#AGBTPH – Nikolas Papadopoulos, Detection of rare somatic mutations in bodily fluids in the era of precision medicine: Challenges and opportunities

Johns Hopkins School of Medicine

Data Analysis in Cancer Management: Risk Assessment.  Looking for germ line variants in families.

In cancer management: on tissue.  Prognosis, classification and response.

IN absence of tissue: are there still cancer cells present?  are there actionable changes?  Are there signs of cancer?

Use ctDNA to answer some of these questions.  ctDNA is minority of DNA that’s present.  half life is 30 min – one hour.

Challenges:

  • Technical – specificity and sensitivity
  • Biological: is there detectable amount of DNA in fluid
  • Interpretation: How do you do it?

Favourite markers: somatic mutations, indels and rearrangements.  Very good specificity.

Technical challenges – finding the needle in the haystack.  How do you do it?  Used to be that you would search the hay one by one… digital PCR for instance.  Now, we marry the digital signal to NGS.  Disadvantages: error rare is high for the clinical applications.  Early detection.

Safe-SEQs: Method

  • assignment of unique identifier to each molecule
  • a UID is used to group reads from a common template.
  • Use a PCR based approach. (Allows detection at 0.01%.)
  • divide results into mutant, non-mutant and artifact.

Which tumour types can we detect in cancer?  sensitivity depends on cancer type.  Best for bladder, colorectal, ovarian, gastroesophageal, etc.  Bad for Thyroid, Glioma, prostate, renal cell, etc.

ctDNA is a dynamic biomarker.  Can be used to follow tumour.

Stage II: colorectal cancer.  80% of patients cures by surgery alone.  Good setting for ctDNA.  230 patients.  Plasma for 4 to 10 weeks.  >1000 plasmas analyzed.   Physicians blinded to ctDNA.

Panel used to probe small number of variants.  ctDNA is a strong marker for recurrence.  If it’s not there, patients fare very well.

ctDNA detects minimal residual disease and predicts recurrence.  Provides real-time measurement of disease burden.

Detection of Resistance.

Liquid biopsy resistance mutations.  Pre-treatment, no mutations, post treatment, many did. 0-12 mutations.

Early detection: the holy grail

The most difficult and the longest to get out of the lab.  We know it works.  People will be missed if not part of screening.  Localized disease are frequent.

For early detection, the source makes a difference.  Stool works better than plasma for colorectal cancer.  CNS tumours are not detectable in plasma, pap smear tests work best for ovarian and endometrial cancer.

Doing both plasma and pap smear, improves over either one alone.

Pancreatic cancers are tough – However, a lot of cysts are being found.  Cyst types have different genes mutated.  Cyst fluid gathered in a test – SNP analysis done, 1026 resected cysts.

Goal: reduce surgeries, without leaving anything behind.

Summary: It can be done, it can be done well.

Vision: Prevent advance tumours by integrating such testes into routine physical exams.

#AGBTPH – The Great Debate (Panels vs. Exomes vs Genomes)

  • Richard Gibbs, Baylor College of Medicine
  • Heidi Rehm, Harvard Medical School, Partners Healthcare
  • Steven Kingsmore, Rady Children’s Hospital

Heidi Rehm is up first.

  • No one test fits all.  Should be influenced by phenotype, insurance, gene of interest, etc. Some genes are incredibly hard to work with (e.g.. hearing loss genes) so many genome and exome approaches, so panels would be better.  30% of hearing loss issues are CNV, which are very difficult to detect by exome.  If diagnosis is really tough, and there is no panel that caters to the symptoms, then go for genome.
  • We used to operate with labs that focused on single genes – but now our labs are broad, but we’ve lost all of the experts on single genes.  knowledge management is very hard in this environment.  Sometimes genomes miss things that panels catch, and vice versa.   At end of day, it’s all context specific.  If you have to factor in insurance coverage – and insurance companies really aren’t interested in secondary findings.  etc.
  • Address all of the other complexities: SVs, CNVs, etc. etc.  But these are expensive, but physicians will still go with the cheapest labs.

Richard Gibbs is next

  • The two points of view here are :
    • whole genomes are better
    • no, it’s more nuanced, it depends on the context
  • Most of us are probably in the second camp, if we were pressed.
  • The quality of the genomes, exomes and panels is still a huge factor.   Genomes DO miss things.
  • Exomes are getting better – we’re now up to about 84% coverage of every base in every gene in the coding genome. All genes are there, and most are only missing a small number of bases.
  • Genomes cover everything, but calling is a big deal.
  • CNV coverage is poor on Exomes, but good on genomes and panels.
  • Cost is linear between panels, exomes and genomes.  Informativeness relatively linear as well, but you can explode cost if you want to do more on a genome.
  • If you have a choice between trio of exomes vs genomes, you need to consider that too.
  • The vendor is selling instruments that have distorted prices for exomes, which could be as cheap as panels, if they wanted.

Steven Kingsmore:

  • Going to be provocative, and push hard. [devil’s advocate]
  • The Genome IS the ultimate test, and it’s incredibly informative.  Everyone needs a genome!
  • Genomes
    • Doesn’t miss all exomes of all genes
    • You don’t miss all intronic variants you’d like to see.
    • You can look at all of the reads to see what you’re missing in the gene of interest.  can’t really look at that in panels and exomes.
  • Problem is that they’re expensive.  At least 4 times more than an exome.  (and that’s 4 times more than a panel) Payers don’t pay us much.  How much money do you want to lose.  People don’t mine the genome either – they’re doing genomes, but only get back what they’re looking for.
  • Special cases, though: Cancer needs to go REALLY deep – 30x isn’t enough for somatic mutations.  Exomes will win there.
  • Hybrid Genomes – the next great things: matching long read sequencing with short read sequencing. Rich mining of indels/SVs.  SO EXCITING!
  • Panel < Exome < Geomes << Rich/Hybrid Genome (EVERYTHING ON STEROIDS….  which costs even more.)

Moderator: Why are hybrids a good thing? why is it so much better?  We can already get SVs, but what we can’t see, why would be expect to see it then?

RG: Just on the cusp of giving you all the rich information that we want.  If cost wasn’t a limit, we’d probably come up with a $40,000 bit for each genome.  Yes, we want rockets to mars, but the cost is much more nuanced now.

SK: Everyone is spending the same amount on a battery of tests, but genomes give us that anyhow.

RG: But we’re still missing stuff because of the technology.  You’ll still miss critical expansion regions anyhow.

Q: We’re so far beyond where the physicians are, but ultimately, we’re dealing with uninterpretable content in the genome.  Why is this really relevant?  The real question: there’s value in all of these, but if we want this to translate to the clinic, how we convince them any of this is useful?

HR: The strategy for reporting is very different between panel, exome, genome.  Panels, require an investigation of every variant in gene.  Even if you have VUS, you can follow up. In genome exome, you can not interpret every variant, so you filter.  Sometimes those filters don’t do a good job.  So, we miss things not because they’re technically missed, but because the clinicians and bioinformatics are failing to do what they should.

First lawsuit for misinterpretation of variant – the lab reported the variant, and requested parental testing in order to augment to pathogenic.  The doctor didn’t pass any of that on to the family, and the family found out years later.    The physicians have to be brought into the conversation.

SK: Have to respond to HR.  Panels tend to overdiagnose.  You have 10 genes, and darn it, you’re going to make a diagnosis.  You tend to want to call something.  Under diagnosis is the other end, because you miss other genes. Panels are cheap, and that’s why we do it, genomes will eventually be there.  Respond to questions on Physicians: moving to hybrid genomes will help, because then physicians just have to know one word: Hybrid-genome.   They don’t need to know protocols and panels… Patients shouldn’t have to bounce through specialist till the right test is run.

RG: Is it true that panels are over diagnosed?  It is a persistent problem.

HR: See just as many over diagnoses from genomes and exomes – it’s not unique to panels.

Q: (Fawzan) Point 1: litigation, Point 2: variant is there, but we fail to see it.  That combination is terrible, so we’re amplifying the odds of being sued!  We aren’t discussing that enough.

HR: I don’t think any of us are liable for doing the tests that are requested or doing their best for the patient.  Whoever it is, if you’re doing your best, follow your protocol, then you’re not open to liability.  If we diverge from protocol, or don’t validate, that’s when we become liable.  There’s not always a right or wrong.  We should be doing better, but that’s not unique to medicine.

Q (Follow up): If you’re in a court room, that 30% -40% difference in diagnosis rate may play differently.

HR: That’s why we do so many validation tests – The bioinformatics is maturing, and early versions didn’t do a great job – but it’s improving all the time.  It’s easy to say what should be done, but technically doing the pipeline is much more complex, data is more complex, nomenclature is not perfect.  Filters are very very hard to do, and pipelines need to be validated extensively.  it’s a challenge.

Q (Follow up):  When we miss something it’s usually because the filters are wrong – and with genomes this is just again opening up to liability.

SK: Your argument is silly.

Q (Follow up) : just being provocative!

SK: [reductio ad absurdum]  Patient goes to doctor with a headache…

Q: It’s contextual, we all agree.  In the practice setting, where diagnostics are being ordered.  The constraints on the doctors are even more tight.  How do you think about getting any of these tests (even panels!) accepted outside the academic environment.

RG: Kingsmore promotes genetic literacy by not burying people – applying filters that make the data less complex for physicians. We’ve even put filters in place to mimic that.

HR: Panel done on exome or genome backbone is good – That is a good transition.  Virtual panels are constructed in a way that appeals to the physician to mirror the standard of care for an off-the shelf test is a good intermediate – and allows physicians to return to that data and unmask the next set of genes.  iteratively go forth to reduce the cost, but not change complexity for the physician.

(@notSoJunkDNA): This debate ignored cancer – for another day.  We see resources like Exac, which help aggregate data.  Thus Whole Genome is the only investment in the future – which should be factored into the cost.

HR: Yes, by building that resource, we are making huge impact. Exac is single most useful resource of the last 20 years.  However, we can’t put that expense on our patients.  We can’t be shortlisted, we still need to care for patients.

RG: want to use a different argument to disagree with SK.  Whole genomes are not good enough yet.  Lets not burn all our dollars now before the genomes are great quality, lets get to the great genome, then do it.

SK:  Yes, there are flaws with genomes, but the point is well made: in an ideal world we should be getting genomes on everyone, and put them aggregated into the public domain to allow us to tackle major issues.  Really like that idea, and willing to help subsidize the incremental cost of that. The idea that you get one report, and can return to the genome and reanalyze them makes a huge impact.

M: We can generate them, but we can’t analyze them.  It’s not a great genome if you can’t analyze it.

RG: Eventually  someone is going to go back and use better methods to reanalyze what we’ve done.

Q: Hard studies are tough, but worth doing.  Q to RG: if you had a family member, would you really do 30x WGS, with existing technology  Wouldn’t you do Germline/Somatic?

RG: It’s different between cancer and mendelian.  It’s also different from family, from discovery from managing a health care portfolio.  Hypothetical emotional questions have be separate from the data questions.

HR: we have limited resources to do analysis anyhow.

RG: Have to ask what’s advancing the research agenda.

SK: we do have a lack of objective evidence. Most of our community is doing panels like this one, where we see who argues the best.  Analysis used to be an art – parameterization takes a huge amount of work and investment.  What we’re doing with sequencing is the same, and when the hard data shows up, we’ll convince the payers.

Q:  What price does it shift over from WES to WGS?

SK: I don’t think we’ve done clinical utility studies – it’s the missing piece.  There are studies starting to look at this.  There are way more studies needed for cost effectiveness.

HR: it’s not just prices, we’re losing something with the broader scope.  It’s a tradeoff that has to be examined, outside of cost.

M: RG, If you could put a certain test on a certain machine.  If you could run your exome on a different machine, are policies limiting the best highest quality health care?

RG: conflicting answer:  SK is right, we haven’t got all the studies done.  Even if it’s free, we don’t know how it would work.  However there are medical tests that have a price inflection that is convincing.  <$100 exomes, we would open up many new avenues.

HR: Need to look at numbers, what do the old vs. new numbers say about the changing tests.

M: We could drive down costs if we could use exomes on different machines…. that’s major issue.

Q: How do you think consumer genomics is going to change our field?   (Like GMO?)

SK: Great point – while we argue over which is best, there is an impending nightmare where traditional medicine becomes eclipsed.  Like Herbal medicine, chiropractors… there are things that medical fields didn’t embrace, but public wants it.  Patients will start wandering into their physicians with results  and looking for information.

Q (Follow up): Especially if quality they bring in is terrible.

HR: Terrible backlash to GMO because there was no public debate, and that lack of debate had huge negative impact.    It’s really important that we have those discussions.

Q: Moving target scenarios: cancer mutations, antibiotic resistance.  How do we balance coverage and cost – what’s best approach in that setting?

SK: Cancer, you want both exome and genome, match germline, tumour….. [everything!]

M: Out of time!  What should reimbursement do?

HR: It should be paying, all the hype has missed the fact that this is incredibly helpful for those who get diagnosis.  It’s on the community to do a better job to communicate and share resources so we can decide which tests are the right tests.

RG: Swayed that the top down tests are actually be good.  We shouldn’t be comparing exome vs panel.. Genomes aren’t there, but we should be looking them in the future.

SK: We’re privileged to be living in the day of NGS, and we should be looking at enabling all of the options for clinicians!

#AGBTPH – William Gahl, The NIH Undiagnosed Diseases Program: Expansion to a national and international network

National Human Genome Research Institute

Starts with Acknowledgements.

Goals: help patients get diagnosis… and to discover new diseases.

Anyone can submit an application, and the physician has to send a summary.  Then NIH triages the case to decide whether to take it.  Reject about 75%.  See them for 1 week (free, including travel) as inpatients.  Get done about the equivalent of what can be done in 1 year at a regular hospital, with a barrage of tests.  Take more children than adults, (40%).  Lots of publications!

This program requires good publicity to keep the budget from being cut.  Congress has generally been supportive.

Investigative studies.

Examples: Discovery:   5 adults from Kentucky.  Calcification of arteries and joints.  Parents were 3rd cousins.  (share 1/128th of genes), thus likely homozygous region.  Parents were both heterozygous, but homozygous in affected siblings.  ROH: 22.4MB.  One good candidate gene: NT5E.  Details make sense.

70% of patients have skin biopsy, from which fibroblasts were cultured.  Could go back and look for others.  Could do rescue with vector.

[Into the biochemistry, but too fast to copy down…. Has to do with CD73, AMP-> Adenosine, which signals to inhibit TNAP.  If that doesn’t happen, they convert PPi to Pi, which causes mineralization.]

Second example: young girl with dysmorphisms, hypotonia, etc.

Did urine screening, which turned up a tetrasaccharide.  Hypothesize deficiency of glucosidase I gene.  Compound het turned up in patient in that gene.  Story doesn’t end there.  Also had low immunoglobulins.  Ig was inserted into SCID mouse, and half life was very short – turns out that the sugars are required to protect Igs.

Even more…. did not cause them to have infections.  T-Cells found that viral replication was reduced compared to controls.  Virus produced was less infective. (Secondary infection reduced).  Viruses require glycosylation machinery which wasn’t working!

Third example: parents are first cousins.

Whole exome sequencing: 6 individuals, including 2 parents, 2 unaffected.  1 variant fit: mutation in AFG3L2.  Mitochondrial m-AAA protease.  There are already neurological diseases associated.  Base interacted with both the ability of the protein to homo-dimerise and interact with another protein… so patients had BOTH diseases.

There are Rare diseases, Very rare diseases, and VERY VERY rare diseases that they’ve worked on.  [Pages and pages of them…]

More examples..

Half of diagnostics, half the diagnoses are not made based on NGS… based on doctors and nurses and personal coming together to discuss cases.

Expanded to a Nationwide program.  Expanded to 7 sites, with common protocol.  Share Identifiable information within sites, and able to share de-identified information publicly.

There is also now a gateway.  information is shared through dbGAP, others.

There is a global undiagnosed diseases network…

Phenotype databases (UDPICS), includes exomes and variant analysis.  Anyone can get access to this, if requested.

Sharing is important – Example:  22 year old woman with dystonia.  By sharing information, they found a physician with 19 other cases with the same gene (in London)… and it made a huge difference in outcome.  Patients were eventually diagnosed and 5/19 treated with deep brain simulation, which has worked exceptionally well.

#AGBTPH – Ramnik Xavier, Microbes, genes and gut immunity

Broad Institute.

Studying health and disease in human:

  • Genetics
  • Microbiome
  • Environment – area of weakness, how do we study this?

Cost of sample prep has dropped dramatically, and that helps us with data gathering.  IBD has enjoyed the greatest success in terms of GWAS discovery.  Our sophistication in doing the analysis has had the biggest impact.

Dysbiosis implicated in disease in human and animal models.  Crones is a good example, where disease causes imbalances in gut bacteria.  Disease activity correlates with micro biome population.  Just counting bugs so far… but look at function.  Those that disappear are the ones that regulate activity in normal gut.  Maybe first insult is the alteration in gut metabolism.

What happens if you look at tissue at those sites?  Also show that you have an altered redox balance.  The host response is tilted in same direction.

What about Viruses?  Similar trends seen – with changes in virus populations altered.

2000 healthy individuals recruited in Netherlands.  Gut microbiome and gwas.  In healthy individuals, there was a richness in loci that related to microbiome health… and other markers too, including food choice.

GWAS for bacterial taxonomies.  (Bonder et al, Nature genetics, accepted)

First example: Ulcerative Colitis.  Crones disease.

If you took all the GWAS, what do they tell you?  They point to pathways that maintain inflammation, healing and homeostasis.  Just one example: Autophagy.  Early GWAS (2007 and 2008) associate autophagy with Crones.  Ability of certain cells (paneth cells?) were altered in cells that lead to crones  – ATG16L1 T330A.  Put that variant into mice, and found that there are less Reg3gamma, which is an antimicrobial. With mutation, bacteria get closer to the cell lining, which potentially leads to more inflammation.

T300A is the risk variant.  There’s a potential caspase cleavage site, show that caspase is likely involved – thus, the protein is probably less stable.

Thus, GWAS has lead to some significant understanding of the disease.   Applications include better prediction of health outcomes for children with crones.

Another Example: CARD9

This protein is a protective gene for IBD. Can we use this to improve therapeutics?  Sure – we know that the risk version binds to a protein, and if you can disrupt that target, then you can prevent the inflammation.  This is a draggable target.

Example on Type I diabetes.  Why study?  3-7% of kids have risk alleles for this type of diabetes.  Incidence is rapidly increasing, and may double in next few years – probably environmental.

Use a “living laboratory”, Finland has very high incidence, while Russia and Estonia do not.  If you take kids who have high risk, and follow them.  You can see major microbiome shifts one year before Type I diabetes kicks in.

VERY cool graph of microbial compositions by country.  Close investigation showed that microbial sensing was affected in kids with Type I diabetes.  LPS different between Russian kids and Finnish kids – the LPS in Finnish kids is not sensed by TLR receptor – preventing the recognition of bacteria in the first year of life.  Kids immune system is not “educated” to recognize bacteria, which later has consequence on whether autoimmune diseases (such as T1D) become triggered.

Future:

  • Tapping into microbiome will help us to understand interactions
  • identify disease triggers

#AGBTPH – John Mattison, The last mile of precision medicine: Big challenges, big opportunities

[John Mattison is from Kaiser Permenante.  I had the opportunity to meet him at lunch today, and was blown away by his perspective, which is really broad and not what I expected from someone at an insurance company.  I also learned about “sitting is the new cancer” from him.  Lots of food for thought.]

Audience questions, who’s downloaded their own EHR? Used Open Notes?  Sequenced Exome? Sequenced Gnoe?  Donated to open research project? 50% of physicians are burned out?  Understand what potentially actionable variants means to physicians? Did you know that Genome research, PHR and bitcoin all share a technology.

Thee natural metaphors:

  • Big Bang:  data expanding at ever increasing rates.
  • Meiosis: an incredible innovation engine. (massive mash up of ideas)
  • Tropical Rainforrest: very poor soil, but have maximum genetic diversity and maximum on conversion/capture of sunlight.

Human Microbiolme Complexity  – BIG part of little data.  100 diseases with microbiologic signatures.  It’s all connected and will be medically relevant.  No such things as a “common” disease.

So many -OMES!   And whole ecosystem of platforms from data, to economic and cognitive… all share exponential growth, but synergistic and convergence.  Have to put all of the data together, and linked for common individual – if you don’t work out how to associate together to make sense of them, you’re losing out.

How many genomes to we need to get to the bottom of disease? More viable genomes that are possible than there are people who have walked on earth.  There is no such thing as a cohort large enough to solve everything.   Example with Autism and speculation about a Homeobox.. neat but too much to jot down.

How do we achieve interoperability, given that there are too many standards?  There are families of use cases that need different representations.  we probably have 10x as many representation than we need.  May need some Darwinian evolution to winnow down.

How do we exploit evidence based practice and practice based evidence?  All about accelerating learning and cycle times.  Clinical practice and data capture -> Analytics modelling and Simulation -> Decision support -> back to clinical practice.  Genetics is only 33%  of early death. [?]

How do we integrate genetic decision support with other -omics without further burning out physicians?  Have to have a really functional and effective integration with EHRs.

Truly informed consent:  Pet issue – GINA left out insurance industry and long term care and disability.    Need to go back an put that in – people who consent for genomic sequencing, and have genomic issues may legally be discriminated against by those industries.

Social aspects – 3 conversations that need to happen:

  • Patient and professional heath care team (inc. genetic councillor)
  • Person with Personal Care team.
  • Patient with the person that houses the patient persona. “Person-centricity”

(Diabetes example.  How does treatment happen? Drugs, vs lifestyle, how do we include genomics?)

Geo-fencing -> Geno-fencing. [New term for me!]

The future: Child gets sick, parents swab child and sequence the DNA of the virus or bacteria, and then get a prescription that targets the exact agent… and medicine is delivered by drone.

Global Alliance for Genomics & Health.  If you aren’t involved, you should be.  Work together for better data sharing.  Slides available on the Github for the Global Alliance.

They use blockchain – can be used to identify cohort, and then trace back to institute and consent agreement.  Hash is one way, of course, so ID is impossible to decrypt.  Can be used by physicians to see everything that’s currently known about people who share a single variant. Public data open and available to all, not corruptible.

Spread like the internet: Every additional node increases the value of the network.

Data can come from anywhere – participant works with a trusted steward to get data into project.

We need this because trust is local. Very efficient, effective low cost way to do research and work with patient.  Can’t be owned by single government.  Makes everything public, but without central authority or government control.

[Awesome visionary talk! One of my favourites from the day.]

#AGBTPH – Josh Peterson, Scaling precision health across an enterprise

Vanderbuilt University Medical Centre.

Precision medicine can not stand alone.  It needs to be linked to the concept of a larger health care system.  We need to learn from implementations, to ensure that patient outcomes are changed.

A wave of genomic data moving in the direction of the EHR.  How do you integrate that data to make it useful, and not just a PDF?

Emerge network working on this.

Concept of Genomic Escrow, hold data until it’s ready to be used that does not go into an EHR.  Promote data over time, as you interpret that data.

PREDICT model.  Brings up relevant variants when prescriptions are applied.  Took 5 years to get all of this implemented.  6 months for each drug interaction.

Use CPIC guidelines.

Created “advisors”, that notifies when prescribing medication.  Results also in patient portal.  Limited view: is there a variant, and does it apply to a specific medication.

For Clopidogrel: (Medication releasing stent)  Number needed to genotype to potentially avert one adverse cardiac outcome: 25. Relatively strong outcome.  Retrospective studies also bore out similar strong case for using this information.

Clinician response: didn’t get complete adoption.  Some doctors were proactive, others more resistant. Obvious in retrospect:  Many of the panel tests were not ordered by physician that didn’t order.  Asking them to take ownership of something they didn’t know about.

Asked physicians: Who should take responsibility for prescription change:  67% said PREDICT staff should contact providers. 27% thought PREDICT should contact patient directly. Not the intended outcome.

Is it worth it for the institution?  How do you tell?  What’s the value of multiplex testing?

Cost effectiveness of test?  Abacavir, Azathioprine, clopidogrel and Simvastatin are cost effective… Warfarin is not particularly cost effective.  You can leverage cost effectiveness of other genes to add warfarin and make it cost effective.  Not effective on it’s own.

How about panels?  Hard to justify universal testing for pharmacogenomics… but may be used at first indication or targeted preemptive testing.  Both generate advantages, but haven’t been demonstrated to be superior.

Simulations of cost-effectiveness:  Can be modelled, and applied to other drugs.

#AGBTPH – Patricia Deverka, Payer perspectives on NIPT.

University of North Carolina.

Payer framework for coverage of diagnostic tests.

  • Analytic Validity (CLIA, FDA, Payers)
  • Clinical Validity (FDA, Payers)
  • Clinical Utility. (Payers)

Clinical coverage criteria:  Mostly looking at publications, look at expert opinions, and independent technology assessments.  Have test developers done their own studies?  Other health care organizations.

Key questions: How to evaluate the benefits and risks that analyze many different genes and variants at once?  (And how do you design the tests that get you there?) When evaluating, why are more genes better?  How can they support appropriate clinical integration?

Challenges: What are barriers?  Data sharing, and different payers have different evidentiary standards for assessing clinical utility.

Beyond common trisomies, how do you use the information in the screens?

We need standards to have greater predictability – what is the evidence required to have consistent payer adoption.

Coverage policy study background.  cfDNA screening has been rapidly integrated into clinical practice.  Decision making process has not been systematically examined.

Method, First looked at 5 payers, covering 128Million people!   Second version had 19 payers, covering [180Million?] people.

Some insights into U.S. insurance… Blue Cross/Blue Shield is a dominant player.

Looked into every policy and details.

All private payers cover high risk.  8/19 cover average risk pregnancies.  None cover micro deletions.  One covers sex chromosome aneuploidy.  None require prior authorization from genetic councillor.

10/19 looked at analytic validity, but recognized they have no way to do it indecently.  Lack access to public data and standards to do validation. Lack of FDA regulation.  Majority referenced blue cross/blue shield study.

Most of them emphasized clinical validity.  Rich evidence base for clinical validity.

Payers looking at the same evidence for average risk pregnancies came to different results – 8 consider it medically necessary, the rest don’t.

Modelled data is enough for determining that outcomes are worth it.  Models may not have included test failures.

Clinical validity: constantly defined as “change in health outcomes.”

Conclusions:

  • For non-invasive prenatal testing, vast majorities are using standard analytic framework for understanding tests.
  • they are evaluating evidence for each chromosome abnormality separately, even if bundled.
  • payers cite same evidence, but can come to different conclusions.
  • Most payers couldn’t independently asses validity.

Medicare covers almost half of births in U.S.  Hard to interpret policy.

Blue Cross Blue Shield say that different [chapters?] make decisions separately, but there is dependence between them, judging by the data.

Summary:

  • cfDNA screening adopted rapidly in certain indications
  • Payers used standard framework for making decisions.
  • Genetic counselling a foreseeable bottleneck.

 

 

#AGBTPH – Diana Bianchi, Noninvasive prenatal DNA testing: The vanguard of genomic medicine.

Why is NIPT the Vanguard of Genomic Medicine?  More than 2M tests performed worldwide since tests became available.  Industry has driven innovation.  Clinical impact: 70% reduction in invasive procedures (worldwide).  Has had consequences on maternal medicine.

Expanding test menus have changed the paradigm for prenatal medicine.

Prenatal is the most mature and translated are of genomic medicine.  NIPT functions as well as a crude liquid biopsy.

Why is prenatal most mature?  We are measuring cell free placental DNA, mixed with maternal DNA.  A liquid placental biopsy.  Placental, really.

Tests for Down syndrome , Edwards syndrome, patau syndrome in high risk populations have high sensitivity and specificity.  Chance of false neg are 1 in 1054, 1 in 930 and 1 in 4265 respectively.  These are just screening tests, but they are VERY good screens.

Contrasts with general population, where probabilities of false negatives are lower, but the prevalence of those issues is lower.

Case for/against routine screening for CNVs:

For: may impact care, independent of maternal age, 1.7% have significant CNVs.

Against: clinical use unproven, high false positive rates, increase in procedures which may be invasive.

Study done showed you needed at least 10 million reads to detect 1Mb copy number variant.

Common micro deletions in testing panels: DiGeorge, Prader-Willi/Angelman, Jacobsen, Langer-Giedion, Cri du chat, Wolf-Hirschhorn, 1p36.  All of them are large (3-9.8Mb).  Do not align with ultrasound abnormalities.

Example focusing on DeGeorge.

RAT (Rare Autosomal Trisomies)

used a bioinformatics quality control parameter to identify potentially abnormal cases.

MT16: interesting. Anecdotally, in mosaics (since it never exists in non-mosaic) the percent of cells in the child can be a marker for complications.

Decisions should not be made upon NIPT screens.  6% of pregnancies with positive screen results for Trisomies terminated the pregnancy without properly confirming results! [Hope I got that right… might be misunderstood.]  Should do amniocentesis, because placental results may not agree, and screens may not actually reflect what’s going on in the fetus.

Tumours are often caught this way.  May be a source of false positives.

Bioinformatics errors are possible.

Not much published on pregnancy termination based on the tests.

[Notes are incomplete at the end…]

 

 

#AGBTPH – AmirAli Talasaz, NGS analysis of circulating tumour DNA from 20,000 advanced cancer patients demonstrates similarity to tissue alteration patterns

AmirAli Talasaz, Guardant Health

Spatial and Temporal heterogeneity is becoming more important.  Resistance clones are a major challenge.

Risk and cost of lung biopsies:  19.3% patients experience adverse events.  Mostly pneumothorax.  By pass this by doing liquid biopsies.  Tumours release cell free DNA – active tumours actively shed.  We can get this via simple blood tests.  cDNA can be used to study tumour heterogeneity.

Tumour cell free signals are very dilute.  Standard NGS would limit what you can see.  Take  two samples: biobank one, process one.  digital sequencing library, involves non-random tagging.  target capture 70 genes followed by error correction and bioinformatics.  After 10’s of thousands, the performance has improved very well.

Call CNV, SNV, SV, epigenetics. Variant calling an interpretation.

Half of reported variants occur below 0.4% MAF.  Reported Somatic variants are highly variable, up to 97% MAF, but median is 0.4%.

Accuracy of Low VAF is excellent using this method – correlates perfectly.

High detection rate across most cancers – better for stuff like liver 92%, brain is in 50’s% because of the blood brain barrier [57%?].

Typical driver mutations are frequently found, ranging from 100% for an EGFR variant, down to 13% –  27% for a different EGFR variant.

TCGA and ctDNA have similar mutation patterns. (some exceptions where ctDNA reflects generally the heterogeneity of cancer)

Fusion calls are similar.

When you have access to treatment data at time of blood draw, you can see actionable resistance variants.  27% of resistance mutations found in ctDNA are potentially actionable.

Example with NSCLC – biomarkers were only complete for 37%.

Example with clinical trial – using ctDNA was able to make excellent predictions in large fraction of cases, with favourable outcomes in majority of cases.

[Wasn’t a real summary so here’s mine: – ctDNA is a thing, and their protocol seems to be working.  Very cool.]

#AGBTPH – David A . Shaywitz, Building a Global Network for Transcriptional Discovery and Clinical Application

[Missed the title – Edit: Here it is!  Thanks to Dr. Shaywitz]

Two futures: do we have the will to harness all of the information we’ve collected to build a future that takes advantage of it.

Balanced between Centralized and Globally Distributed.

DNA nexus is clinical care: distributed patient care.  Centralized Data processing, deliver consistent results.

precisionFDA Appathon – https://precision.fda.gov/challenges/app-a-thon-in-a-box

[Edit – thanks to Dr. Shaywitz for the link!]

Ideal Glogal Network:  Capabilities.  Global reach, security, rich tool set, qualified collaboration, indexing.

Three examples.

Regeneron: Revolutionize how pharma is done. Driven by science instead of trends.

Data is not equal to impact.  Innovation is driven by people, by intention, impact. [A lot of rapid fire hypothetical examples, including nature vs. environment, tipping points, important to understand data.]

Roll at DNAnexus is to empower people to have the tools they need.

Singapore Data Federation: Government sponsors -> improved hospital care.  Improve care by understanding data by understanding populations.

ORIEN Cancer Research Network: Cancer centres brought together to others who want to consume data they have (access to patients).  Cancer centres benefit from collaboration and access to data they couldn’t afford, companies get access to patients they need to fuel research.  Beginning of Networked Future.

DNA nexus is in the centre of all of this.  Drive precision medicine by being the hub that connects all of it.