"Off-target Activity of Targeted Therapies Undergoing Clinical Trials in Cancer"

February 02, 2022

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Yale Cancer Center Grand Rounds | February 1, 2022

Presentation by: Dr. Jason Sheltzer

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00:00And I want to introduce Jason Shelter.
00:06Jason is an assistant professor of
00:09surgery and received his PhD from MIT,
00:12where he worked in the laboratory
00:14of Doctor Angelika Amon in the Koch
00:17Institute for Cancer Research.
00:18After completing his PhD,
00:20he established his own research
00:22group as an independent fellow at
00:24the Cold Spring Harbor Laboratory.
00:26The Shelter Lab is broadly interested
00:29in understanding the genomic changes
00:31that drive cancer progression,
00:32particularly aneuploidy,
00:33which is found in more than
00:3590% of human tumors.
00:37Additionally,
00:38they're working to identify genomic
00:40alterations that create druggable
00:42therapeutic vulnerabilities and cancer.
00:44They have recently discovered
00:46the first ever selective
00:48inhibitor of the kinase CDK 11,
00:50and developing CDK 11 inhibition as a new
00:53strategy to treat malignancies without.
00:56Further delay Jason all yours.
01:01Thanks so much for the kind introduction,
01:04so I'm very excited to be able to share
01:06with you today some research my lab
01:08has done about off target activity of
01:11cancer drugs undergoing clinical trials.
01:13These are my disclosures and this
01:15project really comes from a journal
01:17article that I read a few years ago
01:19that had a statistic in it that I
01:21found to be just absolutely shocking.
01:23If you look at all drugs that enter
01:26clinical testing and oncology,
01:2897% of drug indication pairs that
01:31enter clinical trials fail during that
01:34testing and don't end up receiving
01:36FDA approval and this 97% failure
01:39rate for oncology drugs is the
01:41highest of any field of medicine.
01:43So more cancer drugs fail than
01:45psychiatric drugs or endocrinology,
01:47drugs, or infectious disease,
01:49drugs or anything else.
01:51And if you look at the proximate causes
01:54for trial failure, the most common,
01:57immediate causes that drugs run into
01:59are toxicity and limited efficacy.
02:01That is,
02:02the drugs have too many side
02:03effects for patients to safely take.
02:05Or maybe the patients can safely take them,
02:07but they have limited efficacy and they
02:10don't actually shrink the patient's tumor.
02:12And while these are kind of the.
02:13Proximate causes for oncology
02:15drug trial failure.
02:17I think the underlying reasons
02:19why so many drugs run into these
02:21problems isn't very well understood,
02:24and today I'm going to share some
02:26evidence from my lab towards one
02:28potential explanation for this high
02:30failure rate and the hypothesis that
02:32I'm going to argue for is that many
02:35drugs are entering clinical testing and
02:37oncology with an incorrect understanding
02:40of their mechanism of action,
02:42and I think this mischaracterization
02:44of cancer drugs.
02:45Maybe one factor by no means the only factor,
02:48but one factor that contributes
02:50to this extremely high failure
02:53rate for oncology therapies.
02:55So in my lab,
02:56we've been interested in using
02:58genetic approaches to investigate
03:00the mechanisms of action of
03:02different experimental cancer drugs,
03:04and by searching through the current
03:06literature and looking on clinicaltrials.gov,
03:08we put together a list of 12
03:11different drugs targeting 7 different
03:13cancer related proteins that we
03:15were interested in studying.
03:16These drugs have been used in
03:19about 30 different clinical trials
03:21targeting several 100 cancer patients.
03:23So six of these proteins are reported
03:26to be cancer genetic dependencies.
03:29That is the function of these proteins
03:31is reported to be essential for the
03:33growth and proliferation of cancer cells.
03:36For instance,
03:36pack four is a kinase.
03:38It's been reported that Pack 4
03:41kinase activity is essential for
03:43the growth of colon cancer.
03:44Lung cancer, breast cancer,
03:46and a few other cancer types.
03:48And because of that genetic data
03:51concerning pack four that motivated.
03:54Wiser to develop a small molecule
03:56pack for inhibitor
03:59PF 3758309 which they then
04:01entered into clinical testing.
04:03Caspase 3 is a little bit different.
04:04I'm going to talk about Caspase 3 separately,
04:07so we were interested in testing the
04:09mechanism of action of these drugs
04:11and seeing whether they killed cancer
04:13cells through the inhibition of these
04:15proteins and as a first step towards
04:17this process we wanted to confirm that
04:19the proteins these drugs were targeting
04:22were truly cancer genetic dependencies.
04:24That is, they were essential.
04:25For cancer growth and to investigate this,
04:28we set up a crisper competition
04:29assay to see what happened when
04:31we knocked these jeans out.
04:33To do this CRISPR assay,
04:35we transduced cancer cell lines
04:37with cast 9 and then we transduced
04:40them a second time with a guide
04:42RNA coexpressed along with GFP.
04:45This would then create a mixed
04:47population of GFP positive cells
04:49that had the guide RNA and caused
04:51mutations in the target gene and then
04:54UN transduced non fluorescent cells.
04:56We then measure the percentage
04:58of GFP cells over time.
05:00If the percent of GFP positive
05:02cells decreases overtime,
05:03that tells us that whatever gene
05:05the guide RNA is knocking out,
05:07it must be required for cancer growth
05:09because the GFP positive cells are dying.
05:12In contrast,
05:13if the percent of GFP positive
05:15cells stays about the same,
05:16then that's evidence that whatever
05:18this guide RNA is targeting,
05:20it isn't important for cancer
05:22growth because these GFP positive
05:24cells can grow just fine.
05:25So that's what the assay looked like we
05:28designed and cloned multiple guide RNA's.
05:30Targeting each of the putative
05:32cancer genetic dependencies we were
05:34interested in studying and then we
05:36did a bunch of competition assays
05:38and this is what one of these
05:40competition assays looks like.
05:42So here we're in MD AMB 231 sells
05:45a triple negative breast cancer
05:46cell line as negative controls.
05:48We have guide RNA's targeting
05:50nonessential loci. Rosa 26 and eight.
05:53The S1 guide RNA's targeting.
05:55These genes exhibit no drop out.
05:58As positive controls we have guide RNA's,
06:00targeting the essential replication genes,
06:03or PA3 and PC,
06:04and a guide RNA's targeting these genes,
06:07which are required for DNA replication.
06:09Drop out between 50 fold and 200
06:12fold over 5 passages in culture.
06:15We then looked at the effects of guide RNA's,
06:17targeting each of the putative
06:19cancer genetic dependencies that
06:20we were interested in studying,
06:22and we were really astounded when the
06:25guide RNA's targeting these cancer drug
06:28targets exhibited no dropout whatsoever.
06:30These guide RNAs behaved exactly
06:32the same as guide RNA's,
06:33targeting known non essential
06:35genes like Rosa 26 and a VS1.
06:39This was incredibly surprising to us
06:41because right now there are patients who
06:43are receiving anti htac 6 therapy and.
06:45Anti milk therapy and anti Kim.
06:47One therapy based on the belief that these
06:50proteins are required for cancer growth,
06:53but this experiment suggests that in
06:56these experimental conditions in this
06:58cell line we can eliminate these genes
07:01without any effect on cancer whatsoever.
07:04So this is what it looked
07:05like in one cell line.
07:06We ended up repeating this faceing 32
07:09different cancer cell lines from more
07:10than a dozen different cancer types,
07:12and in each of these experiments
07:14we got the same result.
07:15There is no drop out of the guide RNA's
07:17targeting these drug targets and there
07:19was no evidence that any of these genes
07:22were actually dependency in any cancer type.
07:24So this made us take a step back and think,
07:27well, is there something that
07:28could be going wrong in this assay?
07:29Could we be you know doing
07:31something incorrect here?
07:32And so we thought.
07:34Well, maybe with CRISPR.
07:36We're generating heterozygous
07:37mutations but not homozygous mutations.
07:40You know, maybe we're we're
07:42introducing mutations into these genes,
07:43but we're not really knocking
07:45out the total protein.
07:47So we thought OK,
07:48instead of doing this population
07:50based approach,
07:51let's make single cell Dr Knockout
07:53clones and be as sure as humanly
07:55possible that we were really
07:57eliminating 100% of the target protein.
08:00So we did that.
08:01We used A2 CRISPR guide RNA strategy
08:03where we designed to guide RNA targeting
08:06an upstream exon into downstream
08:08exon so that we could physically
08:10cut the gene out of the genome.
08:12And there would be no protein left.
08:14So we sorted a single cells
08:16that were double positive.
08:18That picked up both guide RNA's
08:20that we transduced in and then we
08:23verified target knockout using
08:25two independent antibodies.
08:26So for instance 1 gene we were
08:29interested in studying as math K14.
08:31This is the gene that encodes
08:33the kinase P38 alpha.
08:34We generated knockout clones and we
08:37verified complete target knockout
08:38using one antibody and then verified
08:41it again using a second antibody.
08:43So that we could be,
08:44you know,
08:45as sure as physically possible that
08:47we had truly eliminated all trace of
08:50these putative cancer drivers from the cell.
08:53However,
08:53when we tested the fitness effects
08:55of these knockout clones,
08:57we got exactly the same result
08:58that we got from the competition
09:01assays knocking out these putative
09:03cancer genetic dependencies had
09:05no effect on cancer growth.
09:07So here, for instance,
09:08is a proliferation assay in
09:10a Melanoma cell line.
09:11We have three map K14 knockout
09:14clones and then two control rows
09:16of 26 clones and these map K14
09:19knockout clones grow exactly as
09:20well as the rows of 26 control.
09:23Jones,
09:23we could also put these cells in soft
09:26Agar challenge their clonogenic ability.
09:28We saw no difference in
09:31Clonogenic ability either.
09:32These knockout cells grew just fine.
09:35So to sum up,
09:36a whole bunch of data that I
09:37don't have time to show you.
09:39We ended up eliminating all six
09:41different cancer driver genes
09:42that we were studying in at least
09:45three different cancer types each,
09:46and there was no fitness effect
09:49whatsoever that we could discuss.
09:51So this was a really strange
09:53finding to us and it made us try
09:54to figure out what was going on.
09:56So we were looking at the
09:57targets of 12 different
09:59anti cancer drugs in various stages
10:00of clinical development and we
10:02looked at these drug targets with
10:04multiple different CRISPR techniques.
10:05We did CRISPR competition assays.
10:08We made CRISPR knockouts,
10:10but concordantly.
10:11They both showed that we could eliminate
10:13these jeans without a detrimental
10:15effect on cancer proliferation.
10:17This then raised the question well,
10:19why were these genes believed to be
10:22cancer essential in the 1st place?
10:24And when we looked into the
10:26literature on these genes,
10:27we found the two main lines of evidence
10:29had identified these genes as cancer,
10:31essential initially.
10:32The first line of evidence identifying
10:35these genes as cancer essential were
10:38experiments done using RNA interference.
10:40The second line of evidence were
10:42experiments done using small molecule drugs,
10:45many of which had then gone
10:47on to enter clinical testing.
10:49So we wanted to see if we could
10:51backtrack a little and understand why
10:52we had come to such a different result
10:55than these previous experiments done.
10:57Using RNA I and small molecule drugs.
11:01So I'll first show you what we
11:02learned when we looked at some
11:04of the prior RNA I experiments.
11:06So this is an RNA I experiment
11:08published in the literature a few
11:10years ago that had identified the
11:12kinase pack for as essential for
11:14the growth of colon cancer cells.
11:16In this experiment,
11:17the investigators took SI
11:19RNA's targeting pack four.
11:21They introduced them into HCT 116,
11:23colon cancer cells,
11:25and they found that the SI RNAs decreased
11:28colon cancer cell survival data like
11:31this motivated Pfizer to enter a pack
11:33for inhibitor into clinical trials.
11:36We had found no fitness effect when we
11:38had knocked out packed 4 using CRISPR,
11:40so we wanted to see if we could
11:42recapitulate this result that
11:44had been published using RNA I.
11:46Two of these SI RNA constructs were
11:48commercially available and we had
11:50HCT 116 cells growing in my lab,
11:52so we purchased these siren's
11:54from this prior publication and
11:56then tested them in our cells.
11:58We transfected these siren's,
12:00the same from the prior publication
12:03into HCT 116 cells,
12:04and we could confirm by Western blot.
12:06These SI RNAs decrease protein
12:09expression as expected and we did
12:12a self survival assay and we could
12:14confirm that they killed HCT 116.
12:16Colon cancer cells exactly
12:18as had been reported.
12:20However, using CRISPR,
12:21we were also able to generate a
12:24pack for knockout clone in this
12:26exact same cancer cell line.
12:28So here we had a pack for knockout clone.
12:30You can see there's no pack for
12:32expression in either the control
12:34or the knockdown condition.
12:36And then when we did a self
12:38survival assay on these cells,
12:40we found that transfecting the
12:42pack 4 knockout cells with pack 4
12:45targeting SI RNA had exactly the same
12:48detrimental impact on colon cancer
12:50survival as it did in the pack for
12:53expressing Rosa 26 control cells.
12:55So these packed 4 targeting SI RNAs
12:58are killing colon cancer cells,
13:00but their ability to kill colon
13:02cancer cells is entirely
13:03independent of the expression
13:05of pack four because they're.
13:06Exactly as lethal in the control cells
13:09expressing pack four as they are in the pack.
13:124 knockout clones that we
13:14generated using crisper.
13:15So this prior experiment was
13:17was totally reproducible.
13:18These sirens killed colon cancer cells,
13:21but just the interpretation was wrong
13:24because the toxicity of these Sir nase,
13:26is just entirely independent
13:28of pack for expression,
13:30and this seems to be commonly
13:32the case where we test SIRN as
13:35and SH RNA's in the literature.
13:37Over CRISPR derived knockout clones.
13:39The SI and SH RNA's may kill cancer cells,
13:42but it's just independent of the expression
13:45of the gene that they were designed against.
13:48The next thing that we wanted to
13:49figure out was what was going on
13:51with the small molecule drugs,
13:52many of which had then gone on to enter
13:55clinical testing and I'll show you
13:56what happened with one of those drugs.
13:59So pack one is a drug that was
14:01described with few years ago in a
14:03paper in nature chemical biology.
14:05It was developed as a Caspase 3
14:08activator compound so the apoptosis
14:10enzyme caspase 3 is normally present
14:12in an inactive procaspase state in
14:15the cell and pack one was developed
14:18to catalyze the conversion of caspase
14:203 from its inactive procaspase
14:22state to its active caspase 3 state,
14:25at which point it would then
14:27kill cancer cells in this drug.
14:29Has been entered into three
14:31different clinical trials.
14:32However,
14:32this mechanism of action was worked
14:35out based on in vitro biochemistry
14:37and no one had described a mutation
14:40in Caspase 3 that conferred resistance
14:42to it or had assessed the effects of
14:45this drug in a Caspase 3 knockout cell.
14:48So using CRISPR we generated multiple
14:51Caspase 3 knockout clones and then
14:53we did a dose response curve.
14:55Examining the viability of wildtype
14:57and Caspase 3 knockout clones in
15:00different concentrations of pack one.
15:03So this is what it looked like
15:04for two control clones,
15:052 clones expressing Arosa 26 guide RNA pack,
15:09one is a potent anti cancer agent.
15:12You can see it has an IC50 value
15:14of around one or two micromolar.
15:16However,
15:16when we did the same assay in the Caspase
15:203 knockout clones that we generated,
15:22we ended up getting exactly
15:24the same drug curve.
15:25This drug is exactly as potent in caspase
15:283 knockout clones as it is in caspase
15:313 expressing Rosa 26 control clones.
15:34It has an IC50 value of 1 to 2 micromolar,
15:38regardless of whether these
15:39cells express caspase 3,
15:41so this drug,
15:42which entered clinical trials as
15:44a caspase 3 activating compound.
15:46Its anti cancer activity actually
15:49comes from something entirely
15:51independent of caspase 3 and this
15:53is actually the case for many
15:54of the drugs that we studied.
15:56So to show you a few more examples,
15:58HDK 6 is a histone deacetylase
16:01Celgene has developed each DAC.
16:036 inhibitors sitter in a statin
16:05richelain ISTAT.
16:06We knocked out HDK 6 but we saw no change
16:09in sensitivity to these
16:11putative HDK 6 inhibitors.
16:13Milk is a cancer related
16:15kinase uncle therapy.
16:16Science is developed this drug,
16:18Novartis, developed this drug.
16:20We use CRISPR to knockout milk.
16:22We saw no change in sensitivity to
16:25these milk inhibitory compounds.
16:28So to sum up a whole bunch of data
16:29that I don't have time to show you,
16:31we found that target knockouts conferred
16:33no resistance for 12 different
16:35cancer drugs that we were studying.
16:37We made these knockouts and did
16:39these tests in at least three
16:41different cancer types each,
16:42so this kind of leaves us in an odd position.
16:46We were studying 12 different preclinical
16:48or clinical anti cancer drugs and
16:50in each of these cases we found that
16:53the reported mechanism of action.
16:55Was actually incorrect.
16:56This then raised the question well if
16:59these drugs are killing cancer cells at
17:01nanomolar or low micromolar potency,
17:04how is it they actually work?
17:05What is it they're actually targeting?
17:07We wanted to see if we could figure
17:08out how they were actually functioning.
17:11We've had the best success so
17:13far with one drug called O TS964.
17:16This is what the drug looks like.
17:18It was described in a paper in science
17:20Translational Medicine a few years
17:22ago as an inhibitor of a kinase called PBK,
17:24which is also called Pop K in the literature,
17:27but using CRISPR.
17:28We knocked out PVK and we
17:30saw no effect whatsoever.
17:32On sensitivity to this compound
17:34telling us that this drug O TS964
17:37must have some other cellular target.
17:40To see if we could figure out
17:41what this drug was actually doing,
17:43we used a genetic based approach
17:46for this approach.
17:47We took highly mutagenized
17:49colon cancer cells, HCT 116.
17:51They have a very high mutation rate
17:53because they're microsatellite unstable
17:55and then we expose these drugs to
17:58a nearly lethal concentration of
17:59O TS96 four such that about 99.9%
18:03of cells on the plate were killed.
18:06However,
18:06there were a few stragglers that
18:08remained when we cut these cells
18:09in the drug for a period of weeks
18:11until these cells were able to grow
18:13and form little micro colonies.
18:15We then subjected these cells to
18:17whole exome sequencing and when we
18:20did sequencing on the resistant clones,
18:22what we were hoping to see was a
18:24mutation that blocked whatever it was.
18:27This drug was actually targeting.
18:29Maybe these cells could survive a
18:31lethal treatment because they had
18:33some mutation preventing drug binding
18:35to whatever O TS96 or was actually doing.
18:38So when we did whole exome sequencing
18:40on these clones,
18:41we were really excited to see that
18:43every clone that we looked at had
18:46the same mutation in it.
18:47Every drug resistant clone had a
18:49mutation in the cyclin dependent kinase,
18:51CDK 11.
18:52They had a glycine to serine substitution,
18:55right smack dab in the middle of
18:57the CDK 11 kinase domain.
18:59So this immediately suggested to
19:01us that maybe this drug,
19:02which had been developed as a PDK inhibitor,
19:05was actually functioning through
19:07inhibition of CDK 11.
19:09Instead, one potential limitation
19:11to this is that, well,
19:13there actually isn't a precedent for this.
19:14CDK 11 hasn't been previously dropped,
19:18so we wanted to see if this mutation
19:20actually had anything to do with
19:22sensitivity to OTS 964 in order to do that,
19:26we wanted to see whether this mutation
19:28that we discovered in the resistance.
19:29Jones was actually sufficient to
19:32confer resistance to OTS 964.
19:34To test this,
19:35we used a CRISPR knockin strategy
19:37where we introduced this glycine
19:39to serine substitution that we
19:41recovered in drug resistant cells.
19:44We knocked it into drug naive
19:46cancer cells and then tested its
19:48effects on on O TS964 sensitivity.
19:50This is what it looked like.
19:53Here we have four different
19:54cancer cell lines treated with a
19:56lethal concentration of O TS964,
19:58with a negative control guide RNA.
20:00Or if we just cut in the CDK 11 gene,
20:03we have no cancer cell viability.
20:05But if we introduce a repair template that
20:08includes the glycine to serine substitution,
20:11then we can restore viability
20:12in the presence of an otherwise
20:14lethal concentration of O TS964.
20:17So this tells us that this mutation is
20:19in fact both necessary and sufficient
20:22for resistance to this compound.
20:24We then followed this up with
20:26some biochemical assays.
20:27We confirmed that O TS964 inhibits CDK 11.
20:31With an IC50 value of around 40
20:34to 50 animal or in vitro,
20:36and we did a cell based target engagement
20:39assay using mass spectrometry,
20:41we found that 100 animal or
20:43treatment with O TS964.
20:45It didn't bind to hundreds of other
20:48cellular kinases, but it bound.
20:50It caused about 70% of binding site
20:53occlusion for CDK 11, and only CDK 11.
20:56So from this work we concluded that
20:59by profiling a mischaracterized
21:01anti cancer agent we were actually
21:03able to serendipitously discover the
21:06first selective inhibitor of CDK 11.
21:10So to sum up what I told you so far,
21:13we're kind of operating in a space
21:15in which the vast majority of new
21:17therapies that get tested in human
21:19patients in oncology don't end up working,
21:21and we put together a collection
21:23of these drugs to study.
21:24And one thing that we found while
21:26studying them is that many of these
21:29drugs have actually been designed
21:30to target proteins that have no
21:32detectable role in cancer growth.
21:34Furthermore,
21:35while these drugs do kill cancer cells,
21:38they largely kill cancer cells
21:39through off target effects rather
21:41than through the target that they
21:43were initially designed against,
21:45and I think that this can increase
21:46the burden of side effects and the
21:48decrease the efficacy when some
21:50of these drugs are actually used.
21:52We don't truly understand how
21:54they're working or where their anti
21:56cancer activity comes from.
21:58Think this conclusion has a number of
22:00important considerations and caveats though.
22:02For instance, there could be
22:04unrecognized cell type specificity.
22:06We did these competitions
22:08in 32 cancer cell lines.
22:10We generated knockout clones
22:11in three cancer types each,
22:13but it was,
22:13you know,
22:14physically, impossible for us to test
22:16every subtype of leukemia or every subtype.
22:20Kidney cancer in existence,
22:21and so we can't fully recognize
22:23rule out some unrecognized cell
22:24type specificity that hasn't been
22:26reported in the literature on these.
22:28Targets. Secondly,
22:30we specifically tested the hypothesis
22:33that these proteins are required
22:35for cell autonomous cancer growth,
22:38that is, cells going from you know,
22:39one cancer cell to 2:00 to 4:00 to 8:00,
22:41and so on, and this had been reported for
22:43each of the drugs that we had studied.
22:45However, if it turned out that,
22:47say, pack four had some role in
22:50angiogenesis or in immune evasion,
22:53or some other non cell autonomous process,
22:55that wouldn't be ruled out for
22:57the cell autonomous proliferation
22:59focused assays that we've done.
23:01I think a third important consideration
23:03is while our data suggests that
23:05these drugs are promiscuous and
23:06may have multiple targets in the
23:08cell just because a cancer drug is
23:10promiscuous doesn't necessarily mean
23:11that it will fail in the clinic.
23:14There are a number of drugs like sunitinib,
23:16Serafin, IB which do have multiple
23:19targets in the cell.
23:21And so,
23:22just because something is promiscuous
23:23doesn't necessarily mean that it will fail.
23:25However,
23:26I think that if our goal in cancer
23:29biology is to kind of reach a plateau
23:31of targeted precision medicine where
23:33you sequence a patient's tumor,
23:35you identify the mutations and
23:38amplifications and alterations and
23:39then design a drug cocktail based
23:41on that particular genetic profile
23:43in order to get to that level.
23:45I think we need to have a really good
23:47understanding of what drugs do and how
23:50their anti cancer activity actually arises.
23:52And what we'd suggest is that
23:54pre clinical genetic validation,
23:56particularly using CRISPR instead of RNA.
23:58I may help us get genetic insight into
24:01how anti cancer drugs work and may
24:03decrease the number of investigational
24:05drugs that enter clinical trials,
24:07but end up failing during clinical testing.
24:10So this is work that was done by my group.
24:12In particular,
24:13two really talented students
24:14and Lynn and Chris Giuliano.
24:16I'd like to acknowledge the
24:18funding and thank you so much,
24:19I'd be happy to answer any
24:20questions that you have.
24:25Thanks very much.
24:26I thought that was really great.
24:28I think you know one of the one of the
24:32things we're all aware of is that when
24:35we combine drugs that the toxicity
24:37goes way up and you know of course,
24:40much of the reason for that is
24:42that many of these drugs are
24:44promiscuous and are doing much
24:46more than what we need them to do.
24:49There's a there was a question a minute ago.
24:53Uh oh, so the from from Jeffrey Townsend.
24:58How were the original 12 drugs selected
25:01and assembled for investigation?
25:03Yep, so I didn't have time to discuss
25:06that extensively in this talk,
25:08but what we were interested in
25:11our underlying hypothesis is that
25:13the gold standard for knowing a
25:15cancer drugs mechanism of action is
25:17the identification of a mutation
25:19that confers resistance to it.
25:20The classic example here is Gleevec
25:22and the mutations in BCR ABL.
25:24Set block, Liebeck activity and our
25:26thinking was that drugs that lacked
25:28that level of genetic validation
25:30were less likely to be acting
25:32through an on target mechanism.
25:34So we selected drugs that
25:35specifically did not have that level
25:37of genetic evidence behind them.
25:41And from from Mike Hurwitz.
25:45Sort of along that line.
25:46Do you find it striking that every
25:48single one of your targets was wrong?
25:50Yeah, so for the sake of time,
25:53yeah, for the sake of time,
25:55I focused on the ones that were
25:57where we discovered that the
25:59mechanism of action was incorrect.
26:02However, we did have a few examples
26:04where we could validate it,
26:06and I'm just trying to here.
26:08I'm going to show just
26:10one example of that now.
26:12So this is not Lynn 3A.
26:15This is a drug that's been reported to
26:18function through P53 activation blocks.
26:20The interaction between MDM two
26:22and P53 we generated P53 knockout
26:26clones using crisper and when we
26:28did this drug sensitivity curve
26:30we found that a nutlin has no
26:33effect on the P53 knockout clones,
26:35while it kills the P53 expressing
26:37Rosa 26 control phones.
26:39So in general so this is.
26:41What we would expect for a drug that
26:43acts for an on target activity.
26:45You know a huge delta between the
26:47target knockouts and the target,
26:48expressing control clones,
26:50and we found a few examples of this.
26:54OK,
26:54and I think this is the last
26:57question from from Karen Anderson.
27:00Did you make the searing mutant of CDK
27:0211 and show that the inhibitor was no
27:04longer effective in biochemical assays?
27:07So we have been doing the
27:09biochemical assays through ACR, oh,
27:10at the moment, we are not skilled
27:12in in vitro biochemistry ourselves,
27:15and so we've just done it with
27:18the the through the CR out and
27:20we'd be glad to to launch the
27:22collaboration to investigate that,
27:23because I think that would be very powerful.
27:26Well, I want to thank both Jason and Kurt.
27:30It makes me proud to have these kinds
27:33of presentations on my first day here.
27:35So thank you very, very much
27:38and we'll see you all next week.