An Update on the War on Cancer: Finding the Spies that Help Tumors Grow

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00:00:10.00 Do you recognize this photo?
00:00:12.10 You might be a little too young to recognize it,
00:00:14.28 but this is then-President Nixon
00:00:17.13 signing the National Cancer Act.
00:00:19.28 It was December 23rd, 1971,
00:00:22.21 when the U.S. declared war on cancer.
00:00:26.23 The National Cancer Act promised 1.6 billion dollars
00:00:30.24 of funding for cancer research
00:00:32.28 and a cure for cancer by 1976.
00:00:36.25 It’s 2021 now.
00:00:38.27 Do you think we’ve won the war on cancer?
00:00:41.13 Probably not, but it’s not all bad news.
00:00:45.04 In fact, U.S. rates for all cancer deaths
00:00:47.26 have declined over the years.
00:00:50.26 If you look at this chart
00:00:52.19 tracking the number of deaths due to cancer over time,
00:00:55.02 starting in the 1970s all the way to 2020,
00:00:58.15 you’ll notice a really interesting trend.
00:01:01.15 For males in blue, for females in red,
00:01:04.14 and for both sexes in black,
00:01:07.13 there’s been a trend — decreasing in terms of cancer-related deaths.
00:01:11.18 So, since we’ve declared war on cancer,
00:01:14.10 less people have been dying due to cancer.
00:01:19.25 And the drop in cancer rates is due to the fact
00:01:22.08 that we’ve made major advances in cancer therapy.
00:01:25.04 Since the 1970s, we’ve developed so many different ways to treat cancer,
00:01:28.24 including targeted therapies,
00:01:31.15 cancer vaccines,
00:01:33.01 and, more recently, immunotherapies.
00:01:36.00 Immunotherapies is a new type of cancer treatment
00:01:39.23 that harnesses your immune system to find and kill cancer cells.
00:01:43.12 It’s made a huge impact on oncology.
00:01:45.28 In fact, it’s made such a big deal of change
00:01:49.21 that in 2018 the Nobel Prize
00:01:51.25 was awarded to two scientists
00:01:53.26 who helped pave the way for immunotherapy:
00:01:55.17 Dr. Allison and Dr. Honjo.
00:01:58.15 And this is me in 2016 with Dr. Honjo.
00:02:01.18 This is before the Nobel Prize fame,
00:02:04.12 but he was famous to me because, you know,
00:02:07.02 I’m a scientist too.
00:02:08.24 And this is me in 2017 at the University of Pennsylvania,
00:02:12.10 when the FDA approved a new type of immunotherapy
00:02:14.27 that is now treating patients with blood cancers.
00:02:18.16 And these are all really great examples of bench to bedside.
00:02:21.18 What that means is the research that we’ve done in the lab
00:02:24.11 is transferable and translatable to patient care.
00:02:29.26 But let’s take a moment and go back to this graph
00:02:31.28 that I showed earlier.
00:02:33.24 If you’ve noticed, even though there has been
00:02:37.00 a steady decline in cancer-related deaths,
00:02:38.27 we’re not nearing zero quite yet.
00:02:41.15 And if you look back at the clinical trial studies,
00:02:44.10 even under the best circumstances,
00:02:46.14 less than 50% of patients respond to
00:02:50.04 the most advanced immunotherapies available.
00:02:52.00 And so the question in the field is, why?
00:02:54.11 Why are so few patients responding to these amazing drugs?
00:02:58.15 And what can we do to bring this number to zero?
00:03:04.02 In order for us to start answering this question,
00:03:06.16 we’ve got to go back to the concept
00:03:09.05 of this “war on cancer”.
00:03:10.20 In order for you to fight a war,
00:03:13.06 there are three things that you must do:
00:03:15.26 one, you must know your enemy;
00:03:18.10 two, you have to have a strong department of defense;
00:03:22.16 and three, you want to have good weapons.
00:03:25.28 Well, we have an amazing department of defense,
00:03:29.09 and that’s your immune system.
00:03:30.25 And we have, as I mentioned,
00:03:32.17 really cool and awesome drugs, or weapons.
00:03:36.26 But what we’re not so great at, what we lack a little bit in,
00:03:39.27 is knowing our enemy.
00:03:41.23 There’s still a lot we don’t understand about cancer.
00:03:45.08 So, the first step in trying to know your enemy
00:03:48.05 is to not make assumptions.
00:03:49.29 Unfortunately, we made a pretty big assumption early on.
00:03:52.18 For a while, people thought that tumors
00:03:55.07 were just a heterogeneous mass of cancer cells,
00:03:58.09 heterogeneous meaning diverse.
00:04:00.28 So, we knew that there was some diversity in the tumor,
00:04:03.29 but we thought it was all one type of cell:
00:04:05.27 cancer cells.
00:04:07.15 But that actually turns out to be not true.
00:04:10.29 When you look inside a tumor,
00:04:12.19 it’s not just a whole bunch of different cancer cells.
00:04:15.20 In fact, when you look inside the tumor,
00:04:17.12 we find that a tumor is really complex,
00:04:21.14 and it’s a dynamic community of normal cells,
00:04:23.25 cancer cells,
00:04:25.10 and non-cellular components.
00:04:28.07 And together, all of these things
00:04:30.14 make up what we now call the tumor microenvironment.
00:04:35.06 So, let’s put this all into context with this “war on cancer”.
00:04:40.03 If we were to consider the human body a nation,
00:04:44.09 and the cancer cells our enemy,
00:04:46.10 and the immune system our national defense,
00:04:50.00 and immunotherapies as a type of weapon,
00:04:52.25 the question is,
00:04:55.06 what are the non-immune normal cells doing there?
00:04:57.21 What is their role in tumor progression
00:05:00.06 or tumor development?
00:05:02.05 And that is the question I want to ask with my research.
00:05:05.16 So, let’s go back to this tumor microenvironment.
00:05:08.11 We’ve known for a really long time that cancer cells
00:05:10.24 exist in the tumor.
00:05:12.06 We also know that immune cells are there.
00:05:14.25 And what we’ve started to appreciate
00:05:17.03 is the presence of all of these non-immune normal cells,
00:05:20.05 including one major cell type
00:05:22.16 called the cancer-associated fibroblasts.
00:05:24.27 And my research primarily focuses on
00:05:26.25 what the cancer-associated fibroblasts are
00:05:29.16 and what they’re doing inside the tumor.
00:05:32.06 So, the question is, what are cancer-associated fibroblasts?
00:05:36.04 Are they spies, aiding and abetting cancer cells?
00:05:40.27 Or are they allies,
00:05:43.02 fighting alongside the immune cells to fight cancer?
00:05:46.03 It’s still not really clear exactly which role
00:05:49.15 these cancer-associated fibroblasts are playing,
00:05:51.05 and it’s led to a lot of confusion in the field.
00:05:55.03 So, let’s take a moment and look at what we do know
00:05:57.25 about cancer-associated fibroblasts.
00:06:00.22 What is the intel?
00:06:02.19 Well, we know that cancer-associated fibroblasts
00:06:04.14 are called activated fibroblasts,
00:06:06.19 which means they are no longer in their resting state.
00:06:09.18 Activated fibroblasts have more functions,
00:06:12.06 they proliferate and grow more,
00:06:14.24 and they produce the non-cellular components
00:06:17.01 that make up the tumor microenvironment as well.
00:06:20.23 They’re also heterogeneous.
00:06:22.02 They’re diverse.
00:06:23.20 There’s not just one type of cancer-associated fibroblasts,
00:06:27.16 but there’s various subtypes, and those subtypes have different functions.
00:06:31.14 Some functions are pro-tumorigenic, or pro-tumor growth,
00:06:34.23 and other functions are anti-tumor growth, anti-tumorigenic.
00:06:39.24 So, if you think about it,
00:06:42.03 cancer-associated fibroblasts can be spies or allies,
00:06:45.07 and that’s really confusing.
00:06:49.17 So, let’s think about it.
00:06:51.00 If cancer-associated fibroblasts are both spies and allies,
00:06:54.23 we have to figure out a way to differentiate between the two,
00:06:57.26 because if we want to target cancer-associated fibroblasts,
00:07:01.11 we need to know to target the spy
00:07:03.18 and not the ally.
00:07:05.10 So, how can we target them, and what can we do?
00:07:09.05 Well, one way we can start to identify or distinguish
00:07:12.10 the spies and the allies
00:07:14.02 is by finding specific unique markers.
00:07:17.24 Oh, no… not… not these kinds of markers.
00:07:20.18 But markers called proteins that are expressed on the surface of cells,
00:07:24.09 or identifiers.
00:07:25.19 A really good analogy is combat helmets from World War II.
00:07:30.15 Here are two images: one of the allied helmets used in World War II,
00:07:34.10 and the others are helmets used by the Axes of evil.
00:07:37.21 And I want you to take a close look at these two helmets.
00:07:41.14 Do you think you can recognize the two helmets
00:07:44.09 simply by looking at them?
00:07:46.19 Probably not.
00:07:47.28 In the same way, it’s been quite difficult
00:07:49.25 to find unique markers to help distinguish
00:07:52.27 the spies and the allies in cancer-associated fibroblasts.
00:07:55.15 And this is an ongoing challenge for researchers.
00:07:59.08 So, maybe there’s an easier way for us to distinguish the two,
00:08:03.15 or maybe we can figure out a way to prevent the activation of spies.
00:08:07.12 The activation of spies,
00:08:09.03 the process from resting fibroblasts
00:08:11.03 to becoming cancer-associated fibroblasts,
00:08:13.05 is called the stromagenic switch.
00:08:15.28 And the idea is if we can block the stromagenic switch,
00:08:18.16 we may prevent the activation of these spies.
00:08:24.01 So, the hypothesis that I have is that in healthy tissues
00:08:28.11 there’s a very strong stop signal
00:08:30.12 that stops the stromagenic switch,
00:08:31.28 preventing the activation of these spies.
00:08:35.07 But in tumors, there’s a mechanism
00:08:36.29 that blocks the stop signal,
00:08:38.29 or breaks the stop signal,
00:08:41.00 so that the stromagenic switch can be turned on,
00:08:44.02 allowing the formation of these spies.
00:08:48.11 In order for us to start testing this hypothesis,
00:08:50.16 we have to go back to what we do know in tumors.
00:08:53.28 And we can look inside colon cancer tumors
00:08:56.18 to find some kind of clue about what could be a potential stop signal.
00:09:01.26 Why is that?
00:09:03.21 Well, colon cancer is known to have a lot of cancer-associated fibroblasts,
00:09:06.15 which means,
00:09:08.11 if there are a lot of cancer-associated fibroblasts,
00:09:10.19 there has to be less stop signal.
00:09:13.22 So, if we look inside the colon tumor
00:09:17.12 and we find a lot of cancer-associated fibroblasts
00:09:18.29 and less of some other thing,
00:09:21.11 that some other thing could potentially be the stop signal.
00:09:25.00 So, that’s exactly what we did.
00:09:26.27 When we looked inside the tumors of colon cancers,
00:09:29.11 we found that colon cancers had less of this protein
00:09:32.09 called IFNAR1.
00:09:33.24 In the top panel,
00:09:35.12 you’ll see normal colon tissue
00:09:37.06 and the brown dots staining for IFNAR1,
00:09:41.10 letting you know that this tissue does have this protein available.
00:09:45.00 However, if you look at the colon carcinoma tissue,
00:09:47.14 or the colon cancer tissue,
00:09:48.29 you’ll notice that there’s absolutely no brown staining
00:09:51.23 or no brown dots,
00:09:53.11 indicating that there’s very little to no IFNAR1 protein.
00:09:57.18 And what this suggests is that,
00:09:59.17 since colon cancers have a lot of cancer-associated fibroblasts
00:10:01.20 and less IFNAR1,
00:10:03.14 it’s possible that IFNAR1
00:10:06.23 may be part of the stop signal.
00:10:09.25 So, what is IFNAR1?
00:10:12.14 IFNAR1 is an important subunit of the type I interferon.
00:10:16.10 Type I interferons are special type of protein
00:10:18.20 called cytokines,
00:10:20.00 and they have many different functions.
00:10:21.24 But what they do in the context of cancer
00:10:23.28 is that they bind to this receptor
00:10:26.17 made up of these two subunits
00:10:28.09 to elicit anti-tumor signals,
00:10:30.00 including activating the immune system
00:10:32.06 to find and kill cancer cells,
00:10:33.18 and also directly killing cancer cells themselves.
00:10:37.16 But what happens inside the context of a tumor
00:10:40.01 is that there are many different factors
00:10:42.04 within the tumor microenvironment
00:10:43.22 that promote the degradation of IFNAR1.
00:10:46.27 And as a result, you lose all of those anti-tumor signaling events.
00:10:52.10 So, let’s go back to the hypothesis.
00:10:57.03 So, in normal situations in healthy tissues,
00:10:59.26 you have type I interferons,
00:11:01.27 these cytokines with multiple functions
00:11:04.03 that bind to the IFNAR1 receptor
00:11:06.10 to activate the stop signal.
00:11:09.01 The stop signal prevents the stromagenic switch,
00:11:11.16 not allowing the activation of spies.
00:11:15.09 However, in the context of tumors,
00:11:16.24 the tumor microenvironment has variable factors
00:11:19.17 that actually promote the downregulation
00:11:21.29 or the degradation of IFNAR1.
00:11:24.14 As a result, you lose that stop signal,
00:11:26.21 and now the stromagenic switch can occur,
00:11:28.21 and cancer-associated fibroblasts
00:11:31.15 — the bad ones, the spies —
00:11:33.00 can be activated.
00:11:34.25 So, now the hypothesis is tumors degrade the stop signal,
00:11:39.00 in this case IFNAR1,
00:11:40.19 to activate spies and ultimately promote tumor growth.
00:11:43.20 And a way to study our hypothesis and test our hypothesis
00:11:47.22 is to use a mutant form of IFNAR1.
00:11:50.03 What the mutant form of IFNAR1 allows us to do
00:11:52.15 is ask whether or not IFNAR1
00:11:54.24 plays a role in that stop signal.
00:11:57.28 So, as I mentioned earlier,
00:11:59.15 in tumors IFNAR1 gets degraded.
00:12:01.16 But if we can stabilize it to prevent its degradation,
00:12:04.14 we should be able to maintain the anti-tumor signals.
00:12:08.08 So, using this mutant IFNAR1,
00:12:10.28 we can start to begin to address our hypothesis
00:12:14.22 of whether or not this receptor subunit
00:12:17.08 is critical for inhibiting the stromagenic switch.
00:12:23.16 So, let’s go back a little bit more in detail.
00:12:26.02 If we stabilize IFNAR1,
00:12:28.25 the stop signal stays on.
00:12:31.02 And because the stop signal stays on,
00:12:33.09 the stromagenic switch cannot occur,
00:12:35.09 and as a result you don’t have the activation
00:12:37.22 of cancer-associated fibroblasts.
00:12:41.10 So, what we did is we injected mice with tumor cells
00:12:44.22 and looked for tumor growth,
00:12:46.10 and we compared tumor size
00:12:48.22 between wild-type mice, where IFNAR1 is degraded
00:12:51.07 in all the cells,
00:12:52.15 and SA mice that carry the mutant form,
00:12:55.19 so IFNAR1 is stable in all the cells.
00:12:58.10 And here’s a chart tracking out the tumor growth over time,
00:13:03.01 and what you’ll see is that
00:13:06.01 tumors grown in the wild-type mice
00:13:08.07 seem to grow faster and larger
00:13:09.29 than in the SA mice.
00:13:12.08 What that suggests is that tumors in mice
00:13:15.07 whose IFNAR1 is degraded
00:13:17.09 are much larger.
00:13:20.06 So, then we went inside the tumor to see what was happening.
00:13:22.22 Are there more activated spies in there?
00:13:25.25 And one way to address whether or not
00:13:28.16 there are more activated spies in the tumor
00:13:30.11 is to stain for different proteins.
00:13:32.02 So, we looked for fibroblast activation protein,
00:13:35.12 also called FAP or “fap”,
00:13:37.21 and fibronectin, shortened into FN,
00:13:40.14 and these two proteins are really good indications
00:13:42.28 that fibroblasts have been activated.
00:13:45.13 And what you can see here in the top panel,
00:13:47.09 in the blue and green stain,
00:13:49.12 is that in the wild type tumors
00:13:51.11 — again, these mice have no IFNAR1 —
00:13:53.29 FAP is highly expressed.
00:13:56.22 There’s a lot of green compared to the SA,
00:13:59.06 where IFNAR1 is stable.
00:14:01.06 And then if you look at FN (fibronectin),
00:14:03.14 another indication that fibroblasts are activated,
00:14:06.01 you’ll see there’s a lot more brown staining
00:14:08.11 in the wild-type tumors
00:14:10.07 compared to the SA tumors.
00:14:11.16 So, what this tells us is that
00:14:14.05 tumors that are grown in wild-type mice,
00:14:15.21 where IFNAR1 is degraded…
00:14:18.23 there are way more cancer-associated fibroblasts,
00:14:21.03 or way more spies.
00:14:23.22 So, our data so far does suggest
00:14:26.01 that IFNAR1 may be a really important part
00:14:28.29 of the stop signal.
00:14:30.11 But let’s take a quick pause here.
00:14:32.09 Let’s go back to the idea of correlation versus causation.
00:14:35.25 What I’ve shown you so far is correlation.
00:14:38.12 Tumors that are grown in mice where IFNAR1 is degraded, in all cells, happen to grow larger,
00:14:44.20 and inside those tumors there happen to be more activated fibroblasts.
00:14:49.03 But that’s all correlative,
00:14:50.10 because all of the cells are losing IFNAR1.
00:14:54.02 In order for us to demonstrate causation
00:14:56.14 that the loss of IFNAR1
00:14:58.28 promotes the activation of spies,
00:15:00.17 we need to have a system
00:15:03.02 where IFNAR1 is specifically degraded in fibroblasts,
00:15:05.22 and as a result we see their activation into spies.
00:15:09.21 So, that’s exactly what we did.
00:15:11.10 We developed a mouse model, a new mouse,
00:15:13.15 a transgenic mouse,
00:15:14.22 that’s genetically modified so that fibroblasts
00:15:18.02 are the only cells where IFNAR1 is degraded.
00:15:21.02 And then we asked again,
00:15:22.21 is tumor growth greater in mice where IFNAR1
00:15:25.05 is specifically deleted in fibroblasts?
00:15:28.17 And what you see here is that, in fact,
00:15:30.26 if you do lose IFNAR1 in fibroblasts,
00:15:33.07 tumor growth is definitely accelerated.
00:15:36.14 So, tumors do grow in mice
00:15:38.19 whose fibroblasts lack IFNAR1
00:15:40.09 and fibroblasts only lose IFNAR1.
00:15:42.24 And when we look inside those tumors, again,
00:15:46.06 we do see that there’s more FAP,
00:15:48.24 more fibroblast activation protein,
00:15:50.25 indicating that fibroblasts that do lack IFNAR1
00:15:54.24 become activated into spies.
00:15:59.20 So, great.
00:16:01.03 What I’ve shown you so far is that
00:16:03.05 the degradation of IFNAR1
00:16:05.10 induces the activation of these spies,
00:16:07.14 these cancer-associated fibroblasts,
00:16:09.12 that then promote tumor growth.
00:16:11.10 Awesome!
00:16:13.15 But we have to now go into the patient.
00:16:16.10 Are similar things happening in humans?
00:16:18.28 Because as great as it is to demonstrate something in a mouse,
00:16:21.09 we have to be able to correspond that information
00:16:23.14 and associate that what’s going…
00:16:25.29 with what is going on in people.
00:16:27.19 And so, then we looked at human colon cancer tumors
00:16:30.17 and we asked whether or not
00:16:33.13 IFNAR1 and FAP were expressed in the same way in the same space.
00:16:38.19 And when we looked inside human colon tumors,
00:16:40.25 we found that in areas where there’s high FAP,
00:16:43.10 — high fibroblast activation protein —
00:16:45.19 there’s almost no IFNAR1.
00:16:48.24 And in areas where there is IFNAR1, we see no FAP.
00:16:52.26 And in the cases where both IFNAR1 and FAP are expressed,
00:16:56.28 they’re spatially segregated —
00:16:58.24 they’re not in the same area.
00:17:00.09 So, what does this mean?
00:17:03.10 It means that in areas where IFNAR1 is abundant,
00:17:08.00 cancer-associated fibroblasts are absent.
00:17:10.01 And this demonstrates to us that when IFNAR1 is lost
00:17:13.25 spies can be activated.
00:17:19.04 So, let’s summarize what we’ve learned today.
00:17:21.22 We know that type I interferons promote anti-tumor signaling,
00:17:25.20 including the stop signal that blocks the stromagenic switch,
00:17:28.26 preventing the activation of spies.
00:17:32.10 However, tumors have figured out a way
00:17:34.22 to degrade this stop signal
00:17:36.16 by getting rid of IFNAR1.
00:17:38.28 And as a result, the stromagenic switch can occur,
00:17:41.03 activating these spies.
00:17:43.15 However, if we stabilize IFNAR1
00:17:46.00 and prevent its degradation,
00:17:47.28 we actually can keep the stop signal on,
00:17:50.23 and as a result we prevent the stromagenic switch
00:17:52.27 and spies are not activated.
00:17:56.20 Now, that you know this information,
00:17:59.20 let’s go back to our concern.
00:18:01.17 Cancer-associated fibroblasts can be spies or allies —
00:18:04.19 very confusing, not the greatest news.
00:18:07.19 But what my research revealed
00:18:10.03 is that we may be able to prevent the activation of the spies
00:18:13.16 by stabilizing IFNAR1.
00:18:16.07 So, good news!
00:18:18.18 And at this time, I like to present this little quote
00:18:21.29 that i think perfectly represents what research is:
00:18:24.24 “Little victories that lead to bigger victories
00:18:27.00 that affect the battles that eventually win the war.
00:18:30.11 Winning begins small!”
00:18:32.18 So, we may not have won the war on cancer,
00:18:35.18 but we’ve definitely won a few little battles,
00:18:38.03 and i think there’s some hope that one day
00:18:40.07 we will win this war.
00:18:42.27 So, what’s next.
00:18:44.24 Where is my research taking me now?
00:18:47.02 Well, I want to go back to this problem:
00:18:49.14 the ability to distinguish the spies from the allies
00:18:52.24 by identifying unique markers.
00:18:54.14 I mentioned earlier that this was an ongoing challenge, right?
00:18:58.01 Well, there are way more tools now,
00:19:00.08 including single cell RNA sequencing,
00:19:01.19 that allows us to really dig into the details a little bit more,
00:19:05.13 allowing us to revisit those helmets or those markers.
00:19:10.01 So, let’s go back to those two helmets
00:19:11.17 that I mentioned earlier,
00:19:13.22 that may be a little bit hard to distinguish at first glance.
00:19:17.11 When you zoom in on these helmets,
00:19:19.13 you’ll notice that there are subtle details that are different.
00:19:22.02 The Italian M33 has this little nub
00:19:24.19 at the top of the helmet,
00:19:26.02 while the Greek M34
00:19:28.23 has two smaller dots in the bottom.
00:19:30.19 And just the way we can see these details by zooming in,
00:19:33.27 using single cell RNA sequencing and brand new technologies,
00:19:36.29 we can zoom into the details of cancer-associated fibroblasts
00:19:40.04 to start dissecting the different markers
00:19:43.09 that may allow us to really separate out
00:19:45.24 the spies from the allies.
00:19:50.23 So, I just want to take this time to thank the lab
00:19:53.05 where I did, predominantly,
00:19:55.11 all of my cancer-associated fibroblast research —
00:19:57.00 that’s the Fuchs lab at the University of Pennsylvania.
00:19:59.26 And now I’m at Yale using single cell RNA sequencing
00:20:02.23 to identify those markers,
00:20:05.01 and I’m so grateful to be a part of this experience.
00:20:07.21 Thank you.

This Talk

Audience:

Student
Educators of Adv. Undergrad / Grad
Researcher
Educators

Recorded: July 2021

Talk Overview

The National Cancer Act was signed into law in 1971, declaring a “War on Cancer.” While cancer deaths have declined remarkably since then due to major advances in therapies, we are still far from eliminating cancer altogether. In her Share Your Research talk, Christina Cho discusses how we could make further progress in our fight against cancer by learning more about the tumor microenvironment and the role of the immune system on tumor growth. Her work focuses on the complicated functions associated with cancer-associated fibroblasts, which can both help and harm cancer cells.

Credits

Kevin McLean (iBiology): Producer
Brittany Anderton (iBiology): Producer
Kornblum (iBiology): Videographer
Jiefei Yuan (iBiology): Editor
Chris George (iBiology): Graphics
Maggie Hubbard (iBiology): Graphics

Comments

Ziqing Chen says

April 15, 2022 at 4:34 pm

Really clear and great talk! Informative and professional!

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