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

Transcript

• 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.