Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
2019 Koch Institute Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA
The 18th Annual Koch Institute Summer Symposium on June 14, 2019 at MIT’s Kresge Auditorium will focus on Machine Learning and Cancer.
Both fields are undergoing dramatic changes, and their integration holds great promise for cancer research, diagnostics, and therapeutics. Cancer treatment and research have advanced rapidly with an increasing reliance on data-driven decisions. The volume, complexity, and diversity of research and clinical data—from genomics and single-cell molecular and image-based profiles to histopathology, clinical imaging, and medical records—far surpasses the capacity of individual scientists and physicians. However, they offer a remarkable opportunity to new approaches for data science and machine learning to provide holistic and intelligible interpretations to trained experts and patients alike. These advances will make it possible to provide far better diagnostics, discover possible chemical pathways for de novo synthesis of therapeutic compounds, predict accurately the risk of individuals for development of specific cancers years before metastatic spread, and determine the combination of agents that will stimulate immune rejection of a tumor or selectively induce the death of all cells in a tumor.
The symposium will address these issues through three sessions:
Machine Learning in Cancer Research: the Need and the Opportunity
Machine Learning to Decipher Cellular and Molecular Mechanisms in Cancer
Machine Learning into the Clinic
Sessions will be followed by a panel discussion of broadly informed experts moderated by MIT President Emerita Susan Hockfield.
Introductory remarks will be given by symposium co-chairs and Koch Institute faculty members Regina Barzilay, Aviv Regev and Phillip Sharp.
Keynote Speakers | Machine Learning in Cancer Research: the Need and the Opportunity
James P. Allison, PhD
MD Anderson Cancer Center
Regina Barzilay, PhD
MIT Computer Science and Artificial Intelligence Lab, Koch Institute for Integrative Cancer Research at MIT
Aviv Regev, PhD
Broad Institute, Koch Institute for Integrative Cancer Research at MIT
Session Speakers
Michael R. Angelo, MD, PhD
Stanford Unviersity
Andrew Beck
PathAI
Stephen H. Friend, MD, PhD
Sage Bionetworks
Tommi Jaakkola, PhD
MIT Computer Science and Artificial Intelligence Lab
Dana Pe’er, PhD
Memorial Sloan Kettering Cancer Center
Peter Sorger, PhD
Harvard Medical School
Olga Troyanskaya, PhD
Princeton University
Brian Wolpin, MD
Dana-Farber Cancer Institute
Panel Discussion | Big Data, Computation and the Future of Health Care
James (Jay) Bradner, MD
Novartis
Clifford A. Hudis, MD
American Society of Clinical Oncology
Constance D. Lehman, MD, PhD
Massachusetts General Hospital
Norman (Ned) Sharpless, MD
National Cancer Institute
Moderator: Susan Hockfield, PhD
Koch Institute for Integrative Cancer Research at MIT
Tuesday, January 8, was another busy day in San Francisco for the JP Morgan Healthcare Conference. One interesting sideline was the idea that the current government shutdown could complicate some deals. Kent Thiry, chief executive officer of dialysis provider DaVita, who is working on the sale of its medical group to UnitedHealth Group this quarter, said, “We couldn’t guarantee that even if the government wasn’t shut down, but we and the buyer are both working toward that goal with the same intensity if not more.”
And in a slightly amusing bit of synchrony, U.S.Food and Drug Administration (FDA)Commissioner Scott Gottlieb’s keynote address that was delivered by way of video conference from Washington, D.C., had his audio cut out in the middle of the presentation. Gottlieb was talking about teen nicotine use and continued talking, unaware that his audio had shut off for 30 seconds. When it reconnected, the sound quality was reportedly poor.
bluebird bio’s chief executive officer, Nick Leschly, gave an update of his company’s pipeline, with a particular emphasis on a proposed payment model for its upcoming LentiGlobin, a gene therapy being evaluated for transfusion-dependent ß-thalassemia (TDT). The gene therapy is expected to be approved in Europe this year and in the U.S. in 2020. Although the price hasn’t been set, figures up to $2.1 million per treatment have been floated. Bluebird is proposing a five-year payment program, a pledge to not raise prices above CPI, and no costs after the payment period.
Eli Lilly’s chief executive officer David Ricks, just days after acquiringLoxo Oncology, offered up projections for this year, noting that 45 percent of its revenue will be created by drugs launched in 2015. Those include Trulicity, Taltz and Verzenio. The company also expects to launch two new molecular entities this year—nasal glucagons, a rescue medicine for high blood sugar (hyperglycemia), and Lasmiditan, a rescue drug for migraine headaches.
CNBC’s Jim CramerinterviewedAllergan chief executive officer Brent Saunders, in particular discussing the fact the company’s shares traded in 2015 for $331.15 but were now trading for $145.60. Cramer noted that the company’s internal fundamentals were strong, with multiple pipeline assets and a strong leadership team. Some of the stock problems are related to what Saunders said were “unforced errors,” including intellectual property rights to Restasis, its dry-eye drug, and Allergan’s dubious scheme to protect those patents by transferring the rights to the Saint Regis Mohawk Tribe in New York. On the positive side, the company’s medical aesthetics portfolio, dominated by Botox, is very strong and the overall market is expected to double.
One of the big areas of conversation is so-called “flyover tech.” Biopharma startups are dominant in Boston and in San Francisco, but suddenly venture capital investors have realized there’s a lot going on in between. New York City-based Radian Capital, for example, invests exclusively in markets outside major U.S. cities.
“At Radian, we partner with entrepreneurs who have built their businesses with a focus on strong economics rather than growth at all costs,” Aly Lovett, partner at Radian, told The Observer. “Historically, given the amount of money required to stand up a product, the software knowledge base, and coastal access to capital, health start-ups were concentrated in a handful of cities. As those dynamics have inverted and as the quality of living becomes a more important factor in attracting talent, we’re not seeing a significant increase in the number of amazing companies being built outside of the Bay Area.”
“Flyover companies” mentioned include Bind in Minneapolis, Minnesota; Solera Health in Phoenix, Arizona; ClearDATA in Austin, Texas; Healthe, in Eden Prairie, Minnesota; HistoSonics in Ann Arbor, Michigan; and many others.
Only a month after its record-breaking IPO, Moderna Therapeutics’ chief executive officer Stephane Bancel, spent time both updating the company’s clinical pipeline and justifying the company’s value despite the stock dropping off 26 percent since the IPO. Although one clinical program, a Zika vaccine, mRNA-1325, has been abandoned, the company has three new drugs coming into the clinic: mRNA-2752 for solid tumors or lymphoma; mRNA-4157, a Personalized Cancer Vaccine with Merck; and mRNA-5671, a KRAS cancer vaccine. The company also submitted an IND amendment to the FDA to add an ovarian cancer cohort to its mRNA-2416 program.
One interesting bit of trivia, supplied on Twitter by Rasu Shrestha, chief innovation officer for the University of Pittsburgh Medical Center, this year at the conference, 33 female chief executive officers were presenting corporate updates … compared to 19 men named Michael. Well, it’s a start.
And for another bit of trivia, Elisabeth Bik, of Microbiome Digest, tweeted, “San Francisco prices are so out of control that one hotel is charging the equivalent of $21.25 for a cup of coffee during a JPMorgan conference.”
Other posts on the JP Morgan 2019 Healthcare Conference on this Open Access Journal include:
37th Annual J.P. Morgan HEALTHCARE CONFERENCE: #JPM2019 for Jan. 8, 2019; Opening Videos, Novartis expands Cell Therapies, January 7 – 10, 2019, Westin St. Francis Hotel | San Francisco, California
Reporter: Stephen J. Williams, PhD
The annual J.P. Morgan Healthcare Conference is the largest and most informative healthcare investment symposium in the industry, bringing together industry leaders, emerging fast-growth companies, innovative technology creators, and members of the investment community.
Joe Biden on the Fight Against Cancer
Former Vice President of the United States joined the J.P. Morgan Healthcare Conference to discuss cancer initiatives.
Novartis Talks Move to Cell and Gene Therapies at JPM
Published: Jan 08, 2019By Alex Keown from Biospace.com
Denis Linine / Shutterstock
Following a strong post-hoc analysis of mid-stage data in the fall of 2018, Novartis announced this morning the company’s experimental humanized anti-P-selectin monoclonal antibody was crizanlizumab granted Breakthrough Therapy Status by the U.S.Food and Drug Administration (FDA).
Crizanlizumab received the designation as a treatment for the prevention of vaso-occlusive crises (VOCs) in patients of all genotypes with sickle cell disease (SCD). VOCs, which can be extremely painful for patients, happen when multiple blood cells stick to each other and to blood vessels, causing blockages.
The designation was awarded following results from the Phase II SUSTAIN trial, which showed that crizanlizumab reduced the median annual rate of VOCs leading to health care visits by 45.3 percent compared to placebo. The SUSTAIN study also showed that crizanlizumab significantly increased the percentage of patients who did not experience any VOCs vs placebo, 35.8 percent vs. 16.9 percent.
The FDA designation came one day after the Swiss pharma giant laid out its map for a future of success, sustainability and, if things work out, respect from consumers. In an interview with CNBC Monday, Novartis Chief Executive Officer Vas Narasimhan noted that the company is looking to become an entity that doesn’t draw its profits from treating disease, but will make money by providing cures. He pointed to the moves Novartis has made toward gene and cellular therapies that have the potential to cure patients of various diseases in what many researchers hope could be a “one-and-done” treatment. Narasimhan told CNBC that cures are what society wants and that is something they will value. The challenge will be determining the payment system.
As an example, the company is eying potential approval of a gene therapy for spinal muscular atrophy (SMA), a fatal genetic disease marked by progressive, debilitating muscle weakness in infants and toddlers. Novartis’ gene therapy Zolgensma is expected to be approved by the FDA this year and could have a price tag of between $4 and $5 million. While significantly high, non-profit SMA groups have already suggested that the gene therapy treatment could be more cost-effective than Spinraza, the only approved SMA treatment on the market.
During its presentation at J.P. Morgan, Novartis pointed to the moves it has made as the company pivots to this future of gene and cell therapies. The presentation noted that over the course of 2018, the company made several deals to sell off non-essential businesses, such as the $13 billion sale of its share of a consumer health business to partner GlaxoSmithKline. Not only that, but Novartis also made significant acquisitions to reshape its portfolio, including the $8.7 billion acquisition of AveXis for the SMA gene therapy. The deal for AveXis wasn’t the only gene therapy deal the company struck. Novartis began 2018 with a deal for Spark Therapeutics’ gene therapy Luxturna, a one-time gene therapy to restore functional vision in children and adult patients with biallelic mutations of the RPE65 (retinal pigment epithelial 65 kDa protein) gene.
In his interview with CNBC, Narasimhan said the company is about “platforms,” which also includes radio-ligand therapy. The company forged ahead in that area with two acquisitions, Advanced Accelerator Applications and Endocyte. Radiopharmaceuticals like Endocyte’s Lu-PSMA-617 are innovative medicinal formulations containing radioisotopes used clinically for both diagnosis and therapy. When the Endocyte deal was announced, Novartis noted the field is expected to become an increasingly important treatment option for patients, as well as a key growth driver for the company’s oncology business.
Other posts on the JP Morgan 2019 Healthcare Conference on this Open Access Journal include:
Role of Informatics in Precision Medicine: Notes from Boston Healthcare Webinar: Can It Drive the Next Cost Efficiencies in Oncology Care?
Reporter: Stephen J. Williams, Ph.D.
Boston Healthcare sponsored a Webinar recently entitled ” Role of Informatics in Precision Medicine: Implications for Innovators”. The webinar focused on the different informatic needs along the Oncology Care value chain from drug discovery through clinicians, C-suite executives and payers. The presentation, by Joseph Ferrara and Mark Girardi, discussed the specific informatics needs and deficiencies experienced by all players in oncology care and how innovators in this space could create value. The final part of the webinar discussed artificial intelligence and the role in cancer informatics.
Below is the mp4 video and audio for this webinar. Notes on each of the slides with a few representative slides are also given below:
Please click below for the mp4 of the webinar:
worldwide oncology related care to increase by 40% in 2020
big movement to participatory care: moving decision making to the patient. Need for information
cost components focused on clinical action
use informatics before clinical stage might add value to cost chain
Key unmet needs from perspectives of different players in oncology care where informatics may help in decision making
Needs of Clinicians
– informatic needs for clinical enrollment
– informatic needs for obtaining drug access/newer therapies
2. Needs of C-suite/health system executives
– informatic needs to help focus of quality of care
– informatic needs to determine health outcomes/metrics
3. Needs of Payers
– informatic needs to determine quality metrics and managing costs
– informatics needs to form guidelines
– informatics needs to determine if biomarkers are used consistently and properly
– population level data analytics
What are the kind of value innovations that tech entrepreneurs need to create in this space? Two areas/problems need to be solved.
innovations in data depth and breadth
need to aggregate information to inform intervention
Different players in value chains have different data needs
Data Depth: Cumulative Understanding of disease
Data Depth: Cumulative number of oncology transactions
technology innovators rely on LEGACY businesses (those that already have technology) and these LEGACY businesses either have data breath or data depth BUT NOT BOTH; (IS THIS WHERE THE GREATEST VALUE CAN BE INNOVATED?)
NEED to provide ACTIONABLE as well as PHENOTYPIC/GENOTYPIC DATA
data depth more important in clinical setting as it drives solutions and cost effective interventions. For example Foundation Medicine, who supplies genotypic/phenotypic data for patient samples supplies high data depth
technologies are moving to data support
evidence will need to be tied to umbrella value propositions
Informatic solutions will have to prove outcome benefit
How will Machine Learning be involved in the healthcare value chain?
increased emphasis on real time datasets – CONSTANT UPDATES NEED TO OCCUR. THIS IS NOT HAPPENING BUT VALUED BY MANY PLAYERS IN THIS SPACE
Interoperability of DATABASES Important! Many Players in this space don’t understand the complexities integrating these datasets
Other Articles on this topic of healthcare informatics, value based oncology, and healthcare IT on this OPEN ACCESS JOURNAL include:
Can Blockchain Technology and Artificial Intelligence Cure What Ails Biomedical Research and Healthcare
Curator: Stephen J. Williams, Ph.D.
Updated 12/18/2018
In the efforts to reduce healthcare costs, provide increased accessibility of service for patients, and drive biomedical innovations, many healthcare and biotechnology professionals have looked to advances in digital technology to determine the utility of IT to drive and extract greater value from healthcare industry. Two areas of recent interest have focused how best to use blockchain and artificial intelligence technologies to drive greater efficiencies in our healthcare and biotechnology industries.
More importantly, with the substantial increase in ‘omic data generated both in research as well as in the clinical setting, it has become imperative to develop ways to securely store and disseminate the massive amounts of ‘omic data to various relevant parties (researchers or clinicians), in an efficient manner yet to protect personal privacy and adhere to international regulations. This is where blockchain technologies may play an important role.
A recent Oncotarget paper by Mamoshina et al. (1) discussed the possibility that next-generation artificial intelligence and blockchain technologies could synergize to accelerate biomedical research and enable patients new tools to control and profit from their personal healthcare data, and assist patients with their healthcare monitoring needs. According to the abstract:
The authors introduce new concepts to appraise and evaluate personal records, including the combination-, time- and relationship value of the data. They also present a roadmap for a blockchain-enabled decentralized personal health data ecosystem to enable novel approaches for drug discovery, biomarker development, and preventative healthcare. In this system, blockchain and deep learning technologies would provide the secure and transparent distribution of personal data in a healthcare marketplace, and would also be useful to resolve challenges faced by the regulators and return control over personal data including medical records to the individual.
The review discusses:
Recent achievements in next-generation artificial intelligence
Basic concepts of highly distributed storage systems (HDSS) as a preferred method for medical data storage
Open source blockchain Exonium and its application for healthcare marketplace
A blockchain-based platform allowing patients to have control of their data and manage access
How advances in deep learning can improve data quality, especially in an era of big data
Advances in Artificial Intelligence
Integrative analysis of the vast amount of health-associated data from a multitude of large scale global projects has proven to be highly problematic (REF 27), as high quality biomedical data is highly complex and of a heterogeneous nature, which necessitates special preprocessing and analysis.
Increased computing processing power and algorithm advances have led to significant advances in machine learning, especially machine learning involving Deep Neural Networks (DNNs), which are able to capture high-level dependencies in healthcare data. Some examples of the uses of DNNs are:
Prediction of drug properties(2, 3) and toxicities(4)
Generative Adversarial Networks (https://arxiv.org/abs/1406.2661): requires good datasets for extensive training but has been used to determine tumor growth inhibition capabilities of various molecules (7)
Recurrent neural Networks (RNN): Originally made for sequence analysis, RNN has proved useful in analyzing text and time-series data, and thus would be very useful for electronic record analysis. Has also been useful in predicting blood glucose levels of Type I diabetic patients using data obtained from continuous glucose monitoring devices (8)
Transfer Learning: focused on translating information learned on one domain or larger dataset to another, smaller domain. Meant to reduce the dependence on large training datasets that RNN, GAN, and DNN require. Biomedical imaging datasets are an example of use of transfer learning.
One and Zero-Shot Learning: retains ability to work with restricted datasets like transfer learning. One shot learning aimed to recognize new data points based on a few examples from the training set while zero-shot learning aims to recognize new object without seeing the examples of those instances within the training set.
Highly Distributed Storage Systems (HDSS)
The explosion in data generation has necessitated the development of better systems for data storage and handling. HDSS systems need to be reliable, accessible, scalable, and affordable. This involves storing data in different nodes and the data stored in these nodes are replicated which makes access rapid. However data consistency and affordability are big challenges.
Blockchain is a distributed database used to maintain a growing list of records, in which records are divided into blocks, locked together by a crytosecurity algorithm(s) to maintain consistency of data. Each record in the block contains a timestamp and a link to the previous block in the chain. Blockchain is a distributed ledger of blocks meaning it is owned and shared and accessible to everyone. This allows a verifiable, secure, and consistent history of a record of events.
Data Privacy and Regulatory Issues
The establishment of the Health Insurance Portability and Accountability Act (HIPAA) in 1996 has provided much needed regulatory guidance and framework for clinicians and all concerned parties within the healthcare and health data chain. The HIPAA act has already provided much needed guidance for the latest technologies impacting healthcare, most notably the use of social media and mobile communications (discussed in this article Can Mobile Health Apps Improve Oral-Chemotherapy Adherence? The Benefit of Gamification.). The advent of blockchain technology in healthcare offers its own unique challenges however HIPAA offers a basis for developing a regulatory framework in this regard. The special standards regarding electronic data transfer are explained in HIPAA’s Privacy Rule, which regulates how certain entities (covered entities) use and disclose individual identifiable health information (Protected Health Information PHI), and protects the transfer of such information over any medium or electronic data format. However, some of the benefits of blockchain which may revolutionize the healthcare system may be in direct contradiction with HIPAA rules as outlined below:
Issues of Privacy Specific In Use of Blockchain to Distribute Health Data
Blockchain was designed as a distributed database, maintained by multiple independent parties, and decentralized
Linkage timestamping; although useful in time dependent data, proof that third parties have not been in the process would have to be established including accountability measures
Blockchain uses a consensus algorithm even though end users may have their own privacy key
Applied cryptography measures and routines are used to decentralize authentication (publicly available)
Blockchain users are divided into three main categories: 1) maintainers of blockchain infrastructure, 2) external auditors who store a replica of the blockchain 3) end users or clients and may have access to a relatively small portion of a blockchain but their software may use cryptographic proofs to verify authenticity of data.
YouTube video on How #Blockchain Will Transform Healthcare in 25 Years (please click below)
In Big Data for Better Outcomes, BigData@Heart, DO->IT, EHDN, the EU data Consortia, and yes, even concepts like pay for performance, Richard Bergström has had a hand in their creation. The former Director General of EFPIA, and now the head of health both at SICPA and their joint venture blockchain company Guardtime, Richard is always ahead of the curve. In fact, he’s usually the one who makes the curve in the first place.
Please click on the following link for a podcast on Big Data, Blockchain and Pharma/Healthcare by Richard Bergström:
References
Mamoshina, P., Ojomoko, L., Yanovich, Y., Ostrovski, A., Botezatu, A., Prikhodko, P., Izumchenko, E., Aliper, A., Romantsov, K., Zhebrak, A., Ogu, I. O., and Zhavoronkov, A. (2018) Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare, Oncotarget9, 5665-5690.
Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., and Zhavoronkov, A. (2016) Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data, Molecular pharmaceutics13, 2524-2530.
Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., and Lu, H. (2017) Deep-Learning-Based Drug-Target Interaction Prediction, Journal of proteome research16, 1401-1409.
Gao, M., Igata, H., Takeuchi, A., Sato, K., and Ikegaya, Y. (2017) Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds, Journal of pharmacological sciences133, 70-78.
Putin, E., Mamoshina, P., Aliper, A., Korzinkin, M., Moskalev, A., Kolosov, A., Ostrovskiy, A., Cantor, C., Vijg, J., and Zhavoronkov, A. (2016) Deep biomarkers of human aging: Application of deep neural networks to biomarker development, Aging8, 1021-1033.
Vandenberghe, M. E., Scott, M. L., Scorer, P. W., Soderberg, M., Balcerzak, D., and Barker, C. (2017) Relevance of deep learning to facilitate the diagnosis of HER2 status in breast cancer, Scientific reports7, 45938.
Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., and Zhavoronkov, A. (2017) druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico, Molecular pharmaceutics14, 3098-3104.
Ordonez, F. J., and Roggen, D. (2016) Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, Sensors (Basel)16.
December 5, 2018 | The boom of blockchain and distributed ledger technologies have inspired healthcare organizations to test the capabilities of their data. Quest Diagnostics, in partnership with Humana, MultiPlan, and UnitedHealth Group’s Optum and UnitedHealthcare, have launched a pilot program that applies blockchain technology to improve data quality and reduce administrative costs associated with changes to healthcare provider demographic data.
The collective body, called Synaptic Health Alliance, explores how blockchain can keep only the most current healthcare provider information available in health plan provider directories. The alliance plans to share their progress in the first half of 2019.
Providing consumers looking for care with accurate information when they need it is essential to a high-functioning overall healthcare system, Jason O’Meara, Senior Director of Architecture at Quest Diagnostics, told Clinical Informatics News in an email interview.
“We were intentional about calling ourselves an alliance as it speaks to the shared interest in improving health care through better, collaborative use of an innovative technology,” O’Meara wrote. “Our large collective dataset and national footprints enable us to prove the value of data sharing across company lines, which has been limited in healthcare to date.”
O’Meara said Quest Diagnostics has been investing time and resources the past year or two in understanding blockchain, its ability to drive purpose within the healthcare industry, and how to leverage it for business value.
“Many health care and life science organizations have cast an eye toward blockchain’s potential to inform their digital strategies,” O’Meara said. “We recognize it takes time to learn how to leverage a new technology. We started exploring the technology in early 2017, but we quickly recognized the technology’s value is in its application to business to business use cases: to help transparently share information, automate mutually-beneficial processes and audit interactions.”
Quest began discussing the potential for an alliance with the four other companies a year ago, O’Meara said. Each company shared traits that would allow them to prove the value of data sharing across company lines.
“While we have different perspectives, each member has deep expertise in healthcare technology, a collaborative culture, and desire to continuously improve the patient/customer experience,” said O’Meara. “We also recognize the value of technology in driving efficiencies and quality.”
Following its initial launch in April, Synaptic Health Alliance is deploying a multi-company, multi-site, permissioned blockchain. According to a whitepaper published by Synaptic Health, the choice to use a permissioned blockchain rather than an anonymous one is crucial to the alliance’s success.
“This is a more effective approach, consistent with enterprise blockchains,” an alliance representative wrote. “Each Alliance member has the flexibility to deploy its nodes based on its enterprise requirements. Some members have elected to deploy their nodes within their own data centers, while others are using secured public cloud services such as AWS and Azure. This level of flexibility is key to growing the Alliance blockchain network.”
As the pilot moves forward, O’Meara says the Alliance plans to open ability to other organizations. Earlier this week Aetna and Ascension announced they joined the project.
“I am personally excited by the amount of cross-company collaboration facilitated by this project,” O’Meara says. “We have already learned so much from each other and are using that knowledge to really move the needle on improving healthcare.”
November 29, 2018 | The US Department of Health and Human Services (HHS) is making waves in the blockchain space. The agency’s Division of Acquisition (DA) has developed a new system, called Accelerate, which gives acquisition teams detailed information on pricing, terms, and conditions across HHS in real-time. The department’s Associate Deputy Assistant Secretary for Acquisition, Jose Arrieta, gave a presentation and live demo of the blockchain-enabled system at the Distributed: Health event earlier this month in Nashville, Tennessee.
Accelerate is still in the prototype phase, Arrieta said, with hopes that the new system will be deployed at the end of the fiscal year.
HHS spends around $25 billion a year in contracts, Arrieta said. That’s 100,000 contracts a year with over one million pages of unstructured data managed through 45 different systems. Arrieta and his team wanted to modernize the system.
“But if you’re going to change the way a workforce of 20,000 people do business, you have to think your way through how you’re going to do that,” said Arrieta. “We didn’t disrupt the existing systems: we cannibalized them.”
The cannibalization process resulted in Accelerate. According to Arrieta, the system functions by creating a record of data rather than storing it, leveraging machine learning, artificial intelligence (AI), and robotic process automation (RPA), all through blockchain data.
“We’re using that data record as a mechanism to redesign the way we deliver services through micro-services strategies,” Arrieta said. “Why is that important? Because if you have a single application or data use that interfaces with 55 other applications in your business network, it becomes very expensive to make changes to one of the 55 applications.”
Accelerate distributes the data to the workforce, making it available to them one business process at a time.
“We’re building those business processes without disrupting the existing systems,” said Arrieta, and that’s key. “We’re not shutting off those systems. We’re using human-centered design sessions to rebuild value exchange off of that data.”
The first application for the system, Arrieta said, can be compared to department stores price-matching their online competitors.
It takes the HHS close to a month to collect the amalgamation of data from existing system, whether that be terms and conditions that drive certain price points, or software licenses.
“The micro-service we built actually analyzes that data, and provides that information to you within one second,” said Arrieta. “This is distributed to the workforce, to the 5,000 people that do the contracting, to the 15,000 people that actually run the programs at [HHS].”
This simple micro-service is replicated on every node related to HHS’s internal workforce. If somebody wants to change the algorithm to fit their needs, they can do that in a distributed manner.
Arrieta hopes to use Accelerate to save researchers money at the point of purchase. The program uses blockchain to simplify the process of acquisition.
“How many of you work with the federal government?” Arrieta asked the audience. “Do you get sick of reentering the same information over and over again? Every single business opportunity you apply for, you have to resubmit your financial information. You constantly have to check for validation and verification, constantly have to resubmit capabilities.”
Wouldn’t it be better to have historical notes available for each transaction? said Arrieta. This would allow clinical researchers to be able to focus on “the things they’re really good at,” instead of red tape.
“If we had the top cancer researcher in the world, would you really want her spending her time learning about federal regulations as to how to spend money, or do you want her trying to solve cancer?” Arrieta said. “What we’re doing is providing that data to the individual in a distributed manner so they can read the information of historical purchases that support activity, and they can focus on the objectives and risks they see as it relates to their programming and their objectives.”
Blockchain also creates transparency among researchers, Arrieta said, which says creates an “uncomfortable reality” in the fact that they have to make a decision regarding data, fundamentally changing value exchange.
“The beauty of our business model is internal investment,” Arrieta said. For instance, the HHS could take all the sepsis data that exists in their system, put it into a distributed ledger, and share it with an external source.
“Maybe that could fuel partnership,” Arrieta said. “I can make data available to researchers in the field in real-time so they can actually test their hypothesis, test their intuition, and test their imagination as it relates to solving real-world problems.”
Blockchain-based genomic data hub platform Shivom recently reached its $35 million hard cap within 15 seconds of opening its main token sale. Shivom received funding from a number of crypto VC funds, including Collinstar, Lateral, and Ironside.
The goal is to create the world’s largest store of genomic data while offering an open web marketplace for patients, data donors, and providers — such as pharmaceutical companies, research organizations, governments, patient-support groups, and insurance companies.
“Disrupting the whole of the health care system as we know it has to be the most exciting use of such large DNA datasets,” Shivom CEO Henry Ines told me. “We’ll be able to stratify patients for better clinical trials, which will help to advance research in precision medicine. This means we will have the ability to make a specific drug for a specific patient based on their DNA markers. And what with the cost of DNA sequencing getting cheaper by the minute, we’ll also be able to sequence individuals sooner, so young children or even newborn babies could be sequenced from birth and treated right away.”
While there are many solutions examining DNA data to explain heritage, intellectual capabilities, health, and fitness, the potential of genomic data has largely yet to be unlocked. A few companies hold the monopoly on genomic data and make sizeable profits from selling it to third parties, usually without sharing the earnings with the data donor. Donors are also not informed if and when their information is shared, nor do they have any guarantee that their data is secure from hackers.
Shivom wants to change that by creating a decentralized platform that will break these monopolies, democratizing the processes of sharing and utilizing the data.
“Overall, large DNA datasets will have the potential to aid in the understanding, prevention, diagnosis, and treatment of every disease known to mankind, and could create a future where no diseases exist, or those that do can be cured very easily and quickly,” Ines said. “Imagine that, a world where people do not get sick or are already aware of what future diseases they could fall prey to and so can easily prevent them.”
Shivom’s use of blockchain technology and smart contracts ensures that all genomic data shared on the platform will remain anonymous and secure, while its OmiX token incentivizes users to share their data for monetary gain.
Blockchain will secure the DNA database for 50 million citizens in the eighth-largest state in India. The government of Andhra Pradesh signed a Memorandum of Understanding with a German genomics and precision medicine start-up, Shivom, which announced to start the pilot project soon. The move falls in line with a trend for governments turning to population genomics, and at the same time securing the sensitive data through blockchain.
Andhra Pradesh, DNA, and blockchain
Storing sensitive genetic information safely and securely is a big challenge. Shivom builds a genomic data-hub powered by blockchain technology. It aims to connect researchers with DNA data donors thus facilitating medical research and the healthcare industry.
With regards to Andhra Pradesh, the start-up will first launch a trial to determine the viability of their technology for moving from a proactive to a preventive approach in medicine, and towards precision health. “Our partnership with Shivom explores the possibilities of providing an efficient way of diagnostic services to patients of Andhra Pradesh by maintaining the privacy of the individual data through blockchain technologies,” said J A Chowdary, IT Advisor to Chief Minister, Government of Andhra Pradesh.
Other Articles in this Open Access Journal on Digital Health include:
European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. wm2@ebi.ac.uk.
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European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK. fiona@ebi.ac.uk.
Abstract
The Ensembl Variant Effect Predictor is a powerful toolset for the analysis, annotation, and prioritization of genomic variants in coding and non-coding regions. It provides access to an extensive collection of genomic annotation, with a variety of interfaces to suit different requirements, and simple options for configuring and extending analysis. It is open source, free to use, and supports full reproducibility of results. The Ensembl Variant Effect Predictor can simplify and accelerate variant interpretation in a wide range of study designs.
Rare diseases can be difficult to diagnose due to low incidence and incomplete penetrance of implicated alleles however variant analysis of whole genome sequencing can identify underlying genetic events responsible for the disease (Nature, 2015). However, a large cohort is required for many WGS association studies in order to produce enough statistical power for interpretation (see post and here). To this effect major sequencing projects have been initiated worldwide including:
And although sequencing costs have dramatically been reduced over the years, the costs to determine the functional consequences of such variants remains high, as thorough basic research studies must be conducted to validate the interpretation of variant data with respect to the underlying disease, as only a small fraction of variants from a genome sequencing project will encode for a functional protein. Correct annotation of sequences and variants, identification of correct corresponding reference genes or transcripts in GENCODE or RefSeq respectively offer compelling challenges to the proper identification of sequenced variants as potential functional variants.
To this effect, the authors developed the Ensembl Variant Effect Predictor (VEP), which is a software suite that performs annotations and analysis of most types of genomic variation in coding and non-coding regions of the genome.
Summary of Features
Annotation: VEP can annotate two broad categories of genomic variants
Sequence variants with specific and defined changes: indels, base substitutions, SNVs, tandem repeats
Larger structural variants > 50 nucleotides
Species and assembly/genomic database support: VEP can analyze data from any species with assembled genome sequence and annotated gene set. VEP supports chromosome assemblies such as the latest GRCh38, FASTA, as well as transcripts from RefSeq as well as user-derived sequences
Transcript Annotation: VEP includes a wide variety of gene and transcript related information including NCBI Gene ID, Gene Symbol, Transcript ID, NCBI RefSeq ID, exon/intron information, and cross reference to other databases such as UniProt
Protein Annotation: Protein-related fields include Protein ID, RefSeq ID, SwissProt, UniParc ID, reference codons and amino acids, SIFT pathogenicity score, protein domains
Noncoding Annotation: VEP reports variants in noncoding regions including genomic regulatory regions, intronic regions, transcription binding motifs. Data from ENCODE, BLUEPRINT, and NIH Epigenetics RoadMap are used for primary annotation. Plugins to the Perl coding are also available to link other databases which annotate noncoding sequence features.
Frequency, phenotype, and citation annotation: VEP searches Ensembl databases containing a large amount of germline variant information and checks variants against the dbSNP single nucleotide polymorphism database. VEP integrates with mutational databases such as COSMIC, the Human Gene Mutation Database, and structural and copy number variants from Database of Genomic Variants. Allele Frequencies are reported from 1000 Genomes and NHLBI and integrates with PubMed for literature annotation. Phenotype information is from OMIM, Orphanet, GWAS and clinical information of variants from ClinVar.
Flexible Input and Output Formats: VEP supports input data format called “variant call format” or VCP, a standard in next-gen sequencing. VEP has the ability to process variant identifiers from other database formats. Output formats are tab deliminated and give the user choices in presentation of results (HTML or text based)
Choice of user interface
Online tool (VEP Web): simple point and click; incorporates Instant VEP Functionality and copy and paste features. Results can be stored online in cloud storage on Ensembl.
VEP script: VEP is available as a downloadable PERL script (see below for link) and can process large amounts of data rapidly. This interface is powerfully flexible with the ability to integrate multiple plugins available from Ensembl and GitHub. The ability to alter the PERL code and add plugins and code functions allows the flexibility to modify any feature of VEP.
VEP REST API: provides robust computational access to any programming language and returns basic variant annotation. Can make use of external plugins.
Watch Video on VES Web Version training on How to Analyze Your Sequence in VEP
Availability of data and materials
The dataset supporting the conclusions of this article is available from Illumina’s Platinum Genomes [93] and using the Ensembl release 75 gene set. Pre-built data sets are available for all Ensembl and Ensembl Genomes species [94]. They can also be downloaded automatically during set up whilst installing the VEP.
Nature. 2015 Mar 12;519(7542):223-8. doi: 10.1038/nature14135. PMID:25533962
Updated 11/15/2018
Research Points to Caution in Use of Variant Effect Prediction Bioinformatic Tools
Although we have the ability to use high throughput sequencing to identify allelic variants occurring in rare disease, correlation of these variants with the underlying disease is often difficult due to a few concerns:
For rare sporadic diseases, classical gene/variant association studies have proven difficult to perform (Meyts et al. 2016)
As Whole Exome Sequencing (WES) returns a considerable number of variants, how to differentiate the normal allelic variation found in the human population from disease-causing pathogenic alleles
For rare diseases, pathogenic allele frequencies are generally low
Therefore, for these rare pathogenic alleles, the use of bioinformatics tools in order to predict the resulting changes in gene function may provide insight into disease etiology when validation of these allelic changes might be experimentally difficult.
In a 2017 Genes & Immunity paper, Line Lykke Andersen and Rune Hartmann tested the reliability of various bioinformatic software to predict the functional consequence of variants of six different genes involved in interferon induction and sixteen allelic variants of the IFNLR1 gene. These variants were found in cohorts of patients presenting with herpes simplex encephalitis (HSE). Most of the adult population is seropositive for Herpes Simplex Virus (HSV) however a minor fraction (1 in 250,000 individuals per year) of HSV infected individuals will develop HSE (Hjalmarsson et al., 2007). It has been suggested that HSE occurs in individuals with rare primary immunodeficiencies caused by gene defects affecting innate immunity through reduced production of interferons (IFN) (Zhang et al., Lim et al.).
References
Meyts I, Bosch B, Bolze A, Boisson B, Itan Y, Belkadi A, et al. Exome and genome sequencing for inborn errors of immunity. J Allergy Clin Immunol. 2016;138:957–69.
Hjalmarsson A, Blomqvist P, Skoldenberg B. Herpes simplex encephalitis in Sweden, 1990-2001: incidence, morbidity, and mortality. Clin Infect Dis. 2007;45:875–80.
Zhang SY, Jouanguy E, Ugolini S, Smahi A, Elain G, Romero P, et al. TLR3 deficiency in patients with herpes simplex encephalitis. Science. 2007;317:1522–7.
Lim HK, Seppanen M, Hautala T, Ciancanelli MJ, Itan Y, Lafaille FG, et al. TLR3 deficiency in herpes simplex encephalitis: high allelic heterogeneity and recurrence risk. Neurology. 2014;83:1888–97.
Genes Immun. 2017 Dec 4. doi: 10.1038/s41435-017-0002-z.
We selected two sets of naturally occurring human missense allelic variants within innate immune genes. The first set represented eleven non-synonymous variants in six different genes involved in interferon (IFN) induction, present in a cohort of patients suffering from herpes simplex encephalitis (HSE) and the second set represented sixteen allelic variants of the IFNLR1 gene. We recreated the variants in vitro and tested their effect on protein function in a HEK293T cell based assay. We then used an array of 14 available bioinformatics tools to predict the effect of these variants upon protein function. To our surprise two of the most commonly used tools, CADD and SIFT, produced a high rate of false positives, whereas SNPs&GO exhibited the lowest rate of false positives in our test. As the problem in our test in general was false positive variants, inclusion of mutation significance cutoff (MSC) did not improve accuracy.
Methodology
Identification of rare variants
Genomes of nineteen Dutch patients with a history of HSE sequenced by WES and identification of novel HSE causing variants determined by filtering the single nucleotide polymorphisms (SNPs) that had a frequency below 1% in the NHBLI Exome Sequencing Project Exome Variant Server and the 1000 Genomes Project and were present within 204 genes involved in the immune response to HSV.
Identified variants (204) manually evaluated for involvement of IFN induction based on IDBase and KEGG pathway database analysis.
In order to validate the predictive value of the software, HEK293T cells, deficient in IRF3, MAVS, and IKKe/TBK1, were cotransfected with the nine variants of the aforementioned genes and a luciferase reporter under control of the IFN-b promoter and luciferase activity measured as an indicator of IFN signaling function. Western blot was performed to confirm the expression of the constructs.
Live Coverage: MedCity Converge 2018 Philadelphia: AI in Cancer and Keynote Address
Reporter: Stephen J. Williams, PhD
8:30 AM -9:15
Practical Applications of AI in Cancer
We are far from machine learning dictating clinical decision making, but AI has important niche applications in oncology. Hear from a panel of innovative startups and established life science players about how machine learning and AI can transform different aspects in healthcare, be it in patient recruitment, data analysis, drug discovery or care delivery.
Ayan: working at IBM and Thompon Rueters with structured datasets and having gone through his own cancer battle, he is now working in healthcare AI which has an unstructured dataset(s)
Carla: collecting medical images over the world, mainly tumor and calculating tumor volumetrics
Tufia: drug resistant breast cancer clinician but interested in AI and healthcareIT at Mayo
John: taking large scale datasets but a machine learning skeptic
moderator: how has imaging evolved?
Carla: ten times images but not ten times radiologists so stressed field needs help with image analysis; they have seen measuring lung tumor volumetrics as a therapeutic diagnostic has worked
moderator: how has AI affected patient recruitment?
Tufia: majority of patients are receiving great care but AI can offer profiles and determine which patients can benefit from tertiary care;
John: 1980 paper on no free lunch theorem; great enthusiasm about optimization algortihisms fell short in application; can extract great information from e.g. images
moderator: how is AI for healthcare delivery working at mayo?
Tufia: for every hour with patient two hours of data mining. for care delivery hope to use the systems to leverage the cognitive systems to do the data mining
John: problem with irreproducible research which makes a poor dataset: also these care packages are based on population data not personalized datasets; challenges to AI is moving correlation to causation
Carla: algorithisms from on healthcare network is not good enough, Google tried and it failed
John: curation very important; good annotation is needed; needed to go in and develop, with curators, a systematic way to curate medial records; need standardization and reproducibility; applications in radiometrics can be different based on different data collection machines; developed a machine learning model site where investigators can compare models on a hub; also need to communicate with patients on healthcare information and quality information
Ayan: Australia and Canada has done the most concerning AI and lifescience, healthcare space; AI in most cases is cognitive learning: really two types of companies 1) the Microsofts, Googles, and 2) the startups that may be more pure AI
Final Notes: We are at a point where collecting massive amounts of healthcare related data is simple, rapid, and shareable. However challenges exist in quality of datasets, proper curation and annotation, need for collaboration across all healthcare stakeholders including patients, and dissemination of useful and accurate information
9:15 AM–9:45 AM
Opening Keynote: Dr. Joshua Brody, Medical Oncologist, Mount Sinai Health System
The Promise and Hype of Immunotherapy
Immunotherapy is revolutionizing oncology care across various types of cancers, but it is also necessary to sort the hype from the reality. In his keynote, Dr. Brody will delve into the history of this new therapy mode and how it has transformed the treatment of lymphoma and other diseases. He will address the hype surrounding it, why so many still don’t respond to the treatment regimen and chart the way forward—one that can lead to more elegant immunotherapy combination paths and better outcomes for patients.
hodgkin’s lymphoma best responder to PD1 therapy (nivolumab) but hepatic adverse effects
CAR-T (chimeric BCR and TCR); a long process which includes apheresis, selection CD3/CD28 cells; viral transfection of the chimeric; purification
complete remissions of B cell lymphomas (NCI trial) and long term remissions past 18 months
side effects like cytokine release (has been controlled); encephalopathy (he uses a hand writing test to see progression of adverse effect)
Vaccines
teaching the immune cells as PD1 inhibition exhausting T cells so a vaccine boost could be an adjuvant to PD1 or checkpoint therapy
using Flt3L primed in-situ vaccine (using a Toll like receptor agonist can recruit the dendritic cells to the tumor and then activation of T cell response); therefore vaccine does not need to be produced ex vivo; months after the vaccine the tumor still in remission
versus rituximab, which can target many healthy B cells this in-situ vaccine strategy is very specific for the tumorigenic B cells
HoWEVER they did see resistant tumor cells which did not overexpress PD-L1 but they did discover a novel checkpoint (cannot be disclosed at this point)
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