Why Doctors Impede Genomic Testing Adoption Rate – Influence of Big Data and Presentations at the PWMC Clinical Genomics Industry Gathering.

I had the fortunate opportunity to attend the PMWC19 – Precision Medicine World Congress held in the sunny silicon valley of California in beginning of 2019. A good snippet was made by genomeweb – we are in a….

“struggle to figure out best practices for implementing genomics in the clinic.”

One of the factors that impacts adoption of genetic screening is the rate of successful diagnosis. Depending on how you pre-filter your patient population before applying your success criteria, the genomic diagnostic rates in publications range from 15 to 35%. Boasts were made at the meeting that some had achieved diagnostic rates as high as 60%, yet most evidence suggests it is near 20% when measured against a broad spectrum disease containing both monogenic and polygenic drivers. What does this mean to the clinician contemplating ordering of a gene panel – ordering a genetic panel screening will result in a diagnosis for only every 5th person for whom a test is ordered. So it is easy to see why many primary care physicians exploring genomic sequencing respond with pessimism.

A common physician response – “Genetic tests are not very useful.”

It might be that, as whole exome sequencing (WES) or whole genome sequencing (WGS) become more common place, diagnostic yields will increase a by a small percentage. Yet one of the challenges to overcome is a strong conservative desire by the clinical genetics community to keep diagnostic test restricted to the genes that are designated as clinically actionable. Commonly referred to as the “ACMG59“. These are the genes for which change-of-function alleles have clear-cut therapeutic options.

What about the other 7000 genes involved in disease, don’t we want to know data there too?

Vertias gave a talk that had very intriguing data. In their data set so far, there were two findings reported. One was population frequency on the ACMG59 and the other nugget of information was population frequency for all other genes associated with disease. Not revealed was the core ratios used for the variant calls, but keep in mind that for the top 20 genes the ratio is 40% VUS, 34% path and 26% benign (see prior post for more detail). Veritas reported close to 20% of “healthy” individuals were harboring at least one pathogenic variant in the ACMG59 and 90% of individuals were harboring at least one pathogenic variant in the 7000 other rare disease genes. Basically 9 out of 10 people are a carrier of something not so good and some of them may have a ticking time bomb of an autosomal dominant driver hiding in their genome. This ratio may go higher if many of the VUS convert to pathogenic.

What will happen once we convert the remaining VUS into pathogenic assignment?

Estimates on the conversion of VUS to pathogenic vary widely. Julie Eggington form the Center of Genomic Interpretation suggest only 1% of VUS will convert to path. Others have estimated it may be as high as 50%. Two lines of evidence lead me to think it is somewhere in between. First line of data comes from a prior blog post. In that post, I describe the data in ClinVar has 23% of all variants as pathogenic (P + LP). When we auger in on a subset data by looking at the most highly studied genes (the “top 20” with most data from a diverse set of data submitters), the path jumps to 34%. So if the highly studied genes are an indicator, a 10 fold jump in variants/gene analyzed is expected to have the lesser studied genes to likely yield about 30% of newly uncovered variants as pathogenic.

The next line of evidence comes from a functional study using saturative mutagenesis studies. A recent study by the Brotman Baty Institute for Precision Medicine and Department of Genomic Sciences at University of Washington illustrates how the Jay Shendure‘s group used a sensitized monoclonal LIG4 knockout HAP1 line to assess variants in BRCA1 for influence on cell survival. Their CRISPR-based approach examined 96.7% of all variants in BRCA1 for a total of 3,893 SNVs examined. The authors note that 24.9% of these SNVs are in ClinVar as either benign or pathogenic without any conflicting interpretations. Yet, if we open it up a bit and ask ratio regardless of conflicts, we get 40% path, 27% benign and 33% VUS, which means 67% are leaning towards definative assignment. Still, even for BRCA1, there are at least 1/3 of the identified variants left in the ambiguity land of a VUS assessment. To provide more clarity to VUS assigned alleles, the author’s functional studies were able to convert 25% of VUS to a pathogenic category (the “non-functional” in their study). Intriguingly, they were able to convert 49.2% of SNVs with conflicting interpretations into clearly pathogenic “non-functional” assessments.

As a result, application of functional studies will have the potential to boost diagnostic yields by close to 30%.

Higher diagnostic yields combined with AI-enhanced patient history will likely give us a boost to diagnostic yield and, hopefully, more than 50% of the time, genetic casualty and/or contribution will be identified in a patient. If this can be achieved, then doctors might start to say about genetic testing: “yes, I find it quite useful.”

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