1 00:00:02,200 --> 00:00:04,280 Speaker 1: I thought I knew what I needed to know about 2 00:00:04,360 --> 00:00:08,080 Speaker 1: what happens when you get measles. You get a fever 3 00:00:08,360 --> 00:00:12,120 Speaker 1: and a rash. Maybe you get very sick. If you're 4 00:00:12,160 --> 00:00:17,000 Speaker 1: really unlucky, you die. But chances are you get measles, 5 00:00:17,040 --> 00:00:20,280 Speaker 1: you get better, and that's the end of it as 6 00:00:20,320 --> 00:00:20,920 Speaker 1: it happens. 7 00:00:21,120 --> 00:00:21,959 Speaker 2: I was wrong. 8 00:00:22,600 --> 00:00:24,840 Speaker 1: I did not know what I needed to know about measles, 9 00:00:24,960 --> 00:00:28,680 Speaker 1: because a recent discovery has blown open our whole idea 10 00:00:29,080 --> 00:00:31,640 Speaker 1: of what the measles virus does to our bodies. 11 00:00:32,000 --> 00:00:35,479 Speaker 3: The world thought that measles was done being discovered, and 12 00:00:35,520 --> 00:00:40,680 Speaker 3: then boom, all of a sudden, there's this new idea 13 00:00:40,960 --> 00:00:45,120 Speaker 3: of something that really had massive, massive consequences on humans 14 00:00:45,120 --> 00:00:46,520 Speaker 3: that we didn't even realize. 15 00:00:47,320 --> 00:00:51,160 Speaker 1: I'm Jacob Goldstein and this is Incubation, a show about viruses. 16 00:00:51,600 --> 00:00:55,040 Speaker 1: Today on the show, how measles attacks your immune system 17 00:00:55,600 --> 00:00:59,520 Speaker 1: and how researchers are trying to use measles to cure cancer. 18 00:01:09,160 --> 00:01:11,360 Speaker 1: My guest for the first half of today's show is 19 00:01:11,440 --> 00:01:17,160 Speaker 1: Michael Minna. He's an epidemiologist slash immunologist slash physician, and 20 00:01:17,200 --> 00:01:20,200 Speaker 1: he's kind of a measles superfan. He did a lot 21 00:01:20,200 --> 00:01:22,160 Speaker 1: of work we'll talk about today while he was a 22 00:01:22,200 --> 00:01:26,160 Speaker 1: professor at Harvard. He's now chief scientific officer at a 23 00:01:26,200 --> 00:01:30,080 Speaker 1: company called Emed. We started by talking about a surprising 24 00:01:30,160 --> 00:01:33,080 Speaker 1: thing that happened after the measles vaccine was introduced in 25 00:01:33,120 --> 00:01:36,520 Speaker 1: the nineteen sixties. As more and more kids were vaccinated 26 00:01:36,560 --> 00:01:39,920 Speaker 1: against measles, the rate at which kids were dying of 27 00:01:39,959 --> 00:01:44,480 Speaker 1: infectious disease went down way more than anybody expected. It 28 00:01:44,520 --> 00:01:47,280 Speaker 1: went down so much that it couldn't be explained by 29 00:01:47,319 --> 00:01:48,480 Speaker 1: measles alone. 30 00:01:49,040 --> 00:01:54,440 Speaker 3: After the vaccine was introduced, we saw market reductions in 31 00:01:54,560 --> 00:02:00,200 Speaker 3: childhood mortality overall following the vaccine, which drove a a 32 00:02:00,200 --> 00:02:04,080 Speaker 3: lot of questions. Why did that happen? Is it the 33 00:02:04,160 --> 00:02:09,200 Speaker 3: vaccine actually acting directly to somehow prevent other infections, or 34 00:02:09,240 --> 00:02:10,600 Speaker 3: is there something else at play? 35 00:02:11,120 --> 00:02:14,079 Speaker 1: Even if zero people died of measles, Even if zero 36 00:02:14,280 --> 00:02:17,760 Speaker 1: children died of measles, you wouldn't get that large of 37 00:02:17,800 --> 00:02:21,560 Speaker 1: a reduction immortality, Right, some weird thing is going. 38 00:02:21,360 --> 00:02:23,040 Speaker 4: On, that's exactly right. 39 00:02:23,160 --> 00:02:25,519 Speaker 1: Weird good thing, weird usually. 40 00:02:25,320 --> 00:02:27,880 Speaker 3: That's right, A very weird good thing was going on. 41 00:02:28,280 --> 00:02:31,600 Speaker 1: So now we have this interesting kind of happy mystery 42 00:02:31,680 --> 00:02:35,000 Speaker 1: in a way, Why are so few children dying of 43 00:02:35,000 --> 00:02:38,720 Speaker 1: infectious disease after the rollout of the measles vaccine. What 44 00:02:38,800 --> 00:02:41,359 Speaker 1: do you do to try and figure out what's going on? 45 00:02:41,840 --> 00:02:47,359 Speaker 3: We said, well, maybe it's because measles had detrimental effects 46 00:02:47,440 --> 00:02:51,600 Speaker 3: on somebody's immune memory that might be putting them at 47 00:02:51,720 --> 00:02:54,760 Speaker 3: risk for other stuff, other infections, And so what we 48 00:02:54,800 --> 00:02:57,359 Speaker 3: did was we said, well, if that's the case, then 49 00:02:57,360 --> 00:03:00,640 Speaker 3: if we look at a lot of data, we map 50 00:03:01,080 --> 00:03:04,200 Speaker 3: the numbers of cases of measles to the numbers of 51 00:03:04,320 --> 00:03:07,919 Speaker 3: deaths from other things besides measles from year to year. 52 00:03:09,000 --> 00:03:13,760 Speaker 3: And what we found was profoundly predictive. Is if you 53 00:03:13,840 --> 00:03:16,760 Speaker 3: asked what were the number of measles cases in nineteen 54 00:03:16,840 --> 00:03:20,519 Speaker 3: forty nine and what were the numbers of deaths from 55 00:03:20,600 --> 00:03:25,720 Speaker 3: non measles infections from nineteen forty nine, nineteen fifty and 56 00:03:25,840 --> 00:03:29,640 Speaker 3: nineteen fifty one. When you occrued all three years, it 57 00:03:29,680 --> 00:03:35,040 Speaker 3: became extraordinarily predictive of how many children would die over 58 00:03:35,080 --> 00:03:38,840 Speaker 3: the next three years of non measles infectious related deaths. 59 00:03:39,440 --> 00:03:41,640 Speaker 1: So, just to be clear, what you found is that 60 00:03:41,920 --> 00:03:45,000 Speaker 1: when there's a measle's outbreak in one year, the rate 61 00:03:45,040 --> 00:03:48,240 Speaker 1: at which kids died from other infectious diseases went up 62 00:03:48,360 --> 00:03:49,440 Speaker 1: in the next few years. 63 00:03:49,560 --> 00:03:50,560 Speaker 3: That's exactly right. 64 00:03:50,680 --> 00:03:53,680 Speaker 1: So you used in your answer just there this phrase 65 00:03:53,760 --> 00:03:57,280 Speaker 1: that I just want to spend a moment on immune memory. 66 00:03:58,000 --> 00:03:59,360 Speaker 1: What is immune memory. 67 00:04:00,040 --> 00:04:05,520 Speaker 3: Immune memory is very similar to our regular memory. All 68 00:04:05,560 --> 00:04:08,680 Speaker 3: of our body is memories, whether it be muscle memory, 69 00:04:08,880 --> 00:04:13,280 Speaker 3: brain memory, or immune memory is stored in cells. The 70 00:04:13,320 --> 00:04:17,480 Speaker 3: way that immune memory works is when you bump up 71 00:04:17,520 --> 00:04:21,440 Speaker 3: against a pathogen, as let's say, a virus like measles 72 00:04:21,520 --> 00:04:26,560 Speaker 3: or coronavirus, whatever it might be, your body actually sees it, 73 00:04:26,560 --> 00:04:30,719 Speaker 3: it recognizes that virus, it learns from it, and it 74 00:04:30,760 --> 00:04:34,440 Speaker 3: actually remembers it in B cells and T cells and 75 00:04:34,520 --> 00:04:39,159 Speaker 3: plasma cells. And that's how our immune system works in 76 00:04:39,240 --> 00:04:42,359 Speaker 3: terms of developing immune memory and utilizing it to combat 77 00:04:43,120 --> 00:04:45,080 Speaker 3: infections that we see in the future. 78 00:04:45,800 --> 00:04:47,960 Speaker 1: Is what you're finding that in some way measles is 79 00:04:48,040 --> 00:04:53,239 Speaker 1: attacking the kid's immune memory. Is that the hypothesis that follows. 80 00:04:53,680 --> 00:04:58,240 Speaker 3: That's exactly the hypothesis that follows. For example, if if 81 00:04:58,279 --> 00:05:02,760 Speaker 3: a six year old got measles, then maybe that measles 82 00:05:02,800 --> 00:05:07,360 Speaker 3: infection could destroy some of the immune memory the defenses 83 00:05:07,360 --> 00:05:11,560 Speaker 3: that that child gained over the last six years and therefore, 84 00:05:11,839 --> 00:05:15,039 Speaker 3: when they are seven or eight years old, are actually 85 00:05:15,160 --> 00:05:19,480 Speaker 3: more vulnerable than they otherwise would have been to those 86 00:05:19,480 --> 00:05:21,680 Speaker 3: infections that they gained the immunity to when they were 87 00:05:21,680 --> 00:05:22,240 Speaker 3: two or three. 88 00:05:23,360 --> 00:05:27,000 Speaker 1: So, okay, you have this hypothesis, how do you test it? 89 00:05:27,120 --> 00:05:29,280 Speaker 1: How do you investigate what's really going on? 90 00:05:29,800 --> 00:05:35,359 Speaker 3: There's a long, rich history of how to predict where 91 00:05:35,400 --> 00:05:38,039 Speaker 3: measles is going to go next. It's actually famous for 92 00:05:38,120 --> 00:05:42,280 Speaker 3: how predictive it is because it is so infectious that 93 00:05:42,320 --> 00:05:44,920 Speaker 3: you just need to know how many people are vaccinated 94 00:05:44,960 --> 00:05:47,400 Speaker 3: in a community, and if there's any measles anywhere in 95 00:05:47,440 --> 00:05:51,320 Speaker 3: the region, you can expect that pretty soon at below 96 00:05:51,360 --> 00:05:53,520 Speaker 3: certain levels, there's going to be an outbreak. 97 00:05:53,360 --> 00:05:55,560 Speaker 1: Below certain levels of vaccination. 98 00:05:55,400 --> 00:05:57,760 Speaker 3: That's right, and that's why measles is considered the canary 99 00:05:57,760 --> 00:06:01,839 Speaker 3: and the coal mine for vaccine rates. Literally the thing 100 00:06:01,920 --> 00:06:04,719 Speaker 3: that pops up on the radar when you say who's 101 00:06:04,920 --> 00:06:08,039 Speaker 3: what communities are having trouble vaccinating their population, and boom, 102 00:06:08,080 --> 00:06:10,440 Speaker 3: if you see measles, you know they're having trouble vaccinating 103 00:06:10,440 --> 00:06:14,240 Speaker 3: their populations. And so what we can do is we 104 00:06:14,279 --> 00:06:19,479 Speaker 3: can leverage everything we know about measles epidemiology to help 105 00:06:19,560 --> 00:06:24,800 Speaker 3: identify where might outbreaks happen, And that's exactly what Rick 106 00:06:24,839 --> 00:06:28,640 Speaker 3: de Swart and his team did. They are in the Netherlands. 107 00:06:28,640 --> 00:06:32,039 Speaker 3: He's at Erasmus which is in Rotterdam, and so he 108 00:06:32,120 --> 00:06:34,719 Speaker 3: was able to say, hey, right in our backyard, there's 109 00:06:34,760 --> 00:06:39,159 Speaker 3: a community that for religious reasons, they chose not to 110 00:06:39,240 --> 00:06:42,600 Speaker 3: vaccinate their children against measles. So they said, well, if 111 00:06:42,640 --> 00:06:44,159 Speaker 3: you're not going to get a vaccine, would you be 112 00:06:44,240 --> 00:06:47,720 Speaker 3: interested or willing to have us just draw a little 113 00:06:47,720 --> 00:06:51,720 Speaker 3: sample of blood from your kids today and should measles 114 00:06:51,800 --> 00:06:53,840 Speaker 3: catch up to them in the future, could we come 115 00:06:53,880 --> 00:06:55,800 Speaker 3: back and draw another sample of blood. 116 00:06:56,720 --> 00:07:00,279 Speaker 1: You have the before measles blood samples, and you have 117 00:07:00,400 --> 00:07:04,120 Speaker 1: from the same children the after measles blood samples. We 118 00:07:04,200 --> 00:07:08,000 Speaker 1: have everything ready to go. What happened there was a. 119 00:07:07,920 --> 00:07:11,560 Speaker 3: Big outbreak, and almost immediately we start seeing that we 120 00:07:11,600 --> 00:07:15,560 Speaker 3: can measure all of these antibodies in the blood samples. 121 00:07:15,840 --> 00:07:18,640 Speaker 3: And then when we actually look at the blood samples 122 00:07:19,240 --> 00:07:21,840 Speaker 3: from right before the kids got measles to those same 123 00:07:22,120 --> 00:07:25,720 Speaker 3: kids blood samples that they collected after, we saw market 124 00:07:25,840 --> 00:07:30,440 Speaker 3: reductions not just in a couple antibodies, but some kids 125 00:07:30,520 --> 00:07:34,920 Speaker 3: lost eighty percent of all of the diversity of their 126 00:07:35,000 --> 00:07:39,280 Speaker 3: antibodies that existed in that blood sample before they got measles, 127 00:07:39,560 --> 00:07:42,320 Speaker 3: and we compared it. We said, well, maybe that's normal. 128 00:07:42,360 --> 00:07:45,480 Speaker 3: So we looked at kids who had gotten vaccinated for measles. 129 00:07:45,800 --> 00:07:48,400 Speaker 3: We look the kids who just had no infections, and 130 00:07:48,440 --> 00:07:51,520 Speaker 3: what we saw was the average person with from any 131 00:07:51,560 --> 00:07:54,880 Speaker 3: two time points, there'd be like five percent difference in 132 00:07:54,920 --> 00:07:58,800 Speaker 3: their overall antibody repertoire. But the measles kids, the kids 133 00:07:58,840 --> 00:08:02,760 Speaker 3: who got measles lost anywhere from twenty percent to eighty 134 00:08:02,840 --> 00:08:07,360 Speaker 3: percent of their whole immunological memory pool. This is the 135 00:08:07,400 --> 00:08:12,679 Speaker 3: whole lifetime of immune memory protection that they've spent years 136 00:08:12,720 --> 00:08:17,080 Speaker 3: building up and building up, just poof wiped away because 137 00:08:17,120 --> 00:08:20,640 Speaker 3: of this measles infection and these kids. What that means 138 00:08:20,720 --> 00:08:23,160 Speaker 3: is now, across millions and millions of kids who were 139 00:08:23,800 --> 00:08:27,280 Speaker 3: before the vaccines, almost every child got measles. And so 140 00:08:28,520 --> 00:08:31,280 Speaker 3: at that scale, when you have so many people getting 141 00:08:31,320 --> 00:08:36,480 Speaker 3: measles and this effect happening, which we call immunological amnesia, 142 00:08:36,559 --> 00:08:40,920 Speaker 3: essentially they forgot their body, forgot because of the infection 143 00:08:41,920 --> 00:08:45,199 Speaker 3: the immune memories that they formed before the infection. What 144 00:08:45,240 --> 00:08:47,439 Speaker 3: that means is that you have all of these kids 145 00:08:47,440 --> 00:08:51,600 Speaker 3: that are more susceptible to other infectious diseases. So most 146 00:08:52,000 --> 00:08:55,440 Speaker 3: most kids would survive. But it turned out that of 147 00:08:55,480 --> 00:09:00,400 Speaker 3: those kids who did die from other things, about half 148 00:09:00,480 --> 00:09:06,080 Speaker 3: of those deaths could be attributed to the immune amnesia 149 00:09:06,120 --> 00:09:08,280 Speaker 3: associated with measles. 150 00:09:08,720 --> 00:09:11,760 Speaker 1: Is there something particularly insidious if that happens at a 151 00:09:11,800 --> 00:09:15,000 Speaker 1: population level? If you imagine a group of people in 152 00:09:15,040 --> 00:09:18,079 Speaker 1: the absence of a vaccine entirely, where it's like, not 153 00:09:18,120 --> 00:09:21,480 Speaker 1: only is each kid more vulnerable, but because all the 154 00:09:21,520 --> 00:09:24,600 Speaker 1: other kids are more vulnerable, everybody is more vulnerable. Is 155 00:09:24,640 --> 00:09:26,320 Speaker 1: there something like that that happens? 156 00:09:26,480 --> 00:09:28,960 Speaker 3: I am so happy you asked that question. 157 00:09:29,800 --> 00:09:30,400 Speaker 1: You're welcome. 158 00:09:31,320 --> 00:09:36,040 Speaker 3: So yes, it is. It's a much much harder thing 159 00:09:36,280 --> 00:09:36,960 Speaker 3: to measure. 160 00:09:37,320 --> 00:09:39,160 Speaker 1: I mean, it's sort of like the reverse of herd 161 00:09:39,160 --> 00:09:42,360 Speaker 1: immunity in some weird broad spectrum way, right. 162 00:09:42,440 --> 00:09:45,400 Speaker 3: So what's so interesting that you bring that up? Because 163 00:09:45,440 --> 00:09:48,560 Speaker 3: before my measles work, I was working on influenza and 164 00:09:48,600 --> 00:09:52,520 Speaker 3: its impacts on other bacterial infections, And during my PhD, 165 00:09:52,600 --> 00:09:58,400 Speaker 3: I coined a term called generalized herd effects, and I 166 00:09:58,600 --> 00:10:02,319 Speaker 3: explicitly didn't call it herd immuni because maybe it's not 167 00:10:02,360 --> 00:10:05,280 Speaker 3: going to reduce things but maybe you could exacerbate things. 168 00:10:05,400 --> 00:10:08,559 Speaker 1: Yeah, how about herd vulnerability? I want the opposite. What's 169 00:10:08,600 --> 00:10:09,960 Speaker 1: the opposite of herding units? 170 00:10:10,280 --> 00:10:13,040 Speaker 3: That's exactly. Herd vulnerability is a great term, and so 171 00:10:13,120 --> 00:10:15,200 Speaker 3: the idea there as well. If you have a pathogen 172 00:10:15,720 --> 00:10:19,880 Speaker 3: that's impacting your susceptibility to a lot of other pathogens, 173 00:10:20,280 --> 00:10:24,360 Speaker 3: you could, you know, create a herd vulnerability because of 174 00:10:24,440 --> 00:10:28,880 Speaker 3: infections of that initial pathogen. And on the contrary, if 175 00:10:28,920 --> 00:10:31,839 Speaker 3: you figure out a vaccine against that initial pathogen, like 176 00:10:31,920 --> 00:10:37,480 Speaker 3: the measles vaccine, you create massive benefits in getting rid 177 00:10:37,559 --> 00:10:39,559 Speaker 3: of that herd vulnerability. 178 00:10:40,240 --> 00:10:43,680 Speaker 1: So what's going on on a cellular level? 179 00:10:44,320 --> 00:10:48,720 Speaker 3: As far as we know, measles is unique in its class. 180 00:10:49,000 --> 00:10:51,319 Speaker 3: It's actually it's an amazing story. So if you give 181 00:10:51,360 --> 00:10:54,079 Speaker 3: me forty seconds to describe it, I. 182 00:10:54,040 --> 00:10:56,960 Speaker 1: Will, Oh, go, you got a minute if you need it. 183 00:10:57,800 --> 00:11:00,920 Speaker 3: So, every virus has a receptor that it binds to 184 00:11:01,000 --> 00:11:03,880 Speaker 3: and needs to latch onto on a cell. For measles, 185 00:11:04,040 --> 00:11:08,160 Speaker 3: it's this molecule that's called CD one fifty, or it's 186 00:11:08,200 --> 00:11:13,800 Speaker 3: called SLAM. SLAM stands for a signaling of Lymphocyte activation molecule, 187 00:11:14,160 --> 00:11:19,199 Speaker 3: and it's when somebody gets a measles infection. The virus 188 00:11:19,440 --> 00:11:22,439 Speaker 3: comes into somebody's lungs and we have these cool dendritic cells. 189 00:11:22,440 --> 00:11:24,480 Speaker 3: And dendritic cells are like these cells of big arms, 190 00:11:24,520 --> 00:11:26,760 Speaker 3: and they go out and reach pathogens that they that 191 00:11:26,800 --> 00:11:29,640 Speaker 3: they know shouldn't be there, and they capture them and 192 00:11:29,640 --> 00:11:32,679 Speaker 3: bring them in and then they shuttle them into the 193 00:11:32,800 --> 00:11:35,720 Speaker 3: lymphoid system, which is where all of our immune cells are. 194 00:11:36,160 --> 00:11:39,040 Speaker 3: And so normally what would happen is the dendritic cells 195 00:11:39,120 --> 00:11:43,319 Speaker 3: would say, hey, immune system, you know, here's a pathogen, 196 00:11:43,720 --> 00:11:46,679 Speaker 3: take it and develop immune memory against it, and so 197 00:11:46,720 --> 00:11:49,400 Speaker 3: it literally hands it off to B cells and T cells. 198 00:11:50,200 --> 00:11:54,319 Speaker 3: In this case, when the dendritic cell does exactly that 199 00:11:54,400 --> 00:11:58,200 Speaker 3: same process, it hands off Measles virus to B cells 200 00:11:58,200 --> 00:12:00,840 Speaker 3: and T cells. And this is a big mistake because 201 00:12:00,840 --> 00:12:03,520 Speaker 3: now you have a virus that, instead of being handed 202 00:12:03,520 --> 00:12:05,280 Speaker 3: off to a B and T cell and having that 203 00:12:05,320 --> 00:12:08,920 Speaker 3: B and T cell you know, ingested and learn from it, 204 00:12:09,000 --> 00:12:12,840 Speaker 3: the measles flips on its receptor utilization and grabs City 205 00:12:12,920 --> 00:12:17,320 Speaker 3: one fifty or slam these molecules on the outside of 206 00:12:17,400 --> 00:12:20,120 Speaker 3: the B cells and the T cells, and it actually 207 00:12:20,960 --> 00:12:22,800 Speaker 3: invades them like a trojan horse. 208 00:12:23,160 --> 00:12:26,400 Speaker 1: Aha. So It's like a like a trick, right, Like 209 00:12:26,800 --> 00:12:30,720 Speaker 1: measles is there acting like a normal virus until it 210 00:12:30,760 --> 00:12:32,880 Speaker 1: gets to the B cells in the T cells in 211 00:12:32,880 --> 00:12:35,760 Speaker 1: the immune system. And normally the B cells in the 212 00:12:35,760 --> 00:12:39,240 Speaker 1: T cells would destroy measles, would destroy the virus. But 213 00:12:39,360 --> 00:12:42,520 Speaker 1: in that case, this doesn't happen, right, So what does happen? 214 00:12:43,000 --> 00:12:43,200 Speaker 4: Now? 215 00:12:43,200 --> 00:12:46,720 Speaker 3: It's in the cushy lymphoid system and it's just full 216 00:12:46,760 --> 00:12:50,880 Speaker 3: of food and it just replicates like crazy inside the 217 00:12:50,880 --> 00:12:56,240 Speaker 3: immune system, all the while sell by cell destroying the 218 00:12:56,400 --> 00:13:00,360 Speaker 3: valuable immune memory is stored inside each of those that 219 00:13:00,400 --> 00:13:01,080 Speaker 3: it's destroying. 220 00:13:01,559 --> 00:13:05,760 Speaker 1: That is very compelling that it's like a virological horror 221 00:13:05,800 --> 00:13:07,560 Speaker 1: movie inside your body. 222 00:13:08,200 --> 00:13:12,200 Speaker 3: It absolutely is. And what we see when we see 223 00:13:12,200 --> 00:13:15,839 Speaker 3: the prototypic measles rash, which is like dots all over 224 00:13:15,920 --> 00:13:19,600 Speaker 3: a child's body, red dots, it is truly the tip 225 00:13:19,640 --> 00:13:22,680 Speaker 3: of the iceberg in terms of where the damage is 226 00:13:22,720 --> 00:13:27,040 Speaker 3: being done. The real damage inside a child is much 227 00:13:27,200 --> 00:13:30,160 Speaker 3: much deeper and much much more profound in terms of, 228 00:13:30,559 --> 00:13:34,800 Speaker 3: you know, destroying a huge, huge population of very important 229 00:13:34,840 --> 00:13:36,400 Speaker 3: cells inside of our body. 230 00:13:37,200 --> 00:13:41,280 Speaker 1: So if you sort of step back and think about 231 00:13:41,320 --> 00:13:44,960 Speaker 1: this idea that measles not only gives you measles but 232 00:13:45,080 --> 00:13:49,439 Speaker 1: makes you vulnerable to lots of other infectious diseases. Does 233 00:13:49,480 --> 00:13:52,240 Speaker 1: it make you think differently about virus? Is about the 234 00:13:52,280 --> 00:13:54,240 Speaker 1: immune system? Like where do you land? 235 00:13:54,920 --> 00:14:01,560 Speaker 3: Measles brings together for me mathematics, biology, ecology and evolution 236 00:14:02,520 --> 00:14:10,440 Speaker 3: and vaccinology, and I love bringing those pieces together, and 237 00:14:10,480 --> 00:14:14,120 Speaker 3: it gives you a very deep appreciation for the delicate 238 00:14:14,160 --> 00:14:20,280 Speaker 3: balance we have between infectious diseases, immunity, cancer, and autoimmune disease, 239 00:14:20,560 --> 00:14:24,640 Speaker 3: and how those all interplay with each other. You know, 240 00:14:24,840 --> 00:14:28,320 Speaker 3: the world thought that measles was done being discovered, and 241 00:14:28,360 --> 00:14:34,000 Speaker 3: then boom, all of a sudden, there's this new idea 242 00:14:34,280 --> 00:14:38,680 Speaker 3: of something that really had massive, massive consequences on humans 243 00:14:38,680 --> 00:14:42,160 Speaker 3: that we didn't even realize. And so it drives this 244 00:14:42,680 --> 00:14:46,560 Speaker 3: renewed excitement around measles vaccination and the importance of it. 245 00:14:46,560 --> 00:14:49,760 Speaker 3: It's not just a cool finding. It hopefully helps us 246 00:14:50,040 --> 00:14:53,760 Speaker 3: move further and further towards eradication of the virus altogether. 247 00:14:56,720 --> 00:14:58,440 Speaker 1: It was great to talk with you. Thank you so 248 00:14:58,520 --> 00:14:59,160 Speaker 1: much for your time. 249 00:15:00,000 --> 00:15:00,600 Speaker 3: Thank you so much. 250 00:15:00,720 --> 00:15:03,960 Speaker 1: It was a lot of fun. Michael Minna is Chief 251 00:15:04,000 --> 00:15:07,880 Speaker 1: Science Officer at EMED Digital Healthcare. He was previously a 252 00:15:07,920 --> 00:15:11,360 Speaker 1: professor at the Harvard School of Public Health. When we 253 00:15:11,400 --> 00:15:25,920 Speaker 1: come back using measles to fight cancer, going back all 254 00:15:25,960 --> 00:15:29,400 Speaker 1: the way to the eighteen hundreds, which was before anybody 255 00:15:29,520 --> 00:15:32,640 Speaker 1: even knew what a virus really was, there have been 256 00:15:32,760 --> 00:15:36,400 Speaker 1: occasional reports of cancer patients who get some kind of 257 00:15:36,480 --> 00:15:41,200 Speaker 1: viral infection and then go into remission from cancer, and 258 00:15:41,320 --> 00:15:44,760 Speaker 1: at a certain level this makes sense. Viruses are highly 259 00:15:44,800 --> 00:15:48,920 Speaker 1: evolved to enter and destroy cells. Normally we think of 260 00:15:48,920 --> 00:15:51,280 Speaker 1: this as a bad thing, but if a virus is 261 00:15:51,440 --> 00:15:55,000 Speaker 1: entering and destroying cancer cells, this inter a cell and 262 00:15:55,040 --> 00:15:58,880 Speaker 1: destroy it property might be a very good thing. By 263 00:15:58,960 --> 00:16:02,880 Speaker 1: the nineteen fifties, researchers were actively trying to figure out 264 00:16:03,160 --> 00:16:07,080 Speaker 1: how to use viruses to treat cancer. But then new 265 00:16:07,160 --> 00:16:12,120 Speaker 1: kinds of cancer drugs were discovered, basically chemotherapy, and researchers 266 00:16:12,120 --> 00:16:16,360 Speaker 1: got less interested in that virus cancer link. My guest 267 00:16:16,360 --> 00:16:19,400 Speaker 1: for this half of the show is Stephen Russell. Stephen 268 00:16:19,480 --> 00:16:23,280 Speaker 1: has spent his career trying to use viruses to cure cancer, 269 00:16:23,680 --> 00:16:26,440 Speaker 1: and as you'll hear, he and other researchers in the 270 00:16:26,440 --> 00:16:30,240 Speaker 1: field have made real progress. When Stephen was starting his 271 00:16:30,280 --> 00:16:33,360 Speaker 1: career in the nineteen eighties, he was interested in using 272 00:16:33,480 --> 00:16:37,640 Speaker 1: retroviruses as possible cancer treatments. Then he told me he 273 00:16:37,760 --> 00:16:39,360 Speaker 1: turned his attention to measles. 274 00:16:39,880 --> 00:16:42,080 Speaker 2: Yeah, well, measles became the next love. 275 00:16:42,440 --> 00:16:45,560 Speaker 1: You fell in love with measles? Yeah, of course, why'd 276 00:16:45,600 --> 00:16:46,920 Speaker 1: you fall in love with measles? 277 00:16:47,720 --> 00:16:50,880 Speaker 2: Well? All viruses are quite beautiful, I have to say, 278 00:16:50,920 --> 00:16:56,320 Speaker 2: and the life cycles are extraordinarily elegant. But measles I 279 00:16:56,400 --> 00:17:00,040 Speaker 2: could do things with. I knew that there was a 280 00:17:00,120 --> 00:17:04,240 Speaker 2: very remarkable case of a boy with a retro orbital 281 00:17:04,480 --> 00:17:08,119 Speaker 2: Burkitt lymphoma, a very aggressive lymphoma that was sort of 282 00:17:08,200 --> 00:17:11,760 Speaker 2: bulging his eye out, And he went to a clinic 283 00:17:11,800 --> 00:17:13,919 Speaker 2: and was told, well, come back in a couple of 284 00:17:13,920 --> 00:17:17,280 Speaker 2: weeks and we'll start the therapy. And he came back 285 00:17:17,320 --> 00:17:20,920 Speaker 2: in a couple of weeks and the tumor had just resolved. 286 00:17:21,320 --> 00:17:25,760 Speaker 2: But in the meantime he'd had a severe measles infection. Huh, 287 00:17:25,800 --> 00:17:28,560 Speaker 2: And so it looked like it was pretty certain that 288 00:17:28,600 --> 00:17:31,520 Speaker 2: the measles infection had driven this response. 289 00:17:31,600 --> 00:17:36,520 Speaker 1: That he had Burko lymphoma also caused by a virus. Right, 290 00:17:36,600 --> 00:17:39,359 Speaker 1: the first tumor we knew was caused by a virus 291 00:17:39,359 --> 00:17:42,359 Speaker 1: Epstein by yeah, so go on, I apologize. 292 00:17:42,720 --> 00:17:46,720 Speaker 2: So anyway, I looking at measles, it seemed to tick 293 00:17:46,760 --> 00:17:49,520 Speaker 2: a lot of boxes. But there was this whole history 294 00:17:49,560 --> 00:17:53,119 Speaker 2: of the development of a vaccine strain of measles, and 295 00:17:53,200 --> 00:17:57,359 Speaker 2: measles had been the vaccine had been created by taking 296 00:17:57,400 --> 00:18:01,520 Speaker 2: a virus from the throat of a patient with measles. 297 00:18:02,160 --> 00:18:04,240 Speaker 2: He was an eleven year old boy at the time, 298 00:18:04,359 --> 00:18:08,560 Speaker 2: David Edmonstone in nineteen fifty four, and then growing that 299 00:18:08,720 --> 00:18:13,200 Speaker 2: virus on cancer cells in tissue culture, and the virus 300 00:18:13,240 --> 00:18:18,199 Speaker 2: had quired the ability to propagate efficiently in cancer cells, 301 00:18:18,240 --> 00:18:21,200 Speaker 2: but it lost the ability to cause measles. 302 00:18:21,440 --> 00:18:24,520 Speaker 1: And wait, I just just to be clear, this was 303 00:18:24,600 --> 00:18:27,280 Speaker 1: just they weren't trying when they were doing this to 304 00:18:27,320 --> 00:18:31,960 Speaker 1: fight cancer. They were just trying to develop a measles vaccine. 305 00:18:31,960 --> 00:18:34,400 Speaker 1: They were just saying, we're going to grow this measles 306 00:18:34,720 --> 00:18:37,679 Speaker 1: in culture over time and make it be attenuator, make 307 00:18:37,720 --> 00:18:40,600 Speaker 1: it be weaker. And it adapted in such a way 308 00:18:40,640 --> 00:18:43,879 Speaker 1: that it preferred to infect tumor cells and not to 309 00:18:44,000 --> 00:18:45,200 Speaker 1: infect non tumor. 310 00:18:45,040 --> 00:18:48,200 Speaker 2: Cells because of the way they adapted it. Because remember 311 00:18:48,240 --> 00:18:51,679 Speaker 2: that the cells that they were growing in the lab 312 00:18:51,840 --> 00:18:54,560 Speaker 2: that they could put the measles virus on were basically 313 00:18:54,600 --> 00:18:55,440 Speaker 2: cancer cells. 314 00:18:55,800 --> 00:18:58,080 Speaker 1: And is that just because those are easy cells to grow? 315 00:18:58,119 --> 00:18:59,400 Speaker 1: Because they liked to propagate. 316 00:18:59,520 --> 00:19:03,680 Speaker 2: Yeah, yeah, it's very, very difficult to grow non cancerous cells. Yeah. 317 00:19:03,880 --> 00:19:06,200 Speaker 1: It's like the problem with cancer, right, it just loves 318 00:19:06,240 --> 00:19:07,680 Speaker 1: to divide. 319 00:19:07,840 --> 00:19:12,040 Speaker 2: Yeah. Yeah. So they put this virus on the cells 320 00:19:12,080 --> 00:19:16,520 Speaker 2: in culture, and the virus had actually adapted and it 321 00:19:16,560 --> 00:19:21,680 Speaker 2: had learned to use a receptor that is more abundant 322 00:19:21,760 --> 00:19:25,040 Speaker 2: on cancer cells than on normal cells, and it was 323 00:19:25,200 --> 00:19:28,680 Speaker 2: losing all sorts of things that it needed in order 324 00:19:28,720 --> 00:19:32,280 Speaker 2: to cause disease because it didn't need them to be 325 00:19:32,400 --> 00:19:36,959 Speaker 2: able to propagate the cancer cells. So it spontaneously attenuated 326 00:19:37,000 --> 00:19:40,800 Speaker 2: through a lot of mutations that arose in the viral genome. 327 00:19:41,600 --> 00:19:43,960 Speaker 2: And so there it was and had been given to 328 00:19:44,040 --> 00:19:49,160 Speaker 2: billions of people, and it looked like it was fairly 329 00:19:49,320 --> 00:19:54,600 Speaker 2: well adapted to test in human studies against cancer. 330 00:19:55,760 --> 00:19:59,040 Speaker 1: The measles virus used in the vaccine didn't make people sick, 331 00:19:59,280 --> 00:20:03,159 Speaker 1: and it tended to attack cancer cells. So Stephen started 332 00:20:03,200 --> 00:20:05,959 Speaker 1: using that form of the virus in studies to see 333 00:20:06,000 --> 00:20:09,879 Speaker 1: if measles could treat cancer. Eventually, he landed on a 334 00:20:09,960 --> 00:20:13,520 Speaker 1: kind of cancer called multiple myeloma that can take hold 335 00:20:13,560 --> 00:20:16,800 Speaker 1: in the bone marrow and suppress the immune system. And 336 00:20:16,960 --> 00:20:21,080 Speaker 1: there was one multiple myeloma patient in particular who had 337 00:20:21,080 --> 00:20:24,440 Speaker 1: a huge effect on how Stephen thought about using measles 338 00:20:24,440 --> 00:20:27,920 Speaker 1: to fight cancer. The patient's name was Stacy. 339 00:20:27,720 --> 00:20:33,560 Speaker 2: Rholtz, So Stacy. She had multiple myeloma. She had been 340 00:20:34,359 --> 00:20:39,280 Speaker 2: on treatment for ten years, on and off, but probably 341 00:20:39,320 --> 00:20:42,200 Speaker 2: more on and off treatment for the first ten years 342 00:20:42,240 --> 00:20:46,040 Speaker 2: of her diagnosis, because every time she stopped the treatment, 343 00:20:46,800 --> 00:20:49,480 Speaker 2: or even if she continued on it, the disease would 344 00:20:49,480 --> 00:20:52,920 Speaker 2: come back, and then she needs switch to something else. 345 00:20:53,480 --> 00:20:56,119 Speaker 2: She had a large tumor on her forehead that was 346 00:20:56,200 --> 00:21:01,080 Speaker 2: destroying the underlying bone and compressing her brain. She had 347 00:21:01,400 --> 00:21:04,960 Speaker 2: four other solid tumors, and then her bone marrow was 348 00:21:05,040 --> 00:21:09,439 Speaker 2: diffusely infiltrated with myeloma and it was moving fast, and 349 00:21:09,520 --> 00:21:12,240 Speaker 2: she was out of treatment options at the time. I mean, 350 00:21:12,240 --> 00:21:12,840 Speaker 2: there was nothing. 351 00:21:12,880 --> 00:21:14,800 Speaker 1: She was going to die soon. 352 00:21:14,920 --> 00:21:18,400 Speaker 2: She was going to die, yeah, and she had three 353 00:21:18,520 --> 00:21:22,359 Speaker 2: children still at school and everything to play for. She 354 00:21:22,440 --> 00:21:25,760 Speaker 2: was fifty years old at the time. We'd moved up 355 00:21:25,840 --> 00:21:30,040 Speaker 2: through every dose level that FDA had negotiated with us, 356 00:21:30,080 --> 00:21:33,480 Speaker 2: based on a starting dose level of a million, we'd 357 00:21:33,520 --> 00:21:36,960 Speaker 2: gone up to just tenfold short of a billion. 358 00:21:37,440 --> 00:21:39,800 Speaker 1: Like, how much did she get compared to how much 359 00:21:39,800 --> 00:21:41,439 Speaker 1: somebody gets when they get the measles vaxing. 360 00:21:41,800 --> 00:21:45,280 Speaker 2: It's about ten million doses of vaccine. 361 00:21:44,920 --> 00:21:48,040 Speaker 1: Okay for this one person, So she's getting quite a lot. 362 00:21:48,560 --> 00:21:49,800 Speaker 1: And what happens. 363 00:21:50,320 --> 00:21:55,280 Speaker 2: Well as with other patients, she had a reaction to 364 00:21:55,480 --> 00:21:59,359 Speaker 2: the infusion of virus. So she got the virus infused. 365 00:21:59,440 --> 00:22:03,640 Speaker 2: She felt fine for a couple of hours, and then 366 00:22:03,840 --> 00:22:08,160 Speaker 2: she started shivering and shaking and her temperature rose and 367 00:22:08,240 --> 00:22:11,920 Speaker 2: she felt pretty unwell overnight, but by the following morning 368 00:22:12,000 --> 00:22:13,119 Speaker 2: it had settled down. 369 00:22:13,359 --> 00:22:16,600 Speaker 1: And is that essentially like a response to a massive infection. 370 00:22:16,840 --> 00:22:18,680 Speaker 1: She's essentially has this massive infection. 371 00:22:19,400 --> 00:22:24,439 Speaker 2: Yes, it's similar to that, although it wasn't causing measles 372 00:22:24,480 --> 00:22:27,720 Speaker 2: in her so it was it was more the body 373 00:22:27,800 --> 00:22:31,439 Speaker 2: reacting to the foreign stuff in the blood and a 374 00:22:31,560 --> 00:22:35,399 Speaker 2: very kind of rapid reaction. That settled down and then 375 00:22:35,840 --> 00:22:40,720 Speaker 2: she left hospital, went home and after a few days 376 00:22:41,440 --> 00:22:46,280 Speaker 2: she started noticing that the tumor on her forehead she 377 00:22:46,400 --> 00:22:51,480 Speaker 2: and her family had named it Evan, and this tumor Evan, 378 00:22:51,640 --> 00:22:56,280 Speaker 2: started to shrink and actually melted away. And you know, 379 00:22:56,359 --> 00:23:04,000 Speaker 2: we conducted thorough evaluates on her intervals thereafter and we 380 00:23:04,000 --> 00:23:07,000 Speaker 2: were staggered to see that she went into a complete 381 00:23:07,280 --> 00:23:12,360 Speaker 2: disease remission. She felt fantastic. You know. We thought at 382 00:23:12,400 --> 00:23:14,960 Speaker 2: the time, Okay, we've got it. This is the way 383 00:23:15,000 --> 00:23:19,760 Speaker 2: to cure multiple myeloma. And so we gave it to 384 00:23:19,800 --> 00:23:23,439 Speaker 2: a lot of other multiple myeloma patients and it didn't 385 00:23:23,440 --> 00:23:27,480 Speaker 2: work nearly so well. There were some partial responses, but 386 00:23:27,560 --> 00:23:31,479 Speaker 2: there was really nothing as dramatic as Stacy. So we 387 00:23:31,600 --> 00:23:36,240 Speaker 2: studied Stacy pretty intensively to try and understand what was 388 00:23:36,320 --> 00:23:40,399 Speaker 2: special about her because that would be the key to 389 00:23:40,720 --> 00:23:43,440 Speaker 2: the success of virotherapy. I mean, what we knew from 390 00:23:43,480 --> 00:23:46,920 Speaker 2: Stacy as Wow, this can actually happen. You can give 391 00:23:46,960 --> 00:23:52,359 Speaker 2: a virus systemically nothing else, and you can get a 392 00:23:52,440 --> 00:23:57,440 Speaker 2: dramatic resolution of tumor at all sites. So the studies 393 00:23:57,480 --> 00:24:00,040 Speaker 2: that we did on Stacy showed that number one, she 394 00:24:00,119 --> 00:24:05,920 Speaker 2: had no anti measles antibody detectable. She had, however, been 395 00:24:06,040 --> 00:24:10,119 Speaker 2: vaccinated as a child and then she had lost her 396 00:24:10,160 --> 00:24:15,080 Speaker 2: immunity and she'd been revaccinated after her first stem cell transplant. 397 00:24:15,840 --> 00:24:19,760 Speaker 2: The immunity had come back, and she'd lost it again 398 00:24:20,119 --> 00:24:23,880 Speaker 2: after the second stem cell transplant. Huh, So she had 399 00:24:23,920 --> 00:24:27,680 Speaker 2: no antibodies to block the virus from getting to the target. 400 00:24:27,920 --> 00:24:31,679 Speaker 1: So basically her immune system was the same as the 401 00:24:31,680 --> 00:24:34,360 Speaker 1: immune system of a person who has never had measles 402 00:24:34,400 --> 00:24:36,320 Speaker 1: and not been vaccinated for measles. 403 00:24:36,359 --> 00:24:40,480 Speaker 2: Not quite, because we looked at her tea cells, the 404 00:24:40,640 --> 00:24:43,920 Speaker 2: cells that come into the tumor and attack the virus 405 00:24:43,960 --> 00:24:48,639 Speaker 2: infected cells, and she had a very high level of 406 00:24:48,880 --> 00:24:50,640 Speaker 2: anti measles tea cells. 407 00:24:51,560 --> 00:24:55,680 Speaker 1: As it turned out, this was a perfect combination. Stacy 408 00:24:55,720 --> 00:24:59,520 Speaker 1: didn't have any antibodies, so the measles virus was free 409 00:24:59,560 --> 00:25:02,320 Speaker 1: to go into her body and infect her tumor cells. 410 00:25:02,880 --> 00:25:06,879 Speaker 1: But she did have anti measles T cells, so once 411 00:25:06,920 --> 00:25:10,320 Speaker 1: the measles infected the tumor cells, those T cells could 412 00:25:10,400 --> 00:25:14,200 Speaker 1: attack her tumor cells. So now she had both the 413 00:25:14,240 --> 00:25:18,680 Speaker 1: measles virus and her own T cells attacking and destroying 414 00:25:18,800 --> 00:25:22,760 Speaker 1: the tumor. Stephen told me he learned a lot from 415 00:25:22,800 --> 00:25:25,479 Speaker 1: Stacy's case and it helped him figure out how to 416 00:25:25,560 --> 00:25:27,160 Speaker 1: move forward with his research. 417 00:25:27,920 --> 00:25:31,200 Speaker 2: Yeah, so there are two pathways that we took. One 418 00:25:31,240 --> 00:25:34,639 Speaker 2: was to switch to a different virus that people do 419 00:25:34,760 --> 00:25:39,359 Speaker 2: not have prior exposure to, and so we started working 420 00:25:39,440 --> 00:25:44,720 Speaker 2: with the sicular stomatitis virus VSV, which causes naturally a 421 00:25:44,800 --> 00:25:47,879 Speaker 2: blistering illness in cattle. Uh huh. 422 00:25:47,920 --> 00:25:51,760 Speaker 1: So that's that's one way to get around the immunity 423 00:25:51,800 --> 00:25:55,680 Speaker 1: to measles problem. Right, You're using a virus that most 424 00:25:55,680 --> 00:25:58,520 Speaker 1: people don't get and are therefore not immune to. 425 00:26:00,160 --> 00:26:04,199 Speaker 2: Is that going it's going, it's going well. Where you know, 426 00:26:04,359 --> 00:26:07,960 Speaker 2: it's not in every cancer that we see anything, but 427 00:26:08,320 --> 00:26:10,719 Speaker 2: the results that we have at the moment are looking 428 00:26:10,960 --> 00:26:16,720 Speaker 2: very promising in certain indications. The other approach we took 429 00:26:17,240 --> 00:26:23,720 Speaker 2: was to stealth measles virus so that it would still 430 00:26:23,760 --> 00:26:26,959 Speaker 2: be measles virus. It would still be that vaccine strain, 431 00:26:27,040 --> 00:26:29,960 Speaker 2: but it would have a new coat huh, that was 432 00:26:30,119 --> 00:26:36,120 Speaker 2: no longer recognizable by circulating antibodies. And so we would 433 00:26:36,160 --> 00:26:38,960 Speaker 2: in that situation we would have a virus we could 434 00:26:39,000 --> 00:26:44,280 Speaker 2: give systemically that would penetrate the tumor and that would 435 00:26:44,359 --> 00:26:48,399 Speaker 2: then be subject to attack by these T cells that 436 00:26:48,520 --> 00:26:53,960 Speaker 2: exist in people who've been measles immunized. Well, does it 437 00:26:54,000 --> 00:26:55,879 Speaker 2: work in mice? 438 00:26:56,560 --> 00:26:57,800 Speaker 1: Okay, it's a start. 439 00:26:57,880 --> 00:27:01,480 Speaker 2: We haven't taken that one into human clinical tests. 440 00:27:01,560 --> 00:27:05,560 Speaker 1: Yet, let's talk for a minute about using viruses to 441 00:27:05,640 --> 00:27:10,400 Speaker 1: treat cancer more broadly. Right, people have been trying other 442 00:27:11,160 --> 00:27:14,240 Speaker 1: viruses to treat other cancers. What's the state of the 443 00:27:14,280 --> 00:27:15,840 Speaker 1: field more generally? 444 00:27:16,520 --> 00:27:19,919 Speaker 2: There has been incremental progress and there are viruses that 445 00:27:20,000 --> 00:27:25,359 Speaker 2: are looking very promising in brain cancer injected into the brain, 446 00:27:25,440 --> 00:27:31,119 Speaker 2: tumor in bladder cancer instilled into the bladder. And I 447 00:27:31,160 --> 00:27:38,000 Speaker 2: think many ongoing programs which show great promise. So I'm 448 00:27:38,040 --> 00:27:42,960 Speaker 2: still a complete believer in the capability of viruses to 449 00:27:43,040 --> 00:27:46,680 Speaker 2: really bring a transformation in the approach to cancer therapy. 450 00:27:48,480 --> 00:27:54,480 Speaker 2: I feel like it's coming soon, but not everybody agrees 451 00:27:54,520 --> 00:27:54,800 Speaker 2: with me. 452 00:27:57,560 --> 00:28:00,159 Speaker 1: I appreciate your time so much. It was great to tell. 453 00:28:00,200 --> 00:28:03,280 Speaker 2: With you, great talking with you too. Thank you very much. 454 00:28:04,359 --> 00:28:07,560 Speaker 1: Stephen Russell founded the Department of Molecular Medicine at the 455 00:28:07,600 --> 00:28:10,920 Speaker 1: Mayo Clinic. He is currently the CEO of Viriad, a 456 00:28:11,000 --> 00:28:13,840 Speaker 1: company that is trying to use viruses to treat cancer. 457 00:28:15,080 --> 00:28:17,719 Speaker 1: One last thing, Stephen told me that he's still in 458 00:28:17,760 --> 00:28:21,399 Speaker 1: touch with Stacy Airholtz, the patient who had multiple myeloma 459 00:28:21,440 --> 00:28:24,840 Speaker 1: and went into complete remission after being treated with measles. 460 00:28:25,160 --> 00:28:28,879 Speaker 2: Stacy A. Holtz is you know, a guiding light for me. 461 00:28:29,520 --> 00:28:32,480 Speaker 2: I'm friends with her, I'm in contact with her on 462 00:28:32,520 --> 00:28:37,040 Speaker 2: a regular basis, and she's got grandkids. She's having a 463 00:28:37,040 --> 00:28:38,960 Speaker 2: happy life. She's a very positive woman. 464 00:28:40,080 --> 00:28:42,400 Speaker 1: Thanks to both of our guests today, Michael Minna and 465 00:28:42,440 --> 00:28:45,600 Speaker 1: Stephen Russell. Next week on the show, I talked to 466 00:28:45,680 --> 00:28:49,120 Speaker 1: a scientist who discovered an entirely new kind of virus, 467 00:28:50,000 --> 00:28:52,800 Speaker 1: and it turns out this virus is everywhere. 468 00:28:53,200 --> 00:28:57,640 Speaker 4: Boiling Springs Lake, deep Sea Sediment's Harrian air sample, monkey feces, 469 00:28:57,760 --> 00:29:02,080 Speaker 4: dragonfly guts, soil, just outside the lab at Portland State University, 470 00:29:02,240 --> 00:29:07,480 Speaker 4: basically anywhere that we have looked, we found these Cruci viruses. 471 00:29:09,400 --> 00:29:12,560 Speaker 1: Incubation is a co production of Pushkin Industries and Ruby 472 00:29:12,640 --> 00:29:17,080 Speaker 1: Studio at iHeartMedia. It's produced by Kate Ferby and Brittany Cronin. 473 00:29:17,400 --> 00:29:20,360 Speaker 1: The show is edited by Lacy Roberts. It's mastered by 474 00:29:20,400 --> 00:29:24,960 Speaker 1: Sarah Bruguer, fact checking by Joseph Fridman. Our executive producers 475 00:29:25,000 --> 00:29:28,480 Speaker 1: are Lacey Roberts and Matt Romano. I'm Jacob Goldstein. Thanks 476 00:29:28,480 --> 00:29:29,000 Speaker 1: for listening.