Latest / Zero Shot Podcast DEMO / Revolutionizing Pancreatic Cancer Monitoring with Advanced Liquid Biopsies V6

Transcript

Revolutionizing Pancreatic Cancer Monitoring with Advanced Liquid Biopsies V6 · Zero Shot Podcast DEMO

• 0:00
  Arshad: In this webinar, we delve into advanced liquid biopsies, focusing on their critical role in understanding pancreatic cancer. This discussion will highlight the significance of circulating tumor cells and circulating tumor DNA in enhancing precision oncology.
• 0:19
  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.
• 1:04
  Ken Bloom: So the topic is multisource genomics to monitor clonal evolution for pancreatic cancer.
• 1:11
  Dr.
• 1:11
  Rizal Kurzok from Medical College of Wisconsin will provide an update for the talk.
• 1:16
  Arshad: meeting is being recorded.
• 1:17
  We will have two formats for Q &A.
• 1:22
  As the meeting progresses, please feel free to put your questions in the chat, in the chat box,
• 1:30
  and then we'll accommodate as many of the questions.
• 1:34
  And towards the end, we'll have some time for some live questions as well.
• 1:38
  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.
• 1:45
  Ken Bloom: turn it over to Ken Bloom.
• 1:48
  Ken is OneCell's advisor.
• 1:51
  He's a strategic advisor to OneCell, also leads our CAPCLEAR certified laboratory in the San Francisco Bay Area.
• 2:03
  Arshad: Ken will be providing an overview of OneCell as well as our OncoInsights technology. Next, we will discuss the challenges of traditional biopsies?
• 2:15
  Ken Bloom: be providing an overview of OneCell as well as our OncoInsights technology.
• 2:22
  Right after that, before I turn it over to Rizal, I will ask all of the participants to go into the chat box.
• 2:31
  And there's a link there, which you can click.
• 2:34
  And it'll take you literally not more than 10 seconds to answer questions around what topics are relevant to you guys.
• 2:41
  That'll be important for us to understand.
• 2:43
  And we'll share some of that either towards the end or as a follow -up.
• 2:50
  Arshad: was trained 40 years ago with the idea that tissue biopsy is the gold standard.
• 2:55
  And over those years, we've been able to extract multiomic data from tissue, protein data from immunohistochemistry, and increasingly
• 3:02
  comprehensive molecular data from next -generation sequencing.
• 3:06
  Ken Bloom: biopsy has allowed us to predict prognosis and in the era of precision medicine to predict therapy response and potential resistance mechanisms.
• 3:22
  Arshad: Advanced liquid biopsies provide critical insights into the clonal evolution of pancreatic cancer. What does that contrast reveal?
• 3:32
  Ken Bloom: we really wanted to concentrate on pancreatic ductal adenocarcinoma for several reasons.
• 3:38
  One is because our institution really has a world -class program in this cancer.
• 3:46
  And the second is because it is such a difficult cancer.
• 3:50
  The five -year survival rate is about 13%, despite advances in surgery and systemic therapies.
• 3:58
  And it's actually worse than that.
• 4:00
  There's a recent paper in JAMA that shows that the one -year survival is about 24%.
• 4:06
  So, we have a long way to go to learn how to deal with this cancer.
• 4:11
  And it is projected to become the second leading cause of cancer -related deaths in the United States.
• 4:19
  And so, we really need to improve our understanding of this cancer.
• 4:26
  Pancreatic ductal adenocarcinoma progression involves stepwise accumulation of key driver mutations,
• 4:33
  which are mainly known to be KRAS, the cyclin system,
• 4:41
  CDKN2AB loss, and P53.
• 4:45
  And tumor evolution also includes rapid genomic restructuring characterized by
• 4:52
  copy number variations and large chromosomal rearrangements following
• 4:59
  polyploidization.
• 5:02
  So, this is a difficult cancer.
• 5:06
  And the reason, one of the reasons it's difficult is because of the great heterogeneity between patients
• 5:14
  and even within patients with time.
• 5:17
  And that's really difficult to capture if what you're doing is a baseline tissue biopsy.
• 5:24
  Um, so, um, there are a lot of limitations to bulk sequencing.
• 5:33
  Um, and, uh, some of them are illustrated here.
• 5:37
  Uh, so, we use bulk sequencing to understand, um, cancer.
• 5:44
  But the problem is that it doesn't really show us what happens with time, what happens with
• 5:52
  successive, um, mutational events.
• 5:55
  And, um, it cannot, um, extinguish, it cannot distinguish what is
• 6:03
  happening in different clones of cells.
• 6:06
  So, if we see, do single cell sequencing, we can see, are there co -occurring versus
• 6:14
  mutually exclusive alterations in different cells?
• 6:18
  Might this explain, um, mixed responses to drugs, uh, like PARP inhibitors, KRAS inhibitors,
• 6:26
  and immunotherapy.
• 6:28
  And so, single cell DNA sequencing is really required to resolve the clonal
• 6:36
  architecture of pancreatic ductal adenocarcinoma from early invasion through systemic,
• 6:43
  uh, dissemination and to identify molecular vulnerabilities targetable with precision
• 6:51
  therapies as the, as the disease evolves.
• 6:55
  Uh, so, this is a study that we initiated, um, and, uh, the purpose of
• 7:03
  the study was to isolate single circulating tumor cells from pancreatic cancer patients prior to treatment
• 7:10
  and perform genomic profile to characterize cellular level genomic heterogeneity, and also to
• 7:18
  follow these patients during treatment.
• 7:21
  And, uh, uh, this, uh, study is led by Dr.
• 7:25
  Mondana Kamgar and myself at MCW and Dr.
• 7:29
  Kremolini at, uh, University of Pisa.
• 7:33
  And, uh, we've collected quite a few samples and I think, um, I'm happy to share with you some of the
• 7:40
  insights, uh, from these samples.
• 7:43
  Next, uh, slide, please.
• 7:46
  So, all together, uh, we now have 26 patients, 63 total samples,
• 7:54
  um, and, uh, five time points tracked.
• 7:58
  And remembering that we're looking, um, both at CTCs and CT DNA.
• 8:05
  And we're also continuing to expand, uh, this, uh, project.
• 8:09
  Uh, but I'm going to show you, I think we have some fascinating insights, um, at this, uh, time.
• 8:17
  And, um, I want to share those with you.
• 8:19
  So, uh, first, um, uh, this slide, I think really illustrates, uh, some of the
• 8:27
  insights, uh, from the study.
• 8:29
  Um, so if we look at, uh, patients that have CT DNA only
• 8:36
  versus, uh, CTC DNA only, we're getting a lot more alterations
• 8:44
  from the CTCs, which you can see on the right, than we are getting from the CT DNA.
• 8:51
  And this in the middle are some of the shared alterations, um, that you see.
• 8:57
  So there's a lot of common things, but themes.
• 9:00
  But in addition, I think that both the CT DNA and the CTCs bring their
• 9:08
  own information.
• 9:10
  And I think you can see how rich the information is that is coming, um, from the CTCs.
• 9:17
  Um, so this, uh, slide shows, uh, the longitudinal, uh, CTC,
• 9:25
  uh, DNA and, uh, CT DNA longitudinal
• 9:32
  mutational landscape.
• 9:34
  And I'm going to go into, um, more detail in a moment, but if you go to the
• 9:42
  right side, this is patient by patient.
• 9:44
  And if you go to the right side of the slide, you can see, um, on the, um, Y axis on the
• 9:52
  right side, uh, the red is CT DNA and the green is CTCs.
• 9:59
  And we're going to go to higher level magnification, but at, uh, but you can see already from these slides
• 10:07
  that the CTCs and the CT DNA do not always tell you the same thing.
• 10:14
  Um, they give complimentary information sets.
• 10:20
  Um, so this, uh, focuses in, um, on, uh, what we see
• 10:27
  in, um, for instance, CT DNA, which is in the red and CTCs, which is in the green.
• 10:35
  And, um, what you can see is that in some, in some situations we're seeing the same thing,
• 10:42
  but in other situations we actually see, um, uh, more of certain mutations
• 10:50
  in the CT DNA than in the CTCs.
• 10:54
  Um, and, um, so one of the interesting things that we found
• 11:02
  with this is that, um, as we began to look at the CTCs
• 11:09
  versus the CT DNA, um, the, uh, we began to see something that we found
• 11:17
  initially confusing, but now we think it is absolutely fascinating and really goes again,
• 11:26
  uh, along with evidence published very recently in, uh, nature genetics by the Sloan Kettering group.
• 11:34
  Um, so in the CTCs, we're finding, for instance, we find alterations not found
• 11:42
  in the CT DNA.
• 11:43
  In the CT DNA, we find alterations not found in the CTCs.
• 11:48
  And, um, if we put, and some, of course, uh, of the alterations are found both in the CTCs
• 11:56
  and the CT DNA.
• 11:57
  Um, but this is, I'm going to give you an example of something that we thought was really fascinating and actually
• 12:05
  very relevant to therapy.
• 12:08
  Um, so a subset of, uh, patients with KRAS, as the tumor evolves,
• 12:16
  there are CTCs that appear to lose, uh, their KRAS.
• 12:21
  And, um, this was at first a bit confusing to us, but it turns out this
• 12:29
  is very consistent with what other, um, investigators are finding when they're looking at single cell
• 12:36
  populations.
• 12:38
  Um, and, uh, for instance, the Sloan Kettering group in nature genetics about a year ago
• 12:45
  published that about 40 % of the individual clones in pancreatic cancer were
• 12:53
  losing their KRAS.
• 12:55
  And, um, this is precisely what we're seeing as we follow individual
• 13:02
  CTCs and, um, uh, see that there are different clones of CTCs and not
• 13:10
  all of them retain, for example, um, their CTCs.
• 13:15
  So we're, uh, their KRAS.
• 13:17
  So for instance, we're seeing KRAS negative, uh, CTCs.
• 13:22
  Um, sometimes in the KRAS negative CTCs, the MEC pathway is still,
• 13:30
  um, a driver.
• 13:31
  For instance, they may have SMOD4 or they may have other alterations in that pathway, but not always.
• 13:38
  Some of these, uh, CTCs are probably losing different pathways or exploiting different
• 13:46
  pathways in order to, um, propagate, uh, the pancreatic cancer.
• 13:52
  Uh, so this goes back to our prior slide and I want to now focus on
• 14:00
  the Y -axis to the right and, um, um, the green
• 14:07
  part of that access.
• 14:09
  And that's the CTCs.
• 14:12
  And you can see that there's, um, multiple alterations that can be found in CTCs that cannot
• 14:20
  be found in the CT DNA.
• 14:22
  Um, so before we focused on some of the alterations, some of the CTCs that were losing
• 14:30
  alterations, now we're going to focus on, um, some of the alterations found in CTCs that
• 14:38
  cannot be found, um, in the CT DNA.
• 14:41
  And, uh, so you can see there's a whole, um, group of alterations
• 14:49
  that were being, that were finding in the CTCs.
• 14:53
  And, um, these alterations are not found in the bulk, uh, CT DNA.
• 14:59
  And since the CTCs may reflect the evolution of cancer, pancreatic cancer, and other
• 15:06
  cancers, and also may be seeding some of the metastatic sites, these additional alterations
• 15:14
  may be critical to thinking about therapy.
• 15:18
  And then, um, the final, um, part of this, uh, slide, if you look on the, on
• 15:25
  the Y axis to the right, uh, the bottom of the right axis, you can see the red dots.
• 15:32
  And this is the CT DNA only.
• 15:34
  So there are alterations also found in CT DNA that are not found in CTCs, um,
• 15:42
  less so than vice versa, but there are certain alterations in some of our patients.
• 15:48
  Uh, this shows, um, some of the alterations found in individual patients in the CT DNA and not
• 15:56
  found, um, um, in the CTCs.
• 15:59
  So when we put all of this together, the bottom line is we need both sources of information.
• 16:06
  We need the CT DNA and we need the CTCs.
• 16:10
  And this again puts together, um, some of the alterations found in the CT DNA only, some of the
• 16:18
  alterations found in the CTCs.
• 16:23
  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
• 16:30
  the shared alterations.
• 16:32
  So these are complementary techniques and this type of liquid biopsy is really unique in that it can
• 16:40
  give, um, investigators the full picture of what is going on in an individual patient.
• 16:47
  And, um, likely the difficulty with pancreatic cancer, metastatic pancreatic cancer, and
• 16:55
  probably many other, uh, cancers is that we've been getting a very partial picture.
• 17:02
  Um, if we do tissue biopsy, we get a picture at one point in time of one small piece of tissue.
• 17:10
  If we do a liquid biopsy, a blood biopsy, but we do CT DNA, we can get a serial picture
• 17:18
  of what is happening in the cancer, but it's a bulk serial picture.
• 17:23
  And we're not seeing what is happening in individual cells, which may reflect a tremendously
• 17:31
  important, um, biology of the cancer and also may reflect, um,
• 17:38
  seeding of new metastatic sites and how those clones may be different, um, may be the key, uh,
• 17:46
  to understanding, um, our next, uh, step in steps in therapy.
• 17:52
  So, um, this, um, emphasizes the point that we
• 18:00
  kind of always known, but here we have clear cut evidence for it, um, pancreatic
• 18:08
  cancer and probably every other cancer has a lot of tumor heterogeneity.
• 18:13
  And in addition with time that tumor heterogeneity, um, becomes
• 18:21
  worse.
• 18:22
  It undergoes molecular evolution and, uh, there are, uh, new alterations that are appearing.
• 18:29
  There may be old alterations that are muted or disappear.
• 18:38
  And, um, to reiterate tissue biopsy or just bulk CT DNA
• 18:46
  cannot outline all that is happening at a single cell level.
• 18:51
  And so CTCs demonstrate molecular diversity of the tumor at the baseline, uh, which is
• 18:59
  hidden with bulk CT DNA and tissue analysis.
• 19:04
  CTCs allow you to follow that on a regular basis and see what is
• 19:12
  happening at a single cell level as the cancer progresses.
• 19:16
  So, uh, they're important at baseline and they're important through the life cycle of that, uh, cancer.
• 19:24
  Um, so single cell CTC genomics provides biologic insights beyond
• 19:32
  conventional, um, liquid biopsies and it, uh, reveals tumor subpopulations and
• 19:39
  genomic alterations not captured by CT DNA, broadening the detectable molecular landscape and metastatic
• 19:47
  pancreatic cancer beyond what circulating DNA alone can resolve.
• 19:52
  And again, we're talking about pancreatic cancer, but it's very likely, and that's what our project was on, but
• 20:00
  it's very likely that this really applies to all tumors that tend to metastasize.
• 20:06
  Um, so I want to loop back to this really interesting finding,
• 20:13
  um, that our, uh, project has confirmed prior results, um, from,
• 20:22
  uh, uh, that were published in nature genetics about a year ago.
• 20:25
  Um, so, um, to our surprise and probably to the Sloan Kettering group surprise, uh, when they
• 20:33
  published this, um, we identified CTCs that have KRAS, that are KRAS independent.
• 20:41
  They appear, this is one example, but they appear to have lost their KRAS, um, and they're circulating
• 20:49
  in the bloodstream.
• 20:50
  Um, so this would be invisible to CT DNA based assays, um, that, uh,
• 20:59
  track, uh, tumor DNA in bulk.
• 21:03
  Interestingly, in these KRAS independent clones, in some of them, alternate pathways are
• 21:11
  activated in the MEC signal, MAPK, SMOD.
• 21:17
  Um, and in some cases, um, and in some cases, just completely alternate pathways are activated.
• 21:24
  PI3 kinase, AKT, uh, genomic instability, JAK -STAT.
• 21:30
  And I think we really have to have a good understanding of exactly what is happening in all these different clones if we
• 21:38
  are really going to impact what happens to pancreatic cancer patients.
• 21:42
  So, uh, CTCs demonstrate significantly greater intrapatient heterogeneity
• 21:50
  than CT DNA.
• 21:52
  And I showed this to you several slides back.
• 21:56
  Um, but it's, appears that patients have multiple, each patient has multiple
• 22:03
  different clones that may vary from each other.
• 22:08
  And, um, CTCs enable this high resolution delineation of
• 22:16
  coexisting clonal population and the genomic underpinnings of, uh, what is probably
• 22:24
  emerging resistance mechanisms when we treat.
• 22:27
  Um, so, um, uh, looking at both CT DNA and CTCs,
• 22:35
  it enables dynamic longitudinal tracking.
• 22:46
  Arshad: Ken is OneCell's advisor. The meeting is being recorded.
• 22:52
  Ken Bloom: drug programs, this isn't just biomarker discovery.
• 22:55
  This is real -time pharmacodynamic monitoring.
• 22:58
  You can track whether your drug is hitting its target, whether resistance is emerging, and which patients are likely to respond.
• 23:05
  Any new technology lives or dies on its data, so let's look at the numbers.
• 23:09
  Across more than 2 ,000 patient samples spanning lung, breast, pancreatic, colorectal, liver,
• 23:17
  head and neck, prostate, and gallbladder cancers, the overall CTC positivity rate, or our ability to detect
• 23:25
  CTCs, was 83 % for stages stage 2 through stage 4 patients, but metastatic patients had approached
• 23:32
  100%.
• 23:33
  Let me put that in context.
• 23:35
  Our first generation of CTC capture technology yielded a mean of about 5 CTCs per sample, while our current
• 23:43
  platform yields a mean of about 21 CTCs per sample.
• 23:47
  That's a four -fold improvement, and more cells means more material for downstream analysis, more genomics, more protein
• 23:55
  profiling, more confidence.
• 23:58
  An 83 % to 100 % positivity rate across solid tumors means that the vast majority of your enrolled patients can be monitored
• 24:06
  longitudinally with this platform, and that's a game -changer for adaptive trial design.
• 24:11
  One more important point before we move on, and this is particularly relevant for CNS cancers and thoracic malignancies.
• 24:18
  The technology isn't just limited to blood.
• 24:21
  At one cell, we've demonstrated CTC capture, or tumor cell capture, from cerebral spinal fluid
• 24:28
  and pleural fluid.
• 24:29
  In CNS tumors like glioblastoma and medulloblastoma, where a blood draw tells you almost nothing about what's happening in the brain,
• 24:37
  CSF -based CTC analysis opens a completely new window.
• 24:42
  This is significant for clinical trials and CNS malignancies, where biomarker development has lagged far behind other tumors.
• 24:49
  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.
• 24:58
  iCORM is one cell's integrated precision oncology consult.
• 25:02
  Think of it as the intelligence layer that sits on top of the laboratory data.
• 25:07
  It has two primary interfaces.
• 25:09
  iDiscover for translational research and clinical trial support, and iCare for the treating oncologist.
• 25:16
  For those of you in pharma, iDiscover is the critical piece.
• 25:21
  It integrates multiomic data from CTCs and CT DNA, radiologic imaging, pathology images, and real
• 25:29
  -world evidence data sets.
• 25:31
  It uses AI and machine learning to identify patient populations most likely to respond to a given therapy, flag resistance
• 25:39
  markers as they emerge, and help you understand mechanisms of action in a way that bulk tumor sequencing simply can't provide.
• 25:47
  It also adds the missing temporal layer to AI.
• 25:51
  It allows AI to begin to understand how tumor cells are adapting and what to do about it.
• 25:57
  Imagine being able to enrich your clinical trials for responders before the trial even starts, based on real -time
• 26:05
  CTC protein expression in genomics, rather than on archival tissue.
• 26:10
  That's the promise of iCORM.
• 26:12
  You get smarter enrollment, more informative biomarker endpoints, and ultimately, a cheaper path to approval.
• 26:29
  Arshad: The discussion will cover the limitations of traditional biomarkers and the advantages of CTCs.
• 26:37
  Ken Bloom: We've long sought biomarkers that would allow us to effectively monitor tumor progression and response to therapy.
• 26:44
  In my early career, this was accomplished with blood -based biomarkers like CEA, CA -125, and with imaging
• 26:51
  like CT and PET scans.
• 26:54
  While valuable, blood -based biomarkers are limited by their poor sensitivity and specificity, and
• 27:01
  imaging lacks sensitivity, exposes a vulnerable patient to high doses of radiation, and doesn't identify a lesion until it's of significant
• 27:09
  size.
• 27:11
  CT DNA, or circulating tumor DNA, has emerged as the latest tool in our monitoring arsenal.
• 27:17
  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.
• 27:24
  But CT DNA only gives you the genetic sequence of the fraction of tumor cells that have recently died.
• 27:30
  It can miss low -frequency mutations entirely, and of course, it will likely miss emerging clones that are actively proliferating but have not
• 27:38
  yet died.
• 27:43
  Arshad: However, as biopsies become smaller and smaller, our analysis becomes less reliable because of tumor heterogeneity.
• 27:51
  A five -micron section from a one -centimeter tumor represents only a fraction of a percent of that tumor.
• 27:57
  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
• 28:05
  of the book.
• 28:06
  You get a snapshot, a static picture.
• 28:09
  And while that provides actionable data, it creates a gap in our ability to monitor cancer's evolution.
• 28:16
  And this has direct consequences for patient management.
• 28:23
  More importantly, as antibody drug conjugate therapies have emerged, doesn't give us any useful information about protein expression or localization. However, as biopsies become smaller and smaller, our analysis becomes less reliable because of tumor heterogeneity.
• 28:42
  Ken Bloom: more importantly, as antibody drug conjugate therapies have emerged, CT DNA doesn't give us any useful information about
• 28:50
  protein expression or localization.
• 28:53
  This means we're all making patient selection and endpoint decisions based on incomplete, often outdated information.
• 29:01
  Arshad: But that's a solvable problem, and it's exactly what OneCells platform addresses.
• 29:06
  Our solution is called OncoInsights, our proprietary platform to capture and comprehensively analyze circulating
• 29:14
  tumor cells.
• 29:15
  refresher.
• 29:16
  When a tumor grows, it sheds cells into the bloodstream.
• 29:19
  These are circulating tumor cells, actual cancer cells floating in a patient's blood draw.
• 29:24
  The problem historically has been catching them.
• 29:27
  They are incredibly rare, maybe dozens or less, among billions of normal cells.
• 29:32
  The trick, though, is not just to capture them, but to capture them in a way that's suitable for downstream analysis.
• 29:39
  So this is where things get really exciting because of what you can measure from these CTCs. Is RNA, or whole transcriptome profiling. That's about 16,000 markers. Earliest systems utilized single -marker immunocapture and fixed the cells, making them largely unsuitable for comprehensive downstream analysis.
• 30:01
  Ken Bloom: earliest systems utilized single -marker immunocapture and fixed the cells, making them largely unsuitable for comprehensive downstream analysis.
• 30:09
  Other systems used negative selection by depleting hematopoietic cells or size -based methods, but these suffered from
• 30:17
  high background noise and low purity.
• 30:20
  OncoInsights solves these limitations with a combination of multi -marker affinity capture and a cleavable linker allowing capture of
• 30:28
  live CTCs with no cell damage.
• 30:31
  Instead of a single hook, we use customized capture beads coated with multiple antibodies targeted to a specific cancer
• 30:39
  type.
• 30:40
  For most cancers, a combination of Epcam and Transferrin works effectively, but the beads can be customized to optimize capture.
• 30:47
  For example, many renal cell cancers don't express Epcam at high levels, expressions quite low.
• 30:54
  So adding CD70 to the beads dramatically increases the CT positivity rate.
• 31:00
  Critically, this allows for the capture of the full diversity of tumor cells, not just the most common ones.
• 31:06
  Once the cells are captured, they are released intact using a cleavable linker system.
• 31:11
  No bead contamination, no cell damage.
• 31:14
  So the cells are ready for multi -omic analysis.
• 31:17
  Arshad: The discussion will cover the need for real-time monitoring and its implications for therapy.
• 31:24
  Ken Bloom: So this is where things get really exciting because of what you can measure from these capture CTCs.
• 31:30
  The OncoInsights panel covers three biological layers simultaneously from the same blood draw.
• 31:36
  First, DNA.
• 31:38
  1080 gene panel covering point mutations, insertions, deletions, gene fusions, copy number variations, and
• 31:46
  chromosomal rearrangements.
• 31:47
  It also gives you tumor mutational burden, microsatellite instability status, and homologous
• 31:54
  recombination deficiency status.
• 31:56
  All of the genomic biomarkers that predict response to immunotherapy, PARP inhibitors, and targeted agents.
• 32:05
  Arshad: is protein expression.
• 32:07
  This is currently measured by immunofluorescence on individual cells.
• 32:11
  You can directly quantify the proteins a drug is targeting, such as HER2, TROP2, clodin -18, nectin -4, etc.
• 32:19
  If you're developing an antibody drug conjugate, for example, this tells you whether the target is actually present at the protein level
• 32:27
  on cells circulating right now during treatment.
• 32:30
  is RNA, or whole transcriptome profiling.
• 32:33
  That's about 16 ,000 markers.
• 32:35
  This is your window into gene expression, what pathways are active, what resistance mechanisms might be turning on, and what's changing
• 32:43
  over time.
• 32:50
  With that as background, I'll now turn the slides over to Dr.
• 32:54
  Curtis Rock, who's going to present her exciting data on the clonal evolution in pancreatic cancer.
• 32:59
  But first, there will be a few polling questions.
• 33:02
  So, Arshad, I guess that goes to you next.
• 33:05
  Yes, thanks.
• 33:06
  Thanks, Ken.
• 33:07
  Appreciate that.
• 33:09
  If I can ask all the participants, if you can go to the top of the chat room, there's a link there
• 33:16
  from, it's called forms .gle.
• 33:20
  If you guys just click that link, I promise this won't take more than 10 seconds.
• 33:23
  It's going to ask some basic questions on what are the types of areas that are important to you, and that's it.
• 33:30
  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.
• 33:37
  So, over to Razelle.
• 33:41
  So, a pleasure to work on this project and with this
• 33:49
  really interesting technology.
• 33:54
  Ken Bloom: Um, so, key takeaway.
• 33:58
  We need all of these modalities.
• 34:01
  Um, CTC genomics is a complementary modality for understanding intratumoral heterogeneity,
• 34:09
  clonal dynamics, and resistance evolution in metastatic pancreatic ductal carcinoma.
• 34:15
  Um, and the fact that this platform gives us both CT DNA and
• 34:22
  CTCs at a single cell level, I think these are complementary, um, technologies in one
• 34:30
  platform and really allows us a very high high -level understanding of, um, um, what is
• 34:38
  happening in these patients.
• 34:40
  Um, uh, it gives us a window into an expanded, uh, molecular landscapes into clones that
• 34:48
  might be losing, uh, key drivers like KRAS, maybe replacing them with other drivers,
• 34:56
  maybe not, um, gives better clonal resolution, um, gives better clonal resolution and really allows longitudinal
• 35:04
  tracking of what is happening in an individual patient with treatment.
• 35:09
  Um, so this is one example of a patient, um, uh, treated, uh, and a
• 35:17
  longitudinal assessment.
• 35:19
  And, um, I, I think the things that I'd like to point out is, uh, that these
• 35:27
  patients, which we showed you, um, a high -level view, but you can see this in an
• 35:34
  individual patient, um, with time, the CTCs change.
• 35:40
  Um, so, uh, this patient starts off, uh, with NRAS, P53, and
• 35:48
  SMOD4.
• 35:49
  Uh, SMOD4 activates the MEC pathway.
• 35:53
  It can activate EGFR and HER2 and other pathways as well.
• 35:59
  And then with treatment, um, an HRAS emerges, uh, patient retains the P53.
• 36:07
  Now other alterations are emerging, which may be very important, including BRCA, uh,
• 36:14
  RAD51L, and, um, MUTYH.
• 36:20
  Um, a couple of these alterations like BRCA and RAD51 may be causing HRD
• 36:27
  and, uh, could be, uh, targeted with, um, PARP inhibitors or platinums.
• 36:34
  And then with more time, uh, the BRCA is retained and the P53 is retained.
• 36:40
  Um, but a P53CA alteration emerges.
• 36:44
  And so I think, um, this is really, um, important for understanding what's happening in the
• 36:52
  patient and how is resistance emerging and how should we mold our
• 37:00
  therapies, um, to this evolution that is occurring in an individual patient.
• 37:07
  And that is really what the ultimate goal is of precision or personalized medicine is to understand
• 37:15
  each patient in detail at each point in time of their disease.
• 37:21
  So we can mold the therapy that it would be best suited to that patient rather than guessing at what the
• 37:29
  next therapy should be.
• 37:30
  Um, I think this is really next generation precision oncology.
• 37:35
  Um, and, uh, this allows us to break the resolution barrier in precision oncology, allows us to understand
• 37:43
  biologic, uh, resolution, resistance detection, uh, tumor heterogeneity, um,
• 37:51
  all the different mutations and alterations.
• 37:54
  Um, and, um, um, um, we can go from population average genomics to single
• 38:02
  cell tumor biology, single cell genomics.
• 38:06
  Um, and in addition, uh, CTCs allow one to go beyond DNA.
• 38:12
  And this is really important.
• 38:13
  Um, CTCs allow one to look at, uh, DNA, but it, one can look at RNA.
• 38:19
  One can look at proteins on the same cell.
• 38:23
  One can even do functional tests on these cells.
• 38:26
  Um, so this is really an integrated cell biopsy platform, um, that,
• 38:34
  uh, can, uh, really help us understand diagnostics and, uh, drug
• 38:42
  development, tumor evolution, and what pressure points we need to apply to the tumor at any individual
• 38:50
  time point, um, of that tumor's, uh, progression.
• 38:55
  Rizal, is there any other slide you wanted to discuss in terms of alternative pathways?
• 38:59
  No, that's it.
• 38:59
  I'll just say, okay, perfect.
• 39:01
  Thank you, Rizal.
• 39:02
  Really appreciate, uh, the presentation.
• 39:05
  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
• 39:12
  AACR and I know most of you are likely to be at AACR.
• 39:16
  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
• 39:24
  QR code and schedule a meeting with us.
• 39:28
  It could be 15 minutes, it could be 30 minutes, but it'll be great to see you guys.
• 39:33
  Okay.
• 39:33
  Okay.
• 39:34
  Let's move to the Q and A part.
• 39:35
  Um, I see some questions coming in already.
• 39:39
  Um, excellent.
• 39:41
  So we have, okay.
• 39:42
  So Sejong Chun, uh, asked a question on, yeah.
• 39:47
  So what's the, what's the difference you think, Rizal between the difference that why does CTCs, do you
• 39:55
  have a thesis on why they pick up unique and more mutations in ctDNA?
• 40:01
  Um, why do CTCs, well, I think your, um, one reason you may be picking up, uh, more mutations than
• 40:09
  ctDNA is because, um, uh, you're looking at multiple different CTCs and you have different clones.
• 40:15
  And, um, so, uh, that may be a reason to pick up, uh, more mutations than ctDNA.
• 40:23
  Uh, the ctDNA is bulk and there might be, uh, subclones of CTCs that are,
• 40:31
  um, smaller in number and harder to pick up in the ctDNA.
• 40:35
  Um,