Pharmacoscopy Platform Creates Automated, Personalized Selection of Treatment Options for Patients With Multiple Myeloma

Multiple myeloma treatment selection is critical due to the potential for developing treatment resistance over time to the currently prescribed drugs.

Pharmacy Times® Berend Snijder, PhD, SNF professor at ETH Zurich, discusses the Snijder Laboratory’s research investigating multiple myeloma, an incurable form of blood cancer. The treatment of multiple myeloma can be particularly complex because the cells can become resistant over time to the currently prescribed drugs.

To address the complexity of the treatment selection process, Snijder developed the pharmacoscopy platform, which is a fully automated platform that tests hundreds of drugs and combinations of drugs on bone marrow biopsies in a high-​throughput process. The test results then show which treatment options combined with existing therapeutics could be possible for specific patients, personalizing the treatment selection process based on the needs of that patient.

Pharmacy Times: What is pharmacoscopy?

Berend Snijder, PhD: So, pharmacoscopy is a platform to really see how patient cells respond to hundreds of possible treatment options. And it's based on real-time patient biopsy. So, we take live patient cells, we analyze live patient cells that reach us from the clinic, and then we basically perform high throughput drug screens on those. That means that we divide these patient cells into hundreds of small little chambers and circle 384 well plates and incubate these cells with possible drugs or drug combinations—those could be small compounds, could be antibody cell-based therapies. And after a certain amount of incubation, we perform a multiplex immunofluorescence assay on the cells so that we can stain individual markers expressed on each individual cell originated in the patient biopsy, and then we perform high throughput automated microscopy. So, these are microscopes that just take, in an automated fashion, thousands of pictures of all of the cells in all of these wells. And then we use single-cell image analysis and deep learning to quantify the nature and identity of each cell and its response to all of those different drugs. So, in essence, it's a very efficient way to see how patient cells respond to hundreds of drug treatment options.

Pharmacy Times: How is pharmacoscopy currently being used in oncology?

Snijder: Yeah, so pharmacoscopy has a few really fascinating applications in oncology. One is just basic research. It is sort of the first time that we can really, with single cell resolution, measure the response of each individual cell in a patient biopsy to hundreds of treatment options, right? So, we can learn about drug resistance and drug sensitivity mechanisms, especially when we integrate it with genetics and molecular profiling. We actually also start to realize that because we're sort of the first time that we're, at a massive scale, taking pictures of patient cells—and not just a handful of cells, but millions if not billions of patient cells—it’s a new way to analyze cell phenotypes and morphologies and look at, for instance, cancer heterogeneity in a morphological and functional way. It also has pharma applications, drug discovery and biomarker discovery. So, of course we could see how patient cells respond to known therapies and treatment options, but you can also measure how the cells respond to possible new drugs or drug combinations. So, we can identify the best drugs for any one individual patient and discover automatically or in an integrated fashion for any new drug or drug compounds, what would be the ideal patient set to target with it, to try to treat with a new drug. And we can try to identify biomarkers—that’s molecular biomarkers—that predict whether a patient would respond to treatment or not.

And then the third big use of pharmacoscopy is really precision medicine. So we're running clinical trials, and we’ve completed several clinical trials that really show that when you treat a patient—and this early work was particularly in blood cancers—that show that you can actually treat patients based on the drug recommendations that we generate. And thus far, from one in a first study in mostly lymphomas, and now recently in another feasibility trial, mostly on AML, so leukemias, we can actually show that when you do this, you guide patient treatment based on their own drug responses, that you actually see promising signs of efficacy. So, the pharmacoscopy platform actually identifies effective treatments for individual patients.

Pharmacy Times: How does pharmacoscopy allow for high-throughput when screening the efficacy of various multiple myeloma treatments on patient’s cancer cells?

Image Credit: Adobe Stock - LASZLO

Image Credit: Adobe Stock - LASZLO

Snijder: Yeah, so the infrastructure that pharmacoscopy uses is really the infrastructure that pharma companies have been using already for decades to perform high throughput drug screens. It's 384 well plates, automated microscopy and robotics to guide all of that. And then we've extended that sort of previously existing infrastructure to be compatible with patient biopsies. That is trickier than classical cell line or organized model systems, because now we're talking about really complex cellular populations, multicellular systems, and adherent and non-adherent cells together in the same wells, so these all needed various amounts of adaptation, but in essence, that infrastructure is high throughput and highly automated. So, that means that we could either screen you know, a few thousand possible wells per patient sample or we can screen, depending on the biopsy size, many more, sort of in the order of tens to hundreds of thousands of compounds on a few patient cells or a few patient samples. In the context of a reason myeloma study, what we done was analyzed response to 60 drugs or drug combinations across 100 patient samples. Now, that is 60 times 100, 6000 possible treatments or data points. But actually each one of those, of course, is technical replicate wells and different concentrations of the drugs that we tested. So in that particular study, we analyzed over 70,000 individual assays, right, where one assay is one well, because the number of patient cells responding to a certain drug. And in a data set, we analyzed over 700 million individual patient cells and that's really starting to show how this platform is scalable on high throughput.

Pharmacy Times: What was the impetus for developing the pharmacoscopy screening method for multiple myeloma treatments?

Snijder: Yeah, so there were 2 sort of backgrounds. On the one hand, we've done, we have completed a sort of, first of its kind functional precision medicine trial, mostly in lymphoma patients where we've been so encouraging. This was the [sic] one trial. And here we saw encouraging patient responses to pharmacoscopy-guided treatments. We started a similar study now in my lab in Switzerland, on females [sic] with acute myeloid leukemias, and we hadn't really addressed multiple myeloma, really, the third sort of branch of blood cancers. And multiple myeloma has some unique aspects to it in the sense that there are a lot of approved treatment options for multiple myeloma. So, the rationale we and our clinical partners at the University Hospital Zurich, came with was to say, well, we have all these approved treatment options, but it's not always clear which ones of those approved treatment options would achieve an ideal effect or outcome for each individual patient. At the same time, multiple myeloma is known to be highly heterogeneous. So it's genetically heterogeneous, molecularly and cellularly heterogeneous. So it was really that combination of there are a lot of approved treatments, but they're routinely combined in up to 4 different drug combinations and this clinical and molecular heterogeneity of multiple myeloma that for us was really the trigger to say, let's now do this particular study.

Pharmacy Times: Why did the pharmacoscopy platform need to be specifically adapted to multiple myeloma?

Snijder: What we quickly realized was that, basically, we had to do quite some adaptation in order to get this to work for multiple myeloma. And it's sort of, it's been a big part of the roller coaster of developing this platform because really, what it means is that we're the first time to start to quantify the cellular single cell morphologies and phenotypes or hundreds of millions of patient cells across all of the stages of the patient's disease. And what that means is that you're confronted with a phenotypic and cellular heterogeneity that hasn't quite been embraced before. We're talking about orders of magnitude larger than that has been described as single cell RNA sequencing and of course, more quantitative than traditional methods like flow cytometry. So we really had to adapt and solve that question of cellular heterogeneity. One thing that we discovered is that there's a simple morphological phenotype of particularly malignant myeloma cells. And then we had to basically train deep learning algorithms to identify specifically the drug responses of that subset of patient cells. And what we found was that if we measured their drug response to their drug resistance, we actually were predictive of the drug responses that we measured in the clinic. So, that was a really key adaptation of this platform to handle that cellular and phenotypic heterogeneity myeloma and make the platform clinically predictive.

Pharmacy Times: How did pharmacoscopy help to identify new, more effective treatment options for patients?

Snijder: Because sort of intrinsic to the goals of the study was, on the one hand, we wanted to identify new effective treatment options, particularly new combinations of existing drugs. But at the same time, the rationale was that we might be more effective with existing treatment options, right? So, especially in a disease like multiple myeloma, where you are first, second, third line in the proteasome inhibitors, as well as immunotherapy options, all of which are given in various combinations. Part of the rationale was to say, can we be more effective, even with the older drugs? And I think the data we have generated so far is highly encouraging, in the sense that it looks like we've been able to do both. Of course, it still needs further validation—ideally, actually, interventional trials testing some of these new or optimized treatment regimes.

But at least in this observational setting, one thing we've discovered is that for instance, in multiple myeloma, one of the key questions is, at what point and which patients might benefit most from the existing immunotherapies. These are elotuzumab and daratumumab, for instance. And one of the things we found looking across the whole cohort, so over 100 patient samples analyzed for their responses to immunotherapy but then also follow up, those patients were followed in the clinic to see how well they responded in the clinic to the treatments. And one of the things we found was that it appears there's a simple stratification scheme that just looks at the composition of a patient bone marrow biopsy and, particularly for patients after their first-line treatment, seems to predict whether they will benefit from immunotherapy such as elotuzumab or daratumumab or not. So that is, right, we're doing dysfunctional drug screening platform but then basically, we boil down to a very simple signature that would predict in the clinic whether a patient might benefit from the treatment or not. This needs further validation, but it's a highly encouraging signal.

That's existing treatments. We've also found promising ex vivo sensitivity to novel therapy combinations. And I think a few to highlight there are the checkpoint inhibitors. So, in our study, one thing we found is that there is novel checkpoint inhibitor combinations that seem to work specifically for subsets of patients. Now, this is a bit contentious, and of course, it needs further validation in these extra clinical trials. But the notion that there are a subset of patients that might benefit from checkpoint inhibitor treatment in combination with other therapies, in this case, particularly with the elotuzumab and daratumumab is a highly exciting prospect. And one of the things we found was, for instance, that p53 mutations that typically have a poor prognosis actually seemed to respond well to one such novel immunotherapy combination.

Pharmacy Times: Is use of pharmacoscopy in identifying personalized treatment options for patients scalable for larger cancer centers?

Snijder: Yeah, it's really the key question. And we're increasingly starting to show that this platform is predictive of clinical response and really, if this works, and by all means it looks like it does work, the next question is, how do we make sure that as many patients can benefit from using insights, right? And, indeed, that's a question that we've been working on for a long time now. This simple answer is yes and no. So yes, the platform is scalable. Even the relatively simple infrastructure we've set up in my academic lab has the capacity to test thousands of patients every year. And that is not, you know, an industrial lab, that is just an academic lab, right? So, with relatively modest investment, you could actually scale up this infrastructure, set it up around sites and have the capacity to test hundreds of thousands of patients. But the question is, of course, how do you pay for this? Is this reimbursed? Or does this have to be commercially viable and profitable? And that's then, of course, puts different constraints on the platform. If it does come down to being commercially viable, I think the platform could do with more development. And this is something we're actively working on. Sort of what we consider to be the next generation of the platform has reduced footprint both in size as well as in upfront costs, cheaper, relies on cheaper infrastructure, while maintaining that high clinical predictive power while actually increasing the robustness of the assay. So I think here, what we've developed so far is a wonderful proof of concept that would need further development in order to really bring that scalability to around the world, basically.

Pharmacy Times: What are some further applications for pharmacoscopy under investigation?

Snijder: It really started with the blood cancers, but of course, solid tumors are the majority of cancer deaths around the world. So, one thing we're very actively working on in the lab is adapting this technology to also work on solid tumors. And the challenges are real. It's a difference whether you have a essentially fluid bone marrow biopsy or aspirates, or peripheral blood draw versus a piece of solid tumor tissue. However, what we're seeing are really encouraging signals that the same approach that directly testing a patient biopsy from the patient in this platform also works with solid tumors. And that means we can now do all the things that we’re showing in blood cancer cells on solid tumors, even to the extent that we now just received funding to start a first late-stage clinical trial where we see if we can help guide patient treatments for patients with various non-solid tumors, including brain metastases. So those are, that's one part of the sort of under-investigation use of pharmacoscopy.

The other branch is really immunology. So first, every tumor biopsy that we get, whether it's a peripheral blood sample or solid tumor, pretty much always comes with tumor intrinsic immune cells that we can investigate their function and response. And in our myeloma study, we could already show that the responses we measure to immunotherapies are predictive of clinical response. And that therefore means we can now better investigate what are the unique properties of responding or non-responding tumors to immunotherapies. And the nice—and that can be basically extended to even just say, you know, the immune system of otherwise healthy individuals, so not restricted to cancer patients. And a nice example of this work we recently published last year in Nature was a wonderful collaboration with Gavin Wright and Jarrod Shilts at the time at the Wellcome Sanger in the UK. And what we did together was to characterize how proteins express on the surface of immune cells, when we add them as a perturbation by pharmacoscopy to EDMC cultures, how they actually systematically change the cells or contents of those immune cells. So, it actually is a way to systematically, functionally perturb a single-cell resolution in the immune system of individual people. And so, on the one hand solid tumors, the other hand immune system, and I think taking a step back, the opportunity that pharmacoscopy really brings is that it's a new way to, for the first time, characterize how any person, any individual responds functionally and morphologically with single cell resolution to any set of perturbations.

So we're, together with the Red Cross, essentially, here in Switzerland, we're also characterizing how individual people function in response to a set of perturbations—it could be drugs, could be food supplements, could be vaccines—and really try to get a handle on what is the kind of human variability that we start measuring when we don't just describe people based on their genome or their molecular makeup, but actually on their functional response. And that is sort of a conceptual level, advanced that pharmacotherapy now allows.

Pharmacy Times: Any closing thoughts?

Snijder: The future's bright. There's a lot of work needed, that's clear. It is. But we're also sort of kids in a candy store, right? Everywhere we look we find interesting signals and there are new discoveries to be made, be it on cellular phenotypic heterogeneity, functional responses, discovering new drugs and biomarkers or actually just doing the clinical trials and showing that this platform can help patients. So, it's a very satisfying area to work in.

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