Irene Ghobrial:

Good morning, good afternoon everyone. We are here live in Myeloma 2022 for the first time seeing people in person, discussing amazing data on multiple myeloma. And I’m joined here today by an amazing panel to talk mainly about single-cell sequencing and how can we understand drug resistance, response to therapy, mechanisms in general of both immune and cancer cells, and how can we understand it at a deeper level by the single-cell level? So maybe everyone can introduce themselves and then we can start the discussion. Rodger?

Rodger Tiedemann:

I’m Rodger Tiedemann from the Princess Margaret Cancer Center.

Irene Ghobrial:

Eileen.

Eileen Boyle:

I’m Eileen Boyle from NYU Langone in New York.

Irene Ghobrial:

Yael.

Yael Cohen:

Yael Cohen from the Tel Aviv Medical Center in Israel.

Irene Ghobrial:

Perfect. So again, an amazing time today. And we’re back in person, which is wonderful. Rodger, do you want to tell us more about your 1q data? Fascinating data. I think we were all impressed to see that at a much deeper level at the single-cell level.

Rodger Tiedemann:

Thank you. Well, we studied the role of chromosome 1q, which is one of the most common copy number aberrations in human cancer, and in multiple myeloma is the most common adverse prognostic marker. It’s difficult to study the effects of gain of chromosome 1q on gene expression and cellular programs using whole tumors because you end up having to compare different tumors with different genetic backgrounds. So we studied this by single-cell RNA sequencing. We looked at the effects of gain of chromosome 1q in single cells. And to cut a long story short, we basically demonstrated that gain of chromosome 1q is causing upregulation of oxidative phosphorylation in myeloma cells, and downregulating their energy stress, or alleviating their energy stress. And it’s also suppressing type I interferon responses and inflammation, and tumor immunity. And so there’s dual benefits from gain of chromosome 1q in myeloma, a metabolic effect that improves the efficiency of the cells and alleviates energy stress, and an ability to escape from the immune system. And I think these two facets have a significant impact on tumor fitness and on patient survival.

Irene Ghobrial:

Do you see special therapy as we go into precision medicine and understanding how to personalize therapy for individual patients, do you see a 1q therapy coming along with these data that you’re presenting?

Rodger Tiedemann:

I think there’s really important ramifications from this discovery. I think targeting oxidative phosphorylation in high-risk myelomas is going to be useful, particularly in 1q myeloma. But I can tell you it’s also present in some of the other high-risk myelomas, increased oxidative phosphorylation. And I think the understanding again that 1q helps the tumor cells escape the immune system is going to have an impact on how we apply immunotherapy to myeloma patients in the future.

Irene Ghobrial:

Yeah, absolutely. Eileen, amazing data again, coming into, I think both of you, the studies of pre, post-therapy, how can we understand basically selection, drug resistance, mechanisms of interaction as we go on with therapy? Do you mind if you tell us about your data?

Eileen Boyle:

So I think it is important to look at the interaction between the microenvironment and the tumor and the effect within the context of one single therapy. And what we were able to do, in the context of Dara-KRd treated patients, look at both the tumor characteristics with whole genome sequencing and the microenvironment both at the RNA level and protein level, and able to understand the interaction between the tumor cells and the microenvironment. And we were able for the first time to show that early on, before treatment, we were able to see markers that would indicate a high-risk microenvironment, where patients are not likely to respond to therapy. And also more favorable microenvironments where patients will do well and achieve sustained MRD negativity.

Irene Ghobrial:

And I think that’s exciting to start looking at that picture of both tumor and immune cells in response to therapy and predicting who will do well and who will not. Where do you see that moving in the future? It’s hard to bring single cell for every clinical trial. We’re all going to be unable to do that. And we cannot even bring it to the clinics. How do you see that moving forward?

Eileen Boyle:

So one of the things we are trying to do currently is translate these findings into more clinically applicable methods using things such as flow cytometry, for instance, and designing specific risk panels that will indicate, would be an additional criteria to use this information in prognostic information for patients.

Irene Ghobrial:

That’s wonderful. So going to more drug resistance and more understanding of how to treat patients in a selective way, Yael, do you mind if you tell us about your study?

Yael Cohen:

Sure. So when we were looking at the very tough populations, those who are primary refractory and they fail on the induction regimen, and we were using single-cell analysis to try and understand what is the signature array that really drives this resistance. So we compare them to newly diagnosed myeloma patients. And in fact, we were able to come up with a signature of resistance and to explore the pathways that were affected. And we found various pathways in genes that were up and downregulated. Some who were described also previously by others, and some were novel to do with the proteasome machinery, to do with protein folding, mitochondrial respiration, and ER stress. And then we were able to actually validate this signature also on the CoMMpass dataset to show that it’s really providing a prognostic information beyond that that was previously known. And we were able to find some potential new targets and we experimented with them in the lab. And the initial results showed that indeed this might in some way reverse resistance to carfilzomib. So those are the key findings. Also, I think the longitudinal data that we had was able to look at clonal dynamics and actually we can see when patients are developing a progression under treatment stress we can see how their clones are sometimes shifting, a new clone takes dominance, and to see which are the pathways that are involved in this new clone. So this could hopefully, in the future, help us maybe personalize the treatment with having this deeper understanding of what stands behind the resistance.

Irene Ghobrial:

Yeah, absolutely. And I wonder, was this specific to carfilzomib, or do you think all proteasome inhibitors would have this drug resistance mechanism? And should we be looking for it before we start treating our patients?

Yael Cohen:

Right. So that’s an excellent question. I think to some extent it’s probably something that might be common to other proteasome inhibitors. When we looked at our signature, for example, on the CoMMpass. So it seemed to be very much predictive of a prognosis, but this is further work we need to still look into. So hopefully we will be doing that in the future.

Irene Ghobrial:

And it’s amazing how much single-cell can really bring that new level of understanding of what happens, not only on therapy, but after therapy and how we can understand better the disease, both the cancer cells and the immune cells, specific genetic subtypes, specific therapies, understanding drug resistance. So lots and lots of things to come into that. And I think, I brought it from [inaudible], looking at it at the fruits level, not at the smoothie level. So really going back to that understanding of at the single-cell level, what happens? DNA, RNA, where do we see the technologies? I mean, we’re all doing now mainly transcriptional RNA sequencing, but there are so many multiomic methods, there are so many other things. Where do we see this going in the future?

Rodger Tiedemann:

Yeah. We try to bring multiomics into our data a little bit by developing that inferred sciCNV method so that we can look at DNA copy numbers and transcriptomics. But I think the field is broadening in a big way and we definitely need to move into multiomics because you need to look at the parallels between protein expression and RNA transcription and DNA copy number, and beyond that even. So I think, you know, CITE-seq is where everyone’s moving at the moment to bring in the protein expression, I think that’s going to be really helpful for the next generation of identifying targets and the next generation of immunotherapeutics, understanding those [inaudible] protein expressions. But I do think we can go beyond that with even more multiomics. We’re going to be able to, in the near future, be able to look at everything in a single-cell.

Irene Ghobrial:

Yeah. And you did a lot of CITE-seq too. The information you gained from both proteomics basically, or tagging proteins as well as genomic data and transcriptional data. How much did that improve on your understanding of the rare cell events and so on?

Eileen Boyle:

So I think it was a very reassuring approach where we could confirm findings from the single-cell RNA-seq at the protein level. And sometimes we did find some mismatch which led to more questioning and require further studies to understand really how do you get from the RNA to the protein? Are there other mechanisms involved and what other levels of genomic information we need to get from that.

Irene Ghobrial:

Yeah. And we are now hopefully going into spatial modalities and getting to the next level of not only DNA, RNA epigenetics with ATAC-seq or methylation at the single-cell level or proteomics at the single-cell level, but also putting it in the context of where does it localize in the microenvironment. Are there contexts that you think that would be very critical, especially in your data of understanding resistance?

Yael Cohen:

Yeah, so I think we’ve seen very interesting data today about the microenvironment and about the focal lesions and the spatial variability and those focal lesions being like the source for a progression. And I think that’s a really important topic. You know, in the clinic we really get little data from that, from the way that we tap the data. It’s not something that we really have in our fingertips. And it is so important. So I think further thought should be given on how to get to that data.

Irene Ghobrial:

Perfect. Any final thoughts, things to think of for the future?

Yael Cohen:

I think this tool of single-cell, it’s like having a molecular microscope and it really gives us a whole new dimension of looking at the disease and what is happening. So to me, it’s intriguing how we could get this in some way to the clinic. There are right now hold backs as far as logistics and costs, but I’m sure we will get there in some point.

Irene Ghobrial:

Yeah. Absolutely. And it’s amazing how much data was presented today, and hopefully in the future about understanding mechanisms of disease progression from MGUS and smoldering myeloma, mechanisms of response to therapy, genetic subtypes, and hopefully bringing single-cell level to the patients. So with that, thank you so much. And we hope to see you next year again with us, Myeloma 2023. Thank you.