Latest / Zero Shot Podcast DEMO / Revolutionizing Cancer Diagnostics with OneCell.ai V3

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Revolutionizing Cancer Diagnostics with OneCell.ai V3 · Zero Shot Podcast DEMO

• 0:03
  Mohan Utawar: Mohan is a biotech entrepreneur who is the co -founder and CEO of OneCell .ai.
• 0:09
  He's a repeat founder with deep experience in both diagnostics and therapeutics.
• 0:13
  And as an innovator and investor, he's got more than 30 years of experience in life sciences, precision diagnostics, and healthcare technology.
• 0:21
  the founders: Mohan is a biotech entrepreneur who is the co-founder and CEO of OneCell. How does this next moment reframe what we just heard?
• 0:33
  Mohan Utawar: And what is OneCell .ai?
• 0:35
  OneCell .ai is a precision oncology company that's using AI and single -cell analytics to help pharmaceutical teams
• 0:42
  accelerate the next generation of cancer diagnostics and therapeutics.
• 0:46
  Its platform provides real -time tumor profile that can help drug developers make appropriate patient selection, accelerate development,
• 0:54
  and provide more nuanced understanding of a patient's response to therapy.
• 0:59
  But at the same time, this platform can be used to provide earlier diagnosis and guide personalized treatments by the
• 1:06
  ecologists.
• 1:11
  Interviewer: Mohan, thanks for joining us.
• 1:13
  We're going to talk today about how AI is transforming both cancer care and the development of new therapies, OneCell .ai,
• 1:21
  and how its liquid biopsy diagnostics are used by both clinicians and researchers.
• 1:26
  So, let's start with the liquid biopsies.
• 1:30
  For audience members who may not be familiar with these, can you explain what they mean and how they work?
• 1:38
  Thank you, Amur.
• 1:39
  Thank you for inviting me here.
• 1:41
  It's a pleasure and appreciate an opportunity to talk a little bit about the field as well as what we do, why we are
• 1:49
  excited about what we do at OneCell.
• 1:52
  Cancer is a deadly disease, we all know.
• 1:55
  One shoe doesn't fit all.
• 1:58
  So, personalization of therapy is the only way cancer can be managed.
• 2:04
  This whole field has evolved into what they call precision oncology, which refers to personalization.
• 2:12
  Historically, you know, what happens is somehow, you know, you find something, then the doctor tells you go
• 2:20
  into imaging, right, you do the radiologic scan, and then
• 2:27
  radiology finds something, but then the pathology is called for,
• 2:34
  which is the process of taking a biopsy.
• 2:37
  Biopsy is the process of taking a small tissue sample from the area where you suspect that there is cancer,
• 2:45
  and further analyzing it.
• 2:47
  It's sent to the lab, that tissue sample.
• 2:50
  It's called tissue biopsy.
• 2:52
  Tissue sample is sent to the lab.
• 2:54
  The lab does the processing of the sample.
• 2:57
  A glass slide is created, okay?
• 3:00
  And the pathologist puts it under the microscope, does the reading, and based on the pattern he or she sees, write a
• 3:08
  report.
• 3:09
  And based on that report, a treatment is given, okay?
• 3:13
  This process is called tissue biopsy, okay?
• 3:17
  Now, tissue is an issue because it's a surgical procedure.
• 3:21
  It's expensive, and it's not an easy thing to do.
• 3:25
  Yeah, and it's invasive, right?
• 3:27
  And it's invasive, right?
• 3:28
  It's invasive.
• 3:29
  It's invasive.
• 3:30
  As the field evolved and the science evolved, you know, the information that you get from tissue,
• 3:38
  similar information, you can get it from blood, liquid, okay?
• 3:43
  So that's why Dr.
• 3:46
  Klaus Pantel and Katrina Penebrez in Europe, they coined this term liquid biopsy.
• 3:53
  That means you take a simple blood draw, which is non -invasive or minimally invasive, and then you process it, okay,
• 4:01
  in the lab, and give a similar report.
• 4:04
  So that's the liquid biopsy aspect, right?
• 4:07
  From the sample of blood draw that you create the reports, generate the reports, using
• 4:15
  next -generation sequencing.
• 4:17
  Okay.
• 4:18
  Okay.
• 4:18
  Now, when you're doing the liquid biopsy, like, do, is this circulating, like, tumor cells or circulating tumor
• 4:26
  DNA, are they available for pretty much most cancers, or is that, like, more prevalent in some cancers versus not,
• 4:34
  or even, like, the staging?
• 4:36
  Like, maybe, like, when it goes metastatic, it's more present.
• 4:39
  Like, how does that work?
• 4:41
  What happens is cells die, some natural death and tumor cells, right?
• 4:47
  So, when they die, it spits out the DNA.
• 4:53
  That's the circulating DNA or cell -free DNA, okay?
• 4:58
  Subset of the cell -free DNA, right, from the dead cells, you know, if it is a tumor cells, the death of a tumor cells,
• 5:06
  which spits out the DNA, it's called CT DNA, okay?
• 5:11
  Okay.
• 5:12
  Information that you generate from the CT DNA, circulating tumor DNA, is most relevant,
• 5:19
  okay?
• 5:20
  But
• 5:24
  the founders: Liquid biopsies are non-invasive tests that analyze circulating tumor DNA from a simple blood draw, providing crucial insights without the need for surgical tissue biopsies.
• 5:37
  Mohan Utawar: It's like a needle in the haystack, okay?
• 5:42
  All right.
• 5:43
  Now, however, these are the real culprits.
• 5:46
  the founders: The integration of circulating tumor cells (CTCs) and ctDNA improves diagnostic sensitivity.
• 5:57
  Mohan Utawar: So, what happens is mostly, 95 % of the cancer deaths happens because of the
• 6:04
  reoccurrence, okay?
• 6:08
  The root cause of the reoccurrence, okay, is metastasis, a process where cancer spreads
• 6:15
  from its origin.
• 6:17
  It might be breast cancer, but because of the circulating tumor cells, the cells that are shed, they go
• 6:25
  run around.
• 6:26
  Most of them die.
• 6:28
  Most of them die.
• 6:29
  Most of them are killed by our immune system.
• 6:31
  But some strong ones evade the immune system and they settle somewhere else.
• 6:37
  Okay.
• 6:38
  So, they find the fertile ground.
• 6:40
  Maybe it's lung, maybe it's brain.
• 6:43
  Somewhere they go in settled tune and the secondary tumor grows.
• 6:49
  Okay.
• 6:50
  Okay.
• 6:50
  So, these are like the fugitives on the run.
• 6:53
  But they are the microcosms of creating secondary tumor cells.
• 7:01
  Okay.
• 7:02
  And that's the root cause.
• 7:05
  Okay.
• 7:05
  So, historically, you know, it was very difficult to find those CTCs, those one in a billion
• 7:13
  circulating tumor cells.
• 7:15
  Okay.
• 7:16
  You know, and so, you know, but now the new science, the groundbreaking science to
• 7:23
  capture that one in a billion circulating tumor cells.
• 7:27
  Okay.
• 7:29
  At a very high degree of sensitivity, high degree of precision, the purity of
• 7:36
  capture.
• 7:38
  Okay.
• 7:38
  And the high degree of simplicity and lower cost.
• 7:42
  Okay.
• 7:43
  So, why you are excited about this whole field is because one cell has this groundbreaking
• 7:51
  science.
• 7:51
  And if I take this three axis, order of magnitude, higher sensitivity, order of magnitude, higher purity of
• 7:59
  capture, order of magnitude, lower cost, it's a thousand times better price performance than anything
• 8:07
  that existed before.
• 8:09
  Okay.
• 8:10
  And it seems like, so then that's going to be both from like the technical aspect as well as like the algorithmic or AIS, right?
• 8:18
  Like, are these like the both aspects going here?
• 8:20
  Sure, sure.
• 8:22
  Second generation of precision oncology also was, you know, about
• 8:29
  multidimensional data, multimodal data, multiomics data.
• 8:35
  Cancer is like an elephant and a six blind man touching it and describing what it is.
• 8:42
  A genomics is a science of genetics, right?
• 8:47
  It tells you something very important, but it doesn't give you a full picture.
• 8:52
  Just like a blind person touching, you know, the leg of an elephant.
• 8:57
  If you ask him to describe, you'll say, oh, elephant is like a leg.
• 9:04
  Interviewer: Another blind person touches, you know, let's say the tail and it says, oh, it's like a snake.
• 9:09
  You know, so unfortunately, the genomics, the transcriptomics, the proteomics, the methylomics,
• 9:18
  the metabolomics, these are all fields of science.
• 9:21
  But they are looking at cancer into the silos with their own lenses.
• 9:31
  right?
• 9:32
  CT DNA, okay.
• 9:33
  And is it, like, in pretty much most cancers, you see that, or, like, some cancers more than the others?
• 9:40
  Yeah.
• 9:40
  Some cancers more than the others, but most cancers would have some DNA, CT DNA, right?
• 9:46
  Okay.
• 9:46
  And we're referring to tumor, I mean, the solid tumors, solid tumors, right?
• 9:51
  Yeah.
• 9:52
  So, tissue biopsy, the next generation was liquid biopsy, and liquid biopsy is referring to CT DNA.
• 10:01
  However, it is not a full representation of a tumor inside the body of a cancer patient,
• 10:10
  okay?
• 10:10
  These are essentially DNA fragments coming out of the dead cells,
• 10:18
  okay?
• 10:19
  So, what we are doing now is taking that industry forward to the third generation, okay?
• 10:26
  We call cell biopsy, okay?
• 10:29
  Which is basically a next generation of liquid biopsy, but coming out of the circulating tumor
• 10:37
  cells.
• 10:37
  Now, let me explain the CTC, or circulating tumor cells.
• 10:42
  A gram of tumor, okay, sheds about a million cancer cells in the body,
• 10:50
  in the bloodstreams.
• 10:52
  So, that may seem a lot, but we have trillions of cells in our body.
• 10:56
  So, that means it's one in a billion circulating tumor cells.
• 11:05
  the founders: OneCell. ai's platform uses AI to analyze multi-omics data for better cancer diagnostics.
• 11:14
  Mohan Utawar: Unfortunately, cancer, everything, this has to be all integrated.
• 11:20
  So the first generation of precision oncology was driven by tissue biopsy and genomics
• 11:28
  data.
• 11:29
  Companies like Foundation, they did very, very well.
• 11:32
  They established as the leader.
• 11:35
  But tissue is an issue.
• 11:36
  So second generation, the liquid biopsy, right, which is the blood -based stuff, right?
• 11:42
  You know, that came about.
• 11:44
  But what has been not fully equipped is, you know, again, that
• 11:50
  was really focusing again, you know, on the genomics and, you know, the
• 11:58
  transcriptomics.
• 11:59
  But DNAs, they don't, you know, cannot get you the transcriptomics or a little bit, but
• 12:07
  definitely not proteomics.
• 12:09
  You know, so the third generation, which we call the cell biopsy, you know, has the ability to do all three
• 12:17
  pieces together.
• 12:19
  The genomics, the transcriptomics, and the proteomics.
• 12:25
  Interviewer: Now, what happens is multiomics, you know, it's a big data problem.
• 12:30
  Cancer, as much as it's a disease, it's also a data problem.
• 12:35
  And the data is overwhelming.
• 12:37
  So this is where AI comes in handy.
• 12:40
  In analyzing this multidimensional, multimodal, multiomics data, making
• 12:48
  sense out of it, okay, correlating the data and predicting the outcome is the key
• 12:56
  challenge.
• 12:58
  Right?
• 12:59
  So the challenge here I see is that, like, usually, like, when you do, like, genomics, proteomics experiments, right, like, you have, like, a normal tissue, you have
• 13:06
  a disease, tumor tissue, right?
• 13:08
  And you're looking at, like, differences here.
• 13:10
  But then what you're collecting, that has, like, majority of that is already normal anyway.
• 13:17
  So how do you then detect the specific cancer DNA or the cancer proteins from that?
• 13:23
  Because that's a much higher level challenge than what people usually do, right?
• 13:28
  Absolutely.
• 13:28
  So how do you do that with AI?
• 13:30
  Yeah.
• 13:31
  Very, very good point.
• 13:33
  Excellent point.
• 13:34
  In liquid biopsy, I told you it's all about DNA.
• 13:38
  And you have a challenge because you start with a noisy data.
• 13:41
  You don't know whether that DNA came from a normal cell, dead cells, or a circulating tumor.
• 13:48
  I mean, the, the CT DNA, right?
• 13:51
  Yeah.
• 13:52
  And so when you start with the noisy data, again, you have to apply AI and deep analytics, more resources to basically,
• 14:00
  you know, denoise that data.
• 14:02
  Okay.
• 14:03
  The next generation technologies, particularly the one that we have invented, you are already clean,
• 14:11
  you're starting with a cleaner data because you have figured out, we have figured out, capturing that one in a billion circulating
• 14:19
  tumor cells.
• 14:20
  Okay.
• 14:21
  We already know that our starting point is a tumor cell.
• 14:26
  Oh, okay.
• 14:27
  Okay.
• 14:27
  So you're, so you have the technology to then detect the tumor cell specifically.
• 14:33
  Correct.
• 14:33
  Okay.
• 14:34
  Okay.
• 14:34
  Okay.
• 14:34
  Now, does that like, does that depend on like the kind of like surface markers there are on like the tumor cell or?
• 14:41
  Very good point.
• 14:42
  So CTC is not new.
• 14:45
  It's been there 10 years ago.
• 14:47
  Okay.
• 14:49
  Unfortunately, what happened was the sensitivity wasn't there.
• 14:53
  And the endpoint was you capture the circulating tumor cells, you count and report them.
• 15:01
  Okay.
• 15:01
  Okay.
• 15:01
  So the clinical utility was limited.
• 15:05
  You know, it told us that there is a problem.
• 15:09
  Okay.
• 15:10
  Your pet or CT scan or MRI might be clean.
• 15:14
  But if you find CTCs, that means, you know, there are some problems somewhere.
• 15:21
  Okay.
• 15:22
  So the scientist would say, oh, we found the problem.
• 15:25
  Okay.
• 15:26
  Okay.
• 15:26
  But the doctors would say, so what?
• 15:28
  My patient is happy because the pet is clean.
• 15:32
  And now you're telling me you found a CTC and you can't tell me what to do about it.
• 15:38
  Okay.
• 15:39
  So it's a very classic dilemma.
• 15:41
  Scientists get excited with what if.
• 15:44
  Okay.
• 15:45
  But the doctors are saying, so what?
• 15:48
  What if I found a CTC and the doctor is saying, so what?
• 15:52
  Unless you tell me, what is the actionability?
• 15:55
  And the science stopped there in the past.
• 16:00
  But today with the advent of next generation sequencing and with AI, okay, we are able to analyze
• 16:08
  the information in that single cell.
• 16:10
  Okay.
• 16:12
  Okay.
• 16:12
  The genomic data, the transcriptomic data, the proteomic data, and tell you and tell the doctor, hey,
• 16:21
  you know, it may be the cell coming from lung.
• 16:24
  You need a pet on basic breast, which was the original stuff, right?
• 16:30
  So without AI analyzing the cell information that is hidden in that circulating tumor cell,
• 16:37
  we would not enable the doctor to take those critical clinical decisions.
• 16:44
  Okay.
• 16:44
  So it's really the integration of information from the biology of the cell, the
• 16:52
  science part, and the AI working on analyzing the genomic data, the transcriptomic data,
• 17:00
  the proteomic data, right?
• 17:02
  So like you said, cell surface marker.
• 17:04
  You know, we know historically that if it's, let's say, HER2 marker on the surface of the cell,
• 17:12
  very likely that cell must be coming from breast cancer site.
• 17:17
  If it's an EGFR, very likely, again, no guarantees, but very likely it's coming from a lung cancer, right?
• 17:26
  Also, the way morphology of those cells look is very unique.
• 17:30
  So we use AI to analyze, you know, how the cell looks, the morphology, you know, the size,
• 17:38
  the shape, the density, the contrast, right?
• 17:42
  So there's a lot of imaging as well, then.
• 17:44
  Image analysis of the cell that we capture, right?
• 17:48
  Okay.
• 17:48
  So in addition to the multiomics, you're also doing the image analytics.
• 17:52
  Absolutely.
• 17:53
  Absolutely.
• 17:53
  Got you.
• 17:53
  Even before we do multiomics, we do the analysis of these cells, right?
• 17:59
  Okay.
• 17:59
  We do the characterization using our AI algorithms.
• 18:03
  And we give enough information.
• 18:06
  Now, one other, you know, fact that is actually very revealing is that many times the CTCs are
• 18:14
  individual CTCs.
• 18:15
  But many times you have this, you know, what we call the combinations, right?
• 18:23
  You know, you have the, you know, group of cells hanging together.
• 18:28
  Okay.
• 18:29
  Now, the risk profile of those patients, prognostic is much, much different.
• 18:34
  It's much higher risk.
• 18:35
  Okay.
• 18:36
  Again, imaging reveals those kind of things.
• 18:40
  Okay.
• 18:41
  You know, so, so there's interesting stuff, right?
• 18:43
  Now, how do we capture these circulating tumor cells, right?
• 18:48
  The CTCs, there are two methods for capturing.
• 18:51
  One is size -based, right?
• 18:53
  So, tumor cells are usually larger in size.
• 18:57
  And there are techniques, you know, micro filtration.
• 19:01
  There are techniques to really do the nano fluidics to capture those cells.
• 19:06
  That's one method.
• 19:08
  The other method is called affinity -based.
• 19:11
  So, tumor cells have certain types of proteins on the surface of cells.
• 19:17
  So, Kepcam being one very popular protein that is on all solid tumors that are epithelial in
• 19:24
  nature.
• 19:26
  Okay.
• 19:27
  You know, a transferrin in another protein that is expressed.
• 19:31
  Okay.
• 19:31
  So, what happens in the size -based capture, the cells go through a lot of, you know, pushing
• 19:39
  and pulling.
• 19:40
  Nano fluidics, you know, you're forcing this.
• 19:44
  You know, nano filtration, you're pushing it.
• 19:49
  As is, when cell leaves the tumor, okay, you know, it's in a, it was in a
• 19:57
  very comfortable position sitting in the home.
• 20:00
  And now, it has to fight the whole battle, right?
• 20:03
  Immune system really is, you know, the aggressor which is trying to kill it.
• 20:08
  So, only a few remain.
• 20:12
  They, you know, avoid all the forces against them, you know, and so they are, right?
• 20:17
  But they go through a lot of, you know, fights.
• 20:20
  They go through a lot of transitions, transformation, right?
• 20:24
  Pressure, right?
• 20:24
  So, as a result, size -based capture has not yielded great results.
• 20:31
  Okay.
• 20:32
  Affinity capture, relatively, is less stressful on the cells.
• 20:37
  Okay.
• 20:39
  You know, so that's been historic perspective.
• 20:42
  The groundbreaking science that we have, but again, capture and count doesn't tell you the whole story.
• 20:49
  So, where one cell comes in is a groundbreaking science to isolate the captured
• 20:57
  cell into a live single cell.
• 21:01
  Okay.
• 21:02
  So, traditional circulating tumor cell CTC, the end point was capture and count and give the
• 21:10
  report.
• 21:11
  For us, this is not an end point.
• 21:14
  This is the starting point.
• 21:17
  Okay.
• 21:18
  We begin with this captured and isolated live single cell.
• 21:24
  And we do the imaging modality on it.
• 21:27
  We do the NGS on it.
• 21:29
  We do the multi -omics analysis on it.
• 21:32
  Right?
• 21:33
  And so, we have a much deeper insights that enable the doctors to say what's really going on.
• 21:41
  Is the therapy working?
• 21:43
  If it is working, why it's working?
• 21:46
  If it is not working, why it is not working?
• 21:50
  And what could you do potentially to change the course of the therapy?
• 21:54
  Can we add, you know, can we increase the dose?
• 21:59
  Can we add the combination therapy?
• 22:02
  Can we change something?
• 22:04
  Or if nothing, if it is working, can I reduce the dose level?
• 22:08
  Can I give those chemo holidays?
• 22:10
  Can I, you know, do the de -escalation?
• 22:14
  Right?
• 22:14
  So, all these questions are unanswered in the minds of the oncologist.
• 22:20
  Okay?
• 22:22
  We provide AI assistant to the oncologist.
• 22:26
  Okay?
• 22:27
  Based on this scientific data that is presented, multi -omics, you know, data that is presented for a given patient
• 22:34
  at a given time point, to be able to provide them meaningful, actionable information
• 22:42
  so that they change the course of the treatment.
• 22:46
  Personalize the treatment for a given patient, which will improve the outcome.
• 22:51
  Yes.
• 22:51
  And cancer sometimes changes as well, right?
• 22:53
  So, you have the mutations, some mutations take over versus the other, right?
• 22:57
  So, in that sense, like, if it is growing, like, you know, you can detect, like, okay, what is the new mutation
• 23:05
  that's coming up, right?
• 23:06
  And then the ecologist can then change the treatment accordingly as well.
• 23:10
  It evolves.
• 23:11
  Okay?
• 23:12
  Yes.
• 23:12
  You know, and so many successful companies, you know, and I'll give you one example, Natera.
• 23:18
  It does the tissue at the baseline, at the starting point.
• 23:22
  It hones in on an information about 16 -gene PAL, and it only monitors those 16 genes
• 23:30
  longitudinally.
• 23:32
  One of the most successful companies, one of the most successful tests.
• 23:37
  But it does not address the evolution of cancer.
• 23:42
  Okay?
• 23:43
  You cannot take a snapshot.
• 23:44
  You know, it's like, you know, I've taken, you know, before leaving my home, going to the office, if I take, let's say, you know, Google Maps,
• 23:53
  right?
• 23:54
  And I take a snapshot, and I start driving, and if there is an accident, and, you know, I have to do the stuff.
• 24:01
  I can't just, you know, it cannot be a static thing.
• 24:06
  Cancer evolves.
• 24:07
  So you have to, the testing has to kind of adapt to the evolution of the cancer, you know.
• 24:15
  And I think the newer sciences, newer tests, newer diagnostics is really more reflective of the evolution
• 24:22
  of cancer, more adaptive to the evolution of cancer, and provide the tools to the oncologist, you know, that
• 24:30
  reflects the changes that happen.
• 24:32
  And that's how the outcomes are better.
• 24:35
  Yeah.
• 24:36
  So this uncoincides, right, what you described as the clinical diagnostics.
• 24:40
  So it's a clear lab model.
• 24:43
  Can you talk a bit about that?
• 24:44
  Sure.
• 24:45
  How did you get, like, certified, and what does that entail?
• 24:48
  Yeah.
• 24:48
  Good question.
• 24:49
  There are two ways a diagnostic company can offer, you know, the new innovation.
• 24:54
  Okay.
• 24:56
  More traditional way is basically, you know, your invention, you know, you go through the
• 25:03
  IP protection, and you go through a regulatory pathways, which is the 510K for an FDA
• 25:11
  clearance, right?
• 25:12
  So you find a predicate device, some similar test that has already been approved by FDA,
• 25:19
  and you establish that your test is as good, if not better, right?
• 25:26
  You know, and you do that.
• 25:27
  Now, that takes a long time, you know, two, four years, and many tens of millions of dollars before you see your
• 25:35
  first, you know, revenues, etc.
• 25:38
  The other way is a provision called LDT, lab developed test.
• 25:46
  So if you have a lab that is CLIA and CAP certified, you know,
• 25:54
  CLIA is a regulatory body, you know, that does the inspection of the labs, ensuring that they follow
• 26:02
  proper protocols.
• 26:03
  It is run by a very qualified, board certified pathologist.
• 26:09
  Okay.
• 26:10
  And if you establish and prove that you have developed a test that has a good science
• 26:18
  backing it up, good publication, you have done a good, you know, scientific experiments, quality control, and all
• 26:26
  of that stuff.
• 26:27
  Then, you know, on a limited basis for a limited target, you could offer this, you know, lab.
• 26:35
  Okay.
• 26:36
  So we have adopted a two -prong approach, a two -phase strategy.
• 26:40
  We have a CLIA CAP certified lab in Foster City here in Bay Area, okay, where we could take the samples
• 26:48
  from the patients.
• 26:49
  We process the sample and produce the results and the reports and give it back to the oncologist
• 26:57
  based on which they can basically, you know, take the actions.
• 27:02
  Our initial focus is not commercial diagnostics.
• 27:06
  Our initial focus is to provide these services to pharma and biotech companies that are doing
• 27:14
  clinical studies, clinical trials.
• 27:17
  Okay.
• 27:18
  Okay.
• 27:19
  Okay.
• 27:19
  So, you know, pharma companies are always pushing the envelope.
• 27:23
  They want newer technology.
• 27:24
  They want new science to work.
• 27:26
  Right.
• 27:27
  And so it's a shorter path, helping them accelerate their translation research, their
• 27:35
  clinical trials, gives us a shorter path to revenue and gives us an add -on confidence that our
• 27:42
  test now then can become a companion diagnostic.
• 27:46
  Yes.
• 27:47
  And eventually a commercial diagnostic test.
• 27:50
  Okay.
• 27:51
  Okay.
• 27:51
  And so tell us about, like, the information that you provide to the clinicians or to the clinical trial
• 27:59
  coordinators, right?
• 28:01
  Is that about, okay, well, there's these mutations or, like, what kind of information are you providing to them?
• 28:08
  Our OncoInsight, which is the name of our assay, is an LDT, right?
• 28:13
  So we get two tubes of 10 ml blood each.
• 28:18
  We provide the collection kits.
• 28:21
  They are available at the doctor's office.
• 28:24
  Okay.
• 28:25
  A phlobotomist goes, you know, with the patient.
• 28:27
  We draw the sample and those samples come to us.
• 28:31
  Okay.
• 28:32
  It's a one -day turnaround time.
• 28:35
  It might take two days depending on the location, etc.
• 28:38
  And then we do the processing of these samples, right?
• 28:41
  You know.
• 28:43
  So what do we do?
• 28:44
  It's a two -track.
• 28:45
  You know, the first track is we extract the DNA, you know, from the blood sample.
• 28:50
  And we have developed part of OncoInsights is a CT -DNA section of the test where we have
• 28:58
  developed a very novel 1080 gene panel that works on the liquid biopsy.
• 29:05
  And it produces the results.
• 29:08
  It tells, okay, what are the, you know, what are the genes?
• 29:11
  What are the mutations?
• 29:13
  You know, indels, fusions, very comprehensive stuff that we provide.
• 29:18
  It also provides the meaningful interpretation, not just the analysis of what genes and mutations are
• 29:26
  present.
• 29:27
  But we have created a back -end informatics comprehensive knowledge base.
• 29:33
  Okay.
• 29:34
  Which allows us to provide actionable reporting.
• 29:40
  Okay.
• 29:40
  And so we characterize based on NCCN guidelines.
• 29:44
  Okay.
• 29:45
  That what is the level 1 evidence, level 2 evidence, level 3 evidence.
• 29:50
  Right.
• 29:50
  So very comprehensive stuff that has actionability.
• 29:53
  Because of the presence of these genes or these mutations or these translocations or these things,
• 30:01
  we are able to say that most likely this is the recommendation of these drugs will work, these drugs won't
• 30:09
  work, etc.
• 30:10
  It's a very actionable report that oncologists can use it as a reference.
• 30:16
  Ultimate call is made by the oncologist.
• 30:19
  We only analyze and interpret and present the data.
• 30:25
  Based on that data, oncologists will make a call and do the stuff.
• 30:30
  Right.
• 30:30
  So the onus, that responsibility is with the oncologist.
• 30:34
  We are merely the tools to provide the information to the oncologist.
• 30:38
  The second tube.
• 30:41
  The second tube.
• 30:41
  Sorry.
• 30:41
  Please go ahead.
• 30:42
  Okay.
• 30:43
  So this is the DNA side of the story.
• 30:46
  The second tube then we extract, we capture the circulating tumor cells.
• 30:53
  Okay.
• 30:54
  Okay.
• 30:55
  And then we extract the DNA and the RNA.
• 30:58
  Okay.
• 30:59
  And the proteins.
• 31:00
  And then we run our single cell assay.
• 31:04
  And get the full analysis of what are the DNAs on each single cell.
• 31:09
  And usually a 10 ml blood will give us somewhere between 5 to 15 circulating tumor cells.
• 31:18
  That's 1 in a billion.
• 31:19
  Right.
• 31:20
  And so we are now honing around somewhere 10 cells.
• 31:23
  Right.
• 31:24
  So if we get 10 cells.
• 31:25
  Each single cell.
• 31:28
  Okay.
• 31:29
  We do the whole genome.
• 31:31
  The whole transcriptome.
• 31:33
  And a targeted proteome.
• 31:35
  So about 20 proteins.
• 31:37
  Right.
• 31:37
  And we provide a very comprehensive report of what genes, what mutations,
• 31:45
  what translocation, what fusions, what proteins.
• 31:50
  Right.
• 31:51
  You know, all of that stuff.
• 31:52
  Now, this in conjunction with the ctDNA.
• 31:57
  Right.
• 31:57
  So our Onco insights, we are not saying ctc is better than ctDNA.
• 32:02
  Or ctDNA is better than ctCs.
• 32:05
  What we are saying is that when you combine the two and give an integrated
• 32:12
  report, that is much better than any individual, you know, technology.
• 32:19
  Historically, ctDNA or selfie DNA based tests have shown that 18 to
• 32:27
  19 % of the time you have NMDs, no mutations detected.
• 32:32
  Okay.
• 32:34
  Okay.
• 32:34
  So even if the best companies, the garden, the Natera, the foundation, the tempest of the world, this
• 32:42
  is an industry average.
• 32:43
  Okay.
• 32:44
  So when you combine that with the ctC together, that thing, you know, the sensitivity goes from,
• 32:52
  you know, 70, 80 % to high 90s.
• 32:57
  Oh, wow.
• 32:58
  Okay.
• 32:59
  Because what is found in ctDNA or what is missed in ctDNA, ctCs capture it.
• 33:04
  Yes.
• 33:05
  And this is like more like usually we try and do like, you know, this multi -omics, right?
• 33:11
  Because you have different parts of the overall cell you're capturing.
• 33:17
  But now you're adding another very new angle on top of the multi -omics.
• 33:22
  You're adding the ctc angle as well.
• 33:25
  So, okay.
• 33:26
  Yeah, that's great.
• 33:28
  Right.
• 33:28
  And so this rich information that you're providing.
• 33:32
  So let's talk about like both the settings, right?
• 33:35
  Like the one is a more like the commercial setting.
• 33:37
  And then the other one is in the clinical trial setting.
• 33:40
  I said clinical trial setting, there's a lot going on right now.
• 33:42
  So how like, so based on this, what kind of decisions can be made about the clinical trial, right?
• 33:49
  Because this patient is there to get a specific therapy.
• 33:52
  So now you come up with this.
• 33:54
  So now what changes?
• 33:55
  Because either the patient is taking that therapy or they're not, right?
• 33:58
  So how does this change?
• 33:59
  Very good point.
• 34:01
  There are two separate tracks here, right?
• 34:03
  One is the clinical diagnostics, which we are doing it in India.
• 34:06
  Okay.
• 34:08
  Where the doctor wants to know what is the right therapy for a right patient at the right time point and the right dose.
• 34:16
  Right?
• 34:16
  So we provide them a full comprehensive report to help her make those decisions.
• 34:23
  Okay?
• 34:23
  That's one.
• 34:24
  The second part of that workflow, the clinical workflow is, okay, you have given the drug.
• 34:32
  Okay.
• 34:34
  Now, is the drug working?
• 34:36
  So therapy response monitoring is another aspect of it, right?
• 34:41
  And there are two pieces to it.
• 34:44
  One is the MRD, the minimum residual disease, right?
• 34:48
  95 % of the time you have taken care of.
• 34:51
  But that little 5%, right?
• 34:54
  That becomes a problem down the road, right?
• 34:57
  And so how to handle that, right?
• 34:59
  So MRD and the therapy response monitoring.
• 35:03
  That's the clinical side.
• 35:05
  Now, you ask me the second question.
• 35:07
  How is this OncoInsight assay helpful in the clinical trial setting for the pharma?
• 35:13
  Okay?
• 35:14
  So I would say three or four key points that we address.
• 35:19
  First and foremost, when you are doing the screening for patient recruitment, is this the
• 35:27
  right patient?
• 35:29
  Usually, you have inclusion criteria.
• 35:32
  But most of the inclusion criteria are very high level.
• 35:36
  Yeah.
• 35:36
  Breast cancer patient, women between the age of this and this.
• 35:41
  You know.
• 35:41
  Yeah.
• 35:41
  It's not going at the molecular level at all, right?
• 35:44
  Or the cellular level.
• 35:45
  Yeah.
• 35:45
  Right?
• 35:46
  Yeah.
• 35:46
  So that's why even when you recruit patients, you know, they don't perform very well.
• 35:53
  Right?
• 35:53
  So by leveraging assays, tests like OncoInsights, a molecular level,
• 36:02
  you have a much better fit.
• 36:04
  So patient selection is a very interesting area where we can help the clinical trials be successful by
• 36:11
  recruiting the right patients.
• 36:13
  Equally important, patient deselection.
• 36:19
  Okay?
• 36:19
  How not to recruit the wrong patients?
• 36:22
  Because one bad apple messes up the whole fruit basket.
• 36:28
  Okay?
• 36:29
  Right?
• 36:30
  And so, again, when you integrate the CT DNA and CTCs, you have a much richer set.
• 36:37
  So we could basically find better patients, you know, recruit the right patient, okay?
• 36:44
  And deselect the wrong patients.
• 36:46
  So that's one, you know, clear value proposition to the pharma companies that are doing biomarker -based chemical
• 36:54
  trials.
• 36:55
  Okay?
• 36:55
  And we know biomarker -based trials have at least two times higher probability of success from the
• 37:03
  FDA clearance.
• 37:05
  Okay?
• 37:06
  But typical biomarkers that were based on tissue was very hard, right?
• 37:11
  Because then you have to have tissue biopsy.
• 37:13
  You know, so cell biopsy or a liquid biopsy is relatively easy because you just take the blood sample.
• 37:20
  So it costs less and it's much easier to monitor and manage, right?
• 37:26
  So that's one.
• 37:26
  Yeah.
• 37:27
  And it's also faster most probably, right?
• 37:29
  It's much faster, right?
• 37:30
  Much, much faster, right?
• 37:32
  That's one.
• 37:33
  The second thing is, okay, you have now recruited the patient.
• 37:37
  You started, you have dosed the patient, right?
• 37:42
  Is it working or not working?
• 37:45
  In the today's point, right?
• 37:47
  Let's say if you're doing immunocancer therapy, whether my therapy is working or not working, I have to wait for three to four
• 37:55
  months and I have to go expose the patient to either a CT scan or an MRI or a PET.
• 38:02
  And then I would know using what they call it as a racist criteria, okay, whether the tumor
• 38:09
  is shrinking, whether it is not shrinking or whether it is growing.
• 38:15
  But look at the time frame now for every patient under study.
• 38:20
  I have to wait for three, four months before I know whether my therapy is working or not working, okay?
• 38:26
  Let's say you're targeting the drug with Herceptin, HER2, okay?
• 38:32
  And, you know, you've given the drug and now you have to wait.
• 38:36
  Now, instead of that, you know, three to four months wait, within a matter of couple of weeks,
• 38:43
  okay, if I could tell you whether the drug is working or not working, wouldn't that be a great value?
• 38:51
  Yeah.
• 38:52
  Right?
• 38:52
  How do we do that today?
• 38:54
  Okay.
• 38:55
  By capturing circulating tumor cells, right?
• 39:00
  First of all, if the drug is really working, the tumor cells should really go down.
• 39:04
  Yes.
• 39:04
  The CT DNA should really go down.
• 39:07
  Yes.
• 39:07
  Right?
• 39:08
  Okay.
• 39:08
  But you have an 18 -19 % of NMDs in CT DNA, right?
• 39:14
  So you miss out those 20%, right?
• 39:17
  But if I can capture CTCs, right?
• 39:21
  And if I say, oh, I have CTCs where HER2 is still expressed on the surface of the CTCs,
• 39:28
  that means my therapy is not quite working.
• 39:32
  Okay.
• 39:35
  Okay.
• 39:35
  And then, by doing this multi -omics analytics, if I could say, what is the reason why it is not working?
• 39:43
  Okay.
• 39:44
  And again, I am making it up, but there was a marker, let's say EGFR.
• 39:48
  So if there is an EGFR positive marker, then we know historically that HER2 will not work.
• 39:57
  Okay.
• 39:58
  You know, so you don't have to wait for three, four months.
• 40:03
  You can make those critical decisions now.
• 40:07
  And you say, based on multi -omics profile, okay, what ails combination therapy?
• 40:13
  Or can we switch over?
• 40:14
  Can we change something?
• 40:16
  Right?
• 40:16
  Or can we add something to it?
• 40:18
  Right?
• 40:19
  You know, a combination therapy to have a resistance prediction.
• 40:24
  Right?
• 40:24
  If you can predict the resistance, then actionable information will help you add some combo therapy,
• 40:32
  okay, anticipating the resistance, so that the outcomes would be better.
• 40:38
  So, you see, the combination of information, multi -omics data from a CTCs or a single cell,
• 40:46
  coupled with AI to do the prediction, is going to transform this field and make the
• 40:54
  clinical trials much more successful.
• 40:59
  Mohan Utawar: Okay?
• 40:59
  Because you are taking actions proactively.
• 41:02
  You are not waiting for three months, then realizing that, oh, my drug didn't work.
• 41:07
  Okay.
• 41:08
  That's a loss of time and loss of money.
• 41:11
  Yeah.
• 41:12
  And it's also, the tumor has progressed by that time,
• 41:21
  Interviewer: joining us.
• 41:22
  You're welcome.
• 41:22
  We'll see you next time.
• 41:22
  Thank you.
• 41:22
  Thanks.
• 41:22
  Thanks .不会oster