WuXi NextCODE Takes on Cancer: Breakthroughs and Innovation in Sequencing using TCGA and AI

Hannes Smarason NextCODE TCGA cancer

Sequencing reads of a sample prepared by the traditional whole-genome sequencing workflow for fresh-frozen samples and data generated using the SeqPlus whole-genome FFPE method. The green and purple indicate reads sequenced in the forward and reverse directions, respectively, and yellow represents bases with non-reference sequence. The center of the image shows a C to A mutation in each of the tumor samples.

Cancer is one of the most active fields in genomics, spurring mountains of research papers and scores of clinical trials. WuXi NextCODE (WXNC) is committed to pushing this field forward and so we had a special “Genomes for Breakfast” session devoted to this topic at the recent ASHG17 event. Featured talks addressed our pathbreaking work in how to extract impactful findings from the renowned TCGA dataset; get better sequencing results from FFPE samples; and apply deep learning to drug discovery, drug repurposing, and identifying subtypes for diagnostics and clinical trials.

The Cancer Genome Atlas (TCGA) is one of the most useful public genomic cancer databases available and has already led to numerous critical discoveries, including entirely new drug targets as well as better insights into tumor origination, development, and spread. It includes data from approximately 11,000 patients and covers 33 cancer types. Data types include WES, RNA-Seq, mi RNA, CNV, Methylation array, and clinical sample data. The data is big and complex, and can include multiple samples from one patient, which is crucial to know when doing analyses.

During his ASHG talk, Jim Lund, WXNC’s Director of Tumor Product Development, shared some insights into how we put this rich data source to work in concert with our own unique data and analytical tools, in a process he dubs “multiomics analysis.” He described how we specially process the data and use our unique analytical platform to help scientists find just what they are looking for. Researchers can search the data by cancer type, age of diagnosis, sex, ethnicity, year of diagnosis, sample type (e.g. metastatic, new primary), and more.

Multiple pivotal studies using this dataset have already been published, including some examining the prevalence of specific mutations across human cancer types as well as in-depth profiling of specific tumors, such as breast cancer and lung adenocarcinoma. Layering different types of data, such as reads from DNA and RNA, allows much more accurate detection of features such as variants with allele-specific effects on gene expression. The user-friendly but sophisticated data interface makes it easier to see such findings. Over the years, our own database and our capabilities have both grown exponentially, creating a powerful tool for multiomics cancer research. You can see Jim putting the portal through its paces in a recent webinar.

In his talk, Shannon Bailey described how Whole Genome Sequencing (WGS) can be applied to formalin-fixed paraffin-embedded (FFPE) tumor samples, which are stored by the hundreds of thousands in repositories around the world. Shannon is the Associate Director of our Cancer Genetics division. He pointed out that while these samples are abundant and often paired with extensive clinical and outcome data, there are specific hurdles to using these for the type of large-scale retrospective studies many groups are eager to carry out.

For one thing the genetic material in such samples can be degraded, crosslinked, or in low quantities. Of all these problems, the biggest issue is getting sufficient quantity of quality DNA for sequencing. Numerous studies have found that these types of samples are difficult to work with and often provide very low success rates for gene sequencing studies. Clearly, fresh frozen samples provide much better results, but they are also much harder to obtain.

In response, our team has developed the WXNC SeqPlus FFPE extraction method, which provides substantially improved coverage compared to traditional methods and even approximates the results obtained with fresh frozen samples at 10X depth, with similar numbers of heterozygous and homozygous calls.

We tested SeqPlus in a study that comprised 516 tumor-normal pairs (i.e., 1,032 samples) that had been stored for 3 to 6 years. The targeted sequencing depth was 30X for the normal tissue and 70X for tumor tissue. The starting amount of DNA was 400 ng. The results were excellent, with SeqPlus delivering a coverage analysis just about 1% below what the fresh frozen control samples achieved. Further, a comparison of our analyses to results from the TCGA, using fresh frozen samples, showed striking similarity. These study results give us confidence that SeqPlus is a new “power tool” for FFPE sequencing studies. This webinar describes the process.

Sequencing reads of a sample prepared by the traditional whole-genome sequencing workflow for fresh-frozen samples and data generated using the SeqPlus whole-genome FFPE method. The green and purple indicate reads sequenced in the forward and reverse directions, respectively, and yellow represents bases with non-reference sequence. The center of the image shows a C to A mutation in each of the tumor samples.

Another area of great interest at WXNC is artificial intelligence (AI). We have been pioneers in AI for pulling novel insights out of massive multiple datasets. Leading this effort is Tom Chittenden, our Vice President of Statistical Sciences, Founding Director of the Advanced AI Research Labs, and a Lecturer on Pediatrics and Biological Engineering at Harvard Medical School and MIT. He also spoke at the breakfast series.

Our AI capabilities improve the tools we have and expand their capabilities. For example, using our AI tools, we can improve functional annotation of missense variants to an accuracy of >99%, integrate multiple types of data to discover new genes and elaborate pathways, and improve tumor subtype and drug-response classification accuracy by combining DNA- and RNA-seq, among other data types. These tools can be used for such varied purposes as target discovery, drug repurposing, and defining responders and non-responders in clinical trials.

We’ve already helped to develop breakthrough results, such as identifying an intriguing new target for both cardiovascular and cancer drug discovery. We’ve also classified breast and lung cancer subtypes with 97% to 100% accuracy, classified 8,200 tumors of 22 TCGA cancer types with >99% accuracy, and discovered a completely novel pan-cancer molecular survival signature.

The power of our deepCODE AI tools is in part thanks to a novel, causal statistical-learning method and deep-learning classification strategy. But another advantage is that they were built on our global platform for genomic data, which underpins the majority of the world’s largest genomics efforts and includes all major global reference databases. Our database stores, manages, and integrates any type of genomic data and correlates it with phenotype, ‘omics’, biology, outcome, and virtually any other type of data that may be relevant to a particular medical challenge.

If you want to know more, I recently gave an interview to WXpress outlining WXNC’s AI strategy. As we continue to deepen our commitment to this field, I’m sure we’ll have more exciting results to share.

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News Flash: Key Advances in Big Genomics from WuXi NextCODE Highlighted

Jeff Gulcher, CSO and co-founder of WuXi NextCODE

WuXi NextCODE‘s CSO and co-founder, Jeff Gulcher, spoke with Frontline Genomics at this year’s ASHG meeting about our recent breakthrough with FFPE sequencing, advancing toward using AI to diagnose cancer, how we are integrating complex datasets, and the importance of having a global platform. Here is a link to that article.

WuXi NextCODE Wins Scrip Award: As Reported in Our New Blog

WuXi NextCODE wins Scrip award

WuXi NextCODE was awarded the 2017 Scrip Award for Best Specialist CRO for enabling the pharmaceutical industry to take advantage of genomics. Dr Mark Hughes (middle), WuXi NextCODE’s Business Development Director for Europe, accepted the award on behalf of the company.

Below is news about our recent Scrip Award for Best Specialist CRO. This story is from our new WuXi NextCODE blog.

We’re thrilled to share that Scrip, the global biopharmaceutical industry news and analysis service, has named WuXi NextCODE as specialist contract research organization of the year for 2017.

The award, announced at the 13th annual Scrip Awards dinner in London, singled out WuXi NextCODE’s unique Contract Genomics Organization (CGO) capabilities for putting the full power of our world-leading genomics platform into the hands of biotechnology and pharmaceutical companies around the world.

WuXi NextCODE was selected from among many leading service providers and half a dozen finalists by an expert panel of judges comprised of independent, senior industry experts from around the world.

“Our aim is to enable anyone to use the genome to the benefit of people everywhere, so it is particularly gratifying to see our work recognized by the industry that creates new medicine,” said Hannes Smarason, CEO of WuXi NextCODE.

“We have built our CGO business to make it possible for organizations around the world to access our comprehensive range of best-in-class capabilities in genomics. We make it possible for biotechnology and pharmaceutical companies to take advantage of the best technology and largest global datasets, and to do so precisely according to their needs rather than a major fixed cost.”

WuXi NextCODE Solutions
WuXi NextCODE’s global platform is unique in offering truly comprehensive and integrated solutions for sequencing and querying genomic Big Data. Solutions include:

  • Sourcing global cohorts
  • Experimental and trial design
  • Research and clinical DNA sequencing
  • Access to large datasets for diagnostics and research
  • State-of-the-art genomic sequencing of FFPE samples
  • The unrivalled GOR database management system
  • Clinical interpretation and case-control research analytics
  • AI and deep learning

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

wuxi-nextcode-fudan-university

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.

From Rare to Common: How Rare Diseases Could Advance Schizophrenia Treatment

Rapidly advancing our understanding of rare diseases is a key area of focus for us at WuXiNextCODE. We believe genomics can both transform our ability to understand and diagnose rare conditions, and that this is going to point us is the direction of developing new treatments. At the same time, there is a growing body of evidence and even approved new therapies that show that an understanding of rare diseases can also shed new light on the genetics of complex diseases, such as heart disease, arthritis, and schizophrenia.

Understanding complex diseases is a mammoth challenge because multiple genes are usually involved as well as environmental factors. It’s particularly hard with neurologic conditions. No animal models can really mimic what happens in people’s brains, and human studies usually only provide hints of the information needed to identify potential treatments.

But rare diseases are often caused by single variants that perturb specific and identifiable biological pathways. That’s why recent genetic studies of rare types of early-onset psychosis have inspired so much interest among researchers studying schizophrenia. This disease affects more than 50 million people worldwide, but early-onset cases are very rare, suggesting they may be extreme manifestations.

A new line of inquiry into this condition emerged after a group of our close collaborators at Boston Children’s Hospital, including a scientist now at WuXi NextCODE, used chromosomal microarray analysis and whole exome sequencing in a six-year-old with profound symptoms of psychosis. They discovered this patient had a variation in the ATP1A3 gene, which was not previously associated with schizophrenic symptoms. The team wondered: was that mutation helping cause his symptoms? Would the same mutation be found in other children with early-onset schizophrenia? Could this new lead point to a biological pathway common to many people, young and old, with these same symptoms?

That would be a real breakthrough, both for this child and potentially for many other people.

The Puzzle of Schizophrenia Genetics

Schizophrenia is one of the most serious and common mental illnesses. It is often very difficult to treat, in part because of available drugs’ side effects. There are already about a dozen anti-psychotics on the market for this condition. Besides causing serious side effects, treatment must also usually be life-long. Doctors often have to try different drugs until they find something that works and which the patient can tolerate. Even then, the patient’s response can change over time.

The genetics of the disease are still not well understood. Studies of families and populations show it is heritable – the more affected close relatives someone has, the more likely that person will develop it. Many families are afflicted by both schizophrenia and bipolar disease, suggesting the two conditions are biologically related.  Both conditions seem to be associated with multiple mutations to possibly dozens of genes. Still, even in identical twins – who share exactly the same mutations – it’s not uncommon for only one twin to be affected.  Clearly, there is something other than genes afoot.

Scientists, notably including our colleagues at deCODE genetics, have put their fingers on a few genes and key pathways. Another large genomic study, with more than 30,000 cases and 100,000 controls, pointed to over 100 potential spots in the genome with mutations associated with schizophrenia. Both have found an association with mutations in a region called MHC (Major Histocompatibility Complex), a result that reinforced a then percolating idea that schizophrenia might be related to immune dysfunction.  And then just this week, Chinese researchers reported a new trove of suggestive genetic factors. But despite these massive gene hunts, we are still far from a complete picture of what genes cause this disease and how.

A Promising New Lead?

As described in the BCH blog Vector, The BCH team who found that ATp1A3 mutation in the six-year-old boy decided to do some more digging. The chromosomal microarray analysis showed that he was missing an entire chunk of DNA – one copy of the chromosomal region 16p13.11.  Next, they searched their database and found several other children with variations in that area.  One had a duplication of the 16p13.11 region, rather than a deletion. She had started experiencing hallucinations at the age of 4.  Those findings prompted the BCH researchers to launch a large-scale study, which has already enrolled at least 50 children with early-onset psychosis and will be able to leverage WuXi NextCODE’s informatics and global knowledgebase to find more cases, at BCH and beyond.

The researchers hope that ultimately their studies will not only help children with early-onset schizophrenia but also point to the biological pathways that cause the more prevalent form of the disease, which usually strikes adolescents and young adults.

Such research will hopefully provide firm leads on novel pathways that can be used to identify new drug targets. There is a tremendous need for new medicines. Most of the antipsychotic drugs we have today were developed back in the 1950s and act on the dopamine and/or serotonin receptors. They don’t improve all of patients’ symptoms, and as noted earlier, they can have serious side effects.

By uncovering new biological pathways, groups like the researchers at BCH, able to leverage massive global genomic data like that we are able to provide, aim to uncover such targets and begin the journey to providing better options for patients with rare and common diseases alike.

If you are attending ASHG this month, join us to hear more about how rare disease studies can inform our understanding of common diseases at two of our “Genomes for Breakfast” events:  Using Population Genomics to Understand Common and Rare Disease (Oct. 18), and Using NGS to Diagnose Rare Disease – Experiences from three continents (Oct. 19).

 

 

 

New Breast Cancer Study Underscores the Need for More Sequencing

Gene sequencing for breast cancer. More than the usual suspects at play.

Ever since actress Angelina Jolie’s highly publicized preventive mastectomy ignited discussion about BRCA 1 and BRCA2, almost everyone has heard about these genes and how they can increase risk of breast cancer.  Some people even refer to them as “the breast cancer genes.” But how genes cause this disease is much more complicated than just through the most well known BRCA mutations, as a recent study in JAMA of Ashkenazi Jewish women has demonstrated. http://jamanetwork.com/journals/jamaoncology/fullarticle/2644652

This intriguing study raises a crucial question: How much sequencing is enough when diagnosing breast cancer in the age of targeted therapies? The number of these therapies keeps growing, as does our knowledge of the links between what drugs work for women with particular mutations. But at what point should we say we have uncovered enough mutations to make a proper diagnosis? And in a field in which we know there’s a lot we don’t know, is there such a thing as enough?

The good thing is that sequencing costs are going down. “It used to be that just testing for a single gene cost several thousand dollars,” says Jim Lund, Director of Tumor Product Development at WuXi NextCODE.  “Now a panel of genes costs that and whole exome sequencing is slightly more.” At the same time, the number of mutations that are discovered and studied is increasing – in the genomes of patients and the genomes of their tumors.

The data here has a message about data itself: in principle, we should be generating as much sequencing data as possible. By generating it, storing it for vast numbers of patients and their healthy relatives, creating more comprehensive databases of all disease-linked variants, and then reanalyzing patient and tumor samples as more is learned, we can improve the risk assessment and the speed and accuracy of diagnosis for patients everywhere. Since we can do this, the question isn’t whether we can afford to do more sequencing, but why anyone would argue that we can afford not to.

The researchers who led the recent JAMA study used multiplex genomic sequencing on breast tumor samples from 1007 patients. They tested for a total of 23 known and candidate genes.  It has been well documented that women of Ashkenazi descent have a higher risk of breast and ovarian cancer, and that is at least in part because of three particular BRCA1 and BRCA2 mutations. These are called founder mutations, because they probably originated among some of the earliest members of this ethnic group, and have been propagated because of a strong history of marriage within the same community.

But the researchers working on this study wanted to know if there were mutations in other genes besides BRCA that made it more likely these particular women would develop breast cancer. The patients were from 12 major cancer centers; had a first diagnosis of invasive breast cancer; self-identified as having Ashkenazi Jewish ancestry; and had all participated in the New York Breast Cancer Study (NYBCS).

Surprisingly, only 104 of the patients were carrying one of the infamous founder alleles. Seven patients had non-founder mutations in BRCA1 or BRCA2, and 31 had mutations in other genes linked to increased risk of breast cancer, including CHEK2. The vast majority of these women carried none of the mutations that are “obvious suspects” for breast cancer. “We do not know why those women got breast cancer,” says Shannon T. Bailey, Associate Director of Cancer Genetics at WuXi NextCODE.

It’s important to note that thousands of different cancer-predisposing mutations have been found in BRCA1 and BRCA2 alone. Every population studied to date includes people with such mutations.  The three founder mutations that have been established as being common among Ashkenazis are estimated to account for about 10% of breast cancers in this group. The rest of BRCA1 and BRCA2 mutations are considered extremely rare under any circumstances.

“If you look at the genes on the panel used in this study, it looks as if they are mostly associated with DNA damage and there are no cell cycle regulating genes included,” says Bailey. “But there are all kinds of mutations that cause breast cancer, even in noncoding regulatory zones.” As a result, even the best designed panel may fall short.

That’s why this study is so important. It tells us that even with founder mutations, family history matters but it doesn’t yet always tell you everything you’d like to know. Of the women with the founder BRCA mutations, only about half had a mother or sister with breast or ovarian cancer.  It’s also already well known that just carrying a BRCA1 or BRCA2 mutation is no guarantee the patient will get cancer. For reasons we don’t yet understand, these mutations raise overall risk, but not everyone who carries one will develop the disease. So while BRCA mutations are important, we need lots more information about other genes too.

The authors of this JAMA report suggest that Ashkenazi patients with breast cancers should have “comprehensive sequencing,” including, perhaps, complete sequencing of BRCA1 and BRCA2 and possibly testing for other breast cancer genes as well.

And what about other patients?  WuXi NextCODE’s Lund points out that even the most highly regarded recommendations for breast cancer testing do not cite specific panels. Those recommendations come from the U.S. Government Task Force [https://www.uspreventiveservicestaskforce.org/Page/Document/RecommendationStatementFinal/brca-related-cancer-risk-assessment-genetic-counseling-and-genetic-testing] and the NCCN Clinical Practice Guidelines. Women with a family history will likely get more comprehensive testing, but beyond that it is not clear exactly how to proceed in every case.

At WuXi NextCODE we believe that this is clear evidence pointing to the value of doing more sequencing across all ethnic groups – for healthy individuals, patients, and their tumors, and pushing towards sequencing as standard of care. This would expand our knowledge of the genetic risk factors for breast and other cancers; provide vast new cohorts for research; and deliver the most actionable insights to patients, from risk assessment through diagnosis and then ongoing as new discoveries are made.

All of the participants in this JAMA study consented to have their sequence data used to advance research. They are already helping to do that, and this is just one study of thousands that are now underway and that are helping us to expand our data- and knowledgebases with the ultimate aim of delivering even better outcomes for all people and patients everywhere.

Let’s Speed the Genomic Revolution, UK CMO Says

Sally Davies genomics

Whatever path various societies take to tap the power of the genome to improve human health, a recent report from England’s Chief Medical Officer, Dame Sally Davies, calls out key elements for realizing that future sooner rather than later.

England’s Chief Medical Officer wants to build on the success of Genomics England’s 100,000 Genomes Project and take her country swiftly into the age of precision medicine. The goal is to get patients optimal treatment more quickly and with fewer side effects. That means using genomics to more accurately guide prescribing—initially for cancer, infections, and rare diseases—but increasingly for all conditions and overall wellness and prevention.

Dame Sally Davies’ vision is anchored in the work that Genomics England is engaged in today and to which WuXi NextCODE and other leading genomics organizations have contributed. It’s a rallying cry that many voices are joining and underpins our work not only in England, but also similar efforts we are helping to advance in countries near and far, from Ireland to Singapore.

Her call is particularly forceful in three areas that she rightly singles out as critical to realizing the potential of precision medicine to revolutionize healthcare:

  • Industrial scale: Genomics has in many ways been treated and developed as a “cottage industry,” yielding important advances. But the need is massive scale in the era of population health (e.g., whole-genome sequencing, or WGS).
  • Privacy AND data sharing: Dame Sally wants to provide and ensure high standards of privacy protection for genomic data but is adamant that this should not come at the price of stifling the data sharing and large-scale collaboration that will transform medical care and many patients’ lives. She wants to move beyond “genetic exceptionalism,” which holds that genomic data is fundamentally different or more valuable than other data. Like other sensitive data, we can protect genomic data well and use it for public benefit.
  • Public engagement: She calls for a new “social contract” in which we, as individuals and members of society, recognize that all of us will benefit if we allow data about our genomes to be studied. That holds whether we are talking within our own countries or globally.

In England, as elsewhere, these shifts require the input of political leaders, regulators, and a range of healthcare professionals, including researchers as well as care providers. Crucially, such a transformation also requires a level of commitment on the part of patients throughout the National Health Service (NHS) and citizenry in general. If England takes this bold step forward, it could have tremendous effects. But “NHS must act fast to keep its place at the forefront of global science,” said Davies. “This technology has the potential to change medicine forever.”

To date, more than 30,000 people have had their genomes sequenced as part of the 100,000 Genomes Project. But there are 55 million people in the UK, and Dame Sally would like to see genomic testing become as normal as blood tests and biopsies for cancer patients: She wants to “democratize” genomic medicine, making it available to every patient that needs it.

We share and are, indeed, taking part in helping to realize much of Dame Sally’s vision as we work to accelerate Genomics England’s work and engage with our partners globally. As we know, different societies have different models of healthcare and different approaches to research and care delivery. But the ability for people anywhere to tap into the power of the genome to improve their health is at the very core of our own mission as an organization, and we applaud Dame Sally for calling out some of the key elements for realizing that future sooner rather than later.

Whatever path different societies choose to follow toward precision medicine, her recent report provides one enlightening view of a starting point for making the leap.

Domain Expertise: Jumpstarting Artificial Intelligence in Biomedicine

Is artificial intelligence the “single most transformative technology in modern history?” That’s the view of Tom Chittenden, who leads WuXiNextCODE’s AI program. And Tom is not alone in his enthusiasm, as numerous analysts are predicting this technology will be one of the fastest growing fields in the world.

In recent talks at Boston’s BioIT World and the EmTech conference in Hong Kong, Tom described some of the strides we’ve been making with our DeepCODE AI tools. Their power is in part thanks to a novel, causal statistical-learning method and deep-learning classification strategy. But another advantage is that they were built on—and are extending the reach of—our global platform for genomic data. That means that Tom’s team has that rare combination of both of the key ingredients to AI making an impact in biomedicine: cutting-edge algorithms AND deep domain expertise and access to the biggest datasets.

Tom—who also holds appointments at Harvard, MIT, and Boston Children’s Hospital—and his growing team have the former in spades; our platform and expertise in genomics provide a key edge in the latter. Our platform has been built over more than 20 years and today underpins the majority of the world’s largest genomics efforts and includes all major global reference databases. It stores, manages, and integrates any type of genomic data and correlates it with phenotype, ‘omics’, biology, outcome, and virtually any other type of data that may be relevant to a particular medical challenge.

That means that we can routinely train and test our AI tools on some of the most comprehensive data sets in the world, such as that in The Cancer Genome Atlas (TCGA). “Today we can take ‘omics data and clinical information and map those to curated resources such as SNOMED CT and biomedical ontologies, and then use AI to identify patterns that lead us to novel findings,” Tom says.

This is a powerful approach to tease out which of hundreds of genetic variants are really involved in a particular disease, based on which ones are actually associated with aberrant expression pathways. You may find hundreds of genetic mutations in a single type of breast cancer tumor, for example, but it is determining which ones are drivers of the disease that matters.

Put simply, AI can lead us to both better diagnoses and easier discovery of more and better drug targets, by taking a range of genomic data and marrying it to clinical information and scientific knowledge. AI is not just going to better match patients to the right drugs, it is going to help further our understanding of the relationships between genes and complex molecular signaling networks, one of the most challenging arenas in our field and the most sought-after starting point for discovering validated pathways and targets.

Valuable insights in real-world medical challenges are already emerging from this AI effort uniquely developed on and applied to the genomic and medical data that counts.

WuXi NextCODE  recently presented preliminary data from analyses using our novel AI technology to diagnose subtypes of tumors. Our DeepCODE tools were validated on six patient-derived tumor xenografts from mouse models, and then tested against approximately 8,200 human tumors from a collection of 22 cancer types in The National Cancer Institute’s TCGA collection. That study included five ‘omics data types. We achieved 98% accuracy overall, and our analyses of human breast and lung cancer subtypes were accurate in 96% and 99% of cases, respectively. This points to an improvement over current methods for matching patients to treatments for their particular cancer, and we have refined that accuracy further still. This capability is also going to be central to the development of liquid biopsies.

http://hannessmarason.com/blog/2017/04/04/a-perfect-pairing-ai-and-precision-medicine/

In another oncology study, using the same multi-omics data, DeepCODE identified a signal predictive of survival across 21 cancers, pointing to novel and holistic pathways for developing broad oncotherapies.

A recent study published in Nature, meanwhile, describes a potential new role for a well-known growth factor. That report, led by Yale University scientist Michael Simons, looked at blood vessel growth regulation—a crucial process in some very common conditions, including cardiovascular disease and cancer. Our Shanghai team provided RNA sequencing for this study. Our Cambridge AI team drove some of the key insights pointing to novel disease mechanisms.

Simons’ team studied knockout mice, whose fibroblast growth factor (FGF) receptor genes were turned off. They proved, for the first time, that FGFs have a key role in blood vessel growth, uncovering some metabolic processes that were “a complete surprise,” according to scientists on the team. Further, they mapped out pathways that could help provide new drug leads.

http://hannessmarason.com/blog/2017/05/15/bringing-artificial-intelligence-cardiovascular-medicine-cancer-genomics-action/

Our AI team is just getting started. We’re looking forward to many more intriguing findings from this group as they leverage their expertise and massive amounts of the relevant data to improve medicine and healthcare.

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

cancer databases

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.

Genomics: Forging Patient-Centric Communities

patient-centric-communities-hannes-smarason

Genomics has become a foundation for virtual patient-centric communities involving patients, caregivers, clinicians, and researchers worldwide.

In recent years, genomics has become a foundation for virtual patient-centric communities – communities built on the Internet and through social media that:

  • Connect people touched by a disease or disorder; or
  • Reach out to broad populations affected by rare diseases, many of whom are undiagnosed.

These patient-centric communities are dedicated to sharing information and providing support in order to break down the barriers of isolation and uncertainty that can compromise care and adversely affect quality of life for patients and their families.  As we learn more about the genetic variations that contribute to diverse conditions, virtual communities that are fueled by genomics contribute an ever-expanding resource.

Virtual communities have greatly affected patients and caregivers worldwide, and the relationships forged through genomics are essential to clinicians and researchers as well.  Genomics not only serves to link patients to each other but also to connect those patients to research initiatives that use genetic sequencing to diagnose conditions and guide treatment, thus improving patient outcomes today while influencing research for better therapies tomorrow.

RareConnect, for example, is an online platform that connects patients, caregivers, clinicians, and researchers in more than eighty disease-specific communities.  Another leader in this arena is PatientsLikeMe, which has activities that encompass more than 400,000 members with over 2,500 conditions.  Many diseases and conditions are identified by genetic abnormalities or characteristics.  Participants in RareConnect, PatientsLikeMe, and similar sites are drawn in part to the ways in which genomics could contribute to an accurate diagnosis, a novel treatment, and ultimately a cure.

The use of genomics to build communities has been especially important for rare diseases.  For patients and caregivers affected by the rarest of rare diseases – the disorders so rare that only a handful of known cases exist worldwide – the transformative role of genomics is that much more powerful.

An excellent article in The Atlantic tells the story of one young woman whose experience illustrates this phenomenon.  A genomic study identified the genetic mutation that underlies Lilly Grossman’s movement disorder.  The information provided by genomics has enabled the formation of a virtual community.

Lilly’s case has acted as a magnet for others with the same mutation. Families with the same problem read about Lilly’s case and contacted the Grossmans. Doctors and geneticists looked at their own patients and saw a new explanation behind puzzling symptoms. Before, there were isolated pockets of people around the world, dealing with their own problems, alone for all they knew. Now, there’s a community.

The connections forged through genomics are essential to patients, often children, and their caregivers, often families.  Genomics can provide the vital link, the piece of information that identifies individuals with similar experiences – the community of people who understand.  Patient-centric communities are one way in which the increasing availability of cost-effective genetic sequencing is transforming patient experiences, shortening diagnostic odysseys, and improving clinical care.

Many such communities are also critical for advocacy and fundraising.  Parent Project Muscular Dystrophy (PPMD), for instance, has worked effectively to promote Duchenne muscular dystrophy research and speed the discovery of potential treatments. PPMD has demonstrated how parents and caregivers can effect meaningful change, raising both awareness and financial resources – and even being a leading voice in support of FDA approval of therapeutics.

The intersection of genomics and social media increasingly drives progress, too. The Charlotte & Gwyneth Gray Foundation, for example, has raised an estimated $3.5 million to support CLN6-Batten disease research – through a crowdfunding initiative launched less than a year ago.

And coalitions of patient-centric communities can achieve significant advances through the power of numbers. Thus Genetic Alliance, a network of more than 10,000 organizations, was a key player in passage of the Genetic Information Nondiscrimination Act and in development of the National Patient-Centered Clinical Research Network.

Initiatives run the gamut from efforts to identify a handful of individuals with rare diseases to projects that aim to enroll thousands of participants.  Earlier this month, the University of Washington launched MyGene2, a site where families with rare conditions can publicly post their stories, establishing connections not only with those who share similar stories but also with clinicians and researchers.  At the other end of the spectrum, 23andMe has partnered with a number of Parkinson’s community groups on a project to gather genetic data from more than 11,000 individuals.

And, in the last year, the Simons Foundation Autism Research Initiative (SFARI) launched SPARK, a project to collect genomic information from 50,000 people with autism and their families.  At WuXi NextCODE we are delighted to participate in this endeavor by providing direct online access to the data.

Genomics has played a critical role in the evolution of patient-centric communities.  Groups that have developed resources and advice for patients and families are increasingly collaborating with clinicians and researchers.  Through voluntary contributions of personal knowledge – and genomic data – participants in patient communities are expanding the impact of genomics on medicine.  The growing power of virtual communities has facilitated numerous initiatives to improve patient outcomes through improved diagnosis, optimized standards of care, and new directions for promising research.

From rare diseases to disorders that affect millions, all stakeholders increasingly use genomics to translate individual experiences and expertise into meaningful improvements in the lives of patients and their caregivers.  Genomics sits at the powerful nexus between evidenced-based medicine and the empowered patient.  At WuXi NextCODE we are proud to advance the role of genomics not only in patient care but also in the evolution of strong, effective patient-centric communities.