"Oncogenic MAP Kinase Signaling Network" and "Leveraging Social Media Analysis to Inform Tobacco Prevention"

Transcript

• 00:00So it's a couple minutes after the hour,
• 00:02so why don't we get started?
• 00:04Welcome to grand rounds.
• 00:08We have two really interesting talks today.
• 00:12For those of you who don't know me,
• 00:16I'll just.
• 00:18Point out that my name is Ryan Crop.
• 00:19I'm very new to Yale as the new medical
• 00:24director for the Clinical Trials Office.
• 00:27And it's pleasure to start meeting everyone.
• 00:31So we have two took off.
• 00:32As I mentioned the 1st.
• 00:36Benjamin Turk has been
• 00:39kind enough to join us.
• 00:40He's an associate professor of
• 00:42pharmacology and director of
• 00:44Medical Studies and pharmacology.
• 00:45He's a member of the CSN program.
• 00:49His graduate work was in biological
• 00:52chemistry at MIT and then a postdoc
• 00:55with Luke Cantley also in Boston,
• 00:58and he works understanding
• 01:00molecular mechanisms underlying
• 01:02signaling pathways and how they're
• 01:05organized into large networks,
• 01:07and his lab has been.
• 01:08Setting protein modifying enzymes
• 01:11and particularly kinase and
• 01:13proteases that are important in
• 01:15in signaling networks and today he
• 01:18will be talking about the aquaponic
• 01:21not kinase signaling network.
• 01:23Thank you Doctor Turk.
• 01:26OK, thank you. I will share my screen.
• 01:32See.
• 01:36OK can you see my screen in the pointer?
• 01:39Yeah perfect OK great.
• 01:41Well thank you for that introduction and
• 01:44also for the invitation to present our
• 01:48work on MAP kinase signaling networks.
• 01:51So as we all know,
• 01:52one of the hallmarks of cancer is
• 01:55uncontrolled cell proliferation and
• 01:57survival and cancer cells accomplish this,
• 02:00at least in part through Co
• 02:03opting signaling pathways that are
• 02:05normally activated downstream of
• 02:08peptide growth factor receptors.
• 02:10I'm gonna be talking about one of
• 02:12the major arms of growth factor
• 02:14signaling the wrasse, RAF, MEK,
• 02:16Erk, signaling cascade.
• 02:18So owing to high frequency mutations
• 02:21in rosanky genes as well as mutation,
• 02:23amplification of growth factor receptors,
• 02:26this pathway is amongst the most
• 02:29highly hyper activated in or more
• 02:32frequent most frequently hyperactivated
• 02:34in human cancers.
• 02:35And though the pathway has been the
• 02:38subject of intense study for for decades now,
• 02:41there are still some.
• 02:42Open questions in the field that our
• 02:44lab and of course many others are,
• 02:46are trying to understand and to to
• 02:48sum up some of these questions that
• 02:51I'm really talking about today.
• 02:52One question,
• 02:53what are the functionally important
• 02:55components of MAP kinase signaling networks?
• 02:59So obviously the kinases that form
• 03:01the core cascade are are have been
• 03:03well studied or well understood,
• 03:05but we have less understanding of
• 03:07other regulators of the pathway.
• 03:09So for example the protein phosphatases.
• 03:12That act on these kinases,
• 03:14and thus attenuate signaling through
• 03:16the pathway and it in addition.
• 03:20We don't have a complete catalogue
• 03:22of the substrates of Erk that act as
• 03:26the critical effectors in mediating
• 03:28the cancer cell phone. It type.
• 03:31So in addition,
• 03:33one question we're interested in is
• 03:35how specific connections are made
• 03:37between the kinases and the regulators
• 03:39and substrates in this pathway.
• 03:41So there's been a lot of really beautiful
• 03:44structural work emerging recently on
• 03:46the upstream components of the pathway,
• 03:48in particular,
• 03:49how Rask connects to RAF and MEK.
• 03:51But again,
• 03:52our understanding of the more downstream
• 03:55components where where we have these
• 03:57critical effector kinases and their
• 03:59substrates is is less well understood.
• 04:01And then lastly,
• 04:02one thing we know is that the
• 04:05persistent high level of activation
• 04:07of this pathway that one gets with
• 04:11new genic activation really doesn't
• 04:14faithfully recapitulate the sort
• 04:16of normal dynamics of activation
• 04:18when we'd see in response to a
• 04:21growth factor in a normal cell.
• 04:23And this can lead to a phenomenon
• 04:26that someone sometimes called
• 04:28network rewiring and how.
• 04:30New or which new connections are
• 04:32made in these networks and which
• 04:34connections are broken is is is
• 04:35something that's important to
• 04:37know in terms of having a complete
• 04:39understanding of tumor cell biology.
• 04:41So I'm I'm gonna tell two stories
• 04:44briefly today.
• 04:45The first has to do with the oncogenic
• 04:48map kinase signaling in in Melanoma.
• 04:52OK,
• 04:52so as many of you probably know,
• 04:55malignant melanomas are really
• 04:57driven by hyperactive Erk,
• 04:59MAP kinase signaling and so about half of
• 05:03melanomas Harbor mutations in the BRAF gene.
• 05:06Most frequently the V 600 year Leal that
• 05:09leads to high level constitutive activation.
• 05:12And the remaining tumors have mutations.
• 05:15Most of them have mutations either
• 05:17in the NRAS, GTP ace, the NF One Ras.
• 05:22GTP is activating protein that negatively
• 05:25regulates the pathway or gain of
• 05:28function mutations in in MEK MEK,
• 05:30one which is just around stream of UVB
• 05:34graph and and of course the dependence of
• 05:37melanomas on this pathway has really driven
• 05:41the development and eventual FDA approval.
• 05:44Of kinase inhibitors that target both
• 05:46B RAF and MEK that are currently
• 05:49used to treat Melanoma,
• 05:51and while there is a high response rate
• 05:55for tumors that harbor be RAF mutations,
• 05:58the the problem with these drugs
• 06:00and really all targeted therapies is
• 06:02that the responses are not durable
• 06:04and patients will relapse within
• 06:05a few months to a couple of years.
• 06:09And the most common way that one sees
• 06:12resistance to these inhibitors is through
• 06:14re activation of the Erk MAP kinase pathway.
• 06:17Despite the continued presence of
• 06:19inhibitor but one can also see
• 06:21activation of bypass pathways like the
• 06:23P I3 kinase mtor pathway leading to
• 06:26resistance and obviously there's been
• 06:28a lot of interest in understanding
• 06:30these mechanisms of of tumor cell
• 06:32resistance to these therapeutic agents.
• 06:34With the idea that if you understand
• 06:35how cells become resistant you might
• 06:37be able to devise.
• 06:39Addition,
• 06:39new therapeutic strategies that
• 06:41might be more durable.
• 06:43So we got into this area through a
• 06:46genetic loss of function screen and SH
• 06:48RNA screen that one of my graduate students,
• 06:52Eunice Cho conducted to identify
• 06:54genes that modulate sensitivity to
• 06:57MEK inhibitors in Melanoma cells and
• 07:00this work was published last year.
• 07:03People are interested in getting
• 07:04more of the details before I talk
• 07:06about the specifics of this research,
• 07:08I have to briefly plug the Yale Cancer
• 07:11Center Functional Genomics core that
• 07:13I Co direct with David Calderwood.
• 07:15And really,
• 07:16the the the the mission of this
• 07:18core is to facilitate these loss
• 07:20of function genetic screens.
• 07:22CRISPR CAS 9 screens or SH RNA screens
• 07:24like I'm going to talk about and so
• 07:26hopefully this talk will give you a
• 07:28flavor of the kinds of information
• 07:29you can get out of these screens
• 07:32and inspire you to contact us and
• 07:34and set up your own.
• 07:36So I don't have a lot of time to talk
• 07:37about the details of how the screen works.
• 07:39Needless to say, we.
• 07:42In introduce Melanoma cell line
• 07:45with a pooled SH RNA library.
• 07:48In this one.
• 07:48In this case,
• 07:49one targeting kinases and
• 07:51phosphatases and then we propagate
• 07:52in either the presence or absence.
• 07:54One of two MEK inhibitors tromette
• 07:56never sell you met in IB and then we
• 07:59look at which hairpins become enriched
• 08:01or depleted from the population at the
• 08:03end of the screen and and what this
• 08:06will should tell us our our what jeans.
• 08:09Impact the sensitivity mekan hitters and
• 08:13hopefully identify additional genetic
• 08:16modifiers of of map kinase signaling.
• 08:18So I'm going to jump to the
• 08:20top hit that came out of this
• 08:22screen which was a phosphatase.
• 08:24Assyrian threonine phosphatase called
• 08:26PPP six seed, and what you can see
• 08:29here is that amongst all of the
• 08:30hair pins that were in our library,
• 08:32those that target PPP succeed were
• 08:35specifically enriched in the presence
• 08:37of either Solomon if or trim it nib.
• 08:39But under control conditions they
• 08:41were not enriched in the population.
• 08:42And what that means is that when you
• 08:44treat cells with a MEK inhibitor,
• 08:46they grow better if you knock down.
• 08:49PPP 6C OK.
• 08:51So seeing PPP 6C as a hit in the
• 08:54screen really caught our eye and the
• 08:57reason for that is that something
• 08:59like 7 to 9% of melanomas have been
• 09:01shown through genomic analysis.
• 09:03Whole exome sequencing to harbor
• 09:06what are thought to be loss
• 09:08of function mutations in PP6C.
• 09:10So we thought identifying this
• 09:12phosphatase in the screen for modulators
• 09:15of drug sensitivity in Melanoma cell
• 09:17lines was probably not a coincidence.
• 09:19So first thing we did was to try to.
• 09:21Verify this result.
• 09:23So we derived PDP60 knockout cells
• 09:26through CRISPR CAS 9 gene editing,
• 09:28and sure enough,
• 09:29if we titrate in MEK inhibitor in
• 09:31this case a trim it and if you can
• 09:33see that knocking out PPP6C leads to
• 09:36substantial resistance to the inhibitor
• 09:38and the other thing that we're observing,
• 09:40which is kind of interesting,
• 09:42is that actually sells deleted for
• 09:45PPP6C grow more poorly than wild type
• 09:49cell line than the wild type cell line.
• 09:51But that growth is at growth effect is
• 09:54actually rescued by low concentrations
• 09:56of the MEK inhibitor and this is
• 09:59reminiscent of a phenomenon that's
• 10:01been seen in in preclinical models,
• 10:04that's called inhibitor addiction and
• 10:08basically what what this means is that
• 10:11it's it's typically characterized
• 10:13by cells having hyperactive map
• 10:15kinase signaling and hyperactive
• 10:17map kinase signaling is toxic to
• 10:19cells and they can be brought back.
• 10:21Down into the range that's optimal for
• 10:24cell growth with low concentrations
• 10:26of an inhibitor,
• 10:28and so that was it in a sort of a
• 10:30immediate clue that of what might
• 10:32be going on here.
• 10:34That if loss of PPP6C caused hyper
• 10:36activation of MAP kinase signaling,
• 10:39that would explain why you get
• 10:41resistance because it requires higher
• 10:43concentrations of drug to suppress the
• 10:45pathway enough to inhibit cell growth.
• 10:48And also explain this drug
• 10:50addiction phenotype.
• 10:51And sure enough, that's what we see.
• 10:53So basically,
• 10:54if we look at a number of
• 10:57distinct PPP 60 knockout clones,
• 11:00we can see profound hyperphosphorylation
• 11:04hyperactivation of of MEK and of Erk
• 11:06and we can rescue that hyperactivation
• 11:09by re expressing a wild type allele
• 11:12of PPP 6C but not a phosphatase dead
• 11:16allele that's catalytically inactive.
• 11:18OK,
• 11:19and we extended these observations
• 11:20to a whole panel of cell lines.
• 11:23I'm only showing a few of them here,
• 11:25basically,
• 11:26regardless of lineages we lookin cell
• 11:29lines that either harbor BRAF mutations,
• 11:32or crass Oren RAST mutations
• 11:34with a couple of exceptions.
• 11:37We see that when we knock down PPP
• 11:4060 by SH RNA, we get increased mech
• 11:43and or increased ORC phosphorylation.
• 11:45So we do think this is a general phenomenon,
• 11:47at least in the context of
• 11:48oncogenic map kinase. Signaling so.
• 11:53PPP succeed is a phosphatase and
• 11:57in experiments that I I I won't
• 11:59have time to tell you about.
• 12:01We had ruled out activation of upstream
• 12:04components of the pathway and had
• 12:07a good handle on this PB6C acting
• 12:10at the level of MEK because it's a
• 12:13phosphatase may most straightforward
• 12:14explanation would be that it directly
• 12:16dephosphorylates Mac and we we
• 12:18do think that's what's going on.
• 12:20So in in vitro phosphatase assays
• 12:22we could show that.
• 12:23Purified PP6P6C complexes.
• 12:27Candy phosphorylate MEK,
• 12:28but they don't be phosphorylate Erk,
• 12:30so there seems to be some substrate
• 12:32specificity for the upstream component
• 12:34and probably more compelling we could
• 12:37detect at least an indirect physical
• 12:39interaction between Mac and PPP 6C.
• 12:41So PP6C is the catalytic
• 12:44subunit of holoenzyme that is,
• 12:46heterotrimeric,
• 12:47that includes regulatory subunits
• 12:49that have ascribed roles and binding
• 12:52to substrates and recruiting
• 12:54them for dephosphorylation.
• 12:55And we could see in komuna precipitation
• 12:59assays that pulling down any of
• 13:02the three regulatory subunits.
• 13:04I will bring down Mac but not so
• 13:06much with the catalytic subunit,
• 13:08sort of confirming a role for these
• 13:11regulatory subunits in in recruiting.
• 13:13MEC two to the complex.
• 13:16So I mentioned that PPP 6C is
• 13:19recurrently mutated in melanomas
• 13:20and so we wanted to look at whether
• 13:23these mutations affected signaling
• 13:24through the MAP kinase pathway.
• 13:27And so we perform rescue experiments
• 13:29where we re expressed series of the
• 13:33the most frequently observed mutants
• 13:35in our PP60 knockout cells and what we
• 13:39observed is with a single exception
• 13:42that these mutants were either
• 13:45entirely or partially defective.
• 13:48In their ability to mediate
• 13:51mech dephosphorylation,
• 13:52so we conclude that these are
• 13:55likely partial loss of function
• 13:57mutations and it sort of makes
• 14:00sense that they're functioning to
• 14:03increase signaling through the core
• 14:05pathway that drives melanomas.
• 14:08That is, the map kinase signaling pathway.
• 14:10So, unfortunately,
• 14:11PPP 60 mutations are are rare enough
• 14:15that we we really don't know the.
• 14:18Clinical relevance of these
• 14:20mutations to pathway activation,
• 14:23but we were able to mine some data from
• 14:25C bio portal and it did appear as if
• 14:28there was a significant correlation
• 14:31between the M RNA expression level
• 14:33of PPP6C and the level of either
• 14:36phospho Erk or Phospho MEK as seen
• 14:38in reverse phase protein arrays.
• 14:40So we do believe that PPP 6C is
• 14:43modulating flux through the pathway in
• 14:46tumors and and may be a factor that
• 14:49influences. Therapeutic response.
• 14:50OK,
• 14:51so in conclusion of this first part
• 14:54we've identified PPP 6C as a new
• 14:57player in restraining oncogenic map
• 14:59kinase signaling through dephosphorylation
• 15:00of MEK and that loss of function.
• 15:03Mutations of PPP 60 lead to hyper
• 15:06activated Erk signaling some of the
• 15:08open questions that we're trying to pursue.
• 15:10Now,
• 15:11how is PPP 6C regulated?
• 15:13So this phenomenon where PPP 6C is
• 15:16required to restrain MEK activation
• 15:18has has something that we really
• 15:20only see in the setting of oncogenic
• 15:22activation of the pathway.
• 15:23And that suggests to us that maybe
• 15:26there's a negative
• 15:27feedback loop where pathway activation
• 15:29leads to activation of PPP6C
• 15:32towards the phosphorylation of MEK,
• 15:34and we'd like to understand how that happens.
• 15:36And of course, it may be that there
• 15:38are other signaling outputs substrates
• 15:40other than mech that are functionally
• 15:43important for tumors where you see lots
• 15:45of people pay 60 and we're interested
• 15:47in trying to identify those as well.
• 15:50So for the remaining time,
• 15:51I'm going to switch gears a little bit
• 15:53and move downstream in the pathway to
• 15:55do the the the kinase in the bottom
• 15:58of the map kinase cascade IIRC,
• 16:01and here the we're going to be
• 16:03talking a little bit more about the
• 16:06structural basis for how connections
• 16:08in the pathway is are made,
• 16:10and also some of these network rewiring
• 16:13phenomena they introduced at the
• 16:14beginning and so the work I'm going
• 16:16to talk about is the work of really
• 16:19talented graduate student who's.
• 16:20Currently in the lab Julissa Torres
• 16:22Robles and what she was interested in
• 16:26in looking at our oncogenic mutations in
• 16:29in Erk 2 itself or encoded by the map K1G.
• 16:32So as I said at the outset,
• 16:34you have high frequency mutations in
• 16:36multiple cancer types of Rasen draft but
• 16:39at lower frequency you do see mutations
• 16:41in some of the downstream components.
• 16:43The Erk mutations in particular are
• 16:45sort of interesting because you don't
• 16:47see them in the same tumor types
• 16:49that you do the Rasen draft mutation.
• 16:51So where, where as Rasen rap mutations
• 16:53you you see in melanomas,
• 16:55lung cancers, colorectal cancers,
• 16:57pancreatic cancer,
• 16:58the Erk.
• 16:592 mutations are largely restricted
• 17:01to squamous cell carcinomas,
• 17:03so about 8% of cervical squamous cell
• 17:06carcinomas have recurrent or two
• 17:08mutations and about 2% of head and neck.
• 17:11Squamous cell carcinomas have these
• 17:13mutations and they've attracted some
• 17:15attention in that setting because
• 17:17of potential association between
• 17:19the presence of those mutations.
• 17:21And clinical responses to EGF
• 17:25receptor inhibitors.
• 17:27So one of the things that kind
• 17:29of attracted us to this is the
• 17:31the nature of these mutations.
• 17:32They're sort of unusual when you compare
• 17:35them to other activating mutations and
• 17:37protein kinases that you see in cancer.
• 17:40So unlike say,
• 17:41BRAF mutations or EGF receptor mutations,
• 17:44these mutations don't intrinsically
• 17:46hyper activate the kinase and they
• 17:48all map at least in three dimensional
• 17:51space to a really interesting
• 17:53region of the kinase catalytic.
• 17:55So this is a region that falls
• 17:56outside of the catalytic cleft.
• 17:58That's known as the common docking group,
• 18:01and it's called that because
• 18:02it serves as a hub for protein
• 18:05protein interactions with ERP two,
• 18:07so this docking groove binds to
• 18:10a number of substrates of Erk,
• 18:13but it also binds to irks regulators,
• 18:15so the Mach one and Mach 2 which are
• 18:19the positive regulars that phosphorylate
• 18:20and turn on or combined at this site,
• 18:23and the dual specificity phosphatase that
• 18:26dephosphorylates find it this site as well.
• 18:29So this sort of presents a little
• 18:32bit of a conundrum because I just,
• 18:34you know,
• 18:34told you that this is a
• 18:36really functionally important
• 18:37part of the of the molecule yet,
• 18:40and so you might expect that mutations
• 18:42at this site would be loss of function.
• 18:44But of course just logically it
• 18:46would seem that mutations in, IIRC,
• 18:48that you find in cancer should be
• 18:51gain of function and and the reason
• 18:53why this is is that these mutations
• 18:56actually cause selective disruption of
• 18:58these protein protein interactions.
• 19:01So for example,
• 19:03we know that these cancer associated
• 19:05Earth mutants are still able to
• 19:07interact with MEK one and MEK two,
• 19:09and so they can be activated normally,
• 19:11but they no longer interact with
• 19:13the dual specificity phosphatase.
• 19:14So incels, this leads to an imbalance
• 19:17between their activation and inactivation,
• 19:19and you accumulate the hyper
• 19:21phosphorylated active form of the kinase,
• 19:24but that's not all there is to it because
• 19:26it turns out that at least one of the
• 19:29major signaling outputs of Earth that is the.
• 19:31Chinese risk is also
• 19:33broken by these mutations,
• 19:35so these mutants don't interact with
• 19:36risk and they don't phosphorylate risk,
• 19:38and so that makes you raised
• 19:40a few questions in our mind.
• 19:43So first of all,
• 19:45what is the scope of interactions
• 19:47with Erk that are selectively
• 19:49disrupted by her mutations?
• 19:51We simply don't know this at this
• 19:53point and and from a kind of
• 19:55structural or biochemical standpoint.
• 19:57Why are some interactions broken
• 19:59and some spared something that
• 20:01we we also don't understand?
• 20:03And so.
• 20:04In order to address this question,
• 20:06Jay Lisa conducted a proteome wide
• 20:09screen to identify sequences that can
• 20:11interact with the Erk docking group,
• 20:13and again I don't have time
• 20:15to explain this in detail.
• 20:17What we did was mine the human
• 20:19proteome for short amino acid
• 20:22stretches of amino acid sequence.
• 20:24That sort of had sequence similarity
• 20:27to known interacting sequences like you
• 20:30would find in in Mach one and Mach 2.
• 20:34And prepared a genetically encoded
• 20:35library of about 12,000 sequences.
• 20:37So these are short sequences,
• 20:39fragments of proteins that are
• 20:4114 amino acids long.
• 20:42And then we use those in a
• 20:45pooled competitive yeast.
• 20:46Two hybrid screening format and
• 20:47and the the bottom line is that you
• 20:50know similar to sort of an SH RNA or
• 20:53crisper screen if we have a successful
• 20:55interaction between Erk and the interactor,
• 20:58this will become enriched
• 20:59in the population overtime,
• 21:00and we can detect this by next
• 21:02generation sequencing.
• 21:03So when we do this screen with wild type.
• 21:05Work we can see that on gratifyingly,
• 21:09all of the known interactors
• 21:11interacting fragments that were in
• 21:13the library actually scores hits.
• 21:15They become enriched,
• 21:16and furthermore,
• 21:17if we align all of these sequences,
• 21:20we can see a sequence motif.
• 21:22A signature sequence that emerges
• 21:23that seems to be a common feature
• 21:26of sequences that interact with Erk.
• 21:28So a cluster of proline residues and
• 21:30a couple of leucine residues close by,
• 21:33and this is interesting in its own
• 21:35right because it tells us something about.
• 21:37How Erk recruits it's interacting proteins,
• 21:41but what about the mutants? So J.
• 21:43Lisa conducted this same screen with the two
• 21:46most recurrent cancer associated mutations,
• 21:49D321 and E322K and what we saw
• 21:52was kind of what we expected,
• 21:54which is that most of the interactions
• 21:57are preserved about 2/3 of the the
• 21:59interactors that scored his hits for wild
• 22:02type or also interact with the mutants,
• 22:04but about a third of them interacted.
• 22:07Only with the wild type kinase,
• 22:09and furthermore,
• 22:10when we look at the the sequences
• 22:13that interact only with wild type,
• 22:16we actually lose this sequence
• 22:18motif that's characteristic of
• 22:20of of Erk binders in general.
• 22:23And actually there's very little
• 22:25distinguishing feature here,
• 22:26save for the significant selection of a
• 22:29single arching residue in the sequence.
• 22:31So we were a little bit
• 22:32flummoxed by this at first,
• 22:34but first we just wanted to
• 22:36do some basic validation.
• 22:37I I'm I'm starting to run short on time,
• 22:40so I'm going to go through this briefly.
• 22:41Basically,
• 22:42we could confirm that a
• 22:43sensually all of the sequences,
• 22:45but if we if we made synthetic peptides
• 22:48corresponding to these sequences
• 22:50that scored as hits in the screen,
• 22:52we could see that where we expected we
• 22:56saw differential binding in vitro to
• 22:58wild type versus mutant alleles of Erk,
• 23:01one of them in particular peptide
• 23:03coming from the protein ISG 20 had
• 23:05particularly high affinity for Erk and
• 23:07showed the biggest differential binding.
• 23:09Between wild type and mutant forms.
• 23:12So we decided to take a structural
• 23:14biology approach to understand what
• 23:15was going on here in terms of how
• 23:17this interacted with her and with a
• 23:19lot of help from Titus Boggins lab
• 23:21here in the pharmacology department,
• 23:23Jay Lisa was able to solve the X-ray
• 23:25cocrystal structure of wild type work too.
• 23:27In complex with this fragment of the
• 23:30ISG 20 protein and I'm just going to
• 23:32zoom in on the key feature at the
• 23:35region of ISG 20 that binds to IRK.
• 23:38That is close to the hot spot for
• 23:41these mutations we see that the
• 23:43peptide forms a single turn of an
• 23:46alpha Helix and that is enforced.
• 23:48That motive interaction is enforced
• 23:50by a sequence motif that involves a
• 23:53hydrophobic isoleucine residue and
• 23:55then two arginine residues position
• 23:58close by that actually make direct
• 24:01polar contacts to the acidic residues
• 24:04that are mutated in in cancer.
• 24:07And sure enough, if we then go back.
• 24:10And look at our sequences.
• 24:12That bound most preferentially to
• 24:14wild type the the top 9 sequences
• 24:16in the original used to hybrid
• 24:19screening data all have this sequence
• 24:21motif and we could further confirm
• 24:25that this motif was important for
• 24:27binding to wild type IIRC,
• 24:28but not to mutant forms of work
• 24:31through in vitro binding assays
• 24:33that we did with synthetic peptides.
• 24:35So basically,
• 24:36if we if we mutate any
• 24:38of these three residues.
• 24:40We greatly reduce the binding
• 24:42affinity with wild type IIRC,
• 24:44but we have no effect on the
• 24:46already weak binding affinity
• 24:48with the mutant forms of FERC,
• 24:50presumably because the damage had
• 24:51already been done by those mutants.
• 24:54So we think we have a good handle on why
• 24:56some sequences interact specifically
• 24:57with wild type work and are broken.
• 25:00The interactions are broken with the mutants,
• 25:02but we're now trying to do is sort of
• 25:04understand a little bit more about how
• 25:06this relates to tumor cell biology,
• 25:08and this is my last data slide.
• 25:10And So what we've been doing is looking
• 25:11at some of the full length proteins
• 25:14that corresponds to its corresponding
• 25:15hits from the screen, and one that
• 25:17in particular that caught our eye,
• 25:18is the row GTPS exchange factor def.
• 25:22H1, which has been implicated in a positive
• 25:25feedback loop for the Erk signaling pathway.
• 25:29It's a known substrate of of work,
• 25:31and we can confirm that indietro,
• 25:33but also also confirm that these
• 25:36cancer mutated forms of Erk are
• 25:39unable to phosphorylate.
• 25:41FH1, at least in vitro,
• 25:42and we're now following up.
• 25:45On these studies in head and neck
• 25:48squamous cell carcinoma cell lines
• 25:50to see if we can verify this result
• 25:52and understand what this means
• 25:54for for tumor cell biology.
• 25:57So to sum up this part,
• 26:01we've identified that cancer associated
• 26:04mutations that map to these common docking
• 26:08groove of Earth 2 disrupt a subset of
• 26:12interactions and specifically those
• 26:14involving a particular sequence motif.
• 26:16And what we're trying to figure out now,
• 26:18of course,
• 26:19is if selective engagement of these
• 26:21substrates is important for the phenotypic
• 26:23consequences of work to mutation.
• 26:25So with that.
• 26:27I will stop and thank the people
• 26:29who did the work I mentioned,
• 26:31Eunice Cho,
• 26:32who recently left the lab graduated
• 26:35last year who had done all the work on
• 26:38PPP 6C and the work on Earth mutants
• 26:41was conducted by Julissa Torres Robles.
• 26:44I also like to point out my collaborators,
• 26:46David Calderwood,
• 26:47who's my partner in all the
• 26:50functional genomics stuff.
• 26:51Tice Boggins lab who helped us with
• 26:54the crystallography and Mark Gerstein
• 26:55lab that helped us with the the.
• 26:57Library design and computational analysis.
• 27:01And with that I'm happy to take
• 27:03any questions if we have time.
• 27:06Thank you that that that was great
• 27:08and really nice work and and and a
• 27:11good advertisement for the functional
• 27:12genomics core 'cause it looks like some
• 27:15really impressive data we have maybe
• 27:17two or three minutes for questions.
• 27:19If you wouldn't mind just putting him in
• 27:21the chat while people are doing that,
• 27:24can I just ask you a quick question
• 27:27about the the PP6C study?
• 27:31Is it worth you think going back
• 27:34and trying to redo your your.
• 27:36Knock down screen in a background
• 27:40of the the PPP mutant contacts
• 27:44to see if there's other.
• 27:46Targets that could restore
• 27:49sensitivity to the inhibitors.
• 27:51Yeah, I I do believe so.
• 27:52And actually one of the things that
• 27:55we have planned is is such a screen.
• 27:57So the screen that we did before
• 27:59was a focus SH RNA library and what
• 28:01we're gearing up to do is a genome
• 28:04wide CRISPR screen where we compare
• 28:06wildtype cells with the PPP 60
• 28:08knockout cells in the presence or
• 28:10absence of the of the MEK inhibitor,
• 28:13and so we're hoping to get out
• 28:15of that are basically.
• 28:16We should get genetic modifiers
• 28:18that affect the growth of the PPP 60
• 28:21knockout cells and one of the hopes
• 28:23is that we'll identify potentially
• 28:26other signaling outputs of PP6C
• 28:28that are important for growth and
• 28:29maybe drug sensitivity as well.
• 28:32You know, it seems like it makes that
• 28:35make sense in just one other question.
• 28:38I got a little,
• 28:39maybe I misunderstood in terms of the.
• 28:42The prevalence of these mutations
• 28:44in the in the in that phosphatase,
• 28:48and they I I thought you had said
• 28:50that they were relatively common.
• 28:54It's it's 7 to 9% depending on
• 28:57the study, so they're they're.
• 28:59They're not as common it it's.
• 29:01It's actually interesting
• 29:01if you look at the data,
• 29:02they're sort of the I guess
• 29:04the fifth most common,
• 29:04you know after the big guys and Ranson.
• 29:07If one and I think P 53
• 29:09they they're their next
• 29:11and do they get enriched? Have you do?
• 29:13Are there any databases of MEK resistant
• 29:16MEK inhibitor resistant samples that
• 29:18you can look to see whether it's
• 29:20enrichment for that mutation? Yeah,
• 29:21that hasn't really come out of those studies.
• 29:23A lot of those studies have been.
• 29:26Looking at sort of individual
• 29:29patients and you know people
• 29:31have made patients right.
• 29:32Zena graphs and things
• 29:33like that and and done.
• 29:34You know whole exome saying there's no.
• 29:38I mean because they're not
• 29:40particularly common that it really
• 29:42has not come up as a bonafide
• 29:44clinical resistance mechanism.
• 29:47OK, alright thank. Thank you again.
• 29:50Really nice work so why don't we
• 29:53move on to our next presenter?
• 29:56Is Doctor Grace Kang,
• 29:59who's an assistant professor in
• 30:01Department of Psychiatry and a
• 30:04member of our cancer Prevention
• 30:06and Control research program.
• 30:08She did her graduate work in clinical
• 30:11psychology at Saint Johns and in postdoc
• 30:14in adolescent addictions in the in the Yale.
• 30:18A school of Medicine's
• 30:19division of substance abuse.
• 30:21Her current research interests
• 30:23include understanding, substance use,
• 30:25health disparities among youth,
• 30:27and the use of social media for
• 30:29tobacco marketing and and novel
• 30:31tobacco use behaviors among youth,
• 30:32and I think she'll be talking
• 30:35about that today.
• 30:36Her title is leveraging social media
• 30:39analysis to inform tobacco prevention.
• 30:42Dr Kang thank you for for joining us.
• 30:52And I think you're on mute.
• 30:57OK, can you hear me now?
• 30:59Yep perfect OK great thanks.
• 31:02And you could hear you could
• 31:03see my slice here, right?
• 31:04Yeah, OK, awesome, thank you.
• 31:06Well, thank you so much
• 31:08for having me here today.
• 31:10We're gonna really switch gears
• 31:12and talk about social media
• 31:14and youth tobacco prevention,
• 31:16so I will give a brief outline of what
• 31:20we'll what I will talk about today.
• 31:25So I'll first given out overview
• 31:26of why we should care about East
• 31:29figure prevention in the context
• 31:30of tobacco prevention and and,
• 31:32and then the importance of leveraging
• 31:34social media to understand Easter
• 31:37youth behaviors and promotion and and
• 31:39then talk about limitations on current
• 31:41methods to analyze social media and
• 31:44then introduce how advances in new
• 31:46computational methods could be used to.
• 31:50To overcome some of these limitations,
• 31:52and then I'm going to talk about
• 31:54two specific studies in our group
• 31:56using YouTube data to understand E,
• 31:58cigarette content and social media.
• 32:03So cigarette smoking is a leading
• 32:05cause of preventable cause of death,
• 32:07disease, disability and death in
• 32:08the United States and we also know
• 32:11that smoking causes cancer's of
• 32:12a variety of charts in the body.
• 32:15However, cigarette is just one type
• 32:17of tobacco product in the market.
• 32:19There are other types of tobacco
• 32:21products such as cigars,
• 32:22smokeless tobacco, E cigarettes,
• 32:23just to name a few, that Berry in harm.
• 32:27And here what you see is this is
• 32:30a graph from CDC and this shows.
• 32:32Different tobacco products and use
• 32:34rates across the decade and what you
• 32:37see is overall this decrease in tobacco
• 32:39use right and but this dotted green
• 32:42line here is increasing E cigarette
• 32:44use over the years since 2014,
• 32:46E cigarettes have been the most commonly
• 32:49used tobacco product use among youth
• 32:51and in 2020 more than 4.5 million of
• 32:54the US youth are are using E cigarettes.
• 32:58And so when you take E
• 32:59cigarettes into consideration,
• 33:00the overall tobacco use rates.
• 33:02Is increasing among US youth?
• 33:07So for those who are not that
• 33:09familiar with E cigarette,
• 33:10I'll just provide an overview
• 33:12of what a E cigarette is.
• 33:14There are many different types
• 33:15of E cigarettes on the market.
• 33:17These devices are not regulated,
• 33:19so there is a rapid innovation such
• 33:22different product characteristics and E
• 33:24cigarette devices have evolved overtime.
• 33:26It first started out with Cigalikes,
• 33:28which is a which resembles cigarettes.
• 33:31And then evolve into second
• 33:33generation on devices like vape pens,
• 33:35which resembles like a pen.
• 33:37Third generations are these mods which
• 33:41vary in how they're it could be really
• 33:44customized in very different ways,
• 33:46and it could also excel large
• 33:49amounts of excelled aerosol,
• 33:52and then there is this pod mods here
• 33:54that sort of varies and how it looks.
• 33:57The most notable device you
• 33:59may have heard of is Jewel.
• 34:01They recently got popular because.
• 34:03They use nicotine salt instead of freebase.
• 34:05So Freebase nicotine is manipulated
• 34:07so that it has more of the
• 34:09harshness or kick the smokers likes.
• 34:12The nicotine salt is manipulated by
• 34:14lower the pH level so that it's not
• 34:17as harsh and allows for higher levels
• 34:19of nicotine and so the the problem
• 34:21with using nicotine salt is that
• 34:24because it's easier to to debate,
• 34:27you know higher levels of nicotine could
• 34:30be included in this products and therefore.
• 34:33You know the initiation among youth
• 34:35could could be a risk because
• 34:37of his high level of nicotine.
• 34:39So once Jewel started really hitting
• 34:41the market and getting really popular,
• 34:44this fifth generation of devices
• 34:46started entering the market and
• 34:48these are disposable pod devices.
• 34:50They're meant to be single use
• 34:51sometimes with multiple packs.
• 34:53They're small, they're discrete,
• 34:54they look like jewel they contain.
• 34:56They also contain they contain salt,
• 34:58so which has high levels of nicotine
• 35:00and it comes in multiple flavors.
• 35:02And there's a widely and importantly,
• 35:04they're cheap,
• 35:05so you might see a lot of these products on.
• 35:08Come in in your gas stations and
• 35:10other store convenience stores.
• 35:14So how do you cigarettes work?
• 35:15You know, even though these
• 35:17cigarettes vary in how they look,
• 35:19so the anatomy is is the same.
• 35:22So it has a component that
• 35:25holds that you liquid.
• 35:27It has a heating element.
• 35:29Any of the power power source in the
• 35:31form of batteries and is a mouthpiece
• 35:34in which the user could use to inhale
• 35:37the aerosol from from the of the
• 35:39vape and in some in some devices,
• 35:42just inhaling could activate the device.
• 35:44So what's in E liquid is made
• 35:48up of nicotine flavorings.
• 35:49The base is made up of proper link
• 35:51like coal and vegetable glycerin,
• 35:53as well as other additives.
• 35:54So in terms of nicotine,
• 35:56that's that's the main drug.
• 35:58So it stimulates the,
• 35:59stimulates the central nervous system.
• 36:01It raises blood pressure,
• 36:03respiration, heart heart rate,
• 36:04and releases a feeling of pleasure.
• 36:07And the the E cigarette that
• 36:09comes in Freebase comes in zero
• 36:12to 36 milligrams per milliliter.
• 36:14The nicotine salt on their
• 36:16marketed as percentage.
• 36:17So so for example,
• 36:19Jewel come as come as 5%,
• 36:22which is equivalent to about
• 36:2459 milligrams per milliliter.
• 36:25And you know the the issue with
• 36:27labeling is also very important,
• 36:29because you know 5% of anything
• 36:31just sounds little right.
• 36:33But if you actually look at the
• 36:35milligram per milliliter is actually
• 36:37very high level of nicotine.
• 36:38And this is what makes the
• 36:40nicotine is what makes addictive.
• 36:41There are zero level of eliquids
• 36:44and E cigarettes available.
• 36:46However,
• 36:47I should say that that's
• 36:49not that's not very common.
• 36:51These E cigarettes come in
• 36:52many different flavors.
• 36:53There's more than 7000 flavors.
• 36:56You know it comes in the typical
• 36:58like menthol tobacco flavor,
• 36:59but what's really popular or you know,
• 37:01fruit candy store that desert
• 37:03kind of flavors.
• 37:04And also there's also a lot of names
• 37:07that does not allude to actual,
• 37:10you know food,
• 37:11but like obscure names like
• 37:13you know Unicorn milk,
• 37:15or you know vampire blood
• 37:17or things like that.
• 37:18That gets people's attention.
• 37:21It is made up of chemicals.
• 37:24And the people in glycol,
• 37:25vegetable glycerin and the
• 37:27combination of the two is used.
• 37:29The ratio of the two is to create
• 37:32either more aerosol or less aerosol
• 37:34is used to intensify flavors or or
• 37:37a lower the intensity of flavors and
• 37:40nicotine or other chemicals added
• 37:42such as other water and other chemicals.
• 37:44So in addition to you know
• 37:47nicotine flavor flavorings,
• 37:48PG,
• 37:48VG,
• 37:48and other chemicals E cigarette aerosol
• 37:51have known or are shown to have
• 37:54heavy metals volatile organic compounds,
• 37:55and fine and ultrafine particles
• 37:57that can be inhaled deeply into the
• 37:59lungs by both by users as well as bystanders.
• 38:02The long term effects of this
• 38:05vaping is currently unknown.
• 38:07So why?
• 38:08Why should we care right about E cigarettes?
• 38:11So nicotine use among youth increases
• 38:14the risk of lifelong tobacco addiction.
• 38:17And it could also increase the risk
• 38:20for future addiction to other drugs as well.
• 38:22This is this E sticker.
• 38:24Use is considered an epidemic
• 38:26in the United States,
• 38:28so it's NIH, including NCIS.
• 38:31Research priority priority is to
• 38:33prevent you thicker E cigarette use.
• 38:35In fact SCI has RFA specifically
• 38:38focus on preventing E cigarette use
• 38:42among youth and has a collaborative.
• 38:45A grant that's interested in
• 38:48in particularly interested in E
• 38:50cigarette preventing E cigarette use,
• 38:52and then lastly they also have
• 38:55invested considerable resources into
• 38:59developingsmokefree.gov,
• 38:59which has resources to help
• 39:01youth to quit E cigarette use.
• 39:04So we're thinking of how
• 39:05to prevent E cigarette use.
• 39:07We've got to consider a lot of factors right,
• 39:09so there are social,
• 39:11environmental, cognitive,
• 39:12and genetic influences that plays
• 39:14a role in in youth tobacco use.
• 39:17But we also know is that tobacco promotion,
• 39:19marketing, advertising is causally
• 39:21related to youth tobacco use and
• 39:23this has been well established
• 39:25and has been talked about in
• 39:27in in surgeon general reports.
• 39:29So I'm going to focus on social media
• 39:32because now with the advent of social media,
• 39:34tobacco promotion really faces a unique
• 39:37challenge because social media is fast,
• 39:40it's cheap,
• 39:40you could reach a lot of people at a quick
• 39:44speed and it doesn't have sufficient to.
• 39:46To to control its content.
• 39:50So it might not be that surprising
• 39:52to you to hear that you know social
• 39:55media is popular among youth.
• 39:5690% of youth have used social media,
• 39:5975% have at least one active social
• 40:01media profile and 93% report visiting
• 40:04on social media site at least daily.
• 40:07When it comes to understanding how E
• 40:10cigarettes are promoted to youth is
• 40:12so important to understand how it's
• 40:14promoted so pro E cigarette content.
• 40:16Is on social media through paid ads
• 40:19through influencers promoting the
• 40:21products and on post from a share
• 40:24by their peers and other people?
• 40:26And recent studies have or are finding
• 40:28that use of social media among youth
• 40:31is associated with E cigarette use?
• 40:34So while there are many different
• 40:35types of social media platforms in
• 40:37our in our group or I'm going to
• 40:39present research findings specific
• 40:41to YouTube and I'm and I'm sure
• 40:43all of you have used YouTube so
• 40:45you're familiar with it.
• 40:46YouTube is free online streaming service.
• 40:50Is used by 1.9 billion users,
• 40:52which is a third of all Internet users
• 40:54and people spend about a billion hours a
• 40:57day watching watching online YouTube videos.
• 40:59So the the data on the right.
• 41:02The graph here shows this is data from 2018,
• 41:05so it's a bit old,
• 41:06but it shows that among teens YouTube
• 41:09is still popular and actually
• 41:11there's a recent data that's done.
• 41:14I think this year last year that showed
• 41:16that You Tube is still popular among
• 41:19youth despite newer platforms entering.
• 41:21That's popular among youth.
• 41:22We could also see that among those people
• 41:25who use they they're using YouTube often.
• 41:31So E cigarettes have been
• 41:34identified on YouTube.
• 41:35And people have examined.
• 41:37Researchers have examined E cigarette
• 41:39content on YouTube to inform prevention.
• 41:41They have identified certain
• 41:42themes that appear in this video,
• 41:43such as bait tricks that appeal to you and
• 41:47as well as unorthodox or modify users.
• 41:50So how people might hack these devices
• 41:52and use for unintended purposes,
• 41:54people are examine Instagram videos
• 41:57to understand whether there's health
• 41:59warning labels associated with them,
• 42:00as well as how do these videos explain
• 42:04health effects of E cigarettes?
• 42:06And nicotine use as well as
• 42:09the marketing content.
• 42:10These are just some examples of what's
• 42:13been examined on YouTube videos.
• 42:15However, there is a lot of
• 42:16limitation in current methods,
• 42:18so all of these prior studies
• 42:19have used human coding,
• 42:21which means that you know we
• 42:22have humans going in and and and
• 42:24watching a video to identify these
• 42:25themes and really limit the number
• 42:27of videos that could be examined.
• 42:29So in these studies they examine
• 42:31about 50 to 350 videos,
• 42:32but in our previous study we
• 42:34examined big trip videos on YouTube.
• 42:36We found that there is like 156,000
• 42:39videos just on vape tricks along and
• 42:41other studies have found that 2200
• 42:43new E cigarette videos are being.
• 42:45Upload every month.
• 42:47So,
• 42:47advances in computational methods
• 42:49can enhance the methods used to
• 42:51analyze social media data to
• 42:53inform tobacco regulatory science.
• 42:57So the other issue with social
• 42:59media is that social media custom
• 43:02tailors the content to users.
• 43:04So we know that there is a lot of E
• 43:07cigarette content and this I should
• 43:09say this algorithm of how social media
• 43:13content tailors the users is proprietary
• 43:15and we really don't know what kind
• 43:18of content user being exposed to,
• 43:20so understanding the types of content
• 43:22that you would mute or exposed to is
• 43:25really important to inform regulations
• 43:27as well as how to create prevention
• 43:30strategies such as counter marketing.
• 43:33And no study has yet.
• 43:35Try to mimic youth conducting the
• 43:37search and then apply machine learning
• 43:38to understand all the data retrieved.
• 43:44So. So advanced computational
• 43:47methods can be applied to overcome
• 43:50these limitations and and gaps,
• 43:52or another limitation is getting more.
• 43:54How do we get these data or videos
• 43:59rapidly so some platforms provide
• 44:01access via application programming
• 44:03interfaces APIs while other platforms
• 44:05require more involved coding to
• 44:07build data scrapers and API's could
• 44:10potentially deliver thousands or
• 44:12even millions of posts per day.
• 44:14And additionally computational methods.
• 44:16Can be used to understand topics
• 44:19related to tobacco prevention
• 44:20using large social media datasets.
• 44:22So now I will sort of switch gear to
• 44:25talk about two studies that we've
• 44:28used to analyze YouTube content
• 44:29on E cigarettes and these studies
• 44:32use unsupervised machine learning
• 44:33rule based classification,
• 44:35network analysis as well as
• 44:38supervised machine learning.
• 44:39The study one we wanted to understand
• 44:42whether E cigarette content
• 44:43on YouTube differs by U2 youth
• 44:46demographic characteristics.
• 44:47To understand whether you think content
• 44:49is being tailored to certain views.
• 44:51To do this,
• 44:52we create a 16 fictitious viewer
• 44:55profiles and these viewer
• 44:57profiles were separated by age.
• 44:59So 16 year olds and 24 year olds by
• 45:02gender as well as race ethnicity.
• 45:04We may profile for white,
• 45:05black,
• 45:05Hispanic youth and we used factory
• 45:08reset Android phone with Orbot
• 45:10app to delete all personalization
• 45:12based on search results.
• 45:13And these are the search results
• 45:15are words that we use related
• 45:17to E cigarettes and we conducted
• 45:19this search inmate 720.
• 45:20And we obtain 140 videos which
• 45:22is equivalent to about 7 pages
• 45:25of 20 videos per page for each
• 45:27search word and fix your profile.
• 45:30And so after we remove all the
• 45:33duplicates we had 4201 non duplicate
• 45:35videos in our search result.
• 45:38The first we wanted to understand,
• 45:40you know we had to develop a cool bug
• 45:43to understand what we're examining.
• 45:45So what we're interested in examining
• 45:47was like what are the videos being
• 45:49related to E cigarettes, right?
• 45:50So were they product reviews,
• 45:52vape tricks, health information?
• 45:54You know?
• 45:55What were these videos talking about?
• 45:58And then we want to know who are the
• 45:59people who are uploading these videos,
• 46:01where they private users,
• 46:03retailers and we want to know what
• 46:05types of E cigarette products are
• 46:07being featured or the eliquids
• 46:09box mod pods and so on.
• 46:11We also want to see if there were
• 46:13actually selling these products
• 46:15to youth and so we buy we look to
• 46:17see whether this external links
• 46:19for purchasing and discount codes.
• 46:22So once we quoted this book, I'll catbug.
• 46:24We're two independent reviewers
• 46:26randomly review the finalizer themes,
• 46:28and then we establish integrative
• 46:31reliability.
• 46:32And then after that one quarter
• 46:34labeled 1000 videos,
• 46:36which was used to train supervised
• 46:38machine learning algorithms for study one,
• 46:41I'm going to focus on video themes
• 46:43because our goal was to understand
• 46:44whether the video theme content
• 46:46was different among users.
• 46:48However,
• 46:48the methods are the same for both studies.
• 46:52So using network analysis we plotted
• 46:54exposure similarities as a network of
• 46:56demographic attributes and videos.
• 46:58So what you see here is a graph of male,
• 47:00female and by different age groups and
• 47:02the thickness of this purple line indicate
• 47:05the normal number of common videos.
• 47:07So what we see that both 24 year old
• 47:10profiles have the most most videos
• 47:13in common and then it's 24 year
• 47:16old male and 16 year old female.
• 47:18And we also use K means clustering,
• 47:20which is a powerful unsupervised machine
• 47:23learning algorithm that finds similarity
• 47:25between items and grouped them into
• 47:27clusters without the human input.
• 47:29And then we used human data.
• 47:33A human labeled data as an input to
• 47:36graph convolutional network for machine
• 47:39based classification of the 4201 videos,
• 47:42titles and descriptions.
• 47:44And we found that just north of high
• 47:47accuracy and using GCN we were able to
• 47:51identify what the video themes were.
• 47:55So 49% of the videos were product reviews,
• 47:5926.9 videos.
• 48:00Or informational or or modifying
• 48:01so these are videos that teaches
• 48:04people how to use an E cigarette or
• 48:06how to modify or hack in E cigarette
• 48:0815% or health information.
• 48:10Videos about E cigarettes and 9% were
• 48:14just like other types of videos.
• 48:19And so after performing clustering
• 48:21classification, we calculate the
• 48:23percentage of each video type in
• 48:25each category by demographic groups.
• 48:28So what we find here is that.
• 48:32The green color is the product of you,
• 48:34so these are videos that talk about you
• 48:36know like give product reviews on the
• 48:38product and we find that the product
• 48:39reviews represented by the green color
• 48:41is more common among 24 year old profiles.
• 48:45Health health is represented by
• 48:47Orange is similar or cross a little
• 48:50bit more common among males.
• 48:53And what you what's interesting
• 48:54here is that the lighter bluish
• 48:56purplish color here is informational
• 48:58videos where how to use an Instagram
• 49:01or how to modify an Instagram.
• 49:03And that's a lot more common
• 49:05among underage female group.
• 49:07And other other videos are more common,
• 49:10represented by the darker purple
• 49:12here for male 16 year olds,
• 49:14which is concerning because these
• 49:16videos had content like you know
• 49:18related to cannabis vaping and
• 49:20other vape tricks and so on.
• 49:22So there is concerning content
• 49:24that shows that more tailored
• 49:26towards younger younger youth.
• 49:28So our results show that demographic
• 49:31attributes does factor into
• 49:33YouTube algorithmic systems.
• 49:35In the context of esseker
• 49:37related queries on YouTube,
• 49:38we found that the similarities between
• 49:41exposure for male and female 24 year
• 49:43olds and actually higher than than
• 49:46the connection between other pairs.
• 49:47We also found that underage
• 49:49users work more exposed to more
• 49:51instructional videos on E cigarettes,
• 49:53while all the age groups were
• 49:55most exposed to product reviews.
• 49:57So all of this is concerning because.
• 50:00We because this shows that underage profiles,
• 50:04right so 16 year olds are able to or
• 50:06are exposed to E cigarette content
• 50:09despite YouTube having policies
• 50:12about prohibiting Easter great
• 50:14content to their underage viewers,
• 50:16such as product reviews.
• 50:20So now I'll talk about our second study.
• 50:24So we identify we have four areas
• 50:26of interest, which is, you know,
• 50:27what are the video themes?
• 50:28Who are the people uploading these videos?
• 50:31You know what types of E cigarette
• 50:32products are being featured and is
• 50:34their presence of sales and discounts.
• 50:36So what we want to do is we you know we
• 50:38could use human coders to identify them,
• 50:40but we wanted to know can we use
• 50:43supervised machine learning to
• 50:44identify these key areas that could
• 50:47inform E cigarette prevention?
• 50:49So what is machine learning?
• 50:51Machine learning is powerful and it could
• 50:52be used to examine a large data set.
• 50:54So in this case large,
• 50:55many videos machine learning
• 50:57has been used to examine social
• 50:59media content around tobacco use.
• 51:01However,
• 51:02no studies have examined YouTube
• 51:04videos using machine learning.
• 51:06So this is a quick overview of
• 51:09what a machine learning does,
• 51:11so using an algorithm to it uses
• 51:15an algorithm to predict something.
• 51:16So in this case,
• 51:17if we're interested in it saying you know,
• 51:19can we use machine learning to to to
• 51:21identify if a video featuring an E
• 51:24cigarette first we need to teach the
• 51:26algorithm what an E cigarette is, right?
• 51:28So we we teach it, if it's jewel,
• 51:30if it's east, sick, if it's vape,
• 51:32then it's considered an E cigarette
• 51:34and this is A and this is,
• 51:35this data set is now.
• 51:37Used to train the machine learning
• 51:40algorithm and the algorithm learns
• 51:42from this example data set and later
• 51:45uses a different data set to predict
• 51:47whether they could identify an E cigarette.
• 51:50So if it correctly identify
• 51:51that there is an issue,
• 51:53regret that he's a successful model.
• 51:55If it fails to identify where
• 51:57if an E cigarette exists,
• 51:59when it doesn't,
• 52:00then we reach train this machine
• 52:02article rhythm until we could
• 52:04achieve a successful classification.
• 52:09So in our study, this is a model
• 52:11performance of our machine learning
• 52:13models for each of the four categories,
• 52:15F1 score is a measure of test accuracy.
• 52:18It's calculated from the
• 52:19precision and recall of a test.
• 52:23And this is a like a pretty good
• 52:25score considering the complexity of
• 52:27the themes that we were identifying.
• 52:31So what do we find?
• 52:32So this is a little more detailed
• 52:35look into video themes that we use
• 52:37in this case study versus our study.
• 52:40One that's what we have more themes here,
• 52:42and we also similarly identify the
• 52:44product views were the most common.
• 52:45And if you see a picture image here,
• 52:47this is an example of what a
• 52:49product review look like, right?
• 52:50This is Jewel starter Kit
• 52:52unboxing and review.
• 52:53And we also found that 72nd highest
• 52:56video theme was modified video that
• 52:59teaches people how to modify and
• 53:01informational videos on how to use
• 53:03health information was 11% other
• 53:06themes that were still ysaguirre.
• 53:099% of marijuana related things
• 53:11was 6% and other irrelevant theme
• 53:14which is like non E cigarette
• 53:16theme for five percent 5.6% and
• 53:18vape chicks was one point 1%.
• 53:23So product type, so this is so this
• 53:24is all the different types of products
• 53:27that we identified through machine
• 53:29learning and and what this actually
• 53:31shows is that there are a variety of
• 53:34different types of E cigarette products
• 53:36that are being featured on on YouTube.
• 53:39So who are the people who
• 53:41are uploading these videos?
• 53:4354% were weighed enthusiasm,
• 53:44so who are big enthusiasts?
• 53:46These are independent users who post
• 53:49almost exclusively about bathing.
• 53:51So when you go to the channel page to see
• 53:53what kind of videos they've uploaded,
• 53:54it was mostly related to vaping,
• 53:56but they were not directly
• 53:58connected to vaping company,
• 54:00so we cannot verify that
• 54:01their influences or not.
• 54:03So these are some examples of like
• 54:05account of people who've a person.
• 54:08Vape enthusiasts of channel page.
• 54:10As you could see,
• 54:11all the contents related to vaping.
• 54:13This is problematic because when
• 54:15it comes to regulating content,
• 54:17you cannot regulate private users, right?
• 54:21You can't tell the regular
• 54:22person to say you know.
• 54:23Don't post things about vaping.
• 54:25However,
• 54:25you could regulate influencers
• 54:27who get paid by the industry to
• 54:29post their products and the the.
• 54:31The difficulty with vape enthusiasts
• 54:33is that there's no way to tell
• 54:35who are vape enthusiast,
• 54:36who are influencers and her regular users.
• 54:4021% are stores,
• 54:4112% is other sources and six point 4% of
• 54:45medical community and 6% of private users.
• 54:52So 59% of video did not have any
• 54:54discount or links 34% of the videos
• 54:58had external links for purchasing
• 55:00and 5% or have other discount methods
• 55:03and one point 7% had discount.
• 55:04So this is a screenshot of of of
• 55:08instructional videos like beginning
• 55:10beginners vaping tip that also had
• 55:13a link that you could purchase
• 55:15as well as a coupon code.
• 55:17For purchasing,
• 55:18So what do we find in this study?
• 55:22We found that I complicated things
• 55:25relevant to E cigarettes could be
• 55:28identified using machine learning and
• 55:30fictitious youth viewer profiles on YouTube.
• 55:32We identified videos that violated
• 55:35YouTube tobacco policy restricting
• 55:37promotional content to underage minors,
• 55:39such as product reviews and purchasing links.
• 55:42Again, there was a high level
• 55:43of industry presence and such
• 55:45as faith enthusiast at stores.
• 55:49So overall conclusions, you know.
• 55:51Mixed methods such as qualitative
• 55:54analysis using human labellers and
• 55:56computational methods can really reveal
• 55:58E cigarette use content to inform youth,
• 56:01tobacco prevention and social media has
• 56:04really a really rich data and has a good.
• 56:09You know you could have a really good
• 56:12understanding of youth behaviors as well as
• 56:14promotion and sales that youth can access.
• 56:16And again, this is our current
• 56:18occurring a lot on YouTube as well
• 56:20as on other social media platforms.
• 56:22And to prevent youth E cigarette uptake,
• 56:25regulation of social media,
• 56:27a promotion that occurs in
• 56:29social media is really needed.
• 56:32So you know this is one example of
• 56:34how social media could be leveraged
• 56:36using qualitative and computation
• 56:38method to understand certain
• 56:39behaviors that could prevent KENS.
• 56:42Has cancer prevention
• 56:43implications like tobacco use?
• 56:45But certainly this this type of methods could
• 56:48be used to understand other behaviors that
• 56:51has direct implications to preventing cancer,
• 56:54such as, you know,
• 56:55physical activity,
• 56:56diet, obesity as well.
• 56:59So I'd like to acknowledge our funding
• 57:02stores as well as Yale Tobacco Center
• 57:04of the Study on Tobacco Regulation,
• 57:07tobacco product of Youth in addiction and
• 57:11also our team in University, Texas Austin,
• 57:14who is leading the computational methods.
• 57:19So thank you for your attention.
• 57:22Thanks Doctor Kang that
• 57:24that was that was great.
• 57:26And it's open for questions,
• 57:29please put him in the chat.
• 57:30I know we only have a few minutes,
• 57:31but maybe we could stay
• 57:33over for a minute or two.
• 57:35If people have questions.
• 57:39Have you? Reached out to YouTube and showed
• 57:45them your data and asked whether they,
• 57:47I mean it does sound like there's
• 57:50clear you have clear evidence that
• 57:52their policies are being violated.
• 57:54Presumably they have the computational
• 57:56firepower to be able to do similar things.
• 58:00Is it something that they may
• 58:02be convinced to look into?
• 58:04Yeah, that's a great question.
• 58:05You know, I have a paper cut
• 58:07currently under review that's
• 58:09looking at all of the self imposed.
• 58:11Social media policy.
• 58:12Across all the all the social media platforms
• 58:15on tobacco and and not surprisingly,
• 58:17you know all of the social media
• 58:19platforms that do have these policies.
• 58:22They're not being enforced,
• 58:23so so hopefully you know this will
• 58:25bring some more greater attention.
• 58:27Aside from You Tube.
• 58:28But just looking at all the social media
• 58:31platforms and what more could be done.
• 58:33Yeah, and I think could get,
• 58:35you know,
• 58:35I think 1 translational component
• 58:37is that we publish in peer review
• 58:39journals and a lot of this information
• 58:41don't get out into the bigger world
• 58:43and I think just doing some of
• 58:44that legwork might be important in
• 58:46getting some of these attention
• 58:47for two social media platforms.
• 58:51It's important work.
• 58:54So it's it's a few minutes
• 58:55after the hour doesn't look like
• 58:57there's any more questions.
• 58:58So again, thank you to both
• 59:00the the presenters for very
• 59:02interesting discussion and.
• 59:04We'll see you at the next grand rounds.
• 59:06Thank thank you.
• 59:09Thank you.