A Perfect Pairing? AI and Precision Medicine

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.


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.