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Big data

Big data, Pediatrics, Research, Stanford News

Rare gene variants help explain preemies’ lung disease, Stanford study shows

Rare gene variants help explain preemies' lung disease, Stanford study shows

double-helixBecause they’re born before their lungs are fully mature, premature babies are at risk for a serious lung disease. Over the last several decades, this disease, bronchopulmonary dysplasia, has evolved into both a great medical success story and a persistent mystery. But a new Stanford study, published this week, is helping clarify the mysterious part.

First, the success story: Today, doctors can prevent BPD in many babies who would have died of it in the past. Artificial surfactant, which helps keep the air sacs of the lungs open, and extensive research on when it’s appropriate and safe to put preemies on a respirator have both greatly reduced the risk of lung injuries after birth, which can contribute to BPD. The improvement has been especially remarkable for babies born on the later end of the premature spectrum.

However, BPD is still a big problem for infants who arrive more than 12 weeks early. Doctors still have trouble figuring out which of these early preemies are at risk, and why. An editorial accompanying the new Stanford study, which appears in the American Journal of Respiratory and Critical Care Medicine, explains how scientists’ understanding of BPD has evolved:

It is now widely appreciated that the persistence of BPD is strongly linked with factors far beyond postnatal lung injury alone. Importantly, the BPD and related respiratory outcomes clearly have antenatal origins… Growing data support the concept that BPD is at least partly a “fetal disease.”

The editorial names several factors in the prenatal environment that weigh into BPD risk, including certain pregnancy complications and also maternal smoking or drug use. It’s not just the environment that plays into risk, though; twin studies also hint that genes also factor in, and knowing which genes are involved would provide enormous clues to how the disease occurs.

A prior Stanford study that attempted to connect common human gene variants to BPD risk didn’t turn up any good candidates. So, in the new study, the Stanford team focused instead on rare genetic variants. Using data from California’s extensive repository of newborn blood spots (small blood samples collected as part of the state’s program to screen newborns for genetic diseases), they turned up 258 rare gene variants for further investigation, all of which are linked to cell processes that could plausibly be involved in BPD.

“We hope these results will guide future research that can determine the most important pathophysiologic pathways leading to BPD,” said Hugh O’Brodovich, MD, the study’s senior author. The idea isn’t to target the genes themselves for treatment, but rather to help researchers figure out what goes wrong at a molecular level in the lungs of babies who get BPD.

“We also hope this work will be used to discover how clinicians can minimize the chance that an extremely premature baby will develop the disease,” he added.

Previously: Study of outcomes for early preemies highlights complex choices for families and doctors, Stanford-led study suggests changes to brain scanning guidelines for preemies and Counseling parents of the earliest-born preemies: A mom and two physicians talk about the challenges
Photo by James Gaither

Big data, Cardiovascular Medicine, Chronic Disease, In the News, Research, Stanford News

Using “big data” to improve patient care: Researchers explore a-fib treatments

Using "big data" to improve patient care: Researchers explore a-fib treatments

Turakhia photoA Stanford cardiac electrophysiologist and colleagues have used a unique research method to learn more about atrial fibrillation. Mintu Turakhia, MD, and collaborators at Medtronic and Massachusetts General Hospital, extracted data out of decades of continuously recorded medical information from implanted medical devices – pacemakers and defibrillators — in 10,000 heart patients. Then they linked it to medical records, and analyzed it.

The researchers’ goal was to explore whether patients who experienced sudden attacks of a-fib, an irregular and rapid heart rate caused by spasms of the heart’s upper chambers, should be treated with long-term anticoagulants like those who had permanent a-fib or whether perhaps temporary drug therapy could be considered an option. They wanted to know if a patient’s risk of stroke changes as a-fib comes and goes.

The results, which were published recently in Circulation: Arrhythmia and Electrophysiology, found that patients were at an increased risk of stroke the first seven days after their hearts went into a-fib.

A-fib, which afflicts more than 3 million Americans, is known to increase a patient’s risk of stroke – but exactly when this risk occurs is controversial. Currently, physicians recommend long-term anticoagulation for patients, whether the a-fib occurs in sudden attacks or is continuous. This study indicates that transient use of anticoagulants could be an option for some patients and deserves further investigation. Future treatment plans might explore the idea of some kind of wearable device that shows when a patient goes in and out of a-fib, then taking medications just when needed rather than for a lifetime, said Turakhia.

Turakhia told me the study also provides an important example of how using “big data” research methods can ultimately lead to improved clinical care. In an email, he explained:

This is truly a big data approach where we took raw data from implanted pacemakers and implanted defibrillators and linked it to clinical data. The medical device data comes from home remote monitoring systems that patients have and goes to the cloud. We pulled the raw data off the cloud and linked it to VA (Veterans Affairs) electronic health records, VA claims, Medicare claims, and death records. This is truly a novel approach where we are assembling highly disparate data sources and linking them to gain insight into disease.

Previously: A little help from pharmacists helps a-fib patients adhere to prescriptions, Study highlights increased risk of death among patients with atrial fibrillation who take digoxin and What is big data?
Photo of Turakhia by Norbert von der Groeben

Big data, Cancer, Research, Stanford News

A recipe for disaster: Stanford researchers identify mutations that contribute to rare blood cancers

A recipe for disaster: Stanford researchers identify mutations that contribute to rare blood cancers

recipe box“One thing we’ve learned about cancers is that each has its own unique recipe for malignancy. Some use the same ingredients and some a have a wide palate of ingredients.”

This is the analogy Paul Khavari, MD, PhD, professor and chair of dermatology at Stanford, used to describe the mutated genes that turn our own cells against us. The abnormal proteins derived from these genes disrupt the cellular machinery that keeps cell growth under control and monitors the DNA for mistakes. Fast-multiplying, unmonitored cells acquire more mutations in their DNA and the cycle continues.

By the time the cancer is detected, the DNA can be so riddled with mutations and rearrangements that even the power of next generation sequencing to read the DNA of the chromosomes might not be enough to identify the key ingredients – the mutated genes that drive the cancer.

The two T-cell cancers Khavari studies, mycosis fungoides and Sezary syndrome, come from particularly eclectic genetic cookbooks lacking a single obvious cancer-causing mutation. This makes identifying drugs that would fight these cancers extremely difficult.

By turning to clinical and biological data, Khavari’s team selected about 500 genes for deeper investigation. An identical point mutation in a single gene seen in only 5 percent of the examined tumor led them to identify a cell-survival mechanism that had not previously been implicated in any cancer.

In a paper published today in Nature Genetics the researchers reported that almost 40 percent of patients had a genetic abnormality in at least one gene involved in this mechanism. In our press release on the paper, I wrote about how these mutations turn the cells cancerous:

Khavari… likens skin T cells to patrolling sentries, rotating on and off duty. At the end of their shift, the cell-survival mechanism shuts down, and, with no signal, the T cells leave or die. The mutations Khavari’s team found prevent the pathway from turning off, causing T cells to pile up in the skin or circulate through the blood stream. “More and more sentries keep showing up for duty,” said Khavari. “It’s out of control.”

With the mutated genes identified, Khavari plans to introduce them into mice models. By studying their biological effects he hopes to suss out the mutations that are the cancers’ critical ingredients.

To read more about a stem cell treatment for these cancers being developed at Stanford, check out this article in the most recent Stanford Medicine magazine.

Kim Smuga-Otto is a student in UC Santa Cruz’s science communication program and a writing intern in the medical school’s Office of Communication and Public Affairs.

Previously: Smoking gun or hit-and-run? How oncogenes make good cells go bad, When a rash just isn’t a rash: A patient’s battle with mycosis fungoides and Linking cancer gene expression with survival rates, Stanford researchers bring “big data” into the clinic
Photo by April Griffus

Big data, BigDataMed15, Precision health, Public Health, Research, Videos

How the FDA is promoting data sharing and transparency to support innovations in public health

How the FDA is promoting data sharing and transparency to support innovations in public health

Keynote talks and presentations from the 2015 Big Data in Biomedicine conference at Stanford are now available on the Stanford YouTube channel. To continue the discussion of how big data can be harnessed to improve the practice of medicine and enhance human health, we’re featuring a selection of the videos on Scope.

At the 2014 Big Data in Biomedicine conference, Taha Kass-Hout, MD, chief health informatics officer for the U.S. Food and Drug Administration, announced that the federal agency was launching OpenFDA, a scalable search and big-data analytics platform. In May, he returned to the Big Data in Biomedicine stage to offer an update on the initiative and discuss how the FDA is continuing to foster access and transparency of big data in government.

During his talk, Kass-Hout shared some eye-popping statistics about the information available through OpenFDA. The platform houses close to 70,000 product labels for pharmaceuticals; nearly four million reports on adverse events or malfunctions of medical devices; 41,000 records on recalls of foods, pharmaceuticals or devices and over four and a half million reports of adverse events or side-effects of drugs.

He outlined future plans to build a similar public, cloud-based platform to compliment the Obama Administration’s Precision Medicine Initiative. Watch the full talk to learn more about these exciting efforts to unlock the rapidly growing reservoir of biomedical data and spur innovation in public health.

Previously: A look at the MyHeart Counts app and the potential of mobile technologies to improve human health, Discussing patient participation in medical research: “We had to take this into our own hands,” A look at aging and longevity in this “unprecedented” time in history, Mining Twitter to identify cases of foodborne illness and Discussing access and transparency of big data in government

Big data, BigDataMed15, Cardiovascular Medicine, Medical Apps, Stanford News, Videos

A look at the MyHeart Counts app and the potential of mobile technologies to improve human health

A look at the MyHeart Counts app and the potential of mobile technologies to improve human health

Keynote talks and presentations from the 2015 Big Data in Biomedicine conference at Stanford are now available on the Stanford YouTube channel. To continue the discussion of how big data can be harnessed to improve the practice of medicine and enhance human health, we’re featuring a selection of the videos on Scope.

At last count, the number of iPhone owners who have downloaded the MyHeart Counts app and consented to participate in a large-scale, human heart study had reached 40,000. The first-of-its-kind mobile app was designed by Stanford Medicine cardiologists as a way for users to learn about their heart health while simultaneously helping advance the field of cardiovascular medicine.

Built on Apple’s ResearchKit framework, the app leverages the iPhone’s built-in motion sensors to collect data on physical activity and other cardiac risk factors for a research study. The MyHeart Counts study also draws on the strength of Stanford Medicine’s Biomedical Data Science Initiative.

At the 2015 Big Data in Biomedicine conference, Euan Ashley, MD, a cardiologist at Stanford and co-investigator for the MyHeart Counts study, shared some preliminary findings with the audience. Check out the full talk to learn more about how the app is helping researchers better understand Americans’ health habits and what states have the happiest, most physically active and well-rested residents.

Previously: On the move: Big Data in Biomedicine goes mobile with discussion on mHealth, MyHeart Counts shows that smartphones are catching on as new research tool, Lights, camera, action: Stanford cardiologist discusses MyHeart Counts on ABC’s Nightline, MyHeart Counts app debuts with a splash and Stanford launches iPhone app to study heart health.

Big data, Cancer, Genetics, Immunology, Research, Science, Stanford News

Linking cancer gene expression with survival rates, Stanford researchers bring “big data” into the clinic

Linking cancer gene expression with survival rates, Stanford researchers bring "big data" into the clinic

Magic 8 ball“What’s my prognosis?” is a question that’s likely on the mind, and lips, of nearly every person newly diagnosed with any form of cancer. But, with a few exceptions, there’s still not a good way for clinicians to answer. Every tumor is highly individual, and it’s difficult to identify anything more than general trends with regard to the type and stage of the tumor.

Now, hematologist and oncologist Ash Alizadeh, MD, PhD; radiologist Sylvia Plevritis, PhD; postdoctoral scholar Aaron Newman, PhD; and senior research scientist Andrew Gentles, PhD, have created a database that links the gene-expression patterns of individual cancers of 39 types with the survival data of the more than 18,000 patients from whom they were isolated. The researchers hope that the resource, which they’ve termed PRECOG, for “prediction of cancer outcomes from genomic profiles” will provide a better understanding of why some cancer patients do well, and some do poorly. Their research was published today in Nature Medicine.

As I describe in our release:

Researchers have tried for years to identify specific patterns of gene expression in cancerous tumors that differ from those in normal tissue. By doing so, it may be possible to learn what has gone wrong in the cancer cells, and give ideas as to how best to block the cells’ destructive growth. But the extreme variability among individual patients and tumors has made the process difficult, even when focused on particular cancer types.

Instead, the researchers pulled back and sought patterns that might become clear only when many types of cancers, and thousands of patients were lumped together for study:

Gentles and Alizadeh first collected publicly available data on gene expression patterns of many types of cancers. They then painstakingly matched the gene expression profiles with clinical information about the patients, including their age, disease status and how long they survived after diagnosis. Together with Newman, they combined the studies into a final database.

“We wanted to be able to connect gene expression data with patient outcome for thousands of people at once,” said Alizadeh. “Then we could ask what we could learn more broadly.”

The researchers found that they were able to identify key molecular pathways that could stratify survival across many cancer types:

In particular, [they] found that high expression of a gene called FOXM1, which is involved in cell growth, was associated with a poor prognosis across multiple cancers, while the expression of the KLRB1 gene, which modulates the body’s immune response to cancer, seemed to confer a protective effect.

Alizadeh and Plevritis are both members of the Stanford Cancer Institute.

Previously: What is big data?Identifying relapse in lymphoma patients with circulating tumor DNA,  Smoking gun or hit-and-run? How oncogenes make good cells go bad and Big data = big finds: Clinical trial for deadly lung cancer launched by Stanford study
Photo by CRASH:candy

Big data, BigDataMed15, Chronic Disease, Videos

Discussing patient participation in medical research: “We had to take this into our own hands”

Discussing patient participation in medical research: "We had to take this into our own hands"

Keynote talks and presentations from the 2015 Big Data in Biomedicine conference at Stanford are now available on the Stanford YouTube channel. To continue the discussion of how big data can be harnessed to improve the practice of medicine and enhance human health, we’re featuring a selection of the videos on Scope.

Two days before Christmas in 1994, Sharon Terry’s two young children were diagnosed with pseudoxanthoma elasticum (PXE), a rare condition that causes calcium and other minerals to be deposited in the body’s tissue. As Terry told the audience at the 2015 Big Data in Biomedicine conference, “[My husband and I] quickly learned that we, in fact, had to take this into our own hands, like many parents have done before us and many parents have done after us.” Despite not having a science background, Terry co-discovered the gene associated with PXE and created a diagnostic test for the disease; over the years, she has conducted clinical trials and authored 140 peer-reviewed papers, of which 30 are PXE clinical studies.

In the above video, Terry recounts the inspiring journey of how she and her husband worked for two decades with scientists worldwide to advance research on PXE in hopes of developing therapeutic treatments. She also explains her current work as president and CEO of Genetic Alliance to help individuals, families and communities participate in scientific research and promote the sharing of health data to improve health.

Previously: A look at aging and longevity in this “unprecedented” time in history, Parents turn to data after son is diagnosed with ultra-rare disease, Nobel Laureate Michael Levitt explains why “biology is information rich” at Big Data in Biomedicine, At Big Data in Biomedicine, Stanford’s Lloyd Minor focuses on precision health and  Experts at Big Data in Biomedicine: Bigger, better datasets and technology will benefit patients

Big data, Stanford News, Videos

What is big data?

What is big data?

We’ve written a lot about “big data” and the field of data science here on Scope. But for those readers who are still fuzzy on what exactly big data is, or how it’s being used to improve human health, Lloyd Minor, MD, dean of the medical school, and researchers with Stanford’s Biomedical Data Science Initiative (BDSI) are here to help. In the video above they offer their own definitions of big data, discuss how Stanford is leading the way in advancing the field, and share examples of how this so-called “digitization of life” will come to benefit all patients.

Previously: At Big Data in Biomedicine, Stanford’s Lloyd Minor focuses on precision healthExperts at Big Data in Biomedicine: Bigger, better datasets and technology will benefit patientsExamining the potential of big data to transform health careRising to the challenge of harnessing big data to benefit patients and A call to use the “tsunami of biomedical data” to preserve life and enhance health

Applied Biotechnology, Big data, Cancer, Genetics, Research, Science, Stanford News

Peeking into the genome of a deadly cancer pinpoints possible new treatment

Peeking into the genome of a deadly cancer pinpoints possible new treatment

small cell lung cancerSmall cell lung cancer is one of the most deadly kinds of cancers. Typically this aggressive disease is diagnosed fairly late in its course, and the survival rates are so dismal that doctors are reluctant to even subject the patient to surgery to remove the tumor for study. As a result, little is known about the molecular causes of this type of cancer, and no new treatments have been approved by the Food and Drug Administration since 1995.

Now a massive collaboration among researchers around the world, including the University of Cologne in Germany and Stanford, has resulted in the collection of more than 100 human small cell lung cancer tumors. Researchers sequenced the genomes of the tumors and identified some key steps in their development. They also found a potential new weak link for treatment.

The findings were published today in Nature, and Stanford cancer researcher Julien Sage, PhD, one of three co-senior authors of the paper, provided some details in an email:

With this larger number of specimens analyzed, a more detailed picture of the mutations that contribute to the development of small cell lung cancer now emerges. These studies confirmed what was suspected before, that loss of function of the two tumor suppressor genes, Rb and p53, is required for tumor initiation. Importantly, these analyses also identified new therapeutic targets.

The researchers also saw that, in about 25 percent of cases, the Notch protein receptor was also mutated. This protein sits on the surface of a cell; when Notch binds, it initiates a cascade of signaling events within the cell to control its development and growth. As Sage explained:

The mutations in the Notch recepetor were indicative of loss of function, suggesting that Notch normally suppresses small cell lung cancer development. Indeed, when graduate student Jing Lim in my lab activated Notch in mice genetically engineered to develop small cell lung cancer, we found a potent suppression of tumor development. These data identify the Notch signaling pathway as a novel therapeutic target in a cancer type for which new therapies are critically needed.

This is not Sage’s first foray into fighting small cell lung cancer. In 2013, he collaborated with other researchers at Stanford, including oncologist Joel Neal, MD, PhD, to identify a class of antidepressants as a possible therapy for the disease.

Previously: Gene-sequencing rare tumors – and what it means for cancer research and treatment, Listening in on the Ras pathway identifies new target for cancer therapy and Big data = big finds: Clinical trial for deadly lung cancer launched by Stanford study
Image by Yale Rosen

Big data, BigDataMed15, Chronic Disease, Genetics, Videos

Parents turn to data after son is diagnosed with ultra-rare disease

Parents turn to data after son is diagnosed with ultra-rare disease

Keynote talks and presentations from the 2015 Big Data in Biomedicine conference at Stanford are now available on the Stanford YouTube channel. To continue the discussion of how big data can be harnessed to improve the practice of medicine and enhance human health, we’re featuring a selection of the videos on Scope.

Four years ago, Matthew Might, PhD, and his wife, Christina, learned that their son Bertrand was the first person to be diagnosed with ultra-rare genetic disorder called N-Glycanase Disorder. At the 2015 Big Data in Biomedicine conference at Stanford, Might recounted the story of his son’s medical odyssey and explained how a team of Duke University researchers used whole-exome sequencing, which is a protein-focused variant of whole-genome sequencing, on himself, his wife and Bertrand to arrive at his son’s diagnosis.

Watch the video above to find out how Might and his family, who turned a deaf ear to doctors’ advice that nothing could be done for their son, harnessed the power of the Internet to identify 35 more patients with the same disorder and are now leading the charge in helping scientists better understand the disorder.

Previously: Nobel Laureate Michael Levitt explains why “biology is information rich” at Big Data in Biomedicine, At Big Data in Biomedicine, Stanford’s Lloyd Minor focuses on precision health, Experts at Big Data in Biomedicine: Bigger, better datasets and technology will benefit patients, On the move: Big Data in Biomedicine goes mobile with discussion on mHealth and Big Data in Biomedicine panelists: Genomics’ future is bright

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