Latest / Zero Shot Podcast DEMO / Revolutionizing Pancreatic Cancer Treatment with Liquid Biopsies V5

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Revolutionizing Pancreatic Cancer Treatment with Liquid Biopsies V5 · Zero Shot Podcast DEMO

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Interviewer: Thanks, everybody, for joining OneCell's webinar.
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The topic of today's conversation is advanced liquid
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biopsies.
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And in particular, we'll be discussing not just looking at circulating tumor DNA, but also the emerging
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field of single -cell -based liquid biopsies, which formerly a lot of people referred to as circulating tumor cells, also still
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referred to as circulating tumor cells.
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But we'll also describe what is new about these sort of cell -based liquid biopsies, where we can do molecular
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profiling, both in the cell level as well as at the DNA level.
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So the topic is multisource genomics to monitor clonal evolution for pancreatic cancer.
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Dr.
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Rizal Kurzok from Medical College of Wisconsin will provide an update for the talk.
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This meeting is being recorded.
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We will have two formats for Q &A.
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As the meeting progresses, please feel free to put your questions in the chat, in the chat box,
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and then we'll accommodate as many of the questions.
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And towards the end, we'll have some time for some live questions as well.
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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.
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speakers: In this conversation, advanced liquid biopsies provide critical insights into the clonal evolution of pancreatic cancer, enhancing precision oncology.
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Interviewee: turn it over to Ken Bloom.
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Ken is OneCell's advisor.
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He's a strategic advisor to OneCell, also leads our CAPCLEAR certified laboratory in the San Francisco Bay Area.
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He will be providing an overview of OneCell as well as our OncoInsights technology.
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Right after that, before I turn it over to Rizal, I will ask all of the participants to go into the chat box.
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And there's a link there, which you can click.
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And it'll take you literally not more than 10 seconds to answer questions around what topics are relevant to you guys.
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That'll be important for us to understand.
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And we'll share some of that either towards the end or as a follow -up.
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And then I'll turn it over to Rizal, and we'll go for Q &A.
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So that's the agenda for today.
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Interviewer: any further delays, I will turn it over to Ken.
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Over to Ken.
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Perfect.
2:50
Thank you, Arshad.
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Welcome, everybody.
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was trained 40 years ago with the idea that tissue biopsy is the gold standard.
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And over those years, we've been able to extract multiomic data from tissue, protein data from immunohistochemistry, and increasingly
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comprehensive molecular data from next -generation sequencing.
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Tissue biopsy has allowed us to predict prognosis and in the era of precision medicine to predict therapy response and potential resistance
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mechanisms.
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However, as biopsies become smaller and smaller, our analysis becomes less reliable because of tumor heterogeneity.
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A five -micron section from a one -centimeter tumor represents only a fraction of a percent of that tumor.
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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
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of the book.
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You get a snapshot, a static picture.
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And while that provides actionable data, it creates a gap in our ability to monitor cancer's evolution.
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And this has direct consequences for patient management.
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speakers: The history of tissue biopsies shows their limitations in capturing tumor heterogeneity, emphasizing the need for advanced methods.
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Interviewee: name is Ken Bloom.
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I'm a surgical and molecular pathologist and the medical director of OneCell's Foster City Laboratory.
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So let's start with a quick introduction to OneCell .ai.
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We're a precision oncology company headquartered in Foster City, California, with major laboratory operations in Pune and
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Mumbai, India.
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Our labs hold the highest level of clinical accreditation, CLIA and CAP in the U .S., NAVAL and CAP in India.
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And these aren't just logos on the slide.
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They mean that the data coming out of our labs meets the standards required for clinical decision -making.
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Our mission is straightforward but ambitious.
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We're going to focus democratize precision oncology, make it actionable, accessible, affordable for every cancer patient
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everywhere.
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We are working toward impacting the lives of one million cancer patients through breakthroughs in science and AI.
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For those of you in pharma, you can think of OneCell as a potential partner for clinical trial design, patient selection, biomarker
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discovery, and drug development.
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Everything that you're about to hear is built on that infrastructure.
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So now let's talk about the problem, where we've been and where we're going.
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speakers: CTCs provide a more comprehensive view of tumor heterogeneity compared to ctDNA.
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Interviewee: long sought biomarkers that would allow us to effectively monitor tumor progression and response to therapy.
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In my early career, this was accomplished with blood -based biomarkers like CEA, CA -125, and with
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imaging like CT and PET scans.
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While valuable, blood -based biomarkers are limited by their poor sensitivity and specificity, and
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imaging lacks sensitivity, exposes a vulnerable patient to high doses of radiation, and doesn't identify a lesion until it's of significant
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size.
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CT DNA or circulating tumor DNA has emerged as the latest tool in our monitoring arsenal.
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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.
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But CT DNA only gives you the genetic sequence of the fraction of tumor cells that have recently died.
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It can miss low -frequency mutations entirely, and of course, it will likely miss emerging clones that are actively proliferating but have not
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yet died.
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speakers: The topic of today's conversation is advanced liquid biopsies. Ken Bloom, OneCell's advisor, will provide an overview of the company and its innovative OncoInsights technology.
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Interviewee: more importantly, as antibody drug conjugate therapies have emerged, CT DNA doesn't give us any useful information about
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protein expression or localization.
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This means we're all making patient selection and endpoint decisions based on incomplete, often outdated information.
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Interviewer: But that's a solvable problem, and it's exactly what OneCells platform addresses.
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Our solution is called OncoInsights, our proprietary platform to capture and comprehensively analyze circulating tumor
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cells.
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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.
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speakers: 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.
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Any new technology lives or dies on its data, so let's look at the numbers.
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Across more than 2 ,000 patient samples spanning lung, breast, pancreatic, colorectal, liver,
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head and neck, prostate, and gallbladder cancers, the overall CTC positivity rate, or our ability to detect
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CTCs, was 83 % for stages stage 2 through stage 4 patients, but metastatic patients had approached
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100%.
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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
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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
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profiling, more confidence.
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An 83 % to 100 % positivity rate across solid tumors means that the vast majority of your enrolled patients can be monitored
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longitudinally with this platform, and that's a game -changer for adaptive trial design.
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One more important point before we move on, and this is particularly relevant for CNS cancers and thoracic malignancies.
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The technology isn't just limited to blood.
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At one cell, we've demonstrated CTC capture, or tumor cell capture, from cerebral spinal fluid
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and pleural fluid.
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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.
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This is significant for clinical trials and CNS malignancies, where biomarker development has lagged far behind other tumors.
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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.
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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
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-world evidence data sets.
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It uses AI and machine learning to identify patient populations most likely to respond to a given therapy, flag resistance
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markers as they emerge, and help you understand mechanisms of action in a way that bulk tumor sequencing simply can't provide.
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It also adds the missing temporal layer to AI.
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It allows AI to begin to understand how tumor cells are adapting and what to do about it.
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Imagine being able to enrich your clinical trials for responders before the trial even starts, based on real -time
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CTC protein expression in genomics, rather than on archival tissue.
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That's the promise of iCORM.
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You get smarter enrollment, more informative biomarker endpoints, and ultimately, a cheaper path to approval.
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With that as background, I'll now turn the slides over to Dr.
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Curtis Rock, who's going to present her exciting data on the clonal evolution in pancreatic cancer.
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But first, there will be a few polling questions.
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So, Arshad, I guess that goes to you next.
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Yes, thanks.
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Thanks, Ken.
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Appreciate that.
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If I can ask all the participants, if you can go to the top of the chat room, there's a link there
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from, it's called forms .gle.
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If you guys just click that link, I promise this won't take more than 10 seconds.
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It's going to ask some basic questions on what are the types of areas that are important to you, and that's it.
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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.
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So, over to Razelle.
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So, a pleasure to work on this project and with this
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really interesting technology.
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And we really wanted to concentrate on pancreatic ductal adenocarcinoma for several reasons.
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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.
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And it's actually worse than that.
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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.
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And it is projected to become the second leading cause of cancer -related deaths in the United States.
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And so, we really need to improve our understanding of this cancer.
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Pancreatic ductal adenocarcinoma progression involves stepwise accumulation of key driver mutations,
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which are mainly known to be KRAS, the cyclin system,
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CDKN2AB loss, and P53.
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And tumor evolution also includes rapid genomic restructuring characterized by
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copy number variations and large chromosomal rearrangements following
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polyploidization.
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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
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and even within patients with time.
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And that's really difficult to capture if what you're doing is a baseline tissue biopsy.
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Um, so, um, there are a lot of limitations to bulk sequencing.
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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
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successive, um, mutational events.
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And, um, it cannot, um, extinguish, it cannot distinguish what is
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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
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mutually exclusive alterations in different cells?
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Might this explain, um, mixed responses to drugs, uh, like PARP inhibitors, KRAS inhibitors,
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and immunotherapy.
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And so, single cell DNA sequencing is really required to resolve the clonal
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architecture of pancreatic ductal adenocarcinoma from early invasion through systemic,
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uh, dissemination and to identify molecular vulnerabilities targetable with precision
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therapies as the, as the disease evolves.
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Uh, so, this is a study that we initiated, um, and, uh, the purpose of
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the study was to isolate single circulating tumor cells from pancreatic cancer patients prior to treatment
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and perform genomic profile to characterize cellular level genomic heterogeneity, and also to
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follow these patients during treatment.
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And, uh, uh, this, uh, study is led by Dr.
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Mondana Kamgar and myself at MCW and Dr.
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Kremolini at, uh, University of Pisa.
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And, uh, we've collected quite a few samples and I think, um, I'm happy to share with you some of the
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insights, uh, from these samples.
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Next, uh, slide, please.
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So, all together, uh, we now have 26 patients, 63 total samples,
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um, and, uh, five time points tracked.
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And, uh,
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uh, uh, uh, uh, this slide, I think we have some fascinating insights, um, at this, uh, time and, um, I
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want to share those with you.
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So, uh, first, um, uh, this slide, I think really illustrates, uh, some of the
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insights, uh, from the study.
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Um, so if we look at, uh, patients that have CT DNA only
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versus, uh, CTC DNA only, we're getting a lot more alterations
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from the CTCs, which you can see on the right, than we are getting from the CT DNA.
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And this in the middle are some of the shared alterations, um, that you see.
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So there's a lot of common things, but themes.
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But in addition, I think that both the CT DNA and the CTCs bring their
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own information.
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And I think you can see how rich the information is that is coming, um, from the CTCs.
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Um, so this, uh, slide shows, uh, the longitudinal, uh, CTC,
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uh, DNA and, uh, CT DNA longitudinal
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mutational landscape.
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And I'm going to go into, um, more detail in a moment, but if you go to the
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right side, this is patient by patient.
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And if you go to the right side of the slide, you can see, um, on the, um, Y axis on the
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right side, uh, the red is CT DNA and the green is CTCs.
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And we're going to go to higher level magnification, but at, uh, but you can see already from these slides
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that the CTCs and the CT DNA do not always tell you the same thing.
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Um, they give complimentary information sets.
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Um, so this, uh, focuses in, um, on, uh, what we see
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in, um, for instance, CT DNA, which is in the red and CTCs, which is in the green.
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And, um, what you can see is that in some, in some situations we're seeing the same thing,
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but in other situations we actually see, um, uh, more of certain mutations
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in the CT DNA than in the CTCs.
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Um, and, um, so one of the interesting things that we found
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with this is that, um, as we began to look at the CTCs
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versus the CT DNA, um, the, uh, we began to see something that we found
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initially confusing, but now we think it is absolutely fascinating and really goes again,
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uh, along with evidence published very recently in, uh, nature genetics by the Sloan Kettering group.
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Um, so in the CTCs, we're finding, for instance, we find alterations not found
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in the CT DNA.
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In the CT DNA, we find alterations not found in the CTCs.
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And, um, if we put, and some, of course, uh, of the alterations are found both in the CTCs
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and the CT DNA.
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Um, but this is, I'm going to give you an example of something that we thought was really fascinating and actually
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very relevant to therapy.
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Um, so a subset of, uh, patients with KRAS, as the tumor evolves,
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there are CTCs that appear to lose, uh, their KRAS.
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And, um, this was at first a bit confusing to us, but it turns out this
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is very consistent with what other, um, investigators are finding when they're looking at single cell
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populations.
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Um, and, uh, for instance, the Sloan Kettering group in nature genetics about a year ago
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published that about 40 % of the individual clones in pancreatic cancer were
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losing their KRAS.
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And, um, this is precisely what we're seeing as we follow individual
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CTCs and, um, uh, see that there are different clones of CTCs and not
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all of them retain, for example, um, their CTCs.
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So we're, uh, their KRAS.
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So for instance, we're seeing KRAS negative, uh, CTCs.
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Um, sometimes in the KRAS negative CTCs, the MEC pathway is still,
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um, a driver.
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For instance, they may have SMOD4 or they may have other alterations in that pathway, but not always.
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Some of these, uh, CTCs are probably losing different pathways or exploiting different
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pathways in order to, um, propagate, uh, the pancreatic cancer.
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Uh, so this goes back to our prior slide and I want to now focus on
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the Y -axis to the right and, um, um, the green
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part of that access.
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And that's the CTCs.
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And you can see that there's, um, multiple alterations that can be found in CTCs that cannot
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be found in the CT DNA.
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Um, so before we focused on some of the alterations, some of the CTCs that were losing
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alterations, now we're going to focus on, um, some of the alterations found in CTCs that
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cannot be found, um, in the CT DNA.
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And, uh, so you can see there's a whole, um, group of alterations
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that were being, that were finding in the CTCs.
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And, um, these alterations are not found in the bulk, uh, CT DNA.
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And since the CTCs may reflect the evolution of cancer, pancreatic cancer, and other
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cancers, and also may be seeding some of the metastatic sites, these additional alterations
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may be critical to thinking about therapy.
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And then, um, the final, um, part of this, uh, slide, if you look on the, on
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the Y axis to the right, uh, the bottom of the right axis, you can see the red dots.
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And this is the CT DNA only.
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So there are alterations also found in CT DNA that are not found in CTCs, um,
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less so than vice versa, but there are certain alterations in some of our patients.
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Uh, this shows, um, some of the alterations found in individual patients in the CT DNA and not
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found, um, um, in the CTCs.
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So when we put all of this together, the bottom line is we need both sources of information.
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We need the CT DNA and we need the CTCs.
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And this again puts together, um, some of the alterations found in the CT DNA only, some of the
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alterations found in the CTCs.
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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
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the shared alterations.
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So these are complementary techniques and this type of liquid biopsy is really unique in that it can
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give, um, investigators the full picture of what is going on in an individual patient.
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And, um, likely the difficulty with pancreatic cancer, metastatic pancreatic cancer, and
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probably many other, uh, cancers is that we've been getting a very partial picture.
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Um, if we do tissue biopsy, we get a picture at one point in time of one small piece of tissue.
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If we do a liquid biopsy, a blood biopsy, but we do CT DNA, we can get a serial picture
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of what is happening in the cancer, but it's a bulk serial picture.
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And we're not seeing what is happening in individual cells, which may reflect a tremendously
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important, um, biology of the cancer and also may reflect, um,
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seeding of new metastatic sites and how those clones may be different, um, may be the key, uh,
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to understanding, um, our next, uh, step in steps in therapy.
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So, um, this, um, emphasizes the point that we
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kind of always known, but here we have clear cut evidence for it, um, pancreatic
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cancer and probably every other cancer has a lot of tumor heterogeneity.
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And in addition with time that tumor heterogeneity, um, becomes
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worse.
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It undergoes molecular evolution and, uh, there are, uh, new alterations that are appearing.
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There may be old alterations that are muted or disappear.
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And, um, to reiterate tissue biopsy or just bulk CT DNA
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cannot outline all that is happening at a single cell level.
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And so CTCs demonstrate molecular diversity of the tumor at the baseline, uh, which is
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hidden with bulk CT DNA and tissue analysis.
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CTCs allow you to follow that on a regular basis and see what is
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happening at a single cell level as the cancer progresses.
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So, uh, they're important at baseline and they're important through the life cycle of that, uh, cancer.
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Um, so single cell CTC genomics provides biologic insights beyond
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conventional, um, liquid biopsies and it, uh, reveals tumor subpopulations and
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genomic alterations not captured by CT DNA, broadening the detectable molecular landscape and metastatic
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pancreatic cancer beyond what circulating DNA alone can resolve.
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And again, we're talking about pancreatic cancer, but it's very likely, and that's what our project was on, but
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it's very likely that this really applies to all tumors that tend to metastasize.
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Um, so I want to loop back to this really interesting finding,
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um, that our, uh, project has confirmed prior results, um, from,
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uh, uh, that were published in nature genetics about a year ago.
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Um, so, um, to our surprise and probably to the Sloan Kettering group surprise, uh, when they
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published this, um, we identified CTCs that have KRAS, that are KRAS independent.
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They appear, this is one example, but they appear to have lost their KRAS, um, and they're circulating
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in the bloodstream.
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Um, so this would be invisible to CT DNA based assays, um, that, uh,
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track, uh, tumor DNA in bulk.
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Interestingly, in these KRAS independent clones, in some of them, alternate pathways are
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activated in the MEC signal, MAPK, SMOD.
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Um, and in some cases, um, and in some cases, just completely alternate pathways are activated.
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PI3 kinase, AKT, uh, genomic instability, JAK -STAT.
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And I think we really have to have a good understanding of exactly what is happening in all these different clones if we
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are really going to impact what happens to pancreatic cancer patients.
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So, uh, CTCs demonstrate significantly greater intrapatient heterogeneity
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than CT DNA.
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And I showed this to you several slides back.
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Um, but it's, appears that patients have multiple, each patient has multiple
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different clones that may vary from each other.
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And, um, CTCs enable this high resolution delineation of
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coexisting clonal population and the genomic underpinnings of, uh, what is probably
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emerging resistance mechanisms when we treat.
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Um, so, um, uh, looking at both CT DNA and CTCs,
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it enables dynamic longitudinal tracking.
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Serial single cell CTC analysis enables real -time monitoring of clonal evolution during
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therapy, and it offers a direct window into tumor adaptation, uh, selective pressure responses,
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and treatment -driven genomic shifts over time.
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Um, so, key takeaway.
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We need all of these modalities.
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Um, CTC genomics is a complementary modality for understanding intratumoral heterogeneity,
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clonal dynamics, and resistance evolution in metastatic pancreatic ductal carcinoma.
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Um, and the fact that this platform gives us both CT DNA and
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CTCs at a single cell level, I think these are complementary, um, technologies in one
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platform and really allows us a very high high -level understanding of, um, um, what is
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happening in these patients.
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Um, uh, it gives us a window into an expanded, uh, molecular landscapes into clones that
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might be losing, uh, key drivers like KRAS, maybe replacing them with other drivers,
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maybe not, um, gives better clonal resolution, um, gives better clonal resolution and really allows longitudinal
33:00
tracking of what is happening in an individual patient with treatment.
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Um, so this is one example of a patient, um, uh, treated, uh, and a
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longitudinal assessment.
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And, um, I, I think the things that I'd like to point out is, uh, that these
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patients, which we showed you, um, a high -level view, but you can see this in an
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individual patient, um, with time, the CTCs change.
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Um, so, uh, this patient starts off, uh, with NRAS, P53, and
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SMOD4.
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Uh, SMOD4 activates the MEC pathway.
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It can activate EGFR and HER2 and other pathways as well.
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And then with treatment, um, an HRAS emerges, uh, patient retains the P53.
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Now other alterations are emerging, which may be very important, including BRCA, uh,
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RAD51L, and, um, MUTYH.
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Um, a couple of these alterations like BRCA and RAD51 may be causing HRD
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and, uh, could be, uh, targeted with, um, PARP inhibitors or platinums.
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And then with more time, uh, the BRCA is retained and the P53 is retained.
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Um, but a P53CA alteration emerges.
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And so I think, um, this is really, um, important for understanding what's happening in the
34:48
patient and how is resistance emerging and how should we mold our
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therapies, um, to this evolution that is occurring in an individual patient.
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And that is really what the ultimate goal is of precision or personalized medicine is to understand
35:11
each patient in detail at each point in time of their disease.
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So we can mold the therapy that it would be best suited to that patient rather than guessing at what the
35:24
next therapy should be.
35:26
Um, I think this is really next generation precision oncology.
35:31
Um, and, uh, this allows us to break the resolution barrier in precision oncology, allows us to understand
35:39
biologic, uh, resolution, resistance detection, uh, tumor heterogeneity, um,
35:46
all the different mutations and alterations.
35:49
Um, and, um, um, um, we can go from population average genomics to single
35:57
cell tumor biology, single cell genomics.
36:01
Um, and in addition, uh, CTCs allow one to go beyond DNA.
36:07
And this is really important.
36:09
Um, CTCs allow one to look at, uh, DNA, but it, one can look at RNA.
36:15
One can look at proteins on the same cell.
36:19
One can even do functional tests on these cells.
36:22
Um, so this is really an integrated cell biopsy platform, um, that,
36:29
uh, can, uh, really help us understand diagnostics and, uh, drug
36:37
development, tumor evolution, and what pressure points we need to apply to the tumor at any individual
36:45
time point, um, of that tumor's, uh, progression.
36:50
Rizal, is there any other slide you wanted to discuss in terms of alternative pathways?
36:55
No, that's it.
36:55
I'll just say, okay, perfect.
36:56
Thank you, Rizal.
36:57
Really appreciate, uh, the presentation.
37:00
Uh, we already have some questions coming in, but just to remind you guys, as you can see on the slide, uh, we will be at
37:07
AACR and I know most of you are likely to be at AACR.
37:12
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
37:20
QR code and schedule a meeting with us.
37:23
It could be 15 minutes, it could be 30 minutes, but it'll be great to see you guys.
37:28
Okay.
37:29
Okay.
37:29
Let's move to the Q and A part.
37:31
Um, I see some questions coming in already.
37:34
Um, excellent.
37:37
So we have, okay.
37:38
So Sejong Chun, uh, asked a question on, yeah.
37:43
So what's the, what's the difference you think, Rizal between the difference that why does CTCs, do you
37:51
have a thesis on why they pick up unique and more mutations in ctDNA?
37:57
Um, why do CTCs, well, I think your, um, one reason you may be picking up, uh, more mutations than
38:04
ctDNA is because, um, uh, you're looking at multiple different CTCs and you have different clones.
38:11
And, um, so, uh, that may be a reason to pick up, uh, more mutations than ctDNA.
38:19
Uh, the ctDNA is bulk and there might be, uh, subclones of CTCs that are,
38:27
um, smaller in number and harder to pick up in the ctDNA.
38:31
Um, but I, I think the bottom line is that these are complementary techniques and that we're
38:39
getting important information from both of them, but the information from the CTCs is unique because we can see it at a
38:46
single cell level.
38:47
And what it is showing us is that there's a lot of heterogeneity there and that heterogeneity cannot be picked up in the
38:55
ctDNA.
38:58
Sejong, feel free to add any follow up on that.
39:00
Go ahead.
39:01
Yes.
39:01
Thank you so much for that, um, response.
39:03
So another question that I had on that regards is that with the ctDNA, if you increase the read deaths sufficiently,
39:11
would that divergence, um, lessen or would there still be a difference in your perspective?
39:19
So I'm not the technical person.
39:21
Um, uh, so I want the technical person to answer it, but I'm going to tell you what I think.
39:26
Um, I think there would still be a difference, uh, because there may be, um, uh, a, a, uh,
39:35
small numbers of CTCs that have a difference and that technically can't be picked up in the bulk
39:42
ctDNA.
39:43
And yet, even though it's small numbers of CTCs that have that difference, those CTCs may be critical to, um,
39:52
what is happening in the patients with, um, seeding of new metastatic sites or, or reflecting resistant clones.
39:59
So that's my answer, but somebody who's more technical than me might, um, have, uh, be able to add to that.
40:07
Sure.
40:07
I'll chime in on that one.
40:09
Uh, there, there's definitely differences, but before I get into that, I'd just like to add one other point.
40:15
If you draw CtDNA at, you know, draw, take a sample, send it to your favorite CtD, you know,
40:23
garden, your favorite CtDNA lab.
40:25
Draw it in the afternoon, send it to Tempest, draw it in the evening, send it someplace else.
40:30
You won't get the same mutations back, right?
40:32
You're going to get, you're getting basically a small window of cells that are dying at that point of time.
40:37
Now, your question I think was more to the sensitivity.
40:39
And if I raise the sensitivity of the assay, will I be able to detect things?
40:44
And the problem with assays is generally when you raise the sensitivity, you lower the specificity.
40:49
So now I start picking up, you know, it, where do you want to live and die, right?
40:53
You want to get more false positives, more false negatives.
40:56
But in the pharma's perspective, that might be slightly, um, not the way you think.
41:01
So we would like to see the drivers or the targetable ones, which would probably, um, consist of the, um, the larger proportion of the
41:09
mutations detected, which would be relatively consistent across different vendors and, um, different time points as well.
41:16
So that, um, that, that's, that's a conundrum that we're facing all the time.
41:20
But, um, this sort of leads to my next question where with the CTCs across multiple different time points, there were a few mutations that were
41:28
consistent, but there were other ones that appeared and then went away.
41:31
So I'm a little curious on how those were calculated.
41:34
Um, do you think they're drivers of, let's say resistance because, um, in, in that patient, you have the biomarkers going down and eventually
41:41
down the line as tumor burden starts to increase, you see more mutations happening, but at the end of the day, it did seem that TP53
41:49
was the, let's say, surviving clone later.
41:56
I, uh, do you want me to try to answer that?
42:00
Yeah, Ray, that's for you.
42:01
Yep.
42:02
Yep.
42:02
Um, yeah.
42:04
So we actually found that fascinating and, um, uh, uh, we, uh, especially
42:11
found, uh, yes.
42:13
So P53 was the surviving clone in that patient, but we, with time, we don't know what else will come up, but what we
42:21
found most fascinating, I think was the loss of some of those clones, like the loss of K -RAS.
42:29
And, um, uh, to be frank, uh, that totally surprised me, but it was completely
42:36
consistent with the Sloan -Kettering Nature Genetics paper, which, um, I think also surprised us.
42:43
And the reason I think that's really important is from, to me, I'm, um, precision medical
42:50
oncologist, is because if we're looking at therapies, if, um, the clones change, this may
42:58
explain, uh, resistance.
43:00
So if the remaining clones, uh, no longer have K -RAS, that may explain, um, I mean, I
43:08
had all sorts of other explanations before some of this data came out for why patients might be resistant.
43:14
And I think some of those explanations still hold that there may be co -drivers, but now we can add to that explanation
43:22
that there may be loss of certain clones.
43:24
We may have actually killed off certain clones.
43:27
Um, and that means we have to adjust our therapy, um, accordingly.
43:36
Thank you.
43:37
All right.
43:37
Excellent.
43:39
Um, Dean, uh, you had a question.
43:41
I can ask it, but it's better coming directly from you.
43:44
I don't need to put you on the spot.
43:46
Are you still there, Dean?
43:48
Yeah, I'm here.
43:50
Go for it.
43:51
Yeah.
43:51
So, uh, how often are the, how often do you see consistency between the clonal profiles and
43:59
frequencies or profiles say at day one versus after targeted therapy?
44:05
And, and do they get killed in a way that's consistent with the mechanism of the targeted therapy?
44:15
Uh, is that for me?
44:17
Sure.
44:18
Yeah.
44:19
Um, so I don't think we know that answer completely.
44:23
Um, uh, this is why we're doing the study.
44:26
Um, uh, so, uh, in order to determine, so I, I can't answer that.
44:31
Um, I, I, I just don't think we have enough data to know.
44:36
We have done other prior studies, uh, that suggest, um, that, uh,
44:44
the clones do disappear consistent with the, uh, mechanism of the targeted therapy and the emerging
44:51
clones, um, have different alterations or, uh, new mutations.
44:58
Uh, but this is part of this study trying to figure out, um, is this consistent with the therapy that
45:05
we are giving?
45:07
So I don't think we have the data yet.
45:10
Okay.
45:11
Thank you.
45:11
And the follow up question was around, um, you know, it's 10, it looked like the early graph said between 10 and 50 CTCs
45:19
per patient per draw.
45:21
Um, is, could you do some statistics on those?
45:25
I, I see they only reported, you only reported presence or absence of a mutation in each sample, but if you had 10 or
45:33
20, you could say 15 % were, had the mutation.
45:39
Have you looked into that?
45:40
We haven't looked into it, but it's a good idea.
45:44
Okay.
45:44
Um, but I still think, um, if there's substantial numbers of cells that are losing the mutation,
45:52
that's telling you something important, but the actual percentage would be good to look at.
45:58
We agree.
46:00
Thanks.
46:03
All right.
46:03
Feel, guys feel free to either raise your hand and ask a question directly or add it to the, to the chat box.
46:11
Um, Ray, I had a question for you.
46:14
Um, is there, uh, so you've shown the differences and the overlaps, right?
46:20
In terms of number of mutation and the types and the, and the actual mutation.
46:24
Did you observe any differences in the types of mutation between CTD and a versus CTCs?
46:31
Miss sense versus nonsense versus so on and so forth.
46:35
Frame shift.
46:37
Uh, yeah.
46:38
So I think that, uh, some of the slides show that, um, uh, there, there are different, there are definitely differences in the mutations
46:46
that we see in the CTCs and the CT DNA.
46:50
Um, I, I don't know that we can say that there are certain structural, uh, differences, frame shifts
46:57
versus, uh, fusions or, uh, single nucleotide variants.
47:03
Um, uh, maybe, um, the one cell people have more information in that regard.
47:10
Um, but, um, I, I, I think that there's definitely differences in the
47:17
types of mutations that we're picking up.
47:20
There's also convergence in the types of mutations, um, that we see on CTD and a and CTCs, but there's differences.
47:28
And one of the things that I found really interesting was in, uh, some of the CTCs, uh, maybe
47:36
there would be loss loss of KRAS, but there would be other, um, mutations that activate the MEC pathway,
47:44
uh, that would emerge, um, at the same time.
47:48
Sometimes those other pathways didn't emerge, but sometimes they do emerge other mutations that keep that pathway going.
47:57
Yeah.
47:58
Yeah.
47:58
Arshad, just to add on a, just a quick comment.
48:01
That this is one of the reasons why having an AI platform supporting all this is so important.
48:07
You know, we're, we're in brand new territory.
48:09
All these are fantastic questions.
48:11
We don't know the answers to the most of them because the data is just being generated now.
48:16
And I think it's also highlighting how difficult it is to, to understand what these all really mean.
48:23
We have to come up with a hypothesis and then we just look and say, does it support it or not support it?
48:28
But there's a lot of data hidden in all of this.
48:32
Great.
48:33
Uh, and so this is a question to both you and Ken, um, put yourself in the shoes of a, a, someone
48:40
leading a clinical trial, um, either for a targeted therapy or for an ADC.
48:46
I know, Ray, you talked about ADCs as well.
48:49
Um, how, what's the implications for them in terms of how is this approach of
48:56
advanced liquid biopsies useful to them from a clinical development perspective?
49:02
Um, so I think from, um, my, uh, perspective, uh, there are several, uh, ways it's useful.
49:10
Uh, the first is, uh, the baseline liquid biopsies, both CTCs and CT DNA, um,
49:18
may be predictive of, um, which patients will respond with, to which targeted agents.
49:25
Um, the second is, uh, with time, um, if we look at what is
49:33
happening with the CTC evolution, um, it can probably tell us early
49:40
on, um, if the patient's going to respond.
49:45
Um, so we haven't yet done, um, these studies, um, here, uh, but we know from
49:53
other studies, uh, that we can tell, uh, response with liquid biopsy, uh, way before,
50:00
uh, two months, um, with a scan.