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Thank you for the opportunity and thank you for showing up on a early Friday morning.
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I know it's a bit rough at the end of the week.
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OK, here we go.
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So as you all know, immunotherapy effectiveness depends on the disease, the immunotherapy and the patient population.
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Some studies show very high response rates, but most studies the overall response rate is only 20 to 40%.
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And this is not just a challenge with immunotherapies, but we can see this across different treatments.
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And how do we improve that?
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One key is to identify biomarkers to better stratify patients.
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At Standard Biotools our advanced proteomics and multiomic solutions enable groundbreaking discoveries with unmatched precision and scalability.
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This table highlights some of the key discoveries our technologies have enabled and later on and specifically across therapeutic areas and in clinical trials.
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And later on, I'll get into some more specific examples.
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Notably Cytof technology, and I use that as a umbrella term for both our cytometry platform and our spatial biology platform have been in involved and have identified biomarkers in all clinical trials for all of the top 10 immunotherapies.
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Why are technologies so successful in identifying biomarkers?
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Our technologies capture a wide range of phenotypes and cellular functions.
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This enables researchers to better understand today's mechanisms of action for the treatment, and ultimately be able to stratify patients better based on response to those different treatments.
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And specifically thinking about our Hyperion Imaging mass cytometry platform.
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Imaging mass cytometry captures the widest dynamic range of protein expression and this enables researchers to then better understand and capture the heterogeneity present.
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So why does wide dynamic range matter?
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It enables researchers to capture that full range of protein expression and accurately quantify treatment response.
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So in this example from David Rim's lab at Yale University, they're looking at treatment for breast cancer.
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In the looking at the HER2 expression, they found an association with the HER2 extracellular domain and response and they also found a localisation of the CD8 positive T cells with the HER2 extracellular domain.
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And I like this paper for a number of reasons.
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One is it's 80 markers which you know in this day we tend to think of high parameter going pushing beyond 40 and even more.
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But really 80 is still very highly multiplexed in traditional spatial tools and imaging.
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And these 80 markers enable them to capture that heterogeneity.
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Imaging mass cytometry also enabled them to capture the full dynamic range and precisely quantify these relationships.
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In the paper they go on to say that they had tried to do this with more traditional immune fluorescence, but they were unable to accurately quantify and then identify these predictors of response.
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In this example looking at triple negative breast cancer, the researchers were looking at the effect of atezolizumab on triple negative breast cancer.
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They used imaging mass cytometry to characterise the tumour microenvironment and they were able with a 43 marker panel.
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So, what we now think of truly highly multiplexed, they were able to capture that heterogeneity and identify immune predictors of response that may not have been enabled by a smaller panel.
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In this phase two clinical trial the researchers here we're looking at a combination of a media virus therapy and stereotactic radiotherapy in the in triple negative breast cancer.
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And they did identify that the combination therapy increased median overall survival to 14.7 months, which is pretty incredible to think about nearly double. The imaging mass cytometry and CyTOF technology together identified a correlation of response to CD8 positive T cells.
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And this kind of goes with the theme that we're seeing is that with multiomic technologies, we can improve predictive power.
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And that's what this is showing here is they've identified the same population of cells with both imaging mass cytometry and CyTOF.
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And then with the spatial capabilities of imaging mass cytometry, they could identify the spatial localisation.
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So kind of continuing with the theme of multi omics in a study from the University of Sydney looking at the immunotherapy response of patients with lung cancer and this is carried out in Australia.
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And I'll say that some of the collection sites are a very remote sites in Australia, which brings upon its own challenges and the unique features of CyTOF technology and imaging mass cytometry enable that.
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I won't get into specifics, but we do have a booth where I'm happy to take questions what those specific features are later on.
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But they found that by combining these technologies, CyTOF imaging and the SOMA scan, particularly with the SOMA scan, they did the full 11,000, SOMA scan on matched plasma samples, that the untargeted SOMA scan approach complemented this targeted CyTOF approach and together they could get improved predictive power compared to the current standard of care.
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The other thing that we're seeing is a combination of spatial transcriptomics with imaging mass cytometry.
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The beginning of this year, there was a publication from NCI showing a combination of visium and imaging mass cytometry in the same study.
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And now Doctor Caleb Marlin at the Oklahoma Medical Research Foundation has taken that a step further to show on the same tissue section the same slide that he can do 5K 10X Xenium with traditional H&E pathology and imaging mass cytometry on the same slide.
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And some of the features of imaging mass cytometry that enable that is first, it's compatible with the Xenium processing.
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Second, the all in one staining an all-in-one acquisition of imaging mass cytometry retains the tissue integrity.
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And so they've shared some preliminary results showing that the phenotypes, the cells presence, the niches, the environments are preserved between the spatial transcriptomics and the proteomics data.
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So we have several labs across the globe doing this.
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They're all kind of in the late stages finishing up.
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And at this point, I think it's a race to publication.
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We'll see a few publications coming out in the coming months.
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So imaging mass cytometry is a simple workflow.
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It's the same workflow that you're used to with traditional immunohistochemistry.
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We have many ready to go panels that are designed to be modular so you can mix and match. As I mentioned, we have the all in one staining where you stain all of your antibodies at a single times and then a single acquisition. You can very quickly get images off of this system with rapid acquisition and there is no post-image processing or raw file processing.
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The raw file is what you get. And with our latest generation of software, the MCD Smart Viewer that also enables rapid identification and analysis and I'll talk about that in the coming slides.
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So with our latest generation system, we have a number of imaging modes that enable you to get fast whole slide scanning all the way down to high resolution images, single cell, one Micron resolution.
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And this is an example here of how you can very quickly in 20 minutes get this entire tissue area here.
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You can quickly see whole tissue heterogeneity, but you can then use this to identify regions of interest that you want to then interrogate further at higher resolution.
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So this is the same tissue section here.
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So this is a colon cancer tissue section.
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And now with that initial quick preview of the tissue, we can then identify these ROIs and get higher resolution scans.
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And these were acquired in two hours, 42 markers.
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So this is a beautiful image.
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It's a mouse sagittal brain section.
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Not only is it beautiful, it's pretty amazing to think that this is 96 millimetre squared area acquired in just over an hour.
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It's pretty incredible to think of how far the technology has come and how quickly we can get results and see the differences in the tissue.
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It also makes it a little bit frustrating when I have to go and use some of the older equipment that is a little slower and I don't quite have the patience that I used to.
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As I mentioned, we also have the new MCD smart viewer software which enables you to rapidly get information and data analysis.
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So on the left, well this is the mouse glioblastoma stand with a 35 marker neuro-oncology panel on the left.
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We can see different the expression of different proteins and we can get an idea of the phenotypes and cell functions present and kind of identify the themes there.
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The software will also do a pixel-based clustering analysis which we can then correlate to different phenotypes.
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So showing a little bit more about the software and how that works.
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Going back to the colon cancer tissue section on the top again we can quickly see very qualitative information.
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What is the protein expression?
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Where are these different protein markers expressed in the tissue?
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From there we can do pixel-based classification and correlate that to different phenotypes.
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The software will also do a neighbourhood enrichment analysis and give you a score so you can identify different spatial relationships and localizations between the cells present.
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So imaging mass cytometry, use of metal tags enables enhances sensitivity, enables faster panel design and optimisation.
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It results in reproducible data over a wide protein expression, wide dynamic range.
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You can do this with variable resolutions, flexible imaging modes and this enables your clinical trials and both multi-site and longitudinal and ultimately the goal is to better characterise the heterogeneities.
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You understand disease mechanisms and we can understand how patients respond to treatment and then better patient stratification.