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Whole-Genome Sequencing Plus AI Yields Same-Day Genetic Diagnoses

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Sebastiana
Caption: Rapid whole-genome sequencing helped doctors diagnose Sebastiana Manuel with Ohtahara syndrome, a neurological condition that causes seizures. Her data are now being used as part of an effort to speed the diagnosis of other children born with unexplained illnesses. Credits: Getty Images (left); Jenny Siegwart (right).



Back in April 2003, when the international Human Genome Project successfully completed the first reference sequence of the human DNA blueprint, we were thrilled to have achieved that feat in just 13 years. Sure, the U.S. contribution to that first human reference sequence cost an estimated $400 million, but we knew (or at least we hoped) that the costs would come down quickly, and the speed would accelerate. How far we’ve come since then! A new study shows that whole genome sequencing—combined with artificial intelligence (AI)—can now be used to diagnose genetic diseases in seriously ill babies in less than 24 hours.

Take a moment to absorb this. I would submit that there is no other technology in the history of planet Earth that has experienced this degree of progress in speed and affordability. And, at the same time, DNA sequence technology has achieved spectacularly high levels of accuracy. The time-honored adage that you can only get two out of three for “faster, better, and cheaper” has been broken—all three have been dramatically enhanced by the advances of the last 16 years.

Rapid diagnosis is critical for infants born with mysterious conditions because it enables them to receive potentially life-saving interventions as soon as possible after birth. In a study in Science Translational Medicine, NIH-funded researchers describe development of a highly automated, genome-sequencing pipeline that’s capable of routinely delivering a diagnosis to anxious parents and health-care professionals dramatically earlier than typically has been possible [1].

While the cost of rapid DNA sequencing continues to fall, challenges remain in utilizing this valuable tool to make quick diagnostic decisions. In most clinical settings, the wait for whole-genome sequencing results still runs more than two weeks. Attempts to obtain faster results also have been labor intensive, requiring dedicated teams of experts to sift through the data, one sample at a time.

In the new study, a research team led by Stephen Kingsmore, Rady Children’s Institute for Genomic Medicine, San Diego, CA, describes a streamlined approach that accelerates every step in the process, making it possible to obtain whole-genome test results in a median time of about 20 hours and with much less manual labor. They propose that the system could deliver answers for 30 patients per week using a single genome sequencing instrument.

Here’s how it works: Instead of manually preparing blood samples, his team used special microbeads to isolate DNA much more rapidly with very little labor. The approach reduced the time for sample preparation from 10 hours to less than three. Then, using a state-of-the-art DNA sequencer, they sequence those samples to obtain good quality whole genome data in just 15.5 hours.

The next potentially time-consuming challenge is making sense of all that data. To speed up the analysis, Kingsmore’s team took advantage of a machine-learning system called MOON. The automated platform sifts through all the data using artificial intelligence to search for potentially disease-causing variants.

The researchers paired MOON with a clinical language processing system, which allowed them to extract relevant information from the child’s electronic health records within seconds. Teaming that patient-specific information with data on more than 13,000 known genetic diseases in the scientific literature, the machine-learning system could pick out a likely disease-causing mutation out of 4.5 million potential variants in an impressive 5 minutes or less!

To put the system to the test, the researchers first evaluated its ability to reach a correct diagnosis in a sample of 101 children with 105 previously diagnosed genetic diseases. In nearly every case, the automated diagnosis matched the opinions reached previously via the more lengthy and laborious manual interpretation of experts.

Next, the researchers tested the automated system in assisting diagnosis of seven seriously ill infants in the intensive care unit, and three previously diagnosed infants. They showed that their automated system could reach a diagnosis in less than 20 hours. That’s compared to the fastest manual approach, which typically took about 48 hours. The automated system also required about 90 percent less manpower.

The system nailed a rapid diagnosis for 3 of 7 infants without returning any false-positive results. Those diagnoses were made with an average time savings of more than 22 hours. In each case, the early diagnosis immediately influenced the treatment those children received. That’s key given that, for young children suffering from serious and unexplained symptoms such as seizures, metabolic abnormalities, or immunodeficiencies, time is of the essence.

Of course, artificial intelligence may never replace doctors and other healthcare providers. Kingsmore notes that 106 years after the invention of the autopilot, two pilots are still required to fly a commercial aircraft. Likewise, health care decisions based on genome interpretation also will continue to require the expertise of skilled physicians.

Still, such a rapid automated system will prove incredibly useful. For instance, this system can provide immediate provisional diagnosis, allowing the experts to focus their attention on more difficult unsolved cases or other needs. It may also prove useful in re-evaluating the evidence in the many cases in which manual interpretation by experts fails to provide an answer.

The automated system may also be useful for periodically reanalyzing data in the many cases that remain unsolved. Keeping up with such reanalysis is a particular challenge considering that researchers continue to discover hundreds of disease-associated genes and thousands of variants each and every year. The hope is that in the years ahead, the combination of whole genome sequencing, artificial intelligence, and expert care will make all the difference in the lives of many more seriously ill babies and their families.

Reference:

[1] Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Clark MM, Hildreth A, Batalov S, Ding Y, Chowdhury S, Watkins K, Ellsworth K, Camp B, Kint CI, Yacoubian C, Farnaes L, Bainbridge MN, Beebe C, Braun JJA, Bray M, Carroll J, Cakici JA, Caylor SA, Clarke C, Creed MP, Friedman J, Frith A, Gain R, Gaughran M, George S, Gilmer S, Gleeson J, Gore J, Grunenwald H, Hovey RL, Janes ML, Lin K, McDonagh PD, McBride K, Mulrooney P, Nahas S, Oh D, Oriol A, Puckett L, Rady Z, Reese MG, Ryu J, Salz L, Sanford E, Stewart L, Sweeney N, Tokita M, Van Der Kraan L, White S, Wigby K, Williams B, Wong T, Wright MS, Yamada C, Schols P, Reynders J, Hall K, Dimmock D, Veeraraghavan N, Defay T, Kingsmore SF. Sci Transl Med. 2019 Apr 24;11(489).

Links:

DNA Sequencing Fact Sheet (National Human Genome Research Institute/NIH)

Genomics and Medicine (NHGRI/NIH)

Genetic and Rare Disease Information Center (National Center for Advancing Translational Sciences/NIH)

Stephen Kingsmore (Rady Children’s Institute for Genomic Medicine, San Diego, CA)

NIH Support: National Institute of Child Health and Human Development; National Human Genome Research Institute; National Center for Advancing Translational Sciences


More Progress Toward Gene Editing for Kids with Muscular Dystrophy

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Normal and treated muscles with DMD
Caption: Muscles of untreated mouse model of Duchenne muscular dystrophy (left) compared to muscles of similar mice one year after gene-editing treatment (right). Dystrophin production (green) is restored in treated animals, despite therapy-related immune response to the Cas9 editing enzyme (dark spots in white inset). Credit: Charles Gersbach, Duke University, Durham, NC

Thanks to CRISPR and other gene editing technologies, hopes have never been greater for treating or even curing Duchenne muscular dystrophy (DMD) and many other rare, genetic diseases that once seemed tragically out of reach. The latest encouraging news comes from a study in which a single infusion of a CRISPR editing system produced lasting benefits in a mouse model of DMD.

There currently is no way to cure DMD, an ultimately fatal disease that mainly affects boys. Caused by mutations in a gene that codes for a critical protein called dystrophin, DMD progressively weakens the skeletal and heart muscles. People with DMD are usually in wheelchairs by the age of 10, with most dying before the age of 30.

The exquisite targeting ability of CRISPR/Cas9 editing systems rely on a sequence-specific guide RNA to direct a scissor-like, bacterial enzyme (Cas9) to just the right spot in the genome, where it can be used to cut out, replace, or repair disease-causing mutations. In previous studies in mice and dogs, researchers directly infused CRISPR systems directly into the animals bodies. This “in vivo” approach to gene editing successfully restored production of functional dystrophin proteins, strengthening animals’ muscles within weeks of treatment.

But an important question remained: would CRISPR’s benefits persist over the long term? The answer in a mouse model of DMD appears to be “yes,” according to findings published recently in Nature Medicine by Charles Gersbach, Duke University, Durham, NC, and his colleagues [1]. Specifically, the NIH-funded team found that after mice with DMD received one infusion of a specially designed CRISPR/Cas9 system, the abnormal gene was edited in a way that restored dystrophin production in skeletal and heart muscles for more than a year. What’s more, lasting improvements were seen in the structure of the animals’ muscles throughout the same time period.

As exciting as these results may be, much more research is needed to explore both the safety and the efficacy of in vivo gene editing before it can be tried in humans with DMD. For instance, the researchers found that older mice that received the editing system developed an immune response to the bacterially-derived Cas9 protein. However, this response didn’t prevent the CRISPR/Cas9 system from doing its job or appear to cause any adverse effects. Interestingly, younger animals didn’t show such a response.

It’s worth noting that the immune systems of mice and people often respond quite differently. But the findings do highlight some possible challenges of such treatments, as well as approaches to reduce possible side effects. For instance, the latest findings suggest CRISPR/Cas9 treatment might best be done early in life, before an infant’s immune system is fully developed. Also, if it’s necessary to deliver CRISPR/Cas9 to older individuals, it may be beneficial to suppress the immune system temporarily.

Another concern about CRISPR technology is the potential for damaging, “off-target” edits to other parts of the genome. In the new work, the Duke team found that its CRISPR system made very few “off-target” edits. However, the system did make a surprising number of complex edits to the targeted dystrophin gene, including integration of the viral vector used to deliver Cas9. While those editing “errors” might reduce the efficacy of treatment, researchers said they didn’t appear to affect the health of the mice studied.

It’s important to emphasize that this gene editing research aimed at curing DMD is being done in non-reproductive (somatic) cells, primarily muscle tissue. The NIH does not support the use of gene editing technologies in human embryos or human reproductive (germline) cells, which would change the genetic makeup of future offspring.

As such, the Duke researchers’ CRISPR/Cas9 system is designed to work optimally in a range of muscle and muscle-progenitor cells. Still, they were able to detect editing of the dystrophin-producing gene in the liver, kidney, brain, and other tissues. Importantly, there was no evidence of edits in the germline cells of the mice. The researchers note that their CRISPR system can be reconfigured to limit gene editing to mature muscle cells, although that may reduce the treatment’s efficacy.

It’s truly encouraging to see that CRISPR gene editing may confer lasting benefits in an animal model of DMD, but a great many questions remain before trying this new approach in kids with DMD. But that time is coming—so let’s boldly go forth and get answers to those questions on behalf of all who are affected by this heartbreaking disease.

Reference:

[1] Long-term evaluation of AAV-CRISPR genome editing for Duchenne muscular dystrophy. Nelson CE, Wu Y, Gemberling MP, Oliver ML, Waller MA, Bohning JD, Robinson-Hamm JN, Bulaklak K, Castellanos Rivera RM, Collier JH, Asokan A, Gersbach CA. Nat Med. 2019 Feb 18.

Links:

Muscular Dystrophy Information Page (National Institute of Neurological Disorders and Stroke/NIH)

Gersbach Lab (Duke University, Durham, NC)

Somatic Cell Genome Editing (Common Fund/NIH)

NIH Support: National Institute of Arthritis and Musculoskeletal and Skin Diseases; National Institute of Biomedical Imaging and Bioengineering


Working Toward Greater Precision in Childhood Cancers

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Pediatric Cancer

Credit: National Cancer Institute, NIH

Each year, more than 15,000 American children and teenagers will be diagnosed with cancer. While great progress has been made in treating many types of childhood cancer, it remains the leading cause of disease-related death among kids who make it past infancy in the United States [1]. One reason for that sobering reality is our relatively limited knowledge about the precise biological mechanisms responsible for childhood cancers—information vital for designing targeted therapies to fight the disease in all its varied forms.

Now, two complementary studies have brought into clearer focus the genomic landscapes of many types of childhood cancer [2, 3]. The studies, which analyzed DNA data representing tumor and normal tissue from more than 2,600 young people with cancer, uncovered thousands of genomic alterations in about 200 different genes that appear to drive childhood cancers. These so-called “driver genes” included many that were different than those found in similar studies of adult cancers, as well as a considerable number of mutations that appear amenable to targeting with precision therapies already available or under development.


NIH Family Members Giving Back: Rebecca Shlafer

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Rebecca Shlafer

Rebecca Shlafer/Credit: Brady Willette

When Rebecca Shlafer clicks on her office lights each morning at the University of Minnesota Medical Center, Minneapolis, she usually has a good idea of what to expect from the day ahead as lead of a nine-person research team that studies the effects of incarceration on children and families. It’s her volunteer work that can be unpredictable.

For the past eight years, this developmental child psychologist has donated her free time to serve as a guardian ad litem for abused or neglected children who’ve been removed from their homes and placed under protective supervision of Minnesota’s Fourth Judicial District. In that volunteer capacity, Shlafer advocates in court for the well-being of the child, but doesn’t foster the youngster or provide any day-to-day care.


How Kids See the World Depends a Lot on Genetics

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Baby in eye gaze study

Caption: Child watches video while researchers track his eye movements.
Credit: Washington University School of Medicine, St. Louis

From the time we are born, most of us humans closely watch the world around us, paying special attention to people’s faces and expressions. Now, for the first time, an NIH-funded team has shown that the ways in which children look at faces and many other things are strongly influenced by the genes they’ve inherited from their parents.

The findings come from experiments that tracked the eye movements of toddlers watching videos of other kids or adult caregivers. The experiments showed that identical twins—who share the same genes and the same home environment—spend almost precisely the same proportion of time looking at faces, even when watching different videos. And when identical twins watched the same video, they tended to look at the same thing at almost exactly the same time! In contrast, fraternal twins—who shared the same home environment, but, on average, shared just half of their genes—had patterns of eye movement that were far less similar.

Interestingly, the researchers also found that the visual behaviors most affected in children with autism spectrum disorder (ASD)—attention to another person’s eyes and mouth—were those that also appeared to be the most heavily influenced by genetics. The discovery makes an important connection between two well-known features of ASD: a strong hereditary component and poor eye contact with other people.


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