I prepared a lecture for immunology graduate students at Harvard Medical School on clinical features of rheumatoid arthritis (RA) for the G1 IMM302qc class.
This blog post pertains to the Systems Immunology graduate course at Harvard Medical School (Immunology 306qc; see here), which is led by Drs. Christophe Benoist, Nick Haining and Nir Hacohen. My lecture is on the role of human genetics as a tool for understanding the human immune system in health and disease. What follows is an informal description of my lecture. The slide deck for the lecture can be downloaded here. Throughout, I have added key references, with links to the manuscripts and other web-based resources embedded within the blog (and also listed at the end). I highlight five key manuscripts (#1,#2, #3, #4, and #5), which should be reviewed prior to the lecture; the other references, while interesting, are optional.
Overview
It is increasingly clear that humans serve as the best model organism for understanding human health and disease. One reason for this paradigm shift is the lack of fidelity of most animal models to human disease. For systems immunology, the mouse is a powerful model organism to understand fundamental mechanisms of the immune system. However, studies in humans are required to understand how these mechanisms can be translated into new biomarkers and drugs.…
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). …
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“.…
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).…
Genetics can guide the first phase of drug development (identifying drug targets, see here ) as well as late phase clinical trials (e.g., patient segmentation for response/non-responder status, see here ). But is there a convergence between the two areas, or pharmaco-convergence (a term I just made up!)? And are there advantages to a program anchored at both ends in human genetics?
Consider the following two hypothetical examples.
(1) Human genetics identifies loss-of-function (LOF) mutations that protect from disease. The same LOF mutation is associated with an intermediate biomarker, but is not associated with other phenotypes that might be considered adverse drug events. A drug is developed that mimics the effect of the mutation; that is, a drug is developed that inhibits the protein product of the gene. In early mechanistic studies, the drug is shown to influence the intermediate biomarker in a way that is consistent to that predicted by the LOF-protective mutations. Further, because functional studies of the LOF-protective mutations provide insight into relevant biological pathways in humans (e.g., a gene expression signature that correlates with mutation carrier status), additional information is known about genomic signatures of those who carry the LOF-protective mutations (which mimics drug exposure) compared to those who do not carry the LOF-protective mutations (which mimics those who are not exposed to drug).…
Are the same standards applied to genetic and non-genetic tests in clinical medicine? In a review by Munir Pirmohamed and Dyfrig Hughes (download PDFhere), the authors “strongly argue that the slow progress in the implementation of pharmacogenetic (and indeed other genetic) tests can partly be explained by the fact that different criteria are applied when considering genetic testing compared with non-genetic diagnostic tests.” They provide a few compelling examples:
(1) Atomoxetine
There is no regulatory requirement to undertake clinical trials to show that the dosing recommendations for patients with, for example, renal impairment are equivalent in terms of clinical outcomes to those for patients with normal renal function. Indeed, such a stipulation would be impractical and costly, and would never be done during the drug development process, potentially disadvantaging vulnerable patient populations.
Atomoxetine, a drug widely used for attention deficit hyperactivity disorder, is metabolized in the liver by CYP2D6. The SmPC for atomoxetine states that the dose should be reduced by 50% in patients with hepatic impairment (Child-Pugh class B), as drug exposure goes up by twofold. It is also known that drug exposure is increased by a similar amount in CYP2D6 PMs; however, although the SmPC for atomoxetine mentions the effect of CYP2D6 polymorphisms, it does not mandate testing for their presence.…
