They have much to be proud of – the use of VistA has enabled the VA to reach a pharmacy prescription accuracy rate of almost 100%, and the VA outperforms most public sector hospitals on a variety of criteria, enabled by the implementation of VistA. VA hospitals using VistA are one of the few hospital systems that have achieved the qualifications for HIMSS stage 7, the highest level of EHR integration. The goal is to create a usable, next generation EHR that can anticipate and be flexible enough to accommodate where clinical informatics will be headed over the next decade, and the big target is genomic data from the VA’s own Million Veterans Program (MVP).

The ambitious MVP is an ongoing initiative, according to Veterans Affairs Eric K. Shinseki, “It is my honor to join with so many fellow Veterans in keeping VA at the leading edge of genomics research.  This innovative research program will support VA’s mission to provide Veterans and their families with the care they have earned.” The Million Veteran Program is a trailblazing VA effort to consolidate genetic, military exposure, health, and lifestyle information together in one single database.  The database will be used only by authorized researchers with VA, other federal health agencies, and academic institutions within the U.S.—in a secure manner—to conduct health and wellness studies to determine which genetic variations are associated with particular health issues.  By identifying gene-health connections, the program could consequentially advance disease screening, diagnosis, and prognosis and point the way toward more effective, personalized therapies.

In a recent workshop held at the prestigious Institute of Medicine, called “Integrating Large-Scale Genomic Information into Clinical Practice”, several key scientists and clinicians from Genome Centers described the evolution of clinical genetic diagnostics – from gene test panels to whole exome sequencing to whole genome sequencing. With the precipitous drop in the cost of human genome sequencing, the strong recommendation from participants at the workshop was, although we have interim genome-based diagnostic tests, within 2-3 years everyone will be performing whole genome sequencing and analysis because it reveals so much more information about the correlation between genetic variation and disease phenotype^1,2. This mantra is now resounding within the VA’s clinical MVP program, which is evolving with the technology:

¹ Ley at al (2008) DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome.  Nature 456, 66-72 (6 November 2008) | doi:10.1038/nature07485.

² Welch et al (2011) Use of Whole-Genome Sequencing to Diagnose a Cryptic Fusion Oncogene.   JAMA, April 20, 2011. 305 (15): 1577-1584

Highlights:

• Researchers at the Medical College of Wisconsin are taking pioneering steps to make whole-genome sequencing a standard part of diagnostic testing for children with rare inherited disorders not easily diagnosed by traditional methods.
• Howard Jacob, director of the college’s Human and Molecular Genetics Center “has gone the extra mile to convince regulatory and legal people at his institution that this should be part of the diagnostic.”
• In December researchers published their effort to diagnose a six-year-old boy with a severe form of inflammatory bowel disease that failed to respond to treatment. He had already undergone 100 surgeries to try to repair his damaged digestive system.  After sequencing the child’s genome, Jacob’s team identified a mutation on the X chromosome that has been linked to an inherited immune disorder. With the new diagnosis in hand, doctors performed the cord-blood transplant. Eight months later the boy is out of the hospital and doing well.
• Jacob has spent the last year and a half trying to transform what has been a promising medical technology into a routine diagnostic, with standard procedures and clinically certified tests. The team’s most recent success: an unidentified insurance company has said it will cover sequencing costs in cases where the team can demonstrate that it is likely to be cheaper than the typical string of diagnostics. Children with rare diseases often go through a series of tests each of which search one or a few genes for the mutation causing the disease.
• Jacob says he expects to analyze about 20 genomes this year and 100 next year. Thus far, the team has analyzed the genomes of five children and has another seven on the docket. In one case, sequencing revealed that the child would not benefit from a liver transplant, thus saving the liver for another recipient.

Find the full article at: http://www.stsiweb.org/index.php/news_events/detail/making_genome_sequencing_part_of_clinical_care_technology_review4

The wonderful genetic experts at GeneTests have collected global research on over 2000 genetic disorders – GenomeQuest has built that database into its sequence analysis platform.  From a single, whole-genome sequence of a patient, GenomeQuest can generate a comprehensive report on disease susceptibility, diagnosis, and treatment for that patient on those 2000 disorders.

We believe this is a significant step in the genomic “arc” from research to clinical.  But is it significant for health care?   I would argue the answer is a resounding “yes”.

Globally, the world spends about $5T on health care and about 65% of therapy decisions are based on the results of diagnostics tests.  The fastest growing segment of the in vitro diagnostic (IVD) market is molecular diagnostics (MDx) – rising 15% annually and 3X above the category average.  Driving this growth are greater precision and faster turnaround versus traditional techniques, falling costs of genome sequencing, and steady advances in genetic research (eg, NIH alone provides over $5B of annual funding).  Common disease areas for MDx  include cancer, HIV, GC/CT, Hepatitis, and MRSA.

Up to now and to save costs, molecular diagnostic is largely based on a patient’s partial genome — with specialized genetics tests and machines for each disease.  However, because the cost of sequencing is falling so precipitously, diagnostics based on the entire or whole genome is fast becoming practical.  Whole-genome MDx offers transformational advantages – it can fundamentally raise the quality and lower the cost of health care in the following ways:

1. It is more far precise because it can consider the increasingly complex and multi-gene factors of diseases.
2. Armed with this richer whole-genome knowledge, it has the power to not only diagnose but also predict disorder and guide therapy.
3. It allows instrument makers to realize immense economies of scale by manufacturing and optimizing a single instrument type (whole-genome sequencing) for diagnosis of multiple, if not most, genetic disorders.
4. It enhances repeatability and quality in laboratories by allowing them to standardize their diagnostic machines and processes on universal, whole-genome sequencing
5. It allows researchers and organizations to deliver and approve the latest diagnosis discoveries through pure software – clearly a speedier and more streamlined route to patient benefit than through single-disease machines and reagents.

Read the full announcement here.

Everyone from Eric Green at the NHGRI to David Dooling at Washington University’s Genome Center to Kevin Davies at Bio-IT World rightly acknowledges that the $1,000 genome is a misnomer if you decide to include the cost of computing, analyzing, storing, and querying the data. And if you don’t – well, you’ll be a touch over budget.

Nevertheless, recent innovations have changed the playing field. Just two years ago the debate was whether MAQ, the fastest algorithm at the time for mapping reads, was accurate enough based on its gapless strategy. Today, there are some electrifying algorithms out there based on the Burrows Wheeler transform that move 10 times the number of reads through the same compute pipelines. And where Burrows Wheeler doesn’t work – long reads like the kinds we’re seeing more and more – there are fantastic new approaches like GASSST (which we use to get a 7x increase in mapping speeds with arbitrarily long reads and arbitrarily large gaps).

I gave a talk at ABRF this year entitled, “The Bioinformatics Bottleneck,” where I challenged an audience full of bioinformaticians that if we keep getting stuck arguing about the fastest algorithm to map reads and whether to store image files, the industry would simply move on without us. (No, Steve Lincoln, I didn’t out-Steve you.) Illumina, Life Tech, Roche, and now PacBio are going to keep making machines. Beijing Genomics is going to keep buying them. In the heat of the moment I think I said something about not only throwing out the image files, but also throwing out the reads themselves – after all it’s the representation of variation, not the reads, that researchers and clinicians will ultimately care about. As late as March 2010 this was met with shock and even disdain; but today great companies like Complete Genomics and important projects like the Personal Genome Project are doing just that – providing us with the variant calls only; not the reads. Unless of course you want them – your choice, by the way, and may your disk drives spin forever.

Now that’s not to say that we don’t need to keep mapping. And so we do. GenomeQuest is on track to have mapped 100 billion reads in 2010 at year end – about 11 million an hour. Of course if our systems were 100% utilized – an impossible dream for any data center – the number would be far higher. And next year we expect it will be five times that at least. Mapping goes on, powered by innovations in software, and innovations in hardware as well. Earlier this year we announced a partnership with SGI that enabled us to build a purpose-designed architecture designed specifically for mapping. By ensuring reference data availability on every compute node, we minimize network traffic; through significant redundancy in both compute and head nodes we can ensure quality of service. By the end of 2011 we expect to be able to map 300 whole human genomes at 30x coverage per month, or 6,000 exomes per month.

The idea that so many genomes can move through a uniform, industry tested workflow is enchanting. And it sets our sights on the next realm of opportunity: analysis of thousands of genomes at the same time. Projects such as the 1,000 Genome Project (actually planning more like 2,000 – and do you really think Durbin and Altschuler will stop there?) are troves of undiscovered knowledge on the basis of disease. Being able to overlay that on top of your own 100 exome project provides critical information on background. So using the same technology, GenomeQuest has an exciting new suite of products for multi-genome analysis that are currently available for early access. As the year rolls on into 2011, these workflows for computing allelic frequency, performing large-scale tumor/normal studies, and asking population-scale questions across thousands of genomes will be further enhanced in close collaboration with our users.

The end game for healthcare is genomic medicine, enabled by the sequencing of individual patients and the large-scale comparison against populations – from phenotype to clinical presentation to genotype to treatment to response. That’s why we’ve partnered with Beth Israel Deaconess Medical Center – to work closely on the development of whole genome reports that can actually be usable by pathologists, like any other lab test. The idea is to map clinical actions to specific variations and present them in a way that a trained genomics physician can ultimately guide the course of treatment. There is a long road ahead and plenty of stakeholders involved, but being in the clinic in 2010 drives our thinking about the research and clinical development applications that exist today in pharma and academia.

Genomics is still a bit of the wild west – in a single day I can have a meeting with a pharma executive, a seminar on an FDA position-paper, a discussion with agbio researchers on the genomics of polyploid organisms, and a deep dive with a team of bioinformatics talking about word-based hashing algorithms. When genomics is in the clinic this’ll have to stop.

But in the meantime…

Beijing Genomics Institute alone has purchased 128 of these instruments. The Broad has 51. And based on Illumina’s 2010 Q1 10-Q filing, they’ve got a backlog that represents maybe another 200 machines. So by 2011, there may be some 500 of these machines running. Not to mention the GA-IIs, the SOLiD machines, the 454 machines, Helicos, Pac Bio, Ion Torrent, Complete Genomics, and all of the next-next generation single  molecule sequencing companies making big promises.

The Fact Is…

…it’s easy to lose track of what this means. It’s easy to get stuck in today’s problems.

In 2010, we may have something like 1,000 publicly available human genomes at a wide variety of coverage. That’s giving us as a society the benefit of the doubt.

In 2011, the worldwide capacity for whole human genome sequencing will easily reach 50,000 – real data based on orders that have already been placed.

Do we believe this is going to slow down? What incentives does the industry have to dial this down? None that I can think of.

If it’s 50,000 genomes in 2011 (50x increase from 2010), it’s totally reasonable to believe that capacity will grow to 250,000 genomes by 2012 – that’s only a 5x increase from the previous year. Call 2013 a 4x increase over 2012 – that’s a capacity to sequence 1 million genomes, just three years from now.

The only thing in the way of this explosive growth is our ability to absorb the new capacity – and that gets directly to tools that can analyze the data. As the number of genomes increases exponentially, the types of questions we’ll ask of this data will change dramatically. We’re in the middle of an incredible revolution that will move more quickly than many of us appreciate. Let me propose one vision.

2001-2009: A Human Genome

The 10 or so years after the Human Genome Project, through say 2009, were characterized by large-scale research operations to understand the basic biology behind genomics. Gene and target discovery, pathway modeling, disease models, GWAS, expression analysis. Consumers of the Human Genome Project have been academic, pharmaceutical, and biotech researchers. The genome was sequenced, and sequencing was thought to be yesterday’s job.

2010: 1,000 Genomes – Learning the Ropes

In 2010 with the nascent adoption of NGS (if you think it’s widespread today, just wait), new applications have exploded on to the scene: larger-scale resequencing of exomes and whole genomes, RNA sequencing, CHiP-seq, metagenomic sequencing, and a renaissance in the agricultural sciences who can finally run their own versions of the Human Genome Project. The consumers of this early-stage adoption of NGS remain the academic researchers, pharma and biotech researchers, and ag companies. We’re finding new variation across different ethnicities, identifying novel transcripts in previously well-understood genes, and developing exciting new insights in epigenetics. But it’s still basic research. And the bioinformatics community is still arguing about basic approaches to alignments, calling variants, and normalizing across experiments.

2011: 50,000 Genomes – Clinical Flirtation

How do things change when we have the capacity to sequence and analyze 50,000 genomes? Catalogues of human variation will become large-scale for the first time. We’ll build strong correlations between phenotype, genotype, and treatments. Early-stage sequence-based diagnostics will find their way into the leading-edge labs and hospitals. Pharma will take real steps towards the design and optimization of genotype-centric clinical trials. The FDA will provide better guidances towards developing drugs and diagnostics that employ sequencing. We’ll start talking about “Genomicists” in the same way we currently describe Pathologists or Radiologists although there will be very few of them. (Indeed, some Pathologists already believe that genomics will fall in their house.)

2012: 250,000 Genomes – Clinical Early Adoption

With 250,000 genomes, the clinical adoption of sequence data will begin in earnest. Genomics-based diagnostics will be a real business: comments from a recent J.P. Morgan report indicate that lab managers believe that this switch will occur in the next 5 years, particularly in cancer detection and classification. The FDA will support pharmacogenomics-based clinical trials at large. Population studies will continue to drive massive insights into human variation. Leading-edge hospitals will store whole genome data for patients as a part of their medical records. The consumers of NGS are changing from academic and commercial researchers to Pathologists, Genomicists, VPs of Clinical Development in pharma, and young doctors everywhere.

2013: 1 Million Genomes – Consumer Awareness

When the planet has the capacity to sequence 1 million genomes per year, many 1st-world health-care consumers will have enough knowledge to seek out health-care providers who provide these services. Savvy patients, already practiced in researching their own conditions on the Internet prior to a doctor’s visit, will begin to push back on doctors’ recommendations, saying, “before we make a decision on that cancer treatment, I want my genome sequenced to see whether it’ll be effective.” Health and life insurance companies will get into the game, and barring significant ethical battles, will use genomic information to guide treatments, suggest specialists, and even set prices for premiums. Diagnostics for personalized care will double from the previous year. The personal genome will be within reach to many individuals, and the FDA will struggle to keep up with regulation to restrict the use of personal genomes from unapproved diagnostics. It is not at all clear to this author whether the FDA is sufficiently staffed to keep pace with the innovation that will explode from this level of availability of sequencing capacity.

2014: 5 Million Genomes – Consumer Reality

Many cancers in the 1st-world will be sequenced as a regular component of a biopsy. Patterns of drug efficacy will be published and made available against different genotypes. Oncologists will work with statisticians to develop treatment programs. Hospitals will offer whole genome sequencing services to newborns. Chronic pain will be managed on a genotype-by-genotype basis. Medical schools will redesign their curricula to produce physicians and researchers to lead medicine into the Genomics Age and to provide advanced training for the Genomicist specialty.

2015-2020: 25 Million Genomes And Beyond – A Brave New World

The ability to sequence 25 million genomes just five years from now seems well within the industry’s grasp, barring significant issues of uptake and absorption of the data. And applying just a doubling of capacity each year between 2015 and 2020, we would have the capacity to sequence just under 1 billion genomes a year by 2020. This will have drastic impacts on society.

While the health-care industry will continue to adopt sequencing for broader and broader applications, the insurers will do everything in their power to get access to this information both for the microeconomic management of individuals as well as for the macroeconomic indicators of ethnic and regional health that will surely increase their profit margins.

Consumer applications for genomics will flower: want to see whether you are genetically compatible with your new girlfriend? There’s an app for that. DNA sequencing on your iPhone? Believe it. Personalized genomic massage, anyone? This is already happening today – see labs testing for allele 334 of the AVPR1a gene to see whether your new mate has the “cheating gene.” Then imagine the market for consumer applications and gimmicks when your entire genome is already on a USB drive.

Genetic discrimination may need to be addressed in the highest regulatory bodies: do you really want to elect a President whose genome suggests cardiomyopathy? Think this won’t happen? Just imagine the first candidate to release his healthy genome just like his last two years of tax returns, challenging his opponents to do the same. What will the world’s reaction be?

Will LinkedIn and Facebook suggest people you may be related to? Sure, they’ll probably not have your genome, but your genome will be somewhere in a de-identified way, sitting right next to other de-identified genomes. It’s easy to envision software to mine this data that will find your relatives and common ancestors. It may start as a medical application but it won’t be able to stay that way. Just let that software platform tell you that they’ve found a genome of someone who looks like a third cousin and provide a way to reach out to them anonymously. Welcome to ChromosomallyLinkedIn.

Back to Reality

I’m no futurist – most weeks I can barely tell you what my schedule is the following week. So while it’s fun to dream up the next decade, there are too many variables to get it all right and this thought experiment may be off a few years in any direction. We’re squarely in 2010, the year of the 1,000 genomes. The deeper we allow ourselves to look into the future, the less clear it becomes.

But one thing is certain – sequencing capacity world-wide will continue to grow exponentially for at least the next 10 years. This is going to happen. That means sample preparation will get vastly easier, throughput will continue to increase at a dizzying rate, sequencing costs will plummet, and the applications of sequencing will become more mass-market.

And most of all, it means that the software that we use to analyze sequence will need to become a lot simpler to use, and more purpose-built for specific applications. General bioinformatics frameworks are dinosaurs awaiting the impact of the meteor. In the (near) future, no one will be arguing about gapped vs. ungapped alignments. No one will be talking about Phred-like quality scores. No one will be talking about reads, even – they’ll seem like antiquated tiny puzzle pieces from a past when sequencing technology was like a nuclear bomb rather than a precision scalpel.

As I look ahead to develop the long-term vision for the product roadmap for GenomeQuest it’s obvious to me that our immediate-term focus must be on simple, easy-to-use, whole and multi-genome analysis. With the coming of 50,000 genomes next year, our immediate problem is supporting the absorption of this new knowledge. That means continuing to enable the processing of data as quickly as it comes off the sequencers and presenting it to end users in a way they can understand, interact with, and discover. What are all of the proteins affected by this individuals variants and what are the types of modifications we see? How does that impact disease pathways? How is this individual similar to others for whom we have treatment/outcome data?

Today’s consumer of genome sequencing is the researcher or clinician doing basic discovery with thousands or hundreds of thousands of genomes. But the longer-term audience is the clinic itself.

And I for one don’t think we have that long to wait.

Calling all clinicians.

Rants welcomed.
-Richard

According to Bob, it was the 2nd largest crowd ever for his seminars and the largest ever for a software topic — an indicator that reseachers are indeed planning for NGS and eager for answers to their “information bottleneck”.

Over 80 principal investigators, Post Docs, and MDs attended from Harvard hospitals including Beth Israel Deaconess, Children’s Hospital, Dana Farber, Brigham and Womens, Mass General, as well as Harvard Medical School.

Based on the questions, most researchers were interested in the RNA-Seq and Variant Detection applications. Richard Resnick stressed GQ’s cloud-based storage/analysis of results, the GQ Browser for whole-genome analysis, as well as cloud-sharing of results.

Harvard bioinformaticians and computational biologists are welcome to a follow-up seminar or training on the GQ API.

So what?

GenomeQuest is now for bioinformatics and computational biologists (we call them developers for short). These are people who prefer to write code in Unix, and prefer awk, perl, and sed to Firefox, Internet Explorer, Safari, or Chrome.

So why is that important?

With no up-front investment, developers can use the GQ API to write large-scale sequence comparison applications for the cloud without regard for the details of the computing and the reference data. And, they can publish their applications to the GenomeQuest Web Application, so research biologist customers can use and reuse them.

Over at Depth-First there is a blog post about an application in the cheminformatics field: PubCouch: Streams aren’t just for Pipeline Pilot. The author illustrates how a well abstracted Web service avoids the costly database Extract-Transform-Load operations so familiar to most life science development. In the example, the author streams the entire contents of the PubChem FTP server to PubCouch, a web-service based on the NoSQL style document-oriented database CouchDB. CouchDB doesn’t rely on a database, instead it computes the PubChem relationships “on-the-fly” using an approach based on  MapReduce.

So what you say?

The vision is this: Since modern Web-based programming (aka RESTful architecture) hides the details of massive data and computing resources, programmers can focus on “what to do” and not “how to do it” and that increases productivity.

GenomeQuest’s developers have thought deeply about what a scalable computational biology engine should look like in the cloud-based, MapReduce paradigm. If you want to read a primer on the GQ Engine, feel free to check it out.

Soon, we’ll publish the full-blown URL API so that large-scale biological data and computation can be assembled from any Internet connected desktop, using the language of the Web. A command line interface to our Web-services can be found here.

A final remark: Deepak Singh from business|bytes|genes|molecules wonders aloud what is the role of Pipeline Pilot in this new programming paradigm? I’m guessing within a domain, the value proposition might be limited, but across domains these tools will continue to be able to solve even bigger problems by leveraging better designed Web-services.

So what took so long? Until now, it wasn’t necessary. We provided clear business value to pharma for a well-defined use case. An advisory board might have even been a distraction.

So what’s changed? As we build out the sequence data management (SDM) platform, we want to see beyond this year’s application of next generation sequencing (NGS), and make sure we understand where the industry is going.

Dr. Mark Boguski

Mark is a perfect advisor for this initiative. His practical experience at NCBI, Rosetta, and Novartis, and his current vantage point at Harvard Medical School and Beth Israel, places him squarely with a view to the future uses of sequence data in a clinical setting, and with firm grounding in the practical applications of sequence data for the past 20 years.

It’s an honor to have Mark join us as a scientific advisor. We hope to build a diverse advisory team to complement him with skills and experiences that reflect the diversity of talents converging on the digital revolution in biology.