Next-gen sequencing technologies have introduced two paradigm shifts for genomic medicine. First, they’ve accelerated the discovery of pathogenic variants and disease-associated genes for myriad inherited diseases, providing the basis for expanded genetic testing and carrier screens. Second, they’ve provided a superior assay for performing those tests in a clinical setting.
From a researcher’s point of view, it seems inevitable and matter-of-fact that NGS will become a routine part of clinical care. After all, these technologies have fundamentally altered how we study the genetic basis of disease. From that point of view, it can be hard for us to understand why these powerful tools haven’t been rapidly and thoroughly adopted by healthcare providers. In my new digs, I’m fortunate to work directly with clinicians and genetic counselors, which has been enormously helpful in understanding their perspective on genetic testing.
Here’s a useful illustration. Genetic testing panels — focused diagnostic assays that interrogate a specific set of genes associated with a certain phenotype or disorder — have been a routine part of clinical care for decades. NGS has the potential to improve the speed, cost, and diagnostic efficiency of such panels. Indeed, many testing providers are offering NGS-based tests. For any given condition, there might be 10+ commercial tests available.
This is good news for the clinicians, because they have the power to compare multiple options and choose the best one for their needs. As far as I can tell, it comes down to four things:
1. Comprehensiveness.
For panels, the set of genes tested should be as inclusive as possible. The total number tested is one of the first metrics that clinicians ask about. Obviously, this is a moving target: as new disease-associated genes are identified and credentialed, they’ll ideally be incorporated into the test.
Another important aspect of comprehensiveness is the coverage of the panel genes. For historical reasons, clinicians have come to expect that a gene included in a panel test will have 100% coverage, i.e., interrogate every single coding base. In the days of 3730 sequencing, test providers assured that by repeating assays (or even designing new primers) to achieve exhaustive sequencing coverage of all coding bases. Even recently, some test providers that moved to NGS will use capillary sequencing to fill the gaps in coverage.
This model is almost certainly untenable for a rapid, reasonably priced genetic test that relies on NGS. It’s inherent with targeted sequencing that some regions will have low or no coverage in some samples. What’s interesting to me is that some clinicians choose panels over whole-exome because they have the impression that sequencing coverage is somehow more complete in a panel test. Yet most test providers actually perform exome sequencing, but limit their reports the panel genes.
In other words, unless special probes are spiked in, the coverage of a given gene from a company’s panel test is probably the same as you’d get from their exome-wide test.
2. Del/Dup testing.
Panel tests should report not only single-nucleotide variants and small indels, but also structural variants (deletions and duplications) that might span exons or entire genes. This is vital when surveying the known genes for a specific condition, as many of the known causal lesions fall into this category.
Importantly, this requires a separate assay: usually, a custom array with oligos designed for each exon in every panel gene. It’s tempting to try and use the information obtained in targeted sequencing to call these, to save both time and expense. Can exome-based technologies uncover such forms of variation? Absolutely. I developed one of those methods. Yet I can also tell you that they’re inherently noisy — particularly for targeted sequencing data — and I would not be confident in using them to guide clinical care.
3. Fewer VUS.
Variants of Unknown Significance (VUS) on clinical reports are increasingly common, particularly as panels expand. I understand some of the reasons for this from the analysis point of view. However, Every VUS on the report creates a burden for the ordering clinician, and many of them could probably be promoted (to pathogenic) or ruled out with deeper analysis.
Thanks to efforts like the Exome Aggregation Consortium, we now have allele frequency data from cohorts of more than 50,000 individuals. These deep catalogs of human genetic variation should make it possible to discount an ever-larger-swath of rare but neutral variants.
4. Competitive pricing and turnaround.
Most would-be NGS testing providers can meet requirements 1-3 (or come close). The real challenge is to do so in a short timeframe for a market-competitive price. Doing so requires, among other things, robust automation of lab processes and analysis pipelines.
Determining whether or not a test’s price is competitive is a complex problem. More inclusive tests, for example, should logically have higher associated costs. Direct comparisons of competing products are made difficult because of differences in inclusiveness, coverage, reporting, etc.
The responsibility for payment adds another layer of complexity to this problem. A 100-gene panel test might cost the patient $5,000 or more. Often, the patients who need genetic testing the most are ones who really can’t afford it. Particularly if it’s not covered by insurance or Medicaid. I admit, I don’t know much about this part of healthcare, but it seems to me that the ideal genetic test is one whose cost can be justifiably reimbursed by insurance companies.
Conclusion
We take for granted that disruptive technologies tend to see rapid adoption in the research community, where the pressure from competition is high, but the risk is mitigated. None of our activities directly impact the well-being of patients or their families. By comparison, the adoption of new technologies in clinical settings seems painfully slow and tedious. It helps me to remember that clinical decisions have direct consequences on the lives and well-being of real people. With stakes that high, an abundance of caution seems warranted.
You are right about the dilemma in reimbursement. I used to do genetic research, and now I work as a healthcare actuary. People have noticed this problem. Insurers, researchers, and especially clinicians are trying to push this really hard in Hartford, CT. The Jackson Lab for Genomic Medicine has been hosting seminars on this issue for the past two years. Hopefully we will see some improvement soon.