Do not presume that modern-day scientists are too enlightened to be taken in by "chance" phenomenon. For many years, medical scientists sought a cause for sudden infant death syndrome (SIDS). Children were dying in their cribs, unpredictably, as though they had the bad luck to just stop breathing. In the past half century, we have learned that the majority of cases of SIDS are associated with sleeping conditions that limit the infants ability to breathe (e.g., sleeping on stomach, in hot room, with overabundance of soft bedding, etc.).

Today, cancer has become the quintessential "bad luck" disease. The literature gives us lots of examples of "bad luck" cancers that transformed into "specific cause" cancers, when we studied the data.

For example, In a landmark paper published in 1971 by Herbst and coworkers, the authors found an increase in the number of young women who developed an extremely rare cancer: clear cell adenocarcinoma of the cervix or of the vagina. The mothers of these young women had ingested a nonsteroidal synthetic estrogen (diethylsilbestrol, DES) during their pregnancies. In utero exposure to the drug caused a specific rare tumor to occur in the daughters. The offspring were classic "bad luck" cancer victims, having done nothing to put themselves at risk. Herbst had to go back a generation to find the real cause.

Women who developed mesotheliomas, a very rare cancer, in the 1970s and 1980s, were also the victims of "bad luck", until cancer epidemiologists found the common factor that linked these cases. These women had washed the asbestos-laden clothes of their fathers or husbands, who worked in the shipyards during World War II. Their brief exposure to asbestos resulted in mesotheliomas 20+ years later.

Much of what we observe in biology and medicine looks exactly like luck... until we understand the cause. The effect of "bad luck" hypotheses, as they apply to biology and medicine, is to halt scientific progress. Why would scientists waste their time looking for the causes of cancer, if cancers are caused by "bad luck"? The U.S. Environmental Protection Agency certainly can't protect us from bad luck!

I urge everyone reading this blog to also read my prior blog, which provides a rebuttal to the Johns Hopkins "bad luck" cancer hypothesis.

- Jules Berman

tags: johns hopkins, cancer news, bad luck, data repurposing, opinion, criticism, carcinogenesis, rare cancer, rare diseases, cancer incidence, comparative carcinogenesis, Jules J. Berman, Ph.D., M.D., cancer research, new findings, mutation rate, rebuttal, stem cell renewal, probabilistic models, data modeling, randomness, chance, misfortune, accident, unpredictable

Friday, January 2, 2015

Hopkins is wrong. Role of bad luck in cancer not shown!

Amidst much fanfare, Johns Hopkins issued a news release, dated Jan. 1, 2015, under the banner, “Bad Luck of Random Mutations Plays Predominant Role in Cancer, Study Shows" The subtitle to the banner is, "Statistical modeling links cancer risk with number of stem cell divisions.”

Whoever wrote the Hopkins news report doesn't seem to understand that the subtitle contradicts the title. The title implies that the authors have proven an assertion (i.e., that bad luck causes cancer). The subtitle indicates that they have only established an association (i.e., there is a statistical link between cancer incidence and random mutations occurring as stem cells divide). It seems like a quibble, but there is an immense conceptual gulf between a "link" and a "cause". It's easy to find a correlation, but it's hard to prove a causal role. In many cases, correlations simply disappear when the original data is reanalyzed with different analytic methods, or when some of the original assumptions are changed, or when new data is obtained, or when information from some other study provides better results that support an opposing hypothesis.

The Hopkins researchers reviewed the literature to find, "the cumulative total number of divisions of stem cells among 31 tissue types during an average individual’s lifetime." These numbers for the different tissues, correlated closely with the risk of cancer occurring in those tissues. Having arrived at the correlation, "using statistical theory, the pair calculated how much of the variation in cancer risk can be explained by the number of stem cell divisions, which is 0.804 squared, or, in percentage form, approximately 65 percent." Of the 31 tissues they studied, the tumor incidence in 9 of the tissues did not fit their "bad luck" correlation. Tumor incidence in these tissues, according to the news report, must come from some other source, such as environmental carcinogens. The 22 tissues that fit their model were deemed the "bad luck" tumors.

The bad luck hypothesis is not new. Cancer researchers have been trying to titrate the various suspected causes of cancer for decades. In the 1970s, when there was a large push to find chemicals in the environment that cause cancer, it was widely accepted that about 85% of cancers were caused by environment agents; 15% were caused by other things, such as genes, and this last 15% would also include "bad luck" mutations. These numbers were based on statistical inferences from data on the geographic variations in cancer incidence, looking at how the types of cancers occurring in populations changed in different locations on earth and in response to identified carcinogens.

Back in the early '70s, there was an awareness of the special place of "rare cancers" in the discussion. The common cancers (i.e., skin, lung, colon), were all presumed to be caused by environmental toxins (e.g., UV light, cigarettes, food and water contaminants, chronic infections, etc). More than 90% of the burden of cancer in the U.S. is accounted for by just a handful of cancer types (namely, basal cell carcinoma of skin, squamous cell carcinoma of skin, bronchogenic lung cancer, adenocarcinoma of colon, adenocarcinoma of breast, adenocarcinoma of prostate, adenocarcinoma of pancreas, ovarian carcinoma, esophageal cancer, and maybe one or two others). There are over 6,000 different kinds of cancer. All but a half dozen or so of these 6,000 varieties of cancer are rare, accounting in the aggregate for fewer than 10% of the tumors occurring in humans. Many of the rare cancers have well-studied patterns of inheritance. Because there are so many known inherited rare cancers, we tend to assume (perhaps incorrectly) that the bulk of rare cancers are caused by inherited genes (i.e., not caused by random mutations occurring in individuals with cancer).

OK, so lessons learned through the history of cancer research seems to be at odds with the conclusions drawn by the Hopkins team. Let's ignore history, for a moment. Here is a list of present-day concerns that should, at the very least, tone down the conclusions reached by the Hopkins study.

1. There are animals with much higher stem cell renewal than that seen in humans. Consider the whale. Whales have tons of intestines with trillions of dividing cells. If stem cell division and random mutation account for cancer, then you would expect every whale to be chock full of intestinal cancers. They are not. Please, spare me the argument that whales are different from humans and the two species cannot be compared. If you assert that random mutations in the DNA of stem cells is the cause of cancer, then your assertion should apply equally to any organisms that contains DNA and stem cells.

2. Carcinogenesis (i.e., the biological process that leads to cancer) is known to be a multi-step phenomenon. Mutation may be the first step, but many additional steps, leading to cancer, must occur, sometimes playing out over decades. In a multi-step process, you cannot expect any single event (e.g., a random bad luck mutation) to account, by itself, for the incidence of cancer.

3. There is a high cancer rate in mice and rats, both relatively short-lived animals. Wouldn't you expect a low accumulation of random bad mutations in animals that only live a year or two? The rapid evolution of cancers in short-lived animals (i.e., weeks or months) suggests that something in addition to random bad luck mutations must account for carcinogenesis in these animals.

4. Biological systems are complex, and causation is seldom a meaningful concept when many events contribute to a single observed phenomenon. For example, random mutation may occur more frequently in tissues with rapidly dividing stem cells, but rapid division of cells may occur in response to some toxic effect or chronic condition that causes a subpopulation of cells to die. Hence, rapid division of stem cells may be the result of some other "cause". Likewise, chronic toxicity and subsequent stem cell renewal in various tissues may result from higher rates of activation of carcinogens (i.e., metabolism) in those tissues. Hence, stem stem cell renewal may be tightly coupled with a variety of biological influences other than "bad luck".

In summary, the correlation observed by the Hopkins scientists is interesting, and it probably deserves further investigation. But the assertion that "bad luck" causes most human cancers is pretty much meaningless, at the moment.

- Jules J. Berman

p.s. The topic of today's blog is covered in depth in several of my published books, particularly Neoplasms: Principles of Development and Diversity, and expanded in my next blog post.

tags: johns hopkins, cancer news, bad luck, data repurposing, opinion, criticism, carcinogenesis, rare cancer, rare diseases, cancer incidence, comparative carcinogenesis, Jules J. Berman, Ph.D., M.D., cancer research, new findings, mutation rate, rebuttal, stem cell, stem cell renewal

Tuesday, October 14, 2014

Free Preface to Rare Diseases and Orphan Drugs

I just noticed that the frontmatter, individual chapters, and appendices to my new book,
Rare Diseases and Orphan Drugs: Keys to understanding and treating the Common Diseases
is now available from Science Direct.

Though there are charges for downloading the individual chapters, the frontmatter, including the Preface, can be downloaded at no cost. The Preface explains the applicability of rare disease research to finding cures for common diseases. The is theme is catching on in the biomedical research community and is changing the way we study and fund disease research, in general.

Please take a few minutes to download and read the Preface to my book. If you, or any of your relatives and friends, have a rare disease, you may find this book to be a real eye-opener.

Jules J. Berman, Ph.D., M.D.


tags: rare disease, complex disease, genetics of disease, disease genetics, infectious disease, common disease, orphan drugs, orphan disease, disease research, disease funding, medical funding, medical research, free book chapter, trial chapter, sample chapter, review chapter

Sunday, September 21, 2014

Lymphadenopathy: a misnomer

Medical nomenclature contains numerous examples of outdated, but widely used terminology.

The term "lymphadenopathy", meaning lymph node disease, is a case in point. In former times, lymph nodes (as they are known now) were known as lymph glands. It was believed that the lymph fluid circulating in the lymph vessels, was produced by the lymph nodes. Organs that produce chemicals that are circulated to other tissues are referred to as glands (e.g., endocrine glands, exocrine glands). Hence the term "lymph gland". A disease of the lymph gland was termed "lymphadenopathy" from lymph + adenos (Greek for gland) + pathei (Greek for disease).
Derivation of lymph fluid.
Source: National Cancer Institute, public domain

The term for a neoplasm of a lymph node was "lymphadenoma"

The term for inflammation of a lymph node was "lymphadenitis"

Nearly everything about lymph node pathology was saddled to the ill-conceived notion that a lymph node is a type of gland.

We now know that lymph is not produced by the glandular activity of lymph nodes. Lymph is interstitial fluid (i.e., fluid between tissue cells) that is absorbed into lymph vessels. Lymph fluid is somewhat milky because it contains white cells, sloughed from lymph nodes, but the fluid comes from tissue interstitium and its composition is akin to blood plasma.

Modern pathologists have dropped the "adeno" in "lymphadenoma" and replaced it with the less confusing term, "lymphoma".

Regrettably, the terms "lymphadenopathy" and "lymphadenitis" persist into modern usage.

- Jules J. Berman, Ph.D., M.D. tags: lymph node, lymphoid, lymphedema, lymphatics, lymphatic vessels, common disease, orphan disease, orphan drugs, rare disease, subsets of disease, disease genetics, logophile, medical terminology, medical nomenclature, medical dictionary

In June, 2014, my book, entitled Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases was published by Elsevier. The book builds the argument that our best chance of curing the common diseases will come from studying and curing the rare diseases.



I urge you to read more about my book. There's a generous preview of the book at the Google Books site. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.

Wednesday, September 17, 2014

Patient identifiers

I have posted an article on patient identifiers. Here is a short excerpt from the article:

Imagine this scenario. You show up for treatment in the hospital where you were born, and in which you have been seen for various ailments over the past three decades. One of the following events transpires:

1. The hospital has a medical record of someone with your name, but it's not you. After much effort, they find another medical record with your name. Once again, it's the wrong person. After much time and effort, you are told that the hospital has no record for you.

2. The hospital has your medical record. After a few minutes with your doctor, it becomes obvious to both of you that the record is missing a great deal of information, relating to tests and procedures done recently and in the distant past. Nobody can find these missing records. You ask your doctor whether your records may have been inserted into the electronic chart of another patient or of multiple patients. The doctor does not answer your question.

3. The hospital has your medical record, but after a few moments, it becomes obvious that the record includes a variety of tests done on patients other than yourself. Some of the other patients have your name. Others have a different name. Nobody seems to understand how these records got into your chart.

4. You are informed that the hospital has changed its hospital information system, and your old electronic records are no longer available. You are asked to answer a long list of questions concerning your medical history. Your answers will be added to your new medical chart. You can't answer any of the questions with much certainty.

5. You are told that your electronic record was transferred to the hospital information system of a large multi-hospital system. This occurred as a consequence of a complex acquisition and merger. The hospital in which you are seeking care has not yet been deployed within the information structure of the multi-hospital system and has no access to your record. You are assured that the record has not been lost and will be accessible within the decade.

6. You arrive at your hospital to find that it has been demolished and replaced by a shopping center. Your electronic records are gone forever.


These are the kinds of problems that arise when hospitals lack a proper patient identifier system (a common shortcoming). The purpose of the article is to list the features of a patient identifier system, emphasizing the essential role of identifiers in healthcare services and biomedical research.

The full-length article is available at:

http://www.julesberman.info/book/id_deid.htm

In June, 2014, my book, entitled Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases was published by Elsevier. The book builds the argument that our best chance of curing the common diseases will come from studying and curing the rare diseases.



I urge you to read more about my book. There's a generous preview of the book at the Google Books site. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.

- Jules J. Berman, Ph.D., M.D. tags: common disease, orphan disease, orphan drugs, rare disease, subsets of disease, disease genetics, identifiers, identification, ehr, emr, electronic health records, electronic medical record, health informatics, HITECH, medical informatics, pathology informatics

Three neglected principles of Big Data: identifiers, immutability, and introspection

My book, Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information was published last year by Morgan Kaufmann.



There are three crucial topics related to data preparation that are omitted from virtually every other Big Data book: identifiers, immutability, and introspection.

A thoughtful identifier system ensures that all of the data related to a particular data object will be attached to the correct object, through its identifier, and to no other object. It seems simple, and it is, but many Big Data resources assign identifiers promiscuously, with the end result that information related to a unique object is scattered throughout the resource, attached to other objects, and cannot be sensibly retrieved when needed. The concept of object identification is of such overriding importance that a Big Data resource can be usefully envisioned as a collection of unique identifiers to which complex data is attached. Data identifiers are discussed in Chapter 2.

Immutability is the principle that data collected in a Big Data resource is permanent, and can never be modified. At first thought, it would seem that immutability is a ridiculous and impossible constraint. In the real world, mistakes are made, information changes, and the methods for describing information changes. This is all true, but the astute Big Data manager knows how to accrue information into data objects without changing the pre-existing data. Methods for achieving this seemingly impossible trick is described in detail in Chapter 6.

Introspection is a term borrowed from object oriented programming, not often found in the Big Data literature. It refers to the ability of data objects to describe themselves when interrogated. With introspection, users of a Big Data resource can quickly determine the content of data objects and the hierarchical organization of data objects within the Big Data resource. Introspection allows users to see the types of data relationships that can be analyzed within the resource and clarifies how disparate resources can interact with one another. Introspection will be described in detail in Chapter 4.

I urge you to read more about my book. Google books has prepared a generous preview of the book contents. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.

Jules J. Berman, Ph.D., M.D.tags: big data, metadata, data preparation, data analytics, data repurposing, datamining, data mining

Wednesday, September 10, 2014

Pitfalls in Medical Terminology

Back in 2008, I posted a list of medical terms that are easily confused, such as ileum (part of small intestine), and ilium (a pelvic bone). Medical transcriptionists and healthcare workers who input chart data (i.e., just about everybody), should be aware of medical term-pairs that have nearly the same orthography, are often pronounced identically, and have completely different meanings. These words are not picked up by spell checkers (because they are not misspelled). You can avoid such errors if you know what to look for.

Since 2008, there have been many updates to the list:
acinic, actinic
anisakiasis, anisokaryosis
aptotic, apoptotic
arboreal, aboriginal
arteritis, arthritis
auxilliary, axillary
brachial, brachium, branchial
callous, callus
causality, casualty
chlorpropamide, chlorpromazine
chondroid, chordoid
chondroma, chordoma
chorionic, chronic
cingula, singular
coitus, colitis
colic, colonic
colitis, coitus
costal, coastal
cryptogam, cryptogram
cygnet, signet
decease, disease
deceased, desist
digitate, digitize
dioecious, deciduous
diploic, diploid
disc, disk
disease, decease
diseased, deceased
dyskaryosis, dyskeratosis
dysphasia, dysphagia
ectatic, ecstatic
endochondral, enchondral (these are synonyms)
engram, n-gram, ngram
epistasis, epistaxis, epitaxis (the last is a misspelling of the second)
exxon, exon
facial, fascial
facies, faeces
fetal, fatal
firearm, forearm
foreword, forward
hallux, helicis
helicis, hallux
herpetic, herpangina
hydatid, hydatidiform
ileitis, iliitis
ileum, ilium
insular, insulin
intercostal, intercoastal
intubation, incubation
isotope, isotrope
kerasin, kerosene, keratin
keratotic, keratinic, actinic
keratinocytic, keratinolytic
keratosis, ketosis
lipoma, lymphoma
lumbar, lumber
malleolus, malleus
metachronous, metacrinus
milia, milium
miotic, mitotic, meiotic
mitosis, meiosis, myosis, myiasis
monogenic, monogenetic, and Monogenetic (last, related to class Monogenea)
mucous, mucus
myelofibrosis, myofibrosis
myofibroma, myelofibroma
neuroplastic, neoplastic
nucleus, nucleolus
oncocyte, onychocyte
oncology, ontology, ontogeny
organic, organoid
palatal, palatial
paleodontology, paleontology
palette, palate
palpation, palpitation
parasite, pericyte
parental, parenteral
pathogen, parthenogen
pathogenesis, parthenogenesis
pathogenic, pathogenetic (these two are synonyms)
penal, penile, pineal, panel
penicillamine, penicillin
perineal, peroneal, perianal
pleiotropic, pleiotrophic, pleiotypic (the first two are synonyms)
plural, pleural
porphyria, porphyruria
proptosis, ptosis
prostrate, prostate
protuberant, protruberant (the second term is simply a common misspelling)
quinine, quinidine
rachischisis, rachitis, rachischitic, rachitic
radial, radical
relics, relicts
reticle, reticule, radical
rett, ret
rosacea, rosea
semantic, somatic
serous, serious
silicon, silicone
singleton, singultus
sinusitis, synositis
somatic, semantic
sonography, stenography
taenia, tinea
takoma, trachoma
thecoma, thekeoma
torsion, distortion
trachoma, trachea
trichina, trachoma, trichura
trichinosis, trichosis, trichuriasis
trichrome, trichome
trochlear, tracheal
troglobite, troglodyte, trilobite
tuberous sclerosis, tuberculosis
tunicate, tourniquet
urethral, ureteral
vagitis, vaginitis
venous, venus
If you know the meaning of half of the terms in this list, you have a good grasp of medical terminology; but please don't settle for half measures. Physicians, nurses, chart reviewers, and medical transcriptionists should be aware of the correct meaning of each alternate word in these listed pairs.

In June, 2014, my book, entitled Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases was published by Elsevier. The book builds the argument that our best chance of curing the common diseases will come from studying and curing the rare diseases.



I urge you to read more about my book. There's a generous preview of the book at the Google Books site. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.

- Jules J. Berman, Ph.D., M.D. tags: common disease, orphan disease, orphan drugs, rare disease, medical terminology, medical errors, malaprop, malapropism, definition, confusing terms, confused medical terms, medical definitions, medical transcription, nomenclature, terminology, transcription errors, transcription mistakes, EMR, EHR, electronic medical record, electronic chart, electronic health record, avoidable errors, avoidable mistakes, sources of confusion, sources of error, common mistakes, common sources of confusion

Friday, July 18, 2014

Rare Diseases Hiding Among Common Diseases

In June, 2014, my book, entitled Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases was published by Elsevier. The book builds the argument that our best chance of curing the common diseases will come from studying and curing the rare diseases.



Here is an excerpt from Chapter 12:
It is easy to find cases wherein a rare disease accounts for a somewhat uncommon clinical presentation of a common disease.
12.1.2 Rule—Uncommon presentations of common diseases are sometimes rare diseases, camouflaged by a common clinical phenotype.
Brief Rationale—Common diseases tend to occur with a characteristic clinical phenotype and a characteristic history (e.g., risk factors, underlying causes). Deviations from the normal phenotype and history are occasionally significant.
Rare diseases may produce a disease that approximates the common disease; the differences being subtle findings revealed to the most astute observers. Here is some pithy wisdom that senior physicians love to impart to junior colleagues: “When you see hoof prints, look for horses, not zebras.” The message warns young doctors that most clinical findings can be accounted for by common diseases. Nonetheless, physicians must understand that zebras, unlike unicorns and griffins, actually exist. Occasionally, a rare disease will present with the clinical phenotype of a common disease.

For example, mutations of the JAK2 gene are involved in several myeloproliferative conditions, including myelofibrosis, polycythemia vera (see Glossary item, Polycythemia), and at least one form of hereditary thrombocythemia (i.e., increased blood platelets) [9–11]. Surprisingly, somatic blood cells with JAK2 mutations are found in 10% of apparently healthy individuals [12]. The high incidence of JAK2 mutations in the general population, and the known propensity for JAK2 mutations to cause thrombocythemia and thrombosis, should alert physicians to the possibility that some cases of idiopathic thrombosis may be caused by a platelet disorder caused by undiagnosed JAK2 mutation of blood cells. As it happens, it has been shown that a JAK2 mutation can be found in 41% of patients who present with idiopathic chronic portal, splenic and mesenteric venous thrombosis [13]. Such thrombotic events are uncommon in otherwise healthy patients. The search for a zebra, in this case a cryptic myeloproliferative disorder caused by a JAK2 mutation, pays off (see Glossary item, Myeloproliferative disorder).

Zebras can hide among the horses. Consider lung cancer, the number one cause of cancer deaths in the U.S. When lung cancer occurs in a young person, you might wonder if this is a rare disease cloaked as a common disease. Midline carcinoma of children and young adults is an extremely rare type of lung cancer. It is characterized by a NUT gene mutation, not typically found in commonly occurring lung cancers of adults [14]. Hence, midline carcinoma of children and young adults is an example of a rare disease hidden in a common disease. Secretory carcinoma, formerly known as juvenile breast cancer, is a rare form of breast cancer. It has a less aggressive clinical course than commonly occurring breast cancer, and occurs at a younger median age (i.e., about 25 years) than the median age of occurrence of common breast cancer (i.e., 61 years). In 2002, it was discovered that the expression of the ETV6-NTRK3 gene fusion is a primary event in the carcinogenesis of secretory breast carcinoma [15]. Once again, an uncommon presentation of a common tumor was found to hide a rare disease with its own characteristic genetic mutation.

Myelodysplastic syndrome, formerly known as preleukemia, is a rare blood disorder occurring almost exclusively in older individuals. The specific gene causing myelodysplastic syndrome is unknown, but recurrent cytogenetic alterations have been found in bone marrow cells, particularly losses of the long arm of chromosome 5 (i.e., 5q-) and of chromosome 7 (i.e., monosomy 7). Myelodysplastic syndrome occurs in very young children, with extreme rarity. Virtually all such childhood cases involve monosomy 7. An inherited predisposition to lose one copy of chromosome 7 in somatic cells has been reported in kindreds whose children have a high likelihood of developing myelodysplastic syndrome, or of acute leukemia. Hence, it seems that a somatic chromosomal abnormality associated with a rare disease occurring in adults is also associated with an even more rare childhood form of the disease. The childhood disease may occur when an inherited mutation predisposes children to the equivalent somatic chromosomal abnormality observed in the adult form of the disease [16,17].

As a final example, there are two recognized types of acute myelogenous leukemia (AML): AML following myelodysplasia, a preleukemia, and de novo AML, which develops in the absence of an observed preleukemic condition [18]. De novo AML can occur in children or in adults. The de novo AML cases in children have a different set of cytogenetic markers than those observed in adult de novo AML [19].

I urge you to read more about my book. There's a good preview of the book at the Google Books site. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.

- Jules J. Berman, Ph.D., M.D. tags: common disease, orphan disease, orphan drugs, rare disease, subsets of disease, disease genetics, genetics of complex disease, genetics of common diseases, cryptic disease

Thursday, July 17, 2014

Pareto's Principle and Long-Tailed Distribution Curves

In June, 2014, my book, entitled Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases was published by Elsevier. The book builds the argument that our best chance of curing the common diseases will come from studying and curing the rare diseases.



The book has an extensive glossary, that explains the meaning and relevance of medical terms appearing throughout the chapters. The glossary can be read as a stand-along document. Here is an example of one term, "Pareto's Principle", excerpted from the glossary.
Pareto’s principle - Also known as the 80/20 rule, Pareto’s principle holds that a small number of items account for the vast majority of observations. For example, a small number of rich people account for the majority of wealth. Just two countries, India plus China, account for 37% of the world population. Within most countries, a small number of provinces or geographic areas contain the majority of the population of a country (e.g., east and west coastlines of the U.S.). A small number of books, compared with the total number of published books, account for the majority of book sales.

Likewise, a small number of diseases account for the bulk of human morbidity and mortality. For example, two common types of cancer, basal cell carcinoma of skin and squamous cell carcinoma of skin, account for about 1 million new cases of cancer each year in the U.S. This is approximately the sum total for all other types of cancer combined. We see a similar phenomenon when we count causes of death. About 2.6 million people die each year in the U.S. [98]. The top two causes of death account for 1,171,652 deaths (596,339 deaths from heart disease and 575,313 deaths from cancer [99]), or about 45% of all U.S. deaths. All of the remaining deaths are accounted for by more than 7000 conditions.

Sets of data that follow Pareto’s principle are often said to follow a Zipf distribution, or a power law distribution. These types of distributions are not tractable by standard statistical descriptors because they do not produce a symmetric bell-shaped curve. Simple measurements such as average and standard deviation have virtually no practical meaning when applied to Zipf distributions. Furthermore, the Gaussian distribution does not apply, and none of the statistical inferences built upon an assumption of a Gaussian distribution will hold on data sets that observe Pareto’s principle.

I urge you to read more about my book. There's a good preview of the book at the Google Books site. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.

- Jules J. Berman, Ph.D., M.D. tags: 80/20 rule, common disease, data analysis, glossary, orphan disease, orphan drugs, rare disease, statistics

Tuesday, July 15, 2014

Relationship between Hamartoma and Cancer

In June, 2014, my book, entitled Rare Diseases and Orphan Drugs: Keys to Understanding and Treating the Common Diseases was published by Elsevier. The book builds the argument that our best chance of curing the common diseases will come from studying and curing the rare diseases.



The book has an extensive glossary, that explains the meaning and relevance of medical terms appearing throughout the chapters. The glossary can be read as a stand-along document. Here is an example of one term, "hamartoma", excerpted from the glossary.
Hamartoma - Hamartomas are benign tumors that occupy a peculiar zone lying between neoplasia (i.e., a clonal expansion of an abnormal cell) and hyperplasia (i.e., the localized overgrowth of a tissue). Some hamartomas are composed of tissues derived from several embryonic lineages (e.g., ectodermal tissues mixed with mesenchymal tissue). This is almost never the case in cancers, which are clonally derived neoplasms wherein every cell is derived from a single embryonic lineage. Tuberous sclerosis is an inherited hamartoma syndrome. The pathognomonic lesion in tuberous sclerosis is the brain tuber, from which the syndrome takes its name. Tubers of the brain consist of localized but poorly demarcated malformations of neuronal and glial cells. Like other hamartoma syndromes, the germline mutation in tuberous sclerosis produces benign hamartomas as well as carcinomas, indicating that hamartomas and cancers are biologically related. Hamartomas and cancers associated with tuberous sclerosis include cortical tubers of brain, retinal astrocytoma, cardiac rhabdomyoma, lymphangiomyomatosis (very rarely), facial angiofibroma, white ash leaf-shaped macules, subcutaneous nodules, cafe-au-lait spots, subungual fibromata, myocardial rhabdomyoma, multiple bilateral renal angiomyolipoma, ependymoma, renal carcinoma, subependymal giant cell astrocytoma [62].

Another genetic condition associated with hamartomas is Cowden syndrome, also known as multiple hamartoma syndrome. Cowden syndrome is associated with a loss of function mutation in PTEN, a tumor suppressor gene. Features that may be encountered are macrocephaly, intestinal hamartomatous polyps, benign hamartomatous skin tumors (multiple trichilemmomas, papillomatous papules, and acral keratoses), dysplastic gangliocytoma of the cerebellum, and a predisposition to cancers of the breast, thyroid and endometrium.

I urge you to read more about my book. There's a good preview of the book at the Google Books site. If you like the book, please request your librarian to purchase a copy of this book for your library or reading room.

- Jules J. Berman, Ph.D., M.D. tags: rare disease, common disease, orphan disease, orphan drugs, types of cancer, cancer types, tumor types, tumor biology, rare cancers, common cancers, hyperplasia, tissue overgrowth, disease genes, genetic disease, carcinogenesis, glossary