Speeding Diagnosis of Rare Diseases

WuXi NextCODE Claritas

Claritas Genomics combines physician experience with next-generation sequencing and WuXi NextCODE’s analytics to accelerate rare disease diagnosis.

It’s one of the most heartbreaking and frustrating things for parents and pediatricians. When a child presents with a constellation of symptoms that doesn’t point to a known disease, what do you do?

Typically, these kids undergo a battery of tests, some of which will eventually be for single genes suspected to play a role in their health problems. But what if those tests come up negative? That leaves the families and doctors wringing their hands as they wonder what to do next.

That was the case with a patient at Boston Children’s Hospital (BCH). He was a boy who, at six months, wasn’t sitting up, smiling, or doing most of the things babies his age typically do. Instead, he seemed “rigid” to his mom, and then he developed a severe respiratory virus and was hospitalized. He also had repeated seizures and eventually needed a tracheotomy—a tube placed through an incision in his throat to help him breath.

Usually, such kids then begin going through what is known as a “diagnostic odyssey”—a long and arduous journey from doctor to doctor and lab to lab.

BCH doctors are trying a new approach. In 2013, the hospital spun out Claritas Genomics, a specialized genetics diagnostics business that combines the experience of the hospital’s physicians with the power of next-generation sequencing and WuXi NextCODE’s advanced analytics. Timothy Yu, a neurologist and researcher at BCH, helped found Claritas to provide a more holistic approach to rare disease.

WuXi NextCODE’s advanced analytics play a key role in improving the speed and efficiency of such diagnostics. Reading the genome isn’t the major challenge anymore—now the issue is finding the relevant mutations in those three billion base pairs.

The data from a single genome can comprise more than 100 gigabytes, which is enough to fill the hard drive on a good laptop computer. Even the exome, which comprises the parts of the genome that encode proteins, can be 15 gigabytes. To diagnose a rare disease, doctors need to find sequence variations and then scour the research to find out what those actually do. That used to take months to years, and many of the variants were simply classified as being of “unknown significance,” without any further information or the ability to check again as the field of knowledge grew.

WuXi NextCODE’s system has begun to make this a click-and-search task. Our knowledgebase can mine all publicly available global reference datasets simultaneously and in real time to show all there is to know about any given variant and its likely biological impact. By keeping the data in a WuXi NextCODE research database, such as the one BCH is growing every day, our system can also quickly rerun the analysis and provide new information as soon as it becomes known.

Claritas is continually expanding the range of its services. Most recently, the group received conditional approval from the New York Department of Health for three new “region of interest assays” as well as one for mitochondrial DNA. That brings the number of Claritas’s approved tests in that state up to six and means more patients in New York will benefit from this new technology.

Children at BCH with ambiguous diagnoses now regularly undergo a whole exome scan early in their clinical journey. The data is then triaged. It is examined first for the most obvious mutations and then more data is progressively analyzed as necessary. With the consent of parents and security measures for privacy, that data can also become part of research datasets at BCH and other major hospitals around the world, so that the growing data pool can benefit that child and others.

This combination of expertise and technology helped Claritas Genomics find an answer for that baby boy and his family mentioned earlier. Heather Olson, the boy’s treating neurologist, had the boy’s exome scanned through Claritas Genomics, and 130 genetic variations were identified that could have caused one or more of the symptoms. WuXi NextCODE’s system helped narrow that down to only six variants that could have possibly been passed on by the boy’s parents. Olson and Yu finally focused on one, a mutation of the BRAT1 gene, which served as a diagnosis. A paper published by Yu, Olson, and colleagues, which describes this mutation and children affected by it, should help other physicians make this diagnosis more quickly in the future.

Yu presented more on Claritas’s novel platform recently at Boston’s Bio-IT World meeting. He explained how the platform helps doctors to much more quickly and accurately diagnose kids with diseases not previously described.

“Thanks to the speed of the platform, we can get a whole clinical exome completed in as little as two weeks,” he said.

The growing database of genetic variants and their effects also means more patients will get an actual diagnosis, rather than walking away still wondering what could be going on.

The ability to diagnose more cases is a start to unravelling the causes of the estimated 7,000 different rare diseases estimated to exist. And it’s a necessary first step towards developing new therapies for those conditions, too.

email

A New Era, New Vision for WuXi and NextCODE Health

WuXi-NextCODE

WuXi PharmaTech has acquired NextCODE Health to create WuXi NextCODE Genomics, a global leader in genomic medicine. Pairing WuXi’s technology and existing reach with NextCODE’s leading analytics and database promises to advance the pace of genomics research today.

In the fast-paced genomics community, we continually look for new opportunities and strategies to enhance the value of genomics and use the increasingly robust body of genomic data for the advancement of clinical medicine.

We’re excited to announce a new, ambitious vision to do just that, with WuXi’s acquisition of NextCODE Health. NextCODE will be merged with WuXi’s existing Genome Center in wholly-owned subsidiary called WuXi NextCODE Genomics, with unique, comprehensive and global capabilities for using genomic data to deliver better medicine and improve healthcare.

WuXi, a Shanghai-based genomic laboratory service partner for companies in the pharma and biotech community, has already been collaborating with NextCODE to provide analysis services to customers of the WuXi Genome Center. Now, with the in-house capability to analyze, store, and manage the vast amount of genomic data, NextCODE’s industry-leading genome sequence analysis platform will expand WuXi’s core next-generation sequencing benefits and services.

Pairing WuXi’s technology and existing reach with NextCODE’s leading analytics and database promises to advance the pace of genomics research today. More importantly, however, this new era for NextCODE brings exciting opportunities to maximize the most advanced tools available today and contribute to major advances in genomic medicine.

Maintaining Momentum Post-ASHG: Maximizing the Value of Large Genomic Databases

The newly launched NextCODE Exchange provides a browser-based hub for multi-center sharing and collaboration on collective data from massive whole-genome databases like the Haplotype Reference Consortium (HRC).

The newly launched NextCODE Exchange provides a browser-based hub for multi-center sharing and collaboration on collective data from massive whole-genome databases like the Haplotype Reference Consortium (HRC).

The American Society of Human Genetics (ASHG) meeting convened this week in San Diego, bringing together genetics experts from around the world to discuss programs with great potential to advance genomic-based medicine in the years to come.

To maintain the momentum generated this week, we need to find ways to integrate these important ideas, insights and programs, and to maximize the use of the massive databases that have been launched to support research on cancer, rare diseases and other pressing health topics.

One of the databases unveiled during the meeting was the Haplotype Reference Consortium, which aims to become the world’s most comprehensive database of genetic variations. Large databases like the HRC, along with several others already underway, can be tremendously helpful to researchers finding answers to some of the most challenging diseases. But there remains a significant bottleneck: these large, cumbersome databases cannot easily be shared and manipulated, limiting their utility for broad, multi-center genomic research.

The solution lies in the newly launched NextCODE Exchange (see release here). This browser-based hub allows for the sharing and harmonizing of massive whole-genome databases like the HRC to accelerate research. The integrated architecture allows users to visually confirm and validate findings in raw sequences, collaborating and sharing with others around the world who may have complementary research underway.

The momentum generated during ASHG will be multiplied by sharing and learning from the world’s collective genomic data on the NextCODE Exchange. Learn more here.

A Standard Database Architecture Will Build a Stronger Foundation for Genome Discoveries

big data genome sequencing hannes smarason

The general adoption of the Genomically-Ordered Relational database (GOR) as a data standard for storing genomic data may greatly accelerate the spread of sequencing and its effectiveness as a tool for advancing medicine.

It is widely accepted that the ability to share the analysis and insights from DNA sequencing will be a key driver of discovery and innovation. But one current limitation to extending this knowledge is that sequencing and analysis platforms, as well as samples, are often proprietary to and stored at different institutions. Perhaps more important, the structures and formats in which genomic data has customarily been stored—the relational databases developed by the likes of IBM and Oracle—make it unwieldy to analyze as the amount of data grows, and very difficult to share. The upshot is that institutions cannot easily share and consolidate information to generate more robust analyses and clinically relevant insights. This presents a serious hurdle to discovery both in rare disorders, where samples need to be gathered in order to generated adequate analytical power, and in complex ones, where truly massive studies can tease apart different facets of disease and reveal their causes.

Over the past decade, a novel and comprehensive database model has been developed to solve this bottleneck, offering a flexible and fast means to overcome these problems. It is called the Genomically-Ordered Relational database, or GOR, and was designed to manage and query the detailed genomic data amassed by deCODE genetics in Iceland – the world’s first and still by far largest and most comprehensive population-based genomic database.

The thinking behind the GOR is as simple as it is revolutionary. Genomic data is a sort of big data but one with an important difference: It is divided up in distinct packets—the chromosomes—and then arranged within each chromosome in linear fashion. The GOR makes use of this by storing and querying sequence data according to its unique position in the genome, rather than as huge files as long as the sequence. This radically reduces the data burden of querying even large numbers of whole genomes, at the same time making it possible to store and visualize instantly the raw sequence underlying an analysis.

In practice, the GOR thereby enables researchers to home in on specific variants without having first to call up entire patient genomes, and separates raw data from annotations to focus in on only the most relevant search components. It’s these types of functions and features that can be consistently applied across data storing systems to allow for more multi-institutional, collaborative research and consistency in outcomes worldwide.

Leaders in the genomic research community are now beginning to create coalitions and working groups to underpin and coordinate the adoption of standards for sharing genomic data. As these groups create flexible and efficient policy frameworks, the GOR is tested and ready to support the fundamental data requirements of global data sharing and the acceleration of discoveries in genome-based medicine. The general adoption of the GOR as a data standard for storing genomic data may greatly accelerate the spread of sequencing and its effectiveness as a tool for advancing medicine around the world.

Four Factors for Improving Genomic Data for Personalized Medicine

advancing the use of genomic data for personalized medicine

The pace of progress has been astounding with advances in the use of genomic information to provide faster, more accurate, and more in-depth information to enable personalized patient care.

We’ve come a long way in improving the way that a patient’s genome sequence data is analyzed and interpreted to realize the full potential of personalized medicine. Here are four factors helping to overcome barriers and achieve new milestones for using genomic data to provide faster, more accurate, and more in-depth information to guide clinicians in delivering personalized care for patients.

Factor #1: Fast database query of the genome

Problem: Relational database architectures make it possible to store large quantities of sequencing data, but querying whole genome data can be time-consuming and take days to weeks.

Solution: The GOR (Genomic Ordered Relations) database is able to query whole-genome sequences in real time. The reason is that GOR understands the genome in terms of chromosomes, its natural structure, rather than as a continuous string of sequence. That’s both intuitive and innovative. When searching for a variant, tools in the GOR architecture don’t have to scan each individual’s entire sequence; they retrieve the variant straight from its location. Annotation data – information on what diseases or conditions variants have been linked to – are also stored in the same way. The GOR database was pioneered a decade ago by deCODE genetics, one of the first organizations to manage truly large genetic datasets, and is now being used by NextCODE for clinical applications of genomic data.

Factor #2: Fast, reliable identification of disease-causing variants

Problem: Many sequencing analysis pipelines are only powered to process data in a compressed format called Variant Call Format (VCF) files. These comprise only a tiny fraction of the genome, and being working only with VCF files makes it difficult to correct common alignment and allele-calling errors. That can result in both false positive and negative results, or to missing the key causative variants altogether.

Solution: The foundation for improved sensitivity and specificity is the ability to use VCF data on top of the raw sequence data from which it was derived. NextCODE’s pipeline and clinical interfaces, powered by GOR, give users the ability to go back to and visualize raw sequence data at a click. This approach enables genomic analysis and interpretation by seeking out disease-causing genetic variants, either in specific patients, or for research studies in a clinical setting.

Factor #3: Patient genomic information at the fingertips of the clinician

Problem: Many of today’s genomic interpretation tools are too complex and difficult to use by clinicians who may have minimal experience with genetic informatics tools.

Solution: All of the complex informatics required by a clinical analysis tool should disappear at the fingertips of a clinician. It starts by having a robust foundation to the informatics platform, and using the GOR database architecture enables rapid cycling between personal sequence data and broad clinical knowledge. The result is the Clinical Sequence Analyzer (CSA) in which clinicians can simply type in a patient’s symptoms, and CSA will search the patient’s whole genome for variants that may be relevant.

Factor #4: Applying the full power of whole-genome sequencing to cancer tumor analysis

Problem: Many of today’s approaches to the analysis of cancer genomes only look at the immediate next step for a course of treatment, an important capability but only part of a holistic view of a the genetic profile of a patient’s cancer and what can be done to fight it.

Solution: The Tumor Mutation Analyzer makes a more holistic approach possible, analyzing a whole exome or whole genome sequence from a patient’s own genome and from tumor cells. Comparing the two it is possible to isolate the variants likely to be cancer drivers. The distinguishing feature of TMA is the depth of the data it stores and the unprecedented level of detail it provides to more accurately identify variations. This level of detail is especially important in cancer genetics, where the chances of finding previously unknown variants are very high, and even if a mutation is successfully targeted with a course of treatment, another potential driver is often waiting in the wings.

The pace of progress has been astounding with advances in the use of genomic information for patient care. How will the path continue in the future? Stay tuned.


 

Personalized Medicine: The Future is Almost Here

The new era of personalized medicine.

The achievement of low-cost genome sequencing and the use of genomic data to better understand diseases are advancing the exciting new era of personalized medicine.

It’s been more than a decade since the human genome was first sequenced. Since then, we have been on the journey of applying this profound new discovery to create personalized medicine and advance human health.

Two significant triumphs along this human genome journey:

  • Using genomic data to better understand diseases; and
  • Achieving low-cost genome sequencing.

Each of these accomplishments has been a stepping stone into the exciting new era that is dawning now: where genomic information is becoming integrated into medical care.

Using Genomic Data to Better Understand Diseases

Let’s take a look back at the early days of using genomic data to connect the dots between genetic mutations and disease. From 1997-2004, I was part of the leadership team at deCODE, the Icelandic genomic company. This was the period when deCODE was building the world’s most productive human genomics platform, with a database of  tens of thousands of individuals who participated in genetic studies and including the largest database of genomes to this day. deCODE’s genomic engine was able to successfully identify the genetic variations associated with human disease. This resulted in dozens of groundbreaking discoveries that were published in major, peer-reviewed journals.

The legacy of deCODE was the creation of an industrialized platform capable of massive storage and analysis capabilities. This enabled researchers to crunch genomic data to gain insights about genetic variants, or risk factors, associated with many common diseases. deCODE’s premise was that once the genetics of disease was better understood that information could be used to create new ways to diagnose, treat and prevent disease. However, when I left deCODE in 2004, there were still barriers to overcome before this genomic information could be widely applied to the level of an individual patient. Chief among them was that the cost of genome sequencing was still prohibitively high. (deCODE was subsequently acquired by Amgen).

Achieving Low-Cost Genome Sequencing

Back in 2004, the cost to sequence a single human genome was hundreds of thousands of dollars. Today that cost is a few thousand dollars (and, in fact, fast approaching $1,000) for a whole genome sequence. DNA sequencing costs continue to fall, as speed and accuracy increase.

This means we are rapidly approaching a tipping point where, as the sequencing of human genomes becomes more economical, its adoption in the medical community becomes more widespread and genomic data can become more routine in medical care. This is why personalized medicine is becoming a reality.

The Era of Genome Sequencing in Medical Care

The steep drop in the costs of sequencing, combined with the explosion of research on gene variants and disease, mean the time is fast approaching when genome sequencing will become routine in medical care. Today, pathologists perform blood cultures to decide which antibiotics will stop a patient’s bacterial infection. Soon a patient sample can be taken to perform a genome sequencing to analyze the genetic characteristics of a patient to determine ways a disease can be prevented or, if they are sick, which treatments might work best for their disease.

The body of genomic knowledge and the large databank of human genomes built by pioneers like deCODE established the key building blocks that enable genome sequencing to have predictive power for individual patients. As more human genomes are sequenced and more genetic variants are associated with disease, the predictive power of knowing about risk genes and effective treatments for each patient – a.k.a. personalized medicine – will become an essential part of medical care.

Genome Sequencing Being Implemented by Medical Centers

In preparation for the future of personalized medicine, major medical centers in the U.S., Europe and Asia are actively beginning to install DNA sequencers and supercomputers as important tools for integrating genome sequencing into medical care. These medical centers are taking initial steps toward the routine sequencing of every patient’s genome to define the ideal course of prevention and treatment based on variants found in a patient’s genes.

Evidence of this adoption of genome sequencing by medical centers appeared in an article in The New York Times in April 2013 citing that:

  • Medical centers in New York City are spending more than $1 billion on new genomic research centers;
  • Several hospitals around the U.S. are undertaking systematic genome sequencing in patients;
  • Mount Sinai Medical Center has a program in which 24,000 patients participate in a biobank to include their DNA sequence and research over their lifetimes;
  • Memorial Sloan-Kettering Cancer Center sequenced 16,000 tumors from cancer patients in 2012; and
  • Phoenix Children’s Hospital opened a new institute in December 2012 to sequence the genomes of 30 percent of their childhood cancer patients.

For now, the use of whole genome sequencing in medical practice is still in its infancy, but the pace of progress continues to accelerate. Clearly, genome sequencing will soon become part of the nucleus of medical care. This will herald a new era in personalized medicine revolutionizing healthcare as we know it and transforming our lives. When do you think genome sequencing will become a part of the medical decisions in your life?