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Archive for the ‘BioIT: BioInformatics, NGS, Clinical & Translational, Pharmaceutical R&D Informatics, Clinical Genomics, Cancer Informatics’ Category


re:Invent 2020 – Virtual 3 weeks Conference, Nov. 30 – Dec. 18, 2020: How Healthcare & Life Sciences leaders are using AWS to transform their businesses and innovate on behalf of their customers. 

 

Preview the tracks that will be available, along with the general agenda, on the website. 

 

https://reinvent.awsevents.com/

 

https://virtual.awsevents.com/agenda

 

awsreinvent-support@amazon.com

Marketplace

How to sell on AWS Marketplace

It covers how to list your software product on AWS Marketplace.
Migrate & build faster on AWS with services in AWS Marketplace

freelancers and  consulting firms to get work done on AWS, and about Professional Services in  AWS Marketplace
Keyword found in: 2 Tags

Natural Language Processing

Enterprise Intelligent Search with Amazon Kendra

Facilitate real-time access to crucial data with natural language processing, machine learning, and

[NEW LAUNCH!] Introducing Amazon QuickSight Q: Ask questions on data & get answers in seconds

With QuickSight Q, which uses natural language processing and semantic data understanding techniques,

[NEW LAUNCH!] Introducing Amazon QuickSight Q: Ask questions on data & get answers in seconds

Merlin uses natural language processing and semantic data understanding to make sense of the data.

[NEW LAUNCH!] Introducing Amazon QuickSight Q: Ask questions on data & get answers in seconds

Merlin uses natural language processing and semantic data understanding to make sense of the data.

BLOCKCHAIN

Nestlé brings supply chain transparency with Amazon Managed Blockchain

Using AWS and Amazon Managed Blockchain, Nestlé can store supply chain transactions in ways that are
Keyword found in: 3 Tags1 Details

What’s new in Amazon Managed Blockchain

scalable blockchain networks using popular open-source technologies.
Keyword found in: 3 Tags

Trust and transparency in live virtual events

experiences using the Amazon Interactive Video Service, Amazon Chime, Amazon QLDB, and Amazon Managed Blockchain
Keyword found in: 4 Tags1 Details

What’s new in Amazon Managed Blockchain

scalable blockchain networks using popular open-source technologies.
Keyword found in: 3 Tags

What’s new in Amazon Managed Blockchain

scalable blockchain networks using popular open-source technologies.
Keyword found in: 3 Tags

Trust and transparency in live virtual events

experiences using the Amazon Interactive Video Service, Amazon Chime, Amazon QLDB, and Amazon Managed Blockchain
Keyword found in: 4 Tags1 Details

Trust and transparency in live virtual events

experiences using the Amazon Interactive Video Service, Amazon Chime, Amazon QLDB, and Amazon Managed Blockchain
Keyword found in: 4 Tags1 Details

Introduction to Blockchain and Amazon Managed Blockchain

Blockchain can help you overcome those challenges.
Keyword found in: 3 Tags

Construire des workflows complexes de calcul avec AWS Step Functions et AWS Fargate

AWS propose différentes solutions pour gérer les charges de travail distribuées à grande échelle.
Keyword found in: 1 Tag1 Details

Financial Services Technology 2020 and Beyond: Embracing Disruption

In this report, we look at the technology forces that matter, from FinTech to Blockchain to robotics

AWS re:Invent 2020 – Life Sciences Attendee Guide

AWS re:Invent routinely fills several Las Vegas venues with standing-room only crowds, but we are bringing it to you with an all-virtual and free event this year. This year’s conference is gearing up to be our biggest yet and we have an exciting program planned with five keynotes, 18 leadership sessions, and over 500 breakout sessions beginning November 30. Hear how AWS experts and inspiring members of the Life Sciences & Genomics industry are using cloud technology to transform their businesses and innovate on the behalf of their customers.For Life Sciences attendees looking to get the most out of their experience, follow these steps:

  • Register for re:Invent.
  • Take a look at all of the Life Sciences sessions available, as well as lots of other information and additional activities, in our curated Life Sciences Attendee Guide coming soon!
  • Check back on this post regularly, as we’ll continually update it to reflect the newest information.

Life Sciences at re:Invent 2020

AWS enables pharma and biotech companies to transform every stage of the pharma value chain, with services that enhance data liquidity, operational excellence, and customer engagement. AWS is the trusted technology provider with the cost-effective compute and storage, machine learning capabilities, and customer-centric know how to help companies embrace innovation and bring differentiated therapeutics to market faster.

Dates and Presentation Title

BlockChain Architecture

DEC 1, 2020 | 12:00 AM – 12:20 AM EST
Nestlé brings supply chain transparency with Amazon Managed Blockchain
Chain of origin is the Nestlé answer to complete supply chain transparency, from crop to coffee cup, with technology at its heart. Today, consumers want to know about the quality of their product and know where it is sourced from. Using AWS and Amazon Man

… Learn More

Add To Calendar
Blockchain Retail

Nestlé brings supply chain transparency with Amazon Managed Blockchain

Thursday, December 10
DEC 10, 2020 | 3:15 PM – 3:45 PM EST
Trust and transparency in live virtual events
This session demonstrates how to build interactive, trusted, and transparent live virtual experiences using the Amazon Interactive Video Service, Amazon Chime, Amazon QLDB, and Amazon Managed Blockchain to capture cryptographic, immutable, and verifiable records of real-time audience interactions.
Add To Calendar
Architecture Blockchain

Trust and transparency in live virtual events

DEC 10, 2020 | 11:15 PM – 11:45 PM EST
Trust and transparency in live virtual events
This session demonstrates how to build interactive, trusted, and transparent live virtual experiences using the Amazon Interactive Video Service, Amazon Chime, Amazon QLDB, and Amazon Managed Blockchain to capture cryptographic, immutable, and verifiable records of real-time audience interactions.
Add To Calendar
Architecture Blockchain

Trust and transparency in live virtual events

Friday, December 11
DEC 11, 2020 | 7:15 AM – 7:45 AM EST
Trust and transparency in live virtual events
This session demonstrates how to build interactive, trusted, and transparent live virtual experiences using the Amazon Interactive Video Service, Amazon Chime, Amazon QLDB, and Amazon Managed Blockchain to capture cryptographic, immutable, and verifiable records of real-time audience interactions.
Add To Calendar
Architecture Blockchain

Trust and transparency in live virtual events

Tuesday, December 15
DEC 15, 2020 | 4:00 PM – 4:30 PM EST
What’s new in Amazon Managed Blockchain
Amazon Managed Blockchain is a fully managed service that makes it easy for you to create and manage scalable blockchain networks using popular open-source technologies. Blockchain technologies enable groups of organizations to securely transact and

… Learn More

Add To Calendar
Blockchain Financial Services

What’s new in Amazon Managed Blockchain

Wednesday, December 16
DEC 16, 2020 | 12:00 AM – 12:30 AM EST
What’s new in Amazon Managed Blockchain
Amazon Managed Blockchain is a fully managed service that makes it easy for you to create and manage scalable blockchain networks using popular open-source technologies. Blockchain technologies enable groups of organizations to securely transact and

… Learn More

Add To Calendar
Blockchain Financial Services

What’s new in Amazon Managed Blockchain

DEC 16, 2020 | 8:00 AM – 8:30 AM EST
What’s new in Amazon Managed Blockchain
Amazon Managed Blockchain is a fully managed service that makes it easy for you to create and manage scalable blockchain networks using popular open-source technologies. Blockchain technologies enable groups of organizations to securely transact and

… Learn More

Add To Calendar
Blockchain Financial Services

What’s new in Amazon Managed Blockchain

Life Science and Healthcare

DEC 1, 2020 | 12:00 AM – 12:20 AM EST
Building robots to help hospitals become safer and smarter
In hospitals and other healthcare venues, robots increasingly perform contactless delivery and autonomous maintenance services to reduce the risk of exposing patients and medical staff to harmful viruses and bacteria. In this session, see how Solaris JetBrain and M

… Learn More

Add To Calendar
Robotics IoT

Building robots to help hospitals become safer and smarter

华米基于AWS构建全球云上健康服务

Melhores práticas na construção de um data lake para a saúde

DEC 1, 2020 | 5:15 PM – 5:45 PM EST
Transform research environments with Service Workbench on AWS
Reenvison how research environments are spun up by reducing wait times from days to minutes. Service Workbench on AWS promotes repeatability, multi-institutional collaboration, and transparency in the research process. In this session, learn how Harvard Medical School is procuring and deploying domain-specific data, tools, and secure IT environments to accelerate research.
Add To Calendar
Public Sector Healthcare

Transform research environments with Service Workbench on AWS

Wednesday, December 2
DEC 2, 2020 | 1:15 AM – 1:45 AM EST
Transform research environments with Service Workbench on AWS
Reenvison how research environments are spun up by reducing wait times from days to minutes. Service Workbench on AWS promotes repeatability, multi-institutional collaboration, and transparency in the research process. In this session, learn how Harvard Medical School is procuring and deploying domain-specific data, tools, and secure IT environments to accelerate research.
Add To Calendar
Public Sector Healthcare

Transform research environments with Service Workbench on AWS

DEC 2, 2020 | 9:15 AM – 9:45 AM EST
Transform research environments with Service Workbench on AWS
Reenvison how research environments are spun up by reducing wait times from days to minutes. Service Workbench on AWS promotes repeatability, multi-institutional collaboration, and transparency in the research process. In this session, learn how Harvard Medical School is procuring and deploying domain-specific data, tools, and secure IT environments to accelerate research.
Add To Calendar
Public Sector Healthcare

Transform research environments with Service Workbench on AWS

DEC 2, 2020 | 11:00 AM – 11:30 AM EST
Reinventing medical imaging with machine learning on AWS
It is hard to imagine the future of medical imaging without machine learning (ML) as its central innovation engine. Countless researchers, developers, startups, and larger enterprises are engaged in building, training, and deploying ML solutions for medical i

… Learn More

Add To Calendar
Public Sector Healthcare

Reinventing medical imaging with machine learning on AWS

DEC 2, 2020 | 2:45 PM – 3:15 PM EST
Making healthcare more personal with MetroPlus Health
COVID has made a huge impact across the world, and organizations have had to adapt quickly to changing requirements as a result. Learn how MetroPlus Health, a New York City health plan covering over half a million people, leveraged AWS technology to quickly

… Learn More

Add To Calendar
Healthcare Public Sector

Making healthcare more personal with MetroPlus Health

DEC 2, 2020 | 4:15 PM – 4:45 PM EST
Securing protected health information and high-risk datasets
Join this session featuring Jonathan Cook, Chief Technology Officer at Arcadia, for a discussion around securing mission-critical and high-risk datasets such as personal health information (PHI) in the cloud. Learn how Arcadia developed a HITRUST CSF-certified plat

… Learn More

Add To Calendar
Healthcare Life Sciences

Securing protected health information and high-risk datasets

DEC 2, 2020 | 5:45 PM – 6:15 PM EST
ML and analytics addressing nationwide COVID-19 impact and recovery (sponsored … 
In this session, learn how Fractal.AI delivered a platform to help analyze data and make decisions related to COVID-19 progression for the government of Telangana, India, deploying it on AWS in five days. The solution, based on Intel processors, delivered more than 100 dashboards using anonymized government and public datasets with hundreds of thousands of COVID-19 d… Learn More
Add To Calendar
Public Sector Partner Solutions for Business

ML and analytics addressing nationwide COVID-19 impact and recovery (sponsored by Intel)

DEC 2, 2020 | 6:30 PM – 7:00 PM EST
How Vyaire uses AWS analytics to scale ventilator production & save lives
When COVID-19 hit the US, Vyaire Medical, one of the country’s only ventilator manufacturers, knew it would have to scale rapidly while still offering quality machinery. In order to scale to 20 times more than its usual production and gain insights from all its new

… Learn More

Add To Calendar
Analytics Healthcare

How Vyaire uses AWS analytics to scale ventilator production & save lives

DEC 2, 2020 | 7:00 PM – 7:30 PM EST
Reinventing medical imaging with machine learning on AWS
It is hard to imagine the future of medical imaging without machine learning (ML) as its central innovation engine. Countless researchers, developers, startups, and larger enterprises are engaged in building, training, and deploying ML solutions for medical i

… Learn More

Add To Calendar
Public Sector Healthcare

Reinventing medical imaging with machine learning on AWS

DEC 2, 2020 | 10:45 PM – 11:15 PM EST
Making healthcare more personal with MetroPlus Health
COVID has made a huge impact across the world, and organizations have had to adapt quickly to changing requirements as a result. Learn how MetroPlus Health, a New York City health plan covering over half a million people, leveraged AWS technology to quickly

… Learn More

Add To Calendar
Healthcare Public Sector

Making healthcare more personal with MetroPlus Health

Thursday, December 3
DEC 3, 2020 | 12:15 AM – 12:45 AM EST
Securing protected health information and high-risk datasets
Join this session featuring Jonathan Cook, Chief Technology Officer at Arcadia, for a discussion around securing mission-critical and high-risk datasets such as personal health information (PHI) in the cloud. Learn how Arcadia developed a HITRUST CSF-certified plat

… Learn More

Add To Calendar
Healthcare Life Sciences

Securing protected health information and high-risk datasets

DEC 3, 2020 | 1:45 AM – 2:15 AM EST
ML and analytics addressing nationwide COVID-19 impact and recovery (sponsored … 
In this session, learn how Fractal.AI delivered a platform to help analyze data and make decisions related to COVID-19 progression for the government of Telangana, India, deploying it on AWS in five days. The solution, based on Intel processors, delivered more than 100 dashboards using anonymized government and public datasets with hundreds of thousands of COVID-19 d… Learn More
Add To Calendar
Public Sector Partner Solutions for Business

ML and analytics addressing nationwide COVID-19 impact and recovery (sponsored by Intel)

DEC 3, 2020 | 2:30 AM – 3:00 AM EST
How Vyaire uses AWS analytics to scale ventilator production & save lives
When COVID-19 hit the US, Vyaire Medical, one of the country’s only ventilator manufacturers, knew it would have to scale rapidly while still offering quality machinery. In order to scale to 20 times more than its usual production and gain insights from all its new

… Learn More

Add To Calendar
Analytics Healthcare

How Vyaire uses AWS analytics to scale ventilator production & save lives

DEC 3, 2020 | 3:00 AM – 3:30 AM EST
Reinventing medical imaging with machine learning on AWS
It is hard to imagine the future of medical imaging without machine learning (ML) as its central innovation engine. Countless researchers, developers, startups, and larger enterprises are engaged in building, training, and deploying ML solutions for medical i

… Learn More

Add To Calendar
Public Sector Healthcare

Reinventing medical imaging with machine learning on AWS

DEC 3, 2020 | 6:45 AM – 7:15 AM EST
Making healthcare more personal with MetroPlus Health
COVID has made a huge impact across the world, and organizations have had to adapt quickly to changing requirements as a result. Learn how MetroPlus Health, a New York City health plan covering over half a million people, leveraged AWS technology to quickly

… Learn More

Add To Calendar
Healthcare Public Sector

Making healthcare more personal with MetroPlus Health

DEC 3, 2020 | 8:15 AM – 8:45 AM EST
Securing protected health information and high-risk datasets
Join this session featuring Jonathan Cook, Chief Technology Officer at Arcadia, for a discussion around securing mission-critical and high-risk datasets such as personal health information (PHI) in the cloud. Learn how Arcadia developed a HITRUST CSF-certified plat

… Learn More

Add To Calendar
Healthcare Life Sciences

Securing protected health information and high-risk datasets

DEC 3, 2020 | 9:45 AM – 10:15 AM EST
ML and analytics addressing nationwide COVID-19 impact and recovery (sponsored … 
In this session, learn how Fractal.AI delivered a platform to help analyze data and make decisions related to COVID-19 progression for the government of Telangana, India, deploying it on AWS in five days. The solution, based on Intel processors, delivered more than 100 dashboards using anonymized government and public datasets with hundreds of thousands of COVID-19 d… Learn More
Add To Calendar
Public Sector Partner Solutions for Business

ML and analytics addressing nationwide COVID-19 impact and recovery (sponsored by Intel)

DEC 3, 2020 | 10:30 AM – 11:00 AM EST
How Vyaire uses AWS analytics to scale ventilator production & save lives
When COVID-19 hit the US, Vyaire Medical, one of the country’s only ventilator manufacturers, knew it would have to scale rapidly while still offering quality machinery. In order to scale to 20 times more than its usual production and gain insights from all its new

… Learn More

Add To Calendar
Analytics Healthcare

How Vyaire uses AWS analytics to scale ventilator production & save lives

DEC 3, 2020 | 4:15 PM – 4:45 PM EST
Productionizing R workloads using Amazon SageMaker, featuring Siemens
R language and its 16,000+ packages dedicated to statistics and ML are used by statisticians and data scientists in industries such as energy, healthcare, life science, and financial services. Using R, you can run simulations and ML securely and at scale with Amazon S

… Learn More

Add To Calendar
Artificial Intelligence & Machine Learning Enterprise/Migration

Productionizing R workloads using Amazon SageMaker, featuring Siemens

DEC 3, 2020 | 6:15 PM – 6:45 PM EST
Accelerating the transition to telehealth with AWS
Learn how AWS is helping healthcare organizations develop and deploy telehealth solutions quickly and at scale. Join speakers from AWS and MedStar Health as they discuss their experience developing and deploying two call centers in less than a week using Amaz

… Learn More

Add To Calendar
Healthcare Public Sector

Accelerating the transition to telehealth with AWS

Friday, December 4
DEC 4, 2020 | 12:15 AM – 12:45 AM EST
Productionizing R workloads using Amazon SageMaker, featuring Siemens
R language and its 16,000+ packages dedicated to statistics and ML are used by statisticians and data scientists in industries such as energy, healthcare, life science, and financial services. Using R, you can run simulations and ML securely and at scale with Amazon S

… Learn More

Add To Calendar
Artificial Intelligence & Machine Learning Enterprise/Migration

Productionizing R workloads using Amazon SageMaker, featuring Siemens

DEC 4, 2020 | 2:15 AM – 2:45 AM EST
Accelerating the transition to telehealth with AWS
Learn how AWS is helping healthcare organizations develop and deploy telehealth solutions quickly and at scale. Join speakers from AWS and MedStar Health as they discuss their experience developing and deploying two call centers in less than a week using Amaz

… Learn More

Add To Calendar
Healthcare Public Sector

Accelerating the transition to telehealth with AWS

DEC 4, 2020 | 8:15 AM – 8:45 AM EST
Productionizing R workloads using Amazon SageMaker, featuring Siemens
R language and its 16,000+ packages dedicated to statistics and ML are used by statisticians and data scientists in industries such as energy, healthcare, life science, and financial services. Using R, you can run simulations and ML securely and at scale with Amazon S

… Learn More

Add To Calendar
Artificial Intelligence & Machine Learning Enterprise/Migration

Productionizing R workloads using Amazon SageMaker, featuring Siemens

DEC 4, 2020 | 10:15 AM – 10:45 AM EST
Accelerating the transition to telehealth with AWS
Learn how AWS is helping healthcare organizations develop and deploy telehealth solutions quickly and at scale. Join speakers from AWS and MedStar Health as they discuss their experience developing and deploying two call centers in less than a week using Amaz

… Learn More

Add To Calendar
Healthcare Public Sector

Accelerating the transition to telehealth with AWS

Tuesday, December 8
DEC 8, 2020 | 2:30 PM – 3:00 PM EST
How Erickson Living built a COVID-19 outbreak management solution
As innovators in independent living, assisted living, and skilled nursing care, Erickson Living responded to the COVID-19 outbreak by building an infectious disease management system with the AWS Data Lab to prevent the spread of the virus. This session focuses o

… Learn More

Add To Calendar
Analytics Life Sciences

How Erickson Living built a COVID-19 outbreak management solution

DEC 8, 2020 | 5:30 PM – 6:00 PM EST
Rapidly deploying social services on Amazon Connect
Organizations that respond to disruptive, large-scale events need the ability to rapidly scale and iterate on their contact centers to provide services to their constituents. Amazon Connect can be set up in minutes and scale to handle virtually any number of contact

… Learn More

Add To Calendar
Public Sector Business Apps (including Connect)

Rapidly deploying social services on Amazon Connect

DEC 8, 2020 | 5:30 PM – 6:00 PM EST
Improving data liquidity in Roche’s personalized healthcare platform
Roche’s personalized healthcare mission is to accelerate drug discovery and transform the patient journey by using digital technologies and advanced analytics to facilitate greater scientific collaboration and insight sharing. In this session, Roche shares ho

… Learn More

Add To Calendar
Life Sciences Artificial Intelligence & Machine Learning

Improving data liquidity in Roche’s personalized healthcare platform

DEC 8, 2020 | 10:30 PM – 11:00 PM EST
How Erickson Living built a COVID-19 outbreak management solution
As innovators in independent living, assisted living, and skilled nursing care, Erickson Living responded to the COVID-19 outbreak by building an infectious disease management system with the AWS Data Lab to prevent the spread of the virus. This session focuses o

… Learn More

Add To Calendar
Analytics Life Sciences

How Erickson Living built a COVID-19 outbreak management solution

Wednesday, December 9
DEC 9, 2020 | 1:30 AM – 2:00 AM EST
Rapidly deploying social services on Amazon Connect
Organizations that respond to disruptive, large-scale events need the ability to rapidly scale and iterate on their contact centers to provide services to their constituents. Amazon Connect can be set up in minutes and scale to handle virtually any number of contact

… Learn More

Add To Calendar
Public Sector Business Apps (including Connect)

Rapidly deploying social services on Amazon Connect

DEC 9, 2020 | 1:30 AM – 2:00 AM EST
Improving data liquidity in Roche’s personalized healthcare platform
Roche’s personalized healthcare mission is to accelerate drug discovery and transform the patient journey by using digital technologies and advanced analytics to facilitate greater scientific collaboration and insight sharing. In this session, Roche shares ho

… Learn More

Add To Calendar
Life Sciences Artificial Intelligence & Machine Learning

Improving data liquidity in Roche’s personalized healthcare platform

DEC 9, 2020 | 6:30 AM – 7:00 AM EST
How Erickson Living built a COVID-19 outbreak management solution
As innovators in independent living, assisted living, and skilled nursing care, Erickson Living responded to the COVID-19 outbreak by building an infectious disease management system with the AWS Data Lab to prevent the spread of the virus. This session focuses o

… Learn More

Add To Calendar
Analytics Life Sciences

How Erickson Living built a COVID-19 outbreak management solution

DEC 9, 2020 | 9:30 AM – 10:00 AM EST
Rapidly deploying social services on Amazon Connect
Organizations that respond to disruptive, large-scale events need the ability to rapidly scale and iterate on their contact centers to provide services to their constituents. Amazon Connect can be set up in minutes and scale to handle virtually any number of contact

… Learn More

Add To Calendar
Public Sector Business Apps (including Connect)

Rapidly deploying social services on Amazon Connect

DEC 9, 2020 | 9:30 AM – 10:00 AM EST
Improving data liquidity in Roche’s personalized healthcare platform
Roche’s personalized healthcare mission is to accelerate drug discovery and transform the patient journey by using digital technologies and advanced analytics to facilitate greater scientific collaboration and insight sharing. In this session, Roche shares ho

… Learn More

Add To Calendar
Life Sciences Artificial Intelligence & Machine Learning

Improving data liquidity in Roche’s personalized healthcare platform

DEC 9, 2020 | 10:30 AM – 11:00 AM EST
BlueJeans’ explosive growth journey with AWS during the pandemic
Global video provider BlueJeans (a Verizon company) supports employees working from home, healthcare providers shifting to telehealth, and educators moving to distance learning. With so many people now working from home, BlueJeans saw explosive gro

… Learn More

Add To Calendar
Telecommunications Media & Entertainment

BlueJeans’ explosive growth journey with AWS during the pandemic

DEC 9, 2020 | 5:15 PM – 5:45 PM EST
Using AI to automate clinical workflows
Learn how healthcare organizations can harness the power of AI and machine learning to automate clinical workflows, digitize medical information, extract and summarize medical information, protect patient data, and more. Using AWS document understand

… Learn More

Add To Calendar
Artificial Intelligence & Machine Learning Healthcare

Using AI to automate clinical workflows

DEC 9, 2020 | 5:45 PM – 6:15 PM EST
Building patient-centric virtualized trials
As clinical trials increasingly become decentralized and virtual, engaging effectively with patients can be challenging. In this session, hear how Evidation Health architects on AWS to create patient-centric experiences, ingests data from millions of devices in real time

… Learn More

Add To Calendar
Life Sciences Healthcare

Building patient-centric virtualized trials

DEC 9, 2020 | 6:30 PM – 7:00 PM EST
BlueJeans’ explosive growth journey with AWS during the pandemic
Global video provider BlueJeans (a Verizon company) supports employees working from home, healthcare providers shifting to telehealth, and educators moving to distance learning. With so many people now working from home, BlueJeans saw explosive gro

… Learn More

Add To Calendar
Telecommunications Media & Entertainment

BlueJeans’ explosive growth journey with AWS during the pandemic

Thursday, December 10
DEC 10, 2020 | 1:15 AM – 1:45 AM EST
Using AI to automate clinical workflows
Learn how healthcare organizations can harness the power of AI and machine learning to automate clinical workflows, digitize medical information, extract and summarize medical information, protect patient data, and more. Using AWS document understand

… Learn More

Add To Calendar
Artificial Intelligence & Machine Learning Healthcare

Using AI to automate clinical workflows

DEC 10, 2020 | 1:45 AM – 2:15 AM EST
Building patient-centric virtualized trials
As clinical trials increasingly become decentralized and virtual, engaging effectively with patients can be challenging. In this session, hear how Evidation Health architects on AWS to create patient-centric experiences, ingests data from millions of devices in real time

… Learn More

Add To Calendar
Life Sciences Healthcare

Building patient-centric virtualized trials

DEC 10, 2020 | 2:30 AM – 3:00 AM EST
BlueJeans’ explosive growth journey with AWS during the pandemic
Global video provider BlueJeans (a Verizon company) supports employees working from home, healthcare providers shifting to telehealth, and educators moving to distance learning. With so many people now working from home, BlueJeans saw explosive gro

… Learn More

Add To Calendar
Telecommunications Media & Entertainment

BlueJeans’ explosive growth journey with AWS during the pandemic

DEC 10, 2020 | 9:15 AM – 9:45 AM EST
Using AI to automate clinical workflows
Learn how healthcare organizations can harness the power of AI and machine learning to automate clinical workflows, digitize medical information, extract and summarize medical information, protect patient data, and more. Using AWS document understand

… Learn More

Add To Calendar
Artificial Intelligence & Machine Learning Healthcare

Using AI to automate clinical workflows

DEC 10, 2020 | 9:45 AM – 10:15 AM EST
Building patient-centric virtualized trials
As clinical trials increasingly become decentralized and virtual, engaging effectively with patients can be challenging. In this session, hear how Evidation Health architects on AWS to create patient-centric experiences, ingests data from millions of devices in real time

… Learn More

Add To Calendar
Life Sciences Healthcare

Building patient-centric virtualized trials

DEC 10, 2020 | 2:30 PM – 3:00 PM EST
Edge computing innovation with AWS Snowcone and AWS Snowball Edge
In this session, learn how the AWS Snow Family can help you run operations in harsh, non-data center environments and in locations where there is a lack of consistent network connectivity. The AWS Snow Family, comprised of AWS Snowcone and AWS Snowball Edge, offers a number of physical devices and capacity points with built-in computing capabilities. This sess… Learn More
Add To Calendar
Storage Business Apps (including Connect)

Edge computing innovation with AWS Snowcone and AWS Snowball Edge

DEC 10, 2020 | 4:00 PM – 4:30 PM EST
Healthcare executive outlook: Accelerating transformation
Join AWS Healthcare and Life Science Leader Shez Partovi, MD, for a look into how cloud technology can reshape the future of healthcare. In this executive overview, Dr. Partovi shares a vision of a digitally enhanced, data-driven future. Learn how AWS is working

… Learn More

Add To Calendar
Healthcare Life Sciences

Healthcare executive outlook: Accelerating transformation

DEC 10, 2020 | 10:30 PM – 11:00 PM EST
Edge computing innovation with AWS Snowcone and AWS Snowball Edge
In this session, learn how the AWS Snow Family can help you run operations in harsh, non-data center environments and in locations where there is a lack of consistent network connectivity. The AWS Snow Family, comprised of AWS Snowcone and AWS Snowball Edge, offers a number of physical devices and capacity points with built-in computing capabilities. This sess… Learn More
Add To Calendar
Storage Business Apps (including Connect)

Edge computing innovation with AWS Snowcone and AWS Snowball Edge

Friday, December 11
DEC 11, 2020 | 12:00 AM – 12:30 AM EST
Healthcare executive outlook: Accelerating transformation
Join AWS Healthcare and Life Science Leader Shez Partovi, MD, for a look into how cloud technology can reshape the future of healthcare. In this executive overview, Dr. Partovi shares a vision of a digitally enhanced, data-driven future. Learn how AWS is worki

… Learn More

Add To Calendar
Healthcare Life Sciences

Healthcare executive outlook: Accelerating transformation

DEC 11, 2020 | 6:30 AM – 7:00 AM EST
Edge computing innovation with AWS Snowcone and AWS Snowball Edge
In this session, learn how the AWS Snow Family can help you run operations in harsh, non-data center environments and in locations where there is a lack of consistent network connectivity. The AWS Snow Family, comprised of AWS Snowcone and AWS Snowball Edge, offers a number of physical devices and capacity points with built-in computing capabilities. This sess… Learn More
Add To Calendar
Storage Business Apps (including Connect)

Edge computing innovation with AWS Snowcone and AWS Snowball Edge

DEC 11, 2020 | 8:00 AM – 8:30 AM EST
Healthcare executive outlook: Accelerating transformation
Join AWS Healthcare and Life Science Leader Shez Partovi, MD, for a look into how cloud technology can reshape the future of healthcare. In this executive overview, Dr. Partovi shares a vision of a digitally enhanced, data-driven future. Learn how AWS is worki

… Learn More

Add To Calendar
Healthcare Life Sciences

Healthcare executive outlook: Accelerating transformation

Tuesday, December 15
DEC 15, 2020 | 1:00 PM – 1:30 PM EST
Intelligent document processing for the insurance industry
Organizations in the insurance industry, both for healthcare and financial services, extract sensitive information such as names, dates, claims, or medical procedures from scanned images and documents to perform their business operations. These organization

… Learn More

Add To Calendar
Artificial Intelligence & Machine Learning Healthcare

Intelligent document processing for the insurance industry

DEC 15, 2020 | 3:00 PM – 3:30 PM EST
AWS Partners driving innovation amidst COVID-19
This session is open to anyone, but it is intended for current and potential AWS Partners. The COVID-19 pandemic has been a formative event affecting our world physically, emotionally, and economically. Despite the challenges created, AWS Partners have responded quickly and proven their resiliency by enabling digital transformation at unprecedented rates. In this sessi… Learn More
Add To Calendar
Global Partner Summit (GPS) Session

AWS Partners driving innovation amidst COVID-19

DEC 15, 2020 | 9:00 PM – 9:30 PM EST
Intelligent document processing for the insurance industry
Organizations in the insurance industry, both for healthcare and financial services, extract sensitive information such as names, dates, claims, or medical procedures from scanned images and documents to perform their business operations. These organization

… Learn More

Add To Calendar
Artificial Intelligence & Machine Learning Healthcare

Intelligent document processing for the insurance industry

DEC 15, 2020 | 11:00 PM – 11:30 PM EST
AWS Partners driving innovation amidst COVID-19
This session is open to anyone, but it is intended for current and potential AWS Partners. The COVID-19 pandemic has been a formative event affecting our world physically, emotionally, and economically. Despite the challenges created, AWS Partners have responded quickly and proven their resiliency by enabling digital transformation at unprecedented rates. In this sessi… Learn More
Add To Calendar
Global Partner Summit (GPS) Session

AWS Partners driving innovation amidst COVID-19

Wednesday, December 16
DEC 16, 2020 | 5:00 AM – 5:30 AM EST
Intelligent document processing for the insurance industry
Organizations in the insurance industry, both for healthcare and financial services, extract sensitive information such as names, dates, claims, or medical procedures from scanned images and documents to perform their business operations. These organization

… Learn More

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Artificial Intelligence & Machine Learning Healthcare

Intelligent document processing for the insurance industry

DEC 16, 2020 | 7:00 AM – 7:30 AM EST
AWS Partners driving innovation amidst COVID-19
This session is open to anyone, but it is intended for current and potential AWS Partners. The COVID-19 pandemic has been a formative event affecting our world physically, emotionally, and economically. Despite the challenges created, AWS Partners have responded quickly and proven their resiliency by enabling digital transformation at unprecedented rates. In this sessi… Learn More
Add To Calendar
Global Partner Summit (GPS) Session

AWS Partners driving innovation amidst COVID-19

DEC 16, 2020 | 11:45 AM – 12:15 PM EST
Simplify healthcare compliance with third-party solutions
Sensitive health data must be protected to ensure patient privacy, and healthcare organizations need to ensure that IT infrastructure is compliant with changing policies and regulations. In this session, learn how healthcare providers can address compli

… Learn More

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Marketplace Healthcare

Simplify healthcare compliance with third-party solutions

DEC 16, 2020 | 12:45 PM – 1:15 PM EST
An introduction to healthcare interoperability and FHIR Works on AWS
Fast Healthcare Interoperability Resources (FHIR) is gaining popularity around the world as the standard to use for exchanging healthcare data, and it is being increasingly adopted in Europe and Australasia. In the US, it is actually mandated in the 21st Century

… Learn More

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Public Sector Healthcare

An introduction to healthcare interoperability and FHIR Works on AWS

DEC 16, 2020 | 1:15 PM – 1:45 PM EST
Achieving healthcare interoperability with FHIR Works on AWS
The Fast Healthcare Interoperability Resources (FHIR) standard has become increasingly necessary for enabling interoperability between healthcare applications and organizations. Join this session for a deep dive into how the FHIR Works on AWS open-source t

… Learn More

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Public Sector Healthcare

Achieving healthcare interoperability with FHIR Works on AWS

DEC 16, 2020 | 6:00 PM – 6:30 PM EST
AWS at the edge: Using AWS IoT to optimize Amazon wind farms
AWS IoT and edge computing solutions move data processing and analysis closer to where data is generated to enable customers to innovate and achieve more sustainable operations. In this session, learn how Amazon renewable energy projects use AWS IoT to coll

… Learn More

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IoT Manufacturing

AWS at the edge: Using AWS IoT to optimize Amazon wind farms

DEC 16, 2020 | 7:45 PM – 8:15 PM EST
Simplify healthcare compliance with third-party solutions
Sensitive health data must be protected to ensure patient privacy, and healthcare organizations need to ensure that IT infrastructure is compliant with changing policies and regulations. In this session, learn how healthcare providers can address compli

… Learn More

Add To Calendar
Marketplace Healthcare

Simplify healthcare compliance with third-party solutions

DEC 16, 2020 | 8:45 PM – 9:15 PM EST
An introduction to healthcare interoperability and FHIR Works on AWS
Fast Healthcare Interoperability Resources (FHIR) is gaining popularity around the world as the standard to use for exchanging healthcare data, and it is being increasingly adopted in Europe and Australasia. In the US, it is actually mandated in the 21st Century

… Learn More

Add To Calendar
Public Sector Healthcare

An introduction to healthcare interoperability and FHIR Works on AWS

DEC 16, 2020 | 9:15 PM – 9:45 PM EST
Achieving healthcare interoperability with FHIR Works on AWS
The Fast Healthcare Interoperability Resources (FHIR) standard has become increasingly necessary for enabling interoperability between healthcare applications and organizations. Join this session for a deep dive into how the FHIR Works on AWS open-source t

… Learn More

Add To Calendar
Public Sector Healthcare

Achieving healthcare interoperability with FHIR Works on AWS

Thursday, December 17
DEC 17, 2020 | 2:00 AM – 2:30 AM EST
AWS at the edge: Using AWS IoT to optimize Amazon wind farms
AWS IoT and edge computing solutions move data processing and analysis closer to where data is generated to enable customers to innovate and achieve more sustainable operations. In this session, learn how Amazon renewable energy projects use AWS IoT to coll

… Learn More

Add To Calendar
IoT Manufacturing

AWS at the edge: Using AWS IoT to optimize Amazon wind farms

DEC 17, 2020 | 3:45 AM – 4:15 AM EST
Simplify healthcare compliance with third-party solutions
Sensitive health data must be protected to ensure patient privacy, and healthcare organizations need to ensure that IT infrastructure is compliant with changing policies and regulations. In this session, learn how healthcare providers can address compliance

… Learn More

Add To Calendar
Marketplace Healthcare

Simplify healthcare compliance with third-party solutions

DEC 17, 2020 | 4:45 AM – 5:15 AM EST
An introduction to healthcare interoperability and FHIR Works on AWS
Fast Healthcare Interoperability Resources (FHIR) is gaining popularity around the world as the standard to use for exchanging healthcare data, and it is being increasingly adopted in Europe and Australasia. In the US, it is actually mandated in the 21st Century

… Learn More

Add To Calendar
Public Sector Healthcare

An introduction to healthcare interoperability and FHIR Works on AWS

DEC 17, 2020 | 5:15 AM – 5:45 AM EST
Achieving healthcare interoperability with FHIR Works on AWS
The Fast Healthcare Interoperability Resources (FHIR) standard has become increasingly necessary for enabling interoperability between healthcare applications and organizations. Join this session for a deep dive into how the FHIR Works on AWS open-source t

… Learn More

Add To Calendar
Public Sector Healthcare

Achieving healthcare interoperability with FHIR Works on AWS

DEC 17, 2020 | 10:00 AM – 10:30 AM EST
AWS at the edge: Using AWS IoT to optimize Amazon wind farms
AWS IoT and edge computing solutions move data processing and analysis closer to where data is generated to enable customers to innovate and achieve more sustainable operations. In this session, learn how Amazon renewable energy projects use AWS IoT to coll

… Learn More

Add To Calendar
IoT Manufacturing

AWS at the edge: Using AWS IoT to optimize Amazon wind farms

DEC 17, 2020 | 11:15 AM – 11:45 AM EST
Share information by removing language barriers with Amazon Translate
In this session, see how to use Amazon Translate and Amazon Transcribe to create subtitles for educational videos and translate them to the language of the consumer’s choice. The session includes a demonstration of how this process reduced the time it took for information about COVID-19 to be translated to many languages, thus spreading accurate information quickly.
Add To Calendar
Artificial Intelligence & Machine Learning Healthcare

Share information by removing language barriers with Amazon Translate

DEC 17, 2020 | 7:15 PM – 7:45 PM EST
Share information by removing language barriers with Amazon Translate
In this session, see how to use Amazon Translate and Amazon Transcribe to create subtitles for educational videos and translate them to the language of the consumer’s choice. The session includes a demonstration of how this process reduced the time it took for information about COVID-19 to be translated to many languages, thus spreading accurate information quickly.
Add To Calendar
Artificial Intelligence & Machine Learning Healthcare

Share information by removing language barriers with Amazon Translate

Friday, December 18
DEC 18, 2020 | 3:15 AM – 3:45 AM EST
Share information by removing language barriers with Amazon Translate
In this session, see how to use Amazon Translate and Amazon Transcribe to create subtitles for educational videos and translate them to the language of the consumer’s choice. The session includes a demonstration of how this process reduced the time it took for information about COVID-19 to be translated to many languages, thus spreading accurate information quickly.
Add To Calendar
Artificial Intelligence & Machine Learning Healthcare

Share information by removing language barriers with Amazon Translate

Share information by removing language barriers with Amazon Translate
Friday, December 18
DEC 18, 2020 | 3:15 AM – 3:45 AM EST
Share information by removing language barriers with Amazon Translate
In this session, see how to use Amazon Translate and Amazon Transcribe to create subtitles for educational videos and translate them to the language of the consumer’s choice. The session includes a demonstration of how this process reduced the time it took for information about COVID-19 to be translated to many languages, thus spreading accurate information quickly.
Add To Calendar
Artificial Intelligence & Machine Learning Healthcare

Share information by removing language barriers with Amazon Translate

Life Sciences sessions

Bookmark this blog and check back for direct links to each session and add to your re:Invent schedule as soon as the session catalog is released:

LFS201: Life Sciences Industry: Executive Outlook
Learn how AWS technology is helping organizations improve their data liquidity, achieve operational excellence, and enhance customer engagement.

LFS202: Improving data liquidity in Roche’s personalized healthcare platform
Learn how Roche’s personalized healthcare platform is accelerating drug discovery and transforming the patient journey with digital technology.

LFS302: AstraZeneca genomics on AWS: from petabytes to new medicines
Learn how AstraZeneca built an industry leading genomics pipeline on AWS to analyze 2 million genomes in support of precision medicine.

LFS303: Building patient-centric virtualized trials
Learn how Evidation Health architects on AWS to create patient-centric experiences in decentralized and virtual clinical trials.

LFS304: Streamlining manufacturing and supply chain at Novartis
Learn how Novartis is creating real-time analytics and transparency in the pharma manufacturing process and supply chain to bring innovative medicines to market.

LFS305: Accelerating regulatory assessments in life sciences manufacturing
Learn how Merck leveraged Amazon Machine Learning to build an evaluation and recommendation engine for streamlining pharma manufacturing change requests.

Other related sessions of interest:

ENT203: How BMS automates GxP compliance for SAP systems on AWS

GPS211: AWS Partners driving innovation amidst COVID-19

HLC203: Securing Personal Health Information and High Risk Data Sets

WPS202: Transform research environments with Service Workbench on AWS

AIM310: Intelligent document processing for healthcare organizations

Healthcare Attendee Guide

AWS re:Invent routinely fills several Las Vegas venues with standing-room only crowds, but we are bringing it to you with an all-virtual and free event this year. This year’s conference is gearing up to be our biggest yet and we have an exciting program planned for the Healthcare industry with five keynotes, 18 leadership sessions, and over 500 breakout sessions beginning November 30. See how AWS experts and talented members of the Healthcare industry are using cloud technology to transform their businesses and innovate on the behalf of their customers.For Healthcare attendees looking to get the most out of their experience, follow these steps:

  • Register for re:Invent.
  • Take a look at all of the Healthcare sessions available, as well as lots of other information and additional activities, in our curated Healthcare Attendee Guide coming soon!
  • Check back on this post regularly, as we’ll continually update it to reflect the newest information.

Healthcare at re:Invent 2020

AWS is the trusted technology partner to the global healthcare industry. For over 12 years, AWS has established itself as the most mature, comprehensive, and broadly adopted cloud platform and is trusted by thousands of healthcare customers around the world—including the fastest-growing startups, the largest enterprises, and leading government agencies. The secure and compliant AWS technology enables the highly regulated healthcare industry to improve outcomes and lower costs by providing the tools to unlock the potential of healthcare data, predict healthcare events, and build closer relationships with patients and consumers. The healthcare track at re:Invent 2020 will feature customer-led sessions focused on these each of these critical components, accelerating the transformation of healthcare.

Healthcare sessions

Learn more and bookmark each Healthcare session:

HCL201: Healthcare Executive Outlook: Accelerating Transformation
Learn how AWS is working with industry leaders to increase their pace of innovation, unlock the potential of their healthcare data, help predict patient health events, and personalize the healthcare journey for their patients, consumers, and members.

HLC202: Making Healthcare More Personal with MetroPlus Health
Learn how MetroPlus Health leveraged AWS technology to quickly build and deploy an application that personally and proactively reached out to its members during a time of critical need.

HLC203: Securing Personal Health Information and High Risk Data Sets
Learn how Arcadia developed a HITRUST CSF Certified platform by leveraging AWS technology to enable the secure management of data from over 100 million patients.

HLC204: Accelerating the Transition to Virtual Care with AWS
Learn how MedStar Health developed and deployed two call centers in less than week that are supporting more than 3,500 outpatient telehealth sessions a day.

WPS202: Transform research environments with Service Workbench on AWS
Learn how Harvard Medical School is using AWS to procure and deploy domain-specific data, tools, and secure IT environments to accelerate research.

WPS209: Reinventing medical imaging with machine learning on AWS
Learn how Radboud University Medical Center uses AWS to power its machine learning imaging platform with 45,000+ registered researchers and clinicians from all over the world.

WPS211: An introduction to healthcare interoperability and FHIR Works on AWS
Learn about AWS FHIR Works, an open-source project, designed to accelerate the industries use of the interoperability standard, Fast Healthcare Interoperability Resources (FHIR).

WPS304: Achieving healthcare interoperability with FHIR Works on AWS
Learn how Black Pear Software leveraged AWS to build an integration toolkit to help their customers share healthcare data more effectively.

Extras you won’t want to miss out on!

LFS201: Life Sciences Industry: Executive outlook

LFS202: AstraZeneca genomics on AWS: From petabytes to new medicines

LFS303: Building patient-centric virtualized trials

AIM303: Using AI to automate clinical workflows

AIM310: Intelligent document processing for the insurance industry

INO204: Solving societal challenges with digital innovation on AWS

ZWL208: Using cloud-based genomic research to reduce health care disparities

GPS211: AWS Partners driving innovation amidst COVID-19

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Tweets & Retweets by @pharma_BI and @AVIVA1950 at #BioIT20, 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 in Boston

 

Virtual Conference coverage in Real Time: Aviva Lev-Ari, PhD, RN

 

Amazing conference ended at 2PM on October 8, 2020

e-Proceedings 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 Boston

Virtual Conference coverage in Real Time: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/26/19th-annual-bio-it-world-2020-conference-october-6-8-2020-in-boston/

Review Tweets and Retweets

and 2 others liked your Tweet

#BioIT20 Plenary Keynote: cutting innovative approach to #Science #Game On: How #AI, #CitizenScience #HumanComputation are facilitating the next leap forward in #Genomics and in #Biology may be in #PrecisionMedicine in the Future @pharma_BI @AVIVA1950 pic.twitter.com/L52qktkeYc

Retweeted your Tweet
#BioIT20 Plenary Keynote: cutting innovative approach to #Science #Game On: How #AI, #CitizenScience #HumanComputation are facilitating the next leap forward in #Genomics and in #Biology may be in #PrecisionMedicine in the Future @pharma_BI @AVIVA1950 pic.twitter.com/L52qktkeYc

and

liked your Tweet

#BioIT20 Plenary Keynote: cutting innovative approach to #Science #Game On: How #AI, #CitizenScience #HumanComputation are facilitating the next leap forward in #Genomics and in #Biology may be in #PrecisionMedicine in the Future @pharma_BI @AVIVA1950 pic.twitter.com/L52qktkeYc

NIH Office of Data Science Strategy
@NIHDataScience

We’ve made progress with #FAIRData, but we still have a ways to go and our future is bright. #BioIT20 #NIHData

Image

3

Aviva Lev-Ari
@AVIVA1950

#BioIT20

Driving Scientific Discovery with Data Digitization great ideas shared by moderator Timothy Gardner

#CEO Inspiration from History Total Quality Implementation is key for BioScience Data #AI won’t solve the problem #Data #Quality will

Image

Rob Lalonde
@HPC_Cloud_Rob

My #BioIT20 talk, “#Bioinformatics in the #Cloud Age,” is tomorrow at 3:30pm. I discuss cloud migration trends in life sciences and #HPC. Join us! A panel with

and

follows the talk.

1
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Jean Marois
@JeanMarois

My team is participating in Bio-IT World Virtual 2020, October 6-8. Join me! Use discount code 20NUA to save 20%! invt.io/1tdbae9s8lp

#BioIT20

I’m going to Bio-IT World 2020, Oct 6-8, from home! Its a virtual event. Join me!
My team is participating in Bio-IT World Virtual 2020, October 6-8. Join me! Use discount code 20NUA to save 20%! @bioitworld #BioIT20
invt.io
2

NIH Office of Data Science Strategy
@NIHDataScience

One of the challenges we face today: we need an algorithm that can search across the 36+ PB of Sequence Read Archive (SRA) data now in the cloud. Imagine what we could do! #BioIT20 #NIHdata #SRAdata

Image

2

NCBI Staff
@NCBI

NCBI’s virtual #BioIT20 booth will open in 15 minutes. There, you can watch videos, grab some flyers and even speak with an expert! bio-itworld.pathable.co/organizations/ The booth will close at 4:15 PM, but we’ll be back tomorrow, Oct 7 and Thursday, Oct 8 at 9AM.
Bio-IT World
Welcome to Bio-IT World Virtual
bio-itworld.pathable.co
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Show this thread

PERCAYAI
@percayai

Happening soon at #BioIT20: Join our faculty inventor Professor Rich Head’s invited talk “CompBio: An Augmented Intelligence System for Comprehensive Interpretation of Biological Data.”
4

Wendy Anne Warr
@WendyAnneWarr

This was a good discussion
Quote Tweet
Cambridge Innovation
@CIInstitute
·
RT percayai: We’ve put together what’s sure to be a thought-provoking discussion group for #BioIT20 “Why Current Approaches Using #AI in #…
1
2

Cambridge Innovation
@CIInstitute

RT VishakhaSharma_: Excited to speak and moderate a panel on Emerging #AI technologies bioitworld #BioIT20
1

Titian Software
@TitianSoftware

Meet Titian at #BioIT20 on 6-8th October and discover the latest research, science and solutions for exploring the world of precision medicine and the technologies that are powering it: bit.ly/2GjCj4B

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1

PERCAYAI
@percayai

Thanks for joining us, Wendy! You’ve done a great job summing up key points from the discussion. #BioIT20
1

Aviva Lev-Ari
@AVIVA1950

#NIHhealthInitiative #BioItWorld20

Out standing Plenary Keynote on #DataScience

CONNECTED DATA ECOSYSTEM FAIR Foundable, Accessible, Interoperable, reusable

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2

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Systems Biology analysis of Transcription Networks, Artificial Intelligence, and High-End Computing Coming to Fruition in Personalized Oncology

Curator: Stephen J. Williams, Ph.D.

In the June 2020 issue of the journal Science, writer Roxanne Khamsi has an interesting article “Computing Cancer’s Weak Spots; An algorithm to unmask tumors’ molecular linchpins is tested in patients”[1], describing some early successes in the incorporation of cancer genome sequencing in conjunction with artificial intelligence algorithms toward a personalized clinical treatment decision for various tumor types.  In 2016, oncologists Amy Tiersten collaborated with systems biologist Andrea Califano and cell biologist Jose Silva at Mount Sinai Hospital to develop a systems biology approach to determine that the drug ruxolitinib, a STAT3 inhibitor, would be effective for one of her patient’s aggressively recurring, Herceptin-resistant breast tumor.  Dr. Califano, instead of defining networks of driver mutations, focused on identifying a few transcription factors that act as ‘linchpins’ or master controllers of transcriptional networks withing tumor cells, and in doing so hoping to, in essence, ‘bottleneck’ the transcriptional machinery of potential oncogenic products. As Dr. Castilano states

“targeting those master regulators and you will stop cancer in its tracks, no matter what mutation initially caused it.”

It is important to note that this approach also relies on the ability to sequence tumors  by RNA-seq to determine the underlying mutations which alter which master regulators are pertinent in any one tumor.  And given the wide tumor heterogeneity in tumor samples, this sequencing effort may have to involve multiple biopsies (as discussed in earlier posts on tumor heterogeneity in renal cancer).

As stated in the article, Califano co-founded a company called Darwin-Health in 2015 to guide doctors by identifying the key transcription factors in a patient’s tumor and suggesting personalized therapeutics to those identified molecular targets (OncoTarget™).  He had collaborated with the Jackson Laboratory and most recently Columbia University to conduct a $15 million 3000 patient clinical trial.  This was a bit of a stretch from his initial training as a physicist and, in 1986, IBM hired him for some artificial intelligence projects.  He then landed in 2003 at Columbia and has been working on identifying these transcriptional nodes that govern cancer survival and tumorigenicity.  Dr. Califano had figured that the number of genetic mutations which potentially could be drivers were too vast:

A 2018 study which analyzed more than 9000 tumor samples reported over 1.5 million mutations[2]

and impossible to develop therapeutics against.  He reasoned that you would just have to identify the common connections between these pathways or transcriptional nodes and termed them master regulators.

A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples

Chen H, Li C, Peng X, et al. Cell. 2018;173(2):386-399.e12.

Abstract

The role of enhancers, a key class of non-coding regulatory DNA elements, in cancer development has increasingly been appreciated. Here, we present the detection and characterization of a large number of expressed enhancers in a genome-wide analysis of 8928 tumor samples across 33 cancer types using TCGA RNA-seq data. Compared with matched normal tissues, global enhancer activation was observed in most cancers. Across cancer types, global enhancer activity was positively associated with aneuploidy, but not mutation load, suggesting a hypothesis centered on “chromatin-state” to explain their interplay. Integrating eQTL, mRNA co-expression, and Hi-C data analysis, we developed a computational method to infer causal enhancer-gene interactions, revealing enhancers of clinically actionable genes. Having identified an enhancer ∼140 kb downstream of PD-L1, a major immunotherapy target, we validated it experimentally. This study provides a systematic view of enhancer activity in diverse tumor contexts and suggests the clinical implications of enhancers.

 

A diagram of how concentrating on these transcriptional linchpins or nodes may be more therapeutically advantageous as only one pharmacologic agent is needed versus multiple agents to inhibit the various upstream pathways:

 

 

From: Khamsi R: Computing cancer’s weak spots. Science 2020, 368(6496):1174-1177.

 

VIPER Algorithm (Virtual Inference of Protein activity by Enriched Regulon Analysis)

The algorithm that Califano and DarwinHealth developed is a systems biology approach using a tumor’s RNASeq data to determine controlling nodes of transcription.  They have recently used the VIPER algorithm to look at RNA-Seq data from more than 10,000 tumor samples from TCGA and identified 407 transcription factor genes that acted as these linchpins across all tumor types.  Only 20 to 25 of  them were implicated in just one tumor type so these potential nodes are common in many forms of cancer.

Other institutions like the Cold Spring Harbor Laboratories have been using VIPER in their patient tumor analysis.  Linchpins for other tumor types have been found.  For instance, VIPER identified transcription factors IKZF1 and IKF3 as linchpins in multiple myeloma.  But currently approved therapeutics are hard to come by for targets with are transcription factors, as most pharma has concentrated on inhibiting an easier target like kinases and their associated activity.  In general, developing transcription factor inhibitors in more difficult an undertaking for multiple reasons.

Network-based inference of protein activity helps functionalize the genetic landscape of cancer. Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, Califano A:. Nature genetics 2016, 48(8):838-847 [3]

Abstract

Identifying the multiple dysregulated oncoproteins that contribute to tumorigenesis in a given patient is crucial for developing personalized treatment plans. However, accurate inference of aberrant protein activity in biological samples is still challenging as genetic alterations are only partially predictive and direct measurements of protein activity are generally not feasible. To address this problem we introduce and experimentally validate a new algorithm, VIPER (Virtual Inference of Protein-activity by Enriched Regulon analysis), for the accurate assessment of protein activity from gene expression data. We use VIPER to evaluate the functional relevance of genetic alterations in regulatory proteins across all TCGA samples. In addition to accurately inferring aberrant protein activity induced by established mutations, we also identify a significant fraction of tumors with aberrant activity of druggable oncoproteins—despite a lack of mutations, and vice-versa. In vitro assays confirmed that VIPER-inferred protein activity outperforms mutational analysis in predicting sensitivity to targeted inhibitors.

 

 

 

 

Figure 1 

Schematic overview of the VIPER algorithm From: Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, Califano A: Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nature genetics 2016, 48(8):838-847.

(a) Molecular layers profiled by different technologies. Transcriptomics measures steady-state mRNA levels; Proteomics quantifies protein levels, including some defined post-translational isoforms; VIPER infers protein activity based on the protein’s regulon, reflecting the abundance of the active protein isoform, including post-translational modifications, proper subcellular localization and interaction with co-factors. (b) Representation of VIPER workflow. A regulatory model is generated from ARACNe-inferred context-specific interactome and Mode of Regulation computed from the correlation between regulator and target genes. Single-sample gene expression signatures are computed from genome-wide expression data, and transformed into regulatory protein activity profiles by the aREA algorithm. (c) Three possible scenarios for the aREA analysis, including increased, decreased or no change in protein activity. The gene expression signature and its absolute value (|GES|) are indicated by color scale bars, induced and repressed target genes according to the regulatory model are indicated by blue and red vertical lines. (d) Pleiotropy Correction is performed by evaluating whether the enrichment of a given regulon (R4) is driven by genes co-regulated by a second regulator (R4∩R1). (e) Benchmark results for VIPER analysis based on multiple-samples gene expression signatures (msVIPER) and single-sample gene expression signatures (VIPER). Boxplots show the accuracy (relative rank for the silenced protein), and the specificity (fraction of proteins inferred as differentially active at p < 0.05) for the 6 benchmark experiments (see Table 2). Different colors indicate different implementations of the aREA algorithm, including 2-tail (2T) and 3-tail (3T), Interaction Confidence (IC) and Pleiotropy Correction (PC).

 Other articles from Andrea Califano on VIPER algorithm in cancer include:

Resistance to neoadjuvant chemotherapy in triple-negative breast cancer mediated by a reversible drug-tolerant state.

Echeverria GV, Ge Z, Seth S, Zhang X, Jeter-Jones S, Zhou X, Cai S, Tu Y, McCoy A, Peoples M, Sun Y, Qiu H, Chang Q, Bristow C, Carugo A, Shao J, Ma X, Harris A, Mundi P, Lau R, Ramamoorthy V, Wu Y, Alvarez MJ, Califano A, Moulder SL, Symmans WF, Marszalek JR, Heffernan TP, Chang JT, Piwnica-Worms H.Sci Transl Med. 2019 Apr 17;11(488):eaav0936. doi: 10.1126/scitranslmed.aav0936.PMID: 30996079

An Integrated Systems Biology Approach Identifies TRIM25 as a Key Determinant of Breast Cancer Metastasis.

Walsh LA, Alvarez MJ, Sabio EY, Reyngold M, Makarov V, Mukherjee S, Lee KW, Desrichard A, Turcan Ş, Dalin MG, Rajasekhar VK, Chen S, Vahdat LT, Califano A, Chan TA.Cell Rep. 2017 Aug 15;20(7):1623-1640. doi: 10.1016/j.celrep.2017.07.052.PMID: 28813674

Inhibition of the autocrine IL-6-JAK2-STAT3-calprotectin axis as targeted therapy for HR-/HER2+ breast cancers.

Rodriguez-Barrueco R, Yu J, Saucedo-Cuevas LP, Olivan M, Llobet-Navas D, Putcha P, Castro V, Murga-Penas EM, Collazo-Lorduy A, Castillo-Martin M, Alvarez M, Cordon-Cardo C, Kalinsky K, Maurer M, Califano A, Silva JM.Genes Dev. 2015 Aug 1;29(15):1631-48. doi: 10.1101/gad.262642.115. Epub 2015 Jul 30.PMID: 26227964

Master regulators used as breast cancer metastasis classifier.

Lim WK, Lyashenko E, Califano A.Pac Symp Biocomput. 2009:504-15.PMID: 19209726 Free

 

Additional References

 

  1. Khamsi R: Computing cancer’s weak spots. Science 2020, 368(6496):1174-1177.
  2. Chen H, Li C, Peng X, Zhou Z, Weinstein JN, Liang H: A Pan-Cancer Analysis of Enhancer Expression in Nearly 9000 Patient Samples. Cell 2018, 173(2):386-399 e312.
  3. Alvarez MJ, Shen Y, Giorgi FM, Lachmann A, Ding BB, Ye BH, Califano A: Functional characterization of somatic mutations in cancer using network-based inference of protein activity. Nature genetics 2016, 48(8):838-847.

 

Other articles of Note on this Open Access Online Journal Include:

Issues in Personalized Medicine in Cancer: Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing

 

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From Cell Press:  New Insights on the D614G Strain of COVID: Will a New Mutated Strain Delay Vaccine Development?

Reporter: Stephen J. Williams, PhD

Two recent articles in Cell Press, both peer reviewed, discuss the emergence and potential dominance of a new mutated strain of COVID-19, in which the spike protein harbors a D614G mutation.

In the first article “Making Sense of Mutation: What D614G means for the COVID-19 pandemic Remains Unclear”[1] , authors Drs. Nathan Grubaugh, William Hanage, and Angela Rasmussen discuss the recent findings by Korber et al. 2020 [2] which describe the potential increases in infectivity and mortality of this new mutant compared to the parent strain of SARS-CoV2.  For completeness sake I will post this article as to not defer from their interpretations of this important paper by Korber and to offer some counter opinion to some articles which have surfaced this morning in the news.

Making sense of mutation: what D614G means for the COVID-19 pandemic remains unclear

 

Nathan D. Grubaugh1 *, William P. Hanage2 *, Angela L. Rasmussen3 * 1Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT 06510, USA 2Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA 3Center for Infection and Immunity, Columbia Mailman School of Public Health, New York, NY 10032, USA Correspondence: grubaughlab@gmail.com

 

Abstract: Korber et al. (2020) found that a SARS-CoV-2 variant in the spike protein, D614G, rapidly became dominant around the world. While clinical and in vitro data suggest that D614G changes the virus phenotype, the impact of the mutation on transmission, disease, and vaccine and therapeutic development are largely unknown.

Introduction: Following the emergence of SARS-CoV-2 in China in late 2019, and the rapid expansion of the COVID19 pandemic in 2020, questions about viral evolution have come tumbling after. Did SARS-CoV-2 evolve to become better adapted to humans? More infectious or transmissible? More deadly? Virus mutations can rise in frequency due to natural selection, random genetic drift, or features of recent epidemiology. As these forces can work in tandem, it’s often hard to differentiate when a virus mutation becomes common through fitness or by chance. It is even harder to determine if a single mutation will change the outcome of an infection, or a pandemic. The new study by Korber et al. (2020) sits at the heart of this debate. They present compelling data that an amino acid change in the virus’ spike protein, D614G, emerged early during the pandemic, and viruses containing G614 are now dominant in many places around the world. The crucial questions are whether this is the result of natural selection, and what it means for the COVID-19 pandemic. For viruses like SARS-CoV-2 transmission really is everything – if they don’t get into another host their lineage ends. Korber et al. (2020) hypothesized that the rapid spread of G614 was because it is more infectious than D614. In support of their hypothesis, the authors provided evidence that clinical samples from G614 infections have a higher levels of viral RNA, and produced higher titers in pseudoviruses from in vitro experiments; results that now seem to be corroborated by others [e.g. (Hu et al., 2020; Wagner et al., 2020)]. Still, these data do not prove that G614 is more infectious or transmissible than viruses containing D614. And because of that, many questions remain on the potential impacts, if any, that D614G has on the COVID-19 pandemic.

The authors note that this new G614 variant has become the predominant form over the whole world however in China the predominant form is still the D614 form.  As they state

“over the period that G614 became the global majority variant, the number of introductions from China where D614 was still dominant were declining, while those from Europe climbed. This alone might explain the apparent success of G614.”

Grubaugh et al. feel there is not enough evidence that infection with this new variant will lead to higher mortality.  Both Korber et al. and the Seattle study (Wagner et al) did not find that the higher viral load of this variant led to a difference in hospitalizations so apparently each variant might be equally as morbid.

In addition, Grubaugh et al. believe this variant would not have much affect on vaccine development as, even though the mutation lies within the spike protein, D614G is not in the receptor binding domain of the spike protein.  Korber suggest that there may be changes in glycosylation however these experiments will need to be performed.  In addition, antibodies from either D614 or G614 variant infected patients could cross neutralize.

 

Conclusions: While there has already been much breathless commentary on what this mutation means for the COVID19 pandemic, the global expansion of G614 whether through natural selection or chance means that this variant now is the pandemic. As a result its properties matter. It is clear from the in vitro and clinical data that G614 has a distinct phenotype, but whether this is the result of bonafide adaptation to human ACE2, whether it increases transmissibility, or will have a notable effect, is not clear. The work by Korber et al. (2020) provides an early base for more extensive epidemiological, in vivo experimental, and diverse clinical investigations to fill in the many critical gaps in how D614G impacts the pandemic.

The link to the Korber Cell paper is here: https://www.cell.com/cell/fulltext/S0092-8674(20)30820-5

Tracking changes in SARS-CoV-2 Spike: evidence that D614G increases infectivity of the COVID-19 virus

DOI: https://doi.org/10.1016/j.cell.2020.06.043

Keypoints

  • The consistent increase of G614 at regional levels may indicate a fitness advantage

 

  • G614 is associated with lower RT PCR Ct’s, suggestive of higher viral loads in patients

 

  • The G614 variant grows to higher titers as pseudotyped virions

Summary

A SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become the most prevalent form in the global pandemic. Dynamic tracking of variant frequencies revealed a recurrent pattern of G614 increase at multiple geographic levels: national, regional and municipal. The shift occurred even in local epidemics where the original D614 form was well established prior to the introduction of the G614 variant. The consistency of this pattern was highly statistically significant, suggesting that the G614 variant may have a fitness advantage. We found that the G614 variant grows to higher titer as pseudotyped virions. In infected individuals G614 is associated with lower RT-PCR cycle thresholds, suggestive of higher upper respiratory tract viral loads, although not with increased disease severity. These findings illuminate changes important for a mechanistic understanding of the virus, and support continuing surveillance of Spike mutations to aid in the development of immunological interventions.

 

References

  1. Grubaugh, N.D., Hanage, W.P., Rasmussen, A.L., Making sense of mutation: what D614G means for the COVID-19 pandemic remains unclear, Cell (2020), doi: https:// doi.org/10.1016/j.cell.2020.06.040.
  2. Korber, B., Fischer, W.M., Gnanakaran, S., Yoon, H., Theiler, J., Abfalterer, W., Hengartner, N., Giorgi, E.E., Bhattacharya, T., Foley, B., et al. (2020). Tracking changes in SARS-CoV-2 Spike: evidence that D614G increases infectivity of the COVID-19 virus. Cell 182.
  3. Endo, A., Centre for the Mathematical Modelling of Infectious Diseases COVID-19 Working Group, Abbott, S., Kucharski, A.J., and Funk, S. (2020). Estimating the overdispersion in COVID-19 transmission using outbreak sizes outside China. Wellcome Open Res 5, 67.
  4. Hu, J., He, C.-L., Gao, Q.-Z., Zhang, G.-J., Cao, X.-X., Long, Q.-X., Deng, H.-J., Huang, L.-Y., Chen, J., Wang, K., et al. (2020). The D614G mutation of SARS-CoV-2 spike protein enhances viral infectivity and decreases neutralization sensitivity to individual convalescent sera. bioRxiv 2020.06.20.161323.
  5. Wagner, C., Roychoudhury, P., Hadfield, J., Hodcroft, E.B., Lee, J., Moncla, L.H., Müller, N.F., Behrens, C., Huang, M.-L., Mathias, P., et al. (2020). Comparing viral load and clinical outcomes in Washington State across D614G mutation in spike protein of SARS-CoV-2. Https://github.com/blab/ncov-D614G.

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Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Reporter: Stephen J. Williams, PhD

 Minisymposium: Evaluating Cancer Genomics from Normal Tissues through Evolution to Metastatic Disease

Oncologic therapy shapes the fitness landscape of clonal hematopoiesis

April 28, 2020, 4:10 PM – 4:20 PM

Presenter/Authors
Kelly L. Bolton, Ryan N. Ptashkin, Teng Gao, Lior Braunstein, Sean M. Devlin, Minal Patel, Antonin Berthon, Aijazuddin Syed, Mariko Yabe, Catherine Coombs, Nicole M. Caltabellotta, Mike Walsh, Ken Offit, Zsofia Stadler, Choonsik Lee, Paul Pharoah, Konrad H. Stopsack, Barbara Spitzer, Simon Mantha, James Fagin, Laura Boucai, Christopher J. Gibson, Benjamin Ebert, Andrew L. Young, Todd Druley, Koichi Takahashi, Nancy Gillis, Markus Ball, Eric Padron, David Hyman, Jose Baselga, Larry Norton, Stuart Gardos, Virginia Klimek, Howard Scher, Dean Bajorin, Eder Paraiso, Ryma Benayed, Maria Arcilla, Marc Ladanyi, David Solit, Michael Berger, Martin Tallman, Montserrat Garcia-Closas, Nilanjan Chatterjee, Luis Diaz, Ross Levine, Lindsay Morton, Ahmet Zehir, Elli Papaemmanuil. Memorial Sloan Kettering Cancer Center, New York, NY, University of North Carolina at Chapel Hill, Chapel Hill, NC, University of Cambridge, Cambridge, United Kingdom, Dana-Farber Cancer Institute, Boston, MA, Washington University, St Louis, MO, The University of Texas MD Anderson Cancer Center, Houston, TX, Moffitt Cancer Center, Tampa, FL, National Cancer Institute, Bethesda, MD

Abstract
Recent studies among healthy individuals show evidence of somatic mutations in leukemia-associated genes, referred to as clonal hematopoiesis (CH). To determine the relationship between CH and oncologic therapy we collected sequential blood samples from 525 cancer patients (median sampling interval time = 23 months, range: 6-53 months) of whom 61% received cytotoxic therapy or external beam radiation therapy and 39% received either targeted/immunotherapy or were untreated. Samples were sequenced using deep targeted capture-based platforms. To determine whether CH mutational features were associated with tMN risk, we performed Cox proportional hazards regression on 9,549 cancer patients exposed to oncologic therapy of whom 75 cases developed tMN (median time to transformation=26 months). To further compare the genetic and clonal relationships between tMN and the proceeding CH, we analyzed 35 cases for which paired samples were available. We compared the growth rate of the variant allele fraction (VAF) of CH clones across treatment modalities and in untreated patients. A significant increase in the growth rate of CH mutations was seen in DDR genes among those receiving cytotoxic (p=0.03) or radiation therapy (p=0.02) during the follow-up period compared to patients who did not receive therapy. Similar growth rates among treated and untreated patients were seen for non-DDR CH genes such as DNMT3A. Increasing cumulative exposure to cytotoxic therapy (p=0.01) and external beam radiation therapy (2×10-8) resulted in higher growth rates for DDR CH mutations. Among 34 subjects with at least two CH mutations in which one mutation was in a DDR gene and one in a non-DDR gene, we studied competing clonal dynamics for multiple gene mutations within the same patient. The risk of tMN was positively associated with CH in a known myeloid neoplasm driver mutation (HR=6.9, p<10-6), and increased with the total number of mutations and clone size. The strongest associations were observed for mutations in TP53 and for CH with mutations in spliceosome genes (SRSF2, U2AF1 and SF3B1). Lower hemoglobin, lower platelet counts, lower neutrophil counts, higher red cell distribution width and higher mean corpuscular volume were all positively associated with increased tMN risk. Among 35 cases for which paired samples were available, in 19 patients (59%), we found evidence of at least one of these mutations at the time of pre-tMN sequencing and in 13 (41%), we identified two or more in the pre-tMN sample. In all cases the dominant clone at tMN transformation was defined by a mutation seen at CH Our serial sampling data provide clear evidence that oncologic therapy strongly selects for clones with mutations in the DDR genes and that these clones have limited competitive fitness, in the absence of cytotoxic or radiation therapy. We further validate the relevance of CH as a predictor and precursor of tMN in cancer patients. We show that CH mutations detected prior to tMN diagnosis were consistently part of the dominant clone at tMN diagnosis and demonstrate that oncologic therapy directly promotes clones with mutations in genes associated with chemo-resistant disease such as TP53.

  • therapy resulted also in clonal evolution and saw changes in splice variants and spliceosome
  • therapy promotes current DDR mutations
  • clonal hematopoeisis due to selective pressures
  • mutations, variants number all predictive of myeloid disease
  • deferring adjuvant therapy for breast cancer patients with patients in highest MDS risk group based on biomarkers, greatly reduced their risk for MDS

5704 – Pan-cancer genomic characterization of patient-matched primary, extracranial, and brain metastases

Presenter/AuthorsOlivia W. Lee, Akash Mitra, Won-Chul Lee, Kazutaka Fukumura, Hannah Beird, Miles Andrews, Grant Fischer, John N. Weinstein, Michael A. Davies, Jason Huse, P. Andrew Futreal. The University of Texas MD Anderson Cancer Center, TX, The University of Texas MD Anderson Cancer Center, TX, Olivia Newton-John Cancer Research Institute and School of Cancer Medicine, La Trobe University, AustraliaDisclosures O.W. Lee: None. A. Mitra: None. W. Lee: None. K. Fukumura: None. H. Beird: None. M. Andrews: ; Merck Sharp and Dohme. G. Fischer: None. J.N. Weinstein: None. M.A. Davies: ; Bristol-Myers Squibb. ; Novartis. ; Array BioPharma. ; Roche and Genentech. ; GlaxoSmithKline. ; Sanofi-Aventis. ; AstraZeneca. ; Myriad Genetics. ; Oncothyreon. J. Huse: None. P. Futreal: None.

Abstract: Brain metastases (BM) occur in 10-30% of patients with cancer. Approximately 200,000 new cases of brain metastases are diagnosed in the United States annually, with median survival after diagnosis ranging from 3 to 27 months. Recently, studies have identified significant genetic differences between BM and their corresponding primary tumors. It has been shown that BM harbor clinically actionable mutations that are distinct from those in the primary tumor samples. Additional genomic profiling of BM will provide deeper understanding of the pathogenesis of BM and suggest new therapeutic approaches.
We performed whole-exome sequencing of BM and matched tumors from 41 patients collected from renal cell carcinoma (RCC), breast cancer, lung cancer, and melanoma, which are known to be more likely to develop BM. We profiled total 126 fresh-frozen tumor samples and performed subsequent analyses of BM in comparison to paired primary tumor and extracranial metastases (ECM). We found that lung cancer shared the largest number of mutations between BM and matched tumors (83%), followed by melanoma (74%), RCC (51%), and Breast (26%), indicating that cancer type with high tumor mutational burden share more mutations with BM. Mutational signatures displayed limited differences, suggesting a lack of mutagenic processes specific to BM. However, point-mutation heterogeneity revealed that BM evolve separately into different subclones from their paired tumors regardless of cancer type, and some cancer driver genes were found in BM-specific subclones. These models and findings suggest that these driver genes may drive prometastatic subclones that lead to BM. 32 curated cancer gene mutations were detected and 71% of them were shared between BM and primary tumors or ECM. 29% of mutations were specific to BM, implying that BM often accumulate additional cancer gene mutations that are not present in primary tumors or ECM. Co-mutation analysis revealed a high frequency of TP53 nonsense mutation in BM, mostly in the DNA binding domain, suggesting TP53 nonsense mutation as a possible prerequisite for the development of BM. Copy number alteration analysis showed statistically significant differences between BM and their paired tumor samples in each cancer type (Wilcoxon test, p < 0.0385 for all). Both copy number gains and losses were consistently higher in BM for breast cancer (Wilcoxon test, p =1.307e-5) and lung cancer (Wilcoxon test, p =1.942e-5), implying greater genomic instability during the evolution of BM.
Our findings highlight that there are more unique mutations in BM, with significantly higher copy number alterations and tumor mutational burden. These genomic analyses could provide an opportunity for more reliable diagnostic decision-making, and these findings will be further tested with additional transcriptomic and epigenetic profiling for better characterization of BM-specific tumor microenvironments.

  • are there genomic signatures different in brain mets versus non metastatic or normal?
  • 32 genes from curated databases were different between brain mets and primary tumor
  • frequent nonsense mutations in TP53
  • divergent clonal evolution of drivers in BMets from primary
  • they were able to match BM with other mutational signatures like smokers and lung cancer signatures

5707 – A standard operating procedure for the interpretation of oncogenicity/pathogenicity of somatic mutations

Presenter/AuthorsPeter Horak, Malachi Griffith, Arpad Danos, Beth A. Pitel, Subha Madhavan, Xuelu Liu, Jennifer Lee, Gordana Raca, Shirley Li, Alex H. Wagner, Shashikant Kulkarni, Obi L. Griffith, Debyani Chakravarty, Dmitriy Sonkin. National Center for Tumor Diseases, Heidelberg, Germany, Washington University School of Medicine, St. Louis, MO, Mayo Clinic, Rochester, MN, Georgetown University Medical Center, Washington, DC, Dana-Farber Cancer Institute, Boston, MA, Frederick National Laboratory for Cancer Research, Rockville, MD, University of Southern California, Los Angeles, CA, Sunquest, Boston, MA, Baylor College of Medicine, Houston, TX, Memorial Sloan Kettering Cancer Center, New York, NY, National Cancer Institute, Rockville, MDDisclosures P. Horak: None. M. Griffith: None. A. Danos: None. B.A. Pitel: None. S. Madhavan: ; Perthera Inc. X. Liu: None. J. Lee: None. G. Raca: None. S. Li: ; Sunquest Information Systems, Inc. A.H. Wagner: None. S. Kulkarni: ; Baylor Genetics. O.L. Griffith: None. D. Chakravarty: None. D. Sonkin: None.AbstractSomatic variants in cancer-relevant genes are interpreted from multiple partially overlapping perspectives. When considered in discovery and translational research endeavors, it is important to determine if a particular variant observed in a gene of interest is oncogenic/pathogenic or not, as such knowledge provides the foundation on which targeted cancer treatment research is based. In contrast, clinical applications are dominated by diagnostic, prognostic, or therapeutic interpretations which in part also depends on underlying variant oncogenicity/pathogenicity. The Association for Molecular Pathology, the American Society of Clinical Oncology, and the College of American Pathologists (AMP/ASCO/CAP) have published structured somatic variant clinical interpretation guidelines which specifically address diagnostic, prognostic, and therapeutic implications. These guidelines have been well-received by the oncology community. Many variant knowledgebases, clinical laboratories/centers have adopted or are in the process of adopting these guidelines. The AMP/ASCO/CAP guidelines also describe different data types which are used to determine oncogenicity/pathogenicity of a variant, such as: population frequency, functional data, computational predictions, segregation, and somatic frequency. A second collaborative effort created the European Society for Medical Oncology (ESMO) Scale for Clinical Actionability of molecular Targets to provide a harmonized vocabulary that provides an evidence-based ranking system of molecular targets that supports their value as clinical targets. However, neither of these clinical guideline systems provide systematic and comprehensive procedures for aggregating population frequency, functional data, computational predictions, segregation, and somatic frequency to consistently interpret variant oncogenicity/pathogenicity, as has been published in the ACMG/AMP guidelines for interpretation of pathogenicity of germline variants. In order to address this unmet need for somatic variant oncogenicity/pathogenicity interpretation procedures, the Variant Interpretation for Cancer Consortium (VICC, a GA4GH driver project) Knowledge Curation and Interpretation Standards (KCIS) working group (WG) has developed a Standard Operating Procedure (SOP) with contributions from members of ClinGen Somatic Clinical Domain WG, and ClinGen Somatic/Germline variant curation WG using an approach similar to the ACMG/AMP germline pathogenicity guidelines to categorize evidence of oncogenicity/pathogenicity as very strong, strong, moderate or supporting. This SOP enables consistent and comprehensive assessment of oncogenicity/pathogenicity of somatic variants and latest version of an SOP can be found at https://cancervariants.org/wg/kcis/.

  • best to use this SOP for somatic mutations and not rearangements
  • variants based on oncogenicity as strong to weak
  • useful variant knowledge on pathogenicity curated from known databases
  • the recommendations would provide some guideline on curating unknown somatic variants versus known variants of hereditary diseases
  • they have not curated RB1 mutations or variants (or for other RBs like RB2? p130?)

 

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Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Early Detection and ctDNA 1:35 – 3:55 PM

Reporter: Stephen J. Williams, PhD

Introduction
Alberto Bardelli

  • circulating tumor DNA has been around but with NGS now we can have more specificity in analyzing ctDNA
  • interest lately in using liquid biopsy to gain insight on tumor heterogeneity versus single needle biopsy of the solid tumor
  • these talks will however be on ctDNA as a diagnostic and therapeutic monitoring modality

Prediction of cancer and tissue of origin in individuals with suspicion of cancer using a cell-free DNA multi-cancer early detection test
David Thiel 

@MayoClinic

  • test has a specificity over 90% and intended to used along with guideline
  • The Circulating  Cell-free Genome Atlas Study (clinical trial NCT02889978) (CCGA) study divided into three substudies: highest performing assay, refining assay, validation of assays
  • methylation based assays worked better than sequencing (bisulfite sequencing)
  • used a machine learning algorithm to help refine assay
  • prediction was >90%; subgroup for high clinical suspicion of cancer
  • HCS sensitivity was 100% and specificity very high; but sensitivity on training set was 40% and results may have been confounded by including kidney cancer
  • TOO tissue of origin was predicted in greater than 99% in both training and validation sets

A first-of-its-kind prospective study of a multi-cancer blood test to screen and manage 10,000 women with no history of cancer

  • DETECT-A study: prospective interventional study; can multi blood test be used prospectively and can lead to a personalized care; can the screen be used to complement current therapy?
  • 10,000 women aged 65-75;  these women could not have previous cancer and conducted through Geisinger Health Network; multi test detects DNA and protein and standard of care screening
  • the study focused on safety so a committee was consulted on each case, and used a diagnostic PET-CT
  • blood test alone not good but combined with protein and CT scans much higher (5 fold increase) detection for breast cancer

Nickolas Papadopoulos

@HopkinsMedicine

Discussant
David Huntsman

  • there are mutiple opportunities yet at same time there are still challenges to utilize these cell free tests in therapeutic monitoring, diagnostic, and screening however sensitivities for some cancers are still too low to use in large scale screening however can supplement current screening guidelines
  • we have to ask about false positive rate and need to concentrate on prospective studies
  • we must consider how tests will be used, population health studies will need to show improved survival

 

Phylogenetic tracking and minimal residual disease detection using ctDNA in early-stage NSCLC: A lung TRACERx study
Chris Abbosh @ucl

  • TRACERx study in collaboration with Charles Swanton.
  • multiplex PCR to track 200 SNVs: correlate tumor tissue biopsy with ctDNA
  • spike in assay shows very good sensitivity and specificity for SNVs variants tracked, did over 400 TRACERx libraries
  • sensitivity increases when tracking more variants but specificity does go down a bit
  • tracking variants can show evidence of subclonal dynamics and evolution and copy number deletion events;  they also show neoantigen editing or changing of their neoantigens
  • this assay can detect low variants in a reproducible manner

The TRACERx (TRAcking Cancer Evolution through therapy (Rx)) lung study is a multi-million pound research project taking place over nine years, which will transform our understanding of non-small cell lung cancer (NSCLC) and take a practical step towards an era of precision medicine. The study will uncover mechanisms of cancer evolution by analysing the intratumour heterogeneity in lung tumours from approximately 850 patients and tracking its evolutionary trajectory from diagnosis through to relapse. At £14 million, it’s the biggest single investment in lung cancer research by Cancer Research UK, and the start of a strategic UK-wide focus on the disease, aimed at making real progress for patients.

Led by Professor Charles Swanton at UCL, the study will bring together a network of experts from different disciplines to help integrate clinical and genomic data and identify patients who could benefit from trials of new, targeted treatments. In addition, it will use a whole suite of cutting edge analytical techniques on these patients’ tumour samples, giving unprecedented insight into the genomic landscape of primary and metastatic tumours and the impact of treatment upon this landscape.

In future, TRACERx will enable us to define how intratumour heterogeneity impacts upon cancer immunity throughout tumour evolution and therapy. Such studies will help define how the clinical evaluation of intratumour heterogeneity can inform patient stratification and the development of combinatorial therapies incorporating conventional, targeted and immune based therapeutics.

Intratumour heterogeneity is increasingly recognised as a major hurdle to achieve improvements in therapeutic outcome and biomarker validation. Intratumour genetic diversity provides a substrate for tumour adaptation and evolution. However, the evolutionary genomic landscape of non-small cell lung cancer (NSCLC) and how it changes through the disease course has not been studied in detail. TRACERx is a prospective observational study with the following objectives:

Primary Objectives

  • Define the relationship between intratumour heterogeneity and clinical outcome following surgery and adjuvant therapy (including relationships between intratumour heterogeneity and clinical disease stage and histological subtypes of NSCLC).
  • Establish the impact of adjuvant platinum-containing regimens upon intratumour heterogeneity in relapsed disease compared to primary resected tumour.

Key Secondary Objectives

  • Develop and validate an intratumour heterogeneity (ITH) ratio index as a prognostic and predictive biomarker in relation to disease-free survival and overall survival.
  • Infer a complete picture of NSCLC evolutionary dynamics – define drivers of genomic instability, metastatic progression and drug resistance by identifying and tracking the dynamics of somatic mutational heterogeneity, and chromosomal structural and numerical instability present in the primary tumour and at metastatic sites. Individual tumour phylogenetic tree analysis will:
    • Establish the order of somatic events in relation to genomic instability onset and metastatic progression
    • Decipher genetic “bottlenecking” events following metastasis and drug therapy
    • Establish dynamics of tumour evolution during the disease course from early to late stage NSCLC.
  • Initiate a longitudinal biobank of circulating tumour cells (CTCs) and circulating-free tumour DNA (cfDNA) to develop analytical methods for the early detection and monitoring of tumour evolution over time.
  • Develop a longitudinal tissue resource to serve as a platform to assess the relationship between genetic intratumour heterogeneity and the host immune response.
  • Define relationships between intratumour heterogeneity and targeted/cytotoxic therapeutic outcome.
  • Use a lung cancer specific gene panel in a certified Good Clinical Practice (GCP) laboratory environment to define clonally dominant disease drivers to address the role of clonal driver dominance in targeted therapeutic response and to guide stratification of lung cancer treatment and future clinical study inclusion (paired primary-metastatic site comparisons in at least 270 patients with relapsed disease).

 

 

Utility of longitudinal circulating tumor DNA (ctDNA) modeling to predict RECIST-defined progression in first-line patients with epidermal growth factor receptor mutation-positive (EGFRm) advanced non-small cell lung cancer (NSCLC)
Martin Johnson

 

Impact of the EML4-ALK fusion variant on the efficacy of lorlatinib in patients (pts) with ALK-positive advanced non-small cell lung cancer (NSCLC)
Todd Bauer

 

From an interview with Dr. Bauer at https://www.lungcancernews.org/2019/08/14/making-headway-with-lorlatinib/

Lorlatinib, a smallmolecule inhibitor of ALK and ROS1, was granted accelerated U.S. Food and Drug Administration approval in November 2018 for patients with ALK-positive metastatic NSCLC whose disease has progressed on crizotinib and at least one other ALK inhibitor or whose disease has progressed on alectinib or ceritinib as the first ALK inhibitor therapy for metastatic disease. Todd M. Bauer, MD, a medical oncologist and senior investigator at Sarah Cannon Research Institute/Tennessee Oncology, PLLC, in Nashville, has been very involved with the development of lorlatinib since the beginning. In the following interview, Dr. Bauer discusses some of lorlatinib’s unique toxicities, as well as his first-hand experiences with the drug.

For further reading: Solomon B, Besse B, Bauer T, et al. Lorlatinib in Patients with ALK-positive non-small-cell lung cancer: results from a global phase 2 study. Lancet. 2018;19(12):P1654-1667.

Abstract

BACKGROUND: Lorlatinib is a potent, brain-penetrant, third-generation inhibitor of ALK and ROS1 tyrosine kinases with broad coverage of ALK mutations. In a phase 1 study, activity was seen in patients with ALK-positive non-small-cell lung cancer, most of whom had CNS metastases and progression after ALK-directed therapy. We aimed to analyse the overall and intracranial antitumour activity of lorlatinib in patients with ALK-positive, advanced non-small-cell lung cancer.

METHODS: In this phase 2 study, patients with histologically or cytologically ALK-positive or ROS1-positive, advanced, non-small-cell lung cancer, with or without CNS metastases, with an Eastern Cooperative Oncology Group performance status of 0, 1, or 2, and adequate end-organ function were eligible. Patients were enrolled into six different expansion cohorts (EXP1-6) on the basis of ALK and ROS1 status and previous therapy, and were given lorlatinib 100 mg orally once daily continuously in 21-day cycles. The primary endpoint was overall and intracranial tumour response by independent central review, assessed in pooled subgroups of ALK-positive patients. Analyses of activity and safety were based on the safety analysis set (ie, all patients who received at least one dose of lorlatinib) as assessed by independent central review. Patients with measurable CNS metastases at baseline by independent central review were included in the intracranial activity analyses. In this report, we present lorlatinib activity data for the ALK-positive patients (EXP1-5 only), and safety data for all treated patients (EXP1-6). This study is ongoing and is registered with ClinicalTrials.gov, number NCT01970865.

FINDINGS: Between Sept 15, 2015, and Oct 3, 2016, 276 patients were enrolled: 30 who were ALK positive and treatment naive (EXP1); 59 who were ALK positive and received previous crizotinib without (n=27; EXP2) or with (n=32; EXP3A) previous chemotherapy; 28 who were ALK positive and received one previous non-crizotinib ALK tyrosine kinase inhibitor, with or without chemotherapy (EXP3B); 112 who were ALK positive with two (n=66; EXP4) or three (n=46; EXP5) previous ALK tyrosine kinase inhibitors with or without chemotherapy; and 47 who were ROS1 positive with any previous treatment (EXP6). One patient in EXP4 died before receiving lorlatinib and was excluded from the safety analysis set. In treatment-naive patients (EXP1), an objective response was achieved in 27 (90·0%; 95% CI 73·5-97·9) of 30 patients. Three patients in EXP1 had measurable baseline CNS lesions per independent central review, and objective intracranial responses were observed in two (66·7%; 95% CI 9·4-99·2). In ALK-positive patients with at least one previous ALK tyrosine kinase inhibitor (EXP2-5), objective responses were achieved in 93 (47·0%; 39·9-54·2) of 198 patients and objective intracranial response in those with measurable baseline CNS lesions in 51 (63·0%; 51·5-73·4) of 81 patients. Objective response was achieved in 41 (69·5%; 95% CI 56·1-80·8) of 59 patients who had only received previous crizotinib (EXP2-3A), nine (32·1%; 15·9-52·4) of 28 patients with one previous non-crizotinib ALK tyrosine kinase inhibitor (EXP3B), and 43 (38·7%; 29·6-48·5) of 111 patients with two or more previous ALK tyrosine kinase inhibitors (EXP4-5). Objective intracranial response was achieved in 20 (87·0%; 95% CI 66·4-97·2) of 23 patients with measurable baseline CNS lesions in EXP2-3A, five (55·6%; 21·2-86·3) of nine patients in EXP3B, and 26 (53·1%; 38·3-67·5) of 49 patients in EXP4-5. The most common treatment-related adverse events across all patients were hypercholesterolaemia (224 [81%] of 275 patients overall and 43 [16%] grade 3-4) and hypertriglyceridaemia (166 [60%] overall and 43 [16%] grade 3-4). Serious treatment-related adverse events occurred in 19 (7%) of 275 patients and seven patients (3%) permanently discontinued treatment because of treatment-related adverse events. No treatment-related deaths were reported.

INTERPRETATION: Consistent with its broad ALK mutational coverage and CNS penetration, lorlatinib showed substantial overall and intracranial activity both in treatment-naive patients with ALK-positive non-small-cell lung cancer, and in those who had progressed on crizotinib, second-generation ALK tyrosine kinase inhibitors, or after up to three previous ALK tyrosine kinase inhibitors. Thus, lorlatinib could represent an effective treatment option for patients with ALK-positive non-small-cell lung cancer in first-line or subsequent therapy.

  • loratinib could be used for crizotanib resistant tumors based on EML4-ALK variants present in ctDNA

Reference:
1. Updated efficacy and safety data from the global phase III ALEX study of alectinib (ALC) vs crizotinib (CZ) in untreated advanced ALK+ NSCLCJ Clin Oncol 36, 2018 (suppl; abstr 9043).

Discussion

Corey Langer

 

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Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 27, 2020 Minisymposium on AACR Project Genie & Bioinformatics 4:00 PM – 6:00 PM

SESSION VMS.MD01.01 – Advancing Cancer Research through an International Cancer Registry: AACR Project GENIE Use Cases
 
Reporter: Stephen J. Williams, PhD

April 27, 2020, 4:00 PM – 6:00 PM
Virtual Meeting: All Session Times Are U.S. EDT

Session Type
Virtual Minisymposium
Track(s)
Bioinformatics and Systems Biology
17 Presentations
4:00 PM – 6:00 PM
– Chairperson Gregory J. Riely. Memorial Sloan Kettering Cancer Center, New York, NY

4:00 PM – 4:01 PM
– Introduction Gregory J. Riely. Memorial Sloan Kettering Cancer Center, New York, NY

Precision medicine requires an end-to-end learning healthcare system, wherein the treatment decisions for patients are informed by the prior experiences of similar patients. Oncology is currently leading the way in precision medicine because the genomic and other molecular characteristics of patients and their tumors are routinely collected at scale. A major challenge to realizing the promise of precision medicine is that no single institution is able to sequence and treat sufficient numbers of patients to improve clinical-decision making independently. To overcome this challenge, the AACR launched Project GENIE (Genomics Evidence Neoplasia Information Exchange).

AACR Project GENIE is a publicly accessible international cancer registry of real-world data assembled through data sharing between 19 of the leading cancer centers in the world. Through the efforts of strategic partners Sage Bionetworks (https://sagebionetworks.org) and cBioPortal (www.cbioportal.org), the registry aggregates, harmonizes, and links clinical-grade, next-generation cancer genomic sequencing data with clinical outcomes obtained during routine medical practice from cancer patients treated at these institutions. The consortium and its activities are driven by openness, transparency, and inclusion, ensuring that the project output remains accessible to the global cancer research community for the benefit of all patients.AACR Project GENIE fulfills an unmet need in oncology by providing the statistical power necessary to improve clinical decision-making, particularly in the case of rare cancers and rare variants in common cancers. Additionally, the registry can power novel clinical and translational research.

Because we collect data from nearly every patient sequenced at participating institutions and have committed to sharing only clinical-grade data, the GENIE registry contains enough high-quality data to power decision making on rare cancers or rare variants in common cancers. We see the GENIE data providing another knowledge turn in the virtuous cycle of research, accelerating the pace of drug discovery, improving the clinical trial design, and ultimately benefiting cancer patients globally.

 

The first set of cancer genomic data aggregated through AACR Project Genomics Evidence Neoplasia Information Exchange (GENIE) was available to the global community in January 2017.  The seventh data set, GENIE 7.0-public, was released in January 2020 adding more than 9,000 records to the database. The combined data set now includes nearly 80,000 de-identified genomic records collected from patients who were treated at each of the consortium’s participating institutions, making it among the largest fully public cancer genomic data sets released to date.  These data will be released to the public every six months. The public release of the eighth data set, GENIE 8.0-public, will take place in July 2020.

The combined data set now includes data for over 80 major cancer types, including data from greater than 12,500 patients with lung cancer, nearly 11,000 patients with breast cancer, and nearly 8,000 patients with colorectal cancer.

For more details about the data, analyses, and summaries of the data attributes from this release, GENIE 7.0-public, consult the data guide.

Users can access the data directly via cbioportal, or download the data directly from Sage Bionetworks. Users will need to create an account for either site and agree to the terms of access.

For frequently asked questions, visit our FAQ page.

  • In fall of 2019 AACR announced the Bio Collaborative which collected pan cancer data in conjuction and collaboration and support by a host of big pharma and biotech companies
  • they have a goal to expand to more than 6 cancer types and more than 50,000 records including smoking habits, lifestyle data etc
  • They have started with NSCLC have have done mutational analysis on these
  • included is tumor mutational burden and using cbioportal able to explore genomic data even further
  • treatment data is included as well
  • need to collect highly CURATED data with PRISM backbone to get more than outcome data, like progression data
  • they might look to incorporate digital pathology but they are not there yet; will need good artificial intelligence systems

 

4:01 PM – 4:15 PM
– Invited Speaker Gregory J. Riely. Memorial Sloan Kettering Cancer Center, New York, NY

4:15 PM – 4:20 PM
– Discussion

4:20 PM – 4:30 PM
1092 – A systematic analysis of BRAF mutations and their sensitivity to different BRAF inhibitors: Zohar Barbash, Dikla Haham, Liat Hafzadi, Ron Zipor, Shaul Barth, Arie Aizenman, Lior Zimmerman, Gabi Tarcic. Novellusdx, Jerusalem, Israel

Abstract: The MAPK-ERK signaling cascade is among the most frequently mutated pathways in human cancer, with the BRAF V600 mutation being the most common alteration. FDA-approved BRAF inhibitors as well as combination therapies of BRAF and MEK inhibitors are available and provide survival benefits to patients with a BRAF V600 mutation in several indications. Yet non-V600 BRAF mutations are found in many cancers and are even more prevalent than V600 mutations in certain tumor types. As the use of NGS profiling in precision oncology is becoming more common, novel alterations in BRAF are being uncovered. This has led to the classification of BRAF mutations, which is dependent on its biochemical properties and affects it sensitivity to inhibitors. Therefore, annotation of these novel variants is crucial for assigning correct treatment. Using a high throughput method for functional annotation of MAPK activity, we profiled 151 different BRAF mutations identified in the AACR Project GENIE dataset, and their response to 4 different BRAF inhibitors- vemurafenib and 3 different exploratory 2nd generation inhibitors. The system is based on rapid synthesis of the mutations and expression of the mutated protein together with fluorescently labeled reporters in a cell-based assay. Our results show that from the 151 different BRAF mutations, ~25% were found to activate the MAPK pathway. All of the class 1 and 2 mutations tested were found to be active, providing positive validation for the method. Additionally, many novel activating mutations were identified, some outside of the known domains. When testing the response of the active mutations to different classes of BRAF inhibitors, we show that while vemurafenib efficiently inhibited V600 mutations, other types of mutations and specifically BRAF fusions were not inhibited by this drug. Alternatively, the second-generation experimental inhibitors were effective against both V600 as well as non-V600 mutations. Using this large-scale approach to characterize BRAF mutations, we were able to functionally annotate the largest number of BRAF mutations to date. Our results show that the number of activating variants is large and that they possess differential sensitivity to different types of direct inhibitors. This data can serve as a basis for rational drug design as well as more accurate treatment options for patients.

  • Molecular profiling is becoming imperative for successful  targeted therapies
  • 500 unique mutations in BRAF so need to use bioinformatic pipeline; start with NGS panels then cluster according to different subtypes or class specific patterns
  • certain mutation like V600E mutations have distinct clustering in tumor types
  • 25% of mutations occur with other mutations; mutations may not be functional; they used highthruput system to analyze other V600 braf mutations to determine if functional
  • active yet uncharacterized BRAF mutations seen in a major proportion of human tumors
  • using genomic drug data found that many inhibitors like verafanib are specific to a specific mutation but other inhibitors that are not specific to a cleft can inhibit other BRAF mutants
  • 40% of 135 mutants were functionally active
  • USE of Functional Profiling instead of just genomic profiling
  • Q?: They have already used this platform and analysis for RTKs and other genes as well successfully
  • Q? how do you deal with co reccuring mutations: platform is able to do RTK plus signaling protiens

4:30 PM – 4:35 PM
– Discussion

4:35 PM – 4:45 PM
1093 – Calibration Tool for Genomic Aggregates (CTGA): A deep learning framework for calibrating somatic mutation profiling data from conventional gene panel data. Jordan Anaya, Craig Cummings, Jocelyn Lee, Alexander Baras. Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, MD, Genentech, Inc., CA, AACR, Philadelphia, PA

Abstract: It has been suggested that aggregate genomic measures such as mutational burden can be associated with response to immunotherapy. Arguably, the gold standard for deriving such aggregate genomic measures (AGMs) would be from exome level sequencing. While many clinical trials run exome level sequencing, the vast majority of routine genomic testing performed today, as seen in AACR Project GENIE, is targeted / gene-panel based sequencing.
Despite the smaller size of these gene panels focused on clinically targetable alterations, it has been shown they can estimate, to some degree, exomic mutational burden; usually by normalizing mutation count by the relevant size of the panels. These smaller gene panels exhibit significant variability both in terms of accuracy relative to exomic measures and in comparison to other gene panels. While many genes are common to the panels in AACR Project GENIE, hundreds are not. These differences in extent of coverage and genomic loci examined can result in biases that may negatively impact panel to panel comparability.
To address these issues we developed a deep learning framework to model exomic AGMs, such as mutational burden, from gene panel data as seen in AACR Project GENIE. This framework can leverage any available sample and variant level information, in which variants are featurized to effectively re-weight their importance when estimating a given AGM, such as mutational burden, through the use of multiple instance learning techniques in this form of weakly supervised data.
Using TCGA data in conjunction with AACR Project GENIE gene panel definitions, as a proof of concept, we first applied this framework to learn expected variant features such as codons and genomic position from mutational data (greater than 99.9% accuracy observed). Having established the validity of the approach, we then applied this framework to somatic mutation profiling data in which we show that data from gene panels can be calibrated to exomic TMB and thereby improve panel to panel compatibility. We observed approximately 25% improvements in mean squared error and R-squared metrics when using our framework over conventional approaches to estimate TMB from gene panel data across the 9 tumors types examined (spanning melanoma, lung cancer, colon cancer, and others). This work highlights the application of sophisticated machine learning approaches towards the development of needed calibration techniques across seemingly disparate gene panel assays used clinically today.

 

4:45 PM – 4:50 PM
– Discussion

4:50 PM – 5:00 PM
1094 – Genetic determinants of EGFR-driven lung cancer growth and therapeutic response in vivoGiorgia Foggetti, Chuan Li, Hongchen Cai, Wen-Yang Lin, Deborah Ayeni, Katherine Hastings, Laura Andrejka, Dylan Maghini, Robert Homer, Dmitri A. Petrov, Monte M. Winslow, Katerina Politi. Yale School of Medicine, New Haven, CT, Stanford University School of Medicine, Stanford, CA, Stanford University School of Medicine, Stanford, CA, Yale School of Medicine, New Haven, CT, Stanford University School of Medicine, Stanford, CA, Yale School of Medicine, New Haven, CT

5:00 PM – 5:05 PM
– Discussion

5:05 PM – 5:15 PM
1095 – Comprehensive pan-cancer analyses of RAS genomic diversityRobert Scharpf, Gregory Riely, Mark Awad, Michele Lenoue-Newton, Biagio Ricciuti, Julia Rudolph, Leon Raskin, Andrew Park, Jocelyn Lee, Christine Lovly, Valsamo Anagnostou. Johns Hopkins Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD, Memorial Sloan Kettering Cancer Center, New York, NY, Dana-Farber Cancer Institute, Boston, MA, Vanderbilt-Ingram Cancer Center, Nashville, TN, Amgen, Inc., Thousand Oaks, CA, AACR, Philadelphia, PA

5:15 PM – 5:20 PM
– Discussion

5:20 PM – 5:30 PM
1096 – Harmonization standards from the Variant Interpretation for Cancer Consortium. Alex H. Wagner, Reece K. Hart, Larry Babb, Robert R. Freimuth, Adam Coffman, Yonghao Liang, Beth Pitel, Angshumoy Roy, Matthew Brush, Jennifer Lee, Anna Lu, Thomas Coard, Shruti Rao, Deborah Ritter, Brian Walsh, Susan Mockus, Peter Horak, Ian King, Dmitriy Sonkin, Subha Madhavan, Gordana Raca, Debyani Chakravarty, Malachi Griffith, Obi L. Griffith. Washington University School of Medicine, Saint Louis, MO, Reece Hart Consulting, CA, Broad Institute, Boston, MA, Mayo Clinic, Rochester, MN, Washington University School of Medicine, Saint Louis, MO, Washington University School of Medicine, Saint Louis, MO, Baylor College of Medicine, Houston, TX, Oregon Health and Science University, Portland, OR, National Cancer Institute, Bethesda, MD, Georgetown University, Washington, DC, The Jackson Laboratory for Genomic Medicine, Farmington, CT, National Center for Tumor Diseases, Heidelberg, Germany, University of Toronto, Toronto, ON, Canada, University of Southern California, Los Angeles, CA, Memorial Sloan Kettering Cancer Center, New York, NY

Abstract: The use of clinical gene sequencing is now commonplace, and genome analysts and molecular pathologists are often tasked with the labor-intensive process of interpreting the clinical significance of large numbers of tumor variants. Numerous independent knowledge bases have been constructed to alleviate this manual burden, however these knowledgebases are non-interoperable. As a result, the analyst is left with a difficult tradeoff: for each knowledgebase used the analyst must understand the nuances particular to that resource and integrate its evidence accordingly when generating the clinical report, but for each knowledgebase omitted there is increased potential for missed findings of clinical significance.The Variant Interpretation for Cancer Consortium (VICC; cancervariants.org) was formed as a driver project of the Global Alliance for Genomics and Health (GA4GH; ga4gh.org) to address this concern. VICC members include representatives from several major somatic interpretation knowledgebases including CIViC, OncoKB, Jax-CKB, the Weill Cornell PMKB, the IRB-Barcelona Cancer Biomarkers Database, and others. Previously, the VICC built and reported on a harmonized meta-knowledgebase of 19,551 biomarker associations of harmonized variants, diseases, drugs, and evidence across the constituent resources.In that study, we analyzed the frequency with which the tumor samples from the AACR Project GENIE cohort would match to harmonized associations. Variant matches increased dramatically from 57% to 86% when broader matching to regions describing categorical variants were allowed. Unlike precise sequence variants with specified alternate alleles, categorical variants describe a collection of potential variants with a common feature, such as “V600” (non-valine alleles at the 600 residue), “Exon 20 mutations” (all non-silent mutations in exon 20), or “Gain-of-function” (hypermorphic alterations that activate or amplify gene activity). However, matching observed sequence variants to categorical variants is challenging, as the latter are typically only described as unstructured text. Here we describe the expressive and computational GA4GH Variation Representation specification (vr-spec.readthedocs.io), which we co-developed as members of the GA4GH Genomic Knowledge Standards work stream. This specification provides a schema for common, precise forms of variation (e.g. SNVs and Indels) and the method for computing identifiers from these objects. We highlight key aspects of the specification and our work to apply it to the characterization of categorical variation, showcasing the variant terminology and classification tools developed by the VICC to support this effort. These standards and tools are free, open-source, and extensible, overcoming barriers to standardized variant knowledge sharing and search.

https://cancervariants.org/

  • store information from different databases by curating them and classifying them then harmonizing them into values
  • harmonize each variant across their knowledgebase; at any level of evidence
  • had 29% of patients variants that matched when compare across many knowledgebase databases versus only 13% when using individual databases
  • they are also trying to curate the database so a variant will have one code instead of various refseq codes or protein codes
  • VIC is an open consortium

 

 

5:30 PM – 5:35 PM
– Discussion

5:35 PM – 5:45 PM
1097 – FGFR2 in-frame indels: A novel targetable alteration in intrahepatic cholangiocarcinoma. Yvonne Y. Li, James M. Cleary, Srivatsan Raghavan, Liam F. Spurr, Qibiao Wu, Lei Shi, Lauren K. Brais, Maureen Loftus, Lipika Goyal, Anuj K. Patel, Atul B. Shinagare, Thomas E. Clancy, Geoffrey Shapiro, Ethan Cerami, William R. Sellers, William C. Hahn, Matthew Meyerson, Nabeel Bardeesy, Andrew D. Cherniack, Brian M. Wolpin. Dana-Farber Cancer Institute, Boston, MA, Dana-Farber Cancer Institute, Boston, MA, Massachusetts General Hospital, Boston, MA, Brigham and Women’s Hospital, Boston, MA, Dana-Farber Cancer Institute, Boston, MA, Dana-Farber Cancer Institute, Boston, MA, Broad Institute of MIT and Harvard, Cambridge, MA, Massachusetts General Hospital, Boston, MA

5:45 PM – 5:50 PM
– Discussion

5:50 PM – 6:00 PM
– Closing RemarksGregory J. Riely. Memorial Sloan Kettering Cancer Center, New York, NY

 

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Personalized Medicine, Omics, and Health Disparities in Cancer:  Can Personalized Medicine Help Reduce the Disparity Problem?

Curator: Stephen J. Williams, PhD

In a Science Perspectives article by Timothy Rebbeck, health disparities, specifically cancer disparities existing in the sub-Saharan African (SSA) nations, highlighting the cancer incidence disparities which exist compared with cancer incidence in high income areas of the world [1].  The sub-Saharan African nations display a much higher incidence of prostate, breast, and cervix cancer and these cancers are predicted to double within the next twenty years, according to IARC[2].  Most importantly,

 the histopathologic and demographic features of these tumors differ from those in high-income countries

meaning that the differences seen in incidence may reflect a true health disparity as increases rates in these cancers are not seen in high income countries (HIC).

Most frequent male cancers in SSA include prostate, lung, liver, leukemia, non-Hodgkin’s lymphoma, and Kaposi’s sarcoma (a cancer frequently seen in HIV infected patients [3]).  In SSA women, breast and cervical cancer are the most common and these display higher rates than seen in high income countries.  In fact, liver cancer is seen in SSA females at twice the rate, and in SSA males almost three times the rate as in high income countries.

 

 

 

 

 

 

Reasons for cancer disparity in SSA

Patients with cancer are often diagnosed at a late stage in SSA countries.  This contrasts with patients from high income countries, which have their cancers usually diagnosed at an earlier stage, and with many cancers, like breast[4], ovarian[5, 6], and colon, detecting the tumor in the early stages is critical for a favorable outcome and prognosis[7-10].  In addition, late diagnosis also limits many therapeutic options for the cancer patient and diseases at later stages are much harder to manage, especially with respect to unresponsiveness and/or resistance of many therapies.  In addition, treatments have to be performed in low-resource settings in SSA, and availability of clinical lab work and imaging technologies may be limited.

Molecular differences in SSA versus HIC cancers which may account for disparities

Emerging evidence suggests that there are distinct molecular signatures with SSA tumors with respect to histotype and pathology.  For example Dr. Rebbeck mentions that Nigerian breast cancers were defined by increased mutational signatures associated with deficiency of the homologous recombination DNA repair pathway, pervasive mutations in the tumor suppressor gene TP53, mutations in GATA binding protein 3 (GATA3), and greater mutational burden, compared with breast tumors from African Americans or Caucasians[11].  However more research will be required to understand the etiology and causal factors related to this molecular distinction in mutational spectra.

It is believed that there is a higher rate of hereditary cancers in SSA. And many SSA cancers exhibit the more aggressive phenotype than in other parts of the world.  For example breast tumors in SSA black cases are twice as likely than SSA Caucasian cases to be of the triple negative phenotype, which is generally more aggressive and tougher to detect and treat, as triple negative cancers are HER2 negative and therefore are not a candidate for Herceptin.  Also BRCA1/2 mutations are more frequent in black SSA cases than in Caucasian SSA cases [12, 13].

Initiatives to Combat Health Disparities in SSA

Multiple initiatives are being proposed or in action to bring personalized medicine to the sub-Saharan African nations.  These include:

H3Africa empowers African researchers to be competitive in genomic sciences, establishes and nurtures effective collaborations among African researchers on the African continent, and generates unique data that could be used to improve both African and global health.

There is currently a global effort to apply genomic science and associated technologies to further the understanding of health and disease in diverse populations. These efforts work to identify individuals and populations who are at risk for developing specific diseases, and to better understand underlying genetic and environmental contributions to that risk. Given the large amount of genetic diversity on the African continent, there exists an enormous opportunity to utilize such approaches to benefit African populations and to inform global health.

The Human Heredity and Health in Africa (H3Africa) consortium facilitates fundamental research into diseases on the African continent while also developing infrastructure, resources, training, and ethical guidelines to support a sustainable African research enterprise – led by African scientists, for the African people. The initiative consists of 51 African projects that include population-based genomic studies of common, non-communicable disorders such as heart and renal disease, as well as communicable diseases such as tuberculosis. These studies are led by African scientists and use genetic, clinical, and epidemiologic methods to identify hereditary and environmental contributions to health and disease. To establish a foundation for African scientists to continue this essential work into the future work, the consortium also supports many crucial capacity building elements, such as: ethical, legal, and social implications research; training and capacity building for bioinformatics; capacity for biobanking; and coordination and networking.

The World Economic Forum’s Leapfrogging with Precision Medicine project 

This project is part of the World Economic Forum’s Shaping the Future of Health and Healthcare Platform

The Challenge

Advancing precision medicine in a way that is equitable and beneficial to society means ensuring that healthcare systems can adopt the most scientifically and technologically appropriate approaches to a more targeted and personalized way of diagnosing and treating disease. In certain instances, countries or institutions may be able to bypass, or “leapfrog”, legacy systems or approaches that prevail in developed country contexts.

The World Economic Forum’s Leapfrogging with Precision Medicine project will develop a set of tools and case studies demonstrating how a precision medicine approach in countries with greenfield policy spaces can potentially transform their healthcare delivery and outcomes. Policies and governance mechanisms that enable leapfrogging will be iterated and scaled up to other projects.

Successes in personalized genomic research in SSA

As Dr. Rebbeck states:

 Because of the underlying genetic and genomic relationships between Africans and members of the African diaspora (primarily in North America and Europe), knowledge gained from research in SSA can be used to address health disparities that are prevalent in members of the African diaspora.

For example members of the West African heritage and genomic ancestry has been reported to confer the highest genomic risk for prostate cancer in any worldwide population [14].

 

PERSPECTIVEGLOBAL HEALTH

Cancer in sub-Saharan Africa

  1. Timothy R. Rebbeck

See all authors and affiliations

Science  03 Jan 2020:
Vol. 367, Issue 6473, pp. 27-28
DOI: 10.1126/science.aay474

Summary/Abstract

Cancer is an increasing global public health burden. This is especially the case in sub-Saharan Africa (SSA); high rates of cancer—particularly of the prostate, breast, and cervix—characterize cancer in most countries in SSA. The number of these cancers in SSA is predicted to more than double in the next 20 years (1). Both the explanations for these increasing rates and the solutions to address this cancer epidemic require SSA-specific data and approaches. The histopathologic and demographic features of these tumors differ from those in high-income countries (HICs). Basic knowledge of the epidemiology, clinical features, and molecular characteristics of cancers in SSA is needed to build prevention and treatment tools that will address the future cancer burden. The distinct distribution and determinants of cancer in SSA provide an opportunity to generate knowledge about cancer risk factors, genomics, and opportunities for prevention and treatment globally, not only in Africa.

 

References

  1. Rebbeck TR: Cancer in sub-Saharan Africa. Science 2020, 367(6473):27-28.
  2. Parkin DM, Ferlay J, Jemal A, Borok M, Manraj S, N’Da G, Ogunbiyi F, Liu B, Bray F: Cancer in Sub-Saharan Africa: International Agency for Research on Cancer; 2018.
  3. Chinula L, Moses A, Gopal S: HIV-associated malignancies in sub-Saharan Africa: progress, challenges, and opportunities. Current opinion in HIV and AIDS 2017, 12(1):89-95.
  4. Colditz GA: Epidemiology of breast cancer. Findings from the nurses’ health study. Cancer 1993, 71(4 Suppl):1480-1489.
  5. Hamilton TC, Penault-Llorca F, Dauplat J: [Natural history of ovarian adenocarcinomas: from epidemiology to experimentation]. Contracept Fertil Sex 1998, 26(11):800-804.
  6. Garner EI: Advances in the early detection of ovarian carcinoma. J Reprod Med 2005, 50(6):447-453.
  7. Brockbank EC, Harry V, Kolomainen D, Mukhopadhyay D, Sohaib A, Bridges JE, Nobbenhuis MA, Shepherd JH, Ind TE, Barton DP: Laparoscopic staging for apparent early stage ovarian or fallopian tube cancer. First case series from a UK cancer centre and systematic literature review. European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology 2013, 39(8):912-917.
  8. Kolligs FT: Diagnostics and Epidemiology of Colorectal Cancer. Visceral medicine 2016, 32(3):158-164.
  9. Rocken C, Neumann U, Ebert MP: [New approaches to early detection, estimation of prognosis and therapy for malignant tumours of the gastrointestinal tract]. Zeitschrift fur Gastroenterologie 2008, 46(2):216-222.
  10. Srivastava S, Verma M, Henson DE: Biomarkers for early detection of colon cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2001, 7(5):1118-1126.
  11. Pitt JJ, Riester M, Zheng Y, Yoshimatsu TF, Sanni A, Oluwasola O, Veloso A, Labrot E, Wang S, Odetunde A et al: Characterization of Nigerian breast cancer reveals prevalent homologous recombination deficiency and aggressive molecular features. Nature communications 2018, 9(1):4181.
  12. Zheng Y, Walsh T, Gulsuner S, Casadei S, Lee MK, Ogundiran TO, Ademola A, Falusi AG, Adebamowo CA, Oluwasola AO et al: Inherited Breast Cancer in Nigerian Women. Journal of clinical oncology : official journal of the American Society of Clinical Oncology 2018, 36(28):2820-2825.
  13. Rebbeck TR, Friebel TM, Friedman E, Hamann U, Huo D, Kwong A, Olah E, Olopade OI, Solano AR, Teo SH et al: Mutational spectrum in a worldwide study of 29,700 families with BRCA1 or BRCA2 mutations. Human mutation 2018, 39(5):593-620.
  14. Lachance J, Berens AJ, Hansen MEB, Teng AK, Tishkoff SA, Rebbeck TR: Genetic Hitchhiking and Population Bottlenecks Contribute to Prostate Cancer Disparities in Men of African Descent. Cancer research 2018, 78(9):2432-2443.

Other articles on Cancer Health Disparities and Genomics on this Online Open Access Journal Include:

Gender affects the prevalence of the cancer type
The Rutgers Global Health Institute, part of Rutgers Biomedical and Health Sciences, Rutgers University, New Brunswick, New Jersey – A New Venture Designed to Improve Health and Wellness Globally
Breast Cancer Disparities to be Sponsored by NIH: NIH Launches Largest-ever Study of Breast Cancer Genetics in Black Women
War on Cancer Needs to Refocus to Stay Ahead of Disease Says Cancer Expert
Ethical Concerns in Personalized Medicine: BRCA1/2 Testing in Minors and Communication of Breast Cancer Risk
Ethics Behind Genetic Testing in Breast Cancer: A Webinar by Laura Carfang of survivingbreastcancer.org
Live Notes from @HarvardMed Bioethics: Authors Jerome Groopman, MD & Pamela Hartzband, MD, discuss Your Medical Mind
Testing for Multiple Genetic Mutations via NGS for Patients: Very Strong Family History of Breast & Ovarian Cancer, Diagnosed at Young Ages, & Negative on BRCA Test
Study Finds that Both Women and their Primary Care Physicians Confusion over Ovarian Cancer Symptoms May Lead to Misdiagnosis

 

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e-Proceedings 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 Boston

https://bio-itworld.pathable.co/meetings/virtual/3T3SuWw9J2Bceei9s

 Virtual Conference coverage in Real Time: Aviva Lev-Ari, PhD, RN

 

Tweets & Retweets by @pharma_BI and @AVIVA1950 at #BioIT20, 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 in Boston

Virtual Conference coverage in Real Time: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/10/08/tweets-retweets-by-pharma_bi-and-aviva1950-for-bioit20-19th-annual-bio-it-world-2020-conference-october-6-8-2020-in-boston/

October 6, 2020

  • Susan Gregurick

    NIH

    Associate Director for Data Science

  • Connected Data Ecosystem – Project is FAIR
  • Data shareable
  • NIH – agenda on data: diverse sets of data: Images of MRI, cells, of organs, of communities,
  • Share images and link it to tables
  • METADATA 34PB enable search – moving Data to clouds for Large-Scalable Analysis
  • Sequence Read Archive (SRA) – DNA seq.
  • COVID-19 from around the World SRA in Cloud Partnerships enabled
  • Open Science – enhance SW tools for making research cloud-ready
  • NIH has 12 Centers: Genomics, Neuro-imaging
  • SCH – Smart & Connected Health
  • IT, Sensor system hardware, effective usability, medical interpretation, Transformative data Science
  • Cancer, Alzheimer’s, Genomics, Medical Imaging, Brain circuits,
  • Coding it Forward: Students come to NIH Virtually from home to join CIVIL DIGITAL FELLOWSHIP
  • COVID-19: repositories of data for researches:
  1. Treatment for Interventions
  2. Long term Sequelae
  3. Clinical platforms: BigData Catalyst, Allow US, ADSO, National COVID Cohort
  4. Across platforms: workflow after RAS August Deploy: Passport for researchers to access data faster, Privacy-Preserving Tokens, Interoperability across clinical COVID data bases
  5. Metadata super rich to link to other new data sources is a challenging issue to solve across studies

Scott Parker

Sinequa Corp

Director of Product Marketing

  • Disconnect between R&D & IT
  • Intelligence search Applications for sensitive information: Sinequa is a leader
  • shares one index cost for document go down & productivity increases

Rebecca Baker

NIH OD

Dir HEAL Initiative

  • END ADDICTION Project – NIH HEAL Initiative: 20 NIH collaborating on Studies
  • National Overdose Deaths overdose opioid drugs – synthetic Fentanyl
  • Heroin, Cocaine, Methamphetamine
  • During COVID Overdose increased during the pandemic
  • Increase in drug use overall and 67% of Fentanyl
  • Chronic Pain: Daily severe pain: can’t go to work – 25 Million
  • $500 Million/year Sustained Research Investment 25+ HEAL Research Programs
  • HEAL Initiative: Pain management, Translating research, New presention, enhance outcomes for affected newborns, novel medications options Pre-clinical translational research in Pain management
  • Improving treatments for opioid misuse & addiction
  • Opioid disorder people do not receive treatment: justice community, collaborative, ER, pregnant mothers
  • Medication-based treatment – do not stay long enough to achieve long-term recovery
  • People experience Pain differently: Muscular, neurological, : Biomarkers, endpoints, signatures, test non-addictive treatments for specific pains
  • Pain control balance of risks of long-term opioid therapy
  • HEAL Research – infant born after exposure to opioids in utero affect brain growth, born with withdrawal syndromes
  • Diversity of Data under HEAL Initiative –>> Harmonize the data
  • Common Data Elements in HEAL Clinical Research in Pain Management
  • CORE CDE & Supplemental CDE
  • Making HEAL Data FAIR: Findable, Accessible, Interpretable, Reusable
  • LINK HEAL data with communities studies, predict behaviours
  • Data sharing made available to the public
  • HEAL Data Lifecycle
  • effect of change due to change in dosage used – if dat is not collected – then we are not able to explore the relationships
  • Use the data to advance research beyond the current understanding of the problem
  • #NIHhealthInitiative

 

Ari Berman

BioTeam Inc

Chief Executive Officer

  • Distributed Questions from the Audience to the speakers

10:00 AM – 11:25 AM EDT on Tuesday, October 6

How to Hold on to Your Knowledge in an Agile World

Etzard Stolte

Roche Pharma

Global Head

October 7, 2020

The Chicagoland COVID-19 Commons: A Regional Data Commons Powering Research to Support Public Health Efforts

  • Matthew Trunnell

    VP & Chief Data Officer

9:00 AM – 9:20 AM EDT on Wednesday, October 7

  • Seattle & COVID – samples from Seattle Flu Study
  • Public Health Practice vs Research – Data from Human Subjects: Avoid delute the control
  • Chicagoland COVID-19 Data Commons – in Chicago
  1. Neighborhood level in Chicago
  2. common data model
  3. power efforts Predictive modeling : Case rate Total confirmed cases, Death cases
  4. Legal agreement of the Consortium
  5. https://chicagoland.pandemic
  • Commons – resources held in commons non-for profit
  • Data Commons: cloud based SW platforms that are co-located data, computing infrastructure and applications
  • Level 1: Basic, Level 2: Repeatable, Level 3: Governance Level4: Interoperability Level 5: Sustainable
  • COVID-19 Data Common: Public health authorities collects data – nor available to Research community
  • Research community need access to Public health authorities
  • Regional COVID-19 Data Commons: Reasons: Public health decision is LOCAL but specific to the Region
  • Fund raising in the communities
  • Data 1: Clinical Data for Health care Summary of incidence – Signals of ethnic dependencies and co-morbidities
  1. Safe harbor: removal of 18 identifiers
  2. Expert Determination
  • Data 2: Public Data: Environmental,
  • Data 3: Resident-Reported Data on iPhones: multiple languages supported early reports of people feeling unwell

CompBio: An Augmented Intelligence System for Comprehensive Interpretation of Biological Data

Richard Head

Washington Univ

Prof & Dir Genome Technology Access Ctr

9:20 AM – 9:40 AM EDT on Wednesday, October 7

  • Formating, data scrubbing,
  • Replace data fabric with simplified version
  • create “Memory Model” Machine learning does classification of patterns
  • dimensions are the variables
  • “Hyper-dimensional – ingestions of abstracts and articles
  • Example; IL^: Aggregate Memories to create a NORMALIZED Aggregate Memory
  • Relationships explored
  • Complex Knowledge Patterns Generated by the PCMM: Compared Utilization
  • Augmented AI System: Combination PCMM with AI
  • Literature mining CompBio
  • Evidence of Utility: PCMM – Accepted or Published Research Leveraging PCMM Applications
  • Example 1: Cell Metabolism CompBio – A person formulate hypothesis
  • Example 2: Analysis of RNA-Seq a rare mutational subtype of GBM
  1. Hypothesis –>> BioExplorer –>> Multiple relations revealed
  2. Example 3: Animal Models to Human Disease: CompBio – Crohn’s Assertion Engine

Summary – Augmented AI Platform for Biological DIscovery

  • PCMM – Memory modle – hyperdimensional
  • AAI Infrastructure
  • Knowledge map libraries
  • In development Medical Discoveries

PercayAI Team – commercial Development

Kingdom Capital

 

Precision Cancer Medicine

  • Jeffrey Rosenfeld

    Rutgers Univ

    Asst Prof

9:40 AM – 10:00 AM EDT on Wednesday, October 7

 

  1. Hereditary cancer sequencing – BRCA
  2. Tumor cancer sequencing
  • Panel Sizes – 500-1000x – the bigger the panel – more computational time more data need be investigated
  1. Hotspot Panels,
  2. Gene Panels,
  3. Exomes
  • Cell free DNA Testing – Liquid biopsy
  1. Apoptosis
  2. Necrosis
  • FoundationONE
  • Patient Results: ALL mutations found, Mutation Burden,
  • Gene EGFR – no mutation
  • For every Mutation what Therapy is recommended for approved drugs
  • Clinical Trials for the mutations
  • VARIANTS of unknown significance
  • WORKFLOW: many MDs send sample get 38pps report
  • Genomic Classification and Prognosis in AML: Mutations subset and therapies available
  • Paradigm Shift in Classification
  1. 2013 – Lung Adenocarcinoma <<<- –
  2. 2011 – another cancer

 

mTOR System: A Database for Systems-Level Biomarker Discovery in Cancer

  • Iman Tavassoly – CANCELLED

    C2i Genomics

    Physician Scientist

10:20 AM – 10:40 AM EDT on Wednesday, October 7
Add to Calendar

mTOR system is a database I have designed for exploring biomarkers and systems-level data related to mTOR pathway in cancer. This database consists of different layers of molecular markers and quantitative parameters assigned to them through a current mathematical model. This database is an example of merging systems-level data with mathematical models for precision oncology.

FAIR and the (Tr)end of Data Lakes

  • Kees Van Bochove

    The Hyve

    Founder & Owner

10:20 AM – 10:40 AM EDT on Wednesday, October 7

Normalizing Regulatory Data Using Natural Language Processing (NLP)

  • Qais Hatim, Dr.

    FDA CDER

    Visiting Assoc

David Milward

Linguamatics

Senior Director, NLP Technology

10:40 AM – 11:00 AM EDT on Wednesday, October 7

  • ML focus on Disease
  • NLP – different words have same meanings, different expression same meaning, grammer & Meaning
  • Normalizes output
  1. Disease
  2. Genes
  3. Dates
  4. Mutations
  • Transform Unstructured into structured
  • Identifying Gaps in adverse events Labelling: Pain and Opioids
  • Improve drug safety
  • ChemAxon

Supplemental Approval Letters

Coding for Adverse events: “derived values of possible interest”

  • Use of Prominent Terminologies used at the FDA: UNII – Translation into ANSI tesaurus standard
  • Matching to the Variation found within Real Text: synonyms
  • Using ML for Normalization in Disease Context
  • Deep Learning PRE-TRAINING APPROACH for annotated date = supervised learning
  • A set of rules to handle overlapping entities
  • normalized the amp extracted from concepts
  • BERN and Terminologies: BioBERN, PubMed Central, PubMed Articles
  • NER – Named Entity Recognition
  • Evaluation of the Approach

Conclusions

NLP, ML, Hybrid methods, Terminology +ML methods

Building an Artificial Intelligence-Based Vaccine Discovery System: Applications in Infectious Diseases & Personalized Neoantigen-Related Immunotherapy for Treatment of Cancers

  • Kamal Rawal

    Amity Univ

    Assoc Prof

10:40 AM – 11:00 AM EDT on Wednesday, October 7

  • Classification of proteins
  • Data Collection
  • Feature Selection – Most important from 1447 features
  • Deep learning Model: Vaxi-DL: Layers, compilation
  • Overfitting Model strategy
  • Balancing Imbalanced
  • Hyper parameter tuning: Internal parameter of the model
  • Stratified K-Fold Training and Validation
  • Ensembling Approach: many weak classifier to create a STRONG Classifier
  • ROC Curve: Ensemble by Consensus
  • Before and after calibration
  • Benchmarking the system: Vaxi-DL Ensemble by Average vs by Consensus
  • SYSTEM developed: Type protein – find results
  • Rare disease CHARGE Syndrome was used for validation
  • Application to COVID-19 – Methodology
  • Application on Cancer: Which peptide can be used as antigen for prediction of immunogenic peptides

 

Using GPU Computing to Evaluate Variant Calling Strategies

  • George Vacek

    NVIDIA Corp

    Sequencing Strategic Development

  • Eriks Sasha Paegle

    Dell EMC

    Senior Business Development Manager

11:15 AM – 11:30 AM EDT on Wednesday, October 7

  • Navidia: 100 Genomes Cohort generated at NY Genome Center  NHGRI
  • Navidia Parabricks mentioned AZURE
  • Dell EMC: Test environment: Dell Technology Cloud Storage for Multi-Cloud: resources across GCU, AWS, Azure in Northern Virginia regions
  • Multi-Cloud ease of use: without Multi-cloud vs with Faction multi-clouds
  • Ease of use
  • Deep Averaging Network (DAN)
  • NVIDIA CLARA PARABRICK TOOLKIT: Short & Long read, Deep learning, Data Analytics, ML
  • Reference applications – host of customized applications, 3rd Party App, Libraries
  • GPU (Genomics PUs) – Drop in tools for Somatic Pipelines : Clara Parabricks v3.5
  • Partnership of NVIDIA and Petagene announced at BioIT20 – NGS Data compretion
  • Petagene technology allows lossless compression reduce storage costs
  • Project with Sanger Institute – Optimizing Muto-graph Identification
  • completed run in 24 hours instead 31 days
  • Parabricks is a joint project Dell/EMC and NVIDIA

PLENARY KEYNOTE: Game On: How AI, Citizen Science, and Human Computation Are Facilitating the Next Leap Forward

12:30 PM – 1:55 PM EDT on Wednesday, October 7

  • Allison Proffitt

    BioIT World & Diagnostics World

    Editorial Dir

Seth Cooper

Northeastern Univ

Asst Prof

  • Foldit – Scientific discovery using video games in the domain of protein structures and folding
  • Combine Human with machine
  • Score based on competition among players for higher score and collaboration in groups
  • Problem: Chemistry give input.
  • Puzzle available for one week on the Internet, games ongoing,
  • Solution analysis – continually IMPROVE the structure of Protein folding
  • Foldit Tutorials offered online
  • Player accomplishments: Articles by scientists ,
  • development of algorithms discovery
  • Electron Density fitting
  • Enzyme re-design
  • de novo Protein Design – named authors on a paper – scientific process
  • Future Work: Coronovirus Spike protein
  • Small molecule design
  • narrative
  • virtual reality – 3D protein structure for manipulation
  • htp://Fold.it/Educator Mode
  • htp://Fold.it/standalone
  • http://fold.it/
  • seth.cooper@gmail.com

Lee Lancashire, CIO

Cohen Veterans Bioscience – not for profit – advancing Brain health

  • Biotyping and stratification
  • Biomarkers
  • Omics data
  • All meet in the Common – Brain Commons: Clinician, Geneticist, Scientist, Bioinformatician, R Studio, Python, Jupyterhub
  • Multidimensional Biomarkers in Multiple Sclerosis

 

Pietro Michelucci

Human Computation Institute

Director

  • Why machine can’t tackle AI on their own and AI can’t do Precision Medicine on their own
  • young people more than others N of 1 – Precision Mediicne
  • Scandinavians and Russians are immune
  • AI & Precision Medicine: can’t solve the complexity of messy data vs big data
  • Messy data: heterogeneous multidimensional, to many combinations to explore, select which combination to explore vs let the machine generate all the combination and do analysis on all and discover PATTERN
  • Causal vs spurious
  • Logical reasoning, right brain abstract and short cuts – Human brain does routinely
  • Human do better on context: Not all info is in pixels such as context
  • #ADS – SBIR suspected the hypothesis to be tested
  • improving crowd wisdom methods: 20 input by different people PLUS machine
  • combine crowd answers with machine faster and improved accuracy
  • Machine has no intuition – machine bias of Human and of machine is similar
  • Wisdom of Crowd: Bootstrapping hybrid Intelligence: CIVIUM
  • bit.ly/civiumintro

 

 

Jerome Waldispuehl

McGill Univ

Assoc Prof

  • visualization of nucleotide – tools for
  • http://phylo.cs.mcgill.ca
  • GAME: Phylo DNA Puzzles: Goal 202, Score, Top Score
  • Whole-genome multiple
  • Phylo: 350,000 participants, 1MM solutions Improve 40 to 95% computer alignments
  • education & science outreach – reach out to the Public
  • Borderlands Science + game designers: 1MM participants 50MM solutions
  • Joint initiative with a major science project
  • Improvement of 16S rRNA
  • MMOS company in Science games

Towards AI-Guided Cell Profiling of Drugs with Automated High-Content Imaging

Ola Spjuth

Uppsala Univ

Professor

2:10 PM – 2:30 PM EDT on Wednesday, October 7

  • Accelerate drug discovering using AI automation in collaboration with AstraZeneca
  • Closed-loop (autonomous) experimentation
  • collect the best data at the minimal cost
  • Active learning: query active learning model
  • Exploitation [best predictions from given data] vs Exploration
  • Automation in Life Science: micro-plate, stack of micro-plates
  • Robot scientist: come out with hypothesis and conduct research
  • high-throughput biology: Robots vs Disease
  • Cell painting: Imaging with multiplexed dyes: genetic or chemical perturbations
  • classify images into biological mechanisms
  • combinations of toxicants
  • A discovery engine: Toxicity, Efficacy, mechanisms combinations
  • Automating our cell-based lab: fixed setup
  • Open source lab automation suite: Github https://github.com/pharmbio/imagedb
  • Dealing with large scale data [TensorFlow]
  • STACKn.com – AI modeling Life cycle
  • HASTE: Hierarchical analysis of Spacial and Temporal
  • https://pharmb.io

Advanced Imaging and AI Technologies Providing New Image and Data Analysis Challenges and Opportunities

Richard Goodwin

AstraZeneca

Dir & Head of Imaging & AI

2:30 PM – 2:50 PM EDT on Wednesday, October 7

AstraZeneca is empowering its scientists to see the complexity of a disease in unprecedented detail to enable effective development and selection of new medicines. This is enabled though the use of an extensive range of cutting-edge imaging technologies that support studies into the efficacy and safety of drugs through the R&D pipeline. This presentation will introduce the range of novel in vivo and ex vivo imaging technologies employed, describe the data challenges associated with scaling up the use of molecular imaging technologies, and address the new data integration and mining challenges. Novel computational methods are required for large cohort imaging studies that involve tissue based multi-omics analysis, which integrate spatial relationships in unprecedented detail.

  • Small molecule – not suitable for complex diseases
  • focus on quality vs quantity
  • compound for commercial value
  • right safety
  • Imaging supports R&D: Molecular, medical, big data and AI
  • convergence of ML for decision making
  • Spatial imaging: morphology
  • Multiplex imaging like MRI
  • Multimodal analysis: tissue data and invivo holistic understanding of drug delivery
  • spacial transcriptomics proteomics: imaging platforms in R&D
  • AZ invest in imaging technologies already impacting projects: AI-empowered imaging delivering subcellular resolution
  • Mass Spec Imaging (MSI) – ex-vivo imaging techniques- spatial distribution of molecular
  • cartography of cancer: Drug metabolite distribution – NEW understanding of disease and drug distribution in tissue
  • DATA: digitization, integration, analysis, exploration
  • Digital pathology and beyond – AI Image Analysis – AI outperform pathololigst and radiologists
  • Data volume and dimensionality challenge and opportunity
  • Data volume and dimensionality: complete image
  • AZ Oncology – disease is understood for drug discovery using Imaging technology

PANEL: Framework and Approach to Unlock the Potential of Quantum Computing in Drug Discovery

  • Brian Martin

    AbbVie Inc

    Research Fellow & Head

Philipp Harbach

Merck KGaA

Head of In Silico Research in Germany

  • chemistry and manufacturing with QC – end user in Pharmaceutical
  • VC at Merck ask expert in Merck to guide investment of Merck in QC
  • 50 people across Merck [three areas at Merck [Pharmaceutics, Animal Health, Diagnostics]

Celia Merzbacher

SRI Intl

Assoc Dir Quantum Economic Dev Consortium (QEDC)

  • Methodology from Pistoia to be used in QC
  • QC R&D developed in parallel
  • Simulation of all the components is possible

John Wise

Pistoia Alliance Inc (2007)

We are a global, not-for-profit members’ organization working to lower barriers to innovation in life science and healthcare R&D through pre-competitive collaboration.

Consultant

  • How Pharmaceutical Industry can benefit from quantum computing
  • 9 of 10 big Pharma are members of the Pistoia Alliance
  • IP created on specifications

 

Zahid Tharia

Pistoia Alliance Inc

Consultant

  • Barriers to adoption of quantum computing (QC) in Pharma is training of staff and skills in the IT aspects of QC

3:10 PM – 4:00 PM EDT on Wednesday, October 7

In 2019, major life sciences companies mobilized to form a pre-competitive, collaborative quantum computing working group (QuPharm) and delineate a framework and approach to accelerate realizing the potential of quantum acceleration in drug discovery. Learn from industry thought leaders on how to valuate and map problems into quantum algorithms, set up organizations to enable and scale quantum computing pilots and establish effective cross-industry, tech, and start-up collaborations.

Session Wrap-Up Panel Discussion

Etzard Stolte, PhD

Roche Pharma

Global Head

  • no official policy
  • 2020 it become important to be mentioned by management as a potential use in automation
  • continual updates needed – it is manual and a disillusion without a business case
  • Roche try to commodatized tools in AI as Classifiers, automation,

Samiul Hasan

GlaxoSmithKline

Scientific Analytics and Visualization Director

  • AI is perceived as having potential to take off on its own
  • POC – demonstrate the vlaue
  • Proof of Concept – Semantic report – a story vs one off
  • demonstration of value is needed and is continuous

 

 

Bin Li

Millennium The Takeda Oncology Co

Dir Computational Biology & Translational Medicine

  • ML community at Takeda
  • Positive to have, how successful not much yet – not used much yet
  • some models are pretty good do not need improvement

Jens Hoefkens

Accenture

Industry Principal Director

  • Future of AI as support to the Human intuition vs replacement of humans
  • automation like pathology classification
  • Machine and Human working together – not as maker of decisions in clinical settings
  • POC cycle prevent production conversion
  • where is the highest value for production and deploy with scale
  • AI Assisted to sift Genomics data
  • BERT term extraction from Google technology to make sense of data assist the user
  • ML
  • RPA – Robotic concept extraction – 80% accuracy needed by scientists

4:00 PM – 4:20 PM EDT on Wednesday, October 7

October 8, 2020

Trends from the Trenches

Kevin Davies, PhD

CRISPR Journal

Exec VP & Exec Editor

Timothy Cutts

Wellcome Sanger Institute

Head

  • Collaborations with scientists in subSahara
  • pay for data analysis – ownership issues
  • in UK 6 Labs for the entire countries: all send the data to Wellcome Sanger Institute for analysis
  • Metadata is the problem – coordination of each of the 6 labs to send the metadata created problems

 

  • Cindy Crowninshield

    Cambridge Healthtech Institute

    Executive Event Director

Vivien Bonazzi

Deloitte Consulting LLP

Managing Dir & Chief Biomedical Data Scientist

  • How organizations use bioscience data
  • Data Ecosystem: Hardware and software: Cloud and other options
  • Operationalize the two trends:
  1. Platforms: End to end solutions resulting in SILOS, systems are native: data ingestions
  2. Data Commons: Open arch, open source – integration and interdependence issues
  • Biomedical Agencies in NIH various Organizations in the Private sector: Sharing data must be more effective
  • IT, Data Science, Management – COVID – reduced barriers
  • Leadership: Different voices from different people
  • Data strategies & Governance not the whole but small pieces , incentives to share data

Chris Dagdigian

BioTeam Inc

Sr Dir

  • 10th Anniversary to Trends from the Trenches
  • IT infrastructure changes
  • Research IT:
  1. Genomics & BioInformatics
  2. Image-based data acquisition and analysis: CryoEM, 3D microscopy, fMRI image analysis
  3. ML and AI – GPU FPGAs, neural processors: Drive in organizations: bottom up
  4. Chemistry & Molecular Dynamics
  5. Storage and exploitation of data for insights
  6. 2020 Hype vs Reality
  7. Scientific Data: managing and understanding, data movement, federated/access
  8. Big Data: data storage, management & governance standards vs human curated data
  9. IT needs guidance and decisions from Science Team
  10. Culture change for joint management by Science & IT: data fidelity, attribution, allocation top down
  11. NERSC File System quotas & Purging overviewSilos & So
  12. Petabytes of open access data, collaborative research resources: Data rich environments
  13. Data Lakes: Gen3 Data Commons
  14. Data hygiene:metadata is Science side vs IT
  15. Biased Data: Model & Data Bias
  • Failed Predictions:
  1. Compilers matter again – not True
  2. CPU benchmarking is back – WRONG
  3. AMD vs Inter arm64 vs both
  4. Policy driven auto-tiering storage – wrong, USER self-service for tiering, movement and archive decision. Let researchers tier/move/archive based on Project, Experiment or Group
  5. Single storage namespace – Wrong: Data intensive science: scientists must do some IT jobs themselves

Kjiersten Fagnan

Lawrence Berkeley Natl Lab

CIO

  • Genome Project of DOE
  • Data management with other agencies
  • COVID: Collaborations, breaking down barriers, small labs and big labs ALL generate data and sharing
  • that collaboration is needed regardless of COVID – not happen
  • If twoo big one lab can’t handle it all
  • Funding and training does not support the Collaborations because next round of funding depend on individual publications – which requires silos
  • Data cleaning and data management:Standards are annoying and painful – not needed for publishing the results as soon as possible – just that someone else will be able to use it
  • Facebook have hundred of curators – the curation of scientific data requires same hunsrands od curators that are SCIENTISTS and Data scientists

Matthew Trunnell

Pandemic Response Commons, Seattle

VP & Chief Data Officer

  • Data commons for intra- and inter-mural data sharing
  • ML is needed for Data commons
  • Progress in FAIRness, NIH efforts driven by Susan Gregory across NIH all centers
  • Large amount of B-to-B Data sharing UBER sharing with a jurisdiction they operate
  • SNOWFLAKES – new cloud technology
  • COVID – plays an accelerator
  • Cancer vs COVID – transfer knowledge from COVID to Cancer

9:00 AM – 10:40 AM EDT on Thursday, October 8

The “Trends from the Trenches” will celebrate its 10th Anniversary at Bio-IT! Since 2010, the “Trends from the Trenches” presentation, given by Chris Dagdigian, has been one of the most popular annual traditions on the Bio-IT Program. The intent of the talk is to deliver a candid (and occasionally blunt) assessment of the best, the worthwhile, and the most overhyped information technologies (IT) for life sciences. The presentation has helped scientists, leadership, and IT professionals understand the basic topics related to computing, storage, data transfer, networks, and cloud that are involved in supporting data-intensive science. In 2020, Chris will give the “Trends from the Trenches” presentation in its original “state-of-the-state address” followed by guest speakers giving podium talks on relevant topics. An interactive Q&A moderated discussion with the audience follows. Come prepared with your questions and commentary for this informative and lively session.

Q&A

  • Project vs enterprise – Sequencing for internal research vs for clients’ data
  • Tension in governmental agencies – no robust solutions: IT, Science, Management
  • different Use cases need different infrastructure: HW & SW: Storage and data exploration
  • Data Lakes: rule base, enterprising – training is an issue in organizations
  • Management, Scientists, IT in enterprises – terra byte of storage, budgets issues, conversation on the limits that IT can ofer putting more burden on the Scientists for triage and quotas – business and scientific value
  • New capabilities in organizations: hands on in data management tactical of data management not IT bur data engineering
  • Citizen Science: privacy vs plants and microbes – no privacy issues
  • Incentives need be changed for Data Citations in addition to Papers
  • Curation Citations as Authorship citation
  • Data sharing in Cancer: GEN3 – NCI Data Commons, Data Governance and Data Permission (Access) – NCI does work in data commons – much data outside this space
  • EBI – in UK Sanger Institute has the infrastructure in one place
  • Migrating Project based Data structure: that involves scientist decisions that should not be a quota (storage is full)  in the IT space
  • Human to Human communications vs tools for data migration
  • Which Organizations get the data curation and annotation well: Subject matter from day 1 – hard to teach vs data engineering skills; TEAM as a solving is critical in Biomedical space no incentives
  • BBC – Meta tagging system is outstanding
  • NCAST TRANSLATOR – across organizations
  • Changing incentives – MORE organizations will do that task better
  • Common metadata across domains with predict uses of data in the Future – collaboration of CS to create in the science organization tagging like in BBC
  • Chris Anderson

    Clinical OMICs

    Editor in Chief

Ian Fore

NIH NCI

Sr Biomedical Informatics Program Mgr

  • NCI – Cancer Data Commons – concierge services to organization on data services

Ravi Madduri – CVD large cohort

Univ of Chicago

Scientist

 

  • Lara Mangravite

    Sage Bionetworks

    President

  • Kees Van Bochove

    The Hyve

    Founder & Owner

11:10 AM – 11:30 AM EDT on Thursday, October 8

 

BREAKOUT: Driving Scientific Discovery with Data / Digitization

  • Timothy Gardner

    Riffyn Inc

    CEO

11:35 AM – 12:00 PM EDT on Thursday, October 8

 

PLENARY KEYNOTE – 12:00 PM – 1:25 PM EDT on Thursday, October 8

Robert Green

Brigham & Womens Hospital

Co-founder of Genome Medicine

Prof & Dir G2P Research

  • Combining data to rapidly analyze COVID-19 Patients –
  • identify BIOMARKERS for vulnerability
  • Preventive Genomics – Angelina Jolly’s musectomy as a preventive clinical condition
  • Patients access to own genomics data
  • Population screening – to predict risks
  • Genetic Testing to Consumer: Preventive Genomics: conflated genotyping/sequencing and labs/care providers
  • Genetic Testing to Consumer: COST & Benefits – UNCLEAR
  1. diagnosis of unsuspected genetic disease
  2. stratification for surveillance
  3. which pieces of the puzzle need to be brought to bear in patient care
  4. Categories and Reporting criteria: Gene-Disease validity vs Variant Pathogenicity –>> Clinic
  5. MedSeq Project: 10MM randomized study – all genome info shared with Patient, other arm only selective genome data shared with patient: 100 patients 20% carry monogenic condition: Polygenic risk scores:
  6. CAD – high Cholesterol biomarker, A-FIb, DM2, 52% Women 48% Men
  7. No high risk error by PCP discussing and disclosing the results of the sequence
  8. Filtering the results: Indication -based testing vs Screening
  9. BabySeq Project: INFANTS sequencing to prevent disease: 11% carry a mutation in a monogenic gene for a monogenic condition -like abnormal narrowed aorta
  10. MDR – Monogenic Disease Risk
  11. MilSeq Project: US Air Force – Military active duty
  12. 5,8,10 – are all Polygenic studies
  13. Polygenic Risk Scores – High risk
  14. Classification need to be repeated every few years (2 years – re-sequence) due to changes in health and to efficiencies in new discovery in curated data which is improving as on-going
  • Risk benefit – UTILITY – Partners Biobank Return of Genomic Results
  • No interest on knowing by the Public NCCN criteria on chart review 20%
  • Brigham Preventive Genomics via telemedicine – First in the country
  • APC mutation after colonoscopy – obstruction diagnosed
  • @robertgreen

 

Juergen Klenk

Deloitte Consulting LLP

Principal

  • Bradykinin hypothesis for COVID-19
  • liberate the data: People , Data Risk

 

Natalija Jovanovic

Sanofi

Chief Digital Officer

  • AI in Pharma
  • Vaccine preventable diseases – produce 1Billion vaccines a year
  1. reduction of incidence: Pertusis – 92% eradication
  • manage risk profile
  • Science mechanism translatable to machines
  1. high automated ingestible data for AI
  2. Digital is about people: Good data Good algorithms Good GUI

Vivien Bonazzi

Deloitte Consulting LLP

Managing Dir & Chief Biomedical Data Scientist

12:00 PM – 1:25 PM EDT on Thursday, October 8
Add to Calendar

12:00 Organizer’s Remarks

Cindy Crowninshield, RDN, LDN, Executive Event Director, Cambridge Healthtech Institute

12:05 Keynote Introduction

Juergen A. Klenk, PhD, Principal, Deloitte Consulting LLP

12:15 Toward Preventive Genomics: Lessons from MedSeq and BabySeq

Robert Green, MD, MPH, Professor of Medicine (Genetics) and Director, G2P Research Program/Preventive Genomics Clinic, Brigham & Women’s Hospital, Broad Institute, and Harvard Medical School

12:40 AI in Pharma: Where We Are Today and How We Will Succeed in the Future

Natalija Jovanovic, PhD, Chief Digital Officer, Sanofi Pasteur

1:05 LIVE Q&A: Session Wrap-Up Panel Discussion

PANEL MODERATORS:

Juergen A. Klenk, PhD, Principal, Deloitte Consulting LLP

Vivien R. Bonazzi, PhD, Managing Director & Chief Biomedical Data Scientist, Deloitte Consulting LLP

Below are included sessions that are NOT included above. I covered ONLY the above sessions.

Session Availability

1. PLENARY KEYNOTE PRESENTATION

10:15 am ET – NIH’s Strategic Vision for Data Science

Susan K. Gregurick, PhD, Associate Director, Data Science (ADDS) and Director, Office of Data Science Strategy (ODSS), National Institutes of Health

Rebecca Baker, PhD, Director, HEAL (Helping to End Addiction Long-term) Initiative, Office of the Director, National Institutes of Health

2. WORKSHOPS

11:55 am ET – W1: Data Management for Biologics: Registration and Beyond

Monica Wang, PhD, Principal Technology Lead, Scientific Informatics, Takeda

Sebastian Schlicker, Head, Biologics Business Operations, Genedata AG

11:55 am ET – W2: A Crash Course in AI: 0-60 in Three

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http://www.giiconference.com/chi909998/

Welcome to Bio-IT World 2020

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Structure-guided Drug Discovery: (1) The Coronavirus 3CL hydrolase (Mpro) enzyme (main protease) essential for proteolytic maturation of the virus and (2) viral protease, the RNA polymerase, the viral spike protein, a viral RNA as promising two targets for discovery of cleavage inhibitors of the viral spike polyprotein preventing the Coronavirus Virion the spread of infection

 

Curators and Reporters: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

 

Therapeutical options to coronavirus (2019-nCoV) include consideration of the following:

(a) Monoclonal and polyclonal antibodies

(b)  Vaccines

(c)  Small molecule treatments (e.g., chloroquinolone and derivatives), including compounds already approved for other indications 

(d)  Immuno-therapies derived from human or other sources

 

 

Structure of the nCoV trimeric spike

The World Health Organization has declared the outbreak of a novel coronavirus (2019-nCoV) to be a public health emergency of international concern. The virus binds to host cells through its trimeric spike glycoprotein, making this protein a key target for potential therapies and diagnostics. Wrapp et al. determined a 3.5-angstrom-resolution structure of the 2019-nCoV trimeric spike protein by cryo–electron microscopy. Using biophysical assays, the authors show that this protein binds at least 10 times more tightly than the corresponding spike protein of severe acute respiratory syndrome (SARS)–CoV to their common host cell receptor. They also tested three antibodies known to bind to the SARS-CoV spike protein but did not detect binding to the 2019-nCoV spike protein. These studies provide valuable information to guide the development of medical counter-measures for 2019-nCoV. [Bold Face Added by ALA]

Science, this issue p. 1260

Abstract

The outbreak of a novel coronavirus (2019-nCoV) represents a pandemic threat that has been declared a public health emergency of international concern. The CoV spike (S) glycoprotein is a key target for vaccines, therapeutic antibodies, and diagnostics. To facilitate medical countermeasure development, we determined a 3.5-angstrom-resolution cryo–electron microscopy structure of the 2019-nCoV S trimer in the prefusion conformation. The predominant state of the trimer has one of the three receptor-binding domains (RBDs) rotated up in a receptor-accessible conformation. We also provide biophysical and structural evidence that the 2019-nCoV S protein binds angiotensin-converting enzyme 2 (ACE2) with higher affinity than does severe acute respiratory syndrome (SARS)-CoV S. Additionally, we tested several published SARS-CoV RBD-specific monoclonal antibodies and found that they do not have appreciable binding to 2019-nCoV S, suggesting that antibody cross-reactivity may be limited between the two RBDs. The structure of 2019-nCoV S should enable the rapid development and evaluation of medical countermeasures to address the ongoing public health crisis.

SOURCE
Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation
  1. Department of Molecular Biosciences, The University of Texas at Austin, Austin, TX 78712, USA.

  2. 2Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
  1. Corresponding author. Email: jmclellan@austin.utexas.edu
  1. * These authors contributed equally to this work.

Science  13 Mar 2020:
Vol. 367, Issue 6483, pp. 1260-1263
DOI: 10.1126/science.abb2507

 

02/04/2020

New Coronavirus Protease Structure Available

PDB data provide a starting point for structure-guided drug discovery

A high-resolution crystal structure of COVID-19 (2019-nCoV) coronavirus 3CL hydrolase (Mpro) has been determined by Zihe Rao and Haitao Yang’s research team at ShanghaiTech University. Rapid public release of this structure of the main protease of the virus (PDB 6lu7) will enable research on this newly-recognized human pathogen.

Recent emergence of the COVID-19 coronavirus has resulted in a WHO-declared public health emergency of international concern. Research efforts around the world are working towards establishing a greater understanding of this particular virus and developing treatments and vaccines to prevent further spread.

While PDB entry 6lu7 is currently the only public-domain 3D structure from this specific coronavirus, the PDB contains structures of the corresponding enzyme from other coronaviruses. The 2003 outbreak of the closely-related Severe Acute Respiratory Syndrome-related coronavirus (SARS) led to the first 3D structures, and today there are more than 200 PDB structures of SARS proteins. Structural information from these related proteins could be vital in furthering our understanding of coronaviruses and in discovery and development of new treatments and vaccines to contain the current outbreak.

The coronavirus 3CL hydrolase (Mpro) enzyme, also known as the main protease, is essential for proteolytic maturation of the virus. It is thought to be a promising target for discovery of small-molecule drugs that would inhibit cleavage of the viral polyprotein and prevent spread of the infection.

Comparison of the protein sequence of the COVID-19 coronavirus 3CL hydrolase (Mpro) against the PDB archive identified 95 PDB proteins with at least 90% sequence identity. Furthermore, these related protein structures contain approximately 30 distinct small molecule inhibitors, which could guide discovery of new drugs. Of particular significance for drug discovery is the very high amino acid sequence identity (96%) between the COVID-19 coronavirus 3CL hydrolase (Mpro) and the SARS virus main protease (PDB 1q2w). Summary data about these closely-related PDB structures are available (CSV) to help researchers more easily find this information. In addition, the PDB houses 3D structure data for more than 20 unique SARS proteins represented in more than 200 PDB structures, including a second viral protease, the RNA polymerase, the viral spike protein, a viral RNA, and other proteins (CSV).

Public release of the COVID-19 coronavirus 3CL hydrolase (Mpro), at a time when this information can prove most vital and valuable, highlights the importance of open and timely availability of scientific data. The wwPDB strives to ensure that 3D biological structure data remain freely accessible for all, while maintaining as comprehensive and accurate an archive as possible. We hope that this new structure, and those from related viruses, will help researchers and clinicians address the COVID-19 coronavirus global public health emergency.

Update: Released COVID-19-related PDB structures include

  • PDB structure 6lu7 (X. Liu, B. Zhang, Z. Jin, H. Yang, Z. Rao Crystal structure of COVID-19 main protease in complex with an inhibitor N3 doi: 10.2210/pdb6lu7/pdb) Released 2020-02-05
  • PDB structure 6vsb (D. Wrapp, N. Wang, K.S. Corbett, J.A. Goldsmith, C.-L. Hsieh, O. Abiona, B.S. Graham, J.S. McLellan (2020) Cryo-EM structure of the 2019-nCoV spike in the prefusion conformation Science doi: 10.1126/science.abb2507) Released 2020-02-26
  • PDB structure 6lxt (Y. Zhu, F. Sun Structure of post fusion core of 2019-nCoV S2 subunit doi: 10.2210/pdb6lxt/pdb) Released 2020-02-26
  • PDB structure 6lvn (Y. Zhu, F. Sun Structure of the 2019-nCoV HR2 Domain doi: 10.2210/pdb6lvn/pdb) Released 2020-02-26
  • PDB structure 6vw1
    J. Shang, G. Ye, K. Shi, Y.S. Wan, H. Aihara, F. Li Structural basis for receptor recognition by the novel coronavirus from Wuhan doi: 10.2210/pdb6vw1/pdb
    Released 2020-03-04
  • PDB structure 6vww
    Y. Kim, R. Jedrzejczak, N. Maltseva, M. Endres, A. Godzik, K. Michalska, A. Joachimiak, Center for Structural Genomics of Infectious Diseases Crystal Structure of NSP15 Endoribonuclease from SARS CoV-2 doi: 10.2210/pdb6vww/pdb
    Released 2020-03-04
  • PDB structure 6y2e
    L. Zhang, X. Sun, R. Hilgenfeld Crystal structure of the free enzyme of the SARS-CoV-2 (2019-nCoV) main protease doi: 10.2210/pdb6y2e/pdb
    Released 2020-03-04
  • PDB structure 6y2f
    L. Zhang, X. Sun, R. Hilgenfeld Crystal structure (monoclinic form) of the complex resulting from the reaction between SARS-CoV-2 (2019-nCoV) main protease and tert-butyl (1-((S)-1-(((S)-4-(benzylamino)-3,4-dioxo-1-((S)-2-oxopyrrolidin-3-yl)butan-2-yl)amino)-3-cyclopropyl-1-oxopropan-2-yl)-2-oxo-1,2-dihydropyridin-3-yl)carbamate (alpha-ketoamide 13b) doi: 10.2210/pdb6y2f/pdb
    Released 2020-03-04
  • PDB structure 6y2g
    L. Zhang, X. Sun, R. Hilgenfeld Crystal structure (orthorhombic form) of the complex resulting from the reaction between SARS-CoV-2 (2019-nCoV) main protease and tert-butyl (1-((S)-1-(((S)-4-(benzylamino)-3,4-dioxo-1-((S)-2-oxopyrrolidin-3-yl)butan-2-yl)amino)-3-cyclopropyl-1-oxopropan-2-yl)-2-oxo-1,2-dihydropyridin-3-yl)carbamate (alpha-ketoamide 13b) doi: 10.2210/pdb6y2g/pdb
    Released 2020-03-04
First page image

Abstract

Coronavirus disease 2019 (COVID-19) is a global pandemic impacting nearly 170 countries/regions and more than 285,000 patients worldwide. COVID-19 is caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), which invades cells through the angiotensin converting enzyme 2 (ACE2) receptor. Among those with COVID-19, there is a higher prevalence of cardiovascular disease and more than 7% of patients suffer myocardial injury from the infection (22% of the critically ill). Despite ACE2 serving as the portal for infection, the role of ACE inhibitors or angiotensin receptor blockers requires further investigation. COVID-19 poses a challenge for heart transplantation, impacting donor selection, immunosuppression, and post-transplant management. Thankfully there are a number of promising therapies under active investigation to both treat and prevent COVID-19. Key Words: COVID-19; myocardial injury; pandemic; heart transplant

SOURCE

https://www.ahajournals.org/doi/pdf/10.1161/CIRCULATIONAHA.120.046941

ACE2

  • Towler P, Staker B, Prasad SG, Menon S, Tang J, Parsons T, Ryan D, Fisher M, Williams D, Dales NA, Patane MA, Pantoliano MW (Apr 2004). “ACE2 X-ray structures reveal a large hinge-bending motion important for inhibitor binding and catalysis”The Journal of Biological Chemistry279 (17): 17996–8007. doi:10.1074/jbc.M311191200PMID 14754895.

 

  • Turner AJ, Tipnis SR, Guy JL, Rice G, Hooper NM (Apr 2002). “ACEH/ACE2 is a novel mammalian metallocarboxypeptidase and a homologue of angiotensin-converting enzyme insensitive to ACE inhibitors”Canadian Journal of Physiology and Pharmacology80 (4): 346–53. doi:10.1139/y02-021PMID 12025971.

 

  •  Zhang, Haibo; Penninger, Josef M.; Li, Yimin; Zhong, Nanshan; Slutsky, Arthur S. (3 March 2020). “Angiotensin-converting enzyme 2 (ACE2) as a SARS-CoV-2 receptor: molecular mechanisms and potential therapeutic target”Intensive Care Medicine. Springer Science and Business Media LLC. doi:10.1007/s00134-020-05985-9ISSN 0342-4642PMID 32125455.

 

  • ^ Gurwitz, David (2020). “Angiotensin receptor blockers as tentative SARS‐CoV‐2 therapeutics”Drug Development Researchdoi:10.1002/ddr.21656PMID 32129518.

 

Angiotensin converting enzyme 2 (ACE2)

is an exopeptidase that catalyses the conversion of angiotensin I to the nonapeptide angiotensin[1-9][5] or the conversion of angiotensin II to angiotensin 1-7.[6][7] ACE2 has direct effects on cardiac functiona and is expressed predominantly in vascular endothelial cells of the heart and the kidneys.[8] ACE2 is not sensitive to the ACE inhibitor drugs used to treat hypertension.[9]

ACE2 receptors have been shown to be the entry point into human cells for some coronaviruses, including the SARS virus.[10] A number of studies have identified that the entry point is the same for SARS-CoV-2,[11] the virus that causes COVID-19.[12][13][14][15]

Some have suggested that a decrease in ACE2 could be protective against Covid-19 disease[16], but others have suggested the opposite, that Angiotensin II receptor blocker drugs could be protective against Covid-19 disease via increasing ACE2, and that these hypotheses need to be tested by datamining of clinical patient records.[17]

REFERENCES

https://en.wikipedia.org/wiki/Angiotensin-converting_enzyme_2

 

FOLDING@HOME TAKES UP THE FIGHT AGAINST COVID-19 / 2019-NCOV

We need your help! Folding@home is joining researchers around the world working to better understand the 2019 Coronavirus (2019-nCoV) to accelerate the open science effort to develop new life-saving therapies. By downloading Folding@Home, you can donate your unused computational resources to the Folding@home Consortium, where researchers working to advance our understanding of the structures of potential drug targets for 2019-nCoV that could aid in the design of new therapies. The data you help us generate will be quickly and openly disseminated as part of an open science collaboration of multiple laboratories around the world, giving researchers new tools that may unlock new opportunities for developing lifesaving drugs.

2019-nCoV is a close cousin to SARS coronavirus (SARS-CoV), and acts in a similar way. For both coronaviruses, the first step of infection occurs in the lungs, when a protein on the surface  of the virus binds to a receptor protein on a lung cell. This viral protein is called the spike protein, depicted in red in the image below, and the receptor is known as ACE2. A therapeutic antibody is a type of protein that can block the viral protein from binding to its receptor, therefore preventing the virus from infecting the lung cell. A therapeutic antibody has already been developed for SARS-CoV, but to develop therapeutic antibodies or small molecules for 2019-nCoV, scientists need to better understand the structure of the viral spike protein and how it binds to the human ACE2 receptor required for viral entry into human cells.

Proteins are not stagnant—they wiggle and fold and unfold to take on numerous shapes.  We need to study not only one shape of the viral spike protein, but all the ways the protein wiggles and folds into alternative shapes in order to best understand how it interacts with the ACE2 receptor, so that an antibody can be designed. Low-resolution structures of the SARS-CoV spike protein exist and we know the mutations that differ between SARS-CoV and 2019-nCoV.  Given this information, we are uniquely positioned to help model the structure of the 2019-nCoV spike protein and identify sites that can be targeted by a therapeutic antibody. We can build computational models that accomplish this goal, but it takes a lot of computing power.

This is where you come in! With many computers working towards the same goal, we aim to help develop a therapeutic remedy as quickly as possible. By downloading Folding@home here [LINK] and selecting to contribute to “Any Disease”, you can help provide us with the computational power required to tackle this problem. One protein from 2019-nCoV, a protease encoded by the viral RNA, has already been crystallized. Although the 2019-nCoV spike protein of interest has not yet been resolved bound to ACE2, our objective is to use the homologous structure of the SARS-CoV spike protein to identify therapeutic antibody targets.

This illustration, created at the Centers for Disease Control and Prevention (CDC), reveals ultrastructural morphology exhibited by coronaviruses. Note the spikes that adorn the outer surface of the virus, which impart the look of a corona surrounding the virion, when viewed electron microscopically. A novel coronavirus virus was identified as the cause of an outbreak of respiratory illness first detected in Wuhan, China in 2019.

Image and Caption Credit: Alissa Eckert, MS; Dan Higgins, MAM available at https://phil.cdc.gov/Details.aspx?pid=23311

Structures of the closely related SARS-CoV spike protein bound by therapeutic antibodies may help rapidly design better therapies. The three monomers of the SARS-CoV spike protein are shown in different shades of red; the antibody is depicted in green. [PDB: 6NB7 https://www.rcsb.org/structure/6nb7]

(post authored by Ariana Brenner Clerkin)

References:

PDB 6lu7 structure summary ‹ Protein Data Bank in Europe (PDBe) ‹ EMBL-EBI https://www.ebi.ac.uk/pdbe/entry/pdb/6lu7 (accessed Feb 5, 2020).

Tian, X.; Li, C.; Huang, A.; Xia, S.; Lu, S.; Shi, Z.; Lu, L.; Jiang, S.; Yang, Z.; Wu, Y.; et al. Potent Binding of 2019 Novel Coronavirus Spike Protein by a SARS Coronavirus-Specific Human Monoclonal Antibody; preprint; Microbiology, 2020. https://doi.org/10.1101/2020.01.28.923011.

Walls, A. C.; Xiong, X.; Park, Y. J.; Tortorici, M. A.; Snijder, J.; Quispe, J.; Cameroni, E.; Gopal, R.; Dai, M.; Lanzavecchia, A.; et al. Unexpected Receptor Functional Mimicry Elucidates Activation of Coronavirus Fusion. Cell 2019176, 1026-1039.e15. https://doi.org/10.2210/pdb6nb7/pdb.

SOURCE

https://foldingathome.org/2020/02/27/foldinghome-takes-up-the-fight-against-covid-19-2019-ncov/

UPDATED 3/13/2020

I am reposting the following Science blog post from Derrick Lowe as is and ask people go browse through the comments on his Science blog In the Pipeline because, as Dr. Lowe states that in this current crisis it is important to disseminate good information as quickly as possible so wanted the readers here to have the ability to read his great posting on this matter of Covid-19.  Also i would like to direct readers to the journal Science opinion letter concerning how important it is to rebuild the trust in good science and the scientific process.  The full link for the following In the Pipeline post is: https://blogs.sciencemag.org/pipeline/archives/2020/03/06/covid-19-small-molecule-therapies-reviewed

A Summary of current potential repurposed therapeutics for COVID-19 Infection from In The Pipeline: A Science blog from Derick Lowe

Covid-19 Small Molecule Therapies Reviewed

Let’s take inventory on the therapies that are being developed for the coronavirus epidemic. Here is a very thorough list of at Biocentury, and I should note that (like Stat and several other organizations) they’re making all their Covid-19 content free to all readers during this crisis. I’d like to zoom in today on the potential small-molecule therapies, since some of these have the most immediate prospects for use in the real world.

The ones at the front of the line are repurposed drugs that are already approved for human use, for a lot of obvious reasons. The Biocentury list doesn’t cover these, but here’s an article at Nature Biotechnology that goes into detail. Clinical trials are a huge time sink – they sort of have to be, in most cases, if they’re going to be any good – and if you’ve already done all that stuff it’s a huge leg up, even if the drug itself is not exactly a perfect fit for the disease. So what do we have? The compound that is most advanced is probably remdesivir from Gilead, at right. This has been in development for a few years as an RNA virus therapy – it was originally developed for Ebola, and has been tried out against a whole list of single-strand RNA viruses. That includes the related coronaviruses SARS and MERS, so Covid-19 was an obvious fit.

The compound is a prodrug – that phosphoramide gets cleaved off completely, leaving the active 5-OH compound GS-44-1524. It mechanism of action is to get incorporated into viral RNA, since it’s taken up by RNA polymerase and it largely seems to evade proofreading. This causes RNA termination trouble later on, since that alpha-nitrile C-nucleoside is not exactly what the virus is expecting in its genome at that point, and thus viral replication is inhibited.

There are five clinical trials underway (here’s an overview at Biocentury). The NIH has an adaptive-design Phase II trial that has already started in Nebraska, with doses to be changed according to Bayesian readouts along the way. There are two Phase III trials underway at China-Japan Friendship Hospital in Hubei, double-blinded and placebo-controlled (since placebo is, as far as drug therapy goes, the current standard of care). And Gilead themselves are starting two open-label trials, one with no control arm and one with an (unblinded) standard-of-care comparison arm. Those might read out first, depending on when they get off the ground, but will be only rough readouts due to the fast-and-loose trial design. The two Hubei trials and the NIH one will add some rigor to the process, but I’m not sure when they’re going to report. My personal opinion is that I like the chances of this drug more than anything else on this list, but it’s still unlikely to be a game-changer.

There’s an RNA polymerase inhibitor (favipiravir) from Toyama, at right, that’s in a trial in China. It’s a thought – a broad-spectrum agent of this sort would be the sort of thing to try. But unfortunately, from what I can see, it has already turned up as ineffective in in vitro tests. The human trial that’s underway is honestly the sort of thing that would only happen under circumstances like the present: a developing epidemic with a new pathogen and no real standard of care. I hold out little hope for this one, but given that there’s nothing else at present, it probably should be tried. As you’ll see, this is far from the only situation like this.

One of the screens of known drugs in China that also flagged remdesivir noted that the old antimalarial drug chloroquine seemed to be effective in vitro. It had been reported some years back as a possible antiviral, working through more than one mechanism, probably both at viral entry and intracellularly thereafter. That part shouldn’t be surprising – chloroquine’s actual mode(s) of action against malaria parasites are still not completely worked out, either, and some of what people thought they knew about it has turned out to be wrong. There are several trials underway with it at Chinese facilities, some in combination with other agents like remdesivir. Chloroquine has of course been taken for many decades as an antimalarial, but it has a number of liabilities, including seizures, hearing damage, retinopathy and sudden effects on blood glucose. So it’s going to be important to establish just how effective it is and what doses will be needed. Just as with vaccine candidates, it’s possible to do more harm with a rushed treatment than the disease is doing itself

There are several other known antiviral drugs are being tried in China, but I don’t have too much hope for those, either. The neuraminidase inhibitors such as oseltamivir (better known as Tamiflu) were tried against SARS and were ineffective; there is no reason to expect anything versus Covid-19 although these drugs are a component of some drug cocktail trials. The HIV protease therapies such as darunavir and the combination therapy Kaletra are in trials, but that’s also a rather desperate long shot, since there’s no particular reason to think that they will have any such protease inhibition against what this new virus has to offer (and indeed, such agents weren’t much help against SARS in the end, either). The classic interferon/ribavirin combination seems to have had some activity against SARS and MERS, and is in two trials from what I can see. That’s not an awful idea by any means, but it’s not a great one, either: if your viral disease has interferon/ribavirin as a front line therapy, it generally means that there’s nothing really good available. No, unless we get really lucky none of these ideas are going to slow the disease down much.

There are a few other repurposed-protease-inhibitors ideas out there, such as this one. (Edit: I had seen this paper but couldn’t track it down, so thanks to those who sent it along). This paper suggests that the TMPRSS2 protease is important for viral entry on the human-cell-side of the process, a pathway that has been noted for other coronaviruses. And it points out that there is a an approved inhibitor (in Japan) for this enzyme (camostat), so that would definitely seem to be worth a trial, probably in combination with remdesivir.

That’s about it for the existing small molecules, from what I can see. What about new ones? Don’t hold your breath, is all I can say. A drug discovery program from scratch against a new pathogen is, as many readers here well know, not a trivial exercise. As this Bloomberg article details, many such efforts in the past (small molecules and vaccines alike) have come to grief because by the time they had anything to deliver the epidemic itself had passed. Indeed, Gilead’s remdesivir had already been dropped as a potential Ebola therapy.

You will either need to have a target in mind up front or go phenotypic. For the former, what you’d see are better characterizations of the viral protease and more extensive screens against it. Two other big target areas are viral entry (which involves the “spike” proteins on the virus surface and the ACE2 protein on human cells) and viral replication. To the former, it’s worth quickly noting that ACE2 is so much unlike the more familiar ACE protein that none of the cardiovascular ACE inhibitors do anything to it at all. And targeting the latter mechanisms is how remdesivir was developed as a possible Ebola agent, but as you can see, that took time, too. Phenotypic screens are perfectly reasonable against viral pathogens as well, but you’ll need to put time and effort into that assay up front, just as with any phenotypic effort, because as anyone who does that sort of work will tell you, a bad phenotypic screen is a complete waste of everyone’s time.

One of the key steps for either route is identifying an animal model. While animal models of infectious disease can be extremely well translated to human therapy, that doesn’t happen by accident: you need to choose the right animal. Viruses in general (and coronaviruses are no exception) vary widely in their effects in different species, and not just across the gaps of bird/reptile/human and the like. No, you’ll run into things where even the usual set of small mammals are acting differently from each other, with some of them not even getting sick at all. This current virus may well have gone through a couple of other mammalian species before landing on us, but you’ll note that dogs (to pick one) don’t seem to have any problem with it.

All this means that any new-target new-chemical-matter effort against Covid-19 (or any new pathogen) is going to take years, and there is just no way around that. Update: see here for just such an effort to start finding fragment hits for the viral protease. This puts small molecules in a very bimodal distribution: you have the existing drugs that might be repurposed, and are presumably available right now. Nothing else is! At the other end, for completely new therapies you have the usual prospects of drug discovery: years from now, lots of money, low success rate, good luck to all of us. The gap between these two could in theory be filled by vaccines and antibody therapies (if everything goes really, really well) but those are very much their own area and will be dealt with in a separate post.

Either way, the odds are that we (and I mean “we as a species” here) are going to be fighting this epidemic without any particularly amazing pharmacological weapons. Eventually we’ll have some, but I would advise people, pundits, and politicians not to get all excited about the prospects for some new therapies to come riding up over the hill to help us out. The odds of that happening in time to do anything about the current outbreak are very small. We will be going for months, years, with the therapeutic options we have right now. Look around you: what we have today is what we have to work with.

Other related articles published in this Open Access Online Scientific Journal include the following:

 

Group of Researchers @ University of California, Riverside, the University of Chicago, the U.S. Department of Energy’s Argonne National Laboratory, and Northwestern University solve COVID-19 Structure and Map Potential Therapeutics

Reporters: Stephen J Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/06/group-of-researchers-solve-covid-19-structure-and-map-potential-therapeutic/

Predicting the Protein Structure of Coronavirus: Inhibition of Nsp15 can slow viral replication and Cryo-EM – Spike protein structure (experimentally verified) vs AI-predicted protein structures (not experimentally verified) of DeepMind (Parent: Google) aka AlphaFold

Curators: Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/08/predicting-the-protein-structure-of-coronavirus-inhibition-of-nsp15-can-slow-viral-replication-and-cryo-em-spike-protein-structure-experimentally-verified-vs-ai-predicted-protein-structures-not/

 

Coronavirus facility opens at Rambam Hospital using new Israeli tech

https://www.jpost.com/Israel-News/Coronavirus-facility-opens-at-Rambam-Hospital-using-new-Israeli-tech-619681

 

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