Mining Evolution

Can a worm get breast cancer? And how would you know if it did (since it doesn't have breasts)?

Biomedical research has made great use of "disease models": conditions in lab organisms that resemble the human diseases that the researchers really want to learn about. By seeing how a model condition develops and how it responds to drugs or other changes, researchers can make better guesses about what might help people. But finding such disease models usually requires some obvious similarity between the outward manifestations of the disease in humans and the animal subjects.

In the Proceedings of the National Academy of Sciences, a team from the University of Texas in Austin led by Edward Marcotte use the underlying molecular relationships to find connections between disorders with no such obvious relationship. In addition to a worm analog of breast cancer, they found an amazing connection between plant and human disorders. The analysis of plants' failure to respond to gravity led them to human genes related to Waardenburg syndrome. This syndrome includes an odd constellation of syndromes resulting from defects in the development of neural crest cells.

I saw Marcotte speak about this fascinating work at the conference I attended last December in Cambridge, Massachusetts. My writeup should be posted soon by the New York Academy of Sciences.

Biologists have repeatedly found that the networks of interacting molecules are organized into modules. Over the course of evolution, these modules can be re-used, often for purposes quite different from their original function. This much is well known, although seeming the persistence of modules over the vast evolutionary separation of plants and people is very dramatic.

What the Austin team did was to devise a methodology to identify related molecular modules in different species even without relying on similar outward manifestations, or phenotypes. They combed the known molecular networks of different species for modules that had a lot of "orthologous" genes: those that had retained similarity--and similar relationships--through evolution. They call the particular traits associated with these genes "orthologous phenotypes," or "phenologs." "We're identifying ancient systems of genes that predate the split of these organisms, that in each case retain their functional coherence," Marcotte said at the conference.

The importance of this scheme is that many molecular networks are poorly mapped, especially in humans. But if a particular gene is part of the network underlying a phenotype in another species--such as poor response of a plant to gravity--it's a good guess that the corresponding gene may be active in the orthologous phenotype in people. The researchers in fact confirmed many of these predicted relationships. Some of these genes were previously known to relate to disease, while others were new. The researchers created a list of hundreds more that they still hope to check.

These genes could give researchers many potential new targets for drugs or other interventions in diseases. So evolution is not just helping us to understand the biologic world we live in, but helping us devise ways to improve human health.

The Language of Life

Ten years after the announcement of the draft human genome, the world of human health seems in many ways unchanged. But it is changing, in many profound ways, says Francis Collins, who led the government-funded part of the genome project and is now the director of the National Institutes of Health.

Collin's new book, The Language of Life: DNA and the Revolution in Personalized Medicine, aims to help the public to understand the changes so far, and those that are still to come. He covers a wide range of topics, but the guiding theme is the promise of "personalized medicine" that tailors treatment for each individual based on their genetic information.

As he shows in his occasional columns in Parade magazine, Collins is a skilled communicator of complex medical topics, including their ethical and personal dimensions. He steers authoritatively but caringly through challenging topics like race-based medicine. On the pros and cons of genetic screening, for example, he describes the desirability of genetic tests as a product of not just the relative and absolute changes in risk associate with a gene, but the seriousness of the disease and the availability of effective intervention.

I confess that I was worried that Collins might let his well-publicized Christian beliefs color this book (his previous book is called The Language of God). They did not. His beliefs arise a few times, for example in the context of stem cell research, but he deals with serious ethical questions with great respect for different points of view. In addition, as should be expected for any modern biomedical researcher, he repeatedly and matter-of-factly draws important insights from evolution.

On the whole, the writing is accessible to general readers, even as Collins discusses complex scientific topics. On occasion, however, he shows an academic's tolerance for complex, caveat-filled verbiage, as when he writes, "Therefore, at the time of this writing, the effort to utilize genetic analysis to optimize the treatment of depression has not yet reached the point of effective implementation." This stilted language is the exception, but he also slips into occasional jargon that might leave some readers temporarily stranded.

A trickier issue is Collins' frequent use of patient anecdotes to illustrate how genetic information can lead to better decisions. These human stories, drawn from his long research and clinical experience, certainly succeed at Collins' goal of inspiring hope for the potential of personalized medicine, as well as showing clearly what it mean to people. But the succession of optimistic stories begins to seem skewed to draw attention away from structural challenges in American medicine that could seriously undermine this potential. When Collins mentions these issues, it tends to be in careful euphemisms: "A recent study estimated that in the United States each year, more than 2 million hospitalized patients suffer serious adverse drug reactions, with more than 100,000 of those resulting in a fatal outcome."

In a similar vein, Collins describes the successful identifications of gene variants associated with macular degeneration. The fact that similar studies for other diseases have been rather disappointing doesn't seem to bother him much. Perhaps his decades in research, including the identification of the cystic fibrosis gene, have made him confident that these problems too will pass. But he comes across as a very optimistic person.

In spite of my quibbles, I think The Language of Life succeeds well at putting the omnipresent news stories about genetic advances in a useful context of individual medical choices. As a writer who covers these areas of science, I didn't learn an awful lot of new things from the book, but I think most people will, and will enjoy themselves in the process.

Targeting Cancer

Amy Harmon of The New York Times had an excellent three-part series this week called "Target Cancer." She follows one clinician/researcher as he pursues a "targeted" treatment for melanoma, which aims at the protein produced by a gene called B-Raf that is mutated more than half of the time in this skin cancer.

The series does a great job in following the emotional roller-coaster ride of the doctor, and of course his patients. One early targeted drug doesn't work at all, perhaps because it also attacks normal cells and the side effects become intolerable before the dose is high enough to affect the cancer. A new drug seems not to do anything, but then the team decides to wait for the drug company to reformulate it to deliver higher effective doses.

The results are spectacular: the new formulation causes a virtually unheard of remission in the cancer, and raises hopes in formerly hopeless patients and in the doctors. The excitement and the potential are palpable as some patients dare to hope and others can't bear to. But within a few months, the patients are dying again.

The new drug is an example of personalized medicine, since it is effective only for patients with a particular mutation. There are a few other examples of therapy tuned to patients with a particular genetic profile, such as the breast-cancer drug erbitux and the anticoagulant warfarin (Coumadin).

But this treatment is actually for cancers with a particular mutation--a mutation the normal cells of the patient don't have. Cancers cells generally have more and more of mutations as the disease progresses, because it disrupts the normal quality-control mechanisms in the cell. A study announced last week (registration required) showed that the specific pattern of mutations could be used to monitor the ebb and flow during treatment, although it doesn't look practical yet for tailoring treatment.

Unfortunately, as described in this series, even when a drug targets a mutation in a particular patient's cancer, cancers often develop alternate routes to proliferation. Harmon alludes to one approach to this problem: a multi-pronged "cocktail" that attacks many possible mutations at once. Such cocktails are standard, for example, in treating HIV/AIDS.

Without vilifying the drug companies, she explains some challenges for these profit-oriented companies in pursuing this approach. In particular, even if the cocktail may ultimately be more effective, getting approval might delay or threaten their profits from the drug they have in hand, even if it only extends life for a few months. This is especially true if other drugs in the cocktail are owned by competing companies. In any case, the difficulties in testing multiple drugs make it much harder to know what is effective and what side effects may appear.

The idea of analyzing molecular networks and attacking them at many points simultaneously is a recurring theme in systems biology. But sometimes it seems very far in the future.

Short RNAs to the Rescue

Ever since scientists realized, just over a decade ago, that exposing cells to short snippets of RNA could affect the activity of matching genes, they have dreamed if harnessing this RNA interference, or RNAi, to fight diseases. In the past week, two groups have announced progress toward that goal, treating chimpanzees with hepatitis C and mice with lung cancer.

RNAi, which rapidly earned a 2006 Nobel Prize, is just one facet of the many ways in which short RNAs regulate gene activity. Researchers have since found numerous types of naturally occurring short RNA that play important roles in development, stem cells, cancer, and other biological processes. These RNA-based mechanisms could seriously revise the emerging understanding of how cellular processes are controlled.

Over the same period, manipulating genetic activity with short RNAs has become an essential tool in biology labs. Cells process various forms of short RNA, such as short-hairpin RNA (shRNA) and small interfering RNA (siRNA) into RNA-protein complexes that reduce (usually) how much protein is made from a messenger RNA that include a complementary (or nearly complementary) sequence.

This technique gives researchers a quick way to learn about what a particular gene does, at least in culture dishes, sidestepping the laborious creation and breeding of genetically-modified critters. (Or if they do put in the time, they can insert genes that allow them to controllably trigger RNAi to knock down a gene only in particular cells or after it has completed an indispensible task in helping an organism to grow.)

But affecting genetic regulation in patients faces the challenges of "delivery" that are well-known in the pharmaceutical industry: To have a beneficial effect, the short RNA must survive in the body, get inside the right cells in large quantities, and not cause too many other effects in other cells. The New York Academy of Sciences has a regular series on the challenges of using RNA for treatment, and I covered one very interesting meeting in 2008.

Molecular survival is the first challenge. Researchers have developed various chemical modifications that help RNA (or a lookalikes) withstand assaults by enzymes that degrade rogue nucleic acids. Santaris, for example, which helped in the hepatitis project, has developed proprietary modifications it calls "locked nucleic acids," or LNA. Other researchers and companies are exploring similar techniques.

Getting the protected RNA to the right tissue is another challenge. Foreign chemicals are naturally cycled to the liver for processing, so it's fairly easy to target this organ. For this reason, the hepatitis results don't really prove that the technique is useful for other tissues. The Santaris release also neglects to mention any publication associated with the research.

The mouse lung cancer result appears in Oncogene. The lead Yale researcher, Frank Slack, regularly studies short RNAs in the worm C. elegans, as I described in a recent report from the New York Academy of Sciences. In this work, he teamed with Mirna Therapeutics, which aims to use the short-RNA-delivery vehicle to replace naturally occurring microRNA that are depleted in cancer, like the let-7 they used for this study. The mouse cancers did not disappear, but they regressed to about a third of their previous size, according to the release. Mirna says that since they are replacing natural microRNAs, their technique shouldn't induce many side effects in other tissues.

A further risk for small-RNA delivery is immune responses. The field of gene therapy is only now recovering from the 1998 death of Jesse Gelsinger in what looks like a massive immune response to the virus used to insert new genes in his cells. Although the short-RNA response will be different, some cellular systems are primed to respond to the foreign nucleic acids brought in by viruses.

It's likely that there will be many twists and turns along the way, and I haven't solicited expert opinions on these studies, but they seem to be intriguing steps toward the goal of using RNA not just to study biology, but to change people's lives.

Denialism

As someone who communicates science for a living, I frequently struggle to understand the widespread distrust of scientific evidence in public and private decisions. I was looking forward to some enlightenment in The New Yorker writer Michael Specter's new book, Denialism: How Irrational Thinking Hinders Scientific Progress, Harms the Planet, and Threatens Our Lives.

I was disappointed. The book scarcely addresses the origins of denialism, or even, as the subtitle advertises, its consequences. Instead, it reads as a cobbled-together series of feature articles, all too long to be called vignettes. The pieces are mostly interesting, well researched and well written, but they include a lot of background material that is peripheral to denialism. As to where the attitude comes from, Specter offers only speculation.

Specter is unlikely to make many converts to "rational thinking," since he frequently comes across as a cheerleader for progress, even as he acknowledges its risks and uncertainties. For example, near the close of his 21-page introduction, he shares a letter from a New Yorker reader: "…the question remains, will this generation of scientists be labeled the great minds of the amazing genetic-engineering era, or the most irresponsible scientists in the history of the world? With the present posture of the scientific community, my money, unfortunately, is on the latter." I regard this is a valid question, but Specter dismisses it: "Those words might as well have been torn from a denialist instruction manual: change is dangerous; authorities are not to be trusted; the present 'posture' of the scientific community has to be one of collusion and conspiracy." He doesn't seem to allow for reckless overconfidence.

Specter doesn't address climate change, which is the only big issue where denialism (as opposed to progress) threatens to "harm the planet." Cynics will note that it's also the issue where denialism promotes corporate interests, rather than opposing them. But the various chapters cover a wide range of topics.

In Vioxx and the Fear of Science, Specter reviews Merck's coverup of the heart risks of their pain medication, Vioxx. This sorry episode has been discussed elsewhere, for example in Melody Peterson's Our Daily Meds, but on its face it has little to do with irrational denial. In fact, in this case, distrust of pharmaceutical companies and the FDA are quite well founded. But in the final section of the chapter that reads like an afterthought, Specter blames much of the public's disregard for scientific evidence such betrayals of trust, although he gives little evidence for this connection.

Specter also uses the Vioxx case to illustrate a common problem: undue attention to acute harms rather than small, distributed benefits. He even argues that the thousands of deaths from Vioxx might have been a reasonable price to pay for its pain relief benefits to millions. Such weaknesses in risk assessment certainly skew many policy and private decisions. But our oft-lamented poor balancing of accepted risks and benefits strikes me as somewhat distinct from denialism, in which scientific evidence for benefit or harm is dismissed entirely.

Both denialism and poor weighing of pros and cons also come to play into the second chapter, Vaccines and the Great Denial. Specter makes it clear there is virtually no science supporting the anti-vaccine movement, and documents the highly misleading selective quotation of a government report in Robert Kennedy's famous Rolling Stone story. This is an easy case to make, but he does it convincingly.

In The Organic Fetish, Specter combines two distinct food-related issues. He shows convincingly that the benefits of "organic" foods are less clear-cut than advocates would like to believe, although I prefer Michael Pollan's wonderful book, The omnivore's dilemma: a natural history of four meals. But Specter's denialism them lets him zig-zag erratically between organic and genetically modified (GM) foods. He compelling despairs over African nations' rejecting GM foods for their starving populations, but he is too willing to accept the standard, long disproven reassurances about the limited spread of modified foods. Still, Specter resoundingly dispels the mythical distinction between modern modifications and those that have been accepted for decades or millennia.

Specter's chapter on food supplements and alternative medicine, The Era of Echinacea, also has an easy target, although he notably includes multivitamin supplements among the snake oils. But again, his discussion lacks a clear explanation of why many people trust these uncontrolled additives more than they do the tightly-regulated products of the pharmaceutical industry.

Race and the Language of Life combines two disparate topics. Specter's discussion of the complex role of genetics in disease is impressively thorough and accurate, and he gives it a human touch with his own genetic testing. But he also invokes the importance of genetics to support the use of race in medicine. Although Specter is no doubt correct that race is often avoided for political reasons, there is a legitimate scientific question that he fails to clarify: how much of the genetic variation in medical response can be explained with traditional notions of race? If within-group variation is large and the differences between groups are largely statistical, the divisive introduction of race may bring little benefit. The messy story behind the heart drug BiDil, approved by the FDA for African Americans, for example, makes it unconvincing as his poster child for race-based medicine.

Surfing the Exponential delves into the nascent field of synthetic biology, covering much the same ground as Specter's recent story in The New Yorker. This chapter is rich in technical detail on the promise of the technology, and to a lesser degree with the risks of making new, self-replicating life forms. Ultimately, though, Specter advocates "a new and genuinely natural environmental movement--one that doesn't fear what science can accomplish, but only what we might do to prevent it."

Such denialism is no more defensible for assessing risks than for judging benefits--both should to be analyzed thoroughly. Fear of the unknown is not always irrational.

New Guidelines for Breast-Cancer Screening

A few weeks ago, as I reported here, Gina Kolata at the New York Times reported that the American Cancer Society was planning to scale back their recommendations on routine screening for prostate and breast cancers.

As discussed by the Knight Tracker, she got a lot of grief for this story, and the next day the Times published a more reserved follow-up story by Tara Parker-Pope, also discussed by the Tracker. In fact, her primary source at the society, Dr. Otis Brawley, later wrote a letter to the editor denying any intention to change the guidelines (although he is on record cautioning about overscreening).

This isn't the first time that a page-one story by Kolata has gotten into trouble. Her 1998 story on cancer drugs was cited as a cautionary tale in my medical-writing course at NYU. That story quoted James Watson as saying (privately, at a banquet) that Judah Folkman was "going to cure cancer in two years" with his amniogenesis inhibitors. Watson later denied saying any such thing.

Nonetheless, Kolata accurately conveyed a painful dilemma of cancer screening: more isn't necessarily better. Not for all cancers, and not for all patients.

The U.S Preventive Services Task Force has now issued revised recommendations for breast-cancer screening for patients who have no indications of high risk. In part, they moved the earliest age for mammography back up from 40 to 50, at which point they recommend a scan every two years rather than every year.

These recommendations were based not primarily on financial costs, but on health risks to patients:

"The harms resulting from screening for breast cancer include psychological harms, unnecessary imaging tests and biopsies in women without cancer, and inconvenience due to false-positive screening results. Furthermore, one must also consider the harms associated with treatment of cancer that would not become clinically apparent during a woman's lifetime (overdiagnosis), as well as the harms of unnecessary earlier treatment of breast cancer that would have become clinically apparent but would not have shortened a woman's life. Radiation exposure (from radiologic tests), although a minor concern, is also a consideration."

The blog, Science-Based Medicine, has a thoughtful and thorough discussion of the issue, written before the recent recommendations. I highly recommend it.

In a rather odd move, the Times published a balancing article by Roni Caryn Rabin on the same day (at least in the paper edition), although it was buried in the "Health" section, not on the front page with Kolata's.

Rabin's story empathetically interviews screening advocates, including people who have been treated for breast cancer. But in its empathy, story misses the opportunity to clarify the issues. Or perhaps in the extended quotes, the author is deliberately allowing the sources to reveal themselves? It's hard to tell.

For example, one woman calls screening her "security blanket." "'If someone ran a computer analysis that determined that wearing a seat belt is not going to protect you from being killed during a crash, would you stop using a seat belt?' Ms. Young-Levi asked."

Although it's hard to imagine, I certainly would stop using a seat belt if the best evidence indicated it, whatever psychological security I might ascribe to it.

The story later quotes another survivor: "'You're going to start losing a lot of women,' said Sylvia Moritz, 54, of Manhattan, who learned she had breast cancer at 48 after an annual mammogram. 'I have two friends in their 40s who were just diagnosed with breast cancer. One of them just turned 41. If they had waited until she was 50 to do a routine mammogram, they wouldn't have to bother on her part — she'd be dead.'"

The author negligently lets this quote stand: the whole point of the recommendations is that if those friends had not been diagnosed, they might be doing just fine now, without the risk of the tests and procedures they underwent because of the diagnosis.

But the unfortunate reality is that we need tests that better predict cancer progression, rather than merely signaling its presence. Without such tests, the recommendations can only trade off lives lost (and other damage) because treatment was unnecessarily aggressive with other lives lost because it wasn't aggressive enough.

Lies, Damn Lies, and…

Statistics don't lie. People do.

I have the greatest respect for statisticians, who methodically sift through messy data to determine what can confidently and honestly be said about them. But even the most sophisticated analysis depends on how the data were obtained. The miniscule false-positive rate for DNA tests, for example, is not going to protect you if the police swap the tissue samples.

One of the core principles in clinical trials is that researchers specify what they're looking for before they see the data. Another is that they don't get to keep trying until they get it right.

But that's just the sort of behavior that some drug companies have engaged in.

In the Pipeline informs us this week of a disturbing article in the New England Journal of Medicine. The authors analyzed twenty different trials conducted by Pfizer and Parke-Davis evaluating possible off-label (non-FDA-approved) uses for their epilepsy drug Neurontin (gabapentin).

If that name sounds familiar, it may be because Pfizer paid a $0.43 billion dollar fine in 2004 for illegally promoting just these off-label uses. As Melody Peterson reported for The New York Times and in her chilling book, "Our Daily Meds," company reps methodically "informed" doctors of unapproved uses, for example by giving them journal articles on company-funded studies. The law then allows the doctors to prescribe the drug for whatever they wish.

But the distortion doesn't stop with the marketing division.

The NEJM article draws on internal company documents that were discovered for the trial. Of 20 clinical trials, only 12 were published. Of these, eight reported a statistically significant outcome that was not the one that was described in the original experimental design. The authors say "…trials with findings that were not statistically significant (P≥0.05) for the protocol-defined primary outcome, according to the internal documents, either were not published in full or were published with a changed primary outcome."

A critical reason to specify the goals, or primary outcome, ahead of time is that the likelihood of getting a statistically significant result by chance increases as more possible outcomes are considered. In genome studies, for example, the criterion for significance is typically reduced by a factor that is the number of genes tested, or equivalently the number of possible outcomes.

None of this would be surprising to Peterson. She described a related practice in which drug companies keep doing trials until they get two positive outcomes, which is what the FDA requires for approval.

By arbitrary tradition, the numerical threshold for statistical significance is taken as a 5% or less chance that an outcome arose by chance (P-value). This means that if you do 20 trials you'll have a very good chance of getting one or more that are "significant," even if there is no effect.

A related issue arose for the recent, highly publicized results of an HIV/AIDS vaccine test in Thailand. Among three different analysis methods, one came up with a P-value of 4%, making it barely significant.

This means is that only one in twenty-five trials like this would get such a result by chance. That makes the trial a success, by the usual measures.

But this trial is just one of many trials for potential vaccines, most of which have shown no effect. The chances that any one of these trials gave a positive result is much larger, presumably more than 5%.

In addition, the Thai vaccine was expected to work by slowing down existing infection. Instead, the data show reduced rates of initial infection. Measured in terms of final outcome (death), it was a success. But in some sense the researchers moved the goalposts.

Sometimes, of course, a large trial can uncover a real but anticipated effect. It makes sense to follow up on these cases, recognizing that a single result is only a hint.

Because of the subtleties in defining the outcome of a complex study, there seems to be no substitute for repeating a trial, stating a clearly defined outcome. Good science writers understand this. It would be nice to think that the FDA did, too, and established procedures to ensure reliable conclusions.

Freedom (From Cancer) Is Not Free

For decades, the American Cancer Society has been a stalwart advocate of steps to reduce cancer risk, including early testing. In a fine story in today's New York Times (registration required), Gina Kolata reports that they are about to back off on those guidelines, for breast and prostate cancers.

The essential issue is that early screening can find small tumors that might never become a problem, or might even disappear on their own. For these tumors, biopsies, further tests, or treatments are an unnecessary financial burden and also a health risk. On the other hand, many rapidly growing tumors may become serious problems in the time between tests.

In a related article (subscription required) published tomorrow [sic] in the Journal of the American Medical Association, entitled "Rethinking Screening for Breast Cancer and Prostate Cancer," three doctors review the disappointing results of twenty years of early detection, and conclude:

"One possible explanation is that screening may be increasing the burden of low-risk cancers without significantly reducing the burden of more aggressively growing cancers and therefore not resulting in the anticipated reduction in cancer mortality."

These results underline the need for measuring the comparative effectiveness of all medical procedures. Not everything that seems like a good idea really is. For every patient whose aggressive early cancer is stopped in its tracks (and whose doctors will vividly remember the events), there are others for whom the trauma, health risk, and expense were unnecessary--and avoidable.

In his book, The Healing of America (which I reviewed here), T.R. Reid notes that the PSA (prostate-specific antigen) test that is routinely given to older men in the U.S. is not paid for by the Public Health Service in the U.K. (p. 120). No doubt this is partially a matter of cost effectiveness. But as his British doctor explained, it is also a matter of medical effectiveness. It may seem brutal to trade off the few lives saved by early testing with lives lost by unnecessary intervention, but such statistical comparisons are, for now, our only option.

Ultimately, though, we need better tests: tests that can identify the molecular or other markers that distinguish between aggressive tumors that people will die from and more passive cancers that people will die with. As the JAMA authors conclude, "To reduce morbidity and mortality from prostate cancer
and breast cancer, new approaches for screening, early detection,
and prevention for both diseases should be considered."

[Update: Paul Raeburn at the Knight Science Journalism Tracker notes that although other outlets covered this issue, Kolata is unique in projecting a revision from the American Chemical Society.]

[Update (11/7/09): Science-Based Medicine has a fantastic, detailed discussion of the science behind this issue. Short message: keep screening.]

Ban the Authors!

Good story on ghostwriting today at the New York Times, "Medical Editors Push for Ghostwriting Crackdown."

In an interview last month, Dr. Cynthia E. Dunbar, the editor in chief of Blood, said that, in the future, the journal would consider a ban of several years for authors caught lying about ghostwriting, in addition to retracting their ghosted articles.

Why consider? Do it.

Pathways to Disease

Most common diseases, including the big killers like heart disease, are "complex": they can't be blamed on single causes like a particular gene. Instead, they result from a complicated interaction of factors that may include lifestyle, environmental exposures, or infection, as well as genetic effects. Moreover, large-scale surveys of genetic influences have confirmed that, in many cases, lots of different genes contribute to disease, each in a small way.

These generalizations also apply to cancer. Cancer differs from the other diseases because most of the genetic changes in cancer cells aren't present in the rest of the patient's cells. Instead, mutations, copy number variations, and large-scale chromosome anomalies accumulate as the disease progresses. These alterations are often abetted by early disruptions of the usual mechanisms for maintaining genome quality during cell division and for executing damaged cells. In spite of these differences, the first major results last fall from The Cancer Genome Atlas comparing the genetics of glioblastomas (deadly and virtually untreatable brain cancers) found no specific mutation was present in all of the tumors. The huge team of researchers did a comprehensive analysis including gene expression, copy number changes and epigenetic changes. But although some changes happened rather frequently, there was no single "smoking gun."

Nonetheless, these studies, in both cancer and other diseases, find clear patterns among the genes whose activity is altered in one way or another. When researchers put the changes in the context of the complex network of molecular interactions in the cell, most of the changes cluster along clear "pathways." As Todd Golub told a meeting I covered last year, just after the glioblastoma results were published: "What was gratifying about this was that this was not just a sprinkling of mutations randomly across the genome, which were difficult to decipher in the context of any kind of mechanistic understanding, but rather these were falling together in a set of pathways that were increasingly well understood in cancer."

I regard the word "pathway" is a bit of a misnomer, since it suggests a linear sequence in which each molecule affects the next one in a chain. In the early days, that was about all that experiments could get at, but researchers have long recognized that networks are messier than this. For example, there may be multiple, parallel influences of one molecule on another, and there are almost always feedback paths in which the final outcome comes back to modify the early steps.

Nonetheless, although they are complex and interconnected, these pathways give researchers a useful shorthand for navigating the rich networks of interactions and for communicating with others. In fact, many researchers specialize in particular pathways, getting to know each molecular member "personally," as well as the effects they have on one another.

Results like the glioblastoma study also show that the pathway level may be a more useful level of "granularity" for thinking about disease than the individual molecules are. Focusing on pathways (or "modules," or "motifs," or whatever) gives us simple-minded humans a better intuitive understanding of a disease, which is important. Moreover, in treatment, researchers can be led astray by focusing on molecular-level changes such as individual genetic variants, since these are not the same for everyone. Targeting specific pathways, for example with combination therapies that attack several "nodes" of the network at once, may prove to be more effective against diverse groups of patients.

But the most important benefit of isolating pathways may be that many of them are shared by different diseases, which is leading to new insights into the relationships between diseases.

The Healing of America

In honor of the constructive and collegial discussion of health-care reform going on in our nation's capital, I've just finished reading Washington Post correspondent T.R. Reid's new book, The Healing of America: A Global Quest for Better, Cheaper, and Fairer Health Care. I first heard about it in a great interview on NPR's Fresh Air.

This highly readable book illustrates with brutal clarity how out of step the U.S. is with other advanced nations. At the same time, Reid shows that we have several proven ways to simultaneously improve the accessibility of health care and reduce its cost--if we are willing to look outside our borders for guidance.

Reid divides the world's health systems into four types, noting that different groups in the U.S. already experience each one:

• The systems that most resemble the widely reviled "socialized medicine" follow the "Beveridge model" of the National Health Service in the U.K.: the government runs both delivery and payment. The Veteran's Administration in the U.S. is similar.
  
• In Canada, a government-run single payer (actually one for each province) pays private practitioners. The U.S. Medicare system follows this model.
  
• The "Bismarck model" used in Germany and many other European countries, as well as Japan, requires everyone to get insurance from mostly private providers, generally with partial payment from employers, and most providers are also private. This is similar to the coverage many employed U.S. citizens get, but the insurers are non-profit and are required to take everyone and the fees for treatment are generally negotiated at the national level.
  
• The "out-of-pocket" model is common in developing countries, where people get whatever care they can afford--and many get nothing. Millions of Americans get the same treatment.
  

Reid doesn't dismiss the downsides to the different approaches-- restricted options in the U.K, long waits for elective procedures in Canada, and merely middle-class pay for doctors in most countries. But at the same time, he notes that patients in these other countries are often completely free to choose their doctors--in contrast with the restrictive insurance-company networks in this country. And all of these countries have significantly lower costs, often half of per-capita costs in the U.S, partly because they spend much less on paperwork.

All the rich countries of the world have opted for universal coverage--except the U.S. For Reid, this moral question should be addressed first: "is access to health care is a basic right?" Or is it acceptable that tens of thousands of Americans die each year for lack of insurance? He thinks that trying to sell reform on cost alone, as the Clintons did in 1994, is misguided.

Nonetheless, Reid clearly expects that bringing everyone into a single plan will provide the joint sense of purpose and the negotiating leverage to reduce costs. Like waiting in line at the grocery store, it's a lot easier to accept limits if everyone is treated equally. Currently, even though the U.S. spends more than anyone on health care, it falls far short on measures such as life expectancy or infant mortality. We're not getting what we pay for.

Targeting Cancer

If personalized medicine ever becomes widespread--and I hope it does--it will probably start with cancers.

In fact, it already has. More than ten years, ago, in 1998, the FDA approved the Genentech monoclonal antibody Herceptin (trastuzumab) as part of treatment for metastatic breast cancer--but only for patients who overexpress the membrane receptor ErbB-2 (also called HER2). For these patients, the extra copies of ErbB-2 generate signals that make the cancer spread more aggressively. The antibody binds to the receptor and diminishes this effect. But Herceptin was only shown to be effective in people who, as shown by laboratory tests, have an excess of the receptor. The approval was conditional on positive test results.

Cancers ought to be the best case for personalized medicine because treatment decisions are made by experts in the disease, based on medical tests and observations. These experts recognize that different tumors respond differently, and they are accustomed to adjusting treatment accordingly. In contrast, for many other diseases, such as mental illnesses, doctors often depend on more subjective symptoms, and patients are susceptible to the default "one size fits all" advertising of pharmaceutical companies. Cancer treatment is still the province of experts.

But it's important to ask whether those experts are doing what they need to, to get the drug to the people who will benefit, and not to the people who will not. A new article in Cancer addresses this question, and the answers are troubling.

The main complaint of the article is that there's not enough data to know. Kathryn Phillips, of the Center for Translational and Policy Research on Personalized Medicine at UCSF, and her colleagues find that in many cases there is no documentation that patients are receiving the right tests to guide their treatment. They also cite other results that

• Perhaps two thirds of patients who could get the test to see if Herceptin would be appropriate may not get it (at least it's not recorded). By implication, many patients aren't getting a treatment that might help them.
  
• A fifth of patients who do get the drug have no record of having gotten the test. This means that patients may be taking a drug, and suffering its cost and side effects, without any evidence that it will help them.
  
• A fifth of the test results may be incorrect.
  

The argument for approving the drug was that it would make treatment cheaper and more effective. That only makes sense if the tests are given, are accurate, and are used to guide treatment. The success of personalized medicine depends on new, reliable procedures for ensuring that treatment is coupled with validated tests. If it can't be done with cancer treatment, it's hard to believe that it's a realistic goal for other diseases.

By the way, the researchers get funding for their research (said to be unrestricted) from the foundations of major health insurance companies. I'm not sure what to make of that.

E pluribus unum

A few diseases can be traced to specific genetic variants. The nerve degeneration of Huntington's Disease, for example, arises exclusively from alterations of either copy of a gene on chromosome 4. This gene specifies a protein that is now called huntingtin. Such diseases are referred to as Mendelian, since they follow the simple rules of inheritance that Gregor Mendel observed in his pea plants.

For most diseases, though, it has proved difficult to find individual genes that explain much of the risk. Instead, the growing evidence from large-scale studies is that many variants contribute, each contributing only weakly. Even then, the genes alone do not condemn a person to the disease, which may also depend on microbes or non-living elements of the environment or on lifestyle. These "complex" diseases include all of the biggies, like heart disease and stroke, cancers, and many mental illnesses.

In some ways, the failure of the "one-gene/one-disorder" hypothesis shouldn't be too surprising. After all, a gene that reliably causes a fatal disease should have been largely weeded out by natural selection. Huntington's disease avoids this fate because it usually appears late in life, often after people have already had children (including, fortunately for us, Arlo Guthrie). Sickle-cell anemia persists because people with a single variant gene are resistant to malaria, although two copies cause the disease.

Nonetheless, lots of other diseases have an import genetic component, which can be determined by comparing the disease rate for close relatives. For example, if pairs of "identical" twins are more likely to both get a disease than are fraternal twins, the difference presumably arises because they share their entire genome, rather than only half.

For simple Mendelian diseases, researchers have extended this approach to locate where the disease gene resides in the chromosomes. This "linkage" analysis looks at which close relatives inherited a disease, and what known chromosome features they also inherited. This technique was applied in the 1980s to locate the Huntington's gene by testing dozens of residents of a Venezuelan village that had unusually many cases.

But human populations aren't particularly well suited for linkage studies. People don't have a lot of children, and they resist attempts at controlled breeding. As a result, it's hard to see weak genetic effects.

To get more subjects, researchers use association studies, which compare the genetics of unrelated individuals. Historically, you really had to know where to look to make associations studies work. But in the past few years researchers have done dozens of "genome-wide association studies," or GWAS, that look without prejudice across the entire human genome.

These studies are tricky. For one thing, since they monitor perhaps a million genetic markers at once, the chances are good that a marker will correlate with the disease by dumb (bad) luck. In individual experiments, researchers traditionally ignore a result if the probability of it arising by chance isn't less than 5% (P<0.05). For testing a million markers, they might need to ignore a result unless the effect is so strong that the probability that it arose by chance is less than perhaps 5x10^-8. To get such a convincing effect requires a lot of human subjects, generally hundreds or thousands. Even so, GWAS results often fail to recur when someone else tries the experiment.

Nonetheless, some genome-wide studies, like two for Alzheimer's I wrote about recently, have uncovered genes repeatedly associated with disease. In addition to variations of the DNA sequence, these studies often include structural variants such as copy-number variations, as well as "epigenetic" tags that change the expression of particular DNA regions. In spite of finding some likely genetic suspects, though, the total effect of all of the known variants is generally less than the known genetic component of these complex diseases. Researchers are actively debating the causes of this discrepancy; probably part of it comes because there are other contributions that are too weak to be seen in these studies.

Because complex diseases depend on the small contributions of many genetic variants, as well as the environment, buying your personal genome often won't tell you much definitive. But by studying these variants, and the way their effects interact in cells, researchers are learning a great deal about the nature of the diseases, including potential strategies for treating them.

Medical "Ghostwriting" Update

The practice of "ghostwriting" can range from unacknowledged editorial assistance to getting someone else to sign on as author of a paper that you wrote.

Reports from the Sixth International Congress of Peer Review and Biomedical Publication, this week in Vancouver, confirm that the practice is widespread, but don't clarify where it mostly falls on this spectrum:

• In a survey, 7.8% of respondents admit that, on their articles in major medical journals, people who could have been listed as co-authors were not.
  
• Looking at the metadata in Word files reveals hidden contributors in many manuscripts.
  

I hope that the journals, and the academic community, can figure out how to clamp down on this practice.

Added 9/13:

At the Knight Tracker, Paul Raeburn commented on the coverage of this conference. In particular, he notes that several stories touted the dangers of pharma ghostwriting stories, when the survey mentioned above does not actually reveal the nature of the unattributed authors.

Structural Variants

The release of the draft map of "the" human genome sequence in 2000 raised hopes that the genetic sources of human variability, and especially disease, would soon be identified. But it has become increasingly clear that the sequence overlooks a major source--perhaps the major source--of genetic variation.

In the years after the sequence was mapped, the International HapMap Project worked to identify some ten million common alterations of individual bases throughout the genome. These "single-nucleotide polymorphisms," or SNPs, constitute a molecular fingerprint or genotype, and companies now offer microarrays to test subsets of them. Researchers look for correlations of disease with particular variants to locate nearby genes that may cause disease. With a few exceptions, though, this process been rather slow, and the genes it finds explain only part of the genetic contribution to disease.

One reason for this--although not the only one--is that the sequence differences don't reflect important genetic differences that arise when large segments of the gene are missing, duplicated, or reversed. Researchers estimate that these "structural variants" affect many more bases than the individual base changes. The importance of these changes was recognized early on in cancer, where they arise from the disruption of the usual quality-control mechanisms of DNA replication, but the past few years have shown that their influence is much more widespread.

The changes were previously invisible because the usual method of sequencing first chops up the DNA into many smaller pieces, whose base sequence is easier to determine. The different sections are then compared in software to see how they match up. With enough overlap and duplication, researchers can make a reasonable guess for the original long sequence. But this method breaks down in regions where sequences occur more than once, because there are many ways to match things up.

In recent years, experimenters have devised several techniques for finding copy-number variations arising from insertions or deletions, as well as inverted sections. For example, Mike Snyder's group at Yale developed a method that I covered for the New York Academy of Sciences (if you're not a member, look at "Go Deep" in the NYAS section of the "Clips" tab at my website). Most of the regions are 3,000-1,000 base pairs in length.

These changes contribute to many diseases. Last year, for example, an international consortium found that structural variants play a role in schizophrenia. But instead of fingering a few key suspects, the results pointed to hundreds of copy-number variations, each one of which has only a small effect. Interestingly, some of the same genetic regions seem to be involved in other mental illnesses.

Like genetic studies that use SNP genotypes, these results highlight the complex nature of many diseases, and the many distinct disruptions that can cause them. Treating these diseases may require a better understanding of the complete networks of interactions that underlie them. At the same time, different diseases seem to have important elements in common, and perhaps should be thought of as members of disease families.

The next few years should see a dramatic increase in the understanding of structural variants in human differences and disease as well as in human evolution.

Alzheimer's and Inflammation

Two online letters, just out in Nature Genetics (here and here), found three genes that had a statistically significant correlation with late-onset Alzheimer's disease. For the past 16 years, only one gene, APOE, had been connected with this common form of the disease, explaining about half of its genetic heritability. In contrast, the rare, early-onset form has a more classic "Mendelian" genetic pattern, in which, if you have a mutation in one of three genes, you have a high probability of getting the disease.

The new results have the common disappointments of the last few years of genome-wide association studies, or GWAS, of complex diseases: (1) The effects of any particular genetic variant are weak, so they can only be seen by studying thousands of subjects. (2) Because half a million candidate mutations are tested simultaneously, it's hard to assess the significance of something that looks like an association. It's easy to pick up false positives by chance alone, so researchers need to apply big corrections. (3) Different studies identify different variants. In this case, the studies agree on a gene called CLU, but each of them also finds another gene that the other study doesn't. (4) The cumulative effect of all the variants found is not enough to explain the observed heritability of the disease. It seems that there must be many other, unidentified contributors, each having only a small effect.

Confirming this weakness, co-author Michael Owen, of Cardiff University in Wales, noted in a supplementary statement on the Nature Genetics website that "the current genes on their own are not strong predictors of risk and are not suitable for risk testing." I'll have a lot more to say about GWAS and disease in future posts.

But although the genes aren't very useful for predicting risk, they do give clues about the biological mechanisms of the disease. Most previous discussions of Alzheimer's, including these two papers, concerns two types of protein deposits in brain cells: "plaques" of β-amyloid protein and "tangles" of tau protein. Both of these deposits are often seen in the brains of Alzheimer's patients after they die. Clusterin, which is the protein coded by CLU, may help clean up the plaques.

But in her supplementary statement, Julie Williams, also of Cardiff, noted that "clusterin has a role in dampening down inflammation in the brain. Up until now increased inflammation seen in the brains of Alzheimer's sufferers had been viewed as a secondary effect of disease. Our results suggest the possibility that inflammation may be primary to disease development."

This reminded me of Paul Ewald's talk at a January 2007 symposium at Hunter College, "Evolution, Health, and Disease," which I covered on behalf of the New York Academy of Sciences. Ewald, of the University of Louisville, noted that inflammation of arterial plaques is a common feature of the atherosclerosis that often leads to heart disease. (The test for inflammation using the C-reactive protein (CRP) is often used to predict heart-attack risk.) But he also noted that the troublesome ε4 variant of the EPOE gene "is the major risk factor, not only for atherosclerosis and stroke, but also for sporadic Alzheimer's and multiple sclerosis," even though the fat transport that influences atherosclerosis is a completely different chemical property than the formation of protein plaques in Alzheimer's or the myelin-sheath destruction in multiple sclerosis. "The idea that ε4 would be bad in all of these different ways," Ewald said, "is really stretching it."

Instead, Ewald suspects that the common element in these various diseases is infection, perhaps by Chlamydia pneumonia. I imagine that his view remains on the fringe, and perhaps it will remain there. But 25 years ago, the idea that many ulcers are caused by a bacteria was also a fringe idea. Barry Marshall and Robin Warren won the 2005 Nobel Prize in Physiology or Medicine for tracing ulcers to Helicobacter pylori. Maybe in 25 years we will find it natural to associate Alzheimer's with infection, too.

Pharma in the News

Two big stories this week about how pharmaceutical industries get doctors to prescribe their drug:

• Some of the marketing plans of Forest Laboratories for their antidepressant Lexapro were made public. Their previous product, Celexa, which was approaching the end of its patent protection, contains a mixture of two mirror-image versions of the same molecule, while Lexapro contains only one. Generating a market for the newer, more expensive replacement, when it is so similar, takes a full-court "marketing" press. (See also this view from a pharmaceutical industry researcher at In the Pipeline.)
  
• For a record $2.3 Billion, Pfizer settles charges about their marketing practices. There have been several previous finds of many hundreds of millions of dollars, but apparently they judged the profit potential to be worth the risk.
  
• In a more encouraging report, the FDA says that 80% of a sample of postmarketing studies are proceeding on schedule. Since these studies include many more patients than those used for initial approval of drugs, they can uncover rare problems that were not statistically apparent in the original studies. Pharmaceutical companies have been criticized for dragging their feet on these studies, since they have little incentive to uncover new problems.

The Responsibilities of Authorship

One of the most difficult parts of our investigation of scientific misconduct by Hendrik Schön was assessing the role of Bertram Batlogg. There were no accusations that he, or any of Hendrik's several other co-authors, participated in the fraud. But Bertram's role was special, and our report commented on that.

Partially it was Bertram's position, at first, as Hendrik's advisor. But more importantly, Bertram, as the senior member of the team and an established and respected experimentalist, brought to the work a sense of authority that Hendrik alone could not have hoped for. Many members of the community and journal editors felt that, by putting his name on the work, Bertram was standing behind the validity of the results.

After the errors in those results became clear, Bertram did not lose his job at ETH Zurich or his grants. Nonetheless, his dream of completing his career as a respected authority for physics in Europe was irrevocably damaged, and he suffered great personal anguish. Some of his colleagues still blame him for failing to live up to the expectations of a co-author.

This background makes the recent revelations about "ghostwriting" of medical articles particularly shocking. People have complained about the practice before, but recent PLoS Medicine and The New York Times recently joined in a lawsuit that documents how pharmaceutical companies have covertly choreographed multiple journal articles supporting their products. (On Friday, PLoS Medicine made this discovered material available online.) As part of this process, for example, companies selected to manage the publications sometimes wrote review articles even before the authors were determined! These authors were later paid to put their authority behind an article that they had not written and may not have even carefully reviewed.

A blog entry from PLoS Medicine summarizes how one of those companies described it to potential clients:

"The first step is to choose the target journal best suited to the manuscript's content. …We will then analyze the data and write the manuscript, recruit a suitable well-recognized expert to lend his/her name as author of the document, and secure his/her approval of its content."

This is not ghostwriting. It is fraud.

Typically these articles are not summaries of ongoing research by the investigator in question. Instead, they are review articles, for which the experience and judgment of the author is ostensibly invoked to help sort through the complicated and possibly conflicting results that have previously published. The tedious process of reviewing of the literature is the content of the article, and cannot be outsourced to an someone hired by a drug company.

Moreover, unlike Schön's work or cases of "honorary co-authorship," in these medical reviews there is often no other author who holds primary responsibility.

Most troubling, the reason the paper even exists is specifically to skew treatment choices in favor a company's products, rather than the medical needs of patients.

There must be zero tolerance of this practice by the universities and teaching hospitals that employ these "authors," by the agencies that fund them, and by the journals that publish their work. Even in the absence of formal censure, though, they should be treated by their colleagues as disgraces to the profession. Which they are.

Where is the FDA?

In a previous post, I fretted that grand aspirations for personalized medicine don't gibe with drug companies' histories of doing whatever they can to expand sales, even to people who won't benefit. You would hope that this would not include clearly deceptive marketing, though, since in the U.S. this is supposed to be monitored for accuracy by the Food and Drug Administration.

You be the judge.

To set the stage, here's a current ad for Aricept, a drug that is approved and widely prescribed for Alzheimer's Disease:

Here's the critical quote:

"Studies showed Aricept slows the progression of Alzheimer's symptoms."

That statement has a clear meaning: if you take this drug you'll decline less than you otherwise would have. Of course this claim (which I've heard repeated by a doctor) is critical, because it implies that you're losing ground every month you don't take this drug, so you'd better get on it soon and stay on it (for the rest of your life). It is not the same thing as saying "Aricept relieves Alzheimer's symptoms," which would mean you could try it for a while and see if it helps.

Fortunately, those ultra-fine-print sheets that come with prescription drugs are now available online. Here's a pdf of the prescribing information for Aricept, from the Aricept.com website, which I'm confident was checked by the FDA. Go ahead and follow the link; it's tough reading, but a good habit, as you'll see.

At the top of the first page, in Figure 1, is a plot of the average cognitive scores over time, with and without the drug. (The shifts are small compared to the spread of individual responses, though, so many people who try it may not notice any effect). Here's the key: after patients are taken off the drug, their scores rapidly decline until they are indistinguishable from those of patients who never took it. But don't take my word for it, here's the conclusion in the fine print:

"This suggests that the beneficial effects of ARICEPT® abate over 6 weeks following discontinuation of treatment and do not represent a change in the underlying disease."

Am I missing something?

Who Will Personalize Medicine?

There's a lot of excitement in the biomedical community about the potential for "personalized medicine"--the tailoring of a patient's treatment to reflect his or her individual biology. The best known prospect is selecting between drug or dosing alternatives based on a DNA tests that may predict how a person will respond, but there are other ways to improve individual outcomes as well. There have been a few specific commercial drugs with genetic tests so far, notably erbitux and warfarin, with mixed results. Nonetheless, I have no doubt that the researchers are sincere in their hopes for improved treatment.

Unfortunately, the history of the pharmaceutical industry offers less basis for optimism. I've been reading the disturbing book Our Daily Meds, by former New York Times reporter Melody Peterson (Amazon, B&N). This is just one of several recent books documenting the cynical manipulation of the prescription-drug process by Big Pharma in service of their own profits. The list of manipulation techniques is long, including inventing new diseases to be treated by their newest drugs, evading requirements for full disclosure of side effects by advertising by hiring celebrity promoters and funding patient groups focused on individual diseases, flooding the scientific literature with ghost-written articles that favor their drug, encouraging prescriptions for off-label uses, creation and marketing of useless "me too" drugs, and much more.

The take-home message is that the pharmaceutical companies rarely limit their sales to patients who would truly benefit from them, which is what personalized medicine really requires. In fact, the companies have taken many opportunities to extend their drugs to diseases in which studies have shown little benefit, to downplay or deny side effects, and to open their markets to include new. unstudied populations. They also tolerate the fact that many recipients don't benefit from the drug they pay for.

Personalized medicine would demand the opposite, shrinking the market for each drug to those who actually respond. At the same time, the complexity and expense of clinical trials that subdivide the patients into subgroups will be much higher. It seems unrealistic to expect our current commercial and regulatory system to rise to this challenge without some major changes.