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.
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.
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.
* * * * * * * * * * * *
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).
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.
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.
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.
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.
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.
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.
Let’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.
Genetic information for individual patients has already successfully infiltrated important areas of clinical practice, notably the diagnosis and treatment of cancer and rare diseases.
Can we now move beyond genomic tests for patients with diseases and begin to use genetic screening of healthy populations to guide preventive medicine and general wellness? The answer is clearly “yes.”
Remarkable progress has been made—and continues to evolve—in using genomics as a fundamental technology to guide the treatment of diseases. Many types of cancer patients now routinely have their genetic information tested to inform a personalized medicine approach to their cancer treatment. Similarly, patients with idiopathic and rare diseases increasingly receive genomic testing to identify the pathogenic mutations that may be driving their disease.
Now, genomic information is evolving into the realm of health and wellness. Genomics can help with preventive medicine, serving as a valuable tool for informing people and their doctors about genetic risk factors hiding in DNA, so that individual health plans can be developed. In a preventive medicine and wellness context, genomic information can give people the opportunity to take control of their health by making lifestyle changes and charting a personally tailored healthcare path.
Medical experts, government organizations, and genomics leaders around the world are pushing the frontier of genomics in health and wellness. In January 2017, the U.S. Centers for Disease Control and Prevention (CDC) held a special workshop to discuss the role of public health in using genetic screening of healthy individuals. The CDC states that it is becoming clearer as science progresses that there are more opportunities for using genetic screening for preventing common diseases across the lifespan.
Today, WuXi NextCODE announced our first pilot analysis of results from a group of 190 healthy individuals using our HealthCODE consumer whole-genome wellness scan. The scan uses proprietary risk modeling to gauge each individual’s inherited risk of 28 common complex diseases, like hypertension, type 2 diabetes and heart attack. On average, these participants from China were at more than 1.5-times average risk of four common diseases.
A clinical-grade scan such as HealthCODE makes it possible to target those individuals at highest inherited risk of certain diseases, so that they and their doctors can act on this information with lifestyle changes, monitoring, and even medicines they should consider to counteract those risks and increase their chances of staying healthy.
Moreover, the results presented today are important as a reflection and component of the power of having a global platform for genomic data. The same leading technology that is being deployed for Chinese consumers interested in using their genome to protect their health is also being used and can be deployed around the world. The key is the ability to model risk accurately for different populations and to use the same platform for interpreting the data and delivering actionable results to individuals while, at the same time, enabling them to participate in research if they wish to do so.
With the rapid progress in genomics, there is a growing sense that genomic advances are leading to new models of health care centered on disease prevention as well as treatments that are tailored to the individual. At the core, the aim of all of us is to reinforce the ability of health care to prevent illness and inform how we can live healthier. The impact can be across a range of outcomes, from better individual health, improved quality of life, and reduced costs to the healthcare system.