Tag Archives: whole-exome sequencing

Pioneering Rare Disease Diagnostics in China—An Interview with Fudan Children’s Hospital Clinicians at ASHG17

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The first year of WuXi NextCODE’s partnership with Fudan Children’s Hospital has delivered 11,000 clinical reports and a diagnosis rate of 33%, matching the throughput and success rate of the world’s leading laboratories.

One year ago, WuXi NextCODE (WXNC) and the Children’s Hospital of Fudan University (CHFU) launched a joint laboratory to put the global gold standard in sequence-based rare disease diagnostics at the service of patients in China. In the first year of that joint effort, the partnership delivered some 11,000 clinical reports—with more than 1,000 new reports now being generated each month—and a diagnosis rate of 33%. This matches the throughput and success rate of the leading laboratories in the world.

Dr Lin Yang of CHFU presented a summary of this remarkable progress at WXNC’s breakfast session on rare disease at the ASHG17 meeting held recently in Orlando. Afterwards, WXNC’s global communications lead, Edward Farmer, sat down to talk about this collaboration and what it means for patients, with Associate Professor and Laboratory Director Dr. Huijin Wang from the clinical team; Dr. Bing Bing Wu, director of the medical diagnostics laboratory; and Assistant Professor Dr Xinran Dong, who leads the bioinformatics team, as well as WXNC Chief Scientific Officer, Jeff Gulcher.

Edward Farmer: It’s a real pleasure to have with us our colleagues from Fudan and to be able to hear about this collaboration in rare disease diagnostics and genome-based testing in the neonatal unit. To start us off, Jeff, can you tell us how this partnership came about and how you see its importance to rare disease diagnostics in China and worldwide?

Jeff Gulcher: It’s been a fantastic partnership that started about two-and-a-half years ago, when we began discussing the possibility of creating a joint laboratory. The aim was to take advantage of WXNC’s technology and sequencing expertise together with Fudan’s expertise, both on the medical side and the interpretation side. The goal was to enable whole exome or medical exome sequencing of very sick children. In parallel, we decided to see if sequencing is useful in the neonatal ICU setting.

Through this partnership, we’ve now sequenced a large number of children and worked together to make diagnoses. Our medical genetics teams have worked closely together to interpret these cases, and in about one-third of the cases, we’ve been able to deliver diagnoses that were not suspected by the treating physician. In many cases, that has led to different treatments, with better outcomes for the children.

Together, we have now sequenced over 11,000 pediatric cases, including some 2,500 neonatal ICU cases, and we are very pleased with this partnership.

Edward Farmer: We have with us several senior people from Fudan. Huijin, let’s begin with you, as director of our joint laboratory. Can you share with us your impressions of this partnership so far and some of your results?

Huijin Wang: We have had a very good experience with this collaboration. We have many cases and, each week, we have a case meeting with the Cambridge WXNC team and we discuss the data and variant curation for the more difficult ones. The results have been impactful for the patients. In many cases, we can deliver a clinical diagnosis, and some of these offer real treatment options.

I remember one case that first came to the neurological clinic with seizures and hypoglycemia. This child had presented with recurrent hypoglycemia at a very early age and was in the NICU. We sequenced the family and found a recessive variant in the FBP1 gene, which the patient had inherited from both parents.

After this diagnosis, the doctor was able to discuss the problem with the family and advise them on how to limit the child’s diet to avoid hypoglycemia. The child is now doing well and no longer experiencing hypoglycemic episodes. And his family came back later and planned to have another child, and we referred them for prenatal diagnosis, and they were able to have another child who is healthy. This was a very successful case and is the sort of story that encourages us and shows us the value of the work we are doing.

Edward Farmer: That is an encouraging result. Lin, as an attending physician, how do you see the impact of introducing this technology into China at scale?

Lin Yang: We have more and more children at our hospital with birth defects or congenital malformations, so we really want to get a diagnosis and whatever possible treatment for them, including new treatments when available.

The collaboration between our hospital and WXNC starts with us deciding whether the case is likely to be the result of a genetic disorder. If it is, we do pre-testing counseling for the whole family before taking DNA samples. We then use WXNC’s capabilities for the sequencing and analysis of the results. Finally, we need to interpret the sequencing results and report them to the parents. It is often very difficult for parents to understand “what is a gene,” “what is a mutation,” “what is the disorder,” and “how can your child benefit from a molecular diagnosis?” So that is a critical part of our work.

But more and more patients are choosing molecular diagnosis and, if they get a correct diagnosis early, they may find a useful and more targeted treatment earlier.

In the NICU, we have some patients that have immune deficiency disorders. These can be very serious conditions, as the children suffer from repeated infections. It is very hard on the whole family. For such cases, if you have a specific diagnosis, there is often a cure. This is very good news for these families in the NICU, as they now have the possibility of getting a molecular diagnosis and then a treatment.

Edward Farmer: Are there any specific examples or cases that you can share with us?

Lin Yang: I had a newborn patient who had very low platelet counts and petachiae (red spots from small bleeds) on his face and body. We found that he has a mutation in the WAS gene, inherited from his mother’s side of the family, which means that his bone marrow is not producing enough platelets. But with a hematopoietic cell transplantation [HCT, which can include bone marrow] from a relative or closely matched donor, he has every chance of being cured of the disease and becoming a healthy boy. He is now waiting for a matched donor.

Edward Farmer: Huijin, you’ve done amazing work so far, and I know you are only getting started, but I wonder what proportion of the patients you see are able to benefit from the work of your lab and the collaboration with WXNC?

Huijin Wang: Currently, we are delivering a diagnosis to about 30% of patients, and we are able to recommend specific clinical treatment for about 20% of our patients.

And very often, we can give some guidance, if not a cure. Sometimes just knowing exactly what the diagnosis is gives patients peace of mind and new options. For example, many can go to a specialty clinic. But just knowing the diagnosis is often a comfort.

There is also a big need, and as a national center of excellence our diagnostics can help people across the country. About 80% of our patients come from outside of Shanghai, so with a diagnosis, they can go back to where they live and take some action there.

Lin Yang: There is also a difference among different diseases. I think we are now able to provide actionable results to about 50% of patients with neuromuscular disorders, and for respiratory maybe something less than that. For NICU, it’s maybe 15% that get a diagnosis, but we want to boost all of these.

We can benefit many more patients with this technology. In our hospital and with the WXNC collaboration, we can see an increasing number of patients. But there are a lot of undiagnosed patients, and in many places, there is not yet access to molecular diagnostics, so we hope this capability spreads to other parts of the country as well.

Edward Farmer: And Xinran, as we’re talking about building the scale and reach of molecular diagnostics, perhaps you can tell us a bit about how you are dealing with all of this data.

Xinran Dong: We have collected a lot of data. And from my bioinformatics perspective, one of the things that the WXNC collaboration is helping us to do is to make good use of the data, both for our clinical cases and for research.

I see part of my job as helping to build this into one of the biggest databases on rare disease in China and maybe the world. This is going to help patients today and advance the discovery of new genes.

Edward Farmer: Clearly there is no lack of ambition here. I want to thank you all for your time, and we look forward to sharing more stories of our work together.

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As Cancer Databases Grow, A Global Platform Leaps the Big Data Hurdle

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As massive cancer databases like The Cancer Genome Atlas (TCGA) proliferate and expand worldwide, WuXi NextCODE expects to see—and to drive—a boom in discoveries of cancer biomarkers that will advance our ability to treat cancer and improve outcomes for patients.

One of the fastest-growing areas in medicine today is the creation of massive cancer databases. Their aim is to provide the scale of data required to unravel the complexity and heterogeneity of cancer—the key to getting patients more precise diagnoses faster, and to getting them the best treatments for their particular disease.

In short, this data has the potential to save lives.

Such databases are not new, but they are now proliferating and expanding at an unprecedented pace. Driven by governments, hospitals, and pharmaceutical companies, they catalogue a growing range of genetic data and biomarkers together with clinical information about their effects on disease, therapy, and outcomes.

Only with such data can we answer the key questions: Does a certain marker suggest that a cancer will be especially aggressive? Does it signal that the tumor responds best to particular treatments? Are there new pathways involved in particular cancers that we can target to develop new drugs?

It’s the cutting edge of oncology, but to be powered to answer these questions, these databases have to be very, very big. They have to bring together whole-genome sequence data on patients and their tumors as well as a host of other ‘omics and biological data. One of the biggest challenges to realizing this potential is to manage and analyze datasets of that scale around the world. It’s one we are addressing in a unique manner through our global platform.

One of the most renowned and widely used of these is The Cancer Genome Atlas, a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI). TCGA data is freely available to those who qualify, and there is a lot of it. It already comprises 2.5 petabytes of data describing tumor tissue and matched normal tissues for 33 tumor types from more than 11,000 patients. Researchers all over the world can apply to use this data for their own studies, and many have.

Yet asking questions of TCGA alone can take months for most groups and requires sophisticated tools. At Boston’s recent Bio-IT World conference, WuXi NextCODE’s director of tumor product development, Jim Lund, explained how we have put TCGA on our global platform—providing a turnkey solution with integrated analytics to transform the data into valuable findings.

Jim and his team have imported into WuXi NextCODE’s cloud platform virtually all key TCGA data: raw whole exome sequence data from patients and tumors, as well as variant calls using MuTect2 and Varscan2; RNA and microRNA sequence and expression data; and data on copy number variation, methylation arrays, and some 150 different clinical attributes. But this data isn’t just hosted in the cloud: it can all now be queried directly and at high speed online, enabling researchers to quickly ask and answer highly complex questions without having to download any data or provide their own bioinformatics software.

To demonstrate the power of this approach, Jim’s team decided to run the same queries in a recent published study that looked at sequence data from the exons of 173 genes in 2,433 primary breast tumors (Pereira et al., Nature 2016). They were specifically looking for driver mutations of cancer’s spread and growth. In a matter of minutes, rather than months, they were able to replicate key mutations identified in the study. That analysis was then extended to all cancer genes, and additional driver genes were found. More important, because they were able to correlate these mutations with clinical outcomes data, they were also able to begin systematically matching specific mutation patterns to patient outcomes.

Next, Jim’s team looked at the genomics of lung adenocarcinoma, the leading cause of death from cancer worldwide. Following up on the findings in another published study (Collison et al., Nature 2014), they profiled the 230 samples examined in the paper and immediately made several observations. Eighteen genes were mutated in a significant number of samples; EGFR mutations (which are well known) were more common in samples from women; and RBM10 mutations were more common in samples from men. These results were extended to 613 samples and shown to be robust. But because they had a wide range of data including mRNA, microRNA, DNA sequencing, and methylation, Jim’s team was further able to suggest some actual biological processes that may be fueling the origin and growth of lung adenocarcinomas.

What’s making this type of research possible? It’s our global platform for genomic data. The platform spans everything required to make the genome useful for helping patients around the world, from CLIA/CAP sequencing to the world’s most widely used system for organizing, mining, and sharing large genomic datasets. At its heart is our database—the Genomically Ordered Relational database (GORdb). Because it references sequence data according to its position on the genome, it makes queries of tens of thousands of samples computationally efficient, enabling the fast, online mining of vast datasets stored in multiple locations.

That’s how we are making the TCGA—and every major reference dataset in the world—available and directly minable by any researcher using our platform. Those users can combine all that data with their own to conduct original research at massive scale.

These breast and lung cancer studies are just two of more than a thousand that have been carried out so far on TCGA data. As more such datasets become available, we expect to see—and to drive—a boom in discoveries of cancer markers that will advance our ability to treat cancer and improve outcomes for patients. For those who want to go further still, our proprietary DeepCODE AI tools offer a means of layering in even more datasets to drive insights even deeper into the biology of cancer and other diseases. And that’s a topic I’ll return to in the weeks ahead.

Four Factors for Improving Genomic Data for Personalized Medicine

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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.