Plenge Lab

I read with interest a recent publication by Khandpur et al in Science Translational Medicine on NETosis in the pathogenesis of rheumatoid arthritis (download PDF here).  It made me think about “cause vs consequence” in scientific discovery.  That is, how does one determine whether a biological process observed in patients with active disease is a cause of disease rather than a consequence of disease?

In reading the article, I learned about how neutrophils cause tissue damage and promote autoimmunity through the aberrant formation of neutrophil extracellular traps (NETs).  Released via a novel form of cell death called NETosis, NETs consist of a chromatin meshwork decorated with antimicrobial peptides typically present in neutrophil granules.  (Read more about NETs on Wikipedia here.) 

Mendelian randomization is a method of using measured variation in genes of known function to examine the causal effect of a modifiable exposure on disease in non-experimental studies (read more here).  It is a powerful to determine if an observation in patients is causal.  For example, if autoantibodies are pathogenic in RA, then DNA variants that influence the formation of autoantibodies should also be associated with risk of RA.  This is indeed the case, as exemplified by variants in a gene, PADI4, the codes for an enzyme involved in peptide citrullination (see here). …

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Date posted: March 16, 2013 | Author: | 1 Comment »

Categories: Precision Medicine

For our website, we have chosen the term “precision medicine” rather than “personalized medicine”.  A recent News article in Nature Medicine reinforces this concept (see here). 

I have had many of my non-genetic physician colleagues comment to me: “We practice personalized medicine every day.  It’s called basic patient care!”  Their point: physicians see patients and make decisions about the best course of treatment based on patient preferences.  For example, one RA patient may prefer to have a drug infusion once per month and another patient may prefer to take a pill each day. 

The Nature Medicine article emphasizes  “the idea that molecular information improves the precision with which patients are categorized and treated“.  While personalized medicine might say “patient X with disease Y should get drug Z”, precision medicine says “patient X has a subset of disease Y — actually, disease Y3, not disease Y1, Y2 or Y4 — and patients with disease Y tend to respond more favorably to drug Z”.  Said another way bt Charles Sawyers, an oncologist at the Memorial Sloan-Kettering Cancer Center in New York: “we are trying to convey a more precise classification of disease into subgroups that in the past have been lumped together because there wasn’t a clear way to discriminate between them“.…

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Date posted: March 13, 2013 | Author: | No Comments »

Categories: Human Genetics Precision Medicine

The value of genetics to clinical prediction depends upon the underlying genetic architecture of complex traits (including disease risk and drug efficacy/toxicity).  It is increasingly clear that common variants contribute to common phenotypes, but that extremely large sample sizes are required to tease apart true signal from the noise at a stringent level of statistical significance.  Occasionally, common variants have a large effect on common phenotypes (e.g., MHC alleles and risk of autoimmunity; VKORC1 and warfarin metabolism), but this seems to be the exception rather than the rule.

 A recent paper published in Nature Genetics explores this concept in more detail (download PDF here).  As stated in the manuscript by Chatterjee and colleagues: “The gap between estimates of heritability based on known loci and those estimated owing to the comprehensive set of common susceptibility variants raises the possibility of substantially improving prediction performance of risk models by using a polygenic approach, one that includes many SNPs that do not reach the stringent threshold for genome-wide significance.”  They measure the ability of models based on current as well as future GWAS to improve the prediction of individual traits.  

The results, which are intriguing, depend not only on the underlying genetic architecture (which is often unknown, especially for PGx traits), but also disease prevalence and familial aggregation:  “We observed that for less common, highly familial conditions, such as T1D and Crohn’s disease, risk models that include family history and optimal polygenic scores based on current GWAS can identify a large majority of cases by targeting a small group of high-risk individuals (for example, subjects who fall in the highest quintile of risk).

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