James King-Holmes / Science Source
SAN DIEGO—Will rheumatologists soon be able to use data from genetics and genome-wide association studies to more accurately predict disease and develop new therapies for rheumatic diseases? At a Nov. 5 session at the 2017 ACR/ARHP Annual Meeting, experts shared their views on how to glean this useful knowledge from genomics studies.
The cost to develop drugs is rapidly increasing, and despite long development pipelines with complex regulatory pathways, many drugs still fail in Phase II clinical trials due to lack of efficacy, said Soumya Raychaudhuri, MD, PhD, associate professor of medicine, Harvard Medical School, and professor of medicine, University of Manchester. According to a 2012 study, since 1995, the number of new molecular entities approved by the U.S. Food and Drug Administration has plateaued, with fewer new therapies being approved for every $1 billion spent in development.1
“We need strategies to come up with better drug targets, and possibly, genetics is one way to get there,” he said. “The implication is that if we pick our targets better in the first place, we may do better in terms of bringing drugs to market.” To improve therapeutic target validation to develop more effective treatments for rheumatic diseases, genetics likely holds the key.
Dr. Raychaudhuri
“Genetics has obviously come to the forefront of drug development strategies, just as the cost of sequencing has rapidly dropped, and also the cost of genotyping has rapidly dropped,” said Dr. Raychaudhuri. “In the last 10 years, our ability to genotype samples very quickly using conventional SNP-based arrays has also allowed us to do incredible genetic studies.” Analysis of SNPs, or single nucleotide polymorphisms, help reveal slight allele frequency differences between cases and controls in study cohorts, and even small differences may be highly statistically significant.
“In 1987, all we knew was the MHC locus but since then, there has been a rapid escalation in the number of loci discovered in RA,” including novel risk loci. More than 100 RA-associated loci are now known. A large genetics and genome-wide association study may reveal SNPs that point to genes in the same pathway that could be useful in RA drug discovery, he said.2
Genetics in Drug Discovery
One gene discovered through genetics and genome-wide association studies that led to lifesaving drug discoveries was PCSK9, said Dr. Raychaudhuri. The original motivation to investigate this gene was its role in familial hypercholesterolemia (FH), a rare inherited condition that lowers the body’s ability to remove LDL cholesterol from the blood. Although FH is rare, researchers set out to learn more about the complex phenotype of cholesterol biology in the hope of helping a wider patient population affected by high cholesterol, he said.
In early studies, gain-of-function mutations in PCSK9 were shown to be associated with an increase in hypercholesterolemia in two families, and researchers then identified common genetic markers in wider studies of more generations of relatives, including those with xanthomas, coronary artery disease and strokes. In 2006, researchers also discovered a loss-of-function mutation in the gene that resulted in reduced cholesterol.3 Further genetics and genome-wide association study analyses explore SNP associations with myocardial infarction risk, and by 2013, a novel monoclonal antibody to inhibit PCSK9 was found to dramatically reduce LDL cholesterol.
“Rare, familial diseases helped us learn how to target cholesterol. Allele function is key here, so knowing whether it was a gain-of-function mutation or a loss-of-function mutation was a critical aspect of this drug discovery process,” he said. “Another key feature of this story was that having an intermediary, causal phenotype. We are interested in preventing stroke and heart disease, but we know that to do that, we can treat LDL levels. In autoimmune diseases, we don’t always have that.”
Another successful genetics and genome-wide association study used for drug discovery involved age-related macular degeneration (AMD). A rare penetrant mutation in CFH, a gene that makes the protein complement factor H, is related to high AMD risk.
“We wanted to find out if, in this locus, we could identify a rare coding functional variant that had a strong effect, it might help us understand the biology of AMD more clearly,” said Dr. Raychaudhuri. Rare coding variants might be useful in drug discovery because they are less likely to be influenced by natural selection. “Common variants, which are the basis of most of our complex traits, are usually selected away by evolutionary forces, so a rare variant might be more useful to derive insights.” Finding rare coding variants with large effects in complex traits like AMD or rheumatic diseases is complex, so many efforts to sequence genes for these phenotypes do not necessarily lend themselves to rare variants, he said.
Researchers knew that individuals with one haplotype, H5, had a dramatically higher risk of getting AMD, so they studied this haplotype in a large cohort to look for a rare coding variant, and found a heterozygous arginine chain in the CFH position that had a strong association. After a larger, targeted sequencing study, researchers discovered a rare variant in the C3 gene. In the complement pathway, CFH binds C3. Using a strategy called burden testing, they found another gene, CFI, which had a dramatic burden of rare variance. “If you look at AMD cases versus controls, you see this rare mutation spread across individuals who have the disease. In controls, you rarely see this mutation. When you look at loss-of-function alleles, all individuals who had this seemed to be getting the disease,” he said. Through genotype data and sequencing, they eventually discovered a rare mutation that resulted in a R1210C substitution associated with AMD risk, he said.4
Currently, Dr. Raychaudhuri and his colleagues are using fine-mapping techniques to study shared loci for rheumatoid arthritis and type 1 diabetes, and finding genetic hits for causal variants in genes associated with both of these diseases that share a pathophysiology but target different organs.
“We think that genetics can be useful strategy to validate drug targets. Rare coding variants can be very useful,” he said.
‘We need strategies to come up with better drug targets, & possibly, genetics is one way to get there.’ —Soumya Raychaudhuri, MD, PhD
Genetics in Lupus Risk
Rheumatology researchers are also using genomic analysis to learn more about risk loci associated with systemic lupus erythematosus (SLE), said Edward K. Wakeland, PhD, chair of the Department of Immunology at University of Texas Southwestern, Dallas. Genetics and genome-wide association study research has revealed more than 50 risk loci for lupus in many ethnic groups, including non-HLA associations, he said.5
“Some loci clearly have the strongest associations with SLE, such as HLA, ILR5 and STAT4. These are loci that have been seen through the years and continue to be prominent, not only in individuals of European descent, but in other ethnicities,” said Dr. Wakeland. Researchers have identified 38 risk loci for SLE that have genome-wide significance, mostly in people of European descent. Fewer loci reach that level of significance in African-American or Hispanic patient cohorts, which are smaller, but more genomic studies of these populations, which are significantly impacted by lupus and other autoimmune diseases, should tell us more, he said.
Researchers are now searching for risk alleles in lupus. Through genetics and genome-wide association study analysis, they try to identify endophenotypes important to SLE risk. “These individual loci are considered to contribute one piece overall to disease. If we could precisely identify the disease allele, and their functional characteristics, this may provide insight into what endophenotypes are interacting to mediate the disease process,” he said.
In a 2016 study, Dr. Wakeland and his colleagues analyzed the nature of risk alleles found in 28 SLE risk loci on the LD block, which have been defined in various ethnic groups.6
“We looked at the tagging SNP identifying the entire segment of the genome that is encompassed by the LD block that this SNP marks,” he said. Using a targeted sequencing strategy, they searched the LD block for causal variants associated with the endophenotype targeted by that particular risk allele. The study included 1,775 samples from 773 SLE patients and 579 healthy controls. “We found strong association even in this small cohort, and looking at these individual markers, saw a variety of SNPs in each locus,” he said. Functional variants in TNFAIP3 and ITGAM had high association with lupus autoimmunity. They also searched for lupus risk alleles, and noticed a higher frequency of SLE cases located on the haplotype 2 region of their genomic map. In the locus for STAT4, which is strongly associated with SLE risk, they found the major tagging SNPs. STAT1 is located in an adjacent locus to STAT4, but not on the LD block. Regulation of both STAT1 and STAT4 is mediated within this portion of the genome, and researchers found that variants of STAT4 and STAT1 are upregulated in association with a lupus risk haplotype.
“If you sequence and detail this LD block area, you’ll find variations that are nonfunctional, some that are potentially functional, and some that are potentially causal. Multiple SNPs that impact a gene and possibly, a separate gene, by doing this, generate an endophenotype or multiple endophenotypes that mediate an increase in disease risk,” said Dr. Wakeland. “If we look at these haplotypes rather than individual SNPs, we find an increase in disease associations. So these haplotypes that we identify through sequencing actually represent a risk allele. It is not necessarily a single variant, but a segment of the genome that is segregating within our population as a unit. In some cases, multiple variations within that region may impact phenotypes, or multiple phenotypes are mediated by changes in regulation. The most effective approach to try to understand genetics of this complicated disease is to begin to look at risk alleles as being a haplotype.”
Researchers are now identifying alleles associated with both lupus risk and disease protection at many loci, “so we need to think also about alternative alleles that may have many differences with the risk allele, and may be expressed at a lower level, that may have an impact and even be protective of the disease. Those particular haplotypes are more common in controls than are the risk haplotypes,” said Dr. Wakeland. Risk characteristics of heterozygotes are not yet understood.
Researchers are also studying genomic characteristics of systemic autoimmunity by looking at benign autoimmunity in healthy populations, said Dr. Wakeland.
“Anti-nuclear antibody (ANA) is a common strategy to identify systemic autoimmunity, but is actually a common phenotype. About 25% of normal population will be ANA positive. And about 8% of normal population will have a very high ANA titer—indistinguishable from what we see in many SLE patients,” he said. Newly diagnosed SLE patients who have not started any therapy are almost always ANA positive. But why do so many healthy individuals also have high ANA levels?
“This may not simply be a production of autoantibody against one particular antigen. Rather, it may be a broad spectrum of autoantibody production against many different antigens,” he said. Genetics and genome-wide association study data are helping researchers map the impact of genes related to ANA positivity in both healthy and SLE populations. While SLE patients make a lot of antibody, healthy individuals do as well, usually IgG, but not nearly at the same levels of seen in lupus patients, he said. Comparative heat maps used in these studies show much lower distribution of antigens in healthy individuals than those with SLE. “Typically, most are non-nuclear antigens. If you look at chromatin or any self-antigens—and look across the map from those who are healthy but ANA positive to those with preclinical or incomplete lupus to those with lupus—there is a transition into a lot of nuclear antigens. Chromatin levels among lupus patients are much stronger than you would see in a healthy individual.”
There is tremendous heterogeneity in the antigens seen in SLE patients in these studies, he said.
“So this breach of tolerance is profound and highly diverse among different individuals. If you look at a large number of antigens, you can see a high amount of variability. We can take a data set like this and begin to make predictions about disease,” he said. Genomics analysis can help distinguish healthy individuals who are ANA positive from those with SLE by pulling out autoantibodies that are highly predictive of lupus. “This indicates that we are on the verge of having an assay that may be of some value for the diagnosis of SLE. Typically, if you are looking at potential SLE patients referred to your office based on the fact that they’re ANA positive, the key step is to distinguish those individuals who have a predisposition to develop lupus as opposed to those who are healthy, but ANA positive.”
This sort of benign autoimmunity is detected throughout the population in both ANA-positive and ANA-negative individuals, said Dr. Wakeland. However, this idea of benign autoimmunity is being questioned, as even ANA-positive phenotypes may correlate with higher risk of disease, according to large research cohorts, such as the Dallas Heart Study, he said. Persistent autoimmunity in an individual who seems healthy could later have clinical outcomes. In the future, more precise endophenotyping may be able to reveal important disease alleles in lupus and other autoimmune diseases, he said.
Susan Bernstein is a freelance journalist based in Atlanta.
References
- Scannell JW, Blanckley A, Boldon H, et al. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Disc. 2012 Mar;11:191–200.
- Okada Y, Wu D, Trynka G, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014 Feb 20;506(7488):376–381.
- Cohen JC, Boerwinkle E, Mosley TH Jr., et al. Sequence variations in PCSK9, low LDL, and protection against coronary artery disease. New Engl J Med. 2006 Mar;354:1264–1272.
- Raychaudhuri S, Iartchouk O, Chin K, et al. A rare penetrant mutation in CFH confers high risk of age-related macular degeneration. Nat Genet. 2011 Oct 23;43(12):1232–1236.
- Langefeld CD, Ainsworth HC, Cunninghame Graham DS, et al. Transancestral mapping and genetic load in systemic lupus erythematosus. Nat Commun. 2017 Jul 17;8:16021.
- Raj P, Rai E, Song R, et al. Regulatory polymorphisms modulate the expression of HLA Class II molecules and promote autoimmunity. eLife. 2016;5:e12089.