Latest / Zero Shot Podcast DEMO / The One-in-a-Billion Cells Behind Cancer Recurrence

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The One-in-a-Billion Cells Behind Cancer Recurrence · Zero Shot Podcast DEMO

• 0:14
  Interviewer: Amr, we've got Mohan Uttarwar on the show today.
• 0:17
  Who is Mohan?
• 0:19
  Mohan is a biotech entrepreneur who is the co -founder and CEO of OneCell .ai.
• 0:24
  He's a repeat founder with deep experience in both diagnostics and therapeutics.
• 0:29
  And as an innovator and investor, he's got more than 30 years of experience in life sciences, precision diagnostics, and healthcare technology.
• 0:42
  speakers: OneCell. ai positions AI and single-cell analytics around cancer diagnostics, therapy development, and real-time tumour profiling.
• 0:54
  Interviewer: I want to understand how OneCell .ai's platform works and the potential for real -time cell profiling to reshape clinical
• 1:01
  trials, drug development, and diagnostics.
• 1:05
  Interviewee: Thank you, Amur.
• 1:06
  Thank you for inviting me here.
• 1:08
  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
• 1:16
  excited about what we do at OneCell.
• 1:18
  Interviewer: Cancer is a deadly disease, we all know.
• 1:22
  One shoe doesn't fit all.
• 1:26
  So personalization of therapy is the only way cancer can be managed.
• 1:30
  This whole field has evolved into what they call precision oncology, which refers to personalization.
• 1:38
  speakers: Historically, what happens is somehow you find something, then the doctor tells you go into
• 1:46
  imaging, right, you do the radiologic scan, and then
• 1:52
  radiology finds something, but then the pathology is called for,
• 2:00
  which is the process of taking a biopsy.
• 2:03
  Biopsy is the process of taking a small tissue sample from the area where you suspect that there is cancer,
• 2:10
  and further analyzing it.
• 2:13
  It's sent to the lab, that tissue sample.
• 2:16
  It's called tissue biopsy.
• 2:17
  Tissue sample is sent to the lab.
• 2:19
  The lab does the processing of the sample.
• 2:23
  A glass slide is created.
• 2:25
  Okay.
• 2:26
  And the pathologist puts it under the microscope, does the reading, and based on the pattern he or she sees, write a
• 2:34
  report.
• 2:34
  And based on that report, a treatment is given.
• 2:38
  Okay.
• 2:39
  This process is called tissue biopsy.
• 2:42
  Okay.
• 2:43
  Now, tissue is an issue because it's a surgical procedure.
• 2:46
  It's expensive, and it's not an easy thing to do.
• 2:51
  Yeah, and it's invasive, right?
• 2:52
  Invasive, right?
• 2:53
  It's invasive.
• 2:55
  It's invasive.
• 2:56
  Interviewer: As the field evolved and the science evolved, you know, the information that you get from tissue, similar
• 3:03
  information you can get it from blood, liquid.
• 3:07
  Okay?
• 3:08
  So that's why Dr.
• 3:10
  Klaus Pantel and Katerina Penebrez in Europe, they coined this term liquid biopsy.
• 3:16
  That means you take a simple blood draw, which is non -invasive or minimally invasive, and then you process it.
• 3:24
  Okay?
• 3:25
  In the lab.
• 3:26
  And give a similar report.
• 3:27
  So that's the liquid biopsy aspect, right?
• 3:31
  From the sample of blood draw, that you create the reports, generate the reports, using
• 3:38
  next -generation sequencing.
• 3:41
  Okay.
• 3:41
  Now, when you're doing the liquid biopsy, like, is this circulating, like, tumor cells or circulating
• 3:49
  tumor DNA, are they available for pretty much most cancers, or is that, like, more prevalent in some cancers versus
• 3:57
  not?
• 3:58
  Or even, like, the staging, like, maybe, like, when it goes metastatic, it's more present.
• 4:03
  Like, how does that work?
• 4:04
  What happens is cells die.
• 4:06
  Some natural death and tumor cells, right?
• 4:10
  So, when they die, it spits out the DNA.
• 4:16
  That's the circulating DNA or cell -free DNA.
• 4:20
  Okay?
• 4:21
  Subset of the cell -free DNA, right?
• 4:24
  From the dead cells, you know, if it is a tumor cells, the death of a tumor cells, which spits out the DNA, it's called
• 4:32
  CT DNA.
• 4:34
  Okay?
• 4:35
  Information that you generate from the CT DNA, circulating tumor DNA, is most relevant.
• 4:42
  speakers: Okay?
• 4:43
  But the problem with the liquid biopsy from the blood -based stuff is, it's very difficult to figure out
• 4:51
  whether it was a cell -free DNA from a normal cell or versus the dead tumor cells,
• 5:08
  Liquid biopsy starts with a minimally invasive blood draw; ctDNA is the cell-free DNA released as tumour cells die.
• 5:19
  Interviewee: Okay?
• 5:19
  And it's a good biomarker.
• 5:21
  It tells you that more the CT DNA, the more the prevalence of cancer.
• 5:27
  So, you map it, right?
• 5:29
  And you have the hypothesis.
• 5:31
  Okay?
• 5:31
  So, it's like the higher stage, greater stage you are, it's likely to have more CT DNA.
• 5:39
  Okay.
• 5:40
  And is it like in pretty much most cancers, you see that, or like some cancers more than the others?
• 5:46
  Yeah.
• 5:46
  Some cancers more than the others, but most cancers would have some DNA, CT DNA.
• 5:51
  Right?
• 5:52
  Okay.
• 5:52
  And we are referring to tumor, I mean, the solid tumors, solid tumors.
• 5:57
  Yeah.
• 5:58
  Yeah.
• 5:58
  So, tissue biopsy, the next generation was liquid biopsy, and liquid biopsy is referring to CT DNA.
• 6:07
  However, it is not a full representation of a tumor inside the body of a
• 6:15
  cancer patient.
• 6:16
  Okay?
• 6:17
  Okay.
• 6:18
  Okay.
• 6:18
  These are essentially DNA fragments coming out of the dead cells.
• 6:26
  speakers: Okay.
• 6:26
  Okay.
• 6:27
  So, what we are doing now is taking that industry forward to the third generation.
• 6:33
  Okay.
• 6:34
  We call cell biopsy.
• 6:36
  Okay.
• 6:37
  Which is basically a next generation of liquid biopsy, but coming out of the circulating tumor cells.
• 6:45
  Now, let me explain the CTC, or circulating tumor cells.
• 6:50
  Interviewee: A gram of tumor, okay, sheds about a million cancer cells in the body, in
• 6:58
  the blood streams.
• 6:59
  So, that may seem a lot, but we have trillions of cells in our body.
• 7:04
  So, that means it's one in a billion circulating tumor cells.
• 7:12
  speakers: Beyond finding rare cells lies the question of what a fuller cancer profile can reveal.
• 7:17
  Sure.
• 7:18
  Second generation of precision oncology also was, you know, about
• 7:24
  multidimensional data, multimodal data, multiomics data.
• 7:39
  The larger aim is to democratise precision oncology, but the immediate biological stakes are recurrence and metastasis.
• 7:48
  Interviewee: So, what happens is mostly, 95 % of the cancer deaths happens because of the
• 7:56
  reoccurrence.
• 7:57
  Okay.
• 7:59
  The root cause of the reoccurrence, okay, is metastasis.
• 8:04
  A process where cancer spread from its origin.
• 8:09
  It might be breast cancer, but because of the circulating tumor cells, the cells that are shed, they go
• 8:16
  run around.
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  Most of them die.
• 8:19
  Most of them are killed by our immune system, but some strong ones evade the immune system and they
• 8:27
  settle somewhere else.
• 8:29
  Okay.
• 8:30
  So, they find the fertile ground.
• 8:32
  Maybe it's lung, maybe it's brain.
• 8:34
  Somewhere they go in settled tune and the secondary tumor grows.
• 8:40
  Okay.
• 8:41
  Okay.
• 8:42
  So, these are like the fugitives on the run, but they are the microcosms
• 8:49
  of creating secondary tumor cells.
• 8:52
  Okay.
• 8:53
  And that's the root cause.
• 8:56
  Okay.
• 8:57
  So, historically, you know, it was very difficult to find those CTCs, those one in a billion
• 9:04
  circulating tumor cells.
• 9:06
  Okay.
• 9:07
  You know, and so, you know, but now the new science, the groundbreaking science,
• 9:14
  to capture that one in a billion circulating tumor cell.
• 9:19
  Okay.
• 9:20
  At a very high degree of sensitivity, high degree of precision, the purity
• 9:28
  of capture.
• 9:30
  Interviewer: Okay.
• 9:31
  And the high degree of simplicity and lower cost.
• 9:36
  Okay.
• 9:36
  So, why you're excited about this whole field is because one cell has this groundbreaking
• 9:43
  science.
• 9:45
  And if I take this three axis, order of magnitude, higher sensitivity, order of magnitude, higher purity of capture,
• 9:53
  order of magnitude, lower cost, it's a thousand times better price performance than anything that existed
• 10:01
  before.
• 10:02
  Okay.
• 10:03
  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.
• 10:10
  Right.
• 10:10
  Like, are these like the both aspects going here?
• 10:13
  Like, sure.
• 10:14
  speakers: That broader picture depends on more than one biological signal.
• 10:19
  Interviewee: genomics is a science of genetics.
• 10:22
  Right.
• 10:23
  It tells you something very important, but it doesn't give you a full picture.
• 10:28
  Just like a blind person touching, you know, the leg of an elephant.
• 10:33
  If you ask him to describe, you'll say, oh, elephant is like a leg.
• 10:38
  Another blind person touches, you know, let's say the tail and it says, oh, it's like a snake.
• 10:43
  Interviewer: You know, so, unfortunately, the genomics, the transcriptomics, the proteomics, the methylomics,
• 10:51
  the metabolomics, these are all fields of science, but they are looking at cancer into the silos with their
• 10:59
  own lenses.
• 11:00
  speakers: The practical challenge is turning separate measurements into a clinically useful picture.
• 11:06
  Interviewer: Companies like Foundation, they did very, very well.
• 11:09
  They established as the leader.
• 11:11
  But tissue is an issue.
• 11:13
  So, second generation, the liquid biopsy, right, which is the blood -based stuff, right, you know, that came about.
• 11:21
  But, what has been not fully equipped is, you know, again, the prote, you know, that
• 11:29
  was really focusing again, you know, on the genomics and, you know, the transcriptomics.
• 11:36
  But DNA is, they don't, you know, cannot get you the transcriptomics or, or, you know, a little bit, but
• 11:44
  definitely not proteomics.
• 11:46
  Okay.
• 11:47
  You know, so the third generation, which we call the cell biopsy, you know, has the ability to do all three pieces
• 11:55
  together.
• 11:56
  The genomics, the transcriptomics, and the proteomics.
• 12:00
  Now, what happens is, multiomics, you know, it's a big data problem.
• 12:06
  Cancer, as much as it's a disease, it's also a data problem.
• 12:10
  And the data is overwhelming.
• 12:13
  So, this is where AI comes in handy.
• 12:16
  Isn't it?
• 12:17
  Analyzing this multidimensional, multimodal, multiomics data, making sense out of it.
• 12:25
  Okay.
• 12:26
  speakers: Correlating the data and predicting the outcome is the key challenge.
• 12:32
  Right.
• 12:32
  So, so the challenge here I see is that like, usually like when you do like genomics, proteomics experiments, right, you have like a normal tissue, you have
• 12:40
  a disease, tumor tissue, right?
• 12:42
  And you're looking at like differences here.
• 12:44
  But then what you're collecting, that has like, majority of that is already normal anyway.
• 12:50
  So, how do you then detect the specific cancer DNA or the cancer proteins from that?
• 12:57
  Because this is, that's a, that's a much higher level challenge than what people usually do, right?
• 13:01
  Absolutely.
• 13:02
  So, how do you do that with the AI?
• 13:04
  Yeah.
• 13:04
  Very, very good point.
• 13:06
  Excellent point.
• 13:17
  Conventional liquid biopsy can begin with DNA whose cellular source is unclear, so AI must first separate signal from noise.
• 13:27
  Interviewer: Okay.
• 13:28
  The next generation technologies, particularly the one that we have invented, you are already clean,
• 13:36
  you're starting with a cleaner data because you have figured out, we have figured out, capturing that one in a billion circulating
• 13:43
  tumor cells.
• 13:46
  Interviewee: Okay.
• 13:46
  So, we already know that our starting point is a tumor cell.
• 13:51
  Oh, okay.
• 13:52
  Okay.
• 13:53
  So, you have the technology to then detect the tumor cell specifically.
• 13:58
  Interviewer: Correct.
• 13:58
  Okay.
• 13:59
  Okay.
• 13:59
  Now, does that like, does that depend on like the kind of like surface markers there are on like the tumor cell or?
• 14:07
  Very good point.
• 14:08
  So, CTC is not new.
• 14:10
  It's been there 10 years ago.
• 14:12
  Okay.
• 14:13
  Unfortunately, what happened was the sensitivity wasn't there and the endpoint was you capture the
• 14:21
  circulating tumor cells, you count and report them.
• 14:25
  Okay.
• 14:26
  So, the clinical utility was limited.
• 14:30
  You know, it told us that there is a problem.
• 14:33
  Okay.
• 14:34
  Your pet or CT scan or MRI might be clean but if you find CTCs, that means, you know,
• 14:43
  there are some problems somewhere.
• 14:47
  speakers: A detected circulating tumour cell can leave a clinician with a clean scan but no clear treatment decision.
• 14:55
  Scientists get excited with what if.
• 14:58
  Okay.
• 14:59
  But the doctors are saying, so what?
• 15:02
  What if I found a CTC and the doctor is saying, so what?
• 15:06
  Unless you tell me what is the actionability and the science stopped there in the past.
• 15:14
  But today with the invent of next generation sequencing and with AI,
• 15:26
  Interviewee: Okay.
• 15:27
  The genomic data, the transcriptomic data, the proteomic data and tell you and tell the doctor, hey,
• 15:36
  you know, it may be the cell coming from lung.
• 15:39
  Interviewer: You need a pet on basic breast which was the original stuff.
• 15:44
  Right?
• 15:44
  So, without AI analyzing the cell information that is hidden in that circulating tumor cell,
• 15:51
  we would not enable the doctor to take those critical clinical decisions.
• 15:57
  speakers: Cancer can involve many genes, so a single test may not identify the molecular change most relevant to treatment.
• 16:06
  Interviewee: So, it's really the integration of information from the biology of the cell, the science
• 16:14
  part, and the AI working on analyzing the genomic data, the transtertomic data, the
• 16:21
  proteomic data.
• 16:23
  Right?
• 16:23
  So, like you said, cell surface marker.
• 16:27
  Interviewer: You know, we know historically that if it's, let's say, HER2 marker on the surface of the cell, very likely
• 16:35
  that cell must be coming from breast cancer site.
• 16:39
  If it's an EGFR, very likely, again, no guarantees, but very likely it's coming from a lung cancer.
• 16:47
  Right?
• 16:48
  Also, the way morphology of those cells look is very unique.
• 16:53
  So, we use AI to analyze, you know, how the cell looks, the morphology, you know, the size, the
• 17:00
  shape, the density, the contrast.
• 17:03
  Right?
• 17:04
  So, there's a lot of imaging as well then.
• 17:06
  Image analysis of the cell that we capture.
• 17:09
  Right?
• 17:10
  Okay.
• 17:10
  So, in addition to the multiomics, you're also doing the image analytics.
• 17:14
  Absolutely.
• 17:15
  Absolutely.
• 17:15
  Gotcha.
• 17:15
  Okay.
• 17:16
  Even before we do multiomics, we do the analysis of these cells.
• 17:21
  Okay.
• 17:22
  We do the characterization using our AI algorithms.
• 17:25
  Aha.
• 17:26
  And we give enough information.
• 17:28
  Now, one other, you know, fact that is actually very revealing is that many times the CTCs are
• 17:36
  individual CTCs, but many times you have this, you know, what we call the
• 17:42
  combinations.
• 17:45
  Right?
• 17:45
  You know, you have the, you know, group of cells hanging together.
• 17:51
  speakers: Okay.
• 17:52
  Again, imaging reveals those kind of things.
• 17:55
  Okay.
• 17:56
  You know, so, so there's interesting stuff.
• 17:59
  Right?
• 17:59
  Now, how do we capture these circulating tumor cells?
• 18:03
  Right?
• 18:03
  The CTCs, there are two methods for capturing.
• 18:06
  One is size -based.
• 18:09
  Right?
• 18:09
  So, tumor cells are usually larger in size.
• 18:13
  And there are techniques, you know, microfiltration.
• 18:17
  There are techniques to really do the nanofluidics to capture those cells.
• 18:22
  That's one method.
• 18:23
  The other method is called affinity -based.
• 18:27
  So, tumor cells have certain types of proteins on the surface of these cells.
• 18:32
  Keppam being one very popular protein that is on all solid tumors that are epithelial in
• 18:40
  nature.
• 18:41
  Okay?
• 18:42
  You know, a transparin in another protein that is expressed.
• 18:46
  Okay?
• 18:47
  So, what happens in the size -based capture, the cells go through a lot of you know, pushing and
• 18:55
  pulling.
• 18:56
  Nanofluidics, you know, you're forcing this.
• 19:00
  you know, nanofiltration.
• 19:03
  You're pushing it.
• 19:22
  Cell capture also depends on what changes as tumour cells enter the bloodstream.
• 19:26
  Interviewee: They go through a lot of transitions, transformation, right?
• 19:30
  Pressure, right?
• 19:31
  So, as a result, size -based capture has not yielded great results.
• 19:36
  Interviewer: Okay?
• 19:37
  Affinity capture relatively is less stressful on the cells.
• 19:43
  Okay?
• 19:44
  You know, so that's been historic perspective.
• 19:48
  The groundbreaking science that we have, but again, capture and count doesn't tell you the whole story.
• 19:56
  So, where one cell comes in is a groundbreaking science to isolate the captured cell
• 20:03
  into a live single cell.
• 20:07
  speakers: The OncoInsight ctDNA track uses a 1,080-gene liquid-biopsy panel alongside the single-cell analysis described here.
• 20:17
  Interviewee: Okay?
• 20:18
  So, traditional circulating tumor cell CTC, the endpoint was capture and count and give the
• 20:26
  report.
• 20:27
  For us, this is not an endpoint, this is the starting point.
• 20:33
  Okay?
• 20:34
  We begin with this captured and isolated live single cell and we do the imaging
• 20:41
  modality on it, we do the NGS on it, we do the multi -omics analysis on it, right?
• 20:49
  And so, we have a much deeper insights that enable the doctors to say what's really going on.
• 20:57
  is the therapy working?
• 20:58
  speakers: The proposed clinical uses include therapy-response monitoring and minimum residual disease—the small amount of disease left after treatment.
• 21:09
  Interviewer: If it is working, why it's working?
• 21:12
  If it is not working, why it is not working?
• 21:16
  And what could you do potentially to change the course of the therapy?
• 21:21
  Can we add, you know, can we increase the dose?
• 21:25
  Can we add the combination therapy?
• 21:28
  Can we change something?
• 21:29
  Or if nothing, if it is working, can I reduce the dose level?
• 21:34
  Can I give those chemo holidays?
• 21:37
  Can I do the de -escalation?
• 21:39
  Right?
• 21:40
  So, all these questions are unanswered in the minds of the oncologist.
• 21:47
  speakers: Patient reports are produced in a CLIA/CAP-certified laboratory in Foster City for oncologists to use in clinical care.
• 21:56
  Interviewee: We provide AI assistant to the oncologist based on this scientific data
• 22:04
  that is presented, multi -omics data that is presented for a given patient at a given time point to
• 22:12
  be able to provide them meaningful, actionable
• 22:18
  speakers: information so that they change the course of the treatment.
• 22:22
  Personalize the treatment for a given patient, which will improve the outcome.
• 22:27
  Yes, and cancer sometimes changes as well, right?
• 22:29
  So, you have some mutations take over versus the other, right?
• 22:34
  So, in that sense, if it is growing, you can detect, what is the new mutation
• 22:42
  that's coming up, right?
• 22:43
  And then the ecologist can then change the treatment accordingly as well.
• 22:46
  It evolves, okay?
• 22:48
  Yes.
• 22:48
  You know, and so many successful companies, you know, and I'll give
• 23:10
  Interviewee: right?
• 23:10
  And I take a snapshot, and I start driving, and if there is an accident, and, you know, I have to do the stuff, I can't just,
• 23:19
  you know, it cannot be a static thing, cancer evolves, so you have to, the testing has to
• 23:27
  kind of adapt to the evolution of the cancer, you know, and I think the newer sciences, newer tests,
• 23:35
  newer diagnostics, is really more reflective of the evolution of cancer, more adaptive to the evolution of cancer,
• 23:43
  and provide the tools to the oncologist, you know, that reflects the changes that happen, that's how the outcomes are better.
• 23:52
  Yeah.
• 23:52
  So this uncoincides, right, what you described as the clinical diagnostic, so it's a clear lab model, can you talk a bit about
• 24:00
  that?
• 24:01
  How did you get, like, certified, and what does that entail?
• 24:05
  Yeah, good question.
• 24:06
  There are two ways a diagnostic company can offer, you know, the new innovation, okay?
• 24:12
  More traditional way is basically, you know, your invention, you know, you go through the
• 24:20
  IP protection, and you go through a regulatory pathways, which is the 510k for an FDA
• 24:28
  clearance, right?
• 24:29
  You find a predicate device, some similar test that has already been approved by FDA, and
• 24:37
  you establish that your test is as good if not better, right?
• 24:42
  You know, and you do that.
• 24:43
  Now, that takes a long time.
• 24:45
  You know, two, four years, and many tens of millions of dollars before you see your first, you know,
• 24:53
  revenues, etc.
• 24:55
  The other way is a provision called
• 25:00
  speakers: LDT, Lab Develop Test.
• 25:02
  So, if you have a lab that is CLIA and CAP certified, you know,
• 25:10
  CLIA is a regulatory body, you know, that does the inspection of the labs, ensuring that they follow
• 25:18
  proper protocols, it is run by a very qualified, board -certified tathologist,
• 25:34
  That laboratory route shapes where the assay is being used first.
• 25:38
  Our initial focus is not commercial diagnostics, our initial focus is to provide these services to
• 25:45
  pharma and biotech companies that are doing clinical studies, clinical trials,
• 25:53
  okay?
• 25:54
  So, you know, pharma companies are always pushing the envelope, they want newer technology, they want new science to work,
• 26:02
  right?
• 26:02
  And so, it's a shorter path, helping them accelerate their translation research, their
• 26:10
  clinical trials, gives us a shorter path to revenue, and gives us an add -on confidence that our
• 26:18
  test now then can become a companion diagnostic test, and eventually a commercial diagnostic test.
• 26:25
  Okay, okay.
• 26:26
  And so, tell us about like the information that you provide to clinicians or to the clinical
• 26:34
  trial coordinators, right?
• 26:37
  Genomics, RNA, proteins, and other omics each illuminate part of a tumour; AI is needed to combine those layers.
• 26:47
  Interviewee: Is that about, okay, well, there's these mutations, or like, what kind of information are you providing to them?
• 26:53
  Our OncoInsight, which is the name of our assay, is an LDT, right?
• 26:59
  So, we get two tubes of 10 ml blood each.
• 27:04
  We provide the collection kits.
• 27:07
  They are available at the doctor's office, okay?
• 27:11
  A flobotomist goes, you know, with the patient, we draw the sample, and those samples come to us, okay?
• 27:18
  It's a one -day turnaround time.
• 27:20
  speakers: The first track maps tumour DNA before the second adds a single-cell view.
• 27:26
  Interviewee: know, what are the genes, what are the mutations, you know, indel, fusions, very comprehensive stuff that we provide.
• 27:34
  It also provides the meaningful interpretation, not just the analysis of what genes and mutations are
• 27:42
  present, but we have created a back -end informatics comprehensive knowledge base, okay,
• 27:50
  which allows us to provide actionable reporting, okay, and so we
• 27:57
  characterize based on NCCN guidelines, okay, that what is the level one evidence, level two evidence, level three
• 28:05
  evidence, right, so very comprehensive stuff that has action ability because of the presence of these genes or these mutations
• 28:13
  or these translocations or these things, we are able to say that most likely this is
• 28:21
  the recommendation of these drugs will work, these drugs won't work, etc.
• 28:26
  It's a very actionable report that oncologists can use it as a reference.
• 28:32
  Ultimate call is made by the oncologist.
• 28:35
  We only analyze and interpret and present the data.
• 28:40
  Interviewer: Based on that data, oncologists will make a call and do the stuff, right, so the onus, that responsibility is with the
• 28:48
  oncologist.
• 28:49
  We are merely the tools to provide the information to the oncologist.
• 28:54
  The second tube, sorry, please go ahead, so this is the DNA side of the story.
• 29:00
  The second tube then, we extract, we capture the circulating tumor cells,
• 29:07
  Interviewee: okay, and then we extract the DNA and the RNA and the proteins, and then
• 29:15
  we run our single cell assay and get the full analysis of what are the DNAs on each single
• 29:23
  cell.
• 29:23
  And usually a 10 ml blood will give us somewhere between 5 to 15 circulating tumor cells.
• 29:31
  Interviewer: That's one in a billion, right?
• 29:33
  And so we are now honing around somewhere 10 cells, right?
• 29:37
  So if we get 10 cells, each single cell, okay, we do the whole genome, the
• 29:45
  whole transcriptome, and a targeted proteome, so about 20 proteins, right?
• 29:51
  And we provide a very comprehensive report of what genes, what mutations,
• 29:59
  what translocations, what fusions, what proteins, right?
• 30:04
  You know, all of that stuff.
• 30:05
  Now, this in conjunction with the ctDNA, right?
• 30:11
  So our Onco insights, we are not saying ctc is better than ctDNA or ctDNA is better than ctc's.
• 30:18
  speakers: What we are saying is that when you combine the two and give an integrated report, that
• 30:26
  is much better than any individual, you know, technology.
• 30:32
  historically, ctDNA or selfie DNA based tests have shown that 18 to
• 30:40
  19 percent of the time, you have NMDs, no mutations detected,
• 31:09
  Interviewer: Oh, wow.
• 31:10
  Okay?
• 31:11
  Because what is found in ctDNA or what is missed in ctDNA, ctc's capture it.
• 31:17
  Yes.
• 31:18
  And this is like more like usually
• 31:22
  Interviewee: -omics, right?
• 31:22
  Because you have different parts of the overall cell you're capturing, but now you're adding another very
• 31:30
  new angle on top of the multi -omics, you're adding the ctc angle as well.
• 31:36
  So, okay, yeah, that's great.
• 31:39
  And so this rich information that you're providing, so let's talk about like both the settings, right?
• 31:46
  Like the one is a more like the commercial setting and then the other one is in the clinical trial setting.
• 31:51
  I said clinical trial setting there's a lot going on right now.
• 31:54
  So how, like, so based on this, what kind of decisions can be made about the clinical trial, right?
• 32:01
  Because this patient is there to get a specific therapy.
• 32:04
  So now you come up with this, so now what changes?
• 32:07
  Because either the patient is taking that therapy or they're not, right?
• 32:09
  So how does this change?
• 32:11
  Very good point.
• 32:13
  There are two separate tracks here, right?
• 32:15
  One is the clinical diagnostics, which we are doing
• 32:17
  speakers: Beyond treatment choice, monitoring asks whether disease remains or therapy is working.
• 32:24
  Interviewer: right?
• 32:24
  And so how to handle that, right?
• 32:26
  So MRD and the therapy response monitoring, that's the clinical side.
• 32:35
  speakers: In clinical trials, the central test is whether response can be seen before imaging catches up.
• 32:41
  Interviewer: Now instead of that three to four months wait, within a matter of couple of weeks,
• 32:48
  okay, if I could tell you whether the drug is working or not working, wouldn't that be a great value?
• 32:56
  Yeah, right?
• 32:57
  How do we do that today?
• 32:59
  speakers: Earlier signals matter only after the right patients enter the study.
• 33:04
  Interviewer: First and foremost, when you are doing the screening for patient recruitment, is this the
• 33:12
  right patient?
• 33:13
  Interviewee: Usually you have inclusion criteria, but most of the inclusion criteria are very high level.
• 33:20
  Breast cancer patient, women, between the age of this and this, you know.
• 33:25
  It's not going at the molecular level at all, right?
• 33:28
  Or the cellular level.
• 33:29
  Yeah.
• 33:30
  Right?
• 33:30
  Yeah.
• 33:30
  So that's why even when you recruit patients, you know, they don't perform very well, right?
• 33:38
  So by leveraging assays, tests, like OncoInsights, a molecular level,
• 33:46
  you have a much better fit.
• 33:48
  patient selection is a very interesting area where we can help the clinical trials be successful by
• 33:55
  recruiting the right patients.
• 33:57
  Equally important, patient deselection.
• 34:02
  speakers: Okay?
• 34:03
  How not to recruit the wrong patients?
• 34:05
  Because one bad apple messes up the whole fruit basket.
• 34:11
  Okay?
• 34:12
  Right?
• 34:13
  And so, again, when you integrate the CT DNA and CTCs, you have a much richer set.
• 34:20
  So we could basically find better patients, you know, recruit the right patient,
• 34:37
  Interviewee: Okay?
• 34:38
  And we know biomarker -based trials have at least two times higher probability of success from
• 34:46
  the FDA clearance.
• 34:47
  Interviewer: Okay?
• 34:48
  But typical biomarkers that were based on tissue was very hard, right?
• 34:53
  Because then you have to have tissue biopsy.
• 34:55
  You know, so cell biopsy or a liquid biopsy is relatively easy because you just take the blood sample.
• 35:02
  So it costs less and it's much easier to monitor and manage, right?
• 35:07
  So that's one.
• 35:08
  Yeah.
• 35:09
  And it's also faster, most probably, right?
• 35:11
  It's much faster, right?
• 35:12
  Much, much faster, right?
• 35:14
  That's one.
• 35:14
  The second thing is, okay, you have now recruited the patient, you started, you have dosed the patient, right?
• 35:22
  Interviewee: Is it working or not working?
• 35:25
  In the today's point, right, let's say if you're doing immunocancer therapy, whether my therapy is working or not working,
• 35:33
  I have to wait for three to four months and I have to go expose the patient to either a CT scan or an
• 35:41
  MRI or a PET and then I would know using what they call it as a resist criteria, okay,
• 35:49
  whether the tumor is shrinking, whether it is not shrinking or whether it is growing.
• 35:55
  But look at the time frame now for every patient under study, I have to wait for three, four months before
• 36:02
  Interviewer: I know whether my therapy is working or not working, okay, let's say you're targeting the drug with
• 36:09
  Herceptin, Her2, okay, and you've given the drug and now you have to wait.
• 36:17
  Okay, by capturing circulating tumor cells, right?
• 36:22
  First of all, if the drug is really working, the tumor cells should really go down.
• 36:26
  Yes.
• 36:27
  The CT DNA should really go down.
• 36:29
  Yes.
• 36:29
  Right?
• 36:30
  Okay, but you have an 18 -19 % of NMDs in CT DNA, right?
• 36:36
  So you miss out those 20%, right?
• 36:40
  But if I can capture CTCs, right, and if I say, oh, I have CTCs where HER2 is
• 36:48
  still expressed on the surface of the CTCs, that means my therapy is not quite working.
• 36:56
  Okay, and then by doing this multiomics analytics, if I could say what is the reason why it is not
• 37:04
  working?
• 37:05
  Okay, and again I am making
• 37:07
  it up, but if there was a marker, let's say EGFR, so if there's an EGFR positive marker, then we
• 37:15
  know historically that HER2 will not work.
• 37:19
  Okay, you know, so you don't have to wait for three, four months.
• 37:24
  Interviewee: you can make those critical decisions now, and you say, based on multiomics profile, okay,
• 37:32
  what ails combination therapy, or can we switch over, can we change something, right, or can we add something to it,
• 37:40
  right, you know, a combination therapy to have a resistance prediction, right, if you can predict the resistance,
• 37:48
  then actionable information will help
• 37:52
  speakers: Trial enrolment often relies on broad criteria rather than molecular or cellular profiles, a gap this approach aims to close.
• 38:03
  you add some combo therapy, okay, anticipating the resistance so that the outcomes would be better.
• 38:11
  So, you see, the combination of information, multiomics data from a CTCs or a single cell,
• 38:20
  coupled with AI to do the prediction, is going to transform this field and make the clinical
• 38:27
  trials much more successful,
• 38:40
  Interviewer: That's a lot of time and loss of money.
• 38:44
  Yeah, and it's also, the tumor has progressed by that time, right?
• 38:47
  By that time, tumor has progressed, right?
• 38:50
  Yeah, and so this is early detection of the tumor increasing or tumor changing, right?
• 38:56
  So, this will be adding a lot of value.
• 39:00
  So, how are you thinking about how can this be commercialized?
• 39:05
  We talked about the clinical trial study, but in terms of the clinicians using that in the U .S.
• 39:11
  and in terms of how you are thinking about commercializing, would there be any pair barriers or so?
• 39:18
  So, what is your plan around that?
• 39:20
  Currently, the way
• 39:21
  is help the biopharma companies, and if they use our assay to accelerate their translation research,
• 39:29
  then one path would be this assay becomes a companion diagnostics.
• 39:34
  So, that's one path we are pursuing.
• 39:38
  Parallelly, we have initiated a number of clinical studies, specific utilities, okay?
• 39:45
  Okay, and once we get the 510 case, for instance, right, then we'll do the Maldi X route,
• 39:53
  okay, to get the reimbursement, right, once
• 39:58
  Interviewee: okay?
• 39:58
  And we will, we have two choices, either we build our own, you know, Salesforce and the whole operation, or we
• 40:06
  partner with larger established labs
• 40:08
  speakers: who already have the partnerships in place, okay?
• 40:13
  And currently, what our interest is, our focus is going to be, we are the R &D, we can come up with the new assets,
• 40:21
  and we, our intent is to leverage strategic partnerships for
• 40:35
  Interviewee: Yes, that makes total sense.
• 40:37
  And now, in terms of like, we talked about the fantastic science and technology around
• 40:43
  speakers: That commercial path rests on rigorous assay validation.
• 40:47
  it with the drug companies, so very, very regimented, very strict, very stringent, you know,
• 40:56
  criterias that we have brought into our practice.
• 40:58
  So we do the analytical validation, then we do the clinical validation, before the assay is ready for putting it into
• 41:06
  the stuff,
• 41:36
  Interviewer: We did one study where we compared our assay with an FDA -cleared assay, you know,
• 41:44
  it's called PGDX, the company which has been acquired by LabCorp.
• 41:50
  Okay, it's one of the very popular, successful, well -established assay.
• 41:54
  And I'm very happy to report to you that we showed 99 .8 % equivalency, you know,
• 42:02
  equivalent to an FDA -cleared assay done completely independently, you know, at a lab which is the University of
• 42:10
  Augusta.
• 42:11
  And that paper will be published very soon.
• 42:15
  Manuscript is ready, we're working through the final details.
• 42:18
  But that gives us the confidence that independent lab was able to conduct an independent study with an FDA
• 42:26
  -cleared assay and we show a substantial equivalency.
• 42:30
  You know, so that becomes interesting.
• 42:33
  That's amazing.
• 42:34
  Congratulations on that.
• 42:36
  Thank
• 42:40
  speakers: Validation matters most if earlier detection can reach patients beyond specialist centres.
• 42:47
  Interviewer: making it accessible, so easily accessible across the country and around the world, and making it affordable.
• 42:54
  Interviewee: Okay?
• 42:55
  So that's our vision, that's our mission.
• 42:58
  And we believe that cell biopsy, coupled with AI, is the transformer.
• 43:06
  Interviewer: Okay?
• 43:07
  Our ultimate goal is to, can this be a simple assay with a simple blood
• 43:15
  draw, be able to detect way early, and I wouldn't say displace completely,
• 43:23
  but significantly reduce the need to wait for three, four months.
• 43:30
  Interviewee: Okay?
• 43:31
  And look at the sensitivity of CT scan.
• 43:35
  Unless the secondary tumor grows to nine to ten millimeter, you can't detect it.
• 43:42
  Okay?
• 43:43
  A nine millimeter tumor means billions of cells.
• 43:47
  And here I am looking at single cell.
• 43:51
  Interviewer: Okay?
• 43:52
  You know, so if we could utilize good AI to analyze the data and show the
• 43:59
  equivalency, substantial equivalency, okay, don't have to wait for three, four months, don't
• 44:07
  have to spend, you know, thousands of dollars, it will transform the care.
• 44:13
  And particularly for survivors, cancer survivors, the people in the remission, where they have to go through
• 44:21
  once a year, twice a year, once a quarter, and go through the agonies and the pains, spend a lot of money, don't have the
• 44:29
  sensitivity.
• 44:30
  So the celebration that my pet is clean is not very long lasting.
• 44:35
  Right?
• 44:36
  Because it didn't get detected because your tumor wasn't quite nine millimeter.
• 44:41
  But within a month after you celebrate your cleanliness, you might come back again.
• 44:47
  Okay?
• 44:48
  So early prediction of a reoccurrence, if I could do through cell biopsy, okay,
• 44:56
  then
• 44:56
  Interviewee: I think we have conquered the big thing.
• 45:00
  Ninety percent of the cancer deaths happens because of the reoccurrence.
• 45:03
  Right?
• 45:05
  And, you know, reoccurrence, the root cause of it is the CTC.
• 45:09
  Interviewer: Right?
• 45:10
  Because of metastasis.
• 45:11
  Right?
• 45:11
  And if you can capture early and create a sensitive specific assay, which is not, which is
• 45:19
  minimally invasive, a simple blood draw, I think it will change the field.
• 45:23
  And that's our mission.
• 45:25
  And our goal in life is to impact at least a million cancer patients' lives across the country and around the world.
• 45:33
  Interviewee: That is fantastic.
• 45:34
  And all our best wishes to you about those.
• 45:38
  Thanks.
• 45:39
  Mohanad Terwar, co -founder and CEO of OneCell .ai.
• 45:43
  Mohan, thank you very much for your time today.
• 45:46
  Thank you very much, Amar and the team.
• 45:51
  Well, that was a great conversation.
• 45:54
  What did you think?
• 45:55
  It was fascinating to learn about the progress being done in this area of diagnostics,
• 46:03
  liquid biopsies, right?
• 46:04
  So we've been living through this when it was tissue biopsies and then liquid biopsies came in and now it's liquid biopsies but only single cell.
• 46:12
  And the precision that we can have now from scientific and technology point of view of really focusing on one cell
• 46:20
  and then really figuring out from there
• 46:22
  Interviewer: what could be what's happening with the tumor.
• 46:27
  It's just fascinating how we're rapidly advancing the science and technology and which is really at the
• 46:35
  forefront of healthcare at this point.
• 46:37
  Interviewee: You know, it must be 20 years now that liquid biopsies kind of first emerged on the scene and
• 46:44
  there was a lot of excitement early on about their potential but the impact has been limited so far.
• 46:50
  To what extent do you think what one cell is doing addresses the existing limitations of liquid
• 46:58
  biopsies?
• 46:59
  So they are not only using just the liquid biopsy and then doing some
• 47:09
  regular experiments.
• 47:10
  Interviewer: they are doing a lot of the multiomics on this.
• 47:12
  So you talked about genomics, transcriptomics, proteomics, as well as a lot of stuff about understanding the morphology of the cell.
• 47:20
  So they're bringing in a lot of different types of analytics together using AI, using analytics.
• 47:28
  So, and there also, he talked about gene assay, right, about over a thousand genes in that.
• 47:34
  So it's really going deep into quite a bit of understanding much more about the cell, much more about the
• 47:42
  DNA, but also about the protein.
• 47:44
  So it provides a lot more information.
• 47:46
  It's also, we have to think about, I mean, what he talked about was the report that they generate, which is not just about, okay, well, this gene here,
• 47:54
  but then they are using NCCN guidelines to then connect this to the drug therapies, and then really
• 48:02
  talking about, okay, well, what are really the actionable mutations that we can see, because that's the biggest problem, right?
• 48:08
  Okay, there's some mutations that you can detect in any DNA, but so what?
• 48:13
  What do you do with that?
• 48:14
  There is no drug for that, right?
• 48:15
  So now detecting these actionable mutations, but also tying them to the therapy options, I think that's going to be great.
• 48:22
  And then it's also as we get more and more therapies, this is going to be more, right?
• 48:27
  Because they're going to be able to say, okay, well, these were mutations that were not necessarily actionable before, but they will be now, so I think that's definitely
• 48:34
  very good.
• 48:36
  And also, I mean, to me, the big thing is to be able to detect early, right?
• 48:40
  Interviewee: mean, like three, he talked about three to four months, that's a huge amount of time in the cancer patient's life after diagnosis.
• 48:48
  So getting any of this information before
• 48:51
  speakers: That response signals also require complex data to become clinically usable.
• 48:56
  Interviewer: data, you need the AI to glue all of that together.
• 49:01
  So yeah, you have to have it, and of course in diagnostics, I definitely think that AI has a big usage in diagnostics because you're trying
• 49:09
  to figure out what's happening in this cell, and the cell has way too many components that you do need AI to
• 49:17
  do the analysis for that.
• 49:19
  The initial focus of one cell is on helping biopharmic companies use the technology to improve their drug development
• 49:27
  efforts and help build the case for its test as a companion diagnostic.
• 49:32
  What do you think of that strategy?
• 49:34
  It's a good strategy.
• 49:35
  I think they are following both strategies, right, like going with the 510k route as well as this.
• 49:42
  Over here, I would say definitely this gives them a lot of experience about how it works
• 49:50
  in the clinical trial setting with how the clinicians will use this information, but also the big advantage there
• 49:58
  is to be able to have it as companion diagnostic.
• 50:01
  So if they can get approved as companion diagnostic for any drug, then the test needs to be used, has to be used,
• 50:10
  whenever that drug is to be prescribed.
• 50:13
  So that I think will be fantastic for them commercially and that also is just going to help in getting the general
• 50:21
  usage application.
• 50:23
  they will be generating a lot of data that will be helping with those applications as well.
• 50:29
  speakers: As you think about the various stakeholders around clinical practice, providers, payers, regulators, drug
• 50:37
  companies, and patients,
• 50:44
  Interviewee: what will it take to win acceptance for doing so with these different stakeholders?
• 50:49
  The way I think was that the physicians and patients are always very interested in understanding what is happening.
• 50:56
  Regulators, it's going to be more about they're doing the trials.
• 51:00
  It's about the insurers and whether they are going to reimburse this or not.
• 51:05
  I would say one of the things is going to be, whenever you talk about AI, how expensive is it?
• 51:10
  speakers: For providers and payers, the question turns on measurable patient benefit.
• 51:17
  Interviewer: going to take some money to do all of that.
• 51:21
  But then what they have to think about is the benefits from here.
• 51:24
  At the level for the patients, but also at the population level, is this going to really
• 51:30
  Interviewee: then help prevent the patient from recurring?
• 51:35
  Is that going to help?
• 51:36
  Is it going to help in increasing the quantity of life, but also the quality of life, is that going to help
• 51:44
  in maybe less therapies later on and the wrong therapies later on because you are detecting
• 51:51
  very early on what should be the right therapies.
• 51:55
  I think those are some of the advantages that the pairs will take into account and then they will do the analysis.
• 52:01
  But I would say that there's definitely the potential to have this in clinical practice.
• 52:08
  I think at some point we have to get there.
• 52:11
  We have to have these early diagnostics to be able to detect cancer early on or the changes in cancer.
• 52:19
  Interviewer: I always think about diagnosis is much better than cure.
• 52:24
  You're always much better off with diagnosis than cure.