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

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:–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.


Personal Genomics Can Drive Preventive Medicine and Wellness

WuXi NextCODE HealthCODE

The next wave of genomics impact in health care is preventive medicine and wellness.

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.”

Today, WuXi NextCODE announced a positive step forward with results from our HealthCODE wellness scan to show the value of genomics in preventive medicine.

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.