Author Archives: Hannes Smarason

About Hannes Smarason

Hannes Smarason is CEO of NextCODE Health, a genomics leader. He is the former CEO of the FL Group (formerly Icelandair) and Chief Business Officer of deCODE Genetics, an Iceland-based genomics company.

Why We Need Big Infrastructure For Tackling Rare Disease

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Hannes Smarason big data rare diseases

Having bigger databases, and having a mechanism that flags genomic variants, is key to optimizing patient care for entire families

Uncovering the genetics of schizophrenia is vital but challenging. As I wrote in my last post, mutations in more than 100 spots in the genome have been linked to the condition. But which ones actually play a role in the disease, and which ones are just there for the ride—innocent bystanders that just happen to occur alongside the real culprits? That’s the crucial question for scientists seeking new treatments for this condition, among them leading researchers and clinicians at our close partner, Boston Children’s Hospital (BCH).

One thing we’ve learned recently is that even a small amount of knowledge about genetic underpinnings of disease can have a big potential benefit for patients. For example, the 16p13.11 region deletion I described in that last post ended up being very important for several patients later, particularly one father and his son, recently described by our colleagues at BCH. This case highlights the importance of expanding the scope and scale of such research, and of updating and alerting patients as more is discovered—not just in schizophrenia, but across rare disease.

In their previous work, the BCH team used chromosomal microarray analysis to determine that a young boy with symptoms of schizophrenia, including psychosis, was missing an entire chunk of DNA—one copy of the chromosomal region 16p13.11, which spans several genes.

Schizophrenia in children is rare, and some researchers believe it could be an extreme variation of the disease, and so might hold important clues for the treatment of this condition in both young and old. A search of our and BCH’s databases showed that several other young patients also showed variations in that region. Just as important, it was confirmed that a parent of one of those patients also carried that deletion, and it seemed likely that another parent (not available for testing, but with reported symptoms of schizophrenia) also likely carried the deletion.

Clearly 16p13.11 seemed to be emerging as a “hotspot” for variations linked to psychosis. But the scientists were only finding this because they could go back and search the databases, and they were working their way backwards from pediatric cases to learn information that might have been medically relevant to the parents as well. All this suggests that having bigger databases, and having a mechanism that flags such variants, is key to optimizing patient care for entire families.

One case uncovered by the BCH scientists, regarding a young man who we will call Jack, brought this into sharp focus. As a teenager, Jack had undergone detailed genetic screening at BCH because of symptoms that included learning disabilities and recurrent seizures. It was determined that he had a 16p13.11 deletion, but at the time of his screening, that mutation hadn’t yet been linked to psychosis. So it became just one more detail in Jack’s medical record.

Separately, a few years later, Jack’s father was diagnosed with ADHD and treated with a high dose of mixed amphetamine salts. Within a few weeks Jack’s father experienced a manic-psychotic episode. He was prescribed an anti-psychotic and eventually recovered. Unfortunately, his son was deeply affected by his father’s breakdown and became withdrawn and depressed. Eventually, Jack also developed psychotic symptoms, which were so serious he was hospitalized.

Jack’s symptoms, thankfully, responded to anti-psychotic medication, but his doctors wondered if there was any connection between the breakdowns suffered by the father and son.

A check of Jack’s medical record revealed the 16p13.11 deletion. And seeing that detail after the link had been made between 16p13.11 and psychosis, his doctors immediately speculated that it might be a cause of Jack’s symptoms. Further, they suspected that mutation could be the “linchpin” causing psychosis in the father and the son. Jack’s father was tested, and he also carries a 16p13.11 deletion.

So here’s the lesson: if Jack’s doctors had known about the link between 16p13.11 and psychosis as soon as it emerged, they might have also suggested testing Jack’s father. If they had, the BCH doctors “believe that the psychosis could have been averted in both father and son.”

In light of this case, the BCH researchers write that they see a keen need for broad, integrated, and sophisticated infrastructure to support genomics-driven precision medicine. They have several recommendations, including that physicians need to receive regularly updated risk information about specific mutations; genetic reports on parents who are “carriers” but seem unaffected should note that problems could arise later, and families that include carriers of variations that increase risk should be monitored and given counseling.

Such activities will be well supported by tools such as WuXi NextCODE’s Genomically Ordered Relational (GOR) database and global platform for diagnosing rare disease and building a global knowledgebase. This can act as one of the key spokes in the “wheel” of genomic diagnostic process. But we also need to build in others, such as means to automatically alert doctors to important knowledge updates, monitor patient records, and connect doctors to specialists who can help refine a diagnosis as new discoveries are made. We and our partners at BCH are committed to helping create these tools.

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Headed to ASHG? If you are attending ASHG this month, join us to hear more about how rare disease studies can inform our understand 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).

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

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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).

 

 

 

One-Two Punch: The Rise of Combination Cancer Therapies

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Wuxi NextCODE genomics and combination therapies

Genomic sequencing is key to developing drugs that can be repurposed alone or used in combination to treat new types of cancer.

One of the biggest new innovations in oncology is the use of combination therapies, and a recent FDA approval for a pair of drugs (dabrafenib and trametinib) for lung cancer underscores that. This is a big step forward and we expect to see many more combinations approved in the future.

But determining which are the right pairings for which particular tumors requires a lot of data. You have to figure out which patients carry particular mutations. That requires accurate testing tools, powerful analytics, and then reams of data to guide you. That’s one of the reasons WuXi NextCODE and others are so focused on building genomic databases and advanced genomic interpretation tools.

”Just collecting and sequencing a cohort is challenging,” says Jim Lund, Director of Tumor Product Development at WuXi NextCODE.  “But having detailed clinical information along with the samples is critical. That multiplies the value of your study.” This type of data creates a “two-way street,” explains Shannon T. Bailey, Associate Director of Cancer Genetics at WuXi NextCODE. “Genomic analysis doesn’t just tell you which drugs you should use, but which treatments will have no effect.”

So what was the latest step forward? Dabrafenib (Tafinlar®) and trametinib (Mekinist®) are now approved in combination for treatment of metastatic non-small cell lung cancer (NSCLC) for patients whose tumors have a specific alteration, called V600E, in the BRAF gene. About 1%-2% of lung tumors carry that mutation, which drives tumor growth and spread through the MAPK signaling pathway. The two drugs target this pathway but through different mechanisms. Dabrafenib is a BRAF inhibitor, and trametinib is a MEK inhibitor.

Perhaps 1%-2% doesn’t seem like a lot of patients. But three things are important here:

  1. Lung cancer is a common and usually deadly disease, so a new approval for even a small number of patients will still have a lot of impact. We must chip away at the mortality rate for this condition however we can, even if the progress seems incremental.
  2. Experts have a lot of hope that combination therapies will deliver the punch we’ve been looking for against many previously intractable cancers. That opens new paths for people who otherwise faced hopelessness.
  3. The more patients receive these combinations, the more we can learn about what works, and why.

The data from today’s patients are a key part of what guide the treatments of tomorrow. This particular approval builds upon more than a decade of data from clinical trials. BRAF V600E has long been a prime suspect as a driver of several cancers. In 2014, the FDA approved this same combination of drugs for melanoma patients whose cancers were positive for the BRAF V600 mutation.

“Genomic sequencing has allowed researchers to identify specific genetic links between different cancers and then repurpose drugs developed for one cancer to treat another form of the disease,” says Lund. “This is data driving precision medicine.”

In tandem with the approval of the two-drug regimen for NSCLC, FDA also gave the OK for the Oncomine DX Target Test, a next-generation sequencing test that can detect BRAF V600E in tumor samples. It screens for multiple biomarkers including BRAF, ALK, ROS1, and EGFR genes. These can all help guide prescribing decisions.

“When we characterize lung tumors from patients, it’s a good idea to test the samples for all the changes that could be targeted by different drugs,” said Bruce E. Johnson of the Dana-Farber Cancer Institute, in a press release about this recent approval. Johnson co-led the clinical trials that were the basis for FDA’s approval of the combination therapy.

It’s also important to realize how difficult it is to carry out these trials. As I noted earlier, this is a very rare mutation. To recruit the 59 patients for the combination trial that led to this NSCLC approval, researchers screened about 6,000 patients. This feat alone represents a “major achievement that underlies the difficulties in completing such a trial,” the study researchers wrote, in an editorial that accompanied the paper about their findings.

These types of advances are spurring researchers to seek more links between established therapies and known mutations. “There are many additional drugs that can be repurposed alone or in combination to treat new types of cancer, and genomic sequencing will be key to both the research and clinical implementation of these new advances in cancer treatment,” says Lund.

WuXi NextCODE, meanwhile, is trying to accelerate progress by providing a comprehensive but user-friendly informatics suite that “allows clinicians to perform complex big data queries and interpretation on a case by case basis, and to annotate that data even if they lack formal computational training,” says Bailey.

Our vision is that eventually, clinicians and individuals all around the world will be deploying their genomes to contributing to our overall knowledge of “which drug—or, perhaps, which combination—works for which patient.”

New Breast Cancer Study Underscores the Need for More Sequencing

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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

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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

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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

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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.

Speeding Diagnosis of Rare Diseases

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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.

Bringing Artificial Intelligence to Cardiovascular Medicine and Cancer: Genomics in Action

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WuXi NextCODE Nature

A Yale research team, with contributions from WuXi NextCODE’s artificial intelligence (AI) and sequencing teams, has discovered a novel mechanism regulating how blood vessels grow.

Artificial Intelligence (AI) can already catch a criminal and identify the right patients for certain types of surgery. But those challenges involve relatively few parameters compared to number of parameters or features involved in linking the 3 billion bases in the human genome with other ‘omics data and all the complexity of human biology. For that very reason, the promise of AI in genomics is as necessary as it is enticing, and WuXi NextCODE is committed to pushing the frontier of this emerging field.

This week, I am encouraged by results from a study published in the latest edition of Nature, which describes how a well-known growth factor may play a previously unknown role in some important diseases. That report, led by Yale University scientist Michael Simons, investigates 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 applied some of the most advanced statistic in their toolset to take the data analysis to the next level.

Simons’ team studied knock out mice, whose fibroblast growth factor (FGF) receptor genes were turned off. The scientists were able to prove, 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.

It’s inspiring to see scientists from around the world using top-notch technology to collaborate on pivotal research questions. This study involved scientists in six different countries.

This FGF study also comes on the heels of our recent announcement about how our deepCODE approach classified 27 different tumor types with greater than 95% accuracy when applied across approximately 9,000 human tumors from The Cancer Genome Atlas (TCGA) collection. [LINK: https://www.wuxinextcode.com/highlights/posters-at-aacr/#/brief–using-ai]

With the rapid rate of progress, it’s not surprising that AI is finding success in genomics. Today’s informatics capabilities allow for assimilating larger and larger datasets with AI applications, and the field is evolving at a rapid pace. Google alone published more than 200 papers on AI in 2016. Like us, they use a deep learning approach.

From facial recognition to genomic solutions
Each AI problem has a different scale. In facial recognition, AI applications analyze relatively few features in the human face (about two dozen). Digital scans of the human eye that use AI techniques are able to segment patients before eye surgeries, and this entails algorithms that consider hundreds of features.

Genomics, of course, involves looking at any number of feature sets among billions of possibilities. It’s an immense challenge, but I think it’s perfectly suited to AI. And with deep-learning tools, we can fish out many more insights than with traditional analyses.

Our goal is to see how AI can help researchers achieve better results in identifying and evaluating new medicines, pinpointing risk factors and disease drivers, finding new combination therapies that work better than single drugs, and more. Our deepCODE tools comprise a novel, multinomial statistical-learning method and deep learning classification strategy. It’s an advanced approach to AI.

This week’s Nature paper is another encouraging sign.

Many of the stickiest problems in medicine are longstanding. The role of FGFs in blood vessel development was poorly understood until now. This group’s findings may help open new avenues of research.

Our team is always seeking to tackle problems with the latest approaches and technologies. Now, in the age of big data, it makes sense to start letting computers do more and more of the work, even some of the actual thinking. Certainly, we pick the questions and frame them. But then, let’s load the data and let the machines help us find the answers. If we can polish this process, and apply it to a growing number of problems, new answers and insights are sure to come.

A Perfect Pairing? AI and Precision Medicine

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AI-and-precision-medicineLet’s start with one of the fastest-growing fields in science today: artificial intelligence (AI). Now, let’s apply another technology that has profound potential for improving patient care:  precision medicine. Some of us think the integration of these two arenas could be a “sweet spot” that leads to some of the decade’s biggest advances in healthcare.

As someone who has worked in genomics for two decades, I am a believer in the combined power of AI and precision medicine. And in my current work, I have the pleasure of pioneering both technologies.

Cancer has been one of the early beachheads for precision medicine, Now, AI is also following that path, with the aim of advancing individualized treatment.

For example, just today, WuXi NextCODE presented preliminary data from analyses using our novel AI technology at the American Association of Cancer Research annual meeting in Washington D.C. We tested the accuracy of our new deepCODE deep learning tools to diagnose subtypes of tumors. Our results suggest these tools do a better job than traditional approaches for classifying tumors and helping determine which patients will respond to which drugs.  Our new AI technology can incorporate all types of omic data, and can also help with drug discovery and finding the best uses for drugs.

How can AI technologies achieve better results in identifying precision treatments in cancer and other diseases?  In the case of our new deepCODE tools, it is in part thanks to a novel, multinomial statistical-learning method and deep learning classification strategy. This approach is designed to support dramatic improvements in drug discovery and development, as well as medical care. But we need to prove the technology’s potential by testing it on real problems in genomic medicine. So, that’s what we are doing.

The initial results are promising. Our deepCODE tools were validated on six patient-derived tumor xenografts from mouse models, and then tested against approximately 9,000 human tumors from a collection of 27 types in The National Cancer Institute’s Cancer Genome Atlas (TCGA) collection. (https://cancergenome.nih.gov/)  We achieved 95% accuracy overall in this test. In analyses of human breast and lung cancer subtypes, deepCODE was accurate in 96% and 99% of cases, respectively. That study included DNA- and RNA-seq data.

These findings are very encouraging.  Breast and lung cancer are both very common malignancies that are increasingly being “divided” into subtypes that have significantly different outcomes and need different treatment regimens. These preliminary data are by no means definitive, but they suggest that AI could bring new certainty to cancer diagnosis.

But why is it even so important to get a fast, accurate molecular diagnosis of a tumor?

Well, here’s the challenge: Today patients who have suspected cancers are typically biopsied.  A snip of the tumor is examined under a microscope and then may be tested for common biological receptors. It can take a while for that to occur. Next, the patient undergoes treatment, and whatever drugs they receive could actually change the tumor’s biology: After that, the drugs initially prescribed might not be the best option anymore.

So how can we know when to switch treatments, and what to switch to?

In the ideal world, anyone diagnosed with cancer would be followed up with an extensive molecular biopsy. In other words, once the initial diagnosis is made, the patient would undergo follow-up tests that involve relatively painless blood draws. From these blood samples (liquid biopsies), the tumor’s DNA would be read, and that would determine how to best monitor and prescribe for that particular patient going forward.

It is an exciting time to be working on integrating AI technology with the primary tools for improving precision medicine in cancer and other diseases.  We’re just at the start of this journey, and we’ll likely find many other ways that AI technology can impact patient healthcare.

Join us here as we follow this intriguing program’s progress.