Latest / Zero Shot Podcast DEMO / Advancements in Liquid Biopsies for Pancreatic Cancer V2

Transcript

Advancements in Liquid Biopsies for Pancreatic Cancer V2 · Zero Shot Podcast DEMO

• 0:00
  Ken Bloom: In this webinar, we explore advanced liquid biopsies and their critical role in monitoring clonal evolution in pancreatic cancer. This discussion highlights the limitations of traditional biopsies and introduces innovative technologies that promise to enhance patient care.
• 0:20
  Thanks, everybody, for joining OneCell's webinar.
• 0:25
  The topic of today's conversation is advanced liquid
• 0:33
  biopsies.
• 0:34
  And in particular, we'll be discussing not just looking at circulating tumor DNA, but also the emerging
• 0:42
  field of single -cell -based liquid biopsies, which formerly a lot of people referred to as circulating tumor cells, also still
• 0:50
  referred to as circulating tumor cells.
• 0:51
  But we'll also describe what is new about these sort of cell -based liquid biopsies, where we can do molecular
• 0:59
  profiling, both in the cell level as well as at the DNA level.
• 1:05
  Dr. Rizal Kurzok: So the topic is multisource genomics to monitor clonal evolution for pancreatic cancer.
• 1:11
  Dr.
• 1:12
  Rizal Kurzok from Medical College of Wisconsin will provide an update for the talk.
• 1:16
  Ken Bloom: This meeting is being recorded.
• 1:18
  We will have two formats for Q &A.
• 1:23
  As the meeting progresses, please feel free to put your questions in the chat, in the chat box,
• 1:31
  and then we'll accommodate as many of the questions.
• 1:35
  And towards the end, we'll have some time for some live questions as well.
• 1:39
  So the way the agenda is going to work is I'll spend a couple of minutes on just sort of setting the stage, which I'm doing right now.
• 1:46
  Traditional tissue biopsies are limited by tumor heterogeneity. The OncoInsights platform captures and analyzes circulating tumor cells (CTCs) effectively.
• 2:00
  Dr. Rizal Kurzok: I'll turn it over to Ken Bloom.
• 2:03
  Ken is OneCell's advisor.
• 2:06
  He's a strategic advisor to OneCell, also leads our CAPCLEAR certified laboratory in the San Francisco Bay Area.
• 2:15
  Ken Bloom: He will be providing an overview of OneCell as well as our OncoInsights technology.
• 2:21
  Dr. Rizal Kurzok: Right after that, before I turn it over to Rizal, I will ask all of the participants to go into the chat box.
• 2:30
  And there's a link there, which you can click.
• 2:33
  And it'll take you literally not more than 10 seconds to answer questions around what topics are relevant to you guys.
• 2:40
  That'll be important for us to understand.
• 2:42
  And we'll share some of that either towards the end or as a follow -up.
• 2:49
  Ken Bloom: Our labs hold the highest level of clinical accreditation, CLIA and CAP in the U .S., NAVAL and CAP in India.
• 2:56
  And these aren't just logos on the slide.
• 2:59
  They mean that the data coming out of our labs meets the standards required for clinical decision -making.
• 3:08
  CTCs provide a more comprehensive view of tumor evolution than CT DNA alone.
• 3:14
  Dr. Rizal Kurzok: But more importantly, as antibody drug conjugate therapies have emerged, CT -DNA doesn't give us any useful information
• 3:22
  about protein expression or localization.
• 3:25
  Ken Bloom: The topic of today's conversation is advanced liquid biopsies. The topic is multisource genomics to monitor clonal evolution for pancreatic cancer.
• 3:37
  Dr. Rizal Kurzok: This means we're all making patient selection and endpoint decisions based on incomplete, often outdated information.
• 3:44
  But that's a solvable problem, and it's exactly what OneCells platform addresses.
• 3:49
  Our solution is called OncoInsights, our proprietary platform to capture and comprehensively analyze circulating
• 3:57
  tumor cells.
• 3:58
  Here's a quick refresher.
• 4:00
  When a tumor grows, it sheds cells into the bloodstream.
• 4:03
  These are circulating tumor cells, actual cancer cells floating in a patient's blood draw.
• 4:09
  The problem historically has been catching them.
• 4:12
  They are incredibly rare, maybe dozens or less, among billions of normal cells.
• 4:16
  The trick, though, is not just to capture them, but to capture them in a way that's suitable for downstream analysis.
• 4:28
  Ken Bloom: The OncoInsights panel covers three biological layers simultaneously from the same blood draw.
• 4:35
  Dr. Rizal Kurzok: OncoInsights solves these limitations with a combination of multi -marker affinity capture and a cleavable linker allowing capture of
• 4:43
  live CTCs with no cell damage.
• 4:46
  Instead of a single hook, we use customized capture beads coated with multiple antibodies targeted to a specific cancer
• 4:53
  type.
• 4:56
  Ken Bloom: For most cancers, a combination of Epcam and Transferrin works effectively, but the beads can be customized to optimize capture.
• 5:03
  For example, many renal cell cancers don't express Epcam at high levels, expressions quite low.
• 5:10
  So adding CD70 to the beads dramatically increases the CT positivity rate.
• 5:15
  Critically, this allows for the capture of the full diversity of tumor cells, not just the most common ones.
• 5:21
  Once the cells are captured, they are released intact using a cleavable linker system.
• 5:27
  No bead contamination, no cell damage.
• 5:30
  So the cells are ready for multi -omic analysis.
• 5:33
  So this is where things get really exciting because of what you can measure from these capture CTCs.
• 5:39
  The OncoInsights panel covers three biological layers simultaneously from the same blood draw.
• 5:46
  First, DNA.
• 5:47
  1080 gene panel covering point mutations, insertions, deletions, gene fusions, copy number variations, and
• 5:55
  chromosomal rearrangements.
• 5:56
  It also gives you tumor mutational burden, microsatellite instability status, and homologous
• 6:04
  recombination deficiency status.
• 6:06
  All of the genomic biomarkers that predict response to immunotherapy, PARP inhibitors, and targeted agents.
• 6:17
  Dr. Rizal Kurzok: Our mission is straightforward but ambitious.
• 6:20
  We're going to be able to do this.
• 6:20
  We're going to be able to do this.
• 6:20
  Democratize precision oncology.
• 6:22
  Make it actionable, accessible, affordable for every cancer patient everywhere.
• 6:28
  We are working toward impacting the lives of one million cancer patients through breakthroughs in science and AI.
• 6:35
  For those of you in pharma, you can think of OneCell as a potential partner for clinical trial design, patient selection, biomarker
• 6:43
  discovery, and drug development.
• 6:45
  Everything that you're about to hear is built on that infrastructure.
• 6:48
  So now let's talk about the problem, where we've been and where we're going.
• 6:52
  Ken Bloom: I was trained 40 years ago with the idea that tissue biopsy is the gold standard.
• 6:58
  And over those years, we've been able to extract multiomic data from
• 7:05
  However, as biopsies become smaller and smaller, our analysis becomes less reliable because of tumor heterogeneity.
• 7:13
  A five -micron section from a one -centimeter tumor represents only a fraction of a percent of that tumor.
• 7:19
  So to put it in a little bit easier terms, that's like trying to understand the entire plot of war and peace by reading one
• 7:27
  sentence of the book.
• 7:28
  You get a snapshot, a static picture.
• 7:31
  And while that provides actionable data, it creates a gap in our ability to monitor cancer's evolution.
• 7:38
  And this has direct consequences for patient management.
• 7:45
  Our solution is called OncoInsights, our proprietary platform to capture and comprehensively analyze circulating tumor cells.
• 7:54
  Dr. Rizal Kurzok: Second is protein expression.
• 7:56
  This is currently measured by immunofluorescence on individual cells.
• 8:00
  You can directly quantify the proteins a drug is targeting, such as HER2, TROP2, clodin -18, nectin -4, etc.
• 8:09
  Ken Bloom: If you're developing an antibody drug conjugate, for example, this tells you whether the target is actually present at the protein level
• 8:16
  on cells circulating right now during treatment.
• 8:20
  Dr. Rizal Kurzok: Third is RNA, or whole transcriptome profiling.
• 8:23
  That's about 16 ,000 markers.
• 8:26
  This is your window into gene expression, what pathways are active, what resistance mechanisms might be turning on, and what's changing
• 8:33
  over time.
• 8:34
  Ken Bloom: For drug programs, this isn't just biomarker discovery.
• 8:38
  This is real -time pharmacodynamic monitoring.
• 8:41
  You can track whether your drug is hitting its target, whether resistance is emerging, and which patients are likely to respond.
• 8:48
  Any new technology lives or dies on its data, so let's look at the numbers.
• 8:53
  Across more than 2 ,000 patient samples spanning lung, breast, pancreatic, colorectal,
• 9:03
  Dr. Rizal Kurzok: radiologic imaging, pathology images, and real -world evidence data sets.
• 9:08
  It uses AI and machine learning to identify patient populations most likely to respond to a given therapy, flag resistance
• 9:16
  markers as they emerge, and help you understand mechanisms of action in a way that bulk tumor sequencing simply can't provide.
• 9:25
  It also adds the missing temporal layer to AI.
• 9:29
  It allows AI to begin to understand how tumor cells are adapting and what to do about it.
• 9:35
  Imagine being able to enrich your clinical trials for responders before the trial even starts, based on real -time
• 9:43
  CTC protein expression in genomics, rather than on archival tissue.
• 9:48
  That's the promise of iCORM.
• 9:50
  You get smarter enrollment, more informative biomarker endpoints, and ultimately, a cheaper path to approval.
• 9:57
  Ken Bloom: With that as background, I'll now turn the slides over to Dr.
• 10:01
  Curtis Rock, who's going to present her exciting data on the clonal evolution in pancreatic cancer.
• 10:06
  But first, there will be a few polling questions.
• 10:09
  So, Arshad, I guess that goes to you next.
• 10:11
  Dr. Rizal Kurzok: Yes, thanks.
• 10:12
  Thanks, Ken.
• 10:13
  Appreciate that.
• 10:15
  If I can ask all the participants, if you can go to the top of the chat room, there's a link there
• 10:22
  from, it's called forms .gle.
• 10:26
  If you guys just click that link, I promise this won't take more than 10 seconds.
• 10:29
  It's going to ask some basic questions on what are the types of areas that are important to you, and that's it.
• 10:35
  Just click on that, type in your name so that we know who responded to what, and then we can move on to the next part of the agenda.
• 10:43
  So, over to Razelle.
• 10:45
  Ken Bloom: So, a pleasure to work on this project and with this
• 10:53
  really interesting technology.
• 10:59
  Dr. Rizal Kurzok: liver, head and neck, prostate, and gallbladder cancers, the overall CTC positivity rate, or our ability to
• 11:07
  detect CTCs, was 83 % for stages stage 2 through stage 4 patients, but metastatic patients had
• 11:14
  approached 100%.
• 11:16
  Let me put that in context.
• 11:18
  Our first generation of CTC capture technology yielded a mean of about 5 CTCs per sample, while our current
• 11:26
  platform yields a mean of about 21 CTCs per sample.
• 11:30
  That's a four -fold improvement, and more cells means more material for downstream analysis, more genomics, more protein
• 11:37
  profiling, more confidence.
• 11:44
  Ken Bloom: Pancreatic ductal adenocarcinoma progression involves stepwise accumulation of key driver mutations,
• 11:51
  which are mainly known to be KRAS, the cyclin system,
• 11:59
  CDKN2AB loss, and P53.
• 12:03
  And tumor evolution also includes rapid genomic restructuring characterized by
• 12:11
  copy number variations and large chromosomal rearrangements following
• 12:17
  polyploidization.
• 12:20
  So, this is a difficult cancer.
• 12:24
  And the reason, one of the reasons it's difficult is because of the great heterogeneity between patients
• 12:32
  and even within patients with time.
• 12:35
  And that's really difficult to capture if what you're doing is a baseline tissue biopsy.
• 12:43
  Um, so, um,
• 12:49
  An 83 % to 100 % positivity rate across solid tumors means that the vast majority of your enrolled patients can be monitored
• 12:56
  longitudinally with this platform, and that's a game -changer for adaptive trial design.
• 13:02
  One more important point before we move on, and this is particularly relevant for CNS cancers and thoracic malignancies.
• 13:09
  The technology isn't just limited to blood.
• 13:11
  At one cell, we've demonstrated CTC capture, or tumor cell capture, from cerebral spinal fluid
• 13:19
  and pleural fluid.
• 13:20
  In CNS tumors like glioblastoma and medulloblastoma, where a blood draw tells you almost nothing about what's happening in the brain,
• 13:28
  CSF -based CTC analysis opens a completely new window.
• 13:32
  This is significant for clinical trials and CNS malignancies, where biomarker development has lagged far behind other tumors.
• 13:40
  Now, generating all this data is only valuable if you can act on it, and that's what the iCORM platform is designed to do.
• 13:52
  there are a lot of limitations to bulk sequencing.
• 13:57
  Um, and, uh, some of them are illustrated here.
• 14:02
  Uh, so, we use bulk sequencing to understand, um, cancer.
• 14:08
  But the problem is that it doesn't really show us what happens with time, what happens with
• 14:16
  successive, um, mutational events.
• 14:20
  And, um, it cannot, um, extinguish, it cannot distinguish what is
• 14:27
  happening in different clones of cells.
• 14:30
  So, if we see, do single cell sequencing, we can see, are there co -occurring versus
• 14:38
  mutually exclusive alterations in different cells?
• 14:43
  Might this explain, um, mixed responses to drugs, uh, like PARP inhibitors, KRAS inhibitors,
• 14:50
  and immunotherapy.
• 14:53
  And so, single cell DNA sequencing is really required to resolve the clonal
• 15:00
  architecture of pancreatic ductal adenocarcinoma from early invasion through systemic,
• 15:08
  uh, dissemination and to identify molecular vulnerabilities targetable with precision
• 15:15
  therapies as the, as the disease evolves.
• 15:19
  Uh, so, this is a study that we initiated, um, and, uh, the purpose of
• 15:27
  the study was to isolate single circulating tumor cells from pancreatic cancer patients prior to treatment
• 15:34
  and perform genomic profile to characterize cellular level genomic heterogeneity, and also to
• 15:42
  follow these patients during treatment.
• 15:45
  And, uh, uh, this, uh, study is led by Dr.
• 15:49
  Mondana Kamgar and myself
• 15:54
  iCORM is one cell's integrated precision oncology consult.
• 15:59
  Think of it as the intelligence layer that sits on top of the laboratory data.
• 16:03
  It has two primary interfaces.
• 16:06
  iDiscover for translational research and clinical trial support, and iCare for the treating oncologist.
• 16:12
  For those of you in pharma, iDiscover is the critical piece.
• 16:17
  It integrates multiomic data from CTCs and CT DNA,
• 16:25
  at MCW and Dr.
• 16:27
  Kremolini at, uh, University of Pisa.
• 16:31
  And, uh, we've collected quite a few samples and I think, um, I'm happy to share with you some of the
• 16:38
  insights, uh, from these samples.
• 16:41
  Next, uh, slide, please.
• 16:44
  So, all together, uh, we now have 26 patients, 63 total samples,
• 16:52
  um, and, uh, five time points tracked.
• 16:56
  And remembering that we're looking, um, both at CTCs and CT DNA.
• 17:03
  And we're also continuing to expand, uh, this, uh, project.
• 17:08
  Uh, but I'm going to show you, I think we have some fascinating insights, um, at this, uh, time.
• 17:15
  And, um, I want to share those with you.
• 17:18
  So, uh, first, um, uh, this slide, I think really illustrates, uh, some of the
• 17:25
  insights, uh, from the study.
• 17:27
  Um, so if we look at, uh, patients that have CT DNA only
• 17:34
  versus, uh, CTC DNA only, we're getting a lot more alterations
• 17:42
  from the CTCs, which you can see on the right, than we are getting from the CT DNA.
• 17:49
  And this in the middle are some of the shared alterations, um, that you see.
• 17:55
  So there's a lot of common things, but themes.
• 17:59
  But in addition, I think that both the CT DNA and the CTCs bring their
• 18:06
  own information.
• 18:08
  And I think you can see how rich the information is that is coming, um, from the CTCs.
• 18:15
  Um, so this, uh, slide shows, uh, the longitudinal, uh, CTC,
• 18:23
  uh,
• 18:25
  DNA and, uh, CT DNA longitudinal mutational landscape.
• 18:31
  And I'm going to go into, um, more detail in a moment, but if you go to the
• 18:39
  right side, this is patient by patient.
• 18:42
  And if you go to the right side of the slide, you can see, um, on the, um, Y axis on the
• 18:50
  right side, uh, the red is CT DNA and the green is CTCs.
• 18:56
  And we're going to go to higher level magnification, but at, uh, but you can see already from these slides
• 19:04
  that the CTCs and the CT DNA do not always tell you the same thing.
• 19:11
  Um, they give complimentary information sets.
• 19:17
  Um, so this, uh, focuses in, um, on, uh, what we see
• 19:24
  in, um, for instance, CT DNA, which is in the red and CTCs, which is in the green.
• 19:32
  And, um, what you can see is that in some, in some situations we're seeing the same thing,
• 19:39
  but in other situations we actually see, um, uh, more of certain mutations
• 19:47
  in the CT DNA than in the CTCs.
• 19:51
  Um, and, um, so one of the interesting things that we found
• 19:59
  with this is that, um, as we began to look at the CTCs
• 20:07
  versus the CT DNA, um, the, uh, we began to see something that we found
• 20:14
  initially confusing, but now we think it is absolutely fascinating and really goes again,
• 20:23
  uh, along with
• 20:24
  evidence published very recently in, uh, nature genetics by the Sloan Kettering group.
• 20:31
  Um, so in the CTCs, we're finding, for instance, we find alterations not found
• 20:39
  in the CT DNA.
• 20:40
  In the CT DNA, we find alterations not found in the CTCs.
• 20:45
  And, um, if we put, and some, of course, uh, of the alterations are found both in the CTCs
• 20:53
  and the CT DNA.
• 20:54
  Um, but this is, I'm going to give you an example of something that we thought was really fascinating and actually
• 21:02
  very relevant to therapy.
• 21:05
  Um, so a subset of, uh, patients with KRAS, as the tumor evolves,
• 21:13
  there are CTCs that appear to lose, uh, their KRAS.
• 21:18
  And, um, this was at first a bit confusing to us, but it turns out this
• 21:26
  is very consistent with what other, um, investigators are finding when they're looking at single cell
• 21:33
  populations.
• 21:35
  Um, and, uh, for instance, the Sloan Kettering group in nature genetics about a year ago
• 21:42
  published that about 40 % of the individual clones in pancreatic cancer were
• 21:50
  losing their KRAS.
• 21:52
  And, um, this is precisely what we're seeing as we follow individual
• 21:59
  CTCs and, um, uh, see that there are different clones of CTCs and not
• 22:07
  all of them retain, for example, um, their CTCs.
• 22:12
  So we're, uh, their KRAS.
• 22:14
  So for instance, we're seeing KRAS negative, uh, CTCs.
• 22:19
  Um, sometimes in the KRAS negative CTCs,
• 22:23
  Dr. Rizal Kurzok: the MEC pathway is still, um, a driver.
• 22:28
  For instance, they may have SMOD4 or they may have other alterations in that pathway, but not always.
• 22:35
  Some of these, uh, CTCs are probably losing different pathways or exploiting different
• 22:42
  pathways in order to, um, propagate, uh, the pancreatic cancer.
• 22:49
  Uh, so this goes back to our prior slide and I want to now focus on
• 22:56
  the Y -axis to the right and, um, um, the green
• 23:04
  part of that access.
• 23:05
  And that's the CTCs.
• 23:08
  And you can see that there's, um, multiple alterations that can be found in CTCs that cannot
• 23:16
  be found in the CT DNA.
• 23:19
  Um, so before we focused on some of the alterations, some of the CTCs that were losing
• 23:26
  alterations, now we're going to focus on, um, some of the alterations found in CTCs that
• 23:34
  cannot be found, um, in the CT DNA.
• 23:38
  And, uh, so you can see there's a whole, um, group of alterations
• 23:45
  that were being, that were finding in the CTCs.
• 23:49
  And, um, these alterations are not found in the bulk, uh, CT DNA.
• 23:55
  And since the CTCs may reflect the evolution of cancer, pancreatic cancer, and other
• 24:03
  cancers, and also may be seeding some of the metastatic sites, these additional alterations
• 24:11
  may be critical to thinking about therapy.
• 24:14
  And then, um, the final, um, part of this, uh, slide, if you look on the, on
• 24:22
  the Y axis
• 24:25
  Ken Bloom: CTCs provide insights into tumor heterogeneity, revealing alterations not captured by CT DNA. This dual approach enhances our understanding of individual patient profiles and treatment responses.
• 24:42
  Dr. Rizal Kurzok: to the right, uh, the bottom of the right axis, you can see the red dots.
• 24:48
  And this is the CT DNA only.
• 24:50
  So there are alterations also found in CT DNA that are not found in CTCs, um,
• 24:58
  less so than vice versa, but there are certain alterations in some of our patients.
• 25:04
  Uh, this shows, um, some of the alterations found in individual patients in the CT DNA and not
• 25:11
  found, um, um, in the CTCs.
• 25:14
  So when we put all of this together, the bottom line is we need both sources of information.
• 25:22
  We need the CT DNA and we need the CTCs.
• 25:26
  And this again puts together, um, some of the alterations found in the CT DNA only, some of the
• 25:34
  alterations found in the CTCs.
• 25:38
  And, uh, there's a lot of alterations that are found in the CTCs that are not found in the CT DNA and then some of
• 25:46
  the shared alterations.
• 25:48
  So these are complementary techniques and this type of liquid biopsy is really unique in that it can
• 25:55
  give, um, investigators the full picture of what is going on in an individual patient.
• 26:03
  And, um, likely the difficulty with pancreatic cancer, metastatic pancreatic cancer, and
• 26:11
  probably many other, uh, cancers is that we've been getting a very partial picture.
• 26:18
  Um, if we do tissue biopsy, we get a picture at one point in time of one small piece of tissue.
• 26:26
  If we do a liquid biopsy, a blood biopsy, but we do CT DNA, we can get a serial picture
• 26:34
  of what is happening in the cancer, but it's a bulk serial picture.
• 26:39
  And we're not seeing what is happening in individual
• 26:44
  Ken Bloom: Understanding the molecular evolution of pancreatic cancer through CTC analysis is crucial for tailoring therapies. This approach allows for real-time monitoring of clonal dynamics and resistance mechanisms.
• 26:59
  Dr. Rizal Kurzok: cells, which may reflect a tremendously important, um, biology of the
• 27:07
  cancer and also may reflect, um, seeding of new metastatic sites and how those
• 27:14
  clones may be different, um, may be the key, uh, to understanding, um, our next, uh,
• 27:22
  step in steps in therapy.
• 27:25
  So, um, this, um, emphasizes the point that we
• 27:32
  kind of always known, but here we have clear cut evidence for it, um, pancreatic
• 27:40
  cancer and probably every other cancer has a lot of tumor heterogeneity.
• 27:45
  And in addition with time that tumor heterogeneity, um, becomes
• 27:53
  worse.
• 27:54
  It undergoes molecular evolution and, uh, there are, uh, new alterations that are appearing.
• 28:02
  There may be old alterations that are muted or disappear.
• 28:10
  And, um, to reiterate tissue biopsy or just bulk CT DNA
• 28:18
  cannot outline all that is happening at a single cell level.
• 28:24
  And so CTCs demonstrate molecular diversity of the tumor at the baseline, uh, which is
• 28:32
  hidden with bulk CT DNA and tissue analysis.
• 28:37
  CTCs allow you to follow that on a regular basis and see what is
• 28:44
  happening at a single cell level as the cancer progresses.
• 28:49
  So, uh, they're important at baseline and they're important through the life cycle of that, uh, cancer.
• 28:57
  Um, so single
• 29:01
  Ken Bloom: cell CTC genomics provides biologic insights beyond conventional, um, liquid
• 29:08
  biopsies and it, uh, reveals tumor subpopulations and genomic alterations not captured by
• 29:16
  CT DNA, broadening the detectable molecular landscape and metastatic pancreatic cancer beyond what
• 29:24
  circulating DNA alone can resolve.
• 29:26
  And again, we're talking about pancreatic cancer, but it's very likely, and that's what our project was on, but
• 29:34
  it's very likely that this really applies to all tumors that tend to metastasize.
• 29:40
  Um, so I want to loop back to this really interesting finding,
• 29:47
  um, that our, uh, project has confirmed prior results, um, from,
• 29:56
  uh, uh, that were published in nature genetics about a year ago.
• 29:59
  Um, so, um, to our surprise and probably to the Sloan Kettering group surprise, uh, when they
• 30:07
  published this, um, we identified CTCs that have KRAS, that are KRAS independent.
• 30:15
  They appear, this is one example, but they appear to have lost their KRAS, um, and they're circulating
• 30:23
  in the bloodstream.
• 30:24
  Um, so this would be invisible to CT DNA based assays, um, that, uh,
• 30:33
  track, uh, tumor DNA in bulk.
• 30:37
  Interestingly, in these KRAS independent clones, in some of them, alternate pathways are
• 30:45
  activated in the MEC signal, MAPK, SMOD.
• 30:51
  Um, and in some cases, um, and in some cases, just completely alternate pathways are activated.
• 30:58
  PI3 kinase, AKT,
• 31:00
  genomic instability, JAK -STAT.
• 31:03
  And I think we really have to have a good understanding of exactly what is happening in all these different clones if we
• 31:11
  are really going to impact what happens to pancreatic cancer patients.
• 31:16
  So, uh, CTCs demonstrate significantly greater intrapatient heterogeneity
• 31:24
  than CT DNA.
• 31:26
  And I showed this to you several slides back.
• 31:29
  Um, but it's, appears that patients have multiple, each patient has multiple
• 31:37
  different clones that may vary from each other.
• 31:41
  And, um, CTCs enable this high resolution delineation of
• 31:49
  coexisting clonal population and the genomic underpinnings of, uh, what is probably
• 31:57
  emerging resistance mechanisms when we treat.
• 32:01
  Um, so, um, uh, looking at both CT DNA and CTCs,
• 32:09
  it enables dynamic longitudinal tracking.
• 32:14
  Serial single cell CTC analysis enables real -time monitoring of clonal evolution during
• 32:22
  therapy, and it offers a direct window into tumor adaptation, uh, selective pressure responses,
• 32:30
  and treatment -driven genomic shifts over time.
• 32:35
  Um, so, key takeaway.
• 32:38
  We need all of these modalities.
• 32:41
  Um, CTC genomics is a complementary modality for understanding intratumoral heterogeneity,
• 32:49
  clonal dynamics, and resistance evolution in metastatic pancreatic ductal carcinoma.
• 32:55
  Um, and the fact that this platform gives
• 33:00
  us both CT DNA and CTCs at a single cell level, I think these are complementary,
• 33:07
  um, technologies in one platform and really allows us a very high high -level
• 33:14
  understanding of, um, um, what is happening in these patients.
• 33:20
  Um, uh, it gives us a window into an expanded, uh, molecular landscapes into clones that
• 33:28
  might be losing, uh, key drivers like KRAS, maybe replacing them with other drivers,
• 33:36
  maybe not, um, gives better clonal resolution, um, gives better clonal resolution and really allows longitudinal
• 33:44
  tracking of what is happening in an individual patient with treatment.
• 33:49
  Um, so this is one example of a patient, um, uh, treated, uh, and a
• 33:57
  longitudinal assessment.
• 33:59
  And, um, I, I think the things that I'd like to point out is, uh, that these
• 34:07
  patients, which we showed you, um, a high -level view, but you can see this in an
• 34:14
  individual patient, um, with time, the CTCs change.
• 34:20
  Um, so, uh, this patient starts off, uh, with NRAS, P53, and
• 34:28
  SMOD4.
• 34:29
  Uh, SMOD4 activates the MEC pathway.
• 34:32
  It can activate EGFR and HER2 and other pathways as well.
• 34:39
  And then with treatment, um, an HRAS emerges, uh, patient retains the P53.
• 34:47
  Now other alterations are emerging, which may be very important, including BRCA, uh,
• 34:54
  RAD51L, and, um, MUTYH.
• 34:59
  Um, a couple of these alterations like BRCA and RAD51 may be causing HRD
• 35:06
  and, uh, could be, uh, targeted with, um, PARP inhibitors or platinums.
• 35:13
  And then with more time, uh, the BRCA is retained and the P53 is retained.
• 35:20
  Um, but a P53CA alteration emerges.
• 35:24
  And so I think, um, this is really, um, important for understanding what's happening in the
• 35:32
  patient and how is resistance emerging and how should we mold our
• 35:39
  therapies, um, to this evolution that is occurring in an individual patient.
• 35:47
  And that is really what the ultimate goal is of precision or personalized medicine is to understand
• 35:55
  each patient in detail at each point in time of their disease.
• 36:00
  So we can mold the therapy that it would be best suited to that patient rather than guessing at what the
• 36:08
  next therapy should be.
• 36:10
  Um, I think this is really next generation precision oncology.
• 36:15
  Um, and, uh, this allows us to break the resolution barrier in precision oncology, allows us to understand
• 36:23
  biologic, uh, resolution, resistance detection, uh, tumor heterogeneity, um,
• 36:30
  all the different mutations and alterations.
• 36:33
  Um, and, um, um, um, we can go from population average genomics to single
• 36:41
  cell tumor biology, single cell genomics.
• 36:45
  Um, and in addition, uh, CTCs allow one to go beyond DNA.
• 36:51
  And this is really important.
• 36:53
  Um, CTCs allow one to look at, uh, DNA, but it, one can look at RNA.
• 36:59
  One
• 36:59
  can look at proteins on the same cell.
• 37:02
  One can even do functional tests on these cells.
• 37:05
  Um, so this is really an integrated cell biopsy platform, um, that,
• 37:13
  uh, can, uh, really help us understand diagnostics and, uh, drug
• 37:21
  development, tumor evolution, and what pressure points we need to apply to the tumor at any individual
• 37:29
  time point, um, of that tumor's, uh, progression.
• 37:34
  Rizal, is there any other slide you wanted to discuss in terms of alternative pathways?
• 37:38
  No, that's it.
• 37:38
  I'll just say, okay, perfect.
• 37:40
  Thank you, Rizal.
• 37:41
  Really appreciate, uh, the presentation.
• 37:43
  Uh, we already have some questions coming in, but just to remind you guys, as you can see on the slide, uh, we will be at
• 37:51
  AACR and I know most of you are likely to be at AACR.
• 37:55
  So would, if there are any questions or if you would like to meet us, you can easily, uh, take a picture of our, put your camera in front of this
• 38:03
  QR code and schedule a meeting with us.
• 38:07
  It could be 15 minutes, it could be 30 minutes, but it'll be great to see you guys.
• 38:12
  Okay.
• 38:12
  Okay.
• 38:12
  Let's move to the Q and A part.
• 38:14
  Um, I see some questions coming in already.
• 38:18
  Um, excellent.
• 38:20
  So we have, okay.
• 38:21
  So Sejong Chun, uh, asked a question on, yeah.
• 38:26
  So what's the, what's the difference you think, Rizal between the difference that why does CTCs, do you
• 38:34
  have a thesis on why they pick up unique and more mutations in ctDNA?
• 38:40
  Um, why do CTCs, well, I think your, um, one reason you may be picking up, uh, more mutations than
• 38:48
  ctDNA is because, um, uh, you're looking at multiple different CTCs and you have different clones.
• 38:54
  And, um, so, uh, that may be a reason
• 39:00
  Serial single cell CTC analysis enables real-time monitoring of clonal evolution during therapy. The ongoing study aims to clarify the relationship between clonal changes and therapy resistance, emphasizing the need for continuous monitoring of CTCs in pancreatic cancer treatment.
• 39:21
  Dr. Rizal Kurzok: to pick up, uh, more mutations than ctDNA.
• 39:24
  Uh, the ctDNA is bulk and there might be, uh, subclones of CTCs that are,
• 39:33
  um, smaller in number and harder to pick up in the ctDNA.
• 39:36
  Um, but I, I think the bottom line is that these are complementary techniques and that we're
• 39:44
  getting important information from both of them, but the information from the CTCs is unique because we can see it at a
• 39:52
  single cell level.
• 39:53
  And what it is showing us is that there's a lot of heterogeneity there and that heterogeneity cannot be picked up in the
• 40:01
  ctDNA.
• 40:03
  Sejong, feel free to add any follow up on that.
• 40:06
  Go ahead.
• 40:06
  Yes.
• 40:07
  Thank you so much for that, um, response.
• 40:09
  So another question that I had on that regards is that with the ctDNA, if you increase the read deaths sufficiently,
• 40:16
  would that divergence, um, lessen or would there still be a difference in your perspective?
• 40:25
  So I'm not the technical person.
• 40:27
  Um, uh, so I want the technical person to answer it, but I'm going to tell you what I think.
• 40:32
  Um, I think there would still be a difference, uh, because there may be, um, uh, a, a, uh,
• 40:41
  small numbers of CTCs that have a difference and that technically can't be picked up in the bulk
• 40:48
  ctDNA.
• 40:49
  And yet, even though it's small numbers of CTCs that have that difference, those CTCs may be critical to, um,
• 40:57
  what is happening in the patients with, um, seeding of new metastatic sites or, or reflecting resistant clones.
• 41:05
  So that's my answer, but somebody who's more technical than me might, um, have, uh, be able to add to that.
• 41:12
  Sure.
• 41:13
  I'll chime in on that one.
• 41:15
  Uh, there, there's definitely differences, but before I get into that, I'd just like to add one other point.
• 41:20
  If you
• 41:23
  Ken Bloom: draw CtDNA at, you know, draw, take a sample, send it to your favorite CtD, you know, garden, your
• 41:30
  favorite CtDNA lab.
• 41:32
  Draw it in the afternoon, send it to Tempest, draw it in the evening, send it someplace else.
• 41:37
  You won't get the same mutations back, right?
• 41:39
  You're going to get, you're getting basically a small window of cells that are dying at that point of time.
• 41:44
  Now, your question I think was more to the sensitivity.
• 41:47
  And if I raise the sensitivity of the assay, will I be able to detect things?
• 41:51
  And the problem with assays is generally when you raise the sensitivity, you lower the specificity.
• 41:56
  So now I start picking up, you know, it, where do you want to live and die, right?
• 42:01
  You want to get more false positives, more false negatives.
• 42:04
  But in the pharma's perspective, that might be slightly, um, not the way you think.
• 42:08
  So we would like to see the drivers or the targetable ones, which would probably, um, consist of the, um, the larger proportion of the
• 42:16
  mutations detected, which would be relatively consistent across different vendors and, um, different time points as well.
• 42:23
  So that, um, that, that's, that's a conundrum that we're facing all the time.
• 42:27
  But, um, this sort of leads to my next question where with the CTCs across multiple different time points, there were a few mutations that were
• 42:35
  consistent, but there were other ones that appeared and then went away.
• 42:38
  So I'm a little curious on how those were calculated.
• 42:41
  Um, do you think they're drivers of, let's say resistance because, um, in, in that patient, you have the biomarkers going down and eventually
• 42:49
  down the line as tumor burden starts to increase, you see more mutations happening, but at the end of the day, it did seem that TP53
• 42:56
  was the, let's say, surviving clone later.
• 43:03
  I, uh, do you want me to try to answer that?
• 43:07
  Yeah, Ray, that's for you.
• 43:08
  Yep.
• 43:09
  Yep.
• 43:09
  Um, yeah.
• 43:11
  So we actually found that fascinating and, um, uh, uh, we, uh, especially
• 43:18
  found, uh, yes.
• 43:21
  So P53 was the surviving
• 43:22
  The findings regarding KRAS loss in CTCs underscore the need for further investigation into the implications of clonal evolution. The variability in mutation detection between different labs highlights the importance of standardizing liquid biopsy protocols.
• 43:42
  Dr. Rizal Kurzok: clone in that patient, but we, with time, we don't know what else will come up, but what we found most fascinating,
• 43:50
  I think was the loss of some of those clones, like the loss of K -RAS.
• 43:55
  And, um, uh, to be frank, uh, that totally surprised me, but it was completely
• 44:02
  consistent with the Sloan -Kettering Nature Genetics paper, which, um, I think also surprised us.
• 44:09
  And the reason I think that's really important is from, to me, I'm, um, precision medical
• 44:17
  oncologist, is because if we're looking at therapies, if, um, the clones change, this may
• 44:24
  explain, uh, resistance.
• 44:26
  So if the remaining clones, uh, no longer have K -RAS, that may explain, um, I mean, I
• 44:34
  had all sorts of other explanations before some of this data came out for why patients might be resistant.
• 44:41
  And I think some of those explanations still hold that there may be co -drivers, but now we can add to that explanation
• 44:48
  that there may be loss of certain clones.
• 44:51
  We may have actually killed off certain clones.
• 44:54
  Um, and that means we have to adjust our therapy, um, accordingly.
• 45:03
  Thank you.
• 45:04
  All right.
• 45:04
  Excellent.
• 45:05
  Um, Dean, uh, you had a question.
• 45:08
  I can ask it, but it's better coming directly from you.
• 45:11
  I don't need to put you on the spot.
• 45:12
  Are you still there, Dean?
• 45:14
  Yeah, I'm here.
• 45:16
  Go for it.
• 45:17
  Yeah.
• 45:18
  So, uh, how often are the, how often do you see consistency between the clonal profiles and
• 45:26
  frequencies or profiles say at day one versus after targeted therapy?
• 45:32
  And, and do they get killed in a way that's consistent with the mechanism of the targeted therapy?
• 45:41
  Ken Bloom: Uh, is that for me?
• 45:44
  Sure.
• 45:45
  Yeah.
• 45:46
  Um, so I don't think we know that answer completely.
• 45:49
  Um, uh, this is why we're doing the study.
• 45:52
  Um, uh, so, uh, in order to determine, so I, I can't answer that.
• 45:58
  Um, I, I, I just don't think we have enough data to know.
• 46:02
  We have done other prior studies, uh, that suggest, um, that, uh,
• 46:10
  the clones do disappear consistent with the, uh, mechanism of the targeted therapy and the emerging
• 46:18
  clones, um, have different alterations or, uh, new mutations.
• 46:24
  Uh, but this is part of this study trying to figure out, um, is this consistent with the therapy that
• 46:32
  we are giving?
• 46:33
  So I don't think we have the data yet.
• 46:36
  Okay.
• 46:37
  Thank you.
• 46:37
  And the follow up question was around, um, you know, it's 10, it looked like the early graph said between 10 and 50 CTCs
• 46:45
  per patient per draw.
• 46:47
  Um, is, could you do some statistics on those?
• 46:51
  I, I see they only reported, you only reported presence or absence of a mutation in each sample, but if you had 10 or
• 46:59
  20, you could say 15 % were, had the mutation.
• 47:05
  Have you looked into that?
• 47:07
  We haven't looked into it, but it's a good idea.
• 47:10
  Okay.
• 47:11
  Um, but I still think, um, if there's substantial numbers of cells that are losing the mutation,
• 47:19
  that's telling you something important, but the actual percentage would be good to look at.
• 47:24
  We agree.
• 47:26
  Thanks.
• 47:29
  All right.
• 47:30
  Feel, guys feel free to either raise your hand and ask a question directly or add it to the, to the chat box.
• 47:37
  Um, Ray, I had a question for you.
• 47:44
  The exploration of mutation types in CTCs versus CT DNA is essential for understanding the full spectrum of tumor evolution. The ongoing research into the dynamics of CTCs and their mutations is vital for understanding the complexities of pancreatic cancer treatment.
• 48:05
  Dr. Rizal Kurzok: is there, uh, so you've shown the differences and the overlaps, right?
• 48:09
  In terms of number of mutation and the types and the, and the actual mutation.
• 48:13
  Did you observe any differences in the types of mutation between CTD and a versus CTCs?
• 48:20
  Miss sense versus nonsense versus so on and so forth.
• 48:24
  Frame shift.
• 48:26
  Uh, yeah.
• 48:27
  So I think that, uh, some of the slides show that, um, uh, there, there are different, there are definitely differences in the mutations
• 48:35
  that we see in the CTCs and the CT DNA.
• 48:39
  Um, I, I don't know that we can say that there are certain structural, uh, differences, frame shifts
• 48:46
  versus, uh, fusions or, uh, single nucleotide variants.
• 48:52
  Um, uh, maybe, um, the one cell people have more information in that regard.
• 48:59
  Um, but, um, I, I, I think that there's definitely differences in the
• 49:06
  types of mutations that we're picking up.
• 49:09
  There's also convergence in the types of mutations, um, that we see on CTD and a and CTCs, but there's differences.
• 49:17
  And one of the things that I found really interesting was in, uh, some of the CTCs, uh, maybe
• 49:25
  there would be loss loss of KRAS, but there would be other, um, mutations that activate the MEC pathway,
• 49:33
  uh, that would emerge, um, at the same time.
• 49:37
  Sometimes those other pathways didn't emerge, but sometimes they do emerge other mutations that keep that pathway going.
• 49:46
  Yeah.
• 49:47
  Yeah.
• 49:47
  Arshad, just to add on a, just a quick comment.
• 49:50
  That this is one of the reasons why having an AI platform supporting all this is so important.
• 49:56
  You know, we're, we're in brand new territory.
• 49:58
  All these are fantastic questions.
• 50:00
  We don't know the answers to the most of them because the data is just being
• 50:06
  Ken Bloom: generated now.
• 50:07
  And I think it's also highlighting how difficult it is to, to understand what these all really mean.
• 50:13
  We have to come up with a hypothesis and then we just look and say, does it support it or not support it?
• 50:18
  But there's a lot of data hidden in all of this.
• 50:23
  Great.
• 50:23
  Uh, and so this is a question to both you and Ken, um, put yourself in the shoes of a, a, someone
• 50:31
  leading a clinical trial, um, either for a targeted therapy or for an ADC.
• 50:37
  I know, Ray, you talked about ADCs as well.
• 50:39
  Um, how, what's the implications for them in terms of how is this approach of
• 50:47
  advanced liquid biopsies useful to them from a clinical development perspective?
• 50:52
  Um, so I think from, um, my, uh, perspective, uh, there are several, uh, ways it's useful.
• 51:00
  Uh, the first is, uh, the baseline liquid biopsies, both CTCs and CT DNA, um,
• 51:09
  may be predictive of, um, which patients will respond with, to which targeted agents.
• 51:16
  Um, the second is, uh, with time, um, if we look at what is
• 51:24
  happening with the CTC evolution, um, it can probably tell us early
• 51:31
  on, um, if the patient's going to respond.
• 51:35
  Um, so we haven't yet done, um, these studies, um, here, uh, but we know from
• 51:43
  other studies, uh, that we can tell, uh, response with liquid biopsy, uh, way before,
• 51:51
  uh, two months, um, with a scan.
• 51:54
  And that, um, is true with targeted therapies is true with chemotherapy.
• 51:59
  So there may be early indicators of response, which we have not had the opportunity yet to, um,
• 52:06
  show here, but I think that, um, there's a lot, there are several studies, some of which we have done that show, um, that,
• 52:14
  um, looking at these serial liquid biopsies, we can see early indicators of response.
• 52:19
  Um, third, um, it may tell us, um, early indicators of resistance, um,
• 52:27
  and not only which patients may be resistant, but why they may be resistant.
• 52:35
  Uh, so it may be because the CT DNA increases.
• 52:39
  It may be because the CTCs increase, but there may be resistance even with a
• 52:47
  decrease in CTCs, if the CTC clones no longer have the target.
• 52:52
  Uh, so I, I think this allows us to decipher, um, uh, the
• 53:00
  heterogeneity in response and resistance, um, that, um, is probably more
• 53:08
  complicated and more interesting and more informative, um, than we previously thought.
• 53:16
  And certainly gives us a lot more information than doing a scan every two or three months or, uh, just looking at CT
• 53:23
  DNA.
• 53:26
  Great.
• 53:27
  Thank you.
• 53:28
  Ken, was there any follow up on that?
• 53:29
  Yeah.
• 53:30
  I'll just add from a pathologist perspective, certainly with ADCs, a lot of the patient selection is based off of biopsy
• 53:38
  material, some of which can be actually quite old.
• 53:42
  Uh, sometimes you can't even find the biopsy on the patient.
• 53:45
  Uh, but you should know anytime you take a biopsy and it's fixed in formalin and prepared for, you know,
• 53:53
  immunohistochemistry, those epitopes have been severely altered by the pre -analytics of, uh, of getting the sample to begin with the
• 54:01
  ischemic times, et cetera, fixation, cross links, proteins,
• 54:05
  and all sorts of bizarre ways.
• 54:07
  We have to do retrieval mechanisms to try to bring those epitopes back.
• 54:11
  Sometimes we bring them back.
• 54:13
  Well, sometimes we open up, uh, to nonspecific findings.
• 54:17
  And so the ability to be able to capture a CTC at the time point of therapy and probe it for proteins
• 54:25
  without any of those effects.
• 54:27
  And I think maybe that wasn't really clear.
• 54:29
  When you capture a live CTC, you don't have to worry about ischemic time fixations, antigen retrieval, all of the things that
• 54:37
  are negatives for immunohistochemistry, wind up going away with CTCs.
• 54:42
  And most importantly, when it's intact, we can absolutely identify and quantify the amount of protein on the surface of the
• 54:50
  cell that their drug would be, uh, available for rather than relying on pathologist eyeballs or AI techniques to
• 54:58
  delineate membrane from cytoplasm.
• 55:00
  I think there's a lot of advantages now.
• 55:02
  Everything else I agree totally with Dr.
• 55:04
  Kershra said, I think she eloquently pointed out all the advantages.
• 55:08
  All right.
• 55:09
  We have two more questions.
• 55:10
  I'm going to quickly go through those.
• 55:11
  So Langdon Miller asked, uh, that one of the critical challenges, uh, is when you're measuring resistance as baseline is what are the
• 55:19
  intrinsic, uh, mechanism resistance or sensitivity mechanisms that may steer the therapy towards one patient or another.
• 55:26
  Are there any known genetic profile in pancreatic cancer that predicts sensitivity or resistance to agents that are commonly employed to treat the
• 55:34
  disease?
• 55:35
  For example, taxanes, gemcitabine, 5 -FU, oxapleton, illinotican, et cetera.
• 55:42
  Uh, so, uh, okay.
• 55:46
  So, uh, in pancreatic cancer in particular, and with chemotherapy in particular, I think that, um,
• 55:54
  uh, we don't know profiles very well, um, and what predicts, um, but I think we're learning and I'm going
• 56:02
  to give you, um, just a couple of examples.
• 56:05
  uh, profile, uh, cells that have, uh, homologous repair deficiency are likely to respond to platinums
• 56:13
  or, uh, to PARP inhibitors.
• 56:17
  Um, we have a paper, uh, out that, uh, shows that, uh,
• 56:25
  uh, uh, profiles that include ATM, um, or check to alterations, um, which
• 56:33
  are usually thought of HRD related genes, but they're actually, um, they can cause HRD, but mostly they don't,
• 56:41
  uh, but they sensitize to topo one, uh, inhibitors, topo one isomerase inhibitors, such
• 56:48
  as irinotekin.
• 56:49
  Um, as far as gemcitabine and taxane, I think that there's a lot less known about what
• 56:57
  profiles, um, might sensitize to those chemotherapies.
• 57:01
  But I will also say that I think the next generation of therapies, uh, for pancreatic
• 57:09
  cancer probably needs to be better than gemcitabine and, uh, abroxane.
• 57:15
  Um, I, I think there's a lot of new drugs coming out and this can inform our use of next generation drugs.
• 57:22
  Okay.
• 57:23
  Before we end, one last question.
• 57:24
  Apparent loss of KRAS, is that supported by orthogonal biopsy methods such as IHC or histology?
• 57:33
  Um, we, uh, yeah, so that's a really good question.
• 57:37
  Um, so we are now cross -checking this loss of KRAS with, um, um, really looking at the cells, making sure
• 57:45
  that, um, uh, that the IHC profiles are correct for pancreatic cancer and so forth.
• 57:52
  Um, but, um, the bottom line is that I feel very confident that this is a real result.
• 57:58
  And the reason I feel confident that this is a real result is first of all, because the technology is
• 58:05
  The confirmation of KRAS loss through advanced liquid biopsy techniques highlights the need for ongoing research into the implications of clonal evolution. The challenges of capturing CTCs and their implications for therapy highlight the need for advanced techniques in liquid biopsy. The ongoing investigation into the relationship between CTCs and therapy resistance is crucial for developing personalized treatment strategies. The integration of CTC analysis into clinical trials can significantly enhance patient selection and treatment personalization. The exploration of genetic profiles in pancreatic cancer is essential for predicting treatment responses.
• 58:46
  Dr. Rizal Kurzok: really good.
• 58:47
  And second of all, um, we, we are basically, uh, confirming that this technology shows
• 58:55
  exactly what Sloan Kettering, uh, showed when they looked at single cell, uh, technology.
• 59:00
  Uh, when you look at this in bulk, you don't see the loss, but when you look at single cell technology, um, in the nature,
• 59:08
  uh, genetics paper, it's a paper by Zhang, Z -H -A -N -G.
• 59:13
  If you want to look it up and it was published at the end of 2024, they found that 42 % of, uh, the clones were
• 59:20
  losing KRAS, which I think is a real surprise.
• 59:23
  But the single cell technology gives you that and bulk technology cannot.
• 59:29
  Great.
• 59:30
  Thank you.
• 59:30
  That was from, uh, Amar Adam.
• 59:31
  So thank you, Amar, for that question.
• 59:33
  With that, uh, really appreciate Ray, uh, for taking your time to, to speak to us and sharing your, your insights.
• 59:41
  Uh, Ken, thank you as well.
• 59:43
  And I really appreciate all of the participants.
• 59:45
  Uh, and we will look forward to speaking to you and seeing you, uh, at, uh, AACR.
• 59:51
  All right.
• 59:51
  Thank you.