Personalized Genetics: Too Soon, Too Little?

9 02 2009

ResearchBlogging.orgPersonalized Medicine is the concept that managing patient’s health should be based on the individual patient’s specific characteristics instead of on the standards of care. Often the term ‘personalized medicine’ is restricted to the use of information about a patient’s genotype or gene expression profile to further tailor medical care to an individual’s needs (see [1])

This so called Personalized Genetics is a beautiful concept. Suppose you could predict people’s risk for a certain disease and be able to prevent it by encouraging positive lifestyle changes and/or start a tailor made therapy, suppose you could predict which patients would respond to an intervention and which people should avoid certain medications. Wouldn’t that be wonderful and much better than treating everybody the same way only to benefit a few?

Research like the human genome project and recent advances in genomics research have boosted progress in the discovery of susceptibility genes and fueled expectations about opportunities of genetic profiling for personalizing.

But are the high expectations justified?

For personalized genetics to be (clinically) effective it must fulfill the following requirements (based on [2]):

  1. Clear and strong association of the gene (expression) variant with the susceptibility to a disease or the outcome of a treatment
  2. Improved prediction compared to other risk factors, including traditional risk factors and clinical judgment…
  3. ..as determined in good quality studies with a sufficient number of events (if the events are rare you cannot accurately predict the outcome)
    (1-3 make up the predictive performance)
  4. The availability of effective interventions or effective alternatives
  5. Cost-effectiveness

    dna-cubes50-berci

According to an editorial in the January issue of the Annals of Internal Medicine “the promise of personalized genetic prediction may be exaggerated and premature” [2]. This is especially true for many complex diseases, where 1 variant alone is unlikely to make the difference.

The editorial is written by John Ioannidis, who is a professor at the University of Ioannina School of Medicine in Greece and has an adjunct appointment at Tufts University School of Medicine in Boston. His research focuses on meta-analysis and evidence-based medicine with special emphasis on research methodology. Ioannidis is a brilliant researcher, epidemiologist and inspiring lecturer (I have attended a lecture of him once at a Cochrane Colloquium). Therefore I would urge everyone interested in personalize genetics to read his editorial.

Here I will give a summary of the editorial entitled “Personalized genetic prediction: too limited, too expensive, or too soon” [2].The editorial summarizes two publications in the same issue of the journal [3,4] and gives an overview of the literature.

Ioannidis stresses that recent studies into the predictive performance of common genetic traits have several shortcomings, including an often weak design with few events (*3)*, incomplete comparison with traditional risk factors (*2) and exaggerated prediction of effects because of the models used (*1).

To date, the genotypic information does not substantially improve the prediction of future cardiovascular disease (CVD), prostate cancer and type 2 diabetes beyond traditional risk factors. In the case of age-related macular degeneration, genetic information does increase the ability to predict progression to the disease. However the predictive power to individualize risks remains relatively weak.

Indeed, a recent paper published in PLOS [5] reinforces that a strong association between single nucleotide polymorphisms (SNPs) and a multifactorial disease like age-related macular degeneration, diabetes type 2, CVD and Crohn disease may be very valuable for establishing etiological hypotheses, but do not guarantee effective discrimination between cases and controls and are therefore of little clinical value yet. For further details with regard to the methods used to determine clinical validity of genetic testing you are encouraged to read the entire (free) paper [5].

Likewise, the study of Paynter et al [3] reviewed by Ioannidis, shows that genetic variation in chromosome 9p21.3 (rs10757274) was strongly and consistently associated with incident CVD in a cohort of white women, but did not improve on the discrimination or classification of predicted risk achieved with traditional risk factors, high-sensitivity C-reactive protein, and family history of premature myocardial infarction. Thus “knowing a patient’s rs10757274 genotype would not help a clinician make better preventive or therapeutic decisions to reduce future risk for heart disease”.
This holds also true for many other potentially causal single SNPs: they have a relatively small effect on their own. Complex diseases are probably the result of numerous gene-gene and gene-environment interactions, which may differ from one population to the other and only explain a small proportion of the trait variance.

Even improved prediction (*1-3) does not necessarily make a predictive test useful. The prevalence of the disease is also an important determinant, i.e. people with high risk gene variants for a rare disease may have a significant higher-than average risk, but still a negligible probability of developing the disease.

Clinical utility of the genetic prediction also depends on the availability of effective interventions (*4) and the cost effectiveness (*5). Another paper in the same Ann. Intern. Med. issue [4] shows that although CYP2C9 and VKORC1 strongly predict the chance of bleeding as a side effect of warfarin treatment, genotype-guided dosing appeared not to be cost-effective for patients requiring initiation of warfarin therapy. Piquant detail: The FDA has approved this kind of genetic testing, although there is no good evidence that such genotyping does in fact reduce the risk of hemorrhage in everyday clinical practice. Such knowledge would require large well designed RCT’s.

Ioannidis emphasizes that despite the poor evidence, genetic testing and commercial use (direct to consumer genetic testing) have already begun and are here to stay. He proposes several safeguards, including transparent and thorough reporting, unbiased continuous synthesis and grading of the evidence and alerting the public that most genetic tests have not yet been shown to be clinically useful. He concludes the editorial as follows:

Helping patients and physicians to decide when to do genetic tests will be a tough task because neither knows much about the rapidly emerging field of genomics. We need to learn more about what our genome can tell us and, more important, what it cannot tell us.

* refers to list, points 1-5

SOURCES and FURTHER READING

1. http://en.wikipedia.org/wiki/Personalized_medicine
2: Ioannidis JP. (2009). Personalized genetic prediction: too limited, too expensive, or too soon? Ann Intern Med, 150 (2), 139-141 DOI: 19153414 {=wrong DOI researchblogs click here to be linked to PubMed)
3: Paynter NP, Chasman DI, Buring JE, Shiffman D, Cook NR, Ridker PM. Cardiovascular disease risk prediction with and without knowledge of genetic variation at chromosome 9p21.3. Ann Intern Med. 2009 Jan 20;150(2):65-72.
4: Eckman MH, Rosand J, Greenberg SM, Gage BF. Cost-effectiveness of using pharmacogenetic information in warfarin dosing for patients with nonvalvular atrial fibrillation. Ann Intern Med. 2009 Jan 20;150(2):73-83.
5: Jakobsdottir J, Gorin MB, Conley YP, Ferrell RE, Weeks DE. Interpretation of genetic association studies: markers with replicated highly
significant odds ratios may be poor classifiers. PLoS Genet. 2009 Feb;5(2):e1000337. Epub 2009 Feb 6 (free full text).

6: Janssens AC, van Duijn CM. Genome-based prediction of common diseases: advances and prospects. Hum Mol Genet. 2008 Oct 15;17(R2):R166-73. Review.

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5 responses

9 02 2009
Tera Eerkes

Great commentary. Accurate, well explained, thorough.

I’m still concerned that this kind of assessment has not penetrated the marketing war room of companies like 23andMe, Navigenics, etc. I wonder if it will only be obvious to them when the already small trickle of customers dries up completely?

14 02 2009
laikaspoetnik

Tera,

Thanks so much for the compliment and your comment.

I share your concern. Unfortunately 23andMe, Navigenics etc are unlikely to have shortage of customers in the near future. People are easily influenced by the promotional campaigns of these companies and their advocates. Also in the web 2.0 world there is firm belief in the promises of personalized genetics. Perhaps because the word “personalizing” is what we like most of all. But it is difficult to convince people that screening and diagnosis only make sense when it improves the predictive value significantly. They rather believe in a promise or are curious what there genetic make-up is like (to be honest I share this curiosity).

17 02 2009
It’s Grand Rounds, What Do You Think? GOSH! // Emergiblog

[...] Laika at Laika’s MedLibLog observes: “Personalized Medicine has not (yet) fulfilled its promises because there are often no good quality studies that show a clear and strong association with a particular gene (expression variant) that IMPROVES prediction.” Check out Personalized Genetics: Too Soon, Too Little? [...]

27 02 2009
PeRSSonalized Medicine - and its alternatives « Laika’s MedLibLog

[...] Medicine – and its alternatives 27 02 2009 A few posts back I just discussed that Personalized Genetics has not fulfilled its promise yet. But what about PeRSSonalized Medicine, just launched by Bertalan [...]

14 06 2010
FDA to Regulate Genetic Testing by DTC-Companies Like 23andMe « Laika's MedLibLog

[...] another post Personalized Genetics: Too Soon, Too Little? I summarized an editorial by Ioannides on the topic. His (and my) conclusion was that “the [...]

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