1 00:00:15,356 --> 00:00:23,876 Speaker 1: Pushkin. Hey, it's Jacob. There's another podcast that I host 2 00:00:23,956 --> 00:00:28,276 Speaker 1: called Incubation. It's a show about viruses, and recently we 3 00:00:28,356 --> 00:00:31,796 Speaker 1: did an episode that I thought would be particularly interesting 4 00:00:31,916 --> 00:00:35,356 Speaker 1: to you, to people who listen to What's Your Problem, 5 00:00:36,076 --> 00:00:39,476 Speaker 1: it's about measles. Measles turns out to be way more 6 00:00:39,516 --> 00:00:44,076 Speaker 1: interesting and way more insidious than we thought, but also 7 00:00:44,436 --> 00:00:49,236 Speaker 1: measles may be a new way to fight cancer. I 8 00:00:49,276 --> 00:00:51,356 Speaker 1: hope you liked the episode. I really found it interesting. 9 00:00:51,916 --> 00:00:54,796 Speaker 1: One last thing, we won't be publishing episodes of What's 10 00:00:54,836 --> 00:00:57,476 Speaker 1: Your Problem over the next few weeks. We'll start up 11 00:00:57,516 --> 00:01:02,116 Speaker 1: again in January. I thought I knew what I needed 12 00:01:02,156 --> 00:01:05,956 Speaker 1: to know about what happens when you get measles. You 13 00:01:05,996 --> 00:01:09,836 Speaker 1: get a fever and a rash. Maybe you get very sick. 14 00:01:10,396 --> 00:01:14,836 Speaker 1: If you're really unlucky, you die. But chances are you 15 00:01:14,876 --> 00:01:17,316 Speaker 1: get measles, you get better, and that's the end of 16 00:01:17,356 --> 00:01:21,556 Speaker 1: it as it happens. I was wrong. I did not 17 00:01:21,756 --> 00:01:24,236 Speaker 1: know what I needed to know about measles. Because a 18 00:01:24,276 --> 00:01:27,956 Speaker 1: recent discovery has blown open our whole idea of what 19 00:01:27,956 --> 00:01:30,236 Speaker 1: the measles virus does to our bodies. 20 00:01:30,596 --> 00:01:34,036 Speaker 2: The world thought that measles was done being discovered and 21 00:01:34,076 --> 00:01:38,716 Speaker 2: then boom, all of a sudden, there's this new idea 22 00:01:39,556 --> 00:01:43,676 Speaker 2: of something that really had massive, massive consequences on humans 23 00:01:43,716 --> 00:01:45,036 Speaker 2: that we didn't even realize. 24 00:01:45,876 --> 00:01:49,716 Speaker 1: I'm Jacob Goldstein and this is Incubation, a show about viruses. 25 00:01:50,196 --> 00:01:53,596 Speaker 1: Today on the show how measles attacks your immune system 26 00:01:54,196 --> 00:01:58,076 Speaker 1: and how researchers are trying to use measles to cure cancer. 27 00:02:07,756 --> 00:02:09,956 Speaker 1: My guest for the first half of today show is 28 00:02:10,036 --> 00:02:15,756 Speaker 1: Michael Minna. He's an epidemiologist slash immunologist, slash physician, and 29 00:02:15,796 --> 00:02:18,756 Speaker 1: he's kind of a measles superfan. He did a lot 30 00:02:18,796 --> 00:02:20,756 Speaker 1: of work we'll talk about today while he was a 31 00:02:20,756 --> 00:02:24,756 Speaker 1: professor at Harvard. He's now chief scientific officer at a 32 00:02:24,756 --> 00:02:28,676 Speaker 1: company called emed. We started by talking about a surprising 33 00:02:28,716 --> 00:02:31,676 Speaker 1: thing that happened after the measles vaccine was introduced in 34 00:02:31,716 --> 00:02:35,116 Speaker 1: the nineteen sixties. As more and more kids were vaccinated 35 00:02:35,116 --> 00:02:38,476 Speaker 1: against measles, the rate at which kids were dying of 36 00:02:38,516 --> 00:02:43,076 Speaker 1: infectious disease went down way more than anybody expected. It 37 00:02:43,116 --> 00:02:45,836 Speaker 1: went down so much that it couldn't be explained by 38 00:02:45,916 --> 00:02:47,516 Speaker 1: measles alone. 39 00:02:47,636 --> 00:02:53,036 Speaker 2: After the vaccine was introduced, we saw market reductions in 40 00:02:53,156 --> 00:02:58,956 Speaker 2: childhood mortality overall following the vaccine, which drove a lot 41 00:02:58,956 --> 00:03:03,316 Speaker 2: of questions, why did that happen? Is it the vaccine 42 00:03:03,356 --> 00:03:07,876 Speaker 2: actually acting directly to somehow prevent other infections, or is 43 00:03:07,916 --> 00:03:09,636 Speaker 2: there something else at play? 44 00:03:09,676 --> 00:03:12,716 Speaker 1: Even if zero people died of measles, even if zero 45 00:03:12,876 --> 00:03:16,356 Speaker 1: children died of measles, you wouldn't get that large of 46 00:03:16,356 --> 00:03:20,116 Speaker 1: a reduction immortality. Right, some weird thing is going. 47 00:03:19,956 --> 00:03:21,636 Speaker 2: On, that's exactly right. 48 00:03:21,756 --> 00:03:24,076 Speaker 1: Weird good thing, weird usually. 49 00:03:23,876 --> 00:03:26,476 Speaker 2: That's right, A very weird good thing was going on. 50 00:03:26,876 --> 00:03:30,196 Speaker 1: So now we have this interesting kind of happy mystery 51 00:03:30,276 --> 00:03:33,596 Speaker 1: in a way. Why are so few children dying of 52 00:03:33,596 --> 00:03:37,316 Speaker 1: infectious disease after the rollout of the measles vaccine? What 53 00:03:37,396 --> 00:03:39,916 Speaker 1: do you do to try and figure out what's going on? 54 00:03:40,436 --> 00:03:45,956 Speaker 2: We said, well, maybe it's because measles had detrimental effects 55 00:03:45,996 --> 00:03:50,236 Speaker 2: on somebody's immune memory that might be putting them at 56 00:03:50,316 --> 00:03:53,356 Speaker 2: risk for other stuff, other infections. And so what we 57 00:03:53,396 --> 00:03:55,916 Speaker 2: did was we said, well, if that's the case, then 58 00:03:55,956 --> 00:03:58,836 Speaker 2: if we look at a lot of data, could we 59 00:03:58,916 --> 00:04:02,676 Speaker 2: map the numbers of cases of measles to the numbers 60 00:04:02,716 --> 00:04:06,476 Speaker 2: of deaths from other things besides measles from year to year? 61 00:04:07,596 --> 00:04:12,196 Speaker 2: And what we found was prof boundly predictive. Is if 62 00:04:12,236 --> 00:04:14,876 Speaker 2: you asked what were the number of measles cases in 63 00:04:14,996 --> 00:04:18,636 Speaker 2: nineteen forty nine and what were the numbers of deaths 64 00:04:18,876 --> 00:04:23,836 Speaker 2: from non measles infections from nineteen forty nine, nineteen fifty 65 00:04:24,236 --> 00:04:27,676 Speaker 2: and nineteen fifty one. When you accrued all three years, 66 00:04:28,116 --> 00:04:32,516 Speaker 2: it became extraordinarily predictive of how many children would die 67 00:04:33,396 --> 00:04:37,436 Speaker 2: over the next three years of non measles infectious related deaths. 68 00:04:38,036 --> 00:04:40,196 Speaker 1: So, just to be clear, what you found is that 69 00:04:40,516 --> 00:04:43,596 Speaker 1: when there's a measle's outbreak in one year, the rate 70 00:04:43,636 --> 00:04:46,836 Speaker 1: at which kids died from other infectious diseases went up 71 00:04:46,916 --> 00:04:47,996 Speaker 1: in the next few years. 72 00:04:48,156 --> 00:04:49,156 Speaker 2: That's exactly right. 73 00:04:49,276 --> 00:04:52,236 Speaker 1: So you used in your answer just there this phrase 74 00:04:52,356 --> 00:04:55,916 Speaker 1: that I just want to spend a moment on immune memory. 75 00:04:56,556 --> 00:04:57,956 Speaker 1: What is immune memory? 76 00:04:58,556 --> 00:05:04,076 Speaker 2: Immune memory is very similar to our regular memory. All 77 00:05:04,156 --> 00:05:08,316 Speaker 2: of our body's memories, whether it be muscle memory, brain memory, 78 00:05:08,956 --> 00:05:12,276 Speaker 2: or immune memory, is stored in cells. The way that 79 00:05:12,396 --> 00:05:16,996 Speaker 2: immune memory works is when you bump up against a 80 00:05:17,036 --> 00:05:20,996 Speaker 2: pathogen as let's say, a virus like measles or coronavirus, 81 00:05:21,036 --> 00:05:25,276 Speaker 2: whatever it might be, your body actually sees it, it 82 00:05:25,876 --> 00:05:29,836 Speaker 2: recognizes that virus. It learns from it, and it actually 83 00:05:29,916 --> 00:05:33,996 Speaker 2: remembers it in B cells and T cells and plasma cells. 84 00:05:34,556 --> 00:05:38,116 Speaker 2: And that's how our immune system works in terms of 85 00:05:38,156 --> 00:05:42,716 Speaker 2: developing immune memory and utilizing it to combat infections that 86 00:05:42,756 --> 00:05:43,676 Speaker 2: we see in the future. 87 00:05:44,356 --> 00:05:46,556 Speaker 1: Is what you're finding that in some way measles is 88 00:05:46,636 --> 00:05:51,836 Speaker 1: attacking the kid's immune memory. Is that the hypothesis that follows. 89 00:05:52,276 --> 00:05:55,996 Speaker 2: That's exactly the hypothesis that follows. For example, if a 90 00:05:56,996 --> 00:06:01,876 Speaker 2: six year old got measles, then maybe that measles infection 91 00:06:02,596 --> 00:06:06,116 Speaker 2: could destroy some of the immune memory the defenses that 92 00:06:06,116 --> 00:06:10,156 Speaker 2: that child gained over the last six years, and therefore 93 00:06:10,436 --> 00:06:13,636 Speaker 2: when they are seven or eight years old, are actually 94 00:06:13,716 --> 00:06:18,036 Speaker 2: more vulnerable than they otherwise would have been to those 95 00:06:18,076 --> 00:06:20,276 Speaker 2: infections that they gained the immunity to when they were 96 00:06:20,276 --> 00:06:20,836 Speaker 2: two or three. 97 00:06:21,916 --> 00:06:25,596 Speaker 1: So, okay, you have this hypothesis, how do you test it? 98 00:06:25,716 --> 00:06:27,876 Speaker 1: How do you investigate what's really going on? 99 00:06:28,396 --> 00:06:33,916 Speaker 2: There's a long, rich history of how to predict where 100 00:06:33,996 --> 00:06:36,596 Speaker 2: measles is going to go next. It's actually famous for 101 00:06:36,676 --> 00:06:40,836 Speaker 2: how predictive it is because it is so infectious that 102 00:06:40,916 --> 00:06:43,516 Speaker 2: you just need to know how many people are vaccinated 103 00:06:43,516 --> 00:06:45,956 Speaker 2: in a community, and if there's any measles anywhere in 104 00:06:45,996 --> 00:06:49,876 Speaker 2: the region, you can expect that pretty soon at below 105 00:06:49,916 --> 00:06:51,276 Speaker 2: certain levels, there's going to be an. 106 00:06:51,196 --> 00:06:54,076 Speaker 1: Outbreak below certain levels of vaccination. 107 00:06:53,996 --> 00:06:56,316 Speaker 2: That's right, and that's why measles is considered the canary 108 00:06:56,356 --> 00:07:00,196 Speaker 2: and the coal mine for vaccine rates. It's literally the 109 00:07:00,196 --> 00:07:02,636 Speaker 2: thing that pops up on the radar when you say 110 00:07:03,036 --> 00:07:06,636 Speaker 2: who's what communities are having trouble vaccinating their population, and boom, 111 00:07:06,636 --> 00:07:08,876 Speaker 2: if you see measles, you know they're having trouble vaccinating 112 00:07:08,916 --> 00:07:12,796 Speaker 2: their populations. And so what we can do is we 113 00:07:12,836 --> 00:07:18,076 Speaker 2: can leverage everything we know about measles epidemiology to help 114 00:07:18,156 --> 00:07:23,356 Speaker 2: identify where might outbreaks happen. And that's exactly what Rick 115 00:07:23,436 --> 00:07:27,156 Speaker 2: de Swart and his team did. They are in the Netherlands. 116 00:07:27,236 --> 00:07:30,636 Speaker 2: He's at Erasmus, which is in Rotterdam, and so he 117 00:07:30,716 --> 00:07:33,316 Speaker 2: was able to say, hey, right in our backyard, there's 118 00:07:33,356 --> 00:07:37,756 Speaker 2: a community that, for religious reasons, they chose not to 119 00:07:37,836 --> 00:07:41,196 Speaker 2: vaccinate their children against measles. So they said, well, if 120 00:07:41,196 --> 00:07:42,716 Speaker 2: you're not going to get a vaccine, would you be 121 00:07:42,836 --> 00:07:46,276 Speaker 2: interested or willing to have us just draw a little 122 00:07:46,316 --> 00:07:50,356 Speaker 2: sample of blood from your kids today and should measles 123 00:07:50,356 --> 00:07:52,436 Speaker 2: catch up to them in the future. Could we come 124 00:07:52,476 --> 00:07:54,396 Speaker 2: back and draw another sample of blood. 125 00:07:55,316 --> 00:07:58,796 Speaker 1: You have the before measles blood samples, and you have 126 00:07:58,956 --> 00:08:02,716 Speaker 1: from the same children the after measles blood samples. We 127 00:08:02,796 --> 00:08:05,716 Speaker 1: have everything ready to go. What happens. 128 00:08:06,156 --> 00:08:09,396 Speaker 2: There was a big outbreak, and almost immediately we aren't 129 00:08:09,436 --> 00:08:13,116 Speaker 2: seeing that we can measure all of these antibodies in 130 00:08:13,156 --> 00:08:16,316 Speaker 2: the blood samples. And then when we actually look at 131 00:08:16,316 --> 00:08:19,636 Speaker 2: the blood samples from right before the kids got measles 132 00:08:19,636 --> 00:08:22,836 Speaker 2: to those same kids blood samples that they collected after, 133 00:08:23,396 --> 00:08:27,716 Speaker 2: we saw marked reductions. Not just in a couple antibodies, 134 00:08:28,196 --> 00:08:31,756 Speaker 2: but some kids lost eighty percent of all of the 135 00:08:31,876 --> 00:08:36,556 Speaker 2: diversity of their antibodies that existed in that blood sample 136 00:08:36,596 --> 00:08:39,396 Speaker 2: before they got measles. And we compared it, we said, well, 137 00:08:40,076 --> 00:08:42,436 Speaker 2: maybe that's normal. So we looked at kids who had 138 00:08:42,436 --> 00:08:45,276 Speaker 2: gotten vaccinated for measles. We like the kids who just 139 00:08:45,316 --> 00:08:48,396 Speaker 2: had no infections, And what we saw was the average 140 00:08:48,436 --> 00:08:52,356 Speaker 2: person from any two time points, there'd be like five 141 00:08:52,436 --> 00:08:56,676 Speaker 2: percent difference in their overall antibody repertoire. But the measles kids, 142 00:08:57,036 --> 00:09:00,636 Speaker 2: the kids who got measles lost anywhere from twenty percent 143 00:09:00,676 --> 00:09:05,676 Speaker 2: to eighty percent of their whole immunological memory pool. This 144 00:09:05,716 --> 00:09:10,716 Speaker 2: is the whole lifetime of immune memory that they've spent 145 00:09:10,876 --> 00:09:14,956 Speaker 2: years building up and building up, just poof wiped away 146 00:09:15,396 --> 00:09:18,836 Speaker 2: because of this measles infection and these kids. What that 147 00:09:18,916 --> 00:09:21,636 Speaker 2: means is now, across millions and millions of kids who 148 00:09:21,636 --> 00:09:25,716 Speaker 2: were before the vaccines, almost every child got measles. And 149 00:09:25,796 --> 00:09:29,516 Speaker 2: so at that scale, when you have so many people 150 00:09:29,556 --> 00:09:35,036 Speaker 2: getting measles and this effect happening, which we call immunological amnesia, 151 00:09:35,156 --> 00:09:39,516 Speaker 2: essentially they forgot their body, forgot because of the infection 152 00:09:40,516 --> 00:09:43,796 Speaker 2: the immune memories that they formed before the infection. What 153 00:09:43,836 --> 00:09:45,956 Speaker 2: that means is that you have all of these kids 154 00:09:46,036 --> 00:09:50,156 Speaker 2: that are more susceptible to other infectious diseases. So most 155 00:09:50,596 --> 00:09:54,036 Speaker 2: most kids would survive, but it turned out that of 156 00:09:54,076 --> 00:09:58,956 Speaker 2: those kids who did die from other things, about half 157 00:09:59,036 --> 00:10:04,676 Speaker 2: of those deaths could be attributed to the immune amnesia 158 00:10:04,716 --> 00:10:06,476 Speaker 2: associated with measles. 159 00:10:07,316 --> 00:10:10,356 Speaker 1: Is there something particularly insane if that happens at a 160 00:10:10,396 --> 00:10:13,596 Speaker 1: population level. If you imagine a group of people in 161 00:10:13,596 --> 00:10:16,636 Speaker 1: the absence of a vaccine entirely, where it's like, not 162 00:10:16,676 --> 00:10:20,076 Speaker 1: only is each kid more vulnerable, but because all the 163 00:10:20,116 --> 00:10:23,196 Speaker 1: other kids are more vulnerable, everybody is more vulnerable. Is 164 00:10:23,196 --> 00:10:24,836 Speaker 1: there something like that that happens. 165 00:10:25,076 --> 00:10:27,556 Speaker 2: I am so happy you asked that question. 166 00:10:28,396 --> 00:10:28,996 Speaker 1: You're welcome. 167 00:10:29,916 --> 00:10:34,636 Speaker 2: So, yes, it is. It's a much much harder thing 168 00:10:34,876 --> 00:10:35,556 Speaker 2: to measure. 169 00:10:35,916 --> 00:10:37,756 Speaker 1: I mean, it's sort of like the reverse of herd 170 00:10:37,756 --> 00:10:40,756 Speaker 1: immunity in some weird broad spectrum way, right. 171 00:10:41,036 --> 00:10:43,956 Speaker 2: So what's so interesting that you bring that up? Because 172 00:10:44,036 --> 00:10:47,156 Speaker 2: before my musle's work, I was working on influenza and 173 00:10:47,196 --> 00:10:51,116 Speaker 2: its impacts on other bacterial infections. And during my PhD, 174 00:10:51,196 --> 00:10:56,956 Speaker 2: I coined a term called generalized herd effects, and I 175 00:10:57,196 --> 00:11:00,876 Speaker 2: explicitly didn't call it herd immunity because maybe it's not 176 00:11:00,956 --> 00:11:03,836 Speaker 2: going to reduce things, but maybe you could exacerbate things. 177 00:11:03,996 --> 00:11:07,156 Speaker 1: Yeah, how about herd vulnerability. I want the opposite. What's 178 00:11:07,196 --> 00:11:08,276 Speaker 1: the opposite of herd. 179 00:11:08,116 --> 00:11:11,636 Speaker 2: Immunity, that's herd vulnerability is a great term, and so 180 00:11:11,716 --> 00:11:13,796 Speaker 2: the idea there as well. If you have a pathogen 181 00:11:14,276 --> 00:11:18,476 Speaker 2: that's impacting your susceptibility to a lot of other pathogens, 182 00:11:18,876 --> 00:11:22,956 Speaker 2: you could, you know, create a herd vulnerability because of 183 00:11:22,996 --> 00:11:27,476 Speaker 2: infections of that initial pathogen. And on the contrary, if 184 00:11:27,476 --> 00:11:30,476 Speaker 2: you figure out a vaccine against that initial pathogen, like 185 00:11:30,476 --> 00:11:36,116 Speaker 2: the measles vaccine, you create massive benefits in getting rid 186 00:11:36,156 --> 00:11:38,156 Speaker 2: of that herd vulnerability. 187 00:11:38,836 --> 00:11:42,276 Speaker 1: So what's going on on a cellular level. 188 00:11:42,916 --> 00:11:47,276 Speaker 2: As far as we know, measles is unique in its class. 189 00:11:47,596 --> 00:11:49,916 Speaker 2: It's actually it's an amazing story. So if you give 190 00:11:49,916 --> 00:11:52,596 Speaker 2: me forty seconds to describe it. 191 00:11:52,516 --> 00:11:55,116 Speaker 1: I will, oh go, you got a minute if you 192 00:11:55,236 --> 00:11:55,516 Speaker 1: need it. 193 00:11:56,356 --> 00:11:59,516 Speaker 2: So, every virus has a receptor that it binds to 194 00:11:59,596 --> 00:12:02,476 Speaker 2: and needs to latch onto on a cell. For measles, 195 00:12:02,596 --> 00:12:06,716 Speaker 2: it's this molecule that's called CD one fifty or it's 196 00:12:06,796 --> 00:12:12,396 Speaker 2: called SLAM. SLAM stands for a signaling of Lymphocyte Activation molecule, 197 00:12:12,756 --> 00:12:17,796 Speaker 2: and it's when somebody gets a measles infection. The virus 198 00:12:18,036 --> 00:12:21,036 Speaker 2: comes into somebody's lungs and we have these cool dendritic cells. 199 00:12:21,036 --> 00:12:23,116 Speaker 2: And dendritic cells are like these cells of big arms, 200 00:12:23,116 --> 00:12:25,356 Speaker 2: and they go out and reach pathogens that they that 201 00:12:25,356 --> 00:12:28,196 Speaker 2: they know shouldn't be there, and they capture them and 202 00:12:28,236 --> 00:12:31,236 Speaker 2: bring them in and then they shuttle them into the 203 00:12:31,356 --> 00:12:34,276 Speaker 2: lymphoid system, which is where all of our immune cells are. 204 00:12:34,716 --> 00:12:37,636 Speaker 2: And so normally what would happen is the dendritic cells 205 00:12:37,716 --> 00:12:41,876 Speaker 2: would say, hey, immune system, you know here's a pathogen, 206 00:12:42,316 --> 00:12:45,276 Speaker 2: take it and develop immune memory against it. And so 207 00:12:45,316 --> 00:12:47,996 Speaker 2: it literally hands it off to B cells and T cells. 208 00:12:48,796 --> 00:12:52,876 Speaker 2: In this case, when the dendritic cell does exactly that 209 00:12:52,996 --> 00:12:56,796 Speaker 2: same process, it hands off Measles virus to B cells 210 00:12:56,796 --> 00:12:59,396 Speaker 2: and T cells. And this is a big mistake because 211 00:12:59,436 --> 00:13:02,076 Speaker 2: now you have a virus that, instead of being handed 212 00:13:02,116 --> 00:13:03,876 Speaker 2: off to a B and T cell and having that 213 00:13:03,916 --> 00:13:07,116 Speaker 2: B and T cell you know ingest it and learn 214 00:13:07,196 --> 00:13:10,716 Speaker 2: from it. The Measles flips on it receptor utilization and 215 00:13:10,796 --> 00:13:14,716 Speaker 2: grabs city one point fifty or slam these molecules on 216 00:13:14,756 --> 00:13:17,396 Speaker 2: the outside of the B cells and the T cells 217 00:13:17,956 --> 00:13:21,396 Speaker 2: and it actually invades them like a trojan horse. 218 00:13:21,756 --> 00:13:24,996 Speaker 1: Aha. So it's like a like a trick, right, Like 219 00:13:25,396 --> 00:13:29,276 Speaker 1: Measles is there acting like a normal virus until it 220 00:13:29,356 --> 00:13:31,436 Speaker 1: gets to the B cells in the T cells in 221 00:13:31,476 --> 00:13:34,356 Speaker 1: the immune system. And normally the B cells in the 222 00:13:34,356 --> 00:13:37,836 Speaker 1: T cells would destroy Measles, would destroy the virus. But 223 00:13:37,956 --> 00:13:41,116 Speaker 1: in that case this doesn't happen, right, So what does happen? 224 00:13:41,556 --> 00:13:44,956 Speaker 2: Now It's in the cushy lymphoid system and it's just 225 00:13:45,076 --> 00:13:49,276 Speaker 2: full of food and it just replicates like crazy inside 226 00:13:49,356 --> 00:13:54,356 Speaker 2: the immune system all the while cell by cell destroying 227 00:13:54,716 --> 00:13:58,516 Speaker 2: the valuable immune memory is stored inside each of those 228 00:13:58,556 --> 00:13:59,676 Speaker 2: cells that it's destroying. 229 00:14:00,156 --> 00:14:04,356 Speaker 1: That is very compelling. That it's like a viralogical horror 230 00:14:04,356 --> 00:14:06,156 Speaker 1: movie inside your body. 231 00:14:06,796 --> 00:14:10,756 Speaker 2: It absolutely is. And what we see when we see 232 00:14:10,796 --> 00:14:14,436 Speaker 2: the prototypic measles rash, which is like dots all over 233 00:14:14,516 --> 00:14:18,196 Speaker 2: a child's body, red dots, it is truly the tip 234 00:14:18,236 --> 00:14:21,276 Speaker 2: of the iceberg in terms of where the damage is 235 00:14:21,316 --> 00:14:25,676 Speaker 2: being done. The real damage inside a child is much 236 00:14:25,796 --> 00:14:28,756 Speaker 2: much deeper and much much more profound in terms of 237 00:14:29,636 --> 00:14:34,396 Speaker 2: destroying a huge, huge population of very important cells inside 238 00:14:34,396 --> 00:14:34,996 Speaker 2: of our body. 239 00:14:35,796 --> 00:14:39,836 Speaker 1: So if you sort of step back and think about 240 00:14:39,876 --> 00:14:43,556 Speaker 1: this idea that measles not only gives you measles but 241 00:14:43,676 --> 00:14:47,996 Speaker 1: makes you vulnerable to lots of other infectious diseases, does 242 00:14:48,036 --> 00:14:51,556 Speaker 1: it make you think differently about viruses, about the immune system, 243 00:14:51,636 --> 00:14:52,796 Speaker 1: Like where do you land? 244 00:14:53,516 --> 00:15:00,156 Speaker 2: Measles brings together for me mathematics, biology, ecology and evolution 245 00:15:01,116 --> 00:15:09,236 Speaker 2: and vaccinology, And I love bringing those pieces together. It 246 00:15:09,236 --> 00:15:13,116 Speaker 2: gives you a very deep appreciation for the delicate balance 247 00:15:13,156 --> 00:15:18,876 Speaker 2: we have between infectious diseases, immunity, cancer, and autoimmune disease, 248 00:15:19,156 --> 00:15:23,196 Speaker 2: and how those all interplay with each other. You know, 249 00:15:23,436 --> 00:15:26,876 Speaker 2: the world thought that measles was done being discovered and 250 00:15:26,916 --> 00:15:32,036 Speaker 2: then boom, all of a sudden, there's this new idea 251 00:15:32,876 --> 00:15:37,236 Speaker 2: of something that really had massive, massive consequences on humans 252 00:15:37,236 --> 00:15:40,716 Speaker 2: that we didn't even realize, and so it drives this 253 00:15:41,276 --> 00:15:45,156 Speaker 2: renewed excitement around measles vaccination and the importance of it. 254 00:15:45,156 --> 00:15:48,316 Speaker 2: It's not just a cool finding. It hopefully helps us 255 00:15:48,636 --> 00:15:52,356 Speaker 2: move further and further towards eradication of the virus altogether. 256 00:15:55,316 --> 00:15:56,996 Speaker 1: It was great to talk with you. Thank you so 257 00:15:57,116 --> 00:15:57,756 Speaker 1: much for your time. 258 00:15:58,276 --> 00:16:00,076 Speaker 2: Well, thank you so much. It was a lot of fun. 259 00:16:01,396 --> 00:16:05,276 Speaker 1: Michael Minna is Chief science Officer at EMED Digital Healthcare. 260 00:16:05,596 --> 00:16:08,276 Speaker 1: He was previously a professor at the Harvard School of 261 00:16:08,316 --> 00:16:13,116 Speaker 1: Public Health. When we come back using measles to fight cancer, 262 00:16:23,756 --> 00:16:26,556 Speaker 1: going back all the way to the eighteen hundreds, which 263 00:16:26,676 --> 00:16:30,116 Speaker 1: was before anybody even knew what a virus really was, 264 00:16:30,716 --> 00:16:34,436 Speaker 1: there have been occasional reports of cancer patients who get 265 00:16:34,476 --> 00:16:38,116 Speaker 1: some kind of viral infection and then go into remission 266 00:16:38,236 --> 00:16:41,676 Speaker 1: from cancer. And at a certain level, this makes sense. 267 00:16:42,156 --> 00:16:46,996 Speaker 1: Viruses are highly evolved to enter and destroy cells. Normally 268 00:16:47,116 --> 00:16:49,156 Speaker 1: we think of this as a bad thing, but if 269 00:16:49,156 --> 00:16:53,036 Speaker 1: a virus is entering and destroying cancer cells, this ter 270 00:16:53,156 --> 00:16:55,996 Speaker 1: a cell and destroy it property might be a very 271 00:16:56,076 --> 00:17:00,796 Speaker 1: good thing. By the nineteen fifties, researchers were actively trying 272 00:17:00,836 --> 00:17:04,156 Speaker 1: to figure out how to use viruses to treat cancer. 273 00:17:04,596 --> 00:17:09,116 Speaker 1: But then new kinds of cancer drugs were discovered, basically chemotherapy, 274 00:17:09,796 --> 00:17:13,676 Speaker 1: and researchers got less interested in that virus cancer link. 275 00:17:14,516 --> 00:17:16,956 Speaker 1: My guest for this half of the show is Stephen Russell. 276 00:17:17,596 --> 00:17:21,036 Speaker 1: Stephen has spent his career trying to use viruses to 277 00:17:21,116 --> 00:17:24,836 Speaker 1: cure cancer, and as you'll hear, he and other researchers 278 00:17:24,836 --> 00:17:28,356 Speaker 1: in the field have made real progress. When Stephen was 279 00:17:28,356 --> 00:17:31,356 Speaker 1: starting his career in the nineteen eighties, he was interested 280 00:17:31,396 --> 00:17:35,716 Speaker 1: in using retroviruses as possible cancer treatments. Then he told 281 00:17:35,796 --> 00:17:37,956 Speaker 1: me he turned his attention to measles. 282 00:17:38,436 --> 00:17:40,676 Speaker 3: Yeah, well, measles became the next love. 283 00:17:40,996 --> 00:17:44,036 Speaker 1: You fell in love with measles? Yeah, of course, why 284 00:17:44,076 --> 00:17:46,556 Speaker 1: do you fall in love with measles? Well? 285 00:17:46,596 --> 00:17:49,556 Speaker 3: All viruses are quite beautiful, I have to say, and 286 00:17:49,916 --> 00:17:55,276 Speaker 3: the life cycles are extraordinarily elegant. But measles I could 287 00:17:55,356 --> 00:17:58,836 Speaker 3: do things with. I knew that there was a very 288 00:17:58,916 --> 00:18:04,156 Speaker 3: remarkable case of a boy with a retro orbital Burkitt lymphoma, 289 00:18:04,196 --> 00:18:07,396 Speaker 3: a very aggressive lymphoma that was sort of bulging his 290 00:18:08,596 --> 00:18:11,356 Speaker 3: eye out, and he went to a clinic and was told, well, 291 00:18:11,396 --> 00:18:13,516 Speaker 3: come back in a couple of weeks and we'll start 292 00:18:13,556 --> 00:18:16,476 Speaker 3: the therapy. And he came back in a couple of 293 00:18:16,476 --> 00:18:20,396 Speaker 3: weeks and the tumor had just resolved. But in the 294 00:18:20,476 --> 00:18:24,676 Speaker 3: meantime he'd had a severe measles infection. Huh, And so 295 00:18:24,756 --> 00:18:27,756 Speaker 3: it looked like it was pretty certain that the measles 296 00:18:27,796 --> 00:18:30,716 Speaker 3: infection had driven this response that he had. 297 00:18:31,276 --> 00:18:35,676 Speaker 1: Burko lymphoma also caused by a virus, right, the first 298 00:18:35,716 --> 00:18:37,916 Speaker 1: tumor we knew was caused by a virus. 299 00:18:37,956 --> 00:18:42,596 Speaker 3: Epstein By Yeah, so go on, I apologize. So anyway, 300 00:18:42,676 --> 00:18:45,636 Speaker 3: I looking at measles, it seemed to tick a lot 301 00:18:45,676 --> 00:18:48,356 Speaker 3: of boxes. But there was this whole history of the 302 00:18:48,396 --> 00:18:52,556 Speaker 3: development of a vaccine strain of measles, and measles had 303 00:18:52,596 --> 00:18:56,516 Speaker 3: been the vaccine had been created by taking a virus 304 00:18:56,556 --> 00:19:01,036 Speaker 3: from the throat of a patient with measles. He was 305 00:19:01,036 --> 00:19:03,996 Speaker 3: an eleven year old boy at the time, David Edmonstone 306 00:19:04,116 --> 00:19:08,036 Speaker 3: in nineteen fifty four, and then growing that virus on 307 00:19:08,236 --> 00:19:12,436 Speaker 3: cancer cells in tissue culture, and the virus had required 308 00:19:12,476 --> 00:19:17,116 Speaker 3: the ability to propagate efficiently in cancer cells, but it 309 00:19:17,316 --> 00:19:19,636 Speaker 3: lost the ability to cause measles. 310 00:19:20,036 --> 00:19:23,076 Speaker 1: And wait, I just just to be clear, this was 311 00:19:23,196 --> 00:19:25,836 Speaker 1: just they weren't trying when they were doing this to 312 00:19:25,916 --> 00:19:30,556 Speaker 1: fight cancer. They were just trying to develop a measles vaccine. 313 00:19:30,556 --> 00:19:32,996 Speaker 1: They were just saying, we're going to grow this measles 314 00:19:33,316 --> 00:19:36,276 Speaker 1: in culture over time and make it be attenuator, make 315 00:19:36,276 --> 00:19:39,156 Speaker 1: it be weaker. And it adapted in such a way 316 00:19:39,196 --> 00:19:42,476 Speaker 1: that it preferred to infect tumor cells and not to 317 00:19:42,556 --> 00:19:43,876 Speaker 1: infect non tumor. 318 00:19:43,636 --> 00:19:46,796 Speaker 3: Cells because of the way they adapted it. Because remember 319 00:19:46,836 --> 00:19:50,236 Speaker 3: that the cells that they were growing in the lab 320 00:19:50,396 --> 00:19:53,116 Speaker 3: that they could put the measles virus on were basically 321 00:19:53,196 --> 00:19:54,036 Speaker 3: cancer cells. 322 00:19:54,396 --> 00:19:56,436 Speaker 1: And is that just because those are easy cells to 323 00:19:56,436 --> 00:19:57,996 Speaker 1: grow because they liked to propagate. 324 00:19:58,156 --> 00:19:58,356 Speaker 4: Yeah. 325 00:19:58,436 --> 00:20:01,916 Speaker 3: Yeah, It's very, very difficult to grow non cancerous sects. 326 00:20:02,036 --> 00:20:04,476 Speaker 1: Yeah, it's like the problem with cancer, right, it just 327 00:20:04,556 --> 00:20:06,276 Speaker 1: loves to divide. 328 00:20:06,436 --> 00:20:10,116 Speaker 3: Yeah. Yeah. So they put this virus on on the 329 00:20:10,276 --> 00:20:14,556 Speaker 3: cells in culture, and the virus had actually adapted and 330 00:20:14,996 --> 00:20:19,516 Speaker 3: it had learned to use a receptor that is more 331 00:20:19,556 --> 00:20:23,436 Speaker 3: abundant on cancer cells than on normal cells, and it 332 00:20:23,516 --> 00:20:26,916 Speaker 3: was losing all sorts of things that it needed in 333 00:20:27,036 --> 00:20:30,676 Speaker 3: order to cause disease because it didn't need them to 334 00:20:30,796 --> 00:20:34,676 Speaker 3: be able to propagate the cancer cells. So it spontaneously 335 00:20:34,716 --> 00:20:38,476 Speaker 3: attenuated through a lot of mutations that arose in the 336 00:20:38,556 --> 00:20:42,076 Speaker 3: viral genome. And so there it was and had been 337 00:20:42,116 --> 00:20:45,796 Speaker 3: given to billions of people, and it looked like it 338 00:20:45,916 --> 00:20:53,156 Speaker 3: was fairly well adapted to test in human studies against cancer. 339 00:20:54,316 --> 00:20:57,596 Speaker 1: The measles virus used in the vaccine didn't make people sick, 340 00:20:57,836 --> 00:21:01,716 Speaker 1: and it tended to attack cancer cells. So Stephen started 341 00:21:01,796 --> 00:21:04,476 Speaker 1: using that form of the virus in studies to see 342 00:21:04,556 --> 00:21:08,436 Speaker 1: if measles could treat cancer. Eventually he landed on a 343 00:21:08,516 --> 00:21:12,116 Speaker 1: kind of cancer called multiple myeloma that can take hold 344 00:21:12,156 --> 00:21:15,356 Speaker 1: in the bone marrow and suppress the immune system. And 345 00:21:15,516 --> 00:21:19,636 Speaker 1: there was one multiple myeloma patient in particular who had 346 00:21:19,676 --> 00:21:23,036 Speaker 1: a huge effect on how Stephen thought about using measles 347 00:21:23,036 --> 00:21:26,516 Speaker 1: to fight cancer. The patient's name was Stacy. 348 00:21:26,316 --> 00:21:32,156 Speaker 3: Erholtz, so Stacy. She had multiple myeloma. She had been 349 00:21:32,956 --> 00:21:37,836 Speaker 3: on treatment for ten years, on and off, but probably 350 00:21:37,876 --> 00:21:40,796 Speaker 3: more on than off treatment for the first ten years 351 00:21:40,836 --> 00:21:44,596 Speaker 3: of her diagnosis, because every time she stopped the treatment, 352 00:21:45,396 --> 00:21:47,996 Speaker 3: or even if she continued on it, the disease would 353 00:21:48,076 --> 00:21:51,516 Speaker 3: come back and then she needs switch to something else. 354 00:21:52,076 --> 00:21:54,716 Speaker 3: She had a large tumor on her forehead that was 355 00:21:54,796 --> 00:21:59,636 Speaker 3: destroying the underlying bone and compressing her brain. She had 356 00:21:59,956 --> 00:22:03,556 Speaker 3: four other solid tumors, and then her bone marrow was 357 00:22:03,636 --> 00:22:07,996 Speaker 3: diffusely infiltrated with myeloma and it was moving fast, and 358 00:22:08,076 --> 00:22:10,516 Speaker 3: she was out of treatment and options at the time. 359 00:22:10,556 --> 00:22:11,396 Speaker 3: I mean, there was nothing. 360 00:22:11,476 --> 00:22:13,396 Speaker 1: She was going to die soon. 361 00:22:13,516 --> 00:22:16,996 Speaker 3: She was going to die, yeah, and she had three 362 00:22:17,076 --> 00:22:20,956 Speaker 3: children still at school and everything to play for. She 363 00:22:21,036 --> 00:22:24,356 Speaker 3: was fifty years old at the time. We'd moved up 364 00:22:24,396 --> 00:22:28,636 Speaker 3: through every dose level that FDA had negotiated with us. 365 00:22:28,676 --> 00:22:32,076 Speaker 3: Based on a starting dose level of a million, we'd 366 00:22:32,076 --> 00:22:35,516 Speaker 3: gone up to just tenfold short of a billion. 367 00:22:36,036 --> 00:22:38,396 Speaker 1: Like, how much did she get compared to how much 368 00:22:38,396 --> 00:22:40,276 Speaker 1: somebody gets when they get the measles vaxing. 369 00:22:40,356 --> 00:22:44,316 Speaker 3: It's about ten million doses of vaccine, okay. 370 00:22:43,956 --> 00:22:46,596 Speaker 1: For this one person, So she's getting quite a lot. 371 00:22:47,156 --> 00:22:48,916 Speaker 1: And what happens. 372 00:22:48,876 --> 00:22:53,876 Speaker 3: Well as with other patients, she had a reaction to 373 00:22:54,036 --> 00:22:57,956 Speaker 3: the infusion of virus. So she got the virus infused. 374 00:22:58,036 --> 00:23:02,236 Speaker 3: She felt fine for a couple of hours, and then 375 00:23:02,396 --> 00:23:06,756 Speaker 3: she started shivering and shaking, and her temperature rose and 376 00:23:06,796 --> 00:23:10,516 Speaker 3: she felt pretty unwell overnight, but by the following morning 377 00:23:10,596 --> 00:23:11,716 Speaker 3: it had settled down. 378 00:23:11,956 --> 00:23:15,196 Speaker 1: And is that essentially like a response to a massive infection. 379 00:23:15,396 --> 00:23:17,276 Speaker 1: She's essentially has this massive infection. 380 00:23:17,956 --> 00:23:22,996 Speaker 3: Yes, it's similar to that, although it wasn't causing measles 381 00:23:23,036 --> 00:23:26,276 Speaker 3: in her so it was it was more the body 382 00:23:26,356 --> 00:23:30,036 Speaker 3: reacting to the foreign stuff in the blood and a 383 00:23:30,156 --> 00:23:33,956 Speaker 3: very kind of rapid reaction. That settled down, and then 384 00:23:34,436 --> 00:23:39,316 Speaker 3: she left hospital, went home and after a few days 385 00:23:40,036 --> 00:23:44,876 Speaker 3: she started noticing that the tumor on her forehead she 386 00:23:44,996 --> 00:23:50,036 Speaker 3: and her family had named it Evan, and this tumor Evan, 387 00:23:50,236 --> 00:23:54,876 Speaker 3: started to shrink and actually melted away. And you know, 388 00:23:54,956 --> 00:24:02,596 Speaker 3: we conducted thorough evaluations on her intervals thereafter, and we 389 00:24:02,596 --> 00:24:05,556 Speaker 3: were staggered to see that she went into a complete 390 00:24:05,876 --> 00:24:10,916 Speaker 3: disease remission. She felt fantastic. You know. We thought at 391 00:24:10,956 --> 00:24:13,556 Speaker 3: the time, Okay, we've got it. This is the way 392 00:24:13,596 --> 00:24:18,316 Speaker 3: to cure multiple myeloma, and so we gave it to 393 00:24:18,396 --> 00:24:21,996 Speaker 3: a lot of other multiple myeloma patients and it didn't 394 00:24:22,036 --> 00:24:26,076 Speaker 3: work nearly so well. There was some partial responses, but 395 00:24:26,156 --> 00:24:30,076 Speaker 3: there was really nothing as dramatic as Stacy. So we 396 00:24:30,196 --> 00:24:34,836 Speaker 3: studied Stacy pretty intensively. To try and understand what was 397 00:24:34,916 --> 00:24:38,996 Speaker 3: special about her, because that would be the key to 398 00:24:39,276 --> 00:24:42,036 Speaker 3: the success of virotherapy. I mean, what we knew from 399 00:24:42,076 --> 00:24:45,516 Speaker 3: Stacy as wow, this can actually happen. You can give 400 00:24:45,516 --> 00:24:50,916 Speaker 3: a virus systemically nothing else, and you can get a 401 00:24:50,996 --> 00:24:56,036 Speaker 3: dramatic resolution of tumor at all sites. So the studies 402 00:24:56,076 --> 00:24:58,476 Speaker 3: that we did on Stacy showed that number one, she 403 00:24:58,636 --> 00:25:04,516 Speaker 3: had no anti measles antibody detectable. She had, however, been 404 00:25:04,636 --> 00:25:08,676 Speaker 3: vaccinated as a child, and then she had lost her 405 00:25:08,716 --> 00:25:13,716 Speaker 3: immune and she'd been revaccinated after her first stem cell transplant, 406 00:25:14,396 --> 00:25:18,316 Speaker 3: the immunity had come back, and she'd lost it again 407 00:25:18,676 --> 00:25:22,476 Speaker 3: after the second stem cell transplant. Huh, So she had 408 00:25:22,516 --> 00:25:26,236 Speaker 3: no antibodies to block the virus from getting to the target. 409 00:25:26,476 --> 00:25:30,276 Speaker 1: So basically, her immune system was the same as the 410 00:25:30,276 --> 00:25:32,956 Speaker 1: immune system of a person who has never had measles 411 00:25:32,996 --> 00:25:34,876 Speaker 1: and not been vaccinated for measles. 412 00:25:34,916 --> 00:25:39,076 Speaker 3: Not quite, because we looked at her T cells, the 413 00:25:39,196 --> 00:25:42,476 Speaker 3: cells that come into the tumor and attack the virus 414 00:25:42,556 --> 00:25:47,236 Speaker 3: infected cells, and she had a very high level of 415 00:25:47,476 --> 00:25:49,236 Speaker 3: anti measles T cells. 416 00:25:50,156 --> 00:25:54,276 Speaker 1: As it turned out, this was a perfect combination. Stacy 417 00:25:54,316 --> 00:25:58,116 Speaker 1: didn't have any antibodies, so the measles virus was free 418 00:25:58,156 --> 00:26:00,916 Speaker 1: to go into her body and infect her tumor cells. 419 00:26:01,436 --> 00:26:05,436 Speaker 1: But she did have anti measles T cells, so once 420 00:26:05,476 --> 00:26:08,836 Speaker 1: the measles infected the tumor cells, those T cells could 421 00:26:08,876 --> 00:26:12,476 Speaker 1: attack hack her tumor cells. So now she had both 422 00:26:12,596 --> 00:26:16,476 Speaker 1: the measles virus and her own T cells attacking and 423 00:26:16,596 --> 00:26:21,076 Speaker 1: destroying the tumor. Stephen told me he learned a lot 424 00:26:21,156 --> 00:26:23,916 Speaker 1: from Stacy's case and it helped him figure out how 425 00:26:23,956 --> 00:26:25,756 Speaker 1: to move forward with his research. 426 00:26:26,516 --> 00:26:29,756 Speaker 3: Yeah, so there are two pathways that we took. One 427 00:26:29,796 --> 00:26:33,236 Speaker 3: was to switch to a different virus that people do 428 00:26:33,356 --> 00:26:37,956 Speaker 3: not have prior exposure to, and so we started working 429 00:26:37,996 --> 00:26:43,316 Speaker 3: with the sicular stomatitis virus VSV, which causes naturally a 430 00:26:43,396 --> 00:26:45,356 Speaker 3: blistering illness in cattle. 431 00:26:45,756 --> 00:26:49,076 Speaker 1: Uh huh. So that's that's one way to get around 432 00:26:49,236 --> 00:26:53,356 Speaker 1: the immunity to measles problem. Right, You're you're using a 433 00:26:53,436 --> 00:26:56,556 Speaker 1: virus that most people don't get and are therefore not 434 00:26:56,636 --> 00:26:59,196 Speaker 1: immune to. How is that going? 435 00:26:59,716 --> 00:27:03,316 Speaker 3: It's going. It's going well. Where you know, it's not 436 00:27:03,556 --> 00:27:07,636 Speaker 3: in every cancer that we see anything, but the results 437 00:27:07,636 --> 00:27:11,756 Speaker 3: that we have at the moment are looking very promising 438 00:27:11,796 --> 00:27:16,876 Speaker 3: in certain indications. The other approach we took was to 439 00:27:17,836 --> 00:27:23,476 Speaker 3: stealth measles virus so that it would still be measles virus. 440 00:27:23,516 --> 00:27:26,156 Speaker 3: It would still be that vaccine strain, but it would 441 00:27:26,156 --> 00:27:31,156 Speaker 3: have a new coat, huh, that was no longer recognizable 442 00:27:31,316 --> 00:27:36,116 Speaker 3: by circulating antibodies, And so we would in that situation, 443 00:27:36,236 --> 00:27:39,556 Speaker 3: we would have a virus we could give systemically that 444 00:27:39,596 --> 00:27:44,676 Speaker 3: would penetrate the tumor, and that would then be subject 445 00:27:44,716 --> 00:27:48,356 Speaker 3: to attack by these t cells that exist in people 446 00:27:48,396 --> 00:27:50,716 Speaker 3: who've been measles immunized. 447 00:27:51,716 --> 00:27:56,356 Speaker 1: Well, does it work in mice? Okay, it's a start. 448 00:27:56,476 --> 00:28:00,436 Speaker 3: We haven't taken that one into human clinical tests yet. 449 00:28:00,996 --> 00:28:04,516 Speaker 1: Let's talk for a minute about using viruses to treat 450 00:28:04,596 --> 00:28:10,316 Speaker 1: cancer more broadly. Right, people have been trying other viruses 451 00:28:10,356 --> 00:28:13,396 Speaker 1: to treat other cancers. What's the state of the field 452 00:28:13,716 --> 00:28:14,436 Speaker 1: more generally? 453 00:28:15,116 --> 00:28:18,516 Speaker 3: There has been incremental progress, and there are viruses that 454 00:28:18,596 --> 00:28:23,676 Speaker 3: are looking very promising in brain cancer injected into the 455 00:28:23,676 --> 00:28:29,196 Speaker 3: brain tumor, in bladder cancer instilled into the bladder, and 456 00:28:29,596 --> 00:28:34,836 Speaker 3: I think many ongoing programs which show great promise. So 457 00:28:36,356 --> 00:28:41,356 Speaker 3: I'm still a complete believer in the capability of viruses 458 00:28:41,436 --> 00:28:45,236 Speaker 3: to really bring a transformation in the approach to cancer therapy. 459 00:28:47,036 --> 00:28:53,076 Speaker 3: I feel like it's coming soon, but not everybody agrees 460 00:28:53,116 --> 00:28:53,356 Speaker 3: with me. 461 00:28:56,156 --> 00:28:58,476 Speaker 1: I appreciate your time so much. It was great to 462 00:28:58,516 --> 00:28:58,996 Speaker 1: talk with you. 463 00:28:59,356 --> 00:29:01,876 Speaker 3: Great talking with you too. Thank you very much. 464 00:29:02,956 --> 00:29:06,156 Speaker 1: Stephen Russell founded the Department of Molecular Medicine at the 465 00:29:06,196 --> 00:29:09,556 Speaker 1: Mayo Clinic. He is currently the CEO of Viria, a 466 00:29:09,596 --> 00:29:12,396 Speaker 1: company that is trying to use viruses to treat cancer. 467 00:29:13,636 --> 00:29:16,276 Speaker 1: One last thing, Stephen told me that he's still in 468 00:29:16,356 --> 00:29:19,956 Speaker 1: touch with Stacy Airholtz, the patient who had multiple myeloma 469 00:29:20,036 --> 00:29:23,436 Speaker 1: and went into complete remission after being treated with measles. 470 00:29:23,756 --> 00:29:27,436 Speaker 3: Stacy Holtz is, you know, a guiding light for me. 471 00:29:28,116 --> 00:29:31,076 Speaker 3: I'm friends with her, I'm in contact with her on 472 00:29:31,116 --> 00:29:35,596 Speaker 3: a regular basis, and she's got grandkids. She's having a 473 00:29:35,636 --> 00:29:37,556 Speaker 3: happy life. She's a very positive woman. 474 00:29:38,676 --> 00:29:40,996 Speaker 1: Thanks to both of our guests today, Michael Minna and 475 00:29:41,036 --> 00:29:44,236 Speaker 1: Stephen Russell. Next week on the show, I talked to 476 00:29:44,236 --> 00:29:47,716 Speaker 1: a scientist who discovered an entirely new kind of virus, 477 00:29:48,556 --> 00:29:51,356 Speaker 1: and it turns out this virus is everywhere. 478 00:29:51,796 --> 00:29:56,236 Speaker 4: Boiling Springs Lake, deep Sea Sediment's Korean air sample, monkey feces, 479 00:29:56,316 --> 00:30:00,676 Speaker 4: dragonfly guts, soil just outside the lab at Portland State University. 480 00:30:00,836 --> 00:30:06,036 Speaker 4: Basically anywhere that we have looked, we found these cruci viruses. 481 00:30:07,996 --> 00:30:11,156 Speaker 1: Incubation is a co production of Pushkin Industries and Ruby 482 00:30:11,196 --> 00:30:15,676 Speaker 1: Studio at iHeartMedia. It's produced by Kate Furby and Brittany Cronin. 483 00:30:15,956 --> 00:30:18,956 Speaker 1: The show is edited by Lacey Roberts. It's mastered by 484 00:30:18,996 --> 00:30:23,556 Speaker 1: Sarah Bruguer, fact checking by Joseph Friedman. Our executive producers 485 00:30:23,556 --> 00:30:27,076 Speaker 1: are Lacey Roberts and Matt Romano. I'm Jacob Goldstein. Thanks 486 00:30:27,076 --> 00:30:27,596 Speaker 1: for listening.