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Pedro Barata: Hello and welcome. My name is Pedro Barata. I'm a GU Medical Oncologist at Case Western University, Seidman Cancer Center in Cleveland, Ohio. It's my true pleasure to be joined today by Dr. Ritesh Kotecha. He's a friend. How are you Ritesh?

Ritesh Kotecha: Very well, thank you Pedro.

Pedro Barata: Ritesh is a Medical Oncologist, a guru in RCC, working at Memorial Sloan Kettering, New York. And first Ritesh congrats on your great job presenting this work at KCA in genetic ancestry and molecular correlations in patients with kidney cancer. Great job.

Ritesh Kotecha: Thank you so much. And really thank you for the opportunity to shed more light onto our work.

Pedro Barata: Absolutely. And I really feel your work is important for a number of reasons and maybe I'll start there, right? I mean this is a huge effort. I mean for those of us who are less familiar with the Memorial Sloan Kettering impact and so forth. You guys have a very comprehensive way of assessing genomics in patients who go and see you, right? So I guess when you look at these work, particularly from what I could gather, you have over 900 patients included in here and that's quite remarkable, right?. And then you define these, have these germline defined ancestral genetic groups and you kind of go into do significant analysis on that. Maybe I'll stop here and ask you, can you walk us through the process? How do you get this data from everybody? I mean, what are you getting from them? Just blood, a couple of tubes? You're doing germline, you're doing something else. Walk us through the process of getting this genomic information for the folks who are listening to this.

Ritesh Kotecha: No, no, that's great. So here at MSK we've had a really strong push to do both somatic and germline testing for patients and germline testing, particularly for those patients who qualify for germline testing, which are patients we think have at least to some degree high risk renal cell cancer. And we define that really in patients who are young. So age under 46, patients who have bilateral kidney cancers. And really a strong push also for patients who have non-clear RCC as well, which will come out through our analysis and our discussion here. From the somatic side, we also have had a really strong push, particularly with our MSK impact assay to do also somatic testing, molecular testing. And really that's some work that, and our group has sort of shown that also integrating sometimes somatic data particularly can help with assigning risk. So we know that patients, for example with PVM1 or BAC1 or TP53, if we add that to the clinical risk criteria that designated from IMDC or MSKCC, knowing those mutations actually upfront can also help from a risk perspective.

So that's why there's been a really strong push here to do this type of testing. Genetic ancestry has been sort of a new development of using some of that data to provide more context for our patient population. Obviously Pedro, disparities for patients with kidney cancers has been really a longstanding challenge for us as a field and as a group to optimize care for our patients. And we do know that there are patients that are differences between how patients might present clinically or cancer specific outcomes that are different between different race or ethnic groups. And so we wanted to have a deeper dive into that from the genetic ancestry perspective. So just to take everyone a step back, genetic ancestry really refers to an individual genetic origin and really incorporates the admixture result from really genetic exchange across different ancestries.

And the way we're able to really infer that data from our MSK impact is use the same markers that are utilized from germline analysis in our MSK impact testing where we basically looked at the same captured regions that are in our targeted panel and we're able to then infer an ancestry for a specific patient.

When we look at ancestry in our group, you're right, we are able to do that kind of in a large cohort of patients. So we looked at this in about over 950 patients that are seen here at MSK, the majority of whom were found to have European ancestry. But then we were able to also find other minority groups. And from an ancestry perspective, these patients were then categorized into either patients who had African, East Asian, South Asian, native American or a separate group that we considered admixed where there were several different ancestry that might contribute to that patient individual.

Pedro Barata: That's a fantastic summary and kudos to have such a great program in place at MSK. Right. So I guess maybe I should ask you of the interesting results, when I look at your data, I really find it interesting because it does really support these underlying tumor biology differences between patients with different ancestry, right? So I'll let you comment on that.

Ritesh Kotecha: So definitely, I mean the first step we went through was to actually just see the correlation between those patients who self-identify with a specific type of race or ethnicity, patients who self-identify as being white, black or Asian and whether the ancestry correlated with that. And we saw there's pretty high concordance from a self-identification perspective to their genetic ancestry. But you're right, when we look at now looking at all those patients, we look at their, for example, their clinical characteristics. There were several things that we sort of found out. The first is we did see that patients, for example, who had African ancestry appeared to really present with more metastatic disease at their initial disease presentation right when they're diagnosed compared to for example, European ancestry or the overall cohort themselves. We also found when we looked at their IMDC risk, right? So that helps us adjudicate not only for example, treatments in this day and age, but also got to give clinicians and patients a sense of potentially disease pace.

Even we found that patients, for example, who are African ancestry also presented with intermediate or poor risk disease more often. We actually did not have any favorable risk patients in our data set, which is really kind of striking and kind of makes you think a little bit, I think from a clinician perspective when you're seeing some of these patients in a much more real time. When we look at their clinical, when we look at their tumor histology also by ancestry, we also found several differences. The first being that clear cell histology really was really enriched in patients who have European ancestry. And we looked at, for example, African ancestry again is a little bit of a comparison. And we saw patients with African ancestry don't actually have clear cell histology in our cohort as often and we found that they have more enrichment of papillary histology and even unclassified with papillary features.

So it made us of think through a little bit of just when you're seeing a patient from a clinical perspective, even before we get to the molecular data, some of the differences that we see between these ancestry groups. When we kind of move into the molecular piece, you're right, our data set here was really robust where we had significant number of patients who had both somatic and germline data that we could sort of probe. And if you look at, for example, the germline data, we saw that roughly about 17% of patients had any detectable germline alteration that would be considered pathogenic or likely pathogenic in our entire cohort of patients. And that was I think around 650 or so. And if you look at the ancestry, you can see here that the patients who had European ancestry were found to have higher enrichment of check two.

And we know that CHEK-2 is a founder mutation in that patient population. If you look at patients with FH mutations as a germline, those are the second highest recurrent alteration we saw in our cohort of RCC. We did see that more enriched in patients with African ancestry or even admixed ancestry compared to some of the others. But there's some other interesting findings here in our cohort. Again, we only saw, we didn't have any, for example, south Asian patients who had any germline alterations and really a lower frequency even in East Asian patients. So again, it's more hypothesis generating. I mean these are very small numbers when you kind of go into these minority groups, but it's sort of like what would we expect almost from a germline perspective that you might see within these groups. Those are the two big mutations that we sort of found that are really enriched in two big populations.

When we look at the somatic data, looking at the actual somatic data for the histology, we then focus all of our efforts really in clear cell. Clear cell is really hallmarked by 3p loss, VHL loss. And in our cohort we found VHL loss for example in about 80% of the patients. But then when we look at those patients, for example, who have African ancestry that have clear cell, we only found VHL loss at about 50%. And if you look at for example, things that we know, alterations that we know might increase a person's disease risk, for example, like BAC1. In our cohort BAC1 was about 17 or 18%, which is what you would expect when you look at the TCGA and sort of larger data sets. But if you look at African ancestry patients, we found BAC1 in 50% of those patients. So again, does that actually portend to some type of different molecular tumor biology that we might be seeing even though all of these patients have clear cell RCC?

Pedro Barata: Well...

Ritesh Kotecha: Yeah, it sort of makes us sort of rethink again these ancestry signals, do they sort of, again confer some other underlying tumor biology that might be inherently different?

Pedro Barata: Right. No, this is absolutely great summary by the way, Ritesh and so important to know these data for the treating physician and to be aware of these differences. I guess let me ask you one last question before I let you go. And of course, I mean the way you describe this comprehensive molecular profiling of tumors, the natural question is what can you offer from the somatic data that you haven't shown yet? Right? Are you thinking about doing RNA Seq and gene expression signatures for example? Do you have data to explore tumor microenvironment? Can you share a little bit with us, what are future plans for these dataset?

Ritesh Kotecha: Sure. I mean, knowing that these are somatic differences and some of these are both epigenetic differences and some are associated with underlying angiogenesis profiles that we see within RNA sequencing data, we've tried to look in house here for patients where we do have RNA sequencing data and we just found that it was a little bit challenging to make sure that we had representation with all the minority groups to really make some of these associations. So what we did was we actually then went to the TCGA, right in the TCGA and if you look in the clear cell or KARC cohort, there are patients who have African ancestry and there are patients that have European ancestry that we've compared differences to. And we saw that in comparison, we did see differences for the underlying RNA sequencing data and that probably represents both immune infiltration you'll see by RNA sequencing and also angiogenesis signatures as well.

So there definitely is downstream biology that we're seeing from a gene expression perspective, that does probably get conferred from the somatic data as well. To take a step forward we're now trying to understand also the metabolomic data that might also be different between these two. There is some abilities for us to try to infer types of metabolites and sort metabolic signatures based on also gene expression or RNA sequencing data. And so we're trying to right now look at the metabolomic differences also again by RNA sequencing to see again, do you see some of the same functional downstream consequences from these different genetic changes?

Pedro Barata: Got it. This is wonderful. Ritesh, I feel like we could spend another hour talking about this. This is fantastic work. Really great. I'm looking forward to the publication on this, which I'm sure you are working on as we speak. But really I just want to congratulate you for an amazing job and for actually taking the time to break it down for us today.

Ritesh Kotecha: No, I...

Pedro Barata: Thank you.

Ritesh Kotecha: I really appreciate it. And just to also make another message that obviously our work here really underscores a lot of the initiatives that we should have when we are studying, for example, underrepresented groups and trying to learn the differences in biology because this probably does, will hopefully have an impact of how we think about this type of tumor and how we think about this type of treatment. And the last thing I'll say is that this work was, is funded primarily from the Kidney Cancer Association Young Investigator Award. So there's a huge, huge initiative from at least the community to really also understand these things, to really optimize the care for patients.

Pedro Barata: Yeah, no, that's amazing. Thank you for those points. Truly, truly important. And I feel that the Kidney CASA community is getting closer and closer, right as it builds at the expense, but people are getting closer. So initiatives like that are actually, it's great for the audience to know that actually the awards are being rented by the KCA are actually going to great work, like the one you just presented. So I'm sure people out there are going to be very happy with that as well.

Ritesh Kotecha: That's right. Thank you.

Pedro Barata: All right, well thank you Ritesh. Take care. Thank you. And I'll see you soon.

Ritesh Kotecha: See you soon. Thank you.