Rady Children’s Hospital.
Translating api whole genome sequencing into precision medicine for infants in intensive care units.
60 slides, 30 minutes… buckle up.
Largely, this was triggered by Obama and Collins. In San Siego, Rady donated $160M and said “make this a reality.”
This is all still an early stage. We’re at the 0.25%… it’s going to take 10 years to deliver on this dream and make it medicine.
Scope: 35M people into california, and we can make it into a precision medicine centre. Focus on Newborns – when a baby is born, doctors will do anything to save a baby’s life. In CA, all babies feed into a network of hospitals down to specialize centres for expert care. It’s a small number of health care systems that deliver care for babies.
Can we provide a scalable service like the NIH’s, and make an impact.
Why? 14% of newborns admitted in NICU or PICU. Leading cause of death is genetic diseases: 8250 genetic diseases. Individually, they are rare, but aggregated they are common. Conventional testing is too slow, and cost of care is $4000/day, so genomics is cheap comparitively.
Surviving: 35 babies in level 5 NICU… median survival is 60 days with genetic diseases…
Why single gene diseases? They are tractable. Looking for 1-2 highly penetrant variants that will poison a protein. We have infrastructure that can deal with this information. Orphan drugs are becoming a part of the scene. Potentially, gene therapy might be scalable and real.
GAP: how do you scale the 26-hour diagnosis nationally. Any clinic? where there are no genetics.. etc.
It is possible to have dynamic EHR agents that monitor constantly. How do you do it for babies? [Review case presented earlier in conference.]
Disease heterogeneity is an issue – children may not have yet grown into phenotype. Vast number of diseases, limited number of presentations. So, start by Data mining medical record, then translate into a differential diagnosis. Use HPO to calculate a projection of symptoms, which can be checked against other disorders.
Computer-generated list of 341 diseases that may fit feature.
Also, then, need a genome/exome. Which one do we do? Speed, sensitivity and specificity. Genomes: one day faster, exomes are cheaper.
[An old Elaine Mardis slide: Fiscal environment: $1000 genome is still a $100,000 analysis.]
Have a big bioinformatics infrastructure. Analytics are very good. But, diagnostic metrics may not be as good. Use standard filtering tools to work out causative variants.
Major goal should be to automate ACMG style classification.
Structural variants should be included. Not yet applied in clinical practice. We are also missing de novo genome assemblies… but that’s coming as well.
When 26 hour process works, it really works.
Big gap: Gemome reimbursement. Quality of evidence is pretty poor. Need more original research, more randomized control studies, Standard testing of new diagnostic tests, is not good enough. Payers are far more interested in other metrics.
Other groups have studied this around the world, using exome sequencing. Diagnosis rate ~28%, making it most effective method. (Can be 25-50%, depending on unknown characteristics.) Quality of phenotype may be a big issue.
WES + EHR can help to raise to 51% diagnosis.
de novo mutations are leading cause of genetic diseases in infants. Really, forced to test trios. This is a “sea-change” for the field.
Study: Trio exome sequencing yields 7.4% more diagnoses over sequencing proband alone. ([Not entirely convincing…]
Another Study: 58% by WES vs. 14% standard methods. [ And more studies – can’t show numbers fast enough.]
Faster you can turn around diagnostic, the faster you can get a change in care.
No recurrent mutations in infants treated… but some presentations are enriched for successful diagnoses.
Move on to Randomized control study: just completed, admitted any NICU patient with phenotype suggestive of genetic tests. 15% molecular diagnosis by standard tests. 41% diagnosis with rapid WGS. Had to end test early because it was clear that WGS was making a massive impact.
Problems and solutions: Focus back on parents and families, who may have different impression/understanding of testing or methods. Don’t have enough experts to fill gap: 850,000 MDS, but only 1100 medical geneticists and 4000 genetic councillors. (Solution: more training, and possibly other experts?)
Triangle of priorities: Pick 2…
-> Scalable Clinical Utility <-> Rapid <-> Low Cost. <-
- Process engineering – scalable highly efficient methods
- Clinical Research – much better evidence than we have now
- Education and Engagement – med students need more training, for instance. (Currently only get a day or a week of genetics…)