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Christie Rizk: Good afternoon, everyone. I'm Christie Rizk, senior editor GenomeWeb, and I'll be your moderator for today. Our webinar today is a Precision Oncology Roundtable, and we will ask the experts about NGS-based tumor profiling. And our sponsor is PierianDx. Our panelists today are Dr. Rakesh Nagarajan of PierianDx, Dr. Gregory Tsongalis of the Dartmouth-Hitchcock Medical Center and Norris Cotton Cancer Center, Dr. Anthony Magliocco, with the Moffitt Cancer Center, and Dr. Ravindra Kolhe of Augusta University.
You may type in a question at any time during the webinar. You can do this through the control panel, which usually appears on the right side of your screen, click on the Q and A box on the upper right side of the control panel. When you click on send to, please select all panelists. We will ask the panelists questions after all the presentations have concluded. First up is Dr. Rakesh Nagarajan, founder and chief biomedical informatics officer at PierianDx. Dr. Nagarajan, please go ahead.
Dr. Nagarajan: Great. Thanks so much, Christie. I'm going to go ahead and jump right into my slide deck. Our first slide here just talks about our One Space Solution Portfolio, I won't spend more than 30 seconds on this. But essentially, we have a set of products and services that enable a clinical lab to perform clinical next generation sequencing on their own, and the way we do this is through three of the services we'll talk about today. The CGW, the Interpretation Services and Validation Services that's offered through Professional Services and I won't really talk about Gateway Lab Services today.
The trend is to move from very focused single marker testing to more and more comprehensive panels, and we'll talk about one of those comprehensive panels today.
That assay that we will focus on is the Illumina TruSight Tumor 170 assay and the content of that has about 170 genes, both DNA and RNA base including, allowing for low DNA input as low as 40 nanograms, it does cover SNVs, indels, copy number variants with respect amplification and gene fusions using the RNA seq results. This assay can obviously be run on the NextSeq and HiSeq instruments.
The assay has a broad solid tumor base of the major cancer types, obviously, that are shown here and again, during detection of both DNA and RNA targets, including copy number variants.
I want to spend just a minute on assay validation and the services we provide their, which includes that for TST170. So the first step in that process is to really designed the experimental approach to help decide numbers and types of sample probably get run on different sequencing runs, determining clinical performance targets and assay claims, selecting, then, samples for analytical and diagnostic validation and then doing both components of that validation with a partner laboratory that includes typically methods faced validation of using reference controls, and then doing a variety of calculations including specificity, sensitivity, positive predictive value, lower limited detection, and diagnostic validation components as well.
I'm going to now, then, focus on the CGW, or the Clinical Genomics Workspace, of workflow and how we integrated the TST170 into that workflow. So, namely, as you know, within CGW, we can do order entry through either manual order entry or integration through HL7 or API based access, as well as both [inaudible 00:04:22].
The process, then, from sequencing for report is much the same within the system, except that some of the details are different for the tissue in 70. One is that we use the TST170 app that's available, either through [inaudible 00:04:40] integration or for local deployment and all of this is seamless to the laboratory, whether they use the [inaudible 00:04:47] solution or the local solution, and then we also have our own modules that I'll describe validation for that calculate tumor mutations burden and microsatellite instability.
Once variants are identified using the TST170 app and have been validated through a validation process, whether with us or the lab has preformed it independently, the CGW, then, allows you to view areas of interest that are filtered by quality, and based on the validation filters that were set up during the initial process, you can also view sample quality metrics both for DNA and RNA and get down to read level data, as well as a variety of other [inaudible 00:05:32] components that I'll talk about in future slides.At this point, I kind of want to drill down into the initial validation we've preformed. Both were TMB and MSI. Let's start with TMB.
Tumor Mutational Burden (TMB), as many of you know, measures the quantity of mutations the tumor contains, and that's being used to predict whether patient will benefit from [inaudible 00:05:58] therapies. Its typically calculated as mutations per megabase. Folks have shown that you can calculate this using whole exome sequencing, but now there are a variety of folks that have also shown that you can do this in targeted panels. That's what we aim to do with TMB calculation in TST170.
This shows analysis that we've done, both using whole exome data as well as limiting the variants in the whole exome to the TST170 capture region, and we use TCGA data sets to calculate tumor mutation burden greater than 10,000 different TCGA samples that represent around 30 tumor types. In this particular calculation, only non-synonymous, somatic variants are considered. When we limit, then, the capture region that the TST170, there's still a high level of concordance, both in value and relative levels across tumor types. That is lower tumor types known to have low tumor mutation burden in the literature continue to have that whether we analyze using whole exome or TST170.
This is further that ... or show here, where through direct correlation on concordance, we can show that either ... all mutations, as shown on the right, or non-synonymous mutations, if you only use them, are both highly correlated to whole exome based analysis after limitation of those variants within the TST170 capture region.
The TMB validation now looks at TMB calculation and using that to differentiate responders from non responders, and, again, using the TST170 capture region, we can show that both the non-small cell lung and melanoma, there's differentiation that we can get. That differentiation is held true whether we use the whole exome or, again, only the TST170.
Moving on to Microsatellite Instability ... again, many of you know Microsatellite and the number of repeats within those microsatellite may be altered in tumor genesis, and this is typically caused by mutations in the [inaudible 00:08:30] repair genes or through epigenetic inactivation of those genes. Obviously, the status of MSI, which typically, today ... I'm sorry ... which, today is really determined by TCR [inaudible 00:08:44] has significant in chance of prognosis and implications for therapeutic response.
Here we have our initial validation and analysis, and, again, we took TCGA results and samples where the MSI was known in those TCGA samples, and, again, we calculated microsatellite instability across thousands of [inaudible 00:09:14] that have microsatelites within the whole exome and then limited to the TST170 capture region. Again, you can see that MSI status is very highly correlated to the number of MSI events, which is what's plotted on the Y axis as compared to the MSI status as determined during PCR, whether it's microsatellite stable, low or high.
I want to jump into the actual visualization reporting in the CGW. Once variants are [inaudible 00:09:54] through the TST170 app and classified and reviewed, our knowledge base creates an automatic classification of these variants, which includes a wide rage of therapeutic, prognostic, diagnostic, and risk information and options, as well as clinical trial matching.
I want to spend just a minute on our knowledgebase itself. Our knowledgebase is comprised of, at the very lowest level, publicly available sources ... so human genome builds, gene RNA protein models, minor allele frequency database information. The published literature, itself ... There are sources that we, then, clean and curate, normalize to a standardized disease oncology, that includes data from COSMIC, TCGA and laboratories who release their results. We have highly curated information from proprietary ... I'm sorry ... from sources such as NCCN, and ASCO, and FDA approved labels, which is [inaudible 00:10:54] for the somatic cancer setting, as well as clinical trials, and finally, we have a proprietary knowledge base of shared interpretations that are shared across our partners of which there are greater than 50 today.
That really forms a very powerful network of sharing interpretations that are, then, been written by medical directors in a particular disease context. Those interpretations, then, in spur across the network in a future [inaudible 00:11:24] for medical directors, whether that variant was found in the same or different of these contexts.
This is an example of one of the many reports templates that we have that laboratories can choose from, and, again, you can report out all the different variant types. You can report out TMB and MSI, a results, in future ... I'm sorry ... pages are not shown here. This is just the first page. You can also see per variant individual trials targeted therapies that you can then edit fully within the system to sign out the report.
I want to end by talking about our interpretation services, which is third service, then, of the four that we're covering today. We truly believe that the right mix of technology, including the platform, and human medical expertise will ultimately help in genomic medicine. We've provided that technology and platform through to the CGW, which, again, provides automated classification of variants, provides the knowledge base for therapeutic, prognostic, diagnostic, and risk options, and can do clinical trial matching based on molecular findings.
The human medical expertise that we not offer through a variant scientist medical director and including sign out services, should you need that and then want that, allows for assessment of a variant classification and interpretation, and updating that information based on [inaudible 00:13:00] literature, drafting clinical interpretations and reclassifying variants based on information that you gather from the system, evaluating pathway based associations and co-variant associations to create cohesive interpretation, and then doing clinical trial matching that is based on pathway crosstalk as well as client specific SOPs.
With that, before I hand it over I just want to summarize that, within the CGW, you get a complete workflow from order entry to a signed out report. We do that for the TST170 as well as a variety of other assays, and then we provide interpretation services at the end of that process, should you want or need that service for either scale reasons or personnel reasons.
Christie Rizk: Thank you, Dr. Nagarajan. As a reminder to webinar participants, if you have a question, please type it into the Q and A box in the control panel.
Next up is Dr. Gregory Tsongalis, director of The Laboratory for Clinical Genomics and Advanced Technology at the Dartmouth-Hitchcock Medical Center and Norris Cotton Cancer Center and Professor of Pathology and Laboratory Medicine at the Audrey and Theodor Geisel School of Medicine at Dartmouth. Dr. Tsongalis, please go ahead.
Dr. Tsongalis: Thank you. Thanks, Christie, and thanks, Rakesh, for the opportunity to share some thoughts today. Hello everyone.
What I'd like to do is just kind of give you an overview of some of the things we think about in the lab when we're talking about next generation sequencing with respect to the different panels we run and some of the things that we're thinking about implementing.
The real power of the technology that we've seen coming out of next gen sequencing or massively parallel sequencing, and the advantages that this technology affords us in the lab with respect to being able to analyze really complex human diseases, like human cancer, and that's focus of the talk today.
I think, before we talk about some of these applications, we have to really, really think about the added value that the laboratory is able to bring to the table with respect to patient management and patient outcome, given that the test that we're providing are not so much used for diagnostic, but very must used for prognosis and selection of different therapies.
Before we start talking about some of the things that we implement in the lab here, I just want to call everyone's attention to this really spectacular guideline paper that came out just this year in The Journal of Molecular Diagnostics that I think everybody should read before you even think about validations of a next generation sequencing assay or panel.
With that in mind, I just want to show you what we're doing, because if you remember back in the day, or still in some labs, we were running singlicate assays for certain genes, certain mutations, and the disease becomes much, much more complex than that. I think everybody appreciates that. We've appreciated that for a while, because we talk about cancer as a colonial disease, that's show in this top panel here, with select mutations being acquired by cells as they progress to become a malignancy and then metastatic disease.
That's really a way underestimation of what really happens, and I think we're appreciating that now with all the tumor cell heterogeneity. But, if you add to the biology of the disease, what's happening in the pharmaceutical side or the treatment side of the disease, it becomes even more complex, because every one of these abnormalities that these cells go through is now, or seems to be now, being targeted by a different therapeutic option for these patients. That becomes really the big impetus for us ... to start looking at technologies that allow us to generate a lot more information than what we could with some of the other more traditional techniques that we're all so familiar with.
I'll just highlight one pathway. This happens to be the pathway for EGFR ... just to show you and highlight the point that there are multiple genes with all kinds of variants in them that act either [singlicate 00:17:40], or in conjunction with each other, or maybe in conjunction with other pathways, and so on, that we used to be able to do one at a time, but its just become way too complex to do that. That's the focus of what most of us have been doing with these panels of genes and applications in the oncology space.
What I like to think about when we're talking about expanding the panels are listed here. The title of my talk was how many genes do you want to test for and so on. It just seems like right when we thought we were doing a great job and we have a nice panel or panels in place, the request comes in for more genes, or there's a new application and we have to add genes, and so on. We talked about what's the clinical utility of this. Are we really going to impact patient care? Will we have cancer specific panels in the lab as there are commercially available, or will we have pan cancer panels that we can use across the board. There are a number that are commercially available, including the 170 that people are talking about today.
What types of variants are we going to detect? We thought we were doing a great job with the SNVs, and then the CNVs came about, and the fusions, and all these things now become important, so you have to figure out what it is you really want to be doing and detecting in the lab.
For me, one of the biggest questions is the next one, and that's; what about the workflow and the lab logistics, including informatics. This is something that I think we way underestimated what it would take to set this up in the laboratory, and we've been doing this for almost four years now.
What about the platforms and the chemistry? There are certain instruments from different vendors depending on the assay you're going to set up and the type of chemistry you want to use. That becomes important for you to evaluate.
Money for equipment and capital ... capital funding for automation is a big deal, where we didn't think we were going to need this a few years ago. We certainly are looking for as much automation as possible. I think training techs in the or the extent of the learning curve is something to keep in mind. The turn around time that your oncologist are expecting and what you can really deliver is another issue. And, then cost and reimbursement, although last year and this year reimbursement seems to be happening a little bit at a time now. We should be good there, I hope, in this next year.
Let me just show you two cases that we had recently. One was a 57 year old female that had non-small cell lung cancer with an ALK-EML4 fusion. That made her eligible for treatment with crizotinib, and after four months the disease progressed, and as we sequenced her we noticed that she also had ... or, the tumor had a TP53 mutation that was associated a really poor prognosis. But, in addition to that, she also had a non-synonymous point mutation in the ALK gene that's associated with resistance to all of the ALK inhibitors. There's only one publication of this in the literature that we could find at the time, anyway.
The second case is a 50 year old male with rectal cancer, had follow up, developed some pelvic mets with-
At follow-up, developed some pelvic mets, was treated with FOLFIRI which is a chemotherapeutic regimen and cetuximab which is an anti-EGFR antibody therapy, and has great response despite the fact that he had a BRAF mutation.
BRAF mutations have been associated with low response to the anti-EGFR therapy. It turns out that in this case, he didn't have the typical V600E mutation. He had a D594G mutation which, again, was reported once in the literature as being favorable response to cetuximab.
I show you these two cases because they really highlight the need to be able to do good sequencing across genes and gene regions with a level of coverage that's going to allow us to detect a lot of different variants, some of them we may not know what they mean right now.
And I think that's the importance of our databases that we're all putting together, but there are others that we would have missed or you'll miss if you're only looking in particular exons of certain genes. I think these two cases and other people have many more that highlight that need and the importance that that will mean for these patients in their management strategy.
Here I'm showing you the team that we have in place here at Dartmouth to be able to do all of these, and we continue like everybody else to ride the ride, so to speak. I'll turn this back over to you, Christie. Thanks.
Christie Rizk: Thank you, Dr. Tsongalis, and we want to remind participants again that if you have a question, please type it into the Q&A box and we will conduct the Q&A once all the presentations is concluded.
Next up is Dr. Anthony Magliocco, Senior Member and Chair of the Department of Anatomic Pathology at Moffitt Cancer Center, Executive Director of Esoteric Laboratories Services and the Morsani Molecular Diagnostic Laboratory, and Scientific Director of the Moffitt Tissue Core. Dr. Magliocco, please go ahead.
Dr. Magliocco: Great, thank you for inviting me to share our experiences at the Moffitt Cancer Center. I'm pleased to be here today.
At Moffitt Cancer Center, one of the key features of our center is a focus on precision medicine and personalized oncology. At Moffitt, like other places, we believe that precision medicine is really defined by having the correct diagnosis about assigning a correct treatment, doing it in a timely way, but also, we're seeing more and more that we need to adjust it because we have multiple lines of therapy now. We have trials that are linked to evolution and it's becoming more and more complex.
We're also understanding that each individual solid tumor is unique; and the type of progression and response to therapy and surgery, et cetera, may also be unique. So, we need the tools to manage that and deal with that in this complex environment.
Now, fortunately, at Moffitt, we were lucky to recruit Dr. Howard McLeod, seen here standing proudly in the center of this image, and he's led our personalized medicine service. This is a comprehensive service. It's really, really helped us in the molecular lab because this service provides personalized medicine consultation to oncologists.
Any time a large panel is ordered or there's a question about what should be done next. His team will work with the oncologists to pick the right panel and help interpret the data. For particularly challenging cases, there's a molecular tumor board where these are discussed on a biweekly basis.
Dr. McLeod has created a training program where he's training multiple fellows actually from a diversity of backgrounds including molecular pathology and pharmacology and others that we hope will go forward in the future to other medical centers across the country and help found new personalized medicine services as well.
There's opportunities in pathway development. In Moffitt, there are prescribed pathways for each treatment type, and we believe this is the foundation to evidence-based medicine. It's also very useful for our payers that if we can convince our payers that we're following a rational approach and a predictable path of treatment, they're more likely to compensate us not only for the testing, but also for the treatment we choose.
More to that end, there's a service to match patients to clinical trials that may be open at Moffitt and beyond, and also to potential off-label opportunities for patients with rare conditions or unusual tumor profiles. Finally, the personalized medicine service does work with payers and it's been very successful in obtaining reimbursement and coverage for Affordable Use and other applications. So, kudos to Dr. McLeod.
Now, in terms of expanding our panel, we've been running next gen for a number of years with smaller focused panels. I believe we've ran over 8000 next gen panels over the last three or four years, and the time came that we needed a bigger panel.
We lean towards TST170 because we've worked with Illumina for a number of years and we feel that they're one of the leaders in this particular field. Our instrument base was Illumina-based instruments. So, we were drawn towards their reagent profiles.
We believe that there's a very broad implementation base of users and that many other users will also use this panel. We've previously used their off-the-shelf TST 26, their heme panel and other solutions from them. We were impressed with the quality of those products in the past and that gave us more confidence moving towards TST170. They also have an opportunity for custom panels.
One of the negative aspects with Illumina is they came from the research world and they're really just cutting their teeth now in the medical diagnostic world. Only a few of their products have gone through FDA process, et cetera, but they are learning as quickly and are certainly poised to enter the medical market with greater potential.
When we look at Illumina, we looked at other panels as well including Archer, and actually, we feel that Archer has a really great panel that we may be implementing into our liquid biopsy service. In terms of Illumina, we also looked at the 500-gene fusion panel which covers 508 gene translocations.
Now, Moffitt, we have many, many demands from our different tumor services. We're trying to satisfy demands from lung, endocrine services, brain, and a variety of other tumor sites. Essentially, we do a survey of what they need and try to match to provide them with an optimal result.
We felt that the 170 was the best available result for our particular needs at Moffitt. We're also planning to launch that along with the 500-gene fusion panel, so we'll have a comprehensive set. Down the road, we are looking at more focused panels perhaps in the 50 to 70-gene range for liquid biopsy purposes, CSF, and cystic fluid analysis.]
Now, one of the values in TST170 that we've discussed a little bit earlier is that it's got comprehensive coverage of a lot of cancer-related variants. This panel was constructed with the intention of actionable targets in mind. So, it's really focused towards actionability.
So, some mutations that may be more defined diagnosis may not necessarily be covered in the panel, and this has caused some consternation for renal services and, perhaps, neuro services and so on. But in terms of treatment selection, it's a very good panel, in my opinion. It also has DNA and RNA aspects which is really optimal for identifying fusion and splice variants, and there's a unified workflow that works fairly well.
The other thing that's quite good about this panel is that it works with fairly low input, as low as 40 ng of DNA and RNA that it can detect as low as the 5%. So, the analytical sensitivity is down to 5% mutant allele frequency which we think is quite good.
We've covered the workflow before. Essentially, the data comes off the instrument. It goes into BaseSpace which is housed by Illumina. It's a HIPAA-compliant cloud and some processing is done there. Then it goes into the Pierian data workflow where it comes out as a report at the end. We heard a little bit about that at the beginning.
We've worked with PierianDx from beginning with our launch of our first next gen panel several years ago, and we found them very convenient to work with and very adaptable for our specific needs, helping us prepare reports in an efficient way and an accurate way for our particular clients and internal use at Moffitt. So, we've been fairly happy with this process and the ability to modify this, adjust filters, and test the workflow.
We're in the middle of this and we're hoping this panel one should go live in the next week or two. We've spent several months running through the recommendations of the diagnostic profiling article from the Journal of Molecular Diagnostics. I've run about 80 of these assays and we're seeing very, very good results which I'll show you as we go through the ...
We've determined that single nucleotide variance. We can detect down to 5%. Amplifications with 10 or more copies can be detected in tumors with 30% tumor load, and fusions and splice variants can be detected with greater than 5 copies in 1 ng of RNA. So, we believe that this meets the performance characteristics for solid tumors in the type of samples that we typically see. It may not be adequate for liquid biopsies, but that's a story for another day.
The sensitivity and specificity of the assay, these are for small variants. There's a greater than 95% sensitivity and specificity at the 5%. One coverage is greater than 250x.
For fusion and splice, there's a greater than 95 specificity for greater than 5 copies of mutant RNA per nanogram; and for amplicons, again, a greater than 95% specificity and sensitivity for variants that are copy number equivalent to greater than 2.2. So, really excellent performance across all of these categories that need to be covered for solid tumors.
We've been busy going through this, and these stars represent that we've detected these in multiple samples, in cross-platforms, and in our control specimens. So, to date, we've been very happy with the performance of the assay and our intent is to bring it live.
The other aspect that I didn't really cover in this is the mutational load which I think will be a greater need as well, greater demand coming from immunotherapeutic services as we now see indications based on microsatellite instability across tumor sites. So, we're really expecting a dramatic increase in demand for these types of assays in the near future.
I'd like to thank Dr. Terry Boyle who led the project in getting this assay validated and housed in all of the systems that Pierian and Illumina have given us to get us to this point. We're hopeful to launch it in the next couple of weeks. That concludes my component. Thank you.
Christie Rizk: Thank you, Dr. Magliocco. As a final reminder to our participants, if you have a question, you still have time to put it into the Q&A box. So, please go ahead.
Next up is Dr. Ravindra Kolhe, Associate Professor of Pathology, Associate Director of the Residency Program, Medical Director at Cytogenetics Laboratory, and Director of the Georgia Esoteric and Molecular Laboratory at Augusta University. Dr. Kolhe, please go ahead.
Dr. Kolhe: And you will see a ... First, I would like to thank [inaudible 00:33:05] team for giving us opportunity to share our experience with TST170. We are a state medical institute, academic-based, and we have a tripartite mission of clinical diagnostics, education, and translational research support on campus.
First, I would like to begin with why we ended up choosing TST170. As most of the labs, we have either a DNA-based or RNA-based panel in our lab. The DNA-based panel is mostly for the heme malignancies, and the RNA-based panel is the sarcoma fusion panel on RNA FP.
But in the previous setting with any type of assay irrespective if it's NGS or even a single-gene assay, a lot of careful consideration has to go through the content of the panel or the genes. Especially, you want to make sure that they have good evidence-based markers selected in the panel, so you're set for next three to five years as a panel for clinical testing.
For me, personally, I think it's very important that these panels are compatible with low quality of DNA and RNA because the surgical pathologists are usually looking at these slides, identify those region of interest in each of these slides and isolate the nucleic acid. Most of the time in the past, they have not been compatible with the assays we have on the NGS platform.
So, when Illumina presented to us the content of the TST170 app in March in 2016, it was really helpful to go through and I was very happy with what the content was. I mean, the assessment of fusions, splice variants, insertions/deletions, SNVs, amplification in one single assay while looking at both DNA and RNA, that definitely created efficiencies in sample usage, time, and cost for us.
I'm going to share a couple of slides with our experience with TST170 initially as a site for the beta testing and later as validation. As of today, we have tested around 89 different samples on the TST170 app. I think eight of those were for the beta testing [inaudible 00:35:43] one of the three sites. As you can see, I think most of this data is probably we have available as a white paper which can be downloaded.
But in our experience, we had a good inter-site performance for all the three variation, SNVs and different variants Illumina has on the panel. Apart from that, all the different samples we have tested have worked very well. I think Tony had also in the previous talk mentioned about their experience and it all has been pretty similar to that.
This is the usual workflow for most, if not all, of our samples. All our samples were FFPE samples. We had a microdissection for DNA and RNA isolation. We use QIAGEN kits for the DNA and RNA isolation which has worked very well in the past for NGS assays.
The library prep was pretty much from the box. We use next sequencing and the initial variant call was in the BaseSpace on the TST170 app. We are collaborating with PierianDx to create a template and architecture for reporting of all these variants, and I think we have consistently seen that in the talk.
The other added advantage was we were able to get the workflow done in such a way that we could run two runs in one given week. We started the first one on Monday and finished the sequencing by Thursday, the overnight sequencing on Wednesday, and start the second run on Wednesday of the same week.
So, this helped us to have two runs on a NextSeq in one given week which really helped us to finish the validation in the given time. As I initially mentioned, we ran eight samples in one run on a NextSeq.
Some of the results from the study and the validation, in our experience, this is written with simple protocol as compared to what we have done with bigger assays or whole exome sequencing. The reagents were pretty integrated as a part of the kit, and I was really happy to see both the informatics provided by Illumina.
I mean, one of the things which I like to point is it was really an NGS in a kit or NGS in a box because it helped us to have a solid library prep along with the different things which we needed for running the assay as well as the initial bioinformatics for calling of the variants.
We had a strong operator and a site as well as run repeatability as a part of the beta testing. The sensitivity and the call concordance was otherwise observed across all sites, runs, and operators. And in our ongoing validation, I think we just started another one this morning, but we have another 81 sample and we had a pretty accurate variant detection which was comparable to all our validation samples through orthogonal methods.
As I initially mentioned, we had all our samples as an FFPE. They extended from punch biopsies to surgical resection. These are common questions I get asked which starts from just before for them.
The RNA we used was 89 ng in total with the input volume of 8.5 microliters, somewhere between 8 to 10 ng/microliter and the DNA we used was around 120 ng total with input volume of 12 microliters with 10 ng/microliter. This has been our experience with overall the TST170 kit.
In summary, we are very happy with the TST170 app. It's a pretty comprehensive DNA/RNA panel. I really feel it's a very cost-effective way to investigate the majority of the malignancies as the coverage is pretty decent.
One of the things which I'm trying to do with Pierian is create templates for reports, and I think at least our reports will have three layers. The first layer will be the tumor-specific information or I like to call this TSI. This layer of information will be very specific for a particular tumor which is they will be based on guidelines for diagnosis, prognosis and therapeutics. For example, if it is colon cancer, it will have the CAP/ASCO/AMP guidelines for the RAS/RAF possibly detection in colon cancer.
The second layer will be the expanded therapeutic targets which would be for the precision and personalized oncology. These are the targets which will be detected based on the different variants we'll identify in the panel.]
The third layer of information will be a combination of the MSI and TMB for immuno-oncology folks. So, these, I think overall will be pretty comprehensive reporting structure. We think we will be able to generate with the help of PierianDx. This is my last slide. Thank you.
Christie Rizk: That's all the time we have for today. We'd like to thank Rakesh Nagarajan, Gregory Tsongalis, Anthony Magliocco, Ravindra Kolhe, and our sponsor PierianDx. If we didn't have time to get to your question, we will try to follow up with our experts.