Correct or Retract Ross et al. Reviews of HER2 as Prognostic in Breast Cancer

Problems with 30 of 107 papers reviewed

Three reviews of HER2 as a prognostic factor in breast cancer have been published by The Oncologist, in 19982003, and 2009.

In the 1998 paper, 10 of the 47 studies were mishandled; correcting the errors overturned the review's conclusion that HER2 is independently prognostic. 

The error rate increased in subsequent reviews. The 2003 update added 34 more papers and 10 new errors. The most recent review, published in 2009, added 26 papers and 10 more new errors. 

All told, in the 2009 review, of the 107 papers reviewed, a total of 30 (28%) are either miscoded or should not have been included:

  • 10 papers coded 'Yes' for multivariate significance should be 'No'
  •  7 papers coded 'Yes' for multivariate significance should be 'NA' 
  • 11 papers should not have been included 
  •  2 papers coded 'No' for multivariate significance should be 'Yes' 

Appendix A below defines what constitutes an error. Appendix A also enumerates and explains the 30 errors contained in the 2009 review.

Stuffing the ballot box

The 11 papers which should not have been included accounted for 7,511 (19%) of the 39,730 patients in the 2009 review. Of the 7,511, the review reported 7,213 supported HER2 as independently prognostic. A single paper, Lal et al. (2005) contributed 3,655 patients to the review, more than twice as many as the next largest study. Like nine of the 11 erroneously included papers, Lal et al. examined the correlation of HER2 with other biomarkers, not HER2 and clinical outcomes.

First author acknowledges possibility of errors, disputes none of them

Jeffrey Ross, the first author on all three reviews, acknowledged the first two might contain errors. Regarding the 1998 review, Ross wrote in email: "It is certainly possible that the studies you have cited were not perfectly listed in my manuscript from so many years ago.” 

With respect to the 2003 review, Ross wrote: "I have no reason to believe that your conclusions are not correct and that there were scattered errors in the meta-analysis of the published literature in our 2003 manuscript."

However, contacted regarding the most recent, 2009 paper, Ross wrote: "Due to time constraints, I am unable at this time to either agree or disagree with your analysis..." In PubMed, the 2009 review is cited 133 times.

No response from The Oncologist

According to the Committee on Publication Ethics (COPE) guidelines, journal editors should consider issuing a correction if "a small portion of an otherwise reliable publication proves to be misleading (especially because of honest error)." 

Three emails documenting possible issues in the Ross et al. reviews, sent to Martin Murphy, executive editor at The Oncologist, have not been answered. The Oncologist is a member of COPE.

Appendix A

Papers counted as representing an error were either miscoded or inappropriately included. Note the 2009 review includes all the papers and errors contained in the 1998 and 2003 reviews.


This examination focuses solely on the reporting of HER2 having independent prognostic value in a multivariate analysis. The reviews misclassified the findings of 19 papers. 

Perhaps most remarkably, seven of the 19 did not report performing a multivariate analysis of HER2 as a prognostic factor. 

Ten papers did perform such an analysis but found HER2 did not predict clinical outcomes although the reviews categorized the 10 as finding HER2 to be independently prognostic. 

Two studies were reported as finding HER2 not prognostic when the papers did find it prognostic. Strangely, one of these false negatives was a paper co-authored by Jeffrey Ross, i.e. he seems to have miscoded one of his own papers.

Inappropriate Inclusion

The three reviews mostly examined papers that included some clinical outcome, such as disease free survival, in HER2 positive and HER2 negative patients. However, particularly in the 2009 review, studies of HER2 were reviewed that did not include any clinical outcome. Of 11 papers that should not have been included, nine correlated HER2 with other biomarkers, not clinical outcomes. 

Inclusion of one the 11 papers, Wright et al., resulted in a double-counting (in Gullick et al.) of a single cohort. 

In the last of the 11, Sandri et al., the paper examined HER2 in serum whereas the other studies in the review were of HER2 overexpression or amplification in tumor cells. The review's conclusions are only for overexpression and/or amplification. 

Enumeration of Errors

Numbers correspond to the study number from Table 1 of the 2009 review.

2. Berger et al.: Yes to Exclude

Correlates HER2 with other biomarkers not clinical outcomes, as the title suggests: "Correlation of c-erbB-2 Gene Amplification and Protein Expression in Human Breast Carcinoma with Nodal Status and Nuclear Grading."

4. Wright et al.: Yes to Exclude

One of three studies incorporated in Gullick et al. (1991), also in the review. As a result, the same 185 patients are counted twice. 

9. Battifora et al.: Yes to No

The paper reports: "Stepwise Cox Regression: This analysis identified independent prognostic factors of DFS and OS when all variables were considered together. Independent predictors of DFS included stage of disease, histology, and nuclear grade. Nuclear grade and stage were the only significant predictors of OS."

13. Lovekin et al.: Yes to No

The paper reports: “Multivariate analysis (Cox, 1972) was used to identify whether c-erbB-2 was of independent prognostic significance. In the context of the temporal variables, tumour size and lymph node stage, cell membrane staining was found to have independent significance as a prognostic factor but significance was lost when histological grade was included in the analysis."

15. Dykins et al.: Yes to NA

No multivariate analysis

19. Paterson et al.: Yes to No

The paper does not state HER2 is independently prognostic in a multivariate analysis or provide the statistics relevant to such a statement. The authors do suggest possible confounding of prognostic factors: “our study design precluded direct determination of the interrelationships of c-erbB-2 [HER2] amplification with conventional disease parameters.”

21. Molina et al.: Yes to NA

No multivariate analysis

28. Press et al.: Yes to NA

No multivariate analysis

30. Descotes et al.: Yes to Exclude

Correlates HER2 with other biomarkers not clinical outcomes, as the title suggests: “Correlation study between Her-2/neu amplification and prognostic factors.”

33. Têtu et al.: Yes to No

The paper reports that HER2 was predictive of treatment resistance: “The difference in survival rates between cases was only significant among patients submitted to adjuvant chemotherapy or hormone therapy."

46. Charpin et al.: Yes to NA

No multivariate analysis

54. Scorilas et al.: No to Yes

Tables 2 and 3 show HER2 overexpression prognostic in multivariate analyses of early relapse and overall survival.

59. Agrup et al.: Yes to NA

No multivariate analysis

67. Jukkola et al.: Yes to No

The abstract reports: "In multivariate regression analysis, only tumour size and nodal involvement were risk factors for poor survival when analysed separately together with c-erbB-2 and receptor status..." 

Section 3.2 states: "In multivariate Cox stepwise regression analysis, tumour size and nodal involvement emerged as independent prognostic factors when analysed separately in combination c-erbB-2, indicating a 2.9 (90% CI 1.9-4.4) risk of death in node-positive patients. For patients with tumour sizes T3 or T4 the risk of death was 2.7 (90% CI 1.4-5.1) and 4.8 (90% CI 2.5-9.5), respectively, c-erbB-2 status did not reach significance in this model, nor when analysed in combination with tumour size, nodal involvement and receptors."

69. Rudolph et al.: Yes to No

HER2 only emerges as prognostic if CR is removed: "When all variables that attained statistical significance in the univariate analysis were included in the multivariate model, the CR was the first and most significant independent indicator of both AOS and DFS (P  .0001; Table 3). Next to CR, only PR status was found to be an independent prognostic factor, albeit of borderline significance."

71. Pinto et al.: Yes to No

HER2 is not independently prognostic: "C-erbB-2 is an independent prognostic indicator when evaluated in conjunction with ploidy and SPF." 

73. Horita et al.: Yes to NA

No multivariate analysis

74. Suo et al.: Yes to No

HER2 is only prognostic when combined with EGFR or HER4. See Table 5. 

76. Rosenthal et al.: No to Yes

A paper on which Ross is senior author found "Multivariate analysis of the combined LN+ and LN− lobular and ductal cases revealed that HER-2/neu amplification (P   0.002), pathologic stage (P < 0.0001), and node positivity (P < 0.0001) were all independent predictors of disease-related death."

78. Spizzo et al.: Yes to No

The paper states: "Multivariate analysis for DROS revealed that nodal status, EpCAM overexpression, tumor size and histological grade were significant prognostic factors. Hormone receptor expression and Her-2/neu overexpression were not significant predictors of DROS. For DFS, nodal status, Ep-CAM overexpression, tumor size and progesterone receptor expression were significant prognostic factors. Her-2/neu overexpression, histologic grade and estrogen receptor expression had no prognostic value for disease-free survival (Table III)."

81. Taucher et al.: Yes to Exclude

Correlates HER2 with other biomarkers not clinical outcomes.

84. Lal et al.: Yes to Exclude

Correlates HER2 with other biomarkers not clinical outcomes, as the title suggests: "Correlation of HER-2 Status With Estrogen and Progesterone Receptors and Histologic Features in 3,655 Invasive Breast Carcinomas"

85. Huang et al.: Yes to Exclude

Correlates HER2 with other biomarkers not clinical outcomes, as the title suggests: "Association between tumour characteristics and HER-2/neu by immunohistochemistry in 1362 women with primary operable breast cancer"

87. Ariga et al.: Yes to Exclude 

Correlates HER2 with other biomarkers not clinical outcomes, as the title suggests: "Correlation of Her-2/neu Gene Amplification with Other Prognostic and Predictive Factors in Female Breast Carcinoma"

89. Prati et al.: Yes to Exclude

Correlates HER2 with other biomarkers not clinical outcomes, as the title suggests: "Histopathologic Characteristics Predicting HER-2/neu Amplification in Breast Cancer"

90. Tanner et al.:Yes to NA

The study does not include a multivariate analysis of HER2 as an independent prognostic factor. In the paper's only multivariate analysis, all the patients were HER2+: 

91. Diallo et al.: Yes to Exclude

Correlates HER2 with other biomarkers not clinical outcomes.

99. Sandri et al. Yes to Exclude

Examines HER2 in serum, as the title suggests: "Serum EGFR and serum HER-2/neu are useful predictive and prognostic markers in metastatic breast cancer patients treated with metronomic chemotherapy"

101. Sunami et al.: Yes to Exclude

Correlates HER2 with other biomarkers not clinical outcomes, as the title suggests: "Estrogen receptor and HER2/neu status affect epigenetic differences of tumor-related genes in primary breast tumors"

106. Ludovini et al.: Yes to No

Found HER2 by IHC and FISH significant in univariate analysis. But only serum HER2 was found prognostic in the multivariate analysis. (See table 5.) 

Why there will never be a model of a cell

Future imperfect: computer models cannot attain life-like fidelity

Biology’s holy grail, a full mechanistic understanding of the workings of life, is beyond reach according to two recent papers. Computer models that closely replicate the phenomena of a single cell are not possible, and the goal has been dropped.

Over the last decade, researchers have tried to grapple with biological complexity by modeling less complicated organisms. Yeast proved too complex and was replaced by organisms with smaller and smaller genomes, all the way down to tiny Mycoplasma pneumoniae. Unable to reduce genomes any further, scientists have radically reduced expectations for models instead.

In Science last month, researchers described the “popular view,” in which “we progress linearly, from conceptual to ever more detailed models.” The popular, linear view is no more. From now on, models “should be judged by how useful they are and what we can learn from them,” according to the paper’s authors, “not by how close we are to the elusive ‘whole cell model’.”

Alex Mogilner, one of authors and a professor at UC Davis, believes some future discovery might make the whole cell model again possible. “Never say never,” he advised. However, a paper from the Institute for Systems Biology forecloses the possibility for all time:

[N]o practically conceivable model will ever represent all possible physical parameters in a system, nor will enough data ever exist to fully constrain them all. It is also experimentally infeasible to measure, and technically prohibitive to model all possible phenomena in a cell, all possible environmental contexts, and all possible genetic perturbations.

There will be no in silico model of a cell, one that fully recapitulates cell behavior and substitutes for wet lab experiments. “Anyone who thinks we can ever obtain a completely deterministic view of an organism will have a hard job to convince me,” said Marc Kirschner, chair of the systems biology department at Harvard. “It is probably true that the number of equations to describe the events in a single cell is so large that this approach will never work,” according to Kirschner. He does hope to be able to predict “to some accuracy” particular responses of a system.

The implications for the future have yet to be worked out, although Mogilner and colleagues observed that such models were envisioned as enabling personalized medicine. For historical purposes, however, these papers bring an end to a monumentally successful, physics-based program for biology that began roughly a century ago.

Biologist Thomas Hunt Morgan successfully pioneered the methods of physics in biology, elucidating the role of chromosomes in heredity. This “turned out to be extraordinarily simple,” as he wrote in 1919, and nature was entirely approachable. “[I]f the world in which we live were as complicated as some of our friends would have us believe,” Morgan wrote, “we might well despair that biology could ever become an exact science.”

Shortly thereafter, physics underwent a crisis of faith as the discipline moved from an intuitive, mechanistic basis into a new and unsettling quantum era which renounced the Newtonian ideal of casually linking everything in space and time. When DNA was discovered decades later, the Newtonian paradise was regained. As a theoretical physicist turned biologist Max Delbrück said in his Nobel Prize lecture:

It might be said that Watson and Crick’s discovery of the DNA double helix in 1953 did for biology what many physicists hoped in vain could be done for atomic physics: it solved all the mysteries in terms of classical models and theories, without forcing us to abandon our intuitive notions about truth and reality.

Not long after, Lee Hood decided to become a biologist after reading an article by Francis Crick in Scientific American. Crick wrote how “the sequence of the bases acts as a kind of genetic code…” which was unknown. Many years later, Hood expressed the belief that “the core of biology is ultimately knowable, and hence, we start with a certainty that is not possible in the other disciplines,” like physics. He forecast being able to predict the behavior of a biological systems “given any perturbation.” His lab at Caltech invented the DNA sequencer.

A draft sequence of the human genome was published in 2000 and Hood founded his Institute for Systems Biology (ISB). The same year, Matt Ridley published his best-selling Genome which predicted a leap from knowing “almost nothing about our genes to knowing everything,” which he described as “the greatest intellectual moment in history. Bar none."

For the next dozen years, researchers from ISB heaved with might and main to realize Hood’s vision. Instead, they now say it is unattainable.

Undoubtedly, there will be disbelief. But Robert Millikan, a founding father of Caltech, didn’t want to believe in Einstein’s photoelectric effect. He won a Nobel Prize for being wrong and proving Einstein right.

This may still be one of the greatest intellectual moments in history, just not what we expected.


Image of yeast adapted from Nelson et al. DOI: 10.1073/pnas.0910874107

Systems and synthetic biology: Neither models nor miracles

From my article on Ars Technica:

The 20th century broke open both the atom and the human genome. Physics deftly imposed mathematical order on the upwelling of particles. Now, in the 21st century, systems biology aims to fit equations to living matter, creating mathematical models that promise new insight into disease and cures. But, after a decade of effort and growth in computing power, models of cells and organs remain crude. Researchers are retreating from complexity towards simpler systems. And, perversely, ever-expanding data are making models more complicated instead of accurate. To an extent, systems biology, rather than climbing upwards to sparkling mathematical vistas, is stuck in a mire of its own deepening details.

Read the rest....

Life expectancy, medical progress, birthday wishes

A wry birthday wish for Ray Kurzweil. I gave this talk at Research Club about the prospects for living forever, which aren't very good.

Many thanks to Research Club and videographer Dustin Zemmel.



If heart drugs keep improving, will we be able to tell?

The Contours of Medical Progress in Depression, Diabetes and Arthritis

Ray Kurzweil does not understand the brain

The Contours of Medical Progress in Depression, Diabetes and Arthritis

Celebrex photo (Allison Turrell)

Although medicine has never been more advanced, progress against the two biggest killers, cancer and heart disease, now comes more slowly than it did decades ago. A skeptical reader suggested that recent breakthroughs in depression, diabetes and arthritis present a truer picture of medical advance. But surveying these other diseases in fact reveals similar contours: the steepest gains occur in the past with progress flattening toward the present.


No antidepressant, however new, surpasses the very first, imipramine, which dates to 1952. Although we are on the third or fourth generation of antidepressants, the newest agents don’t outperform the oldest. Savella, the most recently approved antidepressant, worked the same as imipramine in a meta-study of seven clinical trials. Same for Effexor. Same for Zoloft. Same for Prozac. Unsurprisingly, the newer agents are about the same in terms of efficacy compared with each other. For severe, psychotic depression, imipramine remains the treatment of choice.

Having more therapeutic options greatly aids the art of treating an individual patient, and welcome progress has come on side effects. But we see refinement not revolution. Conceivably research emphasis actually shifts to side effects when progress on defeating a given disease slows. “Polypharmacy,” prescribing more than one drug for a condition, is another such symptom visible in antidepressants.

The reduced (or sometimes merely different) side effects of newer antidepressants come courtesy of very substantial advances in chemistry and receptor biology. Modern compounds strike their targets far more precisely than the older, less discriminating, “dirtier” agents like imipramine. However, consummate skills at the microscopic level are not matched by an understanding of the overall machinery of depression despite a vast expansion of neurological knowledge. In some respects, depression appears more complicated than ever, a tangled skein catching up many different biological pathways, looping through environmental and genetic factors. Breaking free of a complex disease, whether cancer or depression, requires more than snipping the single threads we have found so far.


In diabetes, insulin clearly wins the prize for biggest breakthrough, one achieved in 1923 before there were clinical trials. But none were needed. Insulin very straightforwardly saves the lives of type 1 diabetics who would die without it. Living with diabetes has since become progressively less and less difficult. Insulin has been improved many times, but these are refinements, like the recent fast-acting or long-release variants.

After insulin, the largest absolute gain in glycemic control came in the late 1950s and early 1960s with the introduction of sulfonylureas. For type 2 diabetes, these drugs were frontline until 1995 when metformin jumped to the front because of its greatly reduced side effects. The sulfonylureas actually provide better glycemic control than metformin but ultimately they exhaust the pancreas. After metformin came a series of new drug classes: glitazones in 1999, GLP-1 analogs like Byetta in 2005 and DPP-4 inhibitors like Januvia in 2006. These drugs exploit recently acquired, fine-grain knowledge of biochemical pathways. By contrast, we don't really know how metformin works except that it acts on the pancreas. But the new drugs aren’t “better” than older ones. Instead they provide useful (albeit more expensive) treatment alternatives.

As with depression, increased mastery of microbiological detail in diabetes hasn’t translated into mastery of the disease, a cure. Remarkably, gastric bypass surgery somehow controls type 2 diabetes quickly and not simply because of weight loss. A drug mimicking such an effect would likely be the biggest diabetes breakthrough ever. But the mysterious target has been lurking since 1987. Although today’s suite of diabetes therapies enables more precise glycemic control than ever before, for now, the largest advances are more than half a century old.

Rheumatoid Arthritis

A wave of new biological therapies has washed over rheumatoid arthritis, establishing a new high water mark in relief and remission. For all their scientific brilliance, however, biologicals back up an older drug, methotrexate. In use as early as 1967, methotrexate became the drug of choice in the mid-1980s and it remains first line therapy today.

The most important new biologicals, the TNF inhibitors, arrived in 1998 when the FDA approved Enbrel. However, in head-to-head trials, TNF inhibitors work about the same as methotrexate. Enbrel, for example, performed marginally better than methotrexate in one study while Humira did marginally worse in another. Not surprisingly, the differences between these biologicals are negligible. However, in patients where methotrexate has not worked, adding a TNF inhibitor can make an enormous difference. Remicade plus methotrexate boosted response rates to 52% compared to 17% with methotrexate alone.

The new agents also work much faster and appear to provide much greater protection against joint damage. Still, the biologicals haven’t beaten methotrexate and usually join them in combination therapy. In the case of Remicade, methotrexate is a requirement.

The newest biological for rheumatoid arthritis, Actemra, approved in January, 2010, supplants no other drugs but occupies the last line of treatment, after the TNF inhibitors. Actemra joins the other recently-approved biologicals Rituxan and Orencia sitting at the end of the bench.


Source: Methotrexate works in 70-80% of cases of moderate to severe rheumatoid arthritis. When TNF inhibitors are taken, 70% are taken with methotrexate or another conventional anti-rheumatic drug. TNF inhibitors work for about 75% of patients. The newest agents, Actemra (2010), Rituxan (2006) and Orencia (2005), address the smallest patient group, those failing TNF inhibitors.

 The benefits of new drugs for heart disease, breast cancer, depression, diabetes as well as rheumatoid arthritis have declined over time, with the largest gains realized decades ago. Methotrexate, dating to the early 1980s, provided the biggest gain, followed by TNF inhibitors in the 1990s, with the newest drug Actemra last and least.

However, we are heir to decades of medical advances. The best time in all of human history to have any chronic disease is now (or, better yet, tomorrow). Surprisingly, though, we are adding less to this legacy than previous generations—and we’re a bit oblivious about what’s happening.



If heart drugs keep improving, will we be able to tell?

Life expectancy, medical progress, birthday wishes (video)

If heart drugs keep improving, will we be able to tell?

Even under high magnification, new drug benefits are vanishing

By the end of the 20th century, modern medicine was fending off 190,000 deaths a year from otherwise fatal heart conditions. Funding poured into cardiovascular research, more than doubling from $3.8b in 1995 to $8.4b in 2005. Now from this richly oxygenated drug pipeline, two new heart drugs have emerged. Massive clinical trials depict, at IMAX scale, medicines that seem better, faster, stronger. But it still takes squinting to see the improvements.  And even tests in tens of thousands of people aren’t large enough to show that the new drugs actually save lives.

Once, life-saving effects were visible to the naked eye. In the 1980s, a clinical trial of 17,000 people demonstrated unequivocally that aspirin prevented hundreds of deaths. After a heart attack, aspirin cuts the subsequent risk of death, stroke or heart attack by 2.0%. Improving on aspirin took nearly a decade and a trial of over 19,000 people for the faint effects of a new drug, Plavix, to surface.  Plavix surpassed aspirin by a hard to see, Braille-like bump of 0.5%. But the benefits of Plavix and aspirin, taken together, are additive. After Plavix gained FDA approval in 1997, it won for drug-maker Sanofi-Aventis the second largest pharmaceutical franchise in the world.

The scent of that $8 billion market brought competitors loping, ears-back in pursuit. First came Effient from Eli Lilly. Perhaps to magnify small differences between Effient and Plavix, the company-funded study put heart attacks under a microscope. The trial looked not only at heart attacks with chest pain and other classic symptoms, but also those detectable only by a blood test measuring levels of cardiac enzymes. The precise definition of these invisible heart attacks varies and even changed mid-trial. And whether they matter is disputed. But largely because of the tally of non-fatal heart attacks, the Eli Lilly study showed Effient beating Plavix.

Neither drug, however, defeats death. Enter Brilinta, a new antiplatelet drug from AstraZeneca. A recent clinical trial showed Brilinta not only besting Plavix but saving lives—maybe. The study of nearly 19,000 people still wasn’t big enough to attribute the 89 fewer deaths among Brilinta patients to the new drug or to chance.

Chance is not why cardiovascular clinical trials funded by drug companies tend to report results favorable to the funder. AstraZeneca paid for the Brilinta trial, and two of the study’s authors were employees of the company. AstraZeneca also managed the trial data itself, contrary to good practice. Britain’s National Health Service has expressed doubt about the trial’s blinding—which could suggest that the new drug might have been given to patients who were healthier to begin with. Also raising eyebrows, the trial’s 1,800 North American patients fared worse on Brilinta, although that too could owe to chance. (Brilinta is currently wending through the FDA approval process).

Not only are the benefits of these drugs diminishing and arguable. The number of new drugs is plummeting. From eight in 1995, the number of novel chemical entities approved for heart conditions crashed to zero in 2005. All newly-approved drugs tumbled, from 53 in 1996 to just 18 by 2005.

Surprisingly, it’s not the drug companies’ fault. Huge updrafts of research funding did little to arrest the drug free fall. Not only did cardiovascular research funding double, government funding of all biomedical research ballooned, also doubling between 1998 and 2003. The biomedical research engine now gulps $100 billion annually in the United States. Reassuringly, it powers more scientists than ever and generates 200,000 research papers a year, nearly twice the output of 1995. But research and funding have clearly broken away from drug production. Why?

Research has dived deeper and deeper in search of the fundamental causes of disease. This fantastic voyage ever downward in scale was expected to conclude with the sequencing of the human genome and the molecular pinpointing of the genes that cause disease. Instead, the search is still on. Only 3% of the heritable, genetic basis for early heart attack has been discovered. Scrutinizing the DNA of nearly 3,000 sufferers turned up just nine genes in common, suggesting that there are hundreds more. Worse, early heart attacks have a stronger genetic basis than those occurring after age 65 which represent 90% of all heart attacks.

The research odyssey continues deeper and gets murkier. The genes with the strongest influence on early heart attack don’t, say, produce artery-blocking plaque. Instead they appear to control other, unidentified genes of unknown function. Disentangling this self-referential interplay of genes with each other and genes with environment is the daunting task of epigenetics. Like drug trials, research projects are becoming enormous. Inevitably, there is a Human Epigenome Project, vaster in scope than even its parent, the Human Genome Project.

But as research dives deeper, the medical payoff has become fainter. The tether connecting research to new drugs and health benefits began stretching a quarter century ago. In 1984, a group at Oxford quietly and presciently called for megatrials in the 10,000- to 20,000-person range because most trials were “too small to be of much independent value.” In other words, drug benefits had become too small to be detected without a large trial. In 1985, new drug approvals climbed to record heights. They held there, helped by the arrival of the last key heart medication, the statins, which began lowering cholesterol in 1987. In 1988, the Oxford team published the 17,000-person study of aspirin’s antiplatelet credentials. The era of megatrials began. In 1989, as if on cue, new drug approvals began dropping from their all time highs and have not recovered.

In the realm of heart medications, only modest refinements have ensued. Plavix and other antiplatelet drugs improved very slightly on venerable aspirin but lifesaving benefits have vanished even from megatrials. Similarly, new anticoagulants (with names like bivalirudin and fondaparinux) mostly burnished the achievements of heparin which began saving lives in its first, tiny 1960 trial of 35 patients.

A similar pattern holds for cancer, the number two killer in the United States after heart disease. For breast cancer, the 1960s delivered the biggest breakthrough ever: chemotherapy. It cut mortality by 14% and finally displaced 19th-century radiation treatment as front line therapy. Every therapeutic discovery for breast cancer since chemotherapy has produced only smaller benefits. In the 1970s, modified chemotherapy pared mortality just another 3.1% by employing more toxic drugs developed in the 1950s. Those treatments remain front line to this day. The biggest news since has been tamoxifen, which reduces mortality by 9.2% but only for about three quarters of patients with a particular type of breast cancer. Tamoxifen dates to 1977. The more recent aromatase inhibitors marginally improve on tamoxifen but not in a life-saving way.

The latest generation of cancer drugs, “targeted agents” like Tykerb, approved in 2007, exploit the new, high-definition molecular knowledge. But targeted agents, while higher in precision, have generally lost even the occasional power to cure wielded by older, cruder chemotherapy.

Seemingly the last thing to decouple from new drugs is expectations. In 1998, on the 50th anniversary of the first clinical trial, the Oxford trialists looked ahead to the next half century. They called once again for “greatly increasing” trial size. The reason, they said gently and soberly, is simple: “when it comes to major outcomes it is generally unrealistic to hope for large therapeutic effects.” Instead expectations, like new drug prices, continue to soar, high above shrinking health benefits below.

Photo credit: Buttersweet on Flickr



The Contours of Medical Progress in Depression, Diabetes and Arthritis

Life expectancy, medical progress, birthday wishes (video)