A Promising New Drug for Cystic Fibrosis

February 24, 2011 by Dan Koboldt

Contents: CFTR Gene • Mutation Classes • Vertex Vx-770 Drug • Other Drugs for CF Mutations • Clinical Trials • Books on CF

Today I got a call from a patient with cystic fibrosis, asking if I knew much about a specific mutation called 2184-del-A. It was a striking conversation, particularly because I tend to envision about infants and young children when I think about CF, and this woman was clearly an adult, a working professional, who had some knowledge of her own disease and wanted to know more. She’s particularly interested because of today’s announcement from the CF Foundation and Vertex Pharmaceuticals on the results of a Phase 3 clinical trial for a new CF drug, VX-770. The claim: a significant improvement in lung function among CF patients with the G551D mutation.

For years, I have been occasionally mistaken as a CF expert by non-scientists. This confusion stems from a journal club on the genetics of CF that I gave in graduate school; if you search for “genetics of cystic fibrosis”, my presentation of a 2003 review in Lancet, comes up #5. If you really want to learn from the experts, get the book Cystic Fibrosis: A Guide for Patient and Family by Orenstein, Spahr, and Weiner.

The Cystic Fibrosis Transmembrane Conductor (CFTR) Gene

The CFTR gene encodes a protein in the ATP-Binding Cassette (ABC) family, whose members transport molecules across extracellular (between cell and environment) and intracellular (between compartments within a cell) membranes. The CFTR protein serves as a channel for chloride ions and is important for the creation of sweat and mucus.

Classes of Disease-Causing Mutations

According to the Cystic Fibrosis Mutation Database, more than 1,800 mutations have been described in CFTR, the gene implicated in cystic fibrosis. Because of its prevalence in North American populations, CF is often a component of newborn genetic testing programs. CFTR encodes the cystic fibrosis transmembrane conductance regulator, a chloride channel that transports ions across extra- and intra-cellular membranes.

Traditionally, CFTR mutations are divided into six classes based on their probable effect.

Class I mutations lead to defective protein products
Class II mutations result in defective protein processing
Class III mutations have a defect in the channel regulation
Class IV mutations are defective in conductance through the channel
Class V mutations of abnormal splicing.
Class VI mutations have unknown effects.

Unfortunately, 2184-del-A is in the last category, a mutation of unknown effect. However, a literature search, followed by my own analysis, reveals that the deletion introduces a frameshift at residue 684 and early termination (p.Lys684AsnfsX38). The encoded protein would be severely truncated, with less than half of its 1,480 amino acids. Thus, I suspect that 2184-del-A may represent a class I mutation.

If you think you might be a carrier for CFTR mutations, you can find out with the personal genetic testing service offered by 23andMe.

Gating Mutations and VX-770

Class III and IV mutations are typically associated with milder disease, indicating that some channel function remains. The class III mutation targeted by Vertex’s drug (G551D), encodes a protein with a gating defect; it localizes to the membrane, but its channel is unable to use ATP to speed ion transport. VX-770 helps open the gate at the cell surface, allowing chloride ions to flow through. In the Phase 3 trial, patients receiving the drug had improved lung function and reduced sweat chloride levels, suggesting that the drug is treating the underlying defect.

Other Drugs, Other Mutations

G551D is the third most common mutation, affecting something like 4% of patients. For patients homozygous for “delta-508”, the most common CF mutation, Vertex has another drug in the pipeline. Called VX-809, this drug acts by getting more of the CFTR protein to the cell surface. A phase 2 clinical trial of combined VX-809 and VX-770 treatment in delta-508 patients is currently under way.

Given the diversity of CF-causing mutations, designing drugs for one mutation subset at a time seems like a painful process. But considering that CFTR was the first disease gene isolated, it’s refreshing, at least, to see real progress on the path to curing this disease. I know at least one patient who’s pretty excited about it.

Cystic Fibrosis Treatments in Clinical Trials

This customized search form will let you retrieve information from ClinicalTrials.gov concerning ongoing clinical trials of cystic fibrosis treatments such as VX-770:

Three Excellent Books on Cystic Fibrosis

If you’d like to learn more about cystic fibrosis research, management, and living with the disease, here are some books I’d recommend to you.

	Cystic Fibrosis: A Guide for Patient and Family by David Orenstein, Jonathan E. Spahr, and Daniel J. Weiner This is the current definitive resource for people living with cystic fibrosis. It explains the disease process, outlines the fundamentals of diagnosing and screening, and addresses the challenges of treatment.
	Alex: The Life of a Child by Frank Deford The poignant and uplifting story of Alexandra Deford, a precious and precocious girl, was just eight years old when she died of cystic fibrosis in 1980.
	Little Brave Ones: For Children Who Battle Cystic Fibrosisby Carrie Lux A picture book telling the story of one day in the life of a girl with cystic fibrosis. This book’s intended audience is children with CF, to show them that they’re not alone in having to do daily treatments, take numerous medicines, and have hospital stays.

References
Bompadre, S., Sohma, Y., Li, M., & Hwang, T. (2007). G551D and G1349D, Two CF-associated Mutations in the Signature Sequences of CFTR, Exhibit Distinct Gating Defects The Journal of General Physiology, 129 (4), 285-298 DOI: 10.1085/jgp.200609667

The Fruits of a Thousand Genomes

November 1, 2010 by Dan Koboldt

Last week saw the publication of the 1,000 Genomes Project, which has characterized ~15 million SNPs, 1 million short insertions/deletions (indels), and 20,000 structural variants in seven human populations. This is discovery and genotyping at unprecedented scale, with an astonishing 4.9 terabases (trillion bases) sequenced – the equivalent of about 1,500 human genomes – across three pilot projects:

Deep whole-genome sequencing of trios (mother-father-daughter) from 2 populations
Low-coverage sequencing of 179 unrelated individuals from 4 populations
Exon sequencing of 906 randomly-selected genes in 697 individuals from 7 populations.

The three pilots have shed new light on sequence variation in human genomes and its distribution among human populations. Perhaps unsurprisingly, variation was not evenly distributed in the genome – certain regions (e.g. HLA and sub-telomeres) show high rates of variation, whereas (e.g. a 5 Mbp, gene-dense, highly-conserved region on chromosome 3) show very little. At the chromosomal level, different forms of variation were highly correlated (e.g. SNPs and indels), but there were exceptions for some types of structural variants implicating different mechanisms of mutation.

Novelty and Population-Specificity

The vast majority of SNPs detected were already known to dbSNP. Among known variants, 56% were present in all population panels while 25% were found in only a single panel. In contrast, only 4% of novel variants were found in all panels and 84% were found in only one. This difference supports the notion that the majority of common SNPs in human populations have already been found. There’s more work to do for other forms of variation, though. Many of the novel SVs were detected in all population panels. Half of the common short indels had never been reported.

The smallest two chromosomes – mitochondrial and Y – seemed to benefit the most. There was a lot of heteroplasmy in mitochondrial DNA within individuals – 79% of samples had length heteroplasmy, and 45% had substitution heteroplasmy. On the Y-chromosome, there were 2,870 variable sites, most of which (74%) were novel to public databases. These new variants helped identify several clear, significant sub-clades within the 12 haplotype groups represented in 1,000 Genomes samples.

Coding Regions and Loss-of-Function Variants

In total, the three pilots identified 68,300 non-synonymous variants, almost half of which were novel. Genotyping a subset of these in 620 samples revealed novel NSS variants had dramatically lower minor allele frequency (2.2%) than known ones (26.2%). From this I can draw two conclusions: most novel nonsynonymous variants are rare, and the majority could only have been identified by population-scale sequencing projects like these.

The authors estimate that an individual genome differs from the reference at 10,000 to 11,000 nonsynonymous sites and perhaps 12,000 synonymous sites. A typical genome harbors a much smaller number of loss-of-function (LOF) variants — inframe/frameshift indels, early stops, and splice-site variants — perhaps 340-400 LOF variants per individual, affecting 250-300 genes. Compared to synonymous variants, putative functional variants (nonsynonymous and LOF) tend to have lower allele frequencies and be more population-specific, presumably due to the action of purifying selection against deleterious mutations. Which means, of course, that the really important variants are much harder to find.

Signatures of Natural Selection

Looking in and around genes, the authors found diversity is lowest in exons (50% that of introns) and slightly reduced in 5′ and 3′ UTRs, compared to intronic and intergenic sequences. This signature of natural selection acting upon genes actually has a broad effect; diversity is reduced by 10% in the vicinity of genes compared to gene-distant loci, and that reduction extends up to 85 kbp away. Thus, selection on linked sites appears to restrict variation across the majority of the human genome. Looking across panels, the authors observed that SNPs with large allele frequency differences between populations were enriched for nonsynonymous sites, likely reflecting local adaptation and selection by different continental groups.

Finally, the authors examined the trios to look at a different environment for mutation and selection – immortalized cell lines. Some 952/1001 new mutations in the CEU daughter and 634/669 new mutations in the YRI daughter were not present in the germline, indicating that they occurred either in somatic cells or in the cell lines. Further, the higher number of mutations in the CEU sample may be related to the age of the lines – the CEU line is decades older than the YRI line.

Implications for Future Studies

The findings of the 1,000 Genomes Project thus far have immediate, significant impact on genetic association studies. Using publicly available gene expression data and their expanded catalogue of variants, the authors identified 20-30% more significant expression quantitative trait loci (eQTLs) than had previously been detectable. Thus, it is clear that while existing SNP arrays represent the majority of common variation, a significant amount of rare, phenotypically-relevant variation remains to be incorporated.

References
1000 Genomes Project Consortium (2010). A map of human genome variation from population-scale sequencing. Nature, 467 (7319), 1061-73 PMID: 20981092

Great Mutation. Is It Functional?

October 22, 2010 by Dan Koboldt

As promised, NGS instruments are yielding thousands of new genome sequences. Read lengths and throughputs are increasing. Alignment and analysis algorithms are getting more mature. Databases of sequence variants are growing exponentially. Things are looking pretty good, right? Sure, there are lots of variants still waiting to be discovered. Sure, some of those already reported simply aren’t real. But I think we’re rapidly approaching a point where finding the variants won’t be much of a problem.

Instead, we are facing two significant challenges. First, identifying the subset of variants have functional significance – separating the wheat from the chaff, if you will. Second, understanding how these functional variants contribute to a phenotype. This is soon to be the frontier in genetics and genomics. It merits, I think, a discussion of some of the strategies that have been used to go beyond variant detection, to isolate disease-causing variants and assess their functional impact.

Strategy 1: Process of Elimination

This approach (to my knowledge) is best demonstrated in whole-genome, exome, or pooled sequencing of samples from individuals with rare inherited diseases. It’s essentially a filtering strategy where you start with a list of candidate variants and whittle it down using several criteria:

Pedigree information, especially variants that do not segregate with the disease in Mendelian disorders.
Control variants, usually identified in HapMap samples or other individuals not affected by the disease.
Gene structure information, which serves to eliminate synonymous or non-coding variants.
Evolutionary conservation, to prioritize variants in sequences that are conserved across species.

This strategy has worked well for a handful of rare, inherited diseases like Miller syndrome and severe hypercholesterolemia. There are, however, so many things that can go wrong. The pedigree or assumed mode of inheritance could be wrong. The causal variant might be synonymous or even noncoding (e.g. in a transcription factor binding site). The conservation trick in particular worries me. True, many of the known disease-causing mutations map to conserved amino acid residues, but certainly not all of them.

Strategy 2: Recurrence

This is a developing strategy to identify key mutations and pathway alterations in cancer genomes. Because tumors are genetically unique, and often possess thousands of acquired (somatic) mutations, pedigree analysis and control samples are less informative. Instead, we reason that passenger mutations should occur randomly, mutations key to tumor development and progression are likely to be recurrent (i.e. found in other tumors of the same type). By this reasoning, the more important a mutation, the higher its rate of recurrence. TP53 mutations are a good example of this; in ovarian cancer, more than 80% of tumors carry a TP53 mutation. This is why databases like Sanger’s Catalogue of Somatic Mutations in Cancer (COSMIC) are such powerful tools. As these catalogues grow, having an available panel of additional tumors to screen for novel mutations may become less critical.

Strategy 3: Computational Evaluation

A growing suite of tools and annotation databases enable computational assessments of putative variants to predict their effect in vivo. SIFT and Polyphen are well-known examples of these. The UCSC Genome Browser Database contains dozens of genome-wide annotation datasets (both computational and experimental); many of these are presumed-regulatory regions that form the basis for our “Tier 2” classification (non-coding conserved/regulatory variants). There are also motif-scoring algorithms that evaluate a mutation’s effect on the binding affinity of trancription or splicing factors. These types of inferences are both interesting and helpful, when assessing a mutation’s functional effect. They’re not convincing, however, without supporting experimental evidence.

Strategy 4: Molecular Validation

This may be the most difficult strategy, but potentially the most informative one. A myriad of experimental techniques can be applied to assess a mutation’s functional effect in vivo or in vitro. For coding mutations, the first thing we typically assess is mRNA expression (by RT-PCR or RNA-Seq), to determine (1) if the affected gene is expressed in the tissue of interest (e.g. the retina for studies of retinitis pigmentosa) and (2) whether the mutant allele affects it. Many known disease-causing mutations ablate expression of the mature mRNA, because they introduce splicing defects, mRNA instability, or other effects. A number of other molecular biology tools can also be applied:

Western blot, to determine protein expression
Enzyme activity assays, such as the complex I rescue technique that has been applied to characterize mutations in patients with complex I deficiency (see my last post).
Recombinant DNA techniques, such as a luciferase assay to assess mutations in gene promoters
Colony growth assays, especially for somatic mutations, to determine if mutations confer a growth advantage or invasion potential.

Specialized Sequencing Techniques

A number of recently-developed applications of massively parallel sequencing can be used to assess the functional impact of candidate mutations. RNA-Seq can detect allele-specific expression and alternative splicing. ChIP-Seq can assess protein-DNA interactions and theoretically detect allele-specific DNA binding. Methyl-Seq can be used to profile DNA methylation, either at specific loci or (for methylation pathway mutations) genome-wide. MiRNA-Seq and HITS-CLIP, techniques that measure microRNA expression or isolate miRNA-transcript interactions, also have potential for characterizing mutation effects. Many of these high-throughput techniques stand poised to supplant their traditional experimental counterparts.

Given the wide array of experimental tools, it’s disappointing when reports of new (possible) disease-causing mutations lack sufficient functional validation. I find myself unconvinced when the answer is supported by “it segregates with the disease” or worse, “we filtered everything else.” So when I read new papers that claim to have identified disease-causing variants, my answer is this: Great mutation. Is it functional?

Mutation Detection in Rare Disease by Pooled Sequencing

October 13, 2010 by Dan Koboldt

When it comes to massively parallel sequencing, few areas of human health stand to benefit as much as rare genetic diseases. Indeed, both whole-genome and exome sequencing strategies have identified disease-causing mutations in probands with Charcot-Marie Tooth disease, Miller syndrome, severe brain malformations, and a few other disorders. The Mito10K project took a different approach. They assembled a cohort of mostly unrelated individuals with complex I deficiency (n=103), the most common cause of human respiratory chain diseases.

Mitochondrial Electron Transport Chain (Wikipedia)

Forty-two HapMap samples were included as controls. Instead of employing a whole-genome or exome strategy, they performed deep resequencing of carefully-chosen candidate genes in pools of ~20 samples. And they did it all using a single Illumina flowcell.

Pooled Sequencing of Candidate Genes

The candidates included 103 genes that (i) encoded known complex I proteins, (ii) were implicated in the disease, or (iii) were identified by phylogenetic profiling. The 145 kb target space comprised 653 exons from nuclear genes (138 kb) and two mtDNA regions (7 kb). About 90% of target regions achieved at least 100x coverage; the median redundancy was 3,359x per pool, which works out to ~168x per individual. Next, the authors developed a method (“Syzygy”) to model sequencing error and call variants at very low frequencies. A comparison of calls for the HapMap samples to existing genotype data suggested 92% sensitivity and 99.6% specificity, at sites where coverage was 100x or greater.

Although the pooling strategy worked well for nuclear DNA, there were some problems with the targeted regions in mtDNA. Basically, the distribution of mtDNA was not uniform between samples. That may be due to the fact that while each cell contains exactly 2 copies of each nuclear chromosome, it contains numerous mitochondria and thus numerous copies of the MT chromosome (possibly 20-25 per cell, by one estimate). The resulting shift in sample representation can be quite dramatic. In one pool, for example, 96% of the mtDNA came from a single individual (5% of the pool). The bottom line is that sensitivity to call mutations in pooled samples is going to be lower for mtDNA.

Variant Calling and “Deleteriousness” Prioritization

The unfortunately-named Syzygy method identified 652 variants (high confidence); to boost sensitivity, the authors also employed an ad-hoc approach that called 246 more variants supported by at least 3 reads on each strand (low confidence). The 898 calls were filtered to prioritize variants that seemed likely to underlie a rare and devastating phenotype. In short, the authors removed:

Variants present in healthy individuals (HapMap controls) or public databases (dbSNP, mtDB, 1000 Genomes).
Synonymous or noncoding variants, unless they affected tRNA or splice sites.
Missense variants at positions of low evolutionary conservation

Of 898 detected variants, 216 remained and were validated by multiplexed Sequenom genotyping. Some 82 sites were also Sanger-sequenced to assess the accuracy of the genotyping platform. The comparison revealed 11% false positives and 2% het/hom miscalls, for an overall error rate of 13% for Sequenom assays. Ouch. As for the variant calls, the validation rate was pretty good for high-confidence calls (91/109, or 84%) but rather abysmal for the low-confidence ones (12/107, or 11%). Intriguingly, validation assays identified 12 additional pathogenic variants that were missed by the discovery screen. Based on these data, the sensitivity of the Syzygy method alone was 79.1% (91/115). That’s not bad, but probably not enough for a study whose goal is to identify rare disease-causing variants.

New Diagnoses from Validated Mutations

Some 60 of the sequenced cases lacked a previous molecular-genetic diagnosis. Among these, the authors were able to provide 11 new diagnoses based on mutations in known disease-causing genes. Several lines of supporting evidence were given to support the diagnoses:

6 patients had mutations that were previously known to be disease-causing.
3 patients were homozygous for deleterious mutations that caused splicing defects (observed in cDNA) and no detectable protein (by SDS-page and protein blot).
2 patients had mutations in highly conserved protein domains.

Intriguingly, half of the cases with known mutations (3/6) were compound heterozygotes; that is, they inherited a different defect in the same gene from mother and father. This apparent prevalence of compound hets in monogenic disease is unsettling because they tend to make pedigree analysis complicated and require detection of both variants in heterozygous form, which is more difficult to do by sequencing.

Detection and Characterization of Novel Disease Genes

The key finding of this paper (as suggested by the title) was the implication of two new genes in complex I deficiency: NUBPL and FOXRED1. Pathogenicity of each mutated genes was confirmed by a “rescue” assay in which introduction of wild-type cDNA into patient fibroblasts restored complex I activity. In the absence of rescue, residual complex I activity was markedly reduced (19-40%) in the NUBPL-mutated fibroblasts and strikingly reduced (9-15%) in the FOXRED1-mutated fibroblasts.

The case with NUBPL mutations was particularly interesting. RT-PCR showed that the dominant mRNA species was truncated, and the full-length transcript hardly expressed at all. Sequencing revealed that the shortened fragment had a branch site mutatation that likely caused exon 10 skipping, as well as a missense mutation (Gly56Arg), both on the paternal chromosome. The maternal allele wasn’t expressed. Array-based copy number analysis, however, showed that the maternal chromosome had a complex rearrangement of NUBPL in which exons 1-4 were deleted and exon 7 was duplicated. Obviously this structural variation was not detected in the discovery screen. I think this highlights two things: the importance of structural variation in human disease, and the limitations of targeted sequencing on NGS platforms.

Success and Limitations

As the authors note in their discussion, key to the success of this study was the availability of cellular models of disease, with which the pathogenicity of newly discovered mutations in individual patients could be established. With the two new findings, the 11 newly diagnosed cases, and the 40 or so already-diagnosed cases, the authors now have identified the genetic defect for about half of the cases in their cohort. What about the rest? The authors admit that the causal mutations were likely missed because:

They occur in genes not targeted in this study
They affect targeted genes, but reside in noncoding regulatory regions or novel/unknown exons
They were targeted, but not detected due to limited sensitivity (especially in mtDNA)
They were detected, but filtered out as not likely to be deleterious
They are large-scale deletions or rearrangements, which this approach can’t detect

Despite these limitations, the authors have demonstrated that sequencing carefully-chosen candidate genes in pooled samples, with follow-up validation and experimental support, can successfully identify disease-causing mutations in a good-sized patient cohort. Not bad for a single flowcell.

References

Calvo, S., Tucker, E., Compton, A., Kirby, D., Crawford, G., Burtt, N., Rivas, M., Guiducci, C., Bruno, D., Goldberger, O., Redman, M., Wiltshire, E., Wilson, C., Altshuler, D., Gabriel, S., Daly, M., Thorburn, D., & Mootha, V. (2010). High-throughput, pooled sequencing identifies mutations in NUBPL and FOXRED1 in human complex I deficiency Nature Genetics, 42 (10), 851-858 DOI: 10.1038/ng.659

Ng SB, Buckingham KJ, Lee C, et al (2010). Exome sequencing identifies the cause of a mendelian disorder. Nature genetics, 42 (1), 30-5 PMID: 19915526

Bilgüvar K, Oztürk AK, Louvi A, et al (2010). Whole-exome sequencing identifies recessive WDR62 mutations in severe brain malformations. Nature, 467 (7312), 207-10 PMID: 20729831

Lupski JR, Reid JG, Gonzaga-Jauregui C, et al (2010). Whole-genome sequencing in a patient with Charcot-Marie-Tooth neuropathy. The New England journal of medicine, 362 (13), 1181-91 PMID: 20220177

Lalonde E, Albrecht S, Ha KC, et al (2010). Unexpected allelic heterogeneity and spectrum of mutations in Fowler syndrome revealed by next-generation exome sequencing. Human mutation, 31 (8), 918-23 PMID: 20518025

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