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1:18:14cancer story. Yeah, this is a very cool cancer story. I thought again uses a lot of physics which is uh my favorite kind of biology. Um they're introducing this new tool alpha K. This is out of the Moffett um hospital in Tampa, Florida. >> What they're effectively doing is trying to predict how cancer evolves. >> Okay, >> it's a paradigm shift in the way we think about cancer evolution because before we used to think there's just no rules, >> right? >> It's chaotic, right? The whole thing is just these this cancer thing is just trying its hardest to live. These tumors are trying its hardest to beat whatever we throw at it. And the way that it does is in a very chaotic environment. The genome is is changing very chaotically.
1:18:56And so it's very hard to predict something that is chaotic, right? It's kind of like weather. But at the end of the day, even weather is predictable, right? We have weather models and they're pretty good most of the time, right? Yes, maybe not like Mammoth because Mammoth has mountains and mountains can get in the way of predictions and things like that, but you know for Los Angeles it's like pretty good. >> Um, so can we do the same thing with cancer genomes? >> Particularly what they're trying to tackle is something called annuployy. Okay, there's a paradox in annuployy. We've heard about annuploy when it comes to um disorders like tricommy 21. That's how you get down syndrome, right? you get three copies of the chromosome 21
1:19:38and that leads to down syndrome. >> Okay, >> we should only have two copies for sort of typical organisms. Two copies of each chromosome, we've got 23 chromosomes, so 48 different chromosomes. If we got two copies, we're good to go. >> Okay, but annipoloy is when we have more or less of a specific chromosome. It is very bad and catastrophic for normal cells but cancer cells love it. >> Okay. 90% of solid tumors are annoyed tumors. >> Okay. >> Okay. Here you can look at a cancer cell. There's four of two. There's three of one. Three I guess there's still two.
1:20:20So three is is still chilling. There's four copies of four. Three copies of five. Three copies of 12. Three copies of 11. 10 is still two. You see what I'm saying? Yeah. This like if somebody looked at this chromosome, it's like there's no way this is a normal cell, >> right? >> Cuz there's what what is going on? >> Yeah. Yeah. Yeah. It's just all over the place. >> It's all over the place. There's three of some there. Some are completely deleted and just not even there. So, what what's going on? Cancer loves doing this. Okay. And there's a clear evolutionary advantage >> to doing this for a cancer cell. There's clear a disadvantage for a normal cell, but somehow for cancer, those cells love it, right? there's some kind of benefit that you get from that genetic variation. And it kind of makes sense
1:21:02because the more of a the more copies of a chromosome that you have, the more you can mess around with mutations, right? It's that same concept of the GitHub main versus your branch. If you're a cancer cell, you've got a bunch of your own branches that you're just trying all sorts of stuff to survive, right? Because whoever the patient is and the doctors that are treating the patient, they're throwing radiation, chemotherapy, everything at you. And from a cancer cell's perspective, it's like, I got to change as fast as I can and evolve out of my current environment to get through to the next stage of whatever therapy that they're going to put in. Right? The cancer cell is trying to evolve and survive. And >> if we wanted to look at the possible
1:21:44number of carotypes, carotypes are this set of chromosomes, right? Um, if we were to look at all of the sets of all the different chromosomes that we'd have to model in order to sort of try and figure out some kind of simulation of cancer evolution, it would be something like 10^ the 10 to the 20. So 10 the 20 zeros. >> That's a >> which is a billion billion zeros. Not a billion billion combinations. A billion billion zeros and a one. Okay, it's a lot. It's never going to happen. >> You're right. >> Even with quantum computing, it's never going to happen. Okay, stop trying to make it happen. So instead, these researchers came up with alpha K, which
1:22:24is inferring what's called a local landscape of fitness. And it's it's trying to figure out how the tumor is going through this local landscape of fitness. So the one of the the big the big initial problem is that the scale of trying to just map all the possibilities is so it's just impossibly large and so it's not worth trying to like create the map of everything. >> Yeah. Like a brute force strategy. >> Right. Right. Right. So so even with CL it's just like the scale is too large. So what they're trying to do is basically say can we isolate certain aspects of the landscape that are meaningful
1:23:06>> to to basically uh deal with the scale problem. >> Yes. Exactly. Exactly. Specifically when it comes to this annuploy and before we move off Annuploity I just wanted to talk about um Oh by David Freeberg. >> David Freeberg >> um of the other podcast fame. >> Yes. and he has he's CEO of this company called oh hollowo they are doing annoy effectively they have they have something called um boosted what is it called boosted reproduction or boosted genetics effectively you know when we were born we get half of our genes from our mom half of our genes from our dad >> and so you get one set of chromosomes
1:23:46from your mom one set of chromosomes from your dad and that makes you what he figured was in plants what if we just got what if we just kept the kept the entire set. >> So we had tetroploids, meaning two chromosomes from dad, two chromosomes from mom, right, in the nucleus. And >> I was really surprised that it works like that. It doesn't just the the the plant gel doesn't just die, right? >> To me, trivially it's like >> you should just die. But these plants are bu they're making bigger potatoes. The potatoes are like lasting longer on the shelf. All sorts of stuff. It's like incredible genetic technology and they must have done some crazy
1:24:28engineering to make sure that these you know these cells are like actually lasting as long as they do. The the the I think the main idea behind oh is you know if we look at our agricultural supply chains globally yeah >> which are very susceptible to any number of both environmental as well as geopolitical crises. If folks remember back to the beginning of the Russia Ukraine conflict, one of the things that was brought up is the Ukraine is one of the bread baskets of Europe and if the agriculture goes down there, it's going to have these cascading supply chain issues across not only Europe but other countries as well. And you know, we obviously have anthropogenic climate
1:25:09change issues and all of this stuff. And so finding ways to make the yield >> Mhm. >> on agriculture and crops higher for the same amount of used space. So we don't need to expand to larger amounts of area being uh used for crops, but can we just actually genetically modify the or just be better? More rice, bigger potatoes, bigger tomatoes. So that that's like the fundamental concept >> and and it's in incredible. I mean, apparently it's working, but the way that they went about with that genetics, I just thought it would it would never work. >> And it's f it's fascinating. It's a good thing, you know. >> Yeah, it is a good thing. And I'd love to know ex I'd love to like one day when
1:25:51we get big enough, I'd love to I'd love to have the chief scientist of Oallo on and just ask him like how is this even possible? If you are connected to the chief scientist at Ohalo and you are a fan of the pod and would like to send them a DM uh we would love to dig into this because it's I think a really impactful concept. >> Yeah, I think it's very cool >> and it's very cool technically uh so we we we'd love to connect. However, >> for this story that is not the focus. >> That is not the focus. I just wanted to because the annuploy reminded me of of you know this multiple copies. So now let's get back into the genealogy of cancer. Let's talk about something called the fitness landscape. This is something that we've been alluding to. You alluded to the gradient descent and
1:26:32things like that. In um 1932, there's this guy sele how evolution happens. Okay. What you can think about is a 2D landscape that has hills and valleys. Okay. The axes in 2D. So the direction north south could be how much of gene A do I have? >> East west could be how much of gene B do I have? Okay. So every point is a location in genetic space where it's like I have this much of gene A, this much of gene B. >> There would be hills which is where you're more fit. So it's an advantage to be here. And there's valleys where there's less fit, right? And so you don't want to be in the valley, you want
1:27:13to be towards the hill. >> This is the fitness landscape analogy of evolution. Mhm. >> And physicists love this because everything is a potential energy landscape and we're just looking at trying to get to the highest point. >> Makes sense. And I just want to make sure I'm understanding this graphic. So, we're looking at basically sort of a xyz. >> Yeah. >> Uh uh like plane >> and we see gene A and gene B in the way that you just described. And then the I guess the >> height. Yeah. The height is this population fitness. Yeah. And so the idea is the taller like the the hills are where it's it's highly fit and then the valleys are where it's lowly fit. So it looks like if you've ever played like
1:27:55create a level in Fortnite or any video game and you want to put like you you can generate hills or mountains kind of looks like that that sort of uh imagery uh in terms of just what we're visualizing. >> Yeah. If you if you want to be a sniper you want to get to the highest elevation >> the highest elevation right? So here if you're an organism you want to get to the highest elevation on this fitness landscape. >> Okay. Now this is a nice metaphor and for the longest time it was just a metaphor. >> Mhm. >> Okay. What these guys have done with the alpha K concept is operationalize that metaphor into something that's quantifiable. >> Okay. >> Because what you can do is now make empirical fitness estimates based on data.
1:28:35>> That is what this paper is doing. It's taking that metaphor and it's saying actually this is a real thing >> that we can at least apply very quantitatively to the problem of annuploy >> because in annoy your axes >> don't have to be genes they can be number of a certain chromosome >> you can have this axis be I have one of chromosome 1 I have two of chromosome 1