Latest / Zero Shot Podcast DEMO / Revolutionizing Cancer Care: The Promise of Liquid Biopsies V7

Transcript

Revolutionizing Cancer Care: The Promise of Liquid Biopsies V7 · Zero Shot Podcast DEMO

• 0:00
  Arshad: In this conversation, advanced liquid biopsies, particularly single-cell based methods, enhance the understanding of clonal evolution in pancreatic cancer.
• 0:14
  Thanks, everybody, for joining OneCell's webinar.
• 0:18
  The topic of today's conversation is advanced liquid
• 0:26
  biopsies.
• 0:27
  And in particular, we'll be discussing not just looking at circulating tumor DNA, but also the emerging
• 0:35
  field of single -cell -based liquid biopsies, which formerly a lot of people referred to as circulating tumor cells, also still
• 0:43
  referred to as circulating tumor cells.
• 0:45
  But we'll also describe what is new about these sort of cell -based liquid biopsies, where we can do molecular
• 0:52
  profiling, both in the cell level as well as at the DNA level.
• 0:59
  Ken Bloom: topic is multisource genomics to monitor clonal evolution for pancreatic cancer.
• 1:04
  Dr.
• 1:05
  Rizal Kurzok from Medical College of Wisconsin will provide an update for the talk.
• 1:11
  Arshad: meeting is being recorded.
• 1:13
  We will have two formats for Q &A.
• 1:17
  As the meeting progresses, please feel free to put your questions in the chat, in the chat box,
• 1:25
  and then we'll accommodate as many of the questions.
• 1:29
  And towards the end, we'll have some time for some live questions as well.
• 1:33
  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.
• 1:40
  Ken Bloom, OneCell's advisor, will provide an overview of OneCell and its OncoInsights technology, which is pivotal for precision oncology.
• 1:52
  Ken Bloom: I'll turn it over to Ken Bloom.
• 1:55
  Ken is OneCell's advisor.
• 1:58
  He's a strategic advisor to OneCell, also leads our CAPCLEAR certified laboratory in the San Francisco Bay Area.
• 2:08
  Arshad: be providing an overview of OneCell as well as our OncoInsights technology.
• 2:15
  Ken Bloom: after that, before I turn it over to Rizal, I will ask all of the participants to go into the chat box.
• 2:23
  And there's a link there, which you can click.
• 2:26
  And it'll take you literally not more than 10 seconds to answer questions around what topics are relevant to you guys.
• 2:33
  That'll be important for us to understand.
• 2:35
  And we'll share some of that either towards the end or as a follow -up.
• 2:43
  We've long sought biomarkers that would allow us to effectively monitor tumor progression and response to therapy.
• 2:48
  In my early career, this was accomplished with blood -based biomarkers like CEA, CA -125, and with
• 2:56
  imaging like CT and PET scans.
• 2:59
  While valuable, blood -based biomarkers are limited by their poor sensitivity and specificity, and
• 3:06
  imaging lacks sensitivity, exposes a vulnerable patient to high doses of radiation, and doesn't identify a lesion until it's of significant
• 3:14
  size.
• 3:19
  Arshad: Our labs hold the highest level of clinical accreditation, CLIA and CAP in the U .S., NAVAL and CAP in India.
• 3:26
  And these aren't just logos on the slide.
• 3:29
  They mean that the data coming out of our labs meets the standards required for clinical decision -making.
• 3:35
  Ken Bloom: mission is straightforward but ambitious.
• 3:37
  We're going to focus democratize precision oncology, make it actionable, accessible, affordable for every cancer patient
• 3:45
  everywhere.
• 3:46
  We are working toward impacting the lives of one million cancer patients through breakthroughs in science and AI.
• 3:52
  For those of you in pharma, you can think of OneCell as a potential partner for clinical trial design, patient selection, biomarker
• 4:00
  discovery, and drug development.
• 4:02
  Everything that you're about to hear is built on that infrastructure.
• 4:06
  So now let's talk about the problem, where we've been and where we're going.
• 4:11
  Arshad: was trained 40 years ago with the idea that tissue biopsy is the gold standard.
• 4:16
  And over those years, we've been able to extract multiomic data from tissue, protein data from immunohistochemistry, and increasingly
• 4:23
  comprehensive molecular data from next -generation sequencing.
• 4:27
  Tissue biopsy has allowed us to predict prognosis and in the era of precision medicine to predict therapy response and potential resistance
• 4:35
  mechanisms.
• 4:37
  However, as biopsies become smaller and smaller, our analysis becomes less reliable because of tumor heterogeneity.
• 4:44
  A five -micron section from a one -centimeter tumor represents only a fraction of a percent of that tumor.
• 4:51
  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
• 4:59
  of the book.
• 5:00
  You get a snapshot, a static picture.
• 5:03
  And while that provides actionable data, it creates a gap in our ability to monitor cancer's evolution.
• 5:09
  And this has direct consequences for patient management.
• 5:17
  DNA or circulating tumor DNA has emerged as the latest tool in our monitoring arsenal.
• 5:22
  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.
• 5:29
  But CT DNA only gives you the genetic sequence of the fraction of tumor cells that have recently died.
• 5:36
  It can miss low -frequency mutations entirely, and of course, it will likely miss emerging clones that are actively proliferating but have not
• 5:44
  yet died.
• 5:44
  The OncoInsights platform allows for real-time monitoring of clonal evolution during therapy. The limitations of ctDNA highlight the need for comprehensive monitoring methods that include protein expression and localization.
• 6:01
  Ken Bloom: more importantly, as antibody drug conjugate therapies have emerged, CT DNA doesn't give us any useful information about
• 6:09
  protein expression or localization.
• 6:11
  This means we're all making patient selection and endpoint decisions based on incomplete, often outdated information.
• 6:23
  Arshad: Thanks, Ken.
• 6:24
  Appreciate that.
• 6:25
  If I can ask all the participants, if you can go to the top of the chat room, there's a link there
• 6:33
  from, it's called forms .gle.
• 6:37
  If you guys just click that link, I promise this won't take more than 10 seconds.
• 6:40
  It's going to ask some basic questions on what are the types of areas that are important to you, and that's it.
• 6:46
  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.
• 6:53
  So, over to Razelle.
• 6:58
  So, a pleasure to work on this project and with this
• 7:05
  really interesting technology.
• 7:11
  Ken Bloom: One more important point before we move on, and this is particularly relevant for CNS cancers and thoracic malignancies.
• 7:18
  The technology isn't just limited to blood.
• 7:21
  At one cell, we've demonstrated CTC capture, or tumor cell capture, from cerebral spinal fluid
• 7:28
  and pleural fluid.
• 7:30
  In CNS tumors like glioblastoma and medulloblastoma, where a blood draw tells you almost nothing about what's happening in the brain,
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  CSF -based CTC analysis opens a completely new window.
• 7:42
  This is significant for clinical trials and CNS malignancies, where biomarker development has lagged far behind other tumors.
• 7:49
  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.
• 7:58
  iCORM is one cell's integrated precision oncology consult.
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  Think of it as the intelligence layer that sits on top of the laboratory data.
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  It has two primary interfaces.
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  iDiscover for translational research and clinical trial support, and iCare for the treating oncologist.
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  For those of you in pharma, iDiscover is the critical piece.
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  It integrates multiomic data from CTCs and CT DNA, radiologic imaging, pathology images, and real
• 8:29
  -world evidence data sets.
• 8:31
  It uses AI and machine learning to identify patient populations most likely to respond to a given therapy, flag resistance
• 8:39
  markers as they emerge, and help you understand mechanisms of action in a way that bulk tumor sequencing simply can't provide.
• 8:47
  It also adds the missing temporal layer to AI.
• 8:51
  It allows AI to begin to understand how tumor cells are adapting and what to do about it.
• 8:58
  Imagine being able to enrich your clinical trials for responders before the trial even starts, based on real -time
• 9:05
  CTC protein expression in genomics, rather than on archival tissue.
• 9:10
  That's the promise of iCORM.
• 9:12
  You get smarter enrollment, more informative biomarker endpoints, and ultimately, a cheaper path to approval.
• 9:20
  With that as background, I'll now turn the slides over to Dr.
• 9:24
  Curtis Rock, who's going to present her exciting data on the clonal evolution in pancreatic cancer.
• 9:29
  But first, there will be a few polling questions.
• 9:32
  So, Arshad, I guess that goes to you next.
• 9:35
  Yes, thanks.
• 9:40
  Arshad: that's a solvable problem, and it's exactly what OneCells platform addresses.
• 9:45
  Our solution is called OncoInsights, our proprietary platform to capture and comprehensively analyze circulating tumor
• 9:52
  cells.
• 9:53
  Here's a quick refresher.
• 9:55
  When a tumor grows, it sheds cells into the bloodstream.
• 9:58
  These are circulating tumor cells, actual cancer cells floating in a patient's blood drop.
• 10:04
  The problem historically has been catching them.
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  They are incredibly rare, maybe dozens or less, among billions of normal cells.
• 10:12
  The trick, though, is not just to capture them, but to capture them in a way that's suitable for downstream analysis.
• 10:18
  The OncoInsights platform utilizes advanced techniques for capturing and analyzing CTCs. The OncoInsights platform addresses the challenges of capturing circulating tumor cells (CTCs).
• 10:35
  Ken Bloom: The earliest systems utilized single -marker immunocapture and fixed the cells, making them largely unsuitable for comprehensive downstream
• 10:43
  analysis.
• 10:43
  Other systems use negative selection by depleting hematopoietic cells or size -based methods, but these
• 10:51
  suffered from high background noise and low purity.
• 10:55
  OncoInsights solves these limitations with a combination of multi -marker affinity capture and a cleavable linker allowing capture of
• 11:03
  live CTCs with no cell damage.
• 11:05
  Instead of a single hook, we use customized capture beads coated with multiple antibodies targeted to a specific
• 11:13
  cancer type.
• 11:14
  For most cancers, a combination of Epcam and transferrin works effectively, but the beads can be customized to optimize capture.
• 11:22
  For example, many renal cell cancers don't express Epcam at high levels, expressions quite low.
• 11:29
  So adding CD70 to the beads dramatically increases the CT positivity rate.
• 11:36
  Arshad: this allows for the capture of the full diversity of tumor cells, not just the most common ones.
• 11:42
  Once the cells are captured, they are released intact using a cleavable linker system.
• 11:47
  No bead contamination, no cell damage.
• 11:51
  So the cells are ready for multi -omic analysis.
• 11:53
  So this is where things get really exciting because of what you can measure from these capture CTCs.
• 12:00
  The OncoInsights panel covers three biological layers simultaneously from the same blood draw.
• 12:06
  First, DNA.
• 12:08
  1080 gene panel covering point mutations, insertions, deletions, gene fusions, copy number variations, and
• 12:16
  chromosomal rearrangements.
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  It also gives you tumor mutational burden, microsatellite instability status, and homologous
• 12:24
  recombination deficiency status.
• 12:26
  All of the genomic biomarkers that predict response to immunotherapy, PARP inhibitors, and targeted agents.
• 12:36
  Second is protein expression.
• 12:38
  This is currently measured by immunofluorescence on individual cells.
• 12:42
  You can directly quantify the proteins a drug is targeting, such as HER2, TROP2, clodin -18, nectin -4, etc.
• 12:50
  If you're developing an antibody drug conjugate, for example, this tells you whether the target is actually present at the protein level
• 12:58
  on cells circulating right now during treatment.
• 13:02
  Third is RNA, or whole transcriptome profiling.
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  That's about 16 ,000 markers.
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  This is your window into gene expression.
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  What pathways are active, what resistance mechanisms might be turning on, and what's changing over time.
• 13:16
  Real-time pharmacodynamic monitoring through the OncoInsights platform allows tracking of treatment responses. The OncoInsights platform allows for comprehensive analysis of CTCs.
• 13:32
  Ken Bloom: For drug programs, this isn't just biomarker discovery.
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  This is real -time pharmacodynamic monitoring.
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  You can track whether your drug is hitting its target, whether resistance is emerging, and which patients are likely to respond.
• 13:46
  Any new technology lives or dies on its data, so let's look at the numbers.
• 13:50
  Across
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  Arshad: overall CTC positivity rate, or our ability to detect CTCs, was 83 % for stages stage
• 14:00
  2 through stage 4 patients, but metastatic patients had approached 100%.
• 14:05
  Let me put that in context.
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  Our first generation of CTC capture technology yielded a mean of about 5 CTCs per sample, while our current
• 14:15
  platform yields a mean of about 21 CTCs per sample.
• 14:19
  That's a four -fold improvement, and more cells means more material for downstream analysis, more genomics, more protein
• 14:27
  profiling, more confidence.
• 14:33
  And we really wanted to concentrate on pancreatic ductal adenocarcinoma for several reasons.
• 14:40
  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.
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  The five -year survival rate is about 13%, despite advances in surgery and systemic therapies.
• 15:00
  And it's actually worse than that.
• 15:02
  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.
• 15:13
  And it is projected to become the second leading cause of cancer -related deaths in the United States.
• 15:21
  And so, we really need to improve our understanding of this cancer.
• 15:28
  Ken Bloom: Pancreatic ductal adenocarcinoma progression involves stepwise accumulation of key driver mutations,
• 15:35
  which are mainly known to be KRAS, the cyclin system,
• 15:43
  CDKN2AB loss, and P53.
• 15:47
  And tumor evolution also includes rapid genomic restructuring characterized by
• 15:55
  copy number variations and large chromosomal rearrangements following
• 16:01
  polyploidization.
• 16:04
  So, this is a difficult cancer.
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  And the reason, one of the reasons it's difficult is because of the great heterogeneity between patients
• 16:16
  and even within patients with time.
• 16:19
  And that's really difficult to capture if what you're doing is a baseline tissue biopsy.
• 16:26
  Um, so, um, there are a lot of limitations to bulk sequencing.
• 16:35
  Um, and, uh, some of them are illustrated here.
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  Uh, so, we use bulk sequencing to understand, um, cancer.
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  But the problem is that it doesn't really show us what happens with time, what happens with
• 16:54
  successive, um, mutational events.
• 16:58
  And, um, it cannot, um, extinguish, it cannot distinguish what is
• 17:05
  happening in different clones of cells.
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  So, if we see, do single cell sequencing, we can see, are there co -occurring versus
• 17:16
  mutually exclusive alterations in different cells?
• 17:21
  Might this explain, um, mixed responses to drugs, uh, like PARP inhibitors, KRAS inhibitors,
• 17:28
  and immunotherapy.
• 17:31
  And so, single cell DNA sequencing is really required to resolve the clonal
• 17:38
  architecture of pancreatic ductal adenocarcinoma from early invasion through systemic,
• 17:45
  uh, dissemination and to identify molecular vulnerabilities targetable with precision
• 17:53
  therapies as the, as the disease evolves.
• 17:57
  Uh, so, this is a study that we initiated, um, and, uh, the purpose of
• 18:05
  the study was to isolate single circulating tumor cells from pancreatic cancer patients prior to treatment
• 18:12
  and perform genomic profile to characterize cellular level genomic heterogeneity, and also to
• 18:20
  follow these patients during treatment.
• 18:23
  And, uh, uh, this, uh, study is led by Dr.
• 18:27
  Mondana Kamgar and myself at MCW and Dr.
• 18:31
  Kremolini at, uh, University of Pisa.
• 18:35
  And, uh, we've collected quite a few samples and I think, um, I'm happy to share with you some of the
• 18:42
  insights, uh, from these samples.
• 18:45
  Next, uh, slide, please.
• 18:48
  So, all together, uh, we now have 26 patients, 63 total samples,
• 18:56
  um, and, uh, five time points tracked.
• 19:00
  And, uh, we're looking at CTCs and CTDNA.
• 19:07
  And we're also continuing to expand, uh, this, uh, project.
• 19:12
  Uh, but I'm going to show you, I think we have some fascinating insights, um, at this, uh, time.
• 19:19
  And, um, I want to share those with you.
• 19:22
  So, uh, first, um, uh, this slide, I think really illustrates, uh, some of the
• 19:29
  insights, uh, from the study.
• 19:31
  Um, so if we look at, uh, patients that have CTDNA only
• 19:38
  versus, uh, CTCDNA only, we're getting a lot more alterations
• 19:46
  from the CTCs, which you can see on the right, than we are getting from the CTDNA.
• 19:53
  And this in the middle are some of the shared alterations, um, that you see.
• 19:59
  So there's a lot of common things, but themes.
• 20:03
  But in addition, I think that both the CTDNA and the CTCs bring their
• 20:10
  own information.
• 20:12
  And I think you can see how rich the information is that is coming, um, from the CTCs.
• 20:19
  Um, so this, uh, slide shows, uh, the longitudinal, uh, CTC,
• 20:27
  uh, DNA and, uh, CTDNA longitudinal
• 20:34
  mutational landscape.
• 20:36
  And I'm going to go into, um, more detail in a moment, but if you go to the
• 20:44
  right side, this is patient by patient.
• 20:47
  And if you go to the right side of the slide, you can see, um, on the, um, Y axis on the
• 20:54
  right side, uh, the red is CTDNA and the green is CTCs.
• 21:01
  And we're going to go to higher level magnification, but at, uh, but you can see already from these slides
• 21:09
  that the CTCs and the CTDNA do not always tell you the same thing.
• 21:16
  Um, they give complimentary information sets.
• 21:22
  Um, so this, uh, focuses in, um, on, uh, what we see
• 21:29
  in, um, for instance, CTDNA, which is in the red and CTCs, which is in the green.
• 21:37
  And, um, what you can see is that in some, in some situations we're seeing the same thing,
• 21:44
  but in other situations we actually see, um, uh, more of certain mutations
• 21:52
  in the CTDNA than in the CTCs.
• 21:56
  Um, and, um, so one of the interesting things that we found
• 22:04
  with this is that, um, as we began to look at the CTCs
• 22:12
  versus the CTDNA, um, the, uh, we began to see something that we found
• 22:19
  initially confusing, but now we think it is absolutely fascinating and really goes again,
• 22:28
  uh, along with evidence published very recently in, uh, nature genetics by the Sloan Kettering group.
• 22:36
  Um, so in the CTCs, we're finding, for instance, we find alterations not found
• 22:44
  in the CTDNA.
• 22:45
  In the CTDNA, we find alterations not found in the CTCs.
• 22:50
  And, um, if we put, and some, of course, uh, of the alterations are found both in the CTCs
• 22:58
  and the CTDNA.
• 23:00
  Um, but this is, I'm going to give you an example of something that we thought was really fascinating and actually
• 23:07
  very relevant to therapy.
• 23:10
  Um, so a subset of, uh, patients with KRAS, as the tumor evolves,
• 23:18
  there are CTCs that appear to lose, uh, their KRAS.
• 23:23
  And, um, this was at first a bit confusing to us, but it turns out this is
• 23:31
  very consistent with what other, um, investigators are finding when they're looking at single cell
• 23:38
  populations.
• 23:40
  Um, and, uh, for instance, the Sloan Kettering group in nature genetics about a year ago
• 23:47
  published that about 40 % of the individual clones in pancreatic cancer were
• 23:55
  losing their KRAS.
• 23:57
  And, um, this is precisely what we're seeing as we follow individual
• 24:04
  CTCs and, um, uh, see that there are different clones of CTCs and not
• 24:12
  all of them retain, for example, um, their CTCs.
• 24:17
  So we're, uh, their KRAS.
• 24:19
  So for instance, we're seeing KRAS negative, uh, CTCs.
• 24:24
  Um, sometimes in the KRAS negative CTCs, the MEC pathway is still, um,
• 24:32
  a driver.
• 24:34
  For instance, they may have SMOD4 or they may have other alterations in that pathway, but not always.
• 24:40
  Some of these, uh, CTCs are probably losing different pathways or exploiting different
• 24:48
  pathways in order to, um, propagate, uh, the pancreatic cancer.
• 24:55
  Uh, so this goes back to our prior slide and I want to now focus on
• 25:02
  the Y axis to the right and, um, um, the green
• 25:09
  part of that axis.
• 25:11
  And that's the CTCs.
• 25:14
  And you can see that there's, um, multiple alterations that can be found in CTCs that cannot
• 25:22
  be found in the CT DNA.
• 25:24
  Um, so before we focused on some of the alterations, some of the CTCs that were losing
• 25:32
  alterations, now we're going to focus on, um, some of the alterations found in CTCs that
• 25:40
  cannot be found, um, in the CT DNA.
• 25:43
  And, uh, so you can see there's a whole, um, group of alterations
• 25:51
  that were being, that were finding in the CTCs.
• 25:55
  And, um, these alterations are not found in the bulk, uh, CT DNA.
• 26:01
  And since the CTCs may reflect the evolution of cancer, pancreatic cancer, and other
• 26:08
  cancers, and also may be seeding some of the metastatic sites, these additional alterations
• 26:16
  may be critical to thinking about therapy.
• 26:20
  And then, um, the final, um, part of this, uh, slide, if you look on the, on
• 26:28
  the Y axis to the right, uh, the bottom of the right axis, you can see the red dots.
• 26:34
  And this is the CT DNA only.
• 26:37
  So there are alterations also found in CT DNA that are not found in CTCs, um,
• 26:44
  less so than vice versa, but there are certain alterations in some of our patients.
• 26:50
  Uh, this shows, um, some of the alterations found in individual patients in the CT DNA and not
• 26:58
  found, um, in the CTCs.
• 27:01
  So when we put all of this together, um, the bottom line is we need both sources of information.
• 27:08
  We need the CT DNA and we need the CTCs.
• 27:13
  And this again puts together, um, some of the alterations found in the CT DNA only, some of the
• 27:21
  alterations found in the CTCs.
• 27:25
  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
• 27:32
  of the shared alterations.
• 27:34
  So these are complementary techniques.
• 27:37
  And this type of liquid biopsy is really unique in that it can give, um, investigators the
• 27:45
  full picture of what is going on in an individual patient.
• 27:50
  And, um, likely the difficulty with pancreatic cancer, metastatic pancreatic cancer, and
• 27:58
  probably many other, uh, cancers is that we've been getting a very partial picture.
• 28:05
  Um, if we do tissue biopsy, we get a picture at one point in time of one small piece of tissue.
• 28:13
  If we do a liquid biopsy, a blood biopsy, but we do CT DNA, we can get a serial picture
• 28:21
  of what is happening in the cancer, but it's a bulk serial picture.
• 28:25
  And we're not seeing what is happening in individual cells, which may reflect a tremendously
• 28:33
  important, um, biology of the cancer and also may reflect, um,
• 28:40
  seeding of new metastatic sites and how those clones may be different, uh, may be the key, uh,
• 28:48
  to understanding, um, our next, uh, step in steps in therapy.
• 28:54
  So, um, this, um, emphasizes the point that we
• 29:02
  kind of always known, but here we have clear cut evidence for it.
• 29:08
  Um, pancreatic cancer and probably every other cancer has a lot of tumor heterogeneity.
• 29:15
  And, uh, in addition with time that tumor heterogeneity, um,
• 29:22
  becomes worse.
• 29:24
  It undergoes molecular evolution and, uh, there are, uh, new alterations that are appearing.
• 29:31
  There may be old alterations that are muted or disappear.
• 29:40
  And, um, to reiterate tissue biopsy or just bulk CT DNA
• 29:48
  cannot outline all that is happening at a single cell level.
• 29:54
  And so, uh, CTCs demonstrate molecular diversity of the tumor at the baseline, uh, which is
• 30:02
  hidden with bulk CT DNA and tissue analysis.
• 30:06
  CTCs allow you to follow that on a regular basis and see what is
• 30:14
  happening at a single cell level as the cancer progresses.
• 30:18
  So, uh, they're important at baseline and they're important through the life cycle of that, uh, cancer.
• 30:27
  Um, so single cell CTC genomics provides biologic insights beyond
• 30:34
  conventional, um, liquid biopsies.
• 30:37
  And it, uh, reveals tumor subpopulations and genomic alterations not captured by CT DNA,
• 30:45
  broadening the detectable molecular landscape and metastatic pancreatic cancer beyond what circulating DNA
• 30:53
  alone can resolve.
• 30:55
  And again, we're talking about pancreatic cancer, but it's very likely, and that's what our project was on.
• 31:06
  Um, so, um, uh, that's what our
• 31:14
  project has confirmed prior
• 31:21
  results, um, from, uh, uh, that were published in Nature Genetics about a year ago.
• 31:27
  Um, so, um, um, to our surprise and probably to the Sloan Kettering Group surprise, uh, when they
• 31:35
  published this, um, we identified CTCs that have KRAS, that are KRAS independent.
• 31:43
  They appear, this is one example, but they appear to have lost their KRAS, um, and they're circulating
• 31:51
  in the bloodstream.
• 31:53
  Um, so this would be invisible to CT DNA -based assays, um, that, uh,
• 32:01
  track, uh, tumor DNA in bulk.
• 32:05
  Interestingly, in these KRAS independent clones, in some of them, alternate pathways are
• 32:13
  activated in the MEC signal, MAPK, SMOD.
• 32:19
  Um, and in some cases, um, and in some cases, just completely alternate pathways are activated.
• 32:26
  PI3 kinase, AKT, uh, genomic instability, JAK -STAT.
• 32:32
  And I think we really have to have a good understanding of exactly what is happening in all these different clones if we
• 32:40
  are really going to impact what happens to pancreatic cancer patients.
• 32:45
  So, uh, CTCs demonstrate significantly greater intrapatient heterogeneity
• 32:52
  than ctDNA.
• 32:54
  And I showed this to you several slides back.
• 32:58
  Um, but it's, appears that patients have multiple, each patient has multiple
• 33:06
  different clones that may vary from each other.
• 33:10
  And, um, CTCs enable this high -resolution delineation of
• 33:18
  coexisting clonal population and the genomic underpinnings of, uh, what is probably
• 33:26
  emerging resistance mechanisms when we treat.
• 33:30
  Um, so, um, uh, looking at both ctDNA and CTCs,
• 33:38
  it enables dynamic longitudinal tracking.
• 33:43
  Serial single -cell CTC analysis enables real -time monitoring of clonal evolution during
• 33:51
  therapy, and it offers a direct window into tumor adaptation, uh, selective pressure responses,
• 33:59
  and treatment -driven genomic shifts over time.
• 34:03
  Um, so, key takeaway.
• 34:06
  We need all of these modalities.
• 34:10
  Um, CTC genomics is a complementary modality for understanding intratumoral heterogeneity,
• 34:18
  clonal dynamics, and resistance evolution in metastatic pancreatic ductal carcinoma.
• 34:24
  Um, and the fact that this platform gives us both ctDNA and
• 34:31
  CTCs at a single -cell level, I think these are complementary, um, technologies in one
• 34:39
  platform, and really allows us a very high -level understanding of, um, what is
• 34:47
  happening in these patients.
• 34:49
  Um, uh, it gives us a window into an expanded, uh, molecular landscapes into clones
• 34:57
  that might be losing, uh, key drivers like KRAS, maybe replacing them with other
• 35:04
  drivers, maybe not, um, gives better clonal resolution, um, gives better clonal resolution, and really allows
• 35:12
  longitudinal tracking of what is happening in an individual patient with treatment.
• 35:18
  Um, so this is one example of a patient, um, uh, treated, uh, and a longitudinal
• 35:26
  assessment.
• 35:27
  And, um, um, I, I think the things that I'd like to point out is, uh, that
• 35:35
  these patients, uh, which we showed you, um, a high -level view, but you can see this in an
• 35:43
  individual patient, um, with time, the CTCs change.
• 35:49
  Um, so, uh, this patient starts off, uh, with NRAS, P53, and
• 35:57
  SMOD4.
• 35:58
  Uh, SMOD4 activates the MEC pathway.
• 36:02
  It can activate EGFR and HER2 and other pathways as well.
• 36:08
  And then with treatment, um, an HRAS emerges, uh, patient retains the P53.
• 36:16
  Now, other alterations are emerging, which may be very important, including BRCA, uh,
• 36:23
  RAD51L, and, um, MUTYH.
• 36:28
  Um, a couple of these alterations, like BRCA and RAD51, may be causing HRD
• 36:36
  and, uh, could be, uh, targeted with, um, PARP inhibitors or platinums.
• 36:43
  And then with more time, uh, the BRCA is retained and the P53 is retained.
• 36:49
  Um, but a PIC3CA alteration emerges.
• 36:54
  And so I think, um, this is really, um, important for understanding what's happening in the patient
• 37:02
  and how is resistance emerging?
• 37:04
  And how should we mold our therapies, um, to this evolution
• 37:12
  that is occurring in an individual patient?
• 37:16
  And that is really what the ultimate goal is of precision or personalized medicine is to understand
• 37:24
  each patient in detail at each point in time of their disease.
• 37:30
  So we can mold the therapy that it would be best suited to that patient rather than guessing at what the
• 37:38
  next therapy should be.
• 37:40
  Um, I think this is really next generation precision oncology.
• 37:44
  Um, and, uh, this allows us to break the resolution barrier in precision oncology, allows us to understand
• 37:52
  biologic, uh, resolution, resistance detection, uh, tumor heterogeneity, um,
• 38:00
  all the different mutations and alterations.
• 38:03
  And, um, we can go from population average genomics to single
• 38:10
  cell tumor biology, single cell genomics.
• 38:14
  Um, and in addition, uh, CTCs allow one to go beyond DNA.
• 38:21
  And this is really important.
• 38:22
  Um, CTCs allow one to look at, uh, DNA, but it, one can look at RNA.
• 38:29
  One can look at proteins on the same cell.
• 38:32
  One can even do functional tests on these cells.
• 38:35
  Um, so this is really an integrated cell biopsy platform, um, that,
• 38:43
  uh, can, uh, really help us understand diagnostics and, uh, drug
• 38:51
  development, tumor evolution, and what pressure points we need to apply to the tumor at any individual
• 38:59
  time point, um, of that tumor's, uh, progression.
• 39:04
  Rizal, is there any other slide you wanted to discuss in terms of alternative pathways?
• 39:08
  No, that's it.
• 39:08
  That's it.
• 39:09
  Yes.
• 39:09
  Perfect.
• 39:10
  Thank you, Rizal.
• 39:11
  Really appreciate, uh, the presentation.
• 39:13
  Uh, we already have some questions coming in, but just to remind you guys, as you can see on this slide, uh, we will be at AACR
• 39:21
  and I know most of you are likely to be at AACR.
• 39:25
  So would, if there are any questions or if you would like to meet us, you can easily, uh, take a picture of, or put your camera in front of this
• 39:33
  QR code and schedule a meeting with us.
• 39:37
  It could be 15 minutes.
• 39:38
  It could be 30 minutes, but it'll be great to see you guys.
• 39:42
  Okay.
• 39:42
  Let's move to the Q and A part.
• 39:44
  Um, I see some questions coming in already.
• 39:47
  Um, excellent.
• 39:50
  So we have, okay.
• 39:51
  So Sejong Chun, uh, asked a question on, yeah.
• 39:56
  So what's the, what's the difference you think, Rizal between the difference that why does CTCs,
• 40:03
  do you have a thesis on why they pick up unique and more mutations than ctDNA?
• 40:10
  Um, why do CTCs?
• 40:12
  Well, I think you're, um, one reason you may be picking up, uh, more mutations than ctDNA is because, um,
• 40:20
  uh, you're looking at multiple different CTCs and you have different clones.
• 40:25
  And, um, so, uh, that may be a reason to pick up, um, more mutations than ctDNA.
• 40:32
  Uh, the ctDNA is bulk and there might be, uh, subclones of CTCs that are,
• 40:40
  um, smaller in number and harder to pick up in the ctDNA.
• 40:44
  Um, but I, I think the bottom line is that these are complementary techniques and that we're
• 40:52
  getting important information from both of them, but the information from the CTCs is unique because we can see it at a
• 41:00
  single cell level.
• 41:01
  And what it is showing us is that there's a lot of heterogeneity there and that heterogeneity cannot be picked up in the
• 41:08
  ctDNA.
• 41:11
  Sejong, feel free to add any follow -up on that.
• 41:13
  Go ahead.
• 41:14
  Yes.
• 41:14
  Thank you so much for that, um, response.
• 41:17
  So another question that I had on that regards is that with the ctDNA, if you increase the read deaths sufficiently, would
• 41:24
  that divergence, um, lessen or would there still be a difference in your perspective?
• 41:32
  So I'm not the technical person.
• 41:35
  Um, uh, so I want the technical person to answer it, but I'm going to tell you what I think.
• 41:39
  Um, I think there would still be a difference, uh, because there may be, um, uh,
• 41:47
  uh, small numbers of CTCs that have a difference.
• 41:51
  And that technically can't be picked up in the bulk ctDNA.
• 41:56
  And yet, even though it's small numbers of CTCs that have that difference, those CTCs may be critical to,
• 42:05
  um, what is happening in the patients with, um, seeding of new metastatic sites or, or reflecting resistant clones.
• 42:12
  So that's my answer, but somebody who's more technical than me might, um, have, uh, be able to add to that.
• 42:20
  Sure.
• 42:21
  I'll chime in on that one.
• 42:23
  Uh, there, there's definitely differences, but before I get into that, I'd just like to add one other point.
• 42:28
  If you draw CTC DNA at, you know, draw, take a sample, send it to your favorite CTC, you know,
• 42:36
  garden, your favorite CTC DNA lab, draw it in the afternoon, send it to Tempest, draw out in the evening, send it someplace else.
• 42:43
  You won't get the same mutations back, right?
• 42:45
  You're going to get, you're getting basically a small window of cells that are dying at that point of time.
• 42:50
  Now, your question, I think was more to the sensitivity.
• 42:53
  And if I raise the sensitivity of the assay, will I be able to detect things?
• 42:57
  And the problem with assays is generally when you raise the sensitivity, you lower the specificity.
• 43:02
  So now I start picking up, you know, where do you want to live and die, right?
• 43:07
  You want to get more false positives, more false negatives.
• 43:10
  But in the pharma's perspective, that might be slightly not the way you think.
• 43:15
  So we would like to see the drivers or the targetable ones, which would probably consist a larger proportion of the
• 43:22
  mutations detected, which would be relatively consistent across different vendors and different time points as well.
• 43:29
  So that's a conundrum that we're facing all the time.
• 43:33
  But this sort of leads to my next question, where with the CTCs across multiple different time points, there were a few mutations that
• 43:41
  were consistent, but there were other ones that appeared and then went away.
• 43:44
  So I'm a little curious on how those were calculated.
• 43:48
  Do you think they're drivers of, let's say, resistance?
• 43:51
  Because in that patient, you have the biomarkers going down and eventually down the line as tumor burden starts to increase.
• 43:57
  You see more mutations happening, but at the end of the day, it did seem that TP53 was the, let's say, surviving clone later.