Rare Genomics Institute Quantifies Crowdsourcing, Weighs in on Patient Engagement

Reporter: Aviva Lev-Ari, PhD, RN


By Allison Proffitt

July 30, 2015 | If you ask Jimmy Lin to describe his role in patient engagement, he may well compare himself to a jet pack.

The president of the Rare Genomics Institute sees himself as an empowering accessory for the true heroes: parents of children with rare diseases.

The Rare Genomics Institute is an international nonprofit organization born about four years ago whose mission is to connect patients and their families with the tools, knowledge, and experts necessary to understand the cause of their rare diseases. RGI believes understanding starts with sequencing, and so it partners with research facilities able to perform sequencing and provide a clinician or scientist who can interpret the data. RGI also hosts a fundraising platform to help families raise funds to access those resources.

Last month Lin was appointed by the Patient-Centered Outcomes Research Institute (PCORI) as a member of its Advisory Panel on Patient Engagement. The panel’s goal is to help PCORI refine research funding priorities and ensure that PCORI supports outcomes that matter to patients and other healthcare decision makers.

“It’s no longer researchers at the top and subjects at the bottom. We’re now calling them participants and they should have a role, and they should be able to get back their data,” Lin says. “They should be at the table from the very beginning… They should be acting as co-PI’s [principle investigators].”

That’s very much the case for RGI’s work, Lin says. Each family that applies to work with RGI gets a personal patient advocate. The advocate walks the family through the process to apply for sequencing and raise funds if necessary. While RGI does everything it can to help, a bulk of the work does fall to patients—or in most cases, a young patient’s family.

“We see the parents as the true heroes. They work day and night to see the child through the ups and downs. We see ourselves as the cheerleaders, as the jetpacks to the families, to help these amazing parents access the resources they need to.”

Peer Reviewing Crowdsourcing

While RGI can help with one of the biggest hurdles—access to experts—it does not have the funding to pay all of the fees associated with genomic sequencing and analysis. To bridge the gap, RGI set up a crowdfunding platform for its families.

The funding model is particularly interesting to Lin. He wanted to delve more into how crowdsourcing works to raise funds, but also how it can, “create a community, and educate that community about the scientific impact of genomic medicine.”

With a grant from the Templeton Foundation, in May RGI announced the Amplify Hope Initiative, a study of how crowdfunding can promote scientific research to help rare disease patients. In the first phase of the study, RGI interviewed experts in crowdfunding—individuals who have raised millions for their own children, companies who host crowdfunding platforms, and professional fundraisers—and gathered best practices.

Phase two launched in mid-June. Patients for whom RGI has determined medical need are eligible for the Amplify Hope Initiative. These patients need a physician referral and must show that they have exhausted other genetic panels and microarrays, and thus would benefit from exome sequencing. Two existing RGI sites, Ambry Genetics and Baylor Miraca Genetics Laboratories, will conduct the sequencing. Crowdfunding partners to the project include CrowdRise, Indiegogo Life and YouCaring.

Once enrolled, these patients will begin a free, 30-day Crowdfunding Bootcamp. “We’ve created a curriculum that includes everything we’ve learned,” Lin says.

The program will help families understand available fundraising platforms, plot campaigns, reach out to their networks, and leverage social media more effectively with eye-catching images and videos. Top fundraising experts will be conducting free webinars and offering remote support to showcase the most effective email templates and messaging strategies.

Meanwhile, RGI will be comparing incentive mechanisms and determining how various metrics predict the success of fundraising. When the study is complete, all of the findings will be shared. “At the end, we’ll be posting all of the best practices and making that free and available to everyone who’s interested,” Lin says.

Mr. Lin Goes to Washington

Lin will bring what he’s learned to the PCORI advisory panel on patient engagement as well. He already has a recommendation for how to better serve patients, particularly those with rare diseases for whom clinical trials are always N=1.

“As we move into the genomic era of medicine… we shouldn’t practice genetic exceptionalism in thinking that this is a super special test. Every other test we give back raw results. We should be able to give [genomic test] raw results.”

Lin acknowledges the challenges and advocates for education along with the returned data, but believes especially for rare disease patients having the raw data is crucial.

“For our patients who are on this diagnostic odyssey, the first interpretation may not give them the answer. If they want to take that data to another researcher they often are prohibited from doing that… Clinical genomicists are not doing reinterpretation most of the time. The onus is really on the patient.”

Pressed to clarify what he means by “raw results”, he advocates sharing what patients want—VCFs, aligned BAM files, even FASTQ files. “I think if patients really want their FASTQ files… that could be very helpful,” but he clarifies: “I don’t think all patients are going to ask for their raw data.”


Hybrid Imaging 3D Model of a Human Heart by Cardiac Imaging Techniques: CT and Echocardiography

Reporter: Aviva Lev-Ari, PhD, RN


Group creates 3D printed heart with CT, echo data

By Eric Barnes, AuntMinnie.com staff writer

June 29, 2015 — In what they are calling a major advance, researchers from Michigan have created a 3D model of a human heart using data from two separate cardiac imaging techniques: CT and echocardiography. They believe that such hybrid 3D models will be more accurate than those created from just one imaging modality.

The study team from Spectrum Health Helen DeVos Children’s Hospital in Grand Rapids, MI, hailed the proof-of-concept study as the first use of hybrid imaging in the creation of a 3D heart.

3D image of heart model

3D image of heart model. Image courtesy of Spectrum Health.

Hybrid 3D printing integrates the best aspects of two or more imaging modalities, potentially enhancing diagnosis and improving interventional and surgical planning, said lead author Jordan Gosnell, a cardiac sonographer at the hospital. Previous 3D printing models used only a single modality, which is less accurate than merging two or more datasets.

The study also opens the way for hybrid 3D printing techniques to be used in combination with a third modality: cardiac MR, the study team said in a statement accompanying the results.

First, the researchers used software to register images from CT and 3D transesophageal echocardiography (TEE) scans; they then selectively integrated the datasets to produce the anatomic model of the heart. The results provide more detailed and anatomically accurate 3D renderings and printed models than are available from a single modality, which may allow clinicians to improve their diagnosis and treatment of heart disease.

Each imaging modality has different strengths, and combining the modalities leads to improved results, according to the researchers:

  • CT enhances the outside anatomy of the heart.
  • MRI is superior for the interior of the heart, including the right and left ventricles and the heart’s muscular tissue.
  • 3D TEE offers the best visualization of valve anatomy.

The work was presented at the 2015 Catheter Interventions in Congenital, Structural, and Valvular Heart Disease (CSI) meeting in Frankfurt, Germany, by study co-author Dr. Joseph Vettukattil, who has performed research with 3D and 4D echocardiography. Vettukattil developed the use of multiplanar reformatting (MPR) in echocardiography to evaluate complex heart defects.

“This is a huge leap for individualized medicine in cardiology and congenital heart disease,” Vettukattil said in the statement. “The technology could be beneficial to cardiologists and surgeons. The model will promote better diagnostic capability and improved interventional and surgical planning, which will help determine whether a condition can be treated via transcatheter route or if it requires surgery.”

3D printing from MRI untangles congenital heart surgery, November 21, 2014

Dassault unveils 3D virtual heart model, May 20, 2014

Researchers launch library of 3D heart models, April 18, 2013

Giant virtual reality chamber boosts 3D echo accuracy, August 2, 2007





Originally posted on Education & Advocacy:

Personalized medicine is turning a corner. FDA approved four new indications for personalized medicine in July of 2015, a record for the agency and the field. As you may remember, 20 percent of FDA’s 2014 drug approvals were personalized medicines. At this rate, that percentage will likely be matched or exceeded this year.

But what does that mean for the field?

It means policymakers throughout the health care ecosystem have to play catch up, because ethically and scientifically there is no going back. Science has led us here because personalized medicines save and extend lives. They keep people at work, enjoying their families and contributing to society. They reduce morbidity and the adverse health events associated with older treatments. And for some patients they extend lives long enough to allow for participation in promising clinical trials, which can extend lives even further.

For evidence of personalized medicine’s potential, look no…

View original 342 more words

Cancer, Respiration and the Peril of the Heart in Cancer Patients

Author and Curator: Larry H Bernstein, MD FCAP


Curator: Aviva Lev-Ari, PhD, RN



Cancer and Respiration

Otto Heinrich Warburg, a German physiologist, observed that tumor cells utilize glycolysis more than their normal counterpart cells despite being in normal oxygen conditions (the “Warburg Effect”). In 1931, Warburg won a Nobel Prize for his work on mitochondria. Subsequently he formulated the Warburg Hypothesis, that the cause of cancer is defective mitochondria.

  • The hypothesis focused on the measurement of RESPIRATION
  • Mitochondrion was not then known and referred to as grana
  • He referred to work by Pasteur – 60 years earlier, and the Meyerhof ratio

the discovery of “oncogenes” that directly caused cancer led researchers to believe that the Warburg Hypothesis for cancer causation was simply wrong. As the data on cancer-causing genes became both more comprehensive and more productive, cancer research switched to decoding genes, and a generation of researchers began ignoring metabolism as a factor – very good observation

The connection – LKB1, a gene causing 30% of lung cancers and 25% of cervical cancers was directly activating the enzyme AMPK, known to modulate diabetes and metabolism.

Dr. Shaw asked himself two seminal questions:

  1. “What did a diabetes gene have to do with cancer?
  2. did the cancer gene have anything to do with diabetes?”

AMPK responds to caloric restriction, exercise, hypoxia, low glucose, and metabolic hormones such as ghrelin or adiponectin.

metformin operates through LKB1 and AMPK to lower blood glucose. Since it is well-tolerated, it is the frontline treatment for type 2 diabetes with more than 120 million people taking it every day. However, as Dr. Shaw had postulated, at this time it was also becoming known that metformin reduces the risk of cancer in diabetic patients.

AMPK directly shuts off a major oncogene called TOR, but it only does so when nutrients are low. This oncogene is the causal biochemical event in a number of human cancers, including kidney cancer, tuberous sclerosis, and LAM.

metformin and phenformin both inhibit mitochondria; however, phenformin is nearly 50 times as potent as metformin. Dr. Shaw and his postdoctoral fellows tested both metformin and phenformin as chemotherapeutic agents in mice genetically engineered to mutate different cancer genes in adult lung cells, which results in the mice developing advanced-stage lung tumors. Only in mice lacking the LKB1 cancer gene did Dr. Shaw and his team observe that, after three weeks of treatment with phenformin, there was a major reduction in tumor burden in the mice.

Knowledge of this leads to a profound impact on therapies for cancer because, as Dr. Shaw now knew, it was possible to interfere pharmacologically with this pathway. Disruptions of the “fuel sensing” mechanism means that with cancer cells, they could cause nutrient and oxygen deprivation. This had the medically important effect of signaling AMPK to arrest cell growth. The cancer cells would be influenced to cease proliferating.


The heart. Myxomas…metastatic other. Myxosarcomas? Not myocardiosarcomas. Myocardiocyte is absolutely dependent on oxygen. Apoptosis is not impaired, even though autophagy is needed for repair.


From Our Cardiovascular Disease – Volume Three


Heart – Correlation between Cancer and Cardiovascular Diseases

Causes Reuben Shaw, Ph.D., a geneticist and researcher at the Salk
Institute: Metabolism Influences Cancer

Aviva Lev-Ari, PhD, RN

st-and-researcher-at-the-salk-institute-metabolism-influences-cancer/ Heart Tumors: Etiology and Classification

Aviva Lev-Ari, PhD, RN

lassification/  Amyloidosis with Cardiomyopathy

Author: Larry H Bernstein, MD, FACP


Biomarkrs Stabilizers that prevent Transthyretin-mediated Cardiomyocyte
Amyloidotic Toxicity

Larry H. Bernstein, MD, FCAP

ansthyretin-mediated-cardiomyocyte-amyloidotic-toxicity/  Cancer Symptom Science: On the Mechanisms underlying the Expression
of Cancer-related Symptoms

Aviva Lev-Ari, PhD, RN


Therapies Cardio-oncology and Onco-Cardiology Programs: Treatments for Cancer
Patients with a History of Cardiovascular Disease

Aviva Lev-Ari, PhD, RN

cular-disease/ Radiation and Chemotherapy Therapy: The Pharmacological Risk for
Developing Cardiovascular Disease

Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2014/01/08/20316/ 3rd Annual Canadian Cardiac Oncology Network Conference, June 20 –
21, 2013, Ottawa Convention Centre

Aviva Lev-Ari, PhD, RN


Israel’s Chief Scientist on Mastering the Art of Public-Private Partnership.

This article was published by Gil Press in Forbes.

3D Printing Options:  Printing 3D plastic structures in macroscopic scale or Printing in DNA at the Nanoscale

Reporter: Aviva Lev-Ari, PhD, RN



3D ‘printouts’ at the nanoscale using self-assembling DNA structures

FRI, JUL 24, 2015 04:50 EST

[PRESS RELEASE 22 July 2015] A novel way of making 3D nanostructures from DNA is described in a study published in the renowned journal Nature . The study was led by researchers at Sweden’s Karolinska Institutet who collaborated with a group at Finland’s Aalto University. The new technique makes it possible to synthesize 3D DNA origami structures that are also able to tolerate the low salt concentrations inside the body, which opens the way for completely new biological applications of DNA nanotechnology. The design process is also highly automated, which enables the creation of synthetic DNA nanostructures of remarkable complexity.

The team behind the study likens the new approach to a 3D printer for nanoscale structures. The user draws the desired structure, in the form of a polygon object, in 3D software normally used for computer-aided design or animation. Graph-theoretic algorithms and optimization techniques are then used to calculate the DNA sequences needed to produce the structure.

When the synthesized DNA sequences are combined in a salt solution, they assemble themselves into the correct structure. One of the big advantages of building nanostructures out of DNA is that the bases bind to each other through base-paring in a predictable fashion.

“This new method makes it very easy to design DNA nanostructures and gives more design freedom,” says study leader Björn Högberg from the Department of Medical Biochemistry and Biophysics at Karolinska Institutet. “We can now make structures that were impossible to design previously and we can do it in the same way as one might draw a 3D structure for printing out in macroscopic scale, but instead of making it out of plastic, we print it in DNA at the nanoscale.”

Using this technique, the team has built a ball, spiral, rod and bottle-shaped structure, and a DNA printout of the so-called Stanford Bunny, which is a common test model for 3D modelling. Apart from being simpler compared to former ways of making DNA origami, the method – importantly – does not require high concentrations of magnesium salt.

“For biological applications, the most crucial difference is that we can now create structures that can be folded in, and remain viable in, physiological salt concentrations that are more suitable for biological applications of DNA nanostructures,” explains Dr Högberg.

“ An advantage of the automated design process is that one can now deal systematically with even quite complex structures. Advanced computing methods are likely to be a key enabler in the scaling of DNA nanotechnology from fundamental studies towards groundbreaking applications,” says Professor Pekka Orponen, who directed the team at the Aalto University Computer Science Department.

The possible applications are many. The team at Karolinska Institutet has previously made a DNA nano-caliper used for studying cell signalling. The new technique makes it possible to conduct similar biological experiments in a way that resembles conditions within cells even more closely. DNA nanostructures have also been used to make targeted capsules able to deliver cancer drugs direct to tumour cells, which can reduce the amount of drugs needed.

The study was financed by grants from several bodies, including the Swedish Research Council, the Swedish Foundation for Strategic Research and the Knut and Alice Wallenberg Foundation.

Publication: ‘DNA rendering of polyhedral meshes at the nanoscale’ , Erik Benson, Abdulmelik Mohammed, Johan Gardell, Sergej Masich, Eugen Czeizler, Pekka Orponen & Björn Högberg, Nature , online 23 July 2015, doi: 10.1038/nature14586.

For further information, please contact:
Björn Högberg, PhD, Associate Professor
Department of Medical Biochemistry and Biophysics, Karolinska Institutet
Tel: +46 (0)8 524 870 36
Email: bjorn.hogberg@ki.se

Pekka Orponen, Professor
Department of Computer Science, Aalto University
Tel: +358 (0)500 819 491
Email: pekka.orponen@aalto.fi

Contact the Press Office and download photo:   ki.se/pressroom

Karolinska Institutet is one of the world’s leading medical universities. Its vision is to significantly contribute to the improvement of human health. Karolinska Institutet accounts for over 40 per cent of the medical academic research conducted in Sweden and offers the country´s broadest range of education in medicine and health sciences. The Nobel Assembly at Karolinska Institutet selects the Nobel laureates in Physiology or Medicine.




Drug Discovery & Structural Biology: A Massively Multitask Networks Architecture – Collaboration between Stanford and Google

Reporter: Aviva Lev-Ari, PhD, RN

Massively Multitask Networks for Drug Discovery

Bharath Ramsundar*,†, ◦ RBHARATH@STANFORD.EDU


Patrick Riley◦ PFR@GOOGLE.COM

Dale Webster◦ DRW@GOOGLE.COM

David Konerding◦ DEK@GOOGLE.COM


( *Equal contribution, †Stanford University, ◦Google Inc.)


Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architectures at scale, we gather large amounts of data from public sources to create a dataset of nearly 40 million measurements across more than 200 biological targets. We investigate several aspects of the multitask framework by performing a series of empirical studies and obtain some interesting results:

(1) massively multitask networks obtain predictive accuracies significantly better than single-task methods,

(2) the predictive power of multitask networks improves as additional tasks and data are added,

(3) the total amount of data and the total number of tasks both contribute significantly to multitask improvement, and

(4) multitask networks afford limited transferability to tasks not in the training set.

Our results underscore the need for greater data sharing and further algorithmic innovation to accelerate the drug discovery process



Large-Scale Machine Learning for Drug Discovery

Posted: Monday, March 02, 2015

Discovering new treatments for human diseases is an immensely complicated challenge; Even after extensive research to develop a biological understanding of a disease, an effective therapeutic that can improve the quality of life must still be found. This process often takes years of research, requiring the creation and testing of millions of drug-like compounds in an effort to find a just a few viable drug treatment candidates. These high-throughput screens are often automated in sophisticated labs and are expensive to perform.

Recently, deep learning with neural networks has been applied in virtual drug screening1,2,3, which attempts to replace or augment the high-throughput screening process with the use of computational methods in order to improve its speed and success rate.4 Traditionally, virtual drug screening has used only the experimental data from the particular disease being studied. However, as the volume of experimental drug screening data across many diseases continues to grow, several research groups have demonstrated that data from multiple diseases can be leveraged with multitask neural networks to improve the virtual screening effectiveness.

In collaboration with the Pande Lab at Stanford University, we’ve released a paper titled “Massively Multitask Networks for Drug Discovery“, investigating how data from a variety of sources can be used to improve the accuracy of determining which chemical compounds would be effective drug treatments for a variety of diseases. In particular, we carefully quantified how the amount and diversity of screening data from a variety of diseases with very different biological processes can be used to improve the virtual drug screening predictions.

Using our large-scale neural network training system, we trained at a scale 18x larger than previous work with a total of 37.8M data points across more than 200 distinct biological processes. Because of our large scale, we were able to carefully probe the sensitivity of these models to a variety of changes in model structure and input data. In the paper, we examine not just the performance of the model but why it performs well and what we can expect for similar models in the future. The data in the paper represents more than 50M total CPU hours.


Google, Stanford say big data is key to deep learning for drug discovery

The researches explain the Premise of their methodology:

The efficacy of multitask learning is directly related to the availability of relevant data. Hence, obtaining greater amounts of data is of critical importance for improving the state of the art. Major pharmaceutical companies possess vast private stores of experimental measurements; our work provides a strong argument that increased data sharing could result in benefits for all.

More data will maximize the benefits achievable using current architectures, but in order for algorithmic progress to occur, it must be possible to judge the performance of proposed models against previous work. It is disappointing to note that all published applications of deep learning to virtual screening (that we are aware of) use distinct datasets that are not directly comparable. It remains to future research to establish standard datasets and performance metrics for this field.

. . .

Although deep learning offers interesting possibilities for virtual screening, the full drug discovery process remains immensely complicated. Can deep learning—coupled with large amounts of experimental data—trigger a revolution in this field? Considering the transformational effect that these methods have had on other fields, we are optimistic about the future.




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