Latest / Zero Shot Podcast DEMO / Revolutionizing Pancreatic Cancer Treatment with Liquid Biopsies V8

Transcript

Revolutionizing Pancreatic Cancer Treatment with Liquid Biopsies V8 · Zero Shot Podcast DEMO

• 0:00
  Arshad: In this conversation, advanced liquid biopsies can significantly enhance the understanding and treatment of pancreatic cancer by capturing tumor heterogeneity and clonal evolution.
• 0:17
  Thanks, everybody, for joining OneCell's webinar.
• 0:21
  The topic of today's conversation is advanced liquid
• 0:29
  biopsies.
• 0:30
  And in particular, we'll be discussing not just looking at circulating tumor DNA, but also the emerging
• 0:38
  field of single -cell -based liquid biopsies, which formerly a lot of people referred to as circulating tumor cells, also still
• 0:46
  referred to as circulating tumor cells.
• 0:47
  But we'll also describe what is new about these sort of cell -based liquid biopsies, where we can do molecular
• 0:55
  profiling, both in the cell level as well as at the DNA level.
• 1:01
  Ken Bloom: So the topic is multisource genomics to monitor clonal evolution for pancreatic cancer.
• 1:08
  Dr.
• 1:08
  Rizal Kurzok from Medical College of Wisconsin will provide an update for the talk.
• 1:14
  Arshad: This meeting is being recorded.
• 1:16
  We will have two formats for Q &A.
• 1:20
  As the meeting progresses, please feel free to put your questions in the chat, in the chat box,
• 1:28
  and then we'll accommodate as many of the questions.
• 1:32
  And towards the end, we'll have some time for some live questions as well.
• 1:36
  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.
• 1:44
  Before we continue, let’s clarify who Ken Bloom is and his relevance to this discussion.
• 1:51
  Ken Bloom: turn it over to Ken Bloom.
• 1:54
  Ken is OneCell's advisor.
• 1:57
  He's a strategic advisor to OneCell, also leads our CAPCLEAR certified laboratory in the San Francisco Bay Area.
• 2:06
  Arshad: be providing an overview of OneCell as well as our OncoInsights technology.
• 2:13
  Ken Bloom: after that, before I turn it over to Rizal, I will ask all of the participants to go into the chat box.
• 2:21
  And there's a link there, which you can click.
• 2:25
  And it'll take you literally not more than 10 seconds to answer questions around what topics are relevant to you guys.
• 2:31
  That'll be important for us to understand.
• 2:33
  And we'll share some of that either towards the end or as a follow -up.
• 2:41
  Arshad: labs hold the highest level of clinical accreditation, CLIA and CAP in the U .S., NAVAL and CAP in India.
• 2:47
  And these aren't just logos on the slide.
• 2:50
  They mean that the data coming out of our labs meets the standards required for clinical decision -making.
• 2:59
  Critically, this allows for the capture of the full diversity of tumor cells, not just the most common ones.
• 3:05
  Once the cells are captured, they are released intact using a cleavable linker system.
• 3:10
  No bead contamination, no cell damage.
• 3:14
  So the cells are ready for multi -omic analysis.
• 3:16
  So this is where things get really exciting because of what you can measure from these capture CTCs.
• 3:23
  The OncoInsights panel covers three biological layers simultaneously from the same blood draw.
• 3:29
  First, DNA.
• 3:31
  1080 gene panel covering point mutations, insertions, deletions, gene fusions, copy number variations, and
• 3:39
  chromosomal rearrangements.
• 3:40
  It also gives you tumor mutational burden, microsatellite instability status, and homologous
• 3:47
  recombination deficiency status.
• 3:49
  All of the genomic biomarkers that predict response to immunotherapy, PARP inhibitors, and targeted agents.
• 4:01
  Ken Bloom: mission is straightforward but ambitious.
• 4:03
  We're going to be able to do this, democratize precision oncology, make it actionable, accessible, affordable for every cancer patient
• 4:10
  everywhere.
• 4:11
  We are working toward impacting the lives of one million cancer patients through breakthroughs in science and AI.
• 4:18
  For those of you in pharma, you can think of OneCell as a potential partner for clinical trial design, patient selection, biomarker
• 4:26
  discovery, and drug development.
• 4:28
  Everything that you're about to hear is built on that infrastructure.
• 4:31
  So now let's talk about the problem, where we've been and where we're going.
• 4:36
  Arshad: was trained 40 years ago with the idea that tissue biopsy is the gold standard.
• 4:41
  And over those years, we've been able to extract multiomic data from tissue, protein data from immunohistochemistry, and increasingly
• 4:48
  comprehensive molecular data from next -generation sequencing.
• 4:53
  Tissue biopsy has allowed us to predict prognosis and in the era of precision medicine to predict therapy response and potential resistance
• 5:00
  mechanisms.
• 5:02
  However, as biopsies become smaller and smaller, our analysis becomes less reliable because of tumor heterogeneity.
• 5:09
  A five micron section from a one centimeter tumor represents only a fraction of a percent of that tumor.
• 5:16
  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 sentence
• 5:24
  of the book.
• 5:25
  You get a snapshot, a static picture.
• 5:28
  And while that provides actionable data, it creates a gap in our ability to monitor cancer's evolution.
• 5:35
  And this has direct consequences for patient management.
• 5:39
  Ken Bloom: We've long sought biomarkers that would allow us to effectively monitor tumor progression and response to therapy.
• 5:45
  In my early career, this was accomplished with blood -based biomarkers like CEA, CA -125, and with
• 5:53
  imaging like CT and PET scans.
• 5:55
  While valuable, blood -based biomarkers are limited by their poor sensitivity and specificity, and
• 6:03
  imaging lacks sensitivity, exposes a vulnerable patient to high doses of radiation, and doesn't identify a lesion until it's of significant
• 6:10
  size.
• 6:12
  Arshad: DNA or circulating tumor DNA has emerged as the latest tool in our monitoring arsenal.
• 6:18
  It's real time, and it reflects what's happening now since the half -life of DNA is on the order of a few hours.
• 6:25
  But CT DNA only gives you the genetic sequence of the fraction of tumor cells that have recently died.
• 6:31
  It can miss low -frequency mutations entirely, and of course, it will likely miss emerging clones that are actively proliferating but have not
• 6:39
  yet died.
• 6:41
  Ken Bloom: more importantly, as antibody drug conjugate therapies have emerged, CT DNA doesn't give us any useful information about
• 6:48
  protein expression or localization.
• 6:50
  This means we're all making patient selection and endpoint decisions based on incomplete, often outdated information.
• 7:05
  Arshad: Here's a quick refresher.
• 7:06
  When a tumor grows, it sheds cells into the bloodstream.
• 7:10
  These are circulating tumor cells, actual cancer cells floating in a patient's blood drop.
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  The problem historically has been catching them.
• 7:18
  They are incredibly rare, maybe dozens or less, among billions of normal cells.
• 7:23
  The trick, though, is not just to capture them, but to capture them in a way that's suitable for downstream analysis.
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  The earliest systems utilized single -marker immunocapture and fixed the cells, making them largely unsuitable for comprehensive downstream
• 7:38
  analysis.
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  Other systems use negative selection by depleting hematopoietic cells or size -based methods, but these
• 7:47
  suffered from high background noise and low purity.
• 7:51
  OncoInsights solves these limitations with a combination of multi -marker affinity capture and a cleavable linker allowing capture of
• 7:58
  live CTCs with no cell damage.
• 8:01
  Instead of a single hook, we use customized capture beads coated with multiple antibodies targeted to a specific
• 8:08
  cancer type.
• 8:10
  For most cancers, a combination of Epcam and transferrin works effectively, but the beads can be customized to optimize capture.
• 8:18
  For example, many renal cell cancers don't express Epcam at high levels, expressions quite low.
• 8:24
  So adding CD70 to the beads dramatically increases the CT positivity rate.
• 8:33
  Ken Bloom: is protein expression.
• 8:35
  This is currently measured by immunofluorescence on individual cells.
• 8:38
  You can directly quantify the proteins a drug is targeting, such as HER2, TROP2, clodin -18, nectin -4, etc.
• 8:47
  If you're developing an antibody drug conjugate, for example, this tells you whether the target is actually present at the protein level
• 8:54
  on cells circulating right now during treatment.
• 9:02
  Arshad: overall CTC positivity rate, or our ability to detect CTCs, was 83 % for stages stage
• 9:10
  2 through stage 4 patients, but metastatic patients had approached 100%.
• 9:14
  Let me put that in context.
• 9:17
  Our first generation of CTC capture technology yielded a mean of about 5 CTCs per sample, while our current
• 9:25
  platform yields a mean of about 21 CTCs per sample.
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  That's a four -fold improvement, and more cells means more material for downstream analysis, more genomics, more protein
• 9:36
  profiling, more confidence.
• 9:43
  Thanks, Ken.
• 9:44
  Appreciate that.
• 9:45
  If I can ask all the participants, if you can go to the top of the chat room, there's a link there
• 9:52
  from, it's called forms .gle.
• 9:56
  If you guys just click that link, I promise this won't take more than 10 seconds.
• 10:00
  It's going to ask some basic questions on what are the types of areas that are important to you, and that's it.
• 10:06
  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.
• 10:13
  So, over to Razelle.
• 10:17
  So, a pleasure to work on this project and with this
• 10:25
  really interesting technology.
• 10:31
  Ken Bloom: 83 % to 100 % positivity rate across solid tumors means that the vast majority of your enrolled patients can be monitored longitudinally
• 10:39
  with this platform, and that's a game -changer for adaptive trial design.
• 10:43
  One more important point before we move on, and this is particularly relevant for CNS cancers and thoracic malignancies.
• 10:50
  The technology isn't just limited to blood.
• 10:53
  At one cell, we've demonstrated CTC capture, or tumor cell capture, from cerebral spinal fluid
• 11:00
  and pleural fluid.
• 11:02
  In CNS tumors like glioblastoma and medulloblastoma, where a blood draw tells you almost nothing about what's happening in the brain,
• 11:09
  CSF -based CTC analysis opens a completely new window.
• 11:14
  This is significant for clinical trials and CNS malignancies, where biomarker development has lagged far behind other tumors.
• 11:21
  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.
• 11:30
  iCORM is one cell's integrated precision oncology consult.
• 11:34
  Think of it as the intelligence layer that sits on top of the laboratory data.
• 11:39
  It has two primary interfaces.
• 11:41
  iDiscover for translational research and clinical trial support, and iCare for the treating oncologist.
• 11:48
  For those of you in pharma, iDiscover is the critical piece.
• 11:53
  Arshad: imaging, pathology images, and real -world evidence data sets.
• 11:58
  It uses AI and machine learning to identify patient populations most likely to respond to a given therapy, flag resistance
• 12:06
  markers as they emerge, and help you understand mechanisms of action in a way that bulk tumor sequencing simply can't provide.
• 12:14
  It also adds the missing temporal layer to AI.
• 12:18
  It allows AI to begin to understand how tumor cells are adapting and what to do about it.
• 12:25
  Imagine being able to enrich your clinical trials for responders before the trial even starts, based on real -time
• 12:32
  CTC protein expression in genomics, rather than on archival tissue.
• 12:37
  That's the promise of iCORM.
• 12:39
  You get smarter enrollment, more informative biomarker endpoints, and ultimately, a cheaper path to approval.
• 12:47
  Ken Bloom: that as background, I'll now turn the slides over to Dr.
• 12:50
  Curtis Rock, who's going to present her exciting data on the clonal evolution in pancreatic cancer.
• 12:56
  But first, there will be a few polling questions.
• 12:59
  So, Arshad, I guess that goes to you next.
• 13:02
  Yes, thanks.
• 13:09
  Arshad: we really wanted to concentrate on pancreatic ductal adenocarcinoma for several reasons.
• 13:15
  One is because our institution really has a world -class program in this cancer.
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  And the second is because it is such a difficult cancer.
• 13:27
  The five -year survival rate is about 13%, despite advances in surgery and systemic therapies.
• 13:35
  And it's actually worse than that.
• 13:37
  There's a recent paper in JAMA that shows that the one -year survival is about 24%.
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  So, we have a long way to go to learn how to deal with this cancer.
• 13:48
  And it is projected to become the second leading cause of cancer -related deaths in the United States.
• 13:56
  And so, we really need to improve our understanding of this cancer.
• 14:02
  Ken Bloom: ductal adenocarcinoma progression involves stepwise accumulation of key driver mutations,
• 14:08
  which are mainly known to be KRAS, the cyclin system,
• 14:16
  CDKN2AB loss, and P53.
• 14:20
  And tumor evolution also includes rapid genomic restructuring characterized by
• 14:28
  copy number variations and large chromosomal rearrangements following
• 14:34
  polyploidization.
• 14:38
  So, this is a difficult cancer.
• 14:41
  Arshad: reason, one of the reasons it's difficult is because of the great heterogeneity between patients
• 14:49
  and even within patients with time.
• 14:52
  And that's really difficult to capture if what you're doing is a baseline tissue biopsy.
• 15:00
  Um,
• 15:00
  Ken Bloom: are a lot of limitations to bulk sequencing.
• 15:05
  Um, and, uh, some of them are illustrated here.
• 15:10
  Uh, so, we use bulk sequencing to understand, um, cancer.
• 15:16
  But the problem is that it doesn't really show us what happens with time, what happens with
• 15:24
  successive, um, mutational events.
• 15:28
  And, um, it cannot, um, extinguish, it cannot distinguish what is
• 15:35
  happening in different clones of cells.
• 15:38
  So, if we see, do single cell sequencing, we can see, are there co -occurring versus
• 15:46
  mutually exclusive alterations in different cells?
• 15:51
  Might this explain, um, mixed responses to drugs, uh, like PARP inhibitors, KRAS inhibitors,
• 15:58
  and immunotherapy.
• 16:01
  And so, single cell DNA sequencing is really required to resolve the clonal
• 16:08
  architecture of pancreatic ductal adenocarcinoma from early invasion through systemic,
• 16:16
  uh, dissemination and to identify molecular vulnerabilities targetable with precision
• 16:23
  therapies as the, as the disease evolves.
• 16:27
  Uh, so, this is a study that we initiated, um, and, uh, the purpose of
• 16:35
  the study was to isolate single circulating tumor cells from pancreatic cancer patients prior to treatment
• 16:42
  and perform genomic profile to characterize cellular level genomic heterogeneity, and also to
• 16:50
  follow these patients during treatment.
• 16:53
  And, uh, uh, this, uh, study is led by Dr.
• 16:57
  Mondana Kamgar and myself at MCW and Dr.
• 17:01
  Kremolini at, uh, University of Pisa.
• 17:05
  And, uh, we've collected quite a few samples and I think, um, I'm happy to share with you some of the
• 17:12
  insights, uh, from these samples.
• 17:15
  Next, uh, slide, please.
• 17:18
  So, all together, uh, we now have 26 patients, 63 total samples,
• 17:26
  um, and, uh, five time points tracked.
• 17:30
  And, uh, we're also continuing
• 17:38
  to expand, uh, this, uh, project, uh, but I'm going to show you, I think we have some fascinating insights,
• 17:46
  um, at this, uh, time.
• 17:48
  And, um, um, I want to share those with you.
• 17:52
  So, uh, first, um, uh, this slide, I think really illustrates, uh, some of the insights,
• 18:00
  uh, from the study.
• 18:01
  Um, so if we look at, uh, patients that have CT DNA only
• 18:08
  versus, uh, CTC DNA only, we're getting a lot more alterations
• 18:16
  from the CTCs, which you can see on the right, than we are getting from the CT DNA.
• 18:23
  And this in the middle are some of the shared alterations, um, that you see.
• 18:29
  So there's a lot of common things, but themes.
• 18:33
  But in addition, I think that both the CT DNA and the CTCs bring their
• 18:41
  own information.
• 18:42
  And I think you can see how rich the information is that is coming, um, from the CTCs.
• 18:49
  Um, so this, uh, slide shows, uh, the longitudinal, uh, CTC,
• 18:57
  uh, DNA and, uh, CT DNA longitudinal
• 19:04
  mutational landscape.
• 19:06
  And I'm going to go into, um, more detail in a moment, but if you go to the right
• 19:14
  side, this is patient by patient.
• 19:17
  And if you go to the right side of the slide, you can see, um, on the, um, Y axis on the
• 19:25
  right side, uh, the red is CT DNA and the green is CTCs.
• 19:31
  And we're going to go to higher level magnification, but at, uh, but you can see already from these slides
• 19:39
  that the CTCs and the CT DNA do not always tell you the same thing.
• 19:46
  Um, they give complimentary information sets.
• 19:52
  Um, so this, uh, focuses in, um, on, uh, what we see
• 19:59
  in, um, for instance, CT DNA, which is in the red and CTCs, which is in the green.
• 20:07
  And, um, what you can see is that in some, in some situations we're seeing the same thing,
• 20:14
  but in other situations we actually see, um, uh, more of certain mutations
• 20:22
  in the CT DNA than in the CTCs.
• 20:26
  Um, and, um, so one of the interesting things that we found
• 20:34
  with this is that, um, as we began to look at the CTCs
• 20:42
  versus the CT DNA, um, the, uh, we began to see something that we found
• 20:49
  initially confusing, but now we think it is absolutely fascinating and really goes again,
• 20:58
  uh, along with evidence published very recently in, uh, nature genetics by the Sloan Kettering group.
• 21:06
  Um, so in the CTCs, we're finding, for instance, we find alterations not found
• 21:14
  in the CT DNA.
• 21:15
  In the CT DNA, we find alterations not found in the CTCs.
• 21:20
  And, um, if we put, and some, of course, uh, of the alterations are found both in the CTCs
• 21:28
  and the CT DNA.
• 21:29
  Um, but this is, I'm going to give you an example of something that we thought was really fascinating and actually
• 21:37
  very relevant to therapy.
• 21:40
  Um, so a subset of, uh, patients with KRAS, as the tumor evolves,
• 21:48
  there are CTCs that appear to lose, uh, their KRAS.
• 21:53
  And, um, this was at first a bit confusing to us, but it turns out this
• 22:01
  is very consistent with what other, um, investigators are finding when they're looking at single cell
• 22:08
  populations.
• 22:10
  Um, and, uh, for instance, the Sloan Kettering group in nature genetics about a year ago published
• 22:18
  that about 40 % of the individual clones in pancreatic cancer were losing
• 22:25
  their KRAS.
• 22:27
  And, um, this is precisely what we're seeing as we follow individual
• 22:34
  CTCs and, um, uh, see that there are different clones of CTCs and not
• 22:42
  all of them retain, for example, um, their CTCs.
• 22:47
  So we're, uh, their KRAS.
• 22:49
  So for instance, we're seeing KRAS negative, uh, CTCs.
• 22:54
  Um, sometimes in the KRAS negative CTCs, the MEC pathway is still,
• 23:02
  um, a driver.
• 23:04
  For instance, they may have SMOD4 or they may have other alterations in that pathway, but not always.
• 23:10
  Some of these, uh, CTCs are probably losing different pathways or exploiting different
• 23:18
  pathways in order to, um, propagate, uh, the pancreatic cancer.
• 23:25
  Uh, so this goes back to our prior slide and I want to now focus on
• 23:32
  the Y -axis to the right and, um, um, the green
• 23:39
  part of that access.
• 23:41
  And that's the CTCs.
• 23:44
  And you can see that there's, um, multiple alterations that can be found in CTCs that cannot
• 23:52
  be found in the CT DNA.
• 23:54
  Um, so before we focused on some of the alterations, some of the CTCs that were losing
• 24:02
  alterations, now we're going to focus on, um, some of the alterations found in CTCs that
• 24:10
  cannot be found, um, in the CT DNA.
• 24:13
  And, uh, so you can see there's a whole, um, group of alterations
• 24:21
  that were being, that were finding in the CTCs.
• 24:25
  And, um, these alterations are not found in the bulk, uh, CT DNA.
• 24:31
  And since the CTCs may reflect the evolution of cancer, pancreatic cancer, and other
• 24:38
  cancers, and also may be seeding some of the metastatic sites, these additional alterations
• 24:46
  may be critical to thinking about therapy.
• 24:50
  And then, um, the final, um, part of this, uh, slide, if you look on the, on
• 24:58
  the Y axis to the right, uh, the bottom of the right axis, you can see the red dots.
• 25:04
  And this is the CT DNA only.
• 25:07
  So there are alterations also found in CT DNA that are not found in CTCs, um,
• 25:14
  less so than vice versa, but there are certain alterations in some of our patients.
• 25:20
  Uh, this shows, um, some of the alterations found in individual patients in the CT DNA and not
• 25:28
  found, um, um, in the CTCs.
• 25:31
  Arshad: So when we put all of this together, the bottom line is we need both sources of information.
• 25:38
  We need the CT DNA and we need the CTCs.
• 25:43
  And this again puts together, um, some of the alterations found in the CT DNA only, some of the
• 25:51
  alterations found in the CTCs.
• 25:55
  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
• 26:03
  the shared alterations.
• 26:05
  So these are complementary techniques and this type of liquid biopsy is really unique in that it can
• 26:12
  give, um, investigators the full picture of what is going on in an individual patient.
• 26:20
  And, um, likely the difficulty with pancreatic cancer, metastatic pancreatic cancer, and
• 26:28
  probably many other, uh, cancers is that we've been getting a very partial picture.
• 26:35
  Um, if we do tissue biopsy, we get a picture at one point in time of one small piece of tissue.
• 26:43
  If we do a liquid biopsy, a blood biopsy, but we do CT DNA, we can get a serial picture
• 26:51
  of what is happening in the cancer, but it's a bulk serial picture.
• 26:55
  And we're not seeing what is happening in individual cells, which may reflect a tremendously
• 27:03
  important, um, biology of the cancer and also may reflect, um,
• 27:10
  seeding of new metastatic sites and how those clones may be different, um, may be the key, uh,
• 27:19
  to understanding, um, our next, uh, step in steps in therapy.
• 27:24
  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.
• 28:36
  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 cell CTC genomics provides biologic insights beyond
• 29:04
  conventional, um, liquid biopsies and it, uh, reveals tumor subpopulations and
• 29:12
  genomic alterations not captured by CT DNA, broadening the detectable molecular landscape and metastatic
• 29:19
  pancreatic cancer beyond what circulating DNA alone can resolve.
• 29:25
  And again, we're talking about pancreatic cancer, but it's very likely, and that's what our project was on, but
• 29:32
  it's very likely that this really applies to all tumors that tend to metastasize.
• 29:38
  Um, so I want to loop back to this really interesting finding,
• 29:46
  um, that our, uh, project has confirmed prior results, um, from,
• 29:54
  uh, uh, that were published in nature genetics about a year ago.
• 29:58
  Um, so, um, to our surprise and probably to the Sloan Kettering group surprise, uh, when they
• 30:05
  published this, um, we identified CTCs that have KRAS, that are KRAS independent.
• 30:14
  They appear, this is one example, but they appear to have lost their KRAS, um, and they're circulating
• 30:21
  in the bloodstream.
• 30:23
  Um, so this would be invisible to CT DNA based assays, um, that, uh,
• 30:31
  track, uh, tumor DNA in bulk.
• 30:36
  Interestingly, in these KRAS independent clones, in some of them, alternate pathways are
• 30:44
  activated in the MEC signal, MAPK, SMOD.
• 30:49
  Um, and in some cases, um, and in some cases, just completely alternate pathways are activated.
• 30:57
  PI3 kinase, AKT, uh, genomic instability, JAK -STAT.
• 31:02
  And I think we really have to have a good understanding of exactly what is happening in all these different clones if we
• 31:10
  are really going to impact what happens to pancreatic cancer patients.
• 31:15
  So, uh, CTCs demonstrate significantly greater intrapatient heterogeneity
• 31:22
  than CT DNA.
• 31:25
  And I showed this to you several slides back.
• 31:28
  Um, but it's, appears that patients have multiple, each patient has multiple
• 31:36
  different clones that may vary from each other.
• 31:40
  And, um, CTCs enable this high resolution delineation of
• 31:48
  coexisting clonal population and the genomic underpinnings of, uh, what is probably
• 31:56
  emerging resistance mechanisms when we treat.
• 32:00
  Um, so, um, uh, looking at both CT DNA and CTCs,
• 32:08
  it enables dynamic longitudinal tracking.
• 32:13
  Ken Bloom: Serial single cell CTC analysis enables real -time monitoring of clonal evolution during
• 32:21
  therapy, and it offers a direct window into tumor adaptation, uh, selective pressure responses,
• 32:28
  and treatment -driven genomic shifts over time.