0:00
Thank you very much.
0:01
Appreciate it.
0:01
It's always good to come to a conference where you've got a lot of, you know, like minded individuals focused on biomarkers and precision medicine.
0:09
I know for me the kind of the focus in this precision medicine space is really around, you know, optimising the likelihood of positive responses with patients with cancer.
0:19
So I represent LabCorp and a team of very talented scientists that develop new assays, new capabilities, develop those and bring those to market for that exact purpose.
0:31
So now I think we all kind of know this, maybe we call it now the central dogma of precision medicine, right?
0:39
Matching the right treatment to the right patient at the right time and kind of that is what drives the optimum patient response.
0:47
But I would also add maybe a fourth pillar to this dogma, right?
0:51
And that is the right test is needed to get the right therapy for the right patient at the right time, right?
0:57
And so if you look at this kind of on a slide or on paper, this relatively straightforward, right?
1:02
You develop a drug, in parallel you develop a companion diagnostic or complementary diagnostic.
1:07
It's simple, it's straightforward, right?
1:10
We all know it's always so easy.
1:12
Now in reality, I think any time that you go through the process of developing a drug and an associated diagnostic, the process itself is complicated, right?
1:23
Got a number of different questions that we have to ask.
1:25
First, we'll start out with what is the biomarker, right?
1:28
What is the right strategy for that?
1:29
What is the right technology to measure that particular biomarker?
1:34
A lot of what I'll talk about today is focused in the area of genomics, but it's certainly not the only technology that can be utilised to identify and measure a particular biomarker.
1:43
And then if you go outside of that, how are you going to ultimately take that test, that biomarker test?
1:48
How are you going to distribute it?
1:49
How are you going to get that through regulatory agencies?
1:53
What is the geography needed to do that?
1:55
So it is a very complex set of questions, you know, last and certainly not least of which is how do you actually get the samples from the patient during your clinical trial to enable this testing to occur?
2:08
So it is a very complex set of questions that one may ask in the process of developing a CDx or a complementary diagnostic in parallel with the development of a drug that you may be developing.
2:19
Now the good news is that we have an entire team of people at LabCorp that spent all day, every day thinking about these exact questions.
2:27
We have dedicated people that are thinking about, you know, what is the right regulatory strategy.
2:32
We have dedicated people thinking about how do we go, when your drug gets approved, to make sure that test to enable that drug to be prescribed is available on day one of the approval of your particular drug.
2:44
And then, you know, just associated with the development of the test itself, you have to put certain regulatory requirements, certain quality requirements around the development of such a test.
2:53
Typically those are aligned with ISO 13485 and so we have a series of labs that are accredited through ISO 13485.
3:02
They develop a variety of different types of a diagnostic test.
3:06
So whether those be NextGen sequencing, whether those be, you know, cell therapy measurements for neutralising or total antibodies.
3:14
Got a suite of different labs that focused on different specialties.
3:18
But overall, we spend an inordinate amount of time thinking about what is the right strategy to help you with your drug get the right complementary or companion diagnostic to help facilitate patients receiving that particular therapy.
3:32
So going through a little bit, those are some of the things about how we think through some of these questions, how we enable some of that testing to occur.
3:40
But now I want to go into a little bit more of some of the technologies that we've been developing internally at LabCorp to enable those clinical trials to succeed.
3:49
And what you can see here is a suite of different NGS (primarily)-based products that we've developed over the past, you know, few years.
3:56
And the way that we think about these is much more than a single test, right, is how can we develop a healthcare solution to enable the optimum patient experience and optimal patient care.
4:08
So in certain cases that may be a test that is, you know, a high throughput, a high scale, a centralised test where samples all go into a single lab.
4:16
We run those at scale.
4:18
We have rapid turnaround times; we get the results back out to the sites that are enrolling the patients.
4:23
And that is a great model in certain instances.
4:26
There are other instances where that type of a model doesn't work for whatever reason.
4:31
That type of a model may require the creation of an independent kit that can be sent around the globe where samples can be run locally at individual sites or at individual hospitals.
4:42
And those results reported back without the need to ship those out.
4:45
Now each of these have strengths and weaknesses, but the point is that we are looking across these in their entirety to find the right solution for the right therapy, again, underlying this central dogma of precision medicine.
4:57
So I'll go through a few of these.
4:59
So for example, in the centralised space, we run a lot of comprehensive genomic profiling for tumour tissues through our OmniSeq Insight assays as an example.
5:07
And then over the next few slides, I'll go through a variety of our different kitted solutions for different individual purposes.
5:14
And so these are the three kind of kitted solutions that we have developed to date.
5:18
So if you start at the left, this is what we call elio tissue complete.
5:22
This is a 505 gene hybrid capture based technology for FFPE solid tumour tissue. In the middle, you know in certain instances you don't have tissue available, or you may look to complement that with a cell free DNA or liquid biopsy type assay.
5:37
We have a couple of those with different sets of kind of already existing regulatory requirements.
5:42
So the middle one here on that we call PGDx elio plasma focus Dx.
5:47
This is a 33 gene liquid biopsy panel, really focused on the most clinically actionable biomarkers.
5:54
Relatively small sequencing footprint, runs on a NextSeq 550Dx for example.
5:59
And then there are other instances where again you may want to ask a question specific to your particular programme.
6:05
And maybe that question is I want a comprehensive overview of a number of different genes utilising a liquid biopsy technologies.
6:12
So in that case, we have PGDx, elio plasma complete, which is a 523 gene panel that measures SNVs and indels across the entirety of those genes, also measures copy number alterations and translocations in 12 additional genes.
6:28
So whether or not you're looking at tissue or liquid, whether you want a small liquid panel or a big liquid panel, again, we're trying to bring forth a series of options to enable the healthcare solutions that meet your particular needs.
6:40
And you know, just focusing in on, you know, a couple of these products, right?
6:45
So I think what I see in nearly every clinical protocol that I've seen for the last several years is the, you know, frankly, requirement to be able to enrol patients both on tissue based testing and on liquid based testing, right?
6:59
And in many instances they're asking for both, right?
7:02
They want the flexibility; they want the ability to enrol as part of studies.
7:06
And so if you look at the elio Tissue Complete and the elio plasma Focus Dx, this complementary suite of products actually brings that forward in an already approved set of assays.
7:18
So if you look first at the elio Tissue Complete, this was taken through the US FDA as a 510K back in 2020.
7:25
It was actually the first comprehensive genomic profiling test to go through the US FDA.
7:29
And then recently we're very excited couple of months ago we got certification through IVDR for that same exact test.
7:37
So now we can run that test, you know globally.
7:40
So we can run it in the US under an IVD protocol, we can run it in the EU during under an IVDR protocol.
7:47
We actually run this test around the globe at this point.
7:50
And then about a year ago to kind of complement that tissue based solution, we were able to get a de novo 510K approval or authorization rather for the plasma focused Dx, the smaller liquid panel.
8:02
And we will be moving that through an IVDR process.
8:05
We estimate that kind of receiving that sometime next year.
8:08
And so if you look at these again in a complementary manner, they run on the same instrument.
8:13
They are through a similar set of regulations and will be under a similar set of regulations sometime next year.
8:21
Now lot of technologies, right?
8:23
And we think about, you know, the critical aspects of what drives precision medicine.
8:28
And one of them is the technologies, right?
8:29
We need to make sure that we have the technologies that perform well, they're able to give the robust results back to the patient, to the clinician, to the trial site.
8:38
Now, the other aspect of this is how do you enable that to occur?
8:43
A test is frankly useless if patients don't receive it, if patients don't have access to it.
8:49
And so we've got a very robust global infrastructure to enable this testing to occur.
8:54
So all of those assays that I just mentioned, for example, they're running in Europe at our Geneva site.
8:59
They're run in China at our Shanghai site.
9:02
They're run in the United States at our Baltimore lab rather.
9:06
So we have global infrastructure that enables global clinical trials to be run in a highly regulated or, for exploratory purposes, around the globe.
9:16
And so all of those assets that I was talking through kind of that infrastructure etc, is all aligned for identifying particular biomarkers linked to a particular therapy.
9:26
These are kind of those classic companion diagnostics utilised for therapy selection. But what I want to do now is explore kind of the newest if you will avenue in the area of so-called liquid biopsy testing and that is in the area of molecular minimum residual disease.
9:43
So over the next few slides I'll walk you through a little bit about how we approach this particular question.
9:48
Go through a couple of the studies that we have ongoing and then I'll wrap up with a little bit of a future looking set of data of we're already thinking about what's the next iteration of our molecular residual disease assay.
10:01
So I think we've all seen you know versions of this particular figure what it represents effectively on the X axis is a progression of a cancer through a treatment cycle.
10:12
the Y axis of this is overall tumour burden.
10:14
You can often think of this is circulating tumour DNA, tumour burden, right?
10:18
Basically early on the tumour is smaller, the ctDNA burden is low.
10:23
As that increases over time, you will get a clinical diagnosis depending on the stage or the treatment approach, hopefully that treatment is effective, you'll get a decrease in the overall tumour burden, hopefully goes away in its entirety.
10:36
Unfortunately, that doesn't happen in many patients, and you have clinical recurrence.
10:40
So the point is this is an entire continuum.
10:43
The levels of ctDNA vary depending on where you are on that continuum and you need different assays depending on what your particular requirements for that particular time and point are.
10:53
And so what we're looking for in this particular instance is there right there in the middle.
10:58
It is an instance where a patient is diagnosed with cancer.
11:01
It is, you know, treated with surgery, typically with curative intent.
11:07
The levels of ctDNA then go extremely low.
11:11
So what this means is you need an extraordinarily sensitive assay to be able to detect whether or not you're truly MRD negative or MRD positive.
11:18
And the more sensitive you can drive that down, the more likely you're able to differentiate between the two.
11:23
And then again, in a similar way, if that tumour either recurs or changes dynamically over time, you want a way to quantitatively assess that to occur.
11:32
And so what we're really focusing on over the next few slides are really the area highlighted in the blue box and a little bit on the clinical recurrence side.
11:40
So the assay for molecular residual disease or MRD that we utilise is called LabCorp Plasma Detect.
11:47
So LabCorp plasma Detect is a whole genome tumour informed sequencing approach.
11:51
There's a couple of different ways that people do this, right.
11:54
You have tumour agnostic or tumour naive approaches that don't require an upfront sequencing of a tumour.
11:59
You have the tumour informed approaches, which I would say most people use at this point.
12:04
And the reason they tend to use those, no big surprise if you know what you're looking for, it's easier to find.
12:09
So you can drive the analytical sensitivity down lower with the tumour informed approach.
12:13
So the way that we do this again is there are a couple of different ways that it has been done.
12:19
You can sequence the tumour, and you can create patient specific panels or what we do is we sequence the tumour, we sequence the white blood cells across the entirety of the genome.
12:29
We get a patient specific fingerprint across the entirety of the genome.
12:33
But then rather than looking at individual, you know, single assays and tracking those, we measured the cell free DNA across the entirety of the genome.
12:41
So if you think about it, what we're actually doing here is leveraging the breadth of the alterations across the genome rather than the depth of any individual mutation that's present in that tumour.
12:52
So how do we actually do it?
12:53
Well, I mean, anytime you're sequencing the genome, you're getting a lot of variance.
12:57
It is complex.
12:58
The most critical piece of this is differentiating what is real signal from what is background noise, right?
13:03
And it's one of those things that sounds again, easier said than done.
13:08
So what we actually do is we again sequence the tumour in the normal and we put both of those through a random forest classifier.
13:16
So what that is actually trying to do is assess the likelihood of a particular variant found in a tumour or in the white blood cells are true, are real, right?
13:25
And so it incorporates a number of different features that we've trained over, you know, a decade plus at this point to differentiate and basically create a likelihood score.
13:35
That a particular variant is real.
13:37
If we think it is, we give it a high level of confidence.
13:39
We move those high confidence variants forward into the creation of our individual fingerprint.
13:46
And so the way that once we do that process again, we sequence the cell free DNA and we apply that fingerprint, that high confidence fingerprint.
13:55
And again we run another round to assess the likelihood that the cell free DNA variants are real.
14:00
And we look for that patient specific signature present in the cell free DNA.
14:05
And if present, we're able to, you know, I would say semi quantitatively assess the presence in the level of ctDNA.
14:12
Now that's all has to do really with kind of the background noise ensuring that what you're looking at is real.
14:17
The signal comes from, of course, how many variants are present in an individual tumour.
14:22
That does vary from patient to patient.
14:24
It does vary from tumour type to tumour type.
14:26
And so in the case of a lot of the work that we've done, we've been focused on colorectal cancer, which we've seen it has a relatively high SNV in particular burden.
14:34
But we do have a number of different studies that you'll see ongoing where we've measured a similar type response across a number of different tumours.
14:42
So overall we're looking to identify signal relative to noise.
14:46
And again, the way in which we do that is we sequence the plasma at 30x and then the blood cells or the Buffy coat at 40x, tumour tissue at 80x.
14:56
We combine all that together.
14:57
And I think the interesting thing that we can do here that is very different than those assays which you know, sequence the tumour and the white blood cells and then create the bespoke panels is we can take at a landmark time point the sample to a result in 14 days.
15:14
And if you compare that to a lot of those bespoke generated processes, those can take upwards of four to six weeks.
15:20
So, you know, we're able to dramatically reduce the overall turnaround time at that landmark time point in particular because we're able to sequence all of these in parallel and then informatically analyse those sequentially.
15:32
And again, so 14 day turnaround time at the landmark and then for any subsequent monitoring, we can turn that around in typically less than 7 days.
15:39
And we've been able to see, you know, these estimates perform consistently during the clinical studies and the clinical testing that we've been performing utilising this assay.
15:50
So how does the assay actually perform when you look at it analytically?
15:54
What's shown here?
15:54
A couple of different metrics that we generated during the development or the feasibility portion of this particular assay process.
16:01
So shown on the left is analytical sensitivity.
16:04
You can see a limited detection of 0.005% at LOD95.
16:09
So that means that we're able to detect ctDNA at that level 95 times out of 100 when it's present to that individual level.
16:16
So you may see that number conveyed in different ways.
16:20
There's the percentage, it's also, you know, roughly 50 parts per million, right?
16:23
So that's kind of the level in which we were able to utilise for this version of the assay while maintaining a specificity of 99.6% and relatively consistent measurement of the frequency itself of a CV of 7.2% across 24 runs that we did as part of this particular study.
16:41
And then we took that to an independent cohort.
16:43
This is a validation study.
16:44
And what you can see is very similar performance, again, sensitivity of 50 parts per million, specificity about 99 and a half, and the CV across, you know, just over 8%.
16:54
So we're seeing, we developed the assay, we've now tested it kind of robustly analytically, but I think we all realise that's great.
17:03
How does it actually perform when you put it into a true clinical scenario?
17:07
And so we currently have ongoing, you know, more than 10 different observational retrospective collaborative clinical trials.
17:15
The first of these that I'll touch on just briefly is around stage 3 colon cancer, which we now have a manuscript that has been provisionally accepted in collaboration with fantastic partners of the Netherlands Cancer Institute.
17:29
In that particular cohort, what were that patient population was treated with surgery and then adjuvant chemotherapy.
17:37
And to no big surprise, patients that were ctDNA positive after treatment had a lower progression free and overall survival and a faster time to recurrence.
17:47
In addition to stage 3 colon, we also have an ongoing interventional study with the Netherlands Cancer Institute, we call MEDOCC-CrEATE.
17:56
They do, and we participate in it. In which we're actually looking to utilise the ctDNA results, those MRD results to guide therapy in the experimental arm and then compare that to standard of care in the control arm.
18:08
And this is an ongoing study.
18:09
We're running this consistently with samples coming out of the Netherlands into our lab in Baltimore and turning those around well within the turnaround time that I showed on the previous slide.
18:19
So there's a couple of really interesting studies that we've been running for a little bit, but I want to kind of dig into one that came out actually about 3 weeks ago, right?
18:28
So it's a brand new publication led by authors at Johns Hopkins University in which we were able to participate.
18:35
So at a high level, this particular study is looking at the use of perioperative immunotherapies in diffuse pleural mesothelioma.
18:46
So this particular disease, I wasn't overly familiar with it, so I'm going to walk through some of the details on it.
18:51
It's a standard of care for this treatment is the use of immunotherapy.
18:56
And but the kind of the utility of that around and that perioperative setting, kind of the neoadjuvant and adjuvant settings was not well understood until kind of this study came out.
19:06
So if you look at the overall CONSORT diagram on the right, you can see that overall, 30 patients roughly assigned independently to are either arm A or arm B.
19:16
Those arms are relatively similar with the difference being that arm A received nivo alone and arm B received nivo/ipy combo at the very first of the neoadjuvant time points.
19:30
So day -42 that you can see kind of here in this figure and then received nivo alone for days every two weeks up until surgery if available.
19:40
So the primary endpoint of this particular study was looking at the overall proportion of patients that were able to receive the surgical curative intent.
19:50
And you can see it met those primary objectives with arm A having 81.3% of patients and arm B having 85.7% of patients that were able to receive the surgery.
20:03
What's interesting is if you look at the, you know, the PFS and the OS for arm A, those were 9th.
20:11
9.6 and 19.3 months respectively and you compare that to arm B which had a significantly longer.
20:17
So to keep in mind, this was the patient population that received the combo immunotherapy at six weeks ahead of surgery.
20:27
In that particular case, you're looking at a PFS of 19.8 and an OS of 28.6.
20:32
So a difference in the overall performance that suggests maybe this combo approach will lead to optimal results in a perioperative setting in this particular type of tumour.
20:42
Now as part of this process, we also collected blood samples along the way.
20:47
So we have cycle one day one, cycle two day one, cycle three day one.
20:51
So basically the pre-surgery samples, we also looked at samples pre-surgery and immediately after surgery throughout longitudinally across time.
20:59
And in the instance when a patient recurred, we have those samples as well.
21:03
So to kind of look at how those results end up looking, we can look at a couple individual vignettes that are shown on the right.
21:09
Just to orient you to these figures, they're all basically the same, right?
21:13
Individual time points across the bottom and then the tumour fraction across the Y axis.
21:19
A filled circle indicates that ctDNA was detected.
21:22
The Y axis is the level of it, and an open square indicates that there was no ctDNA detected.
21:29
And what you can see is that in cases where a patient, for example, the one in the top left corner had a PFS of 1.58 months, you can see that there was no response during neoadjuvant immunotherapy.
21:41
The ctDNA fraction is going up throughout the cycles of treatment, did not go down pre-surgery, did not go down post-surgery.
21:49
So that is kind of the unfortunate situation where the patient is simply not responding to treatment.
21:56
If you look at others, for example in Figure D here, you can see an instance where that patient was positive at cycle one day one and cycle two day one.
22:05
But it's cycle three day one pre-surgery and post-surgery, their ctDNA levels have dipped below the limit of detection and that particular patient had a much better PFS of 24.08 months.
22:17
And so you can see kind of this consistent pattern across the board where patients that were responding both during neoadjuvant and then in certain instances adjuvant therapy had consistently better PFS than those that did not.
22:30
And in instances where there was a post treatment recurrence, for example, the figure here in the bottom right, you can see that particular patient's ctDNA levels went up ahead of the detection of recurrence.
22:43
And then as we kind of look at the overall, you know, results of that particular study, again, if you focus, you know, initially the top left figure, what you can see probably to no surprise is those patients that did not ultimately receive surgery had a significantly higher ctDNA fraction than those that did.
23:01
And then if you look at the Kaplan Meyer curves, what you can see is that patients with undetectable ctDNA at cycle 3 fared much better than the patients that did have detectable ctDNA cycle 3.
23:14
And that same pattern persisted if you looked at the pre-surgery sample.
23:17
So basically samples two weeks apart.
23:20
Overall, the conclusion there is if the patient is responding to the neoadjuvant therapy, in that case much better outcomes over time.
23:32
And you can look at that in two ways, right?
23:33
You can look at that as kind of a positive negative and you can also look at that as a patient specific quantitative assessment of whether that patient is responding.
23:41
So that's what's shown in the bottom 2 figures.
23:42
In that case, all results were compared to cycle one day one.
23:46
And if that particular patient showed a more than 95% reduction in ctDNA levels, whether that's at the cycle three day one or at pre-surgery, those patients showed much better outcomes than you know demonstrating A prognostic impact of this type of a measurement.
24:01
So those were all data that were generated utilising, what I'll say is the current version of our MRD assay.
24:09
But we've all seen that the importance of really driving down that analytical limit of detection and the overall performance of the assay.
24:17
So while we're happy with how this is performing, we are certainly not done.
24:21
And so just want to kind of wrap up by showing a little bit around the newest version of the technology that working on that we're working on.
24:28
This is based on what's called PPM-seq out of a company that's called Ultima Genomics.
24:34
So we're utilising the technology that they're developing sequencing on their particular sequencer.
24:38
I think the important thing to note here is that going through this process and happy to walk through the details on this at another time is able to significantly reduce the background noise, not necessarily increase the overall signal, but in doing so is able to significantly improve the signal to noise ratio.
24:55
So what's shown in this particular figure are some early feasibility data that we've generated.
25:00
X axis is parts per million or basically your LOD.
25:03
Your Y axis is a signal to noise ratio.
25:05
And if you compare each of the blue and orange squares kind of that occur consecutively there, what you can see is consistently the blue version, which is our version 1 of the assay, is lower than the version 2.
25:18
So version 2 is providing an improved signal to noise ratio that early studies suggest will enable us to go from an analytical LOD of roughly 50 parts per million to somewhere around 5 to 7.5 parts per million and perhaps lower as we continue to develop algorithms in this space.
25:36
So with that, I just want to thank you all for your time and your attention.
25:40
And if anyone has any questions now or afterwards, colleagues and myself will be around and thank you very much for your attention and your attendance.
