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“Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?”

Creating a Twitter Space for @pharma_BI for Live Broadcasts

Posted in Curation methodology, Key Opinion Leaders in eScientific Publishing - Interviews with, Open Access Journals, Pharmaceutical Industry Competitive Intelligence, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Scientific Publishing, tagged #science2_0, #science3_0, https://twitter.com/#!/pharma_BI, Twitter, Twitter Space, web 2.0, web 3.0 on March 14, 2023| 1 Comment »

How to Create a Twitter Space for @pharma_BI for Live Broadcasts

Right now, Twitter Spaces are available on Android and iOS operating systems ONLY. For use on a PC desktop you must install an ANDROID EMULATOR. This means best to set up the Twitter Space using your PHONE APP not on the desktop or laptop computer. Right now, even though there is the ability to record a Twitter Space, that recording is not easily able to be embedded in WordPress as a tweet is (or chain of tweets). However you can download the recording (takes a day or two) and convert to mpeg using a program like Audacity to convert into an audio format conducible to WordPress.

A while ago I had put a post where I link to a Twitter Space I created for a class on Dissemination of Scientific Discoveries. The post

can be seen at

Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

This online discussion was tweeted out and got a fair amount of impressions (60) as well as interactors (50).

About Twitter Spaces

Spaces is a way to have live audio conversations on Twitter. Anyone can join, listen, and speak in a Space on Twitter for iOS and Android. Currently you can listen in a Space on web.

Quick links

How to use Spaces
Spaces FAQ
Spaces Feedback Community
Community Spaces

How to use Spaces

Instructions for:

How do you start a Space?

Step 1

The creator of a Space is the host. As a host on iOS, you can start a Space by long pressing on the Tweet Composer from your Home timeline and and then selecting the Spaces icon.

You can also start a Space by selecting the Spaces tab on the bottom of your timeline.

Step 2

Spaces are public, so anyone can join as a listener, including people who don’t follow you. Listeners can be directly invited into a Space by DMing them a link to the Space, Tweeting out a link, or sharing a link elsewhere.

Step 3

Up to 13 people (including the host and 2 co-hosts) can speak in a Space at any given time. When creating a new Space, you will see options to Name your Space and Start your Space.

Step 4

To schedule a Space, select Schedule for later. Choose the date and time you’d like your Space to go live.

Step 5

Once the Space has started, the host can send requests to listeners to become co-hosts or speakers by selecting the people icon and adding co-hosts or speakers, or selecting a person’s profile picture within a Space and adding them as a co-host or speaker. Listeners can request permission to speak from the host by selecting the Request icon below the microphone.

Step 6

When creating a Space, the host will join with their mic off and be the only speaker in the Space. When ready, select Start your Space.

Step 7

Allow mic access (speaking ability) to speakers by toggling Allow mic access to on.

Step 8

Get started chatting in your Space.

Step 9

As a host, make sure to Tweet out the link to your Space so other people can join. Select the icon to Share via a Tweet.

Spaces FAQ

Where is Spaces available?

Anyone can join, listen, and speak in a Space on Twitter for iOS and Android. Currently, starting a Space on web is not possible, but anyone can join and listen in a Space.

Who can start a Space?

People on Twitter for iOS and Android can start a Space.

Who can see my Space?

For now, all Spaces are public like Tweets, which means they can be accessed by anyone. They will automatically appear at the top of your Home timeline, and each Space has a link that can be shared publicly. Since Spaces are publicly accessible by anyone, it may be possible for people to listen to a Space without being listed as a guest in the Space.

We make certain information about Spaces available through the Twitter Developer Platform, such as the title of a Space, the hosts and speakers, and whether it is scheduled, in progress, or complete. For a more detailed list of the information about Spaces we make available via the Twitter API, check out our Spaces endpoints documentation.

Can other people see my presence while I am listening or speaking in a Space?

Since all Spaces are public, your presence and activity in a Space is also public. If you are logged into your Twitter account when you are in a Space, you will be visible to everyone in the Space as well as to others, including people who follow you, people who peek into the Space without entering, and developers accessing information about the Space using the Twitter API.

If you are listening in a Space, your profile icon will appear with a purple pill at the top of your followers’ Home timelines. You have the option to change this in your settings.

Instructions for:

Manage who can see your Spaces listening activity

Step 1

On the left nav menu, select the more icon and go to Settings and privacy.

Step 2

Under Settings, navigate to Privacy and safety.

Step 3

Under Your Twitter activity, go to Spaces.

Step 4

Choose if you want to Allow followers to see which Spaces you’re listening to by toggling this on or off.

Your followers will always see at the top of their Home timelines what Spaces you’re speaking in.

What does it mean that Spaces are public? Can anyone listen in a Space?

Spaces can be listened to by anyone on the Internet. This is part of a broader feature of Spaces that lets anyone listen to Spaces regardless of whether or not they are logged in to a Twitter account (or even have a Twitter account). Because of this, listener counts may not match the actual number of listeners, nor will the profile photos of all listeners necessarily be displayed in a Space.

How do I invite people to join a Space?

Invite people to join a Space by sending an invite via DM, Tweeting the link out to your Home timeline, or copying the invite link to send it out.

Who can join my Space?

For now, all Spaces are public and anyone can join any Space as a listener. If the listener has a user account, you can block their account. If you create a Space or are a speaker in a Space, your followers will see it at the top of their timeline.

Who can speak in my Space?

By default, your Space will always be set to Only people you invite to speak. You can also modify the Speaker permissions once your Space has been created. Select the icon, then select Adjust settings to see the options for speaker permissions, which include Everyone, People you follow, and the default Only people you invite to speak. These permissions are only saved for this particular Space, so any Space you create in the future will use the default setting.

Once your Space has started, you can send requests to listeners to become speakers or co-hosts by selecting the icon and adding speakers or selecting a person’s profile picture within a Space and adding them as a co-host or speaker. Listeners can request to speak from the host.

Hosts can also invite other people outside of the Space to speak via DM.

How does co-hosting work?

Up to 2 people can become co-hosts and speak in a Space in addition to the 11 speakers (including the primary host) at one time. Co-host status can be lost if the co-host leaves the Space. A co-host can remove their own co-host status to become a Listener again.

Hosts can transfer primary admin rights to another co-host. If the original host drops from Space, the first co-host added will become the primary admin. The admin is responsible for promoting and facilitating a healthy conversation in the Space in line with the Twitter Rules.

Once a co-host is added to a Space, any accounts they’ve blocked on Twitter who are in the Space will be removed from the Space.

Can I schedule a Space?

Hosts can schedule a Space up to 30 days in advance and up to 10 scheduled Spaces. Hosts can still create impromptu Spaces in the meantime, and those won’t count toward the maximum 10 scheduled Spaces.

Before you create your Space, select the scheduler icon and pick the date and time you’d like to schedule your Space to go live. As your scheduled start time approaches, you will receive push and in-app notifications reminding you to start your Space on time. If you don’t have notifications turned on, follow the in-app steps on About notifications on mobile devices to enable them for Spaces. Scheduled Spaces are public and people can set reminders to be notified when your scheduled Space begins.

How do I edit my scheduled Space(s)?

Follow the steps below to edit any of your scheduled Spaces.

Instructions for:

Manage your scheduled Spaces

Step 1

From your timeline, navigate to and long press on the . Or, navigate to the Spaces Tab at the bottom of your timeline.

Step 2

Select the Spaces icon.

Step 3

To manage your scheduled Spaces, select the scheduler icon at the top.

Step 4

You’ll see the Spaces that you have scheduled.

Step 5

Navigate to the more icon of the Space you want to manage. You can edit, share, or cancel the Space.

If you are editing your Space, make sure to select “Save changes” after making edits.

How do I get notified about a scheduled Space?

Guests can sign up for reminder notifications from a scheduled Space card in a Tweet. When the host starts the scheduled Space, the interested guests get notified via push and in-app notifications.

Can I record a Space?

Hosts can record Spaces they create for replay. When creating a Space, toggle on Record Space.

While recording, a recording symbol will appear at the top to indicate that the Space is being recorded by the host. Once the Space ends, you will see how many people attended the Space along with a link to share out via a Tweet. Under Notifications, you can also View details to Tweet the recording. Under host settings, you will have the option to choose where to start your recording with Edit start time. This allows you to cut out any dead air time that might occur at the beginning of a Space.

If you choose to record your Space, once the live Space ends, your recording will be immediately and publicly available for anyone to listen to whenever they want. You can always end a recording to make it no longer publicly available on Twitter by deleting your recording via the more icon on the recording itself. Unless you delete your recording, it will remain available for replay after the live Space has ended.* As with live Spaces, Twitter will retain audio copies for 30 after they end to review for violations of the Twitter Rules. If a violation is found, Twitter may retain audio copies for up to 120 days in total. For more information on downloading Spaces, please see below FAQ, “What happens after a Space ends and is the data retained anywhere?”

Co-hosts and speakers who enter a Space that is being recorded will see a recording symbol (REC). Listeners will also see the recording symbol, but they will not be visible in the recording.

Recordings will show the host, co-host(s), and speakers from the live Space.

*Note: Hosts on iOS 9.15+ and Android 9.46+ will be able to record Spaces that last indefinitely. For hosts on older app versions, recording will only be available for 30 days. For Spaces that are recorded indefinitely, Twitter will retain a copy for as long as the Space is replayable on Twitter, but for no less than 30 days after the live Space ended.

What is clipping?

Clipping is a new feature we’re currently testing and gradually rolling out that lets a limited group of hosts, speakers, and listeners capture 30 seconds of audio from any live or recorded Space and share it through a Tweet if the host has not disabled the clipping function. To start clipping a Space, follow the instructions below to capture the prior 30 seconds of audio from that Space. There is no limit to the number of clips that participants in a Space can create.

When you enter the Space as a co-host or speaker, you will be informed that the Space is clippable through a tool tip notification above the clipping icon.

Note: Currently, creating a clip is available only on iOS and Android, while playing a clip is available on all platforms to everyone.

Instructions for:

Host instructions: How to turn off clipping

When you start your Space, you’ll receive a notification about what a clip is and how to turn it off, as clipping is on by default. You can turn off clipping at any time. To turn it off, follow the instructions below.

Step 1

Navigate to the more icon.

Step 2

Select Adjust settings .

Step 3

Under Clips, toggle Allow clips off.

Instructions for:

Host and speaker instructions: How to create a clipping

Step 1

In a recorded or live Space that is recorded, navigate to the clipping icon. Please note that, for live Spaces, unless the clipping function is disabled, clips will be publicly available on your Twitter profile after your live Space has ended even though the Space itself will no longer be available.

Step 2

On the Create clip pop-up, go to Next.

Step 3

Preview the Tweet and add a comment if you’d like, similarly to a Quote Tweet.

Step 4

Select Tweet to post it to your timeline.

Why is my clip not displaying captions?

What controls do hosts have over existing clips?

What controls do clip creators have over clips they’ve created?

Other controls over clips: how to report, block, or mute

What controls do I have over my Space?

The host and co-host(s) of a Space have control over who can speak. They can mute any Speaker, but it is up to the individual to unmute themselves if they receive speaking privileges. Hosts and co-hosts can also remove, report, and block others in the Space.

Speakers and listeners can report and block others in the Space, or can report the Space. If you block a participant in the Space, you will also block that person’s account on Twitter. If the person you blocked joins as a listener, they will appear in the participant list with a Blocked label under their account name. If the person you blocked joins as a speaker, they will also appear in the participant list with a Blocked label under their account name and you will see an in-app notification stating, “An account you blocked has joined as a speaker.” If you are entering a Space that already has a blocked account as a speaker, you will also see a warning before joining the Space stating, “You have blocked 1 person who is speaking.”

If you are hosting or co-hosting a Space, people you’ve blocked can’t join and, if you’re added as a co-host during a Space, anyone in the Space who you blocked will be removed from the Space.

What are my responsibilities as a Host or Co-Host of a Space?

As a Host, you are responsible for promoting and supporting a healthy conversation in your Space and to use your tools to ensure that the Twitter Rules are followed. The following tools are available for you to use if a participant in the Space is being offensive or disruptive:

Revoke speaking privileges of other users if they are being offensive or disruptive to you or others
Block, remove or report the user.

Here are some guidelines to follow as a Host or Co-Host:

Always follow the Twitter Rulesin the Space you host or co-host. This also applies to the title of your Space which should not include abusive slurs, threats, or any other rule-violating content.
Do not encourage behavior or content that violates the Twitter Rules.
Do not abuse or misuse your hosting tools, such as arbitrarily revoking speaking privileges or removing users, or use Spaces to carry out activities that break our rules such as following schemes.

How can I block someone in a Space?

How can I mute a speaker in a Space?

How can I see people in my Space?

Hosts, speakers, and listeners can select the icon to see people in a Space. Since Spaces are publicly accessible by anyone, it may also be possible for an unknown number of logged-out people to listen to a Space’s audio without being listed as a guest in the Space.

How can I report a Space?

How can I report a person in a Space?

Can Twitter suspend my Space while it’s live?

How many people can speak in a Space?

How many people can listen in a Space?

What happens after a Space ends and is the data retained anywhere?

Hosts can choose to record a Space prior to starting it. Hosts may download copies of their recorded Spaces for as long as we have them by using the Your Twitter Data download tool.

For unrecorded Spaces, Twitter retains copies of audio from recorded Spaces for 30 days after a Space ends to review for violations of the Twitter Rules. If a Space is found to contain a violation, we extend the time we maintain a copy for an additional 90 days (a total of 120 days after a Space ends) to allow people to appeal if they believe there was a mistake. Twitter also uses Spaces content and data for analytics and research to improve the service.

Links to Spaces that are shared out (e.g., via Tweet or DM) also contain some information about the Space, including the description, the identity of the hosts and others in the Space, as well as the Space’s current state (e.g., scheduled, live, or ended). We make this and other information about Spaces available through the Twitter Developer Platform. For a detailed list of the information about Spaces we make available, check out our Spaces endpoints documentation.

For full details on what data we retain, visit our Privacy Policy.

Who can end a Space?

Does Spaces work for accounts with protected Tweets?

Following the Twitter Rules in Spaces

Spaces Feedback Community

We’re opening up the conversation and turning it over to the people who are participating in Spaces. This Community is a dedicated place for us to connect with you on all things Spaces, whether it’s feedback around features, ideas for improvement, or any general thoughts.

Who can join?

Anyone on Spaces can join, whether you are a host, speaker, or listener.

How do I join the Community?

You can request to join the Twitter Spaces Feedback Community here. By requesting to join, you are agreeing to our Community rules.

Learn more about Communities on Twitter.

Community Spaces

As a Community admin or moderator, you can create and host a Space for your Community members to join.

Note:

Currently, creating Community Spaces is only available to some admins and moderators using the Twitter for iOS and Twitter for Android apps.

Instructions for:

Admins & moderators: How to create a Space

Step 1

Navigate to the Community landing page.

Step 2

Long press on the Tweet Composer and select the Spaces icon.

Step 3

Select Spaces and begin creating your Space by adding in a title, toggling on record Space (optional), and adding relevant topics.

Step 4

Invite admins, moderators, and other people to be a part of your Space.

Members: How to find a Community Space

If a Community Space is live, you will see the Spacebar populate at the top of your Home timeline. To enter the Space and begin listening, select the live Space in the Spacebar.

Community Spaces FAQ

What are Community Spaces?

Spaces Social Narrative

A social narrative is a simple story that describes social situations and social behaviors for accessibility.

Twitter Spaces allows me to join or host live audio-only conversations with anyone.

Joining a Space

When I join a Twitter Space, that means I’ll be a listener. I can join any Space on Twitter, even those hosted by people I don’t know or follow.
I can join a Space by selecting a profile photo with a purple, pulsing outline at the top of my timeline, selecting a link from someone’s Tweet, or a link in a Direct Message (DM).
Once I’m in a Space, I can seethe profile photos and names of some people in the Space, including myself.
I can hearone or multiple people talking at the same time. If it’s too loud or overwhelming, I can turn down my volume.
As a listener, I am not able to speak. If I want to say something, I can send a request to the host. The host might not approve my request though.
If the host accepts my request, I will become a speaker. It may take a few seconds to connect my microphone, so I’ll have to wait.
Now I can unmute myself and speak. Everyone in the Space will be able to hear me.
When someone says something I want to react to, I can choosean emoji to show everyone how I feel. I will be able to see when other people react as well.
I can leave the Space at any time. After I leave, or when the host ends the Space, I’ll go back to my timeline.

Hosting a Space

When I start a Space, that means I’ll be the host. Anyone can join my Space, even people I don’t know and people I don’t follow.
Once I start my space, it may take a few seconds to be connected, so I’ll have to wait.
Now I’m in my Space and I can seemy profile photo. If other logged-in, people have joined, I will be able to see their profile photos, too.
I will start out muted, which is what the microphone with a slash through it means. I can mute and unmute myself, and anyone in my Space, at any time.
I can invitepeople to join my Space by sending them a Direct Message (DM), sharing the link in a Tweet, and by copying the link and sharing it somewhere else like in an email.
Up to 10 other people can have speaking privileges in my Space at the same time, and I can choosewho speaks and who doesn’t. People can also request to speak, and I can choose to approve their request or not.

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson and others

Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

Posted in Artificial Intelligence Applications in Health Care, BioIT: BioInformatics, Blockchain Transactions System, Curation, Curation methodology, De novo synthesis, Discovery process, Historical relevance, IP Development by LPBI Group Team, IP Development by LPBI Group Team & Other Organizations, Key Opinion Leaders in eScientific Publishing - Interviews with, LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management, Open Access Journals, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Scientific Publishing, tagged #science2_0, #science3_0, communication of scientific findings, Curation of Scientific Content, Curation of Scientific Findings, LinkedIn, NFT, science communication, Twitter, web 2.0, web 3.0 on April 1, 2022| 2 Comments »

Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

Curator: Stephen J. Williams, Ph.D.

UPDATED 4/06/2022

A while back (actually many moons ago) I had put on two posts on this site:

Twitter is Becoming a Powerful Tool in Science and Medicine

Each of these posts were on the importance of scientific curation of findings within the realm of social media and the Web 2.0; a sub-environment known throughout the scientific communities as Science 2.0, in which expert networks collaborated together to produce massive new corpus of knowledge by sharing their views, insights on peer reviewed scientific findings. And through this new media, this process of curation would, in itself generate new ideas and new directions for research and discovery.

The platform sort of looked like the image below:

This system lied above a platform of the original Science 1.0, made up of all the scientific journals, books, and traditional literature:

In the old Science 1.0 format, scientific dissemination was in the format of hard print journals, and library subscriptions were mandatory (and eventually expensive). Open Access has tried to ameliorate the expense problem.

Previous image source: PeerJ.com

To index the massive and voluminous research and papers beyond the old Dewey Decimal system, a process of curation was mandatory. The dissemination of this was a natural for the new social media however the cost had to be spread out among numerous players. Journals, faced with the high costs of subscriptions and their only way to access this new media as an outlet was to become Open Access, a movement first sparked by journals like PLOS and PeerJ but then begrudingly adopted throughout the landscape. But with any movement or new adoption one gets the Good the Bad and the Ugly (as described in my cited, above, Clive Thompson article). The bad side of Open Access Journals were

costs are still assumed by the individual researcher not by the journals
the arise of the numerous Predatory Journals

Even PeerJ, in their column celebrating an anniversary of a year’s worth of Open Access success stories, lamented the key issues still facing Open Access in practice

which included the cost and the rise of predatory journals.

In essence, Open Access and Science 2.0 sprung full force BEFORE anyone thought of a way to defray the costs

Can Web 3.0 Finally Offer a Way to Right the Issues Facing High Costs of Scientific Publishing?

What is Web 3.0?

From Wikipedia: https://en.wikipedia.org/wiki/Web3

Web 1.0 and Web 2.0 refer to eras in the history of the Internet as it evolved through various technologies and formats. Web 1.0 refers roughly to the period from 1991 to 2004, where most websites were static webpages, and the vast majority of users were consumers, not producers, of content.^[6]^[7] Web 2.0 is based around the idea of “the web as platform”,^[8] and centers on user-created content uploaded to social-networking services, blogs, and wikis, among other services.^[9] Web 2.0 is generally considered to have begun around 2004, and continues to the current day.^[8]^[10]^[4]

Terminology[edit]

The term “Web3”, specifically “Web 3.0”, was coined by Ethereum co-founder Gavin Wood in 2014.^[1] In 2020 and 2021, the idea of Web3 gained popularity^{[citation needed]}. Particular interest spiked towards the end of 2021, largely due to interest from cryptocurrency enthusiasts and investments from high-profile technologists and companies.^[4]^[5] Executives from venture capital firm Andreessen Horowitz travelled to Washington, D.C. in October 2021 to lobby for the idea as a potential solution to questions about Internet regulation with which policymakers have been grappling.^[11]

Web3 is distinct from Tim Berners-Lee‘s 1999 concept for a semantic web, which has also been called “Web 3.0”.^[12] Some writers referring to the decentralized concept usually known as “Web3” have used the terminology “Web 3.0”, leading to some confusion between the two concepts.^[2]^[3] Furthermore, some visions of Web3 also incorporate ideas relating to the semantic web.^[13]^[14]

Concept[edit]

Web3 revolves around the idea of decentralization, which proponents often contrast with Web 2.0, wherein large amounts of the web’s data and content are centralized in the fairly small group of companies often referred to as Big Tech.^[4]

Specific visions for Web3 differ, but all are heavily based in blockchain technologies, such as various cryptocurrencies and non-fungible tokens (NFTs).^[4] Bloomberg described Web3 as an idea that “would build financial assets, in the form of tokens, into the inner workings of almost anything you do online”.^[15] Some visions are based around the concepts of decentralized autonomous organizations (DAOs).^[16] Decentralized finance (DeFi) is another key concept; in it, users exchange currency without bank or government involvement.^[4] Self-sovereign identity allows users to identify themselves without relying on an authentication system such as OAuth, in which a trusted party has to be reached in order to assess identity.^[17]

Reception[edit]

Technologists and journalists have described Web3 as a possible solution to concerns about the over-centralization of the web in a few “Big Tech” companies.^[4]^[11] Some have expressed the notion that Web3 could improve data security, scalability, and privacy beyond what is currently possible with Web 2.0 platforms.^[14] Bloomberg states that sceptics say the idea “is a long way from proving its use beyond niche applications, many of them tools aimed at crypto traders”.^[15] The New York Times reported that several investors are betting $27 billion that Web3 “is the future of the internet”.^[18]^[19]

Some companies, including Reddit and Discord, have explored incorporating Web3 technologies into their platforms in late 2021.^[4]^[20] After heavy user backlash, Discord later announced they had no plans to integrate such technologies.^[21] The company’s CEO, Jason Citron, tweeted a screenshot suggesting it might be exploring integrating Web3 into their platform. This led some to cancel their paid subscriptions over their distaste for NFTs, and others expressed concerns that such a change might increase the amount of scams and spam they had already experienced on crypto-related Discord servers.^[20] Two days later, Citron tweeted that the company had no plans to integrate Web3 technologies into their platform, and said that it was an internal-only concept that had been developed in a company-wide hackathon.^[21]

Some legal scholars quoted by The Conversation have expressed concerns over the difficulty of regulating a decentralized web, which they reported might make it more difficult to prevent cybercrime, online harassment, hate speech, and the dissemination of child abuse images.^[13] But, the news website also states that, “[decentralized web] represents the cyber-libertarian views and hopes of the past that the internet can empower ordinary people by breaking down existing power structures.” Some other critics of Web3 see the concept as a part of a cryptocurrency bubble, or as an extension of blockchain-based trends that they see as overhyped or harmful, particularly NFTs.^[20] Some critics have raised concerns about the environmental impact of cryptocurrencies and NFTs. Others have expressed beliefs that Web3 and the associated technologies are a pyramid scheme.^[5]

Kevin Werbach, author of The Blockchain and the New Architecture of Trust,^[22] said that “many so-called ‘web3’ solutions are not as decentralized as they seem, while others have yet to show they are scalable, secure and accessible enough for the mass market”, adding that this “may change, but it’s not a given that all these limitations will be overcome”.^[23]

David Gerard, author of Attack of the 50 Foot Blockchain,^[24] told The Register that “web3 is a marketing buzzword with no technical meaning. It’s a melange of cryptocurrencies, smart contracts with nigh-magical abilities, and NFTs just because they think they can sell some monkeys to morons”.^[25]

Below is an article from MarketWatch.com Distributed Ledger series about the different forms and cryptocurrencies involved

From Marketwatch: https://www.marketwatch.com/story/bitcoin-is-so-2021-heres-why-some-institutions-are-set-to-bypass-the-no-1-crypto-and-invest-in-ethereum-other-blockchains-next-year-11639690654?mod=home-page

by Frances Yue, Editor of Distributed Ledger, Marketwatch.com

Clayton Gardner, co-CEO of crypto investment management firm Titan, told Distributed Ledger that as crypto embraces broader adoption, he expects more institutions to bypass bitcoin and invest in other blockchains, such as Ethereum, Avalanche, and Terra in 2022. which all boast smart-contract features.

Bitcoin traditionally did not support complex smart contracts, which are computer programs stored on blockchains, though a major upgrade in November might have unlocked more potential.

“Bitcoin was originally seen as a macro speculative asset by many funds and for many it still is,” Gardner said. “If anything solidifies its use case, it’s a store of value. It’s not really used as originally intended, perhaps from a medium of exchange perspective.”

For institutions that are looking for blockchains that can “produce utility and some intrinsic value over time,” they might consider some other smart contract blockchains that have been driving the growth of decentralized finance and web 3.0, the third generation of the Internet, according to Gardner.

“Bitcoin is still one of the most secure blockchains, but I think layer-one, layer-two blockchains beyond Bitcoin, will handle the majority of transactions and activities from NFT (nonfungible tokens) to DeFi,“ Gardner said. “So I think institutions see that and insofar as they want to put capital to work in the coming months, I think that could be where they just pump the capital.”

Decentralized social media？

The price of Decentralized Social, or DeSo, a cryptocurrency powering a blockchain that supports decentralized social media applications, surged roughly 74% to about $164 from $94, after Deso was listed at Coinbase Pro on Monday, before it fell to about $95, according to CoinGecko.

In the eyes of Nader Al-Naji, head of the DeSo foundation, decentralized social media has the potential to be “a lot bigger” than decentralized finance.

“Today there are only a few companies that control most of what we see online,” Al-Naji told Distributed Ledger in an interview. But DeSo is “creating a lot of new ways for creators to make money,” Al-Naji said.

“If you find a creator when they’re small, or an influencer, you can invest in that, and then if they become bigger and more popular, you make money and they make and they get capital early on to produce their creative work,” according to AI-Naji.

BitClout, the first application that was created by AI-Naji and his team on the DeSo blockchain, had initially drawn controversy, as some found that they had profiles on the platform without their consent, while the application’s users were buying and selling tokens representing their identities. Such tokens are called “creator coins.”

AI-Naji responded to the controversy saying that DeSo now supports more than 200 social-media applications including Bitclout. “I think that if you don’t like those features, you now have the freedom to use any app you want. Some apps don’t have that functionality at all.”

But Before I get to the “selling monkeys to morons” quote,

I want to talk about

THE GOOD, THE BAD, AND THE UGLY

THE GOOD

My foray into Science 2.0 and then pondering what the movement into a Science 3.0 led me to an article by Dr. Vladimir Teif, who studies gene regulation and the nucleosome, as well as creating a worldwide group of scientists who discuss matters on chromatin and gene regulation in a journal club type format.

For more information on this Fragile Nucleosome journal club see https://generegulation.org/fragile-nucleosome/.

Fragile Nucleosome is an international community of scientists interested in chromatin and gene regulation. Fragile Nucleosome is active in several spaces: one is the Discord server where several hundred scientists chat informally on scientific matters. You can join the Fragile Nucleosome Discord server. Another activity of the group is the organization of weekly virtual seminars on Zoom. Our webinars are usually conducted on Wednesdays 9am Pacific time (5pm UK, 6pm Central Europe). Most previous seminars have been recorded and can be viewed at our YouTube channel. The schedule of upcoming webinars is shown below. Our third activity is the organization of weekly journal clubs detailed at a separate page (Fragile Nucleosome Journal Club).

His lab site is at https://generegulation.org/ but had published a paper describing what he felt what the #science2_0 to #science3_0 transition would look like (see his blog page on this at https://generegulation.org/open-science/).

This concept of science 3.0 he had coined back in 2009. As Dr Teif had mentioned

So essentially I first introduced this word Science 3.0 in 2009, and since then we did a lot to implement this in practice. The Twitter account @generegulation is also one of examples

This is curious as we still have an ill defined concept of what #science3_0 would look like but it is a good read nonetheless.

His paper, entitled “Science 3.0: Corrections to the Science 2.0 paradigm” is on the Cornell preprint server at https://arxiv.org/abs/1301.2522

Abstract

Science 3.0: Corrections to the Science 2.0 paradigm

Vladimir B. Teif

The concept of Science 2.0 was introduced almost a decade ago to describe the new generation of online-based tools for researchers allowing easier data sharing, collaboration and publishing. Although technically sound, the concept still does not work as expected. Here we provide a systematic line of arguments to modify the concept of Science 2.0, making it more consistent with the spirit and traditions of science and Internet. Our first correction to the Science 2.0 paradigm concerns the open-access publication models charging fees to the authors. As discussed elsewhere, we show that the monopoly of such publishing models increases biases and inequalities in the representation of scientific ideas based on the author’s income. Our second correction concerns post-publication comments online, which are all essentially non-anonymous in the current Science 2.0 paradigm. We conclude that scientific post-publication discussions require special anonymization systems. We further analyze the reasons of the failure of the current post-publication peer-review models and suggest what needs to be changed in Science 3.0 to convert Internet into a large journal club. [bold face added]

In this paper it is important to note the transition of a science 1.0, which involved hard copy journal publications usually only accessible in libraries to a more digital 2.0 format where data, papers, and ideas could be easily shared among networks of scientists.

As Dr. Teif states, the term “Science 2.0” had been coined back in 2009, and several influential journals including Science, Nature and Scientific American endorsed this term and suggested scientists to move online and their discussions online. However, even at present there are thousands on this science 2.0 platform, Dr Teif notes the number of scientists subscribed to many Science 2.0 networking groups such as on LinkedIn and ResearchGate have seemingly saturated over the years, with little new members in recent times.

The consensus is that science 2.0 networking is:

good because it multiplies the efforts of many scientists, including experts and adds to the scientific discourse unavailable on a 1.0 format
that online data sharing is good because it assists in the process of discovery (can see this evident with preprint servers, bio-curated databases, Github projects)
open-access publishing is beneficial because free access to professional articles and open-access will be the only publishing format in the future (although this is highly debatable as many journals are holding on to a type of “hybrid open access format” which is not truly open access
only sharing of unfinished works and critiques or opinions is good because it creates visibility for scientists where they can receive credit for their expert commentary

There are a few concerns on Science 3.0 Dr. Teif articulates:

A. Science 3.0 Still Needs Peer Review

Peer review of scientific findings will always be an imperative in the dissemination of well-done, properly controlled scientific discovery. As Science 2.0 relies on an army of scientific volunteers, the peer review process also involves an army of scientific experts who give their time to safeguard the credibility of science, by ensuring that findings are reliable and data is presented fairly and properly. It has been very evident, in this time of pandemic and the rapid increase of volumes of preprint server papers on Sars-COV2, that peer review is critical. Many of these papers on such preprint servers were later either retracted or failed a stringent peer review process.

Now many journals of the 1.0 format do not generally reward their peer reviewers other than the self credit that researchers use on their curriculum vitaes. Some journals, like the MDPI journal family, do issues peer reviewer credits which can be used to defray the high publication costs of open access (one area that many scientists lament about the open access movement; where the burden of publication cost lies on the individual researcher).

An issue which is highlighted is the potential for INFORMATION NOISE regarding the ability to self publish on Science 2.0 platforms.

The NEW BREED was born in 4/2012

An ongoing effort on this platform, https://pharmaceuticalintelligence.com/, is to establish a scientific methodology for curating scientific findings where one the goals is to assist to quell the information noise that can result from the massive amounts of new informatics and data occurring in the biomedical literature.

B. Open Access Publishing Model leads to biases and inequalities in the idea selection

The open access publishing model has been compared to the model applied by the advertising industry years ago and publishers then considered the journal articles as “advertisements”. However NOTHING could be further from the truth. In advertising the publishers claim the companies not the consumer pays for the ads. However in scientific open access publishing, although the consumer (libraries) do not pay for access the burden of BOTH the cost of doing the research and publishing the findings is now put on the individual researcher. Some of these publishing costs can be as high as $4000 USD per article, which is very high for most researchers. However many universities try to refund the publishers if they do open access publishing so it still costs the consumer and the individual researcher, limiting the cost savings to either.

However, this sets up a situation in which young researchers, who in general are not well funded, are struggling with the publication costs, and this sets up a bias or inequitable system which rewards the well funded older researchers and bigger academic labs.

C. Post publication comments and discussion require online hubs and anonymization systems

Many recent publications stress the importance of a post-publication review process or system yet, although many big journals like Nature and Science have their own blogs and commentary systems, these are rarely used. In fact they show that there are just 1 comment per 100 views of a journal article on these systems. In the traditional journals editors are the referees of comments and have the ability to censure comments or discourse. The article laments that comments should be easy to do on journals, like how easy it is to make comments on other social sites, however scientists are not offering their comments or opinions on the matter.

In a personal experience,

a well written commentary goes through editors which usually reject a comment like they were rejecting an original research article. Thus many scientists, I believe, after fashioning a well researched and referenced reply, do not get the light of day if not in the editor’s interests.

Therefore the need for anonymity is greatly needed and the lack of this may be the hindrance why scientific discourse is so limited on these types of Science 2.0 platforms. Platforms that have success in this arena include anonymous platforms like Wikipedia or certain closed LinkedIn professional platforms but more open platforms like Google Knowledge has been a failure.

A great example on this platform was a very spirited conversation on LinkedIn on genomics, tumor heterogeneity and personalized medicine which we curated from the LinkedIn discussion (unfortunately LinkedIn has closed many groups) seen here:

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

Issues in Personalized Medicine: Discussions of Intratumor Heterogeneity from the Oncology Pharma forum on LinkedIn

In this discussion, it was surprising that over a weekend so many scientists from all over the world contributed to a great discussion on the topic of tumor heterogeneity.

But many feel such discussions would be safer if they were anonymized. However then researchers do not get any credit for their opinions or commentaries.

A Major problem is how to take the intangible and make them into tangible assets which would both promote the discourse as well as reward those who take their time to improve scientific discussion.

This is where something like NFTs or a decentralized network may become important!

See

https://pharmaceuticalintelligence.com/portfolio-of-ip-assets/

UPDATED 5/09/2022

Below is an online @TwitterSpace Discussion we had with some young scientists who are just starting out and gave their thoughts on what SCIENCE 3.0 and the future of dissemination of science might look like, in light of this new Meta Verse. However we have to define each of these terms in light of Science and not just the Internet as merely a decentralized marketplace for commonly held goods.

This online discussion was tweeted out and got a fair amount of impressions (60) as well as interactors (50).

Set a reminder for my upcoming Space! https://t.co/7mOpScZfGN @Pharma_BI @PSMTempleU #science3_0 @science2_0
— Stephen J Williams (@StephenJWillia2) April 28, 2022

For the recording on both Twitter as well as on an audio format please see below

<blockquote class=”twitter-tweet”><p lang=”en” dir=”ltr”>Set a reminder for my upcoming Space! <a href=”https://t.co/7mOpScZfGN”>https://t.co/7mOpScZfGN</a> <a href=”https://twitter.com/Pharma_BI?ref_src=twsrc%5Etfw”>@Pharma_BI</a> <a href=”https://twitter.com/PSMTempleU?ref_src=twsrc%5Etfw”>@PSMTempleU</a> <a href=”https://twitter.com/hashtag/science3_0?src=hash&ref_src=twsrc%5Etfw”>#science3_0</a> <a href=”https://twitter.com/science2_0?ref_src=twsrc%5Etfw”>@science2_0</a></p>— Stephen J Williams (@StephenJWillia2) <a href=”https://twitter.com/StephenJWillia2/status/1519776668176502792?ref_src=twsrc%5Etfw”>April 28, 2022</a></blockquote> <script async src=”https://platform.twitter.com/widgets.js” charset=”utf-8″></script>

To introduce this discussion first a few startoff material which will fram this discourse

The Intenet and the Web is rapidly adopting a new “Web 3.0” format, with decentralized networks, enhanced virtual experiences, and greater interconnection between people. Here we start the discussion what will the move from Science 2.0, where dissemination of scientific findings was revolutionized and piggybacking on Web 2.0 or social media, to a Science 3.0 format. And what will it involve or what paradigms will be turned upside down?

Old Science 1.0 is still the backbone of all scientific discourse, built on the massive amount of experimental and review literature. However this literature was in analog format, and we moved to a more accesible digital open access format for both publications as well as raw data. However as there was a structure for 1.0, like the Dewey decimal system and indexing, 2.0 made science more accesible and easier to search due to the newer digital formats. Yet both needed an organizing structure; for 1.0 that was the scientific method of data and literature organization with libraries as the indexers. In 2.0 this relied on an army mostly of volunteers who did not have much in the way of incentivization to co-curate and organize the findings and massive literature.

Each version of Science has their caveats: their benefits as well as deficiencies. This curation and the ongoing discussion is meant to solidy the basis for the new format, along with definitions and determination of structure.

We had high hopes for Science 2.0, in particular the smashing of data and knowledge silos. However the digital age along with 2.0 platforms seemed to excaccerbate this somehow. We still are critically short on analysis!

We really need people and organizations to get on top of this new Web 3.0 or metaverse so the similar issues do not get in the way: namely we need to create an organizing structure (maybe as knowledgebases), we need INCENTIVIZED co-curators, and we need ANALYSIS… lots of it!!

Are these new technologies the cure or is it just another headache?

There were a few overarching themes whether one was talking about AI, NLP, Virtual Reality, or other new technologies with respect to this new meta verse and a concensus of Decentralized, Incentivized, and Integrated was commonly expressed among the attendees

The Following are some slides from representative Presentations

Electronic Scientific AGORA: Comment Exchanges by Global Scientists on Articles published in the Open Access Journal @pharmaceuticalintelligence.com – Four Case Studies

eScientific Publishing a Case in Point: Evolution of Platform Architecture Methodologies and of Intellectual Property Development (Content Creation by Curation) Business Model

e-Scientific Publishing: The Competitive Advantage of a Powerhouse for Curation of Scientific Findings and Methodology Development for e-Scientific Publishing – LPBI Group, A Case in Point

@PharmaceuticalIntelligence.com – A Case Study on the LEADER in Curation of Scientific Findings

Real Time Coverage @BIOConvention #BIO2019: Falling in Love with Science: Championing Science for Everyone, Everywhere

Old Industrial Revolution Paradigm of Education Needs to End: How Scientific Curation Can Transform Education

Posted in Biomarkers: Inflammation, Coronavirus Gene Expression, COVID-19, COVID-19, COVID-19: Pandemic Surgery Guidance, Economic Impact of Coronavirus Pandemic, Immune-Mediation (independent immunopathology: lung and reticuloendothelial system), number of asymptomatic infections, Population Health Management, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, SARS-CoV-2, SARS-COV2 Hijacking the Complement and Coagulation Systems, Scientific & Biotech Conferences: Press Coverage, Seasonality & Environmental Factors in Resurgence, Small Molecules in Development of Therapeutic Drugs, Treatment Protocols for COVID-19, Vaccinology, Virology, Virus Infective Acute Respiratory Syndrome: SARS-CoV, tagged covid19, SARS-CoV-2, vaccine development on August 12, 2020| Leave a Comment »

Online Event: Vaccine matters: Can we cure coronavirus? An AAAS Webinar

Online Event: Vaccine matters: Can we cure coronavirus? An AAAS Webinar on COVID19: 8/12/2020

Reporter: Stephen J. Williams. PhD

Source: Online Event

Top on the world’s want list right now is a coronavirus vaccine. There is plenty of speculation about how and when this might become a reality, but clear answers are scarce.Science/AAAS, the world’s leading scientific organization and publisher of the Science family of journals, brings together experts in the field of coronavirus vaccine research to answer the public’s most pressing questions: What vaccines are being developed? When are we likely to get them? Are they safe? And most importantly, will they work?

link: https://view6.workcast.net/AuditoriumAuthenticator.aspx?cpak=1836435787247718&pak=8073702641735492

Presenters

Speaker: Sarah Gilbert, Ph.D.

University of Oxford
Oxford, UK
View Bio

Speaker: Kizzmekia Corbett, Ph.D.

National Institute of Allergy and Infectious Diseases, NIH
Bethesda, MD
View Bio

Speaker: Kathryn M. Edwards, M.D.

Vanderbilt Vaccine Research Program
Nashville, TN
View Bio

Speaker: Jon Cohen

Science/AAAS
San Diego, CA
View Bio

Moderator: Sean Sanders, Ph.D.

Science/AAAS
Washington, DC
View Moderator Bio

Posted in Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Artificial Intelligence in CANCER, Artificial Intelligence in Health Care - Tools & Innovations, Artificial Intelligence in Medicine - Application for Diagnosis, Artificial Intelligence in Medicine - Applications in Therapeutics, Big Data, BioIT: BioInformatics, Biological Engineering, Biological Networks, Biological Networks, Gene Regulation and Evolution, Breast Cancer - impalpable breast lesions, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, cancer metabolism, Deep Learning in Pathology, Genomic Expression, Glioblastoma, Inflammasome, Intelligent Information Systems, Personalized and Precision Medicine & Genomic Research, Prostate Cancer: Monitoring vs Treatment, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Single Cell Genomics, Single-cell sequencing, tumor microenvironment, tagged #AACR20, AACR, Artificial intelligence, Cancer Genomics, functional proteomics, Machine Learning, mass spectrometry, natural language processing, Proteomics, tumor microenvironment on June 24, 2020| Leave a Comment »

Live Notes, Real Time Conference Coverage @AACR #AACR20: Tuesday June 23, 2020 3:00 PM-5:30 PM Educational Sessions

Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 3:00 PM-5:30 PM Educational Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

Posted in AI-assisted Cardiac MRI, Anaerobic Glycolysis, Artificial Intelligence - Breakthroughs in Theories and Technologies, Artificial Intelligence - General, Artificial Intelligence Applications in Health Care, Big Data, Big Data & Analytics, BioBanking, Biochemical pathways, Biological Networks, Gene Regulation and Evolution, Breast Cancer - impalpable breast lesions, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Genomics, Cancer Informatics, cancer metabolism, Cancer Prevention: Research & Programs, Curation, Data Science, Deep Learning in Pathology, Intelligent Information Systems, Metabolic Immuno-Oncology, Metabolism, Oxidative phosphorylation, Phosphorylation, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Scientific & Biotech Conferences: Press Coverage, Scientific Publishing, Scientist: Career considerations, Signaling, Transformative Technologies in Healthcare, tumor microenvironment, tagged #AACR20, AACR, AI, Artificial intelligence, breast cancer, cancer conference, Cancer Genomics, Cancer immunology, cancer immunotherapeutics, cancer metabolic pathways, cancer metabolism, cancer metastasis, Cancer research, cancer researcy funding, cancerinformatics, checkpoint inhibitors, Conference Coverage with Social Media, deep learning, deep learning in drug discovery, Digital pathology, early onset cancer, health disparities, immune checkpoint, Natural killer cell, real time conference coverage, RNASeq, RNASeq Data Analysis on June 23, 2020| Leave a Comment »

Register for FREE at https://www.aacr.org/

uesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session

Tumor Biology, Bioinformatics and Systems Biology

The Clinical Proteomic Tumor Analysis Consortium: Resources and Data Dissemination

This session will provide information regarding methodologic and computational aspects of proteogenomic analysis of tumor samples, particularly in the context of clinical trials. Availability of comprehensive proteomic and matching genomic data for tumor samples characterized by the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) program will be described, including data access procedures and informatic tools under development. Recent advances on mass spectrometry-based targeted assays for inclusion in clinical trials will also be discussed.

Amanda G Paulovich, Shankha Satpathy, Meenakshi Anurag, Bing Zhang, Steven A Carr

Methods and tools for comprehensive proteogenomic characterization of bulk tumor to needle core biopsies

Shankha Satpathy

TCGA has 11,000 cancers with >20,000 somatic alterations but only 128 proteins as proteomics was still young field
CPTAC is NCI proteomic effort
Chemical labeling approach now method of choice for quantitative proteomics
Looked at ovarian and breast cancers: to measure PTM like phosphorylated the sample preparation is critical

Data access and informatics tools for proteogenomics analysis

Bing Zhang

Raw and processed data (raw MS data) with linked clinical data can be extracted in CPTAC
Python scripts are available for bioinformatic programming

Pathways to clinical translation of mass spectrometry-based assays

Meenakshi Anurag

· Using kinase inhibitor pulldown (KIP) assay to identify unique kinome profiles

· Found single strand break repair defects in endometrial luminal cases, especially with immune checkpoint prognostic tumors

· Paper: JNCI 2019 analyzed 20,000 genes correlated with ET resistant in luminal B cases (selected for a list of 30 genes)

· Validated in METABRIC dataset

· KIP assay uses magnetic beads to pull out kinases to determine druggable kinases

· Looked in xenografts and was able to pull out differential kinomes

· Matched with PDX data so good clinical correlation

· Were able to detect ESR1 fusion correlated with ER+ tumors

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session

Survivorship

Artificial Intelligence and Machine Learning from Research to the Cancer Clinic

The adoption of omic technologies in the cancer clinic is giving rise to an increasing number of large-scale high-dimensional datasets recording multiple aspects of the disease. This creates the need for frameworks for translatable discovery and learning from such data. Like artificial intelligence (AI) and machine learning (ML) for the cancer lab, methods for the clinic need to (i) compare and integrate different data types; (ii) scale with data sizes; (iii) prove interpretable in terms of the known biology and batch effects underlying the data; and (iv) predict previously unknown experimentally verifiable mechanisms. Methods for the clinic, beyond the lab, also need to (v) produce accurate actionable recommendations; (vi) prove relevant to patient populations based upon small cohorts; and (vii) be validated in clinical trials. In this educational session we will present recent studies that demonstrate AI and ML translated to the cancer clinic, from prognosis and diagnosis to therapy.
NOTE: Dr. Fish’s talk is not eligible for CME credit to permit the free flow of information of the commercial interest employee participating.

Ron C. Anafi, Rick L. Stevens, Orly Alter, Guy Fish

Overview of AI approaches in cancer research and patient care

Rick L. Stevens

Deep learning is less likely to saturate as data increases
Deep learning attempts to learn multiple layers of information
The ultimate goal is prediction but this will be the greatest challenge for ML
ML models can integrate data validation and cross database validation
What limits the performance of cross validation is the internal noise of data (reproducibility)
Learning curves: not the more data but more reproducible data is important
Neural networks can outperform classical methods
Important to measure validation accuracy in training set. Class weighting can assist in development of data set for training set especially for unbalanced data sets

Discovering genome-scale predictors of survival and response to treatment with multi-tensor decompositions

Orly Alter

Finding patterns using SVD component analysis. Gene and SVD patterns match 1:1
Comparative spectral decompositions can be used for global datasets
Validation of CNV data using this strategy
Found Ras, Shh and Notch pathways with altered CNV in glioblastoma which correlated with prognosis
These predictors was significantly better than independent prognostic indicator like age of diagnosis

Identifying targets for cancer chronotherapy with unsupervised machine learning

Ron C. Anafi

Many clinicians have noticed that some patients do better when chemo is given at certain times of the day and felt there may be a circadian rhythm or chronotherapeutic effect with respect to side effects or with outcomes
ML used to determine if there is indeed this chronotherapy effect or can we use unstructured data to determine molecular rhythms?
Found a circadian transcription in human lung
Most dataset in cancer from one clinical trial so there might need to be more trials conducted to take into consideration circadian rhythms

Stratifying patients by live-cell biomarkers with random-forest decision trees

Guy Fish CEO Cellanyx Diagnostics

Some clinicians feel we may be overdiagnosing and overtreating certain cancers, especially the indolent disease
Platform published in 2018 paper (Clinical Proof-of-concept of a Novel Platform Utilizing Biopsy-derived Live Single Cells, Phenotypic Biomarkers, and Machine Learning Toward a Precision Risk Stratification Test for Prostate Cancer Grade Groups 1 and 2 (Gleason 3 + 3 and 3 + 4)
Problem: their information knowledgebase based on cultured cells
Their platform first used to stratify prostate cancer

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Virtual Educational Session

Tumor Biology, Molecular and Cellular Biology/Genetics, Bioinformatics and Systems Biology, Prevention Research

The Wound Healing that Never Heals: The Tumor Microenvironment (TME) in Cancer Progression

This educational session focuses on the chronic wound healing, fibrosis, and cancer “triad.” It emphasizes the similarities and differences seen in these conditions and attempts to clarify why sustained fibrosis commonly supports tumorigenesis. Importance will be placed on cancer-associated fibroblasts (CAFs), vascularity, extracellular matrix (ECM), and chronic conditions like aging. Dr. Dvorak will provide an historical insight into the triad field focusing on the importance of vascular permeability. Dr. Stewart will explain how chronic inflammatory conditions, such as the aging tumor microenvironment (TME), drive cancer progression. The session will close with a review by Dr. Cukierman of the roles that CAFs and self-produced ECMs play in enabling the signaling reciprocity observed between fibrosis and cancer in solid epithelial cancers, such as pancreatic ductal adenocarcinoma.

Harold F Dvorak, Sheila A Stewart, Edna Cukierman

The importance of vascular permeability in tumor stroma generation and wound healing

Harold F Dvorak

Aging in the driver’s seat: Tumor progression and beyond

Sheila A Stewart

Why won’t CAFs stay normal?

Edna Cukierman

Tuesday, June 23

3:00 PM – 5:00 PM EDT

Live Notes, Real Time Conference Coverage AACR 2020 #AACR20: Tuesday June 23, 2020 Noon-2:45 Educational Sessions

Live Notes, Real Time Conference Coverage AACR 2020: Tuesday June 23, 2020 Noon-2:45 Educational Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

Register for FREE at https://www.aacr.org/

Presidential Address

Elaine R Mardis, William N Hait

Welcome and introduction

William N Hait

Improving diagnostic yield in pediatric cancer precision medicine

Elaine R Mardis

Advent of genomics have revolutionized how we diagnose and treat lung cancer
We are currently needing to understand the driver mutations and variants where we can personalize therapy
PD-L1 and other checkpoint therapy have not really been used in pediatric cancers even though CAR-T have been successful
The incidence rates and mortality rates of pediatric cancers are rising
Large scale study of over 700 pediatric cancers show cancers driven by epigenetic drivers or fusion proteins. Need for transcriptomics. Also study demonstrated that we have underestimated germ line mutations and hereditary factors.
They put together a database to nominate patients on their IGM Cancer protocol. Involves genetic counseling and obtaining germ line samples to determine hereditary factors. RNA and protein are evaluated as well as exome sequencing. RNASeq and Archer Dx test to identify driver fusions
PECAN curated database from St. Jude used to determine driver mutations. They use multiple databases and overlap within these databases and knowledge base to determine or weed out false positives
They have used these studies to understand the immune infiltrate into recurrent cancers (CytoCure)
They found 40 germline cancer predisposition genes, 47 driver somatic fusion proteins, 81 potential actionable targets, 106 CNV, 196 meaningful somatic driver mutations

Tuesday, June 23

12:00 PM – 12:30 PM EDT

Awards and Lectures

NCI Director’s Address

Norman E Sharpless, Elaine R Mardis

Tumor Immunology and Immunotherapy for Nonimmunologists: Innovation and Discovery in Immune-Oncology

Introduction: Elaine Mardis

NCI Director Address: Norman E Sharpless

They are functioning well at NCI with respect to grant reviews, research, and general functions in spite of the COVID pandemic and the massive demonstrations on also focusing on the disparities which occur in cancer research field and cancer care
There are ongoing efforts at NCI to make a positive difference in racial injustice, diversity in the cancer workforce, and for patients as well
Need a diverse workforce across the cancer research and care spectrum
Data show that areas where the clinicians are successful in putting African Americans on clinical trials are areas (geographic and site specific) where health disparities are narrowing
Grants through NCI new SeroNet for COVID-19 serologic testing funded by two RFAs through NIAD (RFA-CA-30-038 and RFA-CA-20-039) and will close on July 22, 2020

Tuesday, June 23

12:45 PM – 1:46 PM EDT

Virtual Educational Session

Immunology, Tumor Biology, Experimental and Molecular Therapeutics, Molecular and Cellular Biology/Genetics

This educational session will update cancer researchers and clinicians about the latest developments in the detailed understanding of the types and roles of immune cells in tumors. It will summarize current knowledge about the types of T cells, natural killer cells, B cells, and myeloid cells in tumors and discuss current knowledge about the roles these cells play in the antitumor immune response. The session will feature some of the most promising up-and-coming cancer immunologists who will inform about their latest strategies to harness the immune system to promote more effective therapies.

Judith A Varner, Yuliya Pylayeva-Gupta

Introduction

Judith A Varner

New techniques reveal critical roles of myeloid cells in tumor development and progression

Different type of cells are becoming targets for immune checkpoint like myeloid cells
In T cell excluded or desert tumors T cells are held at periphery so myeloid cells can infiltrate though so macrophages might be effective in these immune t cell naïve tumors, macrophages are most abundant types of immune cells in tumors
CXCLs are potential targets
PI3K delta inhibitors,
Reduce the infiltrate of myeloid tumor suppressor cells like macrophages
When should we give myeloid or T cell therapy is the issue

Judith A Varner

Novel strategies to harness T-cell biology for cancer therapy

Positive and negative roles of B cells in cancer

Yuliya Pylayeva-Gupta

New approaches in cancer immunotherapy: Programming bacteria to induce systemic antitumor immunity

Tuesday, June 23

12:45 PM – 1:46 PM EDT

Virtual Educational Session

Cancer Chemistry

Chemistry to the Clinic: Part 2: Irreversible Inhibitors as Potential Anticancer Agents

There are numerous examples of highly successful covalent drugs such as aspirin and penicillin that have been in use for a long period of time. Despite historical success, there was a period of reluctance among many to purse covalent drugs based on concerns about toxicity. With advances in understanding features of a well-designed covalent drug, new techniques to discover and characterize covalent inhibitors, and clinical success of new covalent cancer drugs in recent years, there is renewed interest in covalent compounds. This session will provide a broad look at covalent probe compounds and drug development, including a historical perspective, examination of warheads and electrophilic amino acids, the role of chemoproteomics, and case studies.

Benjamin F Cravatt, Richard A. Ward, Sara J Buhrlage

Discovering and optimizing covalent small-molecule ligands by chemical proteomics

Benjamin F Cravatt

Multiple approaches are being investigated to find new covalent inhibitors such as: 1) cysteine reactivity mapping, 2) mapping cysteine ligandability, 3) and functional screening in phenotypic assays for electrophilic compounds
Using fluorescent activity probes in proteomic screens; have broad useability in the proteome but can be specific
They screened quiescent versus stimulated T cells to determine reactive cysteines in a phenotypic screen and analyzed by MS proteomics (cysteine reactivity profiling); can quantitate 15000 to 20,000 reactive cysteines
Isocitrate dehydrogenase 1 and adapter protein LCP-1 are two examples of changes in reactive cysteines they have seen using this method
They use scout molecules to target ligands or proteins with reactive cysteines
For phenotypic screens they first use a cytotoxic assay to screen out toxic compounds which just kill cells without causing T cell activation (like IL10 secretion)
INTERESTINGLY coupling these MS reactive cysteine screens with phenotypic screens you can find NONCANONICAL mechanisms of many of these target proteins (many of the compounds found targets which were not predicted or known)

Electrophilic warheads and nucleophilic amino acids: A chemical and computational perspective on covalent modifier

The covalent targeting of cysteine residues in drug discovery and its application to the discovery of Osimertinib

Richard A. Ward

Cysteine activation: thiolate form of cysteine is a strong nucleophile
Thiolate form preferred in polar environment
Activation can be assisted by neighboring residues; pKA will have an effect on deprotonation
pKas of cysteine vary in EGFR
cysteine that are too reactive give toxicity while not reactive enough are ineffective

Accelerating drug discovery with lysine-targeted covalent probes

Tuesday, June 23

12:45 PM – 2:15 PM EDT

Virtual Educational Session

Molecular and Cellular Biology/Genetics

Virtual Educational Session

Tumor Biology, Immunology

Metabolism and Tumor Microenvironment

This Educational Session aims to guide discussion on the heterogeneous cells and metabolism in the tumor microenvironment. It is now clear that the diversity of cells in tumors each require distinct metabolic programs to survive and proliferate. Tumors, however, are genetically programmed for high rates of metabolism and can present a metabolically hostile environment in which nutrient competition and hypoxia can limit antitumor immunity.

Jeffrey C Rathmell, Lydia Lynch, Mara H Sherman, Greg M Delgoffe

T-cell metabolism and metabolic reprogramming antitumor immunity

Jeffrey C Rathmell

Introduction

Jeffrey C Rathmell

Metabolic functions of cancer-associated fibroblasts

Mara H Sherman

Tumor microenvironment metabolism and its effects on antitumor immunity and immunotherapeutic response

Greg M Delgoffe

Multiple metabolites, reactive oxygen species within the tumor microenvironment; is there heterogeneity within the TME metabolome which can predict their ability to be immunosensitive
Took melanoma cells and looked at metabolism using Seahorse (glycolysis): and there was vast heterogeneity in melanoma tumor cells; some just do oxphos and no glycolytic metabolism (inverse Warburg)
As they profiled whole tumors they could separate out the metabolism of each cell type within the tumor and could look at T cells versus stromal CAFs or tumor cells and characterized cells as indolent or metabolic
T cells from hyerglycolytic tumors were fine but from high glycolysis the T cells were more indolent
When knock down glucose transporter the cells become more glycolytic
If patient had high oxidative metabolism had low PDL1 sensitivity
Showed this result in head and neck cancer as well
Metformin a complex 1 inhibitor which is not as toxic as most mito oxphos inhibitors the T cells have less hypoxia and can remodel the TME and stimulate the immune response
Metformin now in clinical trials
T cells though seem metabolically restricted; T cells that infiltrate tumors are low mitochondrial phosph cells
T cells from tumors have defective mitochondria or little respiratory capacity
They have some preliminary findings that metabolic inhibitors may help with CAR-T therapy

Obesity, lipids and suppression of anti-tumor immunity

Lydia Lynch

Hypothesis: obesity causes issues with anti tumor immunity
Less NK cells in obese people; also produce less IFN gamma
RNASeq on NOD mice; granzymes and perforins at top of list of obese downregulated
Upregulated genes that were upregulated involved in lipid metabolism
All were PPAR target genes
NK cells from obese patients takes up palmitate and this reduces their glycolysis but OXPHOS also reduced; they think increased FFA basically overloads mitochondria
PPAR alpha gamma activation mimics obesity

Tuesday, June 23

12:45 PM – 2:45 PM EDT

Virtual Educational Session

Clinical Research Excluding Trials

The Evolving Role of the Pathologist in Cancer Research

Long recognized for their role in cancer diagnosis and prognostication, pathologists are beginning to leverage a variety of digital imaging technologies and computational tools to improve both clinical practice and cancer research. Remarkably, the emergence of artificial intelligence (AI) and machine learning algorithms for analyzing pathology specimens is poised to not only augment the resolution and accuracy of clinical diagnosis, but also fundamentally transform the role of the pathologist in cancer science and precision oncology. This session will discuss what pathologists are currently able to achieve with these new technologies, present their challenges and barriers, and overview their future possibilities in cancer diagnosis and research. The session will also include discussions of what is practical and doable in the clinic for diagnostic and clinical oncology in comparison to technologies and approaches primarily utilized to accelerate cancer research.

Jorge S Reis-Filho, Thomas J Fuchs, David L Rimm, Jayanta Debnath

Cancer Increases in Younger Populations: Where Are They Coming from?

Tuesday, June 23

12:45 PM – 2:45 PM EDT

High-dimensional imaging technologies in cancer research

David L Rimm

Using old methods and new methods; so cell counting you use to find the cells then phenotype; with quantification like with Aqua use densitometry of positive signal to determine a threshold to determine presence of a cell for counting
Hiplex versus multiplex imaging where you have ten channels to measure by cycling of flour on antibody (can get up to 20plex)
Hiplex can be coupled with Mass spectrometry (Imaging Mass spectrometry, based on heavy metal tags on mAbs)
However it will still take a trained pathologist to define regions of interest or field of desired view

Introduction

Jayanta Debnath

Challenges and barriers of implementing AI tools for cancer diagnostics

Jorge S Reis-Filho

Implementing robust digital pathology workflows into clinical practice and cancer research

Jayanta Debnath

Invited Speaker

Thomas J Fuchs

Founder of spinout of Memorial Sloan Kettering
Separates AI from computational algothimic
Dealing with not just machines but integrating human intelligence
Making decision for the patients must involve human decision making as well
How do we get experts to do these decisions faster
AI in pathology: what is difficult? =è sandbox scenarios where machines are great,; curated datasets; human decision support systems or maps; or try to predict nature
1) learn rules made by humans; human to human scenario 2)constrained nature 3)unconstrained nature like images and or behavior 4) predict nature response to nature response to itself
In sandbox scenario the rules are set in stone and machines are great like chess playing
In second scenario can train computer to predict what a human would predict
So third scenario is like driving cars
System on constrained nature or constrained dataset will take a long time for commuter to get to decision
Fourth category is long term data collection project
He is finding it is still finding it is still is difficult to predict nature so going from clinical finding to prognosis still does not have good predictability with AI alone; need for human involvement
End to end partnering (EPL) is a new way where humans can get more involved with the algorithm and assist with the problem of constrained data
An example of a workflow for pathology would be as follows from Campanella et al 2019 Nature Medicine: obtain digital images (they digitized a million slides), train a massive data set with highthroughput computing (needed a lot of time and big software developing effort), and then train it using input be the best expert pathologists (nature to human and unconstrained because no data curation done)
Led to first clinically grade machine learning system (Camelyon16 was the challenge for detecting metastatic cells in lymph tissue; tested on 12,000 patients from 45 countries)
The first big hurdle was moving from manually annotated slides (which was a big bottleneck) to automatically extracted data from path reports).
Now problem is in prediction: How can we bridge the gap from predicting humans to predicting nature?
With an AI system pathologist drastically improved the ability to detect very small lesions

Virtual Educational Session

Epidemiology

Incidence rates of several cancers (e.g., colorectal, pancreatic, and breast cancers) are rising in younger populations, which contrasts with either declining or more slowly rising incidence in older populations. Early-onset cancers are also more aggressive and have different tumor characteristics than those in older populations. Evidence on risk factors and contributors to early-onset cancers is emerging. In this Educational Session, the trends and burden, potential causes, risk factors, and tumor characteristics of early-onset cancers will be covered. Presenters will focus on colorectal and breast cancer, which are among the most common causes of cancer deaths in younger people. Potential mechanisms of early-onset cancers and racial/ethnic differences will also be discussed.

Stacey A. Fedewa, Xavier Llor, Pepper Jo Schedin, Yin Cao

Cancers that are and are not increasing in younger populations

Stacey A. Fedewa

Early onset cancers, pediatric cancers and colon cancers are increasing in younger adults
Younger people are more likely to be uninsured and these are there most productive years so it is a horrible life event for a young adult to be diagnosed with cancer. They will have more financial hardship and most (70%) of the young adults with cancer have had financial difficulties. It is very hard for women as they are on their childbearing years so additional stress
Types of early onset cancer varies by age as well as geographic locations. For example in 20s thyroid cancer is more common but in 30s it is breast cancer. Colorectal and testicular most common in US.
SCC is decreasing by adenocarcinoma of the cervix is increasing in women’s 40s, potentially due to changing sexual behaviors
Breast cancer is increasing in younger women: maybe etiologic distinct like triple negative and larger racial disparities in younger African American women
Increased obesity among younger people is becoming a factor in this increasing incidence of early onset cancers

Press Coverage

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM

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

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

Live Conference Coverage AACR 2020 in Real Time: Monday June 22, 2020 Late Day Sessions

Posted in Apoptosis, CANCER BIOLOGY & Innovations in Cancer Therapy, Cell Biology, Cell Biology, Signaling & Cell Circuits, Cell death pathways, Cell Processing System in Cell Therapy Process Development, Drug Carrier Design, Drug Delivery Platform Technology, Funding Opportunities for Cancer Research, Proteolysis, Proteosome, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Small Molecules in Development of Therapeutic Drugs, Ubiquitin, tagged Cancer, E3 Ligase, PROTAC, protein degradation on June 22, 2020| Leave a Comment »

Live Conference Coverage AACR 2020 in Real Time: Monday June 22, 2020 Late Day Sessions

Reporter: Stephen J. Williams, PhD

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Carcinogens at Home: Science and Pathways to Prevention

Register for FREE at https://www.aacr.org/

AACR VIRTUAL ANNUAL MEETING II

June 22-24: Free Registration for AACR Members, the Cancer Community, and the Public
This virtual meeting will feature more than 120 sessions and 4,000 e-posters, including sessions on cancer health disparities and the impact of COVID-19 on clinical trials

This Virtual Meeting is Part II of the AACR Annual Meeting. Part I was held online in April and was centered only on clinical findings. This Part II of the virtual meeting will contain all the Sessions and Abstracts pertaining to basic and translational cancer research as well as clinical trial findings.

REGISTER NOW

Virtual Educational Session

Prevention Research, Science Policy, Epidemiology, Survivorship

Chemicals known to cause cancer are used and released to the environment in large volumes, exposing people where they live, work, play, and go to school. The science establishing an important role for such exposures in the development of cancers continues to strengthen, yet cancer prevention researchers are largely unfamiliar with the data drawn upon in identifying carcinogens and making decisions about their use. Characterizing and reducing harmful exposures and accelerating the devel

Julia Brody, Kathryn Z. Guyton, Polly J. Hoppin, Bill Walsh, Mary H. Ward

EMT Still Matters: Let’s Explore! – Dedicated to the Memory of Isaiah J. Fidler

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Tumor Biology, Molecular and Cellular Biology/Genetics, Clinical Research Excluding Trials

During carcinoma progression, initially benign epithelial cells acquire the ability to invade locally and disseminate to distant tissues by activating epithelial-mesenchymal transition (EMT). EMT is a cellular process during which epithelial cells lose their epithelial features and acquire mesenchymal phenotypes and behavior. Growing evidence supports the notion that EMT programs during tumor progression are usually activated to various extents and often partial and reversible, thus pr

Jean-Paul Thiery, Heide L Ford, Jing Yang, Geert Berx

One of These Things Is Not Like the Other: The Many Faces of Senescence in Cancer

Monday, June 22

1:30 PM – 3:00 PM EDT

Virtual Educational Session

Tumor Biology, Experimental and Molecular Therapeutics, Molecular and Cellular Biology/Genetics

Cellular senescence is a stable cell growth arrest that is broadly recognized to act as a barrier against tumorigenesis. Senescent cells acquire a senescence-associated secretory phenotype (SASP), a transcriptional response involving the secretion of inflammatory cytokines, immune modulators, and proteases that can shape the tumor microenvironment. The SASP can initially stimulate tumor immune surveillance and reinforce growth arrest. However, if senescent cells are not removed by the

Clemens A Schmitt, Andrea Alimonti, René Bernards

Recent Advances in Applications of Cell-Free DNA

Monday, June 22

1:30 PM – 3:00 PM EDT

Virtual Educational Session

Clinical Research Excluding Trials, Molecular and Cellular Biology/Genetics

The focus of this educational session will be on recent developments in cell-free DNA (cfDNA) analysis that have the potential to impact the care of cancer patients. Tumors continually shed DNA into the circulation, where it can be detected as circulating tumor DNA (ctDNA). Analysis of ctDNA has become a routine part of care for a subset of patients with advanced malignancies. However, there are a number of exciting potential applications that have promising preliminary data but that h

Michael R Speicher, Maximilian Diehn, Aparna Parikh

Translating Genetics and Genomics to the Clinic and Population

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Methods Workshop

Clinical Research Excluding Trials, Clinical Trials, Experimental and Molecular Therapeutics, Molecular and Cellular Biology/Genetics

This session will describe how advances in understanding cancer genomes and in genetic testing technologies are being translated to the clinic. The speakers will illustrate the clinical impact of genomic discoveries for diagnostics and treatment of common tumor types in adults and in children. Cutting-edge technologies for characterization of patient and tumor genomes will be described. New insights into the importance of patient factors for cancer risk and outcome, including predispos

Heather L. Hampel, Gordana Raca, Jaclyn Biegel, Jeffrey M Trent

Under-representation in Clinical Trials and the Implications for Drug Development

Monday, June 22

1:30 PM – 3:22 PM EDT

Virtual Educational Session

Regulatory Science and Policy, Drug Development, Epidemiology

The U.S. Food and Drug Administration relies on data from clinical trials to determine whether medical products are safe and effective. Ideally, patients enrolled in those trials are representative of the population in which the product will be used if approved, including people of different ages, races, ethnic groups, and genders. Unfortunately, with few patients enrolling in clinical trials, many groups are not well-represented in clinical trials. This session will explore challenges

Ajay K. Nooka, Nicole J. Gormley, Kenneth C Anderson, Ruben A. Mesa, Daniel J. George, Yelak Biru, RADM Richardae Araojo, Lola A. Fashoyin-Aje

Targeted Protein Degradation: Target Validation Tools and Therapeutic Opportunity

Monday, June 22

3:45 PM – 5:45 PM EDT

Virtual Educational Session

Cancer Chemistry

This educational session will cover the exciting emerging field of targeted protein degradation. Key learning topics will include: 1. an introduction to the technology and its relevance to oncology; 2. PROTACS, degraders, and CELMoDs; 3. enzymology and protein-protein interactions in targeted protein degraders; 4. examples of differentiated biology due to degradation vs. inhibition; 5. how to address questions of specificity; and 6. how the field is approaching challenges in optimizing therapies

George Burslem, Mary Matyskiela, Lyn H. Jones, Stewart L Fisher, Andrew J Phillips

Data-Driven Approaches for Choosing Combinatorial Therapies

Monday, June 22

3:45 PM – 5:45 PM EDT

Virtual Educational Session

Bioinformatics and Systems Biology, Experimental and Molecular Therapeutics, Drug Development, Molecular and Cellular Biology/Genetics

Obstacles and opportunities for protein degradation drug discovery

Lyn H. Jones

PROTACs ubiquitin mediated by E3 ligases; first discovered by DeShaies and targeted to specific proteins
PROTACs used in drug discovery against a host of types of targets including kinases and membrane receptors
PROTACs can be modular but lack molecular structural activity relationships
can use chemical probes for target validation
four requirements: candidate exposure at site of action (for example lipophilicity for candidates needed to cross membranes and accumulate in lysosomes), target engagement (ternary occupancy as measured by FRET), functional pharmacology, relevant phenotype
PROTACs hijack the proteosomal degradation system

Proteolysis-targeting chimeras as therapeutics and tools for biological discovery

George Burslem

first PROTAC developed to coopt the VHL ubiquitin ligase system which degrades HIF1alpha but now modified for EREalpha
in screen for potential PROTACS there were compounds which bound high affinity but no degradation so phenotypic screening very important
when look at molecular dynamics can see where PROTAC can add additional protein protein interaction, verifed by site directed mutagenesis
able to target bcr-Abl
he says this is a rapidly expanding field because of all the new E3 ligase targets being discovered

Expanding the horizons of cereblon modulators

Mary Matyskiela

Translating cellular targeted protein degradation to in vivo models using an enzymology framework

Stewart L Fisher

new targeting compounds have an E3 ligase binding domain, a target binding domain and a linker domain
in vivo these compounds are very effective; BRD4 degraders good invitro and in vivo with little effect on body weight
degraders are essential activators of E3 ligases as these degraders bring targets in close proximity so activates a catalytic cycle of a multistep process (has now high turnover number)
in enzymatic pathway the degraders make a productive complex so instead of a kcat think of measuring a kprod or productivity of degraders linked up an E3 ligase
the degraders are also affecting the rebound protein synthesis; so Emax never to zero and see a small rebound of protein synthesis

Drug combinations remain the gold standard for treating cancer, as they significantly outperform single agents. However, due to the enormous size of drug combination space, it is virtually impossible to interrogate all possible combinations. This session will discuss approaches to identify novel combinations using both experimental and computational approaches. Speakers will discuss i) approaches to drug screening in cell lines, the impact of the microenvironment, and attempts to more

Bence Szalai, James E Korkola, Lisa Tucker-Kellogg, Jeffrey W Tyner

Cancer Stem Cells and Therapeutic Resistance

Monday, June 22

3:45 PM – 5:21 PM EDT

Virtual Educational Session

Tumor Biology

Cancer stem cells are a subpopulation of cells with a high capacity for self-renewal, differentiation and resistance to therapy. In this session, we will define cancer stem cells, discuss cellular plasticity, interactions between cancer stem cells and the tumor microenvironment, and mechanisms that contribute to therapeutic resistance.

Robert S Kerbel, Dolores Hambardzumyan, Jennifer S. Yu

Molecular Imaging in Cancer Research

Monday, June 22

3:45 PM – 5:45 PM EDT

Virtual Educational Session

Drug Development, Experimental and Molecular Therapeutics

This session will cover the fundamentals as well as the major advances made in the field of molecular imaging. Topics covered will include the basics for optical, nuclear, and ultrasound imaging; the pros and cons of each modality; and the recent translational advancements. Learning objectives include the fundamentals of each imaging modality, recent advances in the technology, the processes involved to translate an imaging agent from bench to bedside, and how molecular imaging can gui

Julie Sutcliffe, Summer L Gibbs, Mark D Pagel, Katherine W Ferrara

Tumor Endothelium: The Gatekeepers of Tumor Immune Surveillance

Monday, June 22

3:45 PM – 5:45 PM EDT

Virtual Educational Session

Tumor Biology, Immunology, Experimental and Molecular Therapeutics, Drug Development

Tumor-associated endothelium is a gatekeeper that coordinates the entry and egress of innate and adaptive immune cells within the tumor microenvironment. This is achieved, in part, via the coordinated expression of chemokines and cell adhesion molecules on the endothelial cell surface that attract and retain circulating leukocytes. Crosstalk between adaptive immune cells and the tumor endothelium is therefore essential for tumor immune surveillance and the success of immune-based thera

Dai Fukumura, Maria M Steele, Wen Jiang, Andrew C Dudley

Novel Strategies in Cancer Immunotherapy: The Next Generation of Targets for Anticancer Immunotherapy

Monday, June 22

3:45 PM – 5:45 PM EDT

Virtual Educational Session

Immunology, Experimental and Molecular Therapeutics

T-cell immunotherapy in the form of immune checkpoint blockade or cellular T-cell therapies has been tremendously successful in some types of cancer. This success has opened the door to consider what other modalities or types of immune cells can be harnessed for exert antitumor functions. In this session, experts in their respective fields will discuss topics including novel approaches in immunotherapy, including NK cells, macrophage, and viral oncotherapies.

Evanthia Galanis, Kerry S Campbell, Milan G Chheda, Jennifer L Guerriero

Benign Cells as Drivers of Cancer Progression: Fat and Beyond

Monday, June 22

3:45 PM – 5:45 PM EDT

Virtual Educational Session

Tumor Biology, Drug Development, Immunology, Clinical Research Excluding Trials

Carcinomas develop metastases and resistance to therapy as a result of interaction with tumor microenvironment, composed of various nonmalignant cell types. Understanding the complexity and origins of tumor stromal cells is a prerequisite for development of effective treatments. The link between obesity and cancer progression has revealed the engagement of adipose stromal cells (ASC) and adipocytes from adjacent fat tissue. However, the molecular mechanisms through which they stimulate

Guojun Wu, Matteo Ligorio, Mikhail Kolonin, Maria T Diaz-Meco

Dharma Master Jiantai Symposium on Lung Cancer: Know Thy Organ – Lessons Learned from Lung and Pancreatic Cancer Research

Monday, June 22

3:45 PM – 5:45 PM EDT

Virtual Educational Session

Clinical Research Excluding Trials, Experimental and Molecular Therapeutics, Tumor Biology

The term “cancer” encompasses hundreds of distinct disease entities involving almost every possible site in the human body. Effectively interrogating cancer, either in animals models or human specimens, requires a deep understanding of the involved organ. This includes both the normal cellular constituents of the affected tissue as well as unique aspects of tissue-specific tumorigenesis. It is critical to “Know Thy Organ” when studying cancer. This session will focus on two of the most

Trudy G Oliver, Hossein Borghaei, Laura Delong Wood, Howard C Crawford

Clinical Trial Design: Part 1: Novel Approaches and Methods in Clinical Trial Design

Monday, June 22

3:45 PM – 5:45 PM EDT

Virtual Methods Workshop

Clinical Trials

Good clinical trial design has always had to balance the competing interests of effectively and convincingly answering the question with the limitations imposed by scarce resources, complex logistics, and risks and potential benefits to participants. New targeted therapies, immuno-oncology, and novel combination treatments add new challenges on top of the old ones. This session will introduce these concerns and 1) suggest ways to consider what outcomes are relevant, 2) how we can best

Mary W. Redman, Nolan A. Wages, Susan G Hilsenbeck, Karyn A. Goodman

High-Throughput Screens for Drivers of Progression and Resistance

Monday, June 22

3:45 PM – 5:45 PM EDT

Virtual Methods Workshop

Tumor Biology, Drug Development

The sequencing of human cancers now provides a landscape of the genetic alterations that occur in human cancer, and increasingly knowledge of somatic genetic alterations is becoming part of the evaluation of cancer patients. In some cases, this information leads directly to the selection of particular therapeutic approaches; however, we still lack the ability to decipher the significance of genetic alterations in many cancers. This session will focus on recent developments that permit the identification of molecular targets in specific cancers. This information, coupled with genomic characterization of cancer, will facilitate the development of new therapeutic agents and provide a path to implement precision cancer medicine to all patients.

William C Hahn, Mark A Dawson, Mariella Filbin, Michael Bassik

Implementation Science Methods for Cancer Prevention and Control in Diverse Populations: Integration of Implementation Science Methods in Care Settings

Monday, June 22

3:45 PM – 5:15 PM EDT

Defining a cancer dependency map

William C Hahn

Introduction

William C Hahn

Genome-scale CRISPR screens in 3D spheroids identify cancer vulnerabilities

Michael Bassik

Utilizing single-cell RNAseq and CRISPR screens to target cancer stem cells in pediatric brain tumors

Mariella Filbin

many gliomas are defined by discreet mutational spectra that also discriminates based on age and site as well (for example many cortical tumors have mainly V600E Braf mutations while thalamus will be FGFR1
they did single cell RNAseq on needle biopsy from 7 gliomas which gave about 3500 high quality single cells; obtained full length RNA
tumors clustered mainly where the patient it came from but had stromal cell contamination probably so did a deconvolution? Copy number variation showed which were tumor cells and did principle component analysis
it seems they used a human glioma model as training set
identified a stem cell like glioma cell so concentrated on the genes altered in these for translational studies
developed multiple PDX models from patients
PDX transcriptome closest to patient transcriptome but organoid grown in serum free very close while organoids grown in serum very distinct transcriptome
developed a CRISPR barcoded library to determine genes for survival genes
pulled out BMI1 and EZH2 (polycomb complex proteins) as good targets

Virtual Methods Workshop

Prevention Research, Survivorship, Clinical Research Excluding Trials, Epidemiology

Through this Education Session we will use examples from ongoing research to provide an overview of implementation science approaches to cancer prevention and control research. We draw on examples to highlight study design approaches, research methods, and real-world solutions when applying implementation science to achieve health equity. Approaches to defining change in the care setting and measuring sustained changes are also emphasized. Using real examples of patient navigation prog

Graham A Colditz, Sanja Percac-Lima, Nathalie Huguet

COVID-19 and Cancer: Guidance for Clinical Trial Conduct and Considerations for RWE

Monday, June 22

3:45 PM – 5:30 PM EDT

Virtual Educational Session

Regulatory Science and Policy, Epidemiology

This session will consider the use of real-world evidence in the context of oncology clinical trials affected by the COVID-19 pandemic. Key aspects of the FDA’s recent “Guidance on Conduct of Clinical Trials of Medical Products of Medical Products during COVID-19 Public Health Emergency” will be discussed, including telemedicine, accounting for missing data, obtaining laboratory tests and images locally, using remote informed consent procedures, and additional considerations for contin

Wendy Rubinstein, Paul G. Kluetz, Amy P. Abernethy, Jonathan Hirsch, C.K. Wang

Posted in Biological Networks, Biomedical Measurement Science, Cancer - General, Cancer and Current Therapeutics, CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Informatics, CAST - Alternative to CRISPR/Cas9, Computational Biology/Systems and Bioinformatics, Conference Coverage with Social Media, CRISPR/Cas9 & Gene Editing, Funding Opportunities for Cancer Research, Genomic Expression, Health Economics and Outcomes Research, Metabolomics, REAL TIME Conference Coverage Twitter's Hashtags and Handles per Presentation/session, Transposon-encoded CRISPR–Cas systems direct RNA-guided DNA integration, tagged #AACR20, AACR, Cancer research, CRISPR –Cas9 system for genetic engineering, CRISPR/Cas and cell & molecular biology, CRISPR/Cas9 modification, metabolome on June 22, 2020| Leave a Comment »

Live Conference Coverage AACR 2020 in Real Time: Monday June 22, 2020 Mid Day Sessions

Live Conference Coverage AACR 2020 in Real Time: Monday June 22, 2020 Mid Day Sessions

Reporter: Stephen J. Williams, PhD

This post will be UPDATED during the next two days with notes from recordings from other talks

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Pezcoller Foundation-AACR International Award for Extraordinary Achievement in Cancer Research

Register for FREE at https://www.aacr.org/

AACR VIRTUAL ANNUAL MEETING II

REGISTER NOW

The prestigious Pezcoller Foundation-AACR International Award for Extraordinary Achievement in Cancer Research was established in 1997 to annually recognize a scientist of international renown who has made a major scientific discovery in basic cancer research OR who has made significant contributions to translational cancer research; who continues to be active in cancer research and has a record of recent, noteworthy publications; and whose ongoing work holds promise for continued substantive contributions to progress in the field of cancer. For more information regarding the 2020 award recipient go to aacr.org/awards.

John E. Dick, Enzo Galligioni, David A Tuveson

Chemistry to the Clinic: Part 1: Lead Optimization Case Studies in Cancer Drug Discovery

Awardee: John E. Dick

Princess Anne Margaret Cancer Center, Toronto, Ontario

For determining how stem cells contribute to normal and leukemic hematopoeisis

not every cancer cell equal in their Cancer Hallmarks
how do we monitor and measure clonal dynamics
Barnie Clarkson did pivotal work on this
most cancer cells are post mitotic but minor populations of cells were dormant and survive chemotherapy
only one cell is 1 in a million can regenerate and transplantable in mice and experiments with flow cytometry resolved the question of potency and repopulation of only small percentage of cells and undergo long term clonal population
so instead of going to cell lines and using thousands of shRNA looked at clinical data and deconvoluted the genetic information (RNASeq data) to determine progenitor and mature populations (how much is stem and how much is mature populations)
in leukemic patients they have seen massive expansion of a single stem cell population so only need one cell in AML if the stem cells have the mutational hits early on in their development
finding the “seeds of relapse”: finding the small subpopulation of stem cells that will relapse
they looked in BALL;; there are cells resistant to l-aspariginase, dexamethasone, and vincristine
a lot of OXPHOS related genes (in DRIs) that may be the genes involved in this resistance
it a wonderful note of acknowledgement he dedicated this award to all of his past and present trainees who were the ones, as he said, made this field into what it is and for taking it into directions none of them could forsee

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Experimental and Molecular Therapeutics, Drug Development, Cancer Chemistry

How can one continue to deliver innovative medicines to patients when biological targets are becoming ever scarcer and less amenable to therapeutic intervention? Are there sound strategies in place that can clear the path to targets previously considered “undruggable”? Recent advances in lead finding methods and novel technologies such as covalent screening and targeted protein degradation have enriched the toolbox at the disposal of drug discovery scientists to expand the druggable ta

Stefan N Gradl, Elena S Koltun, Scott D Edmondson, Matthew A. Marx, Joachim Rudolph

Informatics Technologies for Cancer Research

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Bioinformatics and Systems Biology, Molecular and Cellular Biology/Genetics

Cancer researchers are faced with a deluge of high-throughput data. Using these data to advance understanding of cancer biology and improve clinical outcomes increasingly requires effective use of computational and informatics tools. This session will introduce informatics resources that support the data management, analysis, visualization, and interpretation. The primary focus will be on high-throughput genomic data and imaging data. Participants will be introduced to fundamental concepts

Rachel Karchin, Daniel Marcus, Andriy Fedorov, Obi Lee Griffith

Engineering and Physical Sciences Approaches in Cancer Research, Diagnosis, and Therapy

Variant analysis is the big bottleneck, especially interpretation of variants
CIVIC resource is a network for curation, interpretation of genetic variants
CIVIC curators go through multiple rounds of editors review
gene summaries, variant summaries
curation follows ACSME guidelines

evidences are accumulated, categories by various ontologies and is the heart of the reports
as this is a network of curators the knowledgebase expands
CIVIC is linked to multiple external informatic, clinical, and genetic databases
they have curated 7017 clinical interpretations, 2527 variants, using 2578 papers, and over 1000 curators
they are currently integrating with COSMIC ClinVar, and UniProt
they are partnering with ClinGen to expand network of curators and their curation effort
CIVIC uses a Python interface; available on website

https://civicdb.org/home

The Precision Medicine Revolution

Precision medicine refers to the use of prevention and treatment strategies that are tailored to the unique features of each individual and their disease. In the context of cancer this might involve the identification of specific mutations shown to predict response to a targeted therapy. The biomedical literature describing these associations is large and growing rapidly. Currently these interpretations exist largely in private or encumbered databases resulting in extensive repetition of effort.

CIViC’s Role in Precision Medicine

Realizing precision medicine will require this information to be centralized, debated and interpreted for application in the clinic. CIViC is an open access, open source, community-driven web resource for Clinical Interpretation of Variants in Cancer. Our goal is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations. For more details refer to the 2017 CIViC publication in Nature Genetics.

U24 funding announced: We are excited to announce that the Informatics Technology for Cancer Research (ICTR) program of the National Cancer Institute (NCI) has awarded funding to the CIViC team! Starting this year, a five-year, $3.7 million U24 award (CA237719), will support CIViC to develop Standardized and Genome-Wide Clinical Interpretation of Complex Genotypes for Cancer Precision Medicine.

Informatics tools for high-throughput analysis of cancer mutations

Rachel Karchin

CRAVAT is a platform to determine, categorize, and curate cancer mutations and cancer related variants
adding new tools used to be hard but having an open architecture allows for modular growth and easy integration of other tools
so they are actively making an open network using social media

Towards FAIR data in cancer imaging research

Andriy Fedorov, PhD

Data and the FAIR Principles

Towards the FAIR principles

While LOD has had some uptake across the web, the number of databases using this protocol compared to the other technologies is still modest. But whether or not we use LOD, we do need to ensure that databases are designed specifically for the web and for reuse by humans and machines. To provide guidance for creating such databases independent of the technology used, the FAIR principles were issued through FORCE11: the Future of Research Communications and e-Scholarship. The FAIR principles put forth characteristics that contemporary data resources, tools, vocabularies and infrastructures should exhibit to assist discovery and reuse by third-parties through the web. Wilkinson et al.,2016. FAIR stands for: Findable, Accessible, Interoperable and Re-usable. The definition of FAIR is provided in Table 1:

Number	Principle
F	Findable
F1	(meta)data are assigned a globally unique and persistent identifier
F2	data are described with rich metadata
F3	metadata clearly and explicitly include the identifier of the data it describes
F4	(meta)data are registered or indexed in a searchable resource
A	Accessible
A1	(meta)data are retrievable by their identifier using a standardized communications protocol
A1.1	the protocol is open, free, and universally implementable
A1.2	the protocol allows for an authentication and authorization procedure, where necessary
A2	metadata are accessible, even when the data are no longer available
I	Interoperable
I1	(meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2	(meta)data use vocabularies that follow FAIR principles
I3	(meta)data include qualified references to other (meta)data
R	Reusable
R1	meta(data) are richly described with a plurality of accurate and relevant attributes
R1.1	(meta)data are released with a clear and accessible data usage license
R1.2	(meta)data are associated with detailed provenance
R1.3	(meta)data meet domain-relevant community standards

A detailed explanation of each of these is included in the Wilkinson et al., 2016 article, and the Dutch Techcenter for Life Sciences has a set of excellent tutorials, so we won’t go into too much detail here.

for outside vendors to access their data, vendors would need a signed Material Transfer Agreement but NCI had formulated a framework to facilitate sharing of data using a DIACOM standard for imaging data

Monday, June 22

1:30 PM – 3:01 PM EDT

Virtual Educational Session

Experimental and Molecular Therapeutics, Cancer Chemistry, Drug Development, Immunology

The engineering and physical science disciplines have been increasingly involved in the development of new approaches to investigate, diagnose, and treat cancer. This session will address many of these efforts, including therapeutic methods such as improvements in drug delivery/targeting, new drugs and devices to effect immunomodulation and to synergize with immunotherapies, and intraoperative probes to improve surgical interventions. Imaging technologies and probes, sensors, and bioma

Claudia Fischbach, Ronit Satchi-Fainaro, Daniel A Heller

Exceptional Responders and Long-Term Survivors

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Survivorship

How should we think about exceptional and super responders to cancer therapy? What biologic insights might ensue from considering these cases? What are ways in which considering super responders may lead to misleading conclusions? What are the pros and cons of the quest to locate exceptional and super responders?

Alice P Chen, Vinay K Prasad, Celeste Leigh Pearce

Exploiting Metabolic Vulnerabilities in Cancer

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Tumor Biology, Immunology

The reprogramming of cellular metabolism is a hallmark feature observed across cancers. Contemporary research in this area has led to the discovery of tumor-specific metabolic mechanisms and illustrated ways that these can serve as selective, exploitable vulnerabilities. In this session, four international experts in tumor metabolism will discuss new findings concerning the rewiring of metabolic programs in cancer that support metabolic fitness, biosynthesis, redox balance, and the reg

Costas Andreas Lyssiotis, Gina M DeNicola, Ayelet Erez, Oliver Maddocks

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Press Coverage

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM

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

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM