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Andrea Miyahira: Hi, I'm Andrea Miyahira at the Prostate Cancer Foundation. Here with me are Doctors Irene Casanova-Salas and Joaquin Mateo from the Vall d'Hebron Institute of Oncology in Spain. They will share their recent publication in Cancer Cell titled "Circulating Tumor Extracellular Vesicles to Monitor Metastatic Prostate Cancer Genomic and Transcriptomic Evolution." Thank you both for joining me today.

Joaquin Mateo: Pleasure to be here, Andrea.

Irene Casanova-Salas: Thank you, Andrea, for the kind introduction. We are very glad to share with you the results of our recently published work, titled "Circulating Tumor Extracellular Vesicles to Monitor Metastatic Prostate Cancer Genomic and Transcriptomic Evolution." In this work, we aim to interrogate the molecular features of the tumor through the multi-omic profiling of extracellular vesicles. This class of vesicles is secreted by normal and tumor cells, and they are key for cancer cell communication. They are implicated in several cancer processes, such as tumor metastasis. These extracellular vesicles secreted by tumors are found in high numbers in plasma and also other fluids such as urine, CSF, or saliva. They are an interesting source of biomarkers because they contain several molecules as cargo, such as DNA, RNA, proteins, and lipids. However, the analysis of the DNA and RNA compartment of these vesicles has not been widely explored.

This is what we pursued in our recent study, where we performed genomic and transcriptomic profiling of EV-DNA and EV-RNA in vitro and in vivo models of metastatic prostate cancer and validated the results in two cohorts of metastatic prostate cancer patients undergoing systemic therapy with androgen receptor signaling inhibitors (ARSI) or taxane-based chemotherapy. Plasma samples were collected longitudinally upon treatment, and dual EV-DNA and RNA, as well as ctDNA extraction, were performed. We first characterized the DNA cargo within these vesicles and found that the DNA within these vesicles is protected from enzymatic degradation in contrast to ctDNA, and it is also bigger in size. We also performed copy number profiling in the DNA cargo of these vesicles and confirmed the tumor origin of these vesicles. Additionally, we found that the EV-DNA tumor fraction correlated with outcomes in these patients.

In addition to the genomic analysis, we were also interested in analyzing the RNA compartment in the same plasma samples. So we developed a novel poly(A)-based capture method to analyze the EV-RNA in these patients. We found that the transcriptomic profiling of these vesicles shows that the groups can be categorized according to their origin. We can see the PBMCs, the healthy volunteers, or the patients depending on the expression of the EV-RNA genes. This actually shows that the patient EV-RNA has a characteristic transcriptomic profile, which is characterized by high expression of some characteristic prostate-specific transcripts. We were also interested in understanding if this tumor EV-RNA cargo could be associated with patient-specific features. Interestingly, we found that those patients that harbor, for instance, AR amplification, which is a key driver gene in metastatic prostate cancer, also had higher expression of EV-RNA AR transcripts.

Similarly, we also found a correlation between TP53 mutation status and the levels of TP53 transcripts in EV-RNA. Those patients that harbor a deleterious mutation in TP53 had lower expression of EV-RNA as well. Finally, we were also interested in studying the role of EV-RNA as a longitudinal response and resistance biomarker. To do so, we studied the changes in EV-RNA upon therapy with either androgen receptor signaling inhibitors or taxane-based chemotherapy. We found that already at four weeks upon treatment, the EV-RNA from patients in the ARSI cohort showed a downregulation of cell proliferation pathways and androgen signaling pathways, and an enrichment of basal-like and neuroendocrine features. In the taxanes cohort, we found that the patients at four weeks of treatment had a downregulation of cell proliferation and apoptosis pathways, confirming that EV-RNA can actually capture these very early changes induced by the treatment.

Overall, with this work, we've seen that EV profiling enables longitudinal interrogation of metastatic prostate cancer, that the EV-DNA genomic profiling recapitulates tumor features and also associates with progression, that RESCUE, the method we developed for this EV-RNA protein coding analysis in circulating EVs, allows us to perform transcriptomic profiling in liquid biopsies, and that EV-RNA also indicates early tumor adaptation changes during therapy in a non-invasive manner. With this, I would like to thank you for the opportunity to share our work, and I will be happy to answer any questions.

Andrea Miyahira: Thank you so much for sharing this really interesting study with us. How does EV-DNA compare with ctDNA for predicting tumor burden and diagnosis of subtypes such as NEPC?

Irene Casanova-Salas: So I think I can start with this one, Joaquin. When we compare directly ctDNA and EV-DNA, we observe more or less the same sensitivity and specificity for the detection of mutations. However, what we think is more interesting about our work is that with the EV compartment, we can also interrogate the RNA expression, which allows us to perform a better patient stratification and subclassification for different types of tumors, such as neuroendocrine tumors. In a similar way to what the community is trying to do with the methylation patterns in ctDNA, we think that with the transcriptomic signatures, we could potentially also use that for patient stratification. So far, we have very few cases that were clearly diagnosed as neuroendocrine in our cohorts. However, we can see that those patients that were treated with ARSI eventually develop these more neuroendocrine or basal-like features, and we can capture that in the EV-RNA.

Andrea Miyahira: Okay, thank you. So what clinical and research applications would you anticipate for EVs in the future, Dr. Mateo?

Joaquin Mateo: Well, I think the short answer is we are still in the early days of understanding how much information we can get out of it. Clearly, the most value that we see here is the possibility of interrogating tumor RNA through a liquid biopsy. We know that cell-free DNA is a very powerful tool, but cell-free RNA is actually much more complicated because RNA degrades in circulation. So this is offering us the possibility of monitoring RNA profiles in liquid biopsies. And that's very important because, as we know, prostate cancer evolves not necessarily through the accumulation of mutations but primarily through transcriptomic changes. So I think that this could be the start of a new tool that allows us to monitor tumor adaptation through liquid biopsies.

Andrea Miyahira: Okay. So exciting. What advice would you give to others hoping to study EVs from cancer patients?

Irene Casanova-Salas: Yeah, I mean, I think it has a lot of potential, and it will really help us to develop these kinds of multi-omic approaches or composite biomarkers. I think some of the challenges with the EV field is the heterogeneity. So there are different types of EVs and different types of contents. Normal cells will also secrete EVs. So I think the challenge comes with the deconvolution of all that signal. In our work, we've developed some pipelines for cell deconvolution that will allow us to confirm the tumor origin. But for sure, it's a field that will need some extra research, and we are focusing some of our projects on that angle as well.

Andrea Miyahira: Okay. Thank you. And-

Joaquin Mateo: Yeah. If I may add, Andrea, also to say that you asked, "What advice can we give to others?" I mean, there was a learning curve here, and a lot of work was put in by Irene and the team into the development of the wet lab, but also the dry lab part of it. And we are more than happy to discuss with anyone interested in the field and to establish collaborations to push the field forward.

Andrea Miyahira: Okay, that's really wonderful to hear. And if you could just give more details on what are your next steps in these studies?

Irene Casanova-Salas: So I think this was a very kind of pilot and pioneer study in the field of transcriptomic analysis in liquid biopsies. Our results are very exciting, but we really want to validate these in bigger cohorts of patients and also to validate if we see similar results when we apply them to broader treatment types. So I think that's probably the first goal that we would like to explore. On a separate note, from the functional perspective, we also want to do some work in our PDX platform in the lab to confirm the changes that we observe in treatment adaptation and how we can better target them in the future.

Joaquin Mateo: Yeah, I think that's an important point because we only have so many samples from patients. So we have very rigid time points, and if we are studying evolution, we want to really understand what is the effect of the treatment in the early days. So probably here, the PDX models are going to help us to understand the changes upon treatment exposure in a more precise way.

Andrea Miyahira: Okay. Well, thank you both for coming on today and sharing this really exciting study with us.

Joaquin Mateo: Thank you.

Andrea Miyahira: Thank you.