big data

There are hundreds of pregnancy apps available. So what is unique about the NIH’s new crowdsourcing project called PregSource?

“We think PregSource is different because we offer the security and reliability of having been developed at the NIH with the participation of well regarded organizations. We’ve developed the content with expert input at every stage. Their are no ads at PregSource. And, of course, we will never, ever share or sell the data with a commercial organization.”

That's Caroline Signore, Principal Investigator at PregSource who says the project began in November of 2017 out of the need for data on typical pregnancies. There was a lot of research and attention, she says at the outset of today’s show, on pregnancy problems, but there has been a big lack of understanding on normal pregnancy with women in the current age. 

PregSource is for any pregnant woman at any stage, and commitment can be at various levels. How does the site incentivize women to share their data and stay committed?

Importantly, data from the site will be available for researchers. What goals does Caroline have for the site in this direction? How will she define success for PregSource?

A major chapter in the history of medicine has been written by UC San Francisco. They are writing the next for precision medicine.

Keith Yamamoto is in both chapters. Since the 1970s, he has been a researcher of cellular and molecular biology at UCSF and now serves as Vice Chancellor of Science Policy and Strategy as well. His career has always had these two parallel tracks of scientist and policy administrator. He has sat on various review boards at the NIH and chaired the Board of Life Sciences at the National Academy of Sciences where he worked with a group that would stimulate President Obama to launch his Precision Medicine Initiative (PMI) back in 2015. Today he shares with us what that was like for him.

Though the political wind blows a different direction at the national level these days, the momentum continues for the PMI at NIH, funded, as Keith points out, by congress with the Faster Cures bill for ten years.

And at UCSF? They are just getting started in precision medicine. With an illustrious history in medicine, an enviable location at the tech hub of the world, and resourced to the gills—the university can boast its sixth year in a row of being the top recipient of NIH funds, and rides around the top of the list for private contributions--UCSF is also one of the hosting universities for California’s own Precision Medicine Initiative, which just a few days ago received a huge boost from Governor Brown's new budget.

What is the university doing with all this gold? Keith says UCSF is building a machine, a precision medicine platform, that brings together data from all the disparate places we’ve talked about here on the program—the omics data, wearables information, electronic health records, etc—into an information commons similar to Google Maps, creating a "knowledge network." It’s a story we’re all familiar with, and if anyone can do it, certainly this is the place.

In addition, Keith describes one of the many collaborations UCSF is involved in called ATOM, or Accelerating Therapeutics for Opportunities in Medicine, a public-private consortium that brings the idea of big data sharing to drug discovery at the pre competitive level.

“What we’re moving is the starting line, rather than the finish line", says Keith.

The NIH has spearheaded similar efforts before. Perhaps with UCSF’s star computer science power this effort will go new places.

For those in our audience attending next week’s Precision Medicine World Conference in Silicon Valley, you can hear Keith talk on Accelerating Cancer Therapies and Exploiting Longitudinal Patient Data: Partnering to Achieve Precision Medicine.

Mike Snyder is well known in the genomics community for his iPOP (integrated personal omics profiling) study. Profiling himself with hundreds of thousands of measurements each day over a period of seven years and a group of a hundred others for about three years, he and his team at Stanford have shown that sequencing and other omics data can be used to predict Type II diabetes, cancer, heart problems and other disease. He’s also published numerous papers comparing NGS instruments. Now he is expanding iPOP with a whole new set of tools: over the counter wearable devices.

Though Fitbit’s sales may be down, Mike says wearables are hot. His team has found that there are over 1,000 health related wearable devices on the market today. He predicts that we will all be wearing them, using data that will be centralized onto the "dashboard" of our smart phones to drive our health decisions.

Those who have used wearables have used them mostly as “activity monitors,” and they tire of the devices after about three months. His lab, says Mike, is looking at wearables differently by using them as “health monitors.”

“The power of these devices is that they will measure continuously your basic physiological parameters, and we think that complements the other sorts of data that we’ve been collecting quite nicely. We actually think these devices can be used to tell when you’re getting sick.”

Just as when omics data predicted his own onset of Type II diabetes, Mike says wearables data helped him quickly diagnose his contraction of Lyme disease. The data in the recent two year study also showed when three others were getting sick—their heart rates went way up over baseline.

What about all the wild goose chases and the chance for hypchondria?

“I’m a believer in letting the data tell us what’s going on,” he says. "I didn’t know my blood oxygen level dropped on flights. In hindsight, it makes a lot of sense. And that’s what everyone says, 'it makes a lot of sense.' But most people didn’t know that. This could be a big issue for those with pulmonary illnesses.”

We end with a brief discussion of Mike’s new book: “Genomics and Personalized Medicine: What Everyone Needs to Know."

In a keynote talk this week for the online Genetics and Genomics conference, computational biology whiz, John Quackenbush, listed some pretty wild correlations found by a Harvard Business School student when he mixed some large data sets.  For example, U.S. spending on science, space, and technology corresponds directly with suicides by hanging, strangulation and suffocation.  We never would have guessed it without the help of big data.

In an L.A. Times article entitled, Why whole genome testing hurts more than it helps, two authors argue that the number of possible connections and patterns in analyzing the three billion bases in the human genome is just "astronomical" and most of the time quite irrelevant to patient care.  They offer more examples of dubious correlations coming from big data seets.  In one study done on 5 million Ontario patients, Canadian statisticians looked for the correlation between astrological signs and hospital diagnoses and found -- are you ready for this? --  "Leos were significantly more likely to be admitted for digestive tract bleeding and Sagittarians were significantly more likely to be admitted for upper-arm fractures.”

Are these spurious correlations the reason that Quackenbush has taken up boxing?  It's the old fashioned methodology:  bang some new relevant correlations into the head.

And finally, we reluctantly come up with several reasons why aging scientists should retire.  There is no mandatory retirement age in the U.S. and younger researchers are understandably feeling neglected by the NIH.  Numbers show that NIH funding awarded to researchers over 65 has doubled since 1998.  And a new plan by the NIH to come up with "emeritus" grants encouraging researchers to wind things down just isn't taking off.  

What to do, what to do?  Enjoy our weekly wrap on life science news.

Direct-to-consumer (DTC) genetic testing has had a bumpy ride.

Back in 2010, Pathway Genomics and Walgreens made a deal to sell DTC genetic tests in thousands of Walgreens drugstores. Within 48 hours of the deal being announced, it collapsed. The FDA sent a letter to Pathway basically asking them what the hell they were doing. Walgreens quickly elected to put the kibosh on the partnership.

Since then, Pathway has reinvented itself as an “information technology company with a genetic testing lab on the side,” according to today's guest, Ardy Arianpour, Pathway’s Chief Commercial Officer.

Late last year Pathway announced a partnership to use IBM’s supercomputer, Watson, to power a new “killer app” called Panorama. This will be a “smart" app available later this year, Ardy says, that will incorporate data from wearables and biomedical literature (through Watson), and be able to recommend certain genetic tests that the company will offer.

However, this time Pathway is being more careful about selling the genetic tests. While the app will be available to every consumer, all genetic tests provided by Pathway must be ordered by a physician. The app becomes then really an educational tool for consumers which might lead them into discussions with their doctors. Ardy says Pathway is developing a separate app for physicians as well.

It's a new day for DTC genetic testing. The FDA just approved for the first time a DTC test offered by 23andMe. Might Pathway's Panorama app with accompanying tests find the right balance between protection of the consumer and the freedom to access our own genetic data?