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Podcast "Caring & Sharing"

This transcript was very slightly edited for legibility.

USS: Welcome to Caring and Sharing -the Advanced Oncology podcast. My name is Uta Schmidt-Straßburger, and I am meeting Dr. Joe Lennerz today. Dr. Lennerz is not only a board-certified pathologist, but he is also serving as the chief scientific officer of BostonGene. Welcome!

JL: Hi, Uta.

USS: You recently pointed my attention to two papers that were published by scientists from your company, and both of them made it to the title pages of the respective journals. One was published in Cancer Cell, and the other one was published in Gastroenterology. And both of them are dealing with profiling of tumor samples in the widest sense. Your colleagues came up with classifications, and I would like to know more about these two different approaches.

JL: Absolutely. Which one should we start with?

USS: Since we are in oncology, and I'm really interested in immunology, since this is my minor, I would like to start with the Cancer Cell one.

JL: Sure. Of course, we are very proud that both made it to the cover story, and that's, I don't know, an old school accomplishment, I would call it, but it's still very exciting. So first off, the Cancer Cell story itself is basically presenting a platform where we're providing an immunoprofiling essay from blood.

Now, at first glance, that sounds fairly simple because every blood draw that you get in your physician office is counting the lymphocytes. And we do the same thing, but in a completely higher complexity fashion. We use several – sort of – advanced laboratory techniques, flow cytometry, high complexity, multicolor labeling in multiple panels that are partially overlapping, so we can reconstruct basically the entire immune system across close to 300 markers, partially overlapping, where you can then get every single subtype counted.

One might call this an actual real complete blood count for the immune system. And then, these highly diverse and complex populations are pushed through an artificial intelligence tool that then provides the output.

Now, before we get to the output, of course, the idea is not new. People have characterized the immune system for decades, and to most immunologists, there's nothing generically new in the paper in terms of what cells we characterize, but what Michael Goldberg, who's the PI and the senior author on the paper and his team, have done is try to bridge this incredible complexity of the immune system, and the rather, I call it archaic way of counting them,  for sites and clinical practice, and we try to land a test somewhere in the middle where it is understandable by a physician, but accounts for the inherent complexity of the immune system.

So that is the, let's say, the position of where that paper is in terms of like the scope.

In terms of the content, we examined a series of healthy and cancerous patients across all kinds of solid human types and performed unsupervised clustering to identify basically what groups of immune cell reactions and numbers can we find in different states. And the very first is healthy versus cancer.

By doing this, let's call it clustering of cell types, we came up with five groups. So they're called G1, G2, G3, G4, G5 and we call them immunotypes or we could call them immunoprofiles. And the easiest way to think about it is like, you know, an old equalizer on the stereo, you know, the visual like the base and the mid base and the, you know, the mids and the travel. Depending on which, let's call it immunotype, is highest in terms of numbers across these cell populations. We would classify a patient as G1 or G2 or G3 or G4 or G5, et cetera, largely oversimplified.

What can you use this for? Well, we intended to use these patterns to predict responses to  mmunotherapy. Now we tested that or we portrayed this in the paper across several populations.

Let's just stop there and say that that is the rough overview of this paper.

USS: All right. So I've got two questions. The first one is how is the panel different from any panel that our colleagues at the Internal Medicine III [hematology and oncology clinic] are running? You know, there is a FACS core facility for the immune-characterization of leukemia and lymphoma cells? And two, what kind of immunotherapy are you talking about?

JL: There's nothing at generically, let's say, ultra-proprietary. The panels are designed, and the combinations of antibodies, of course, have to be validated. The details are in the paper. The markers are the common pattern anti bodies. So, you know, from whatever CD14, CCR3, common markers that - I think - are available in most well-equipped flow cytometry labs, the combinations in which marker we use for which panel and how they're overlapping to fully reconstruct, let's say, the entire landscape.

That's, I would call it a little bit of a trick because you have to make sure that you don't have bleeding over in other channels. So that's a trick. But the markers and the antibodies used to label the cells - I would call them very broad panels - but they're established and well verifiable. There's no magic there, but it is the magic of putting it together in those specific subtypes.

Just quickly, it's not just lymphocytes, there's also monocytes, macrophages, and they combine together into different groups. And by the way, all of this, including the proprietary combination of antibodies is publicly available and this has completely been released with the Cancer Cell paper. So: definitely sharing that approach.

To your second question, which immunotherapies, I think in this particular study, there were several cancer types. Let's pick, head-and- neck cancer, or squamous cell carcinoma as an example, and an array of different therapies, like pembrolizumab. And the idea is, of course, that in prior work, where we characterized the immune system in the tumor, called tumor microenvironment, we get to that in the second paper, we learned a lot about which cells are actually residing within the tumor microenvironment in the tumor and we characterized them very well with whole-exome level transcriptomics. So, we know which cells are in the tumor, but they have to get there somehow, and they come from the bone marrow and they traverse through the blood.

So, we thought, well, if we know that the immune response to, let's say, immune checkpoint inhibitors is different, depending on the different cell types in the microenvironment, it is probable that these different cells - when they traverse through the blood - would serve a similar surrogate function. And that surrogate function, we assessed across multiple solid tumors, including, for example, pancreatic cancer and/ or head and neck squamous cell cancer, melanoma, et cetera, to assess whether we can detect that in the blood. And indeed: We can.

Is it a perfect predictor? That depends on the setting, but in terms of responders versus non-responders, we definitely see different immunotypes in the peripheral blood.

USS: I think already this is a great advantage, because in my understanding, the response to the application of any of the known checkpoint inhibitors is far from being really predictable, so any additional information is a plus. And this is what appealed to me and this paper, I must admit.

JL: Maybe one quick comment. So, we have a cohort of, let's say, 36 or, I think, 35 or 36 head and neck squamous cell carcinomas treated with nivolumab. And the response rate is almost a given, but what I think is even more powerful to predict the response is that this tool, if you call it that, allows you to monitor a patient while being on treatment. And we found it very fascinating that some of the immune phenotypes that we find in the peripheral blood under therapy changes.

For example, there's a much higher rate of non-responders in the G3, 4, and 5 group. And that makes intuitive sense. You know what memory CD8 cells are there and what NKT cells are there. Biologically, that makes sense, but it is, I believe, a very interesting tool that allows oncologists to monitor at multiple time points over the treatment course. Of course, we would like to predict things like severe adverse events as well. And many of those, similar to cytokine release syndrome etc. are mediated by that immune system. There's definitely [room for] other applications of this technology.

USS: It remains fascinating.

JL: Absolutely. And maybe who knows, we may be able to apply this to other settings as well.

USS: I think once newer immunotherapeutic approaches emerge and become accessible to the wider public, it might be a tool to stratify treatments and go from one step to the other. I think, yes, it does make sense. And again, this appealed to me.

Now, the other one that you described appealed to me for an entirely different reason, because I'm coming more from the solid tumors. And your team is describing a transcriptomic-based microenvironment classification in pancreatic ductal adenocarcinoma. Would you like to tell us more about this paper?

JL: Yes, absolutely. This was a collaboration with the Medical College of Wisconsin and, of course, the BostonGene team. So: big, big props to both teams.

What is, of course, an advantage of when you obtain high-level complexity data, like transcriptomic profiling at the exome level, is that you can get really deep insights into the tumor biology. So, we pooled, in this case, I think, over 14 or so publicly available PDAC datasets for a total of 1600 patients. And it is known already that the microenvironment in pancreatic cancer plays a very important role for various reasons.

First, it's, of course, that pancreatic cancer – being extremely aggressive and effectively most of the time, diagnosed in advanced stage – because it's so harmless, there's no pain. Most patients present with diffuse symptoms, but it's usually not like, oh, yeah, that's the common presentation. I think the only presentation that I remember from medical school is the painless jaundice, until proven otherwise, that would be pancreatic cancer, and then you have to exclude it.

So clearly, that local, regional environment changes as pancreatic cancer develops. There's almost like a permissive function to it and fantastic work from animal studies has led to this. Now, in this publication, we described that microenvironment a little bit more and having so many cases and such an in-depth analysis building on what we just discussed, the tumor microenvironment, gives us a different way to look at those tumors and that's presented in that study.

USS: So what conclusions do you derive from these findings?

JL: I mean, the key finding is that there were, sort of, four subtypes and they have letters and abbreviations, but in large oversimplification: Some have more immune cells, some have more fibrosis, and some have less immune cells. That's the very quick.  The second subtype, which is immune enriched and fibrotic, and these have then different characteristics. When you look at, for example, the fibrotic and the immune depleted subtypes.

So clearly, where let's just imply some intent of the human body, where the body of the cancer patient

is reacting with fibrosis –  so fibroblast reaction – and those where there's no immune cells, they had a significantly shorter overall survival for various reasons. Maybe that's a later stage, but the key element is that these have prognostic relevance, which is in a aggressive disease as pancreatic cancer, an extremely helpful, let's say, biomarker, but it's a prognostic biomarker. How to apply this in clinic remains to be determined.

The other part which was very interesting is the immune enriched subtypes, for example, had a much higher rate of lung metastasis. Now, at first glance, that maybe like: So what? Right, like how are you going to use this?

During presentation of advanced stage cancers, it is a question, of course, to correlate certain things. A very common pattern is the phenotype of patients with lung cancer that are typically non- smokers, which has a completely different subset. So many trials can be derived from that. We anticipate that by delineating, for example, that the immune depleted subtype has more liver metastasis versus the immune enriched subtype has more lung metastasis.

We start getting into this area, or let's call it the era of pancreatic cancer, where we can discern different subtypes of this extremely aggressive disease. So, in combination, I believe the characterization of the transcriptome and sub-setting pancreatic cancer into these distinct buckets with different phenotypes, locoregional disease patterns, and different prognosis might actually lead to a distinction of that large group of pancreatic ductal adenocarcinoma, which is currently lumped together into one ball. And final statement there is, of course, untreated versus treated tumors are also entirely different, but now we have an approach to characterize the microenvironment in those different settings.

USS: Thank you.

JL: Of course.

USS: Are there any immediate studies that might be of interest to use the classifications, either one of them, that come to your mind?

JL: Yeah, so what I think needed is, of course, a pathway to deliver this clinically, both studies. I think the one might be a bit easier than the other. Let's say for pancreatic cancer, this classification, we can provide a so-called additional information, for example, on our reports.

Now, how to use this requires, of course, clinical trials and a savvy clinician who will find these patterns and knows what to do with it. But when it comes to trial design and how to apply this to, let's say, novel therapies, for example, radio-sensitivity or immune checkpoint inhibitor therapies, I think having a prognostic and biologically sound approach is probably better than … Basically it's a biomarker. Biomarker stratification, I think, has been proven. Whether that will be successful or not is currently under investigation, of course.

Now, the second part, or the first paper that we discussed with Cancer Cell, that is a bit earlier. I mean, it's understandable. We're currently designing what a report would look like, but that would require some prospective or additional prospective studies to delineate.

Does this really work? I mean, the immune profiling in all cancers? That's a strange hypothesis to make. The key element there, that I believe makes a very strong argument, is: In contrast to circulating tumor DNA, which definitely has a value and it's currently being delineated what the clinical utility is. Here, instead of monitoring fragments of dead and mutant DNA, we're actually monitoring a living system that reacts and responds to various things.

But if a patient now has radiation cystitis and has other immune reactions, those are probable confounders to what signatures we have, which comes back to the point of really understanding this

will require, of course, a lot of studies and a lot more data, but I believe it might be considered a new modality of response or treatment monitoring over time.

So definitely some very good hooks to how to get this into clinical practice. But as always, once you solve one problem, there's like two or three others appearing. That makes it exciting, but I think both are relevant tools for important questions in cancer treatment realm.

USS: it's good to know that there's still something that keeps the scientists going.

JL: [laughing] Absolutely.

USS: Thanks a lot for this insight and if you liked the podcast, just like and subscribe. Thank you, bye-bye.

JL: Thank you, bye-bye.







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