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Tiny But Mighty: Decoding Brain Tumors with Extracellular Vesicles

00:00:13.26 What if I told you that a machine shaped like a box
00:00:17.01 and operated by a woman
00:00:18.19 was crucial to end World War II?
00:00:21.03 A machine far less sophisticated
00:00:23.07 than any of our smartphones was instrumental
00:00:25.15 in decoding Nazi communications,
00:00:27.13 which led to their defeat and to the end of the war.
00:00:30.15 This is interesting, but there is another war going on,
00:00:33.23 a war that is taking more than 16,000 lives every year,
00:00:37.20 that we have been fighting longer than World War I and World War II combined,
00:00:42.04 and the principle behind this machine can help us win it.
00:00:45.17 I’m talking about the war against brain cancer.
00:00:48.23 Like the woman scientists that back in 1940
00:00:51.16 worked to decode Nazi communications
00:00:53.22 and end World War II,
00:00:55.25 I developed a technique to intercept and decode messages
00:00:59.07 that come from the tumor,
00:01:00.25 to find out whether it’s cancer or not
00:01:03.10 and whether the treatment administered to the patient is working.
00:01:07.26 Also, tumors make codes that could help them
00:01:10.20 to shut down immune responses.
00:01:12.06 I’m grateful to have a wonderful team working by my side
00:01:15.08 to use the same principles as they used back in 1940
00:01:19.23 to break the brain tumor code
00:01:21.18 and play my part in the war against brain cancer.
00:01:25.07 What happens when you get a brain tumor?
00:01:27.07 Brain tumors are typically diagnosed through tissue histopathology and MRI.
00:01:30.20 The diagnosis and resection of these tumors
00:01:32.29 normally involve painful and very invasive surgeries
00:01:37.28 and brain biopsies.
00:01:40.08 For the time of diagnosis through surgery to rehabilitation,
00:01:43.00 and sometimes even hospice cares,
00:01:45.10 the total cost runs around $600,000-700,000.
00:01:51.11 Due to inflammation and cell death caused by treatment,
00:01:53.18 MRIs are sometimes not as reliable
00:01:55.24 and not as effective to assess response to treatment.
00:01:59.27 My mentor is also a neurosurgeon
00:02:02.06 that operates on multiple patients every week.
00:02:05.11 On these two MRI scans,
00:02:06.18 there is a real tumor and one that is not real.
00:02:10.00 Can you guess which one is which?
00:02:12.12 Us, either.
00:02:14.13 And both of them underwent surgery regardless,
00:02:17.28 which is a big problem to have.
00:02:21.09 This patient didn’t have to undergo surgery.
00:02:24.10 This scan is just showing inflammation
00:02:26.25 caused by treatment.
00:02:28.13 But unfortunately, MRIs are not that smart to distinguish that.
00:02:33.00 The real tumor is this scan.
00:02:35.07 How would you feel if you or a loved one underwent surgery or treatment
00:02:39.14 without having to?
00:02:40.26 This is why an alternative method to diagnose and monitor treatment outcomes
00:02:44.28 is so necessary,
00:02:46.24 since it will prevent unnecessary interventions to patients.
00:02:50.11 Also, imagine not getting very painful biopsies and surgery,
00:02:54.20 and just a blood draw instead.
00:02:56.25 What if we can study the code from brain tumors using this…
00:03:02.06 these blood draws.
00:03:03.26 Wouldn’t that be cool?
00:03:05.14 And before we address this non-invasive technique,
00:03:08.06 I will explain what type of brain tumor we’re working on.
00:03:11.22 We’re working with glioblastomas, or GBMs,
00:03:14.15 that are the most aggressive and lethal of brain tumors.
00:03:18.08 The standard of care is surgery, chemotherapy, and radiotherapy.
00:03:23.01 And there are more than 18,000 newly diagnosed patients
00:03:26.12 every year in the United States.
00:03:28.21 Despite all of the aggressive treatment that these patients receive,
00:03:32.00 it is very fatal and very lethal,
00:03:34.22 this disease.
00:03:35.29 And also, the patient’s immune system
00:03:38.15 shuts down almost completely.
00:03:41.11 For my thesis project, I focus on grade 4’s,
00:03:44.12 that are the more malignant and the most lethal ones.
00:03:48.16 And we propose using extracellular vesicles,
00:03:50.29 or EVs,
00:03:52.10 as a non-invasive tool for GBM diagnosis
00:03:55.04 and treatment monitoring.
00:03:56.26 EVs are teeny tiny particles
00:03:59.28 that are shed from tumors and also healthy cells.
00:04:03.09 They come in different flavors and varying sizes.
00:04:06.05 We have small EVs that range from 50-100 nanometers in size,
00:04:10.19 large EVs that range from 100-1000 nanometers in size,
00:04:14.22 and apoptotic bodies that are the biggest ones,
00:04:16.20 from 1000-5000 nanometers.
00:04:19.20 They are released from GBMs into the tumor microenvironment,
00:04:23.03 as you may see in the picture,
00:04:25.16 and they contain proteins that are very specific to tumor cells,
00:04:28.26 and we can measure the amount of these proteins
00:04:33.06 that are inside of the vesicles as a cargo.
00:04:36.03 As well, we can characterize other proteins
00:04:38.24 coming from tumors and their sizes,
00:04:41.22 so we are able to distinguish what is really an EV
00:04:45.08 from other types of particles.
00:04:48.07 And like I said, it’s very important to note that
00:04:51.00 these particles could be released from healthy cells
00:04:53.06 and healthy tissues as well,
00:04:55.04 not only from tumors.
00:04:57.21 Tumors need the perfect environment
00:05:00.02 to grow and disseminate throughout the body.
00:05:01.20 Therefore, they need to control nearby cells and tissues
00:05:05.11 to create the conditions that enable their growth
00:05:07.21 and dissemination throughout the body.
00:05:10.15 For this purpose, they send encoded messages
00:05:13.05 inside of the extracellular vesicles
00:05:14.29 to make healthy cells do their bidding.
00:05:19.02 Cracking the tumor code inside of these particles could allow us…
00:05:22.26 and empower doctors with useful techniques
00:05:26.20 for early cancer diagnosis
00:05:28.14 and also successful treatment monitoring
00:05:31.05 for these patients.
00:05:32.27 It will be a liquid biopsy extracted from the blood.
00:05:37.03 And my research aims to break the EV code
00:05:39.14 inside of these particles
00:05:41.20 and devise the counterattack in this war.
00:05:45.12 But before breaking any code,
00:05:47.06 the first thing that we were interested in finding out
00:05:50.16 was the profile of these particles.
00:05:53.05 And the first question that we posed was,
00:05:55.16 can we efficiently isolate small EVs in GBM patients?
00:05:59.23 Small vesicles are enriched for proteins,
00:06:03.27 so that means that there is an increase of proteins
00:06:06.28 in the smallest vesicles that there are,
00:06:10.20 that… these ones range from 50-100 nanometers in size.
00:06:13.12 And these kinds of particles
00:06:16.09 are the focus of my thesis project because of this reason.
00:06:19.04 So, the first thing that we wanted to demonstrate
00:06:22.11 was to validate our technique,
00:06:24.25 the technique that we developed in our lab,
00:06:27.02 and test if we can enrich, or augment,
00:06:30.29 the amount of small EVs in both patients and normal donors.
00:06:35.17 In this graph, in the y-axis,
00:06:37.18 you can see the concentration — that equals particles per mL —
00:06:41.06 and in the x-axis, you can see the size,
00:06:44.04 in nanometers, of these particles.
00:06:46.29 So, for patients, this graph…
00:06:49.22 what it’s showing is that we are successfully able
00:06:53.07 to enrich for particles that range from 50-100 nanometers in size,
00:06:56.24 and the same thing is true for normal donors,
00:06:59.05 as you may see in this next peak.
00:07:01.18 We can efficiently isolate particles
00:07:04.08 that range from 50-100 nanometers in size
00:07:07.07 and, most importantly, that are enriched for proteins,
00:07:11.29 the tumor proteins that we want to study.
00:07:14.20 Also, to further validate our technique,
00:07:17.12 we were interested in testing the expression,
00:07:21.03 the protein expression —
00:07:23.15 the presence of proteins that are characteristic from EVs.
00:07:26.10 All these markers are typical EV markers,
00:07:29.13 and we just wanted to find out if we can find them
00:07:32.18 in the samples derived from patients and normal donors.
00:07:36.20 And the answer is yes —
00:07:38.11 we can see the presence of these proteins
00:07:40.11 in both patients and normal donors,
00:07:42.20 again validating our technique.
00:07:45.13 There are a number of ways that gliomas are stratified,
00:07:48.23 and we try to divide this stratification
00:07:53.20 by different categories
00:07:55.25 to compare the profile of the EVs derived.
00:07:58.14 And the first question that we wanted to address is that…
00:08:02.10 are there any differences in GBM patients’ small EVs
00:08:05.14 based on tumor type?
00:08:06.24 And the first stratification that we tried
00:08:09.11 was based on tumor grades.
00:08:11.06 We have grade 2 gliomas that are low risk —
00:08:13.18 less likely to grow and spread.
00:08:16.11 We have grade 3 gliomas that are higher risk than grade 2,
00:08:19.19 and they can grow quickly.
00:08:22.21 And finally, we have grade 4 GBMs,
00:08:25.07 which are the scope of my thesis project.
00:08:26.26 They are the most lethal ones,
00:08:28.26 and they can grow and spread fastest throughout the body.
00:08:32.07 These are all different types of grades.
00:08:34.07 And we were interested, again,
00:08:36.18 in seeing if there were any differences in the profiling of the EVs
00:08:40.04 derived from these different tumor grades.
00:08:42.21 And the first thing that we wanted to address
00:08:46.07 is if there were any differences whatsoever in particle size.
00:08:48.21 Again, we’re measuring particle size in nanometers,
00:08:51.17 and comparing normal donors, or healthy controls,
00:08:55.09 to the different grades of gliomas.
00:08:58.22 And we found that there were no differences whatsoever
00:09:02.01 in particle size between these tumor grades
00:09:04.19 and normal donors.
00:09:06.11 And also, we were interested
00:09:08.26 in finding out the concentration,
00:09:11.08 the amount of these particles in the blood
00:09:13.08 in these different groups,
00:09:15.07 and we didn’t find any differences
00:09:17.13 in the total number of particles in the blood
00:09:20.09 amongst these different glioma grades.
00:09:23.10 So, the take-home message from this graph is that
00:09:27.02 when we stratify the tumor by grades,
00:09:28.23 we don’t see any differences,
00:09:30.12 and this was very, very helpful to know.
00:09:33.00 The next stratification of tumors that we tried
00:09:36.06 was comparing new versus recurrent tumors.
00:09:40.05 The newly diagnosed tumors are…
00:09:42.12 they have better chances of survival, of course,
00:09:45.10 because it’s the first time that they have been diagnosed,
00:09:47.19 in comparison to the recurrent tumors,
00:09:50.06 that are simply tumors that are coming back for a second, for a third time,
00:09:54.26 and these tumors are normally more aggressive,
00:09:57.23 and they have less…
00:09:59.15 and the patient has less chances of survival.
00:10:02.03 So, we wanted to compare the EVs, the extracellular vesicles,
00:10:06.06 derived from these two types of tumors
00:10:08.17 as well as healthy controls…
00:10:10.04 comparing the EVs derived from healthy controls.
00:10:12.08 And again, we compared particle size,
00:10:14.19 and we didn’t find any differences
00:10:16.28 between new and recurrent tumors and normal donors,
00:10:19.27 nor did we find any differences
00:10:23.15 in the total number of particles
00:10:25.18 between new and recurrent tumors.
00:10:27.02 So, that’s the take-home message here:
00:10:28.27 no differences in this stratification.
00:10:32.02 The next stratification that we wanted to study
00:10:34.26 was tumor mutations.
00:10:36.12 And it is well known that in glioblastoma,
00:10:39.05 and in other types of cancers,
00:10:41.01 these mutations can predict survival.
00:10:43.05 In this curve, in the y-axis,
00:10:45.29 there is the probability of survival in a percentage.
00:10:48.08 Here, we’re comparing for people with mutations
00:10:52.10 versus no mutations in general.
00:10:54.11 And what this graph is showing us is that,
00:10:57.22 for glioblastoma specifically,
00:10:59.07 patients that have mutations tend to do better
00:11:01.18 and have a better prognosis in comparison to patients
00:11:04.08 that have wild type tumors.
00:11:06.22 And this is interesting because
00:11:08.29 for most cancers and other diseases,
00:11:10.22 mutations are normally associated
00:11:13.21 with worse prognosis or worse chances of survival.
00:11:16.27 And in glioblastoma, it’s quite the opposite.
00:11:20.01 And here… something very interesting
00:11:22.03 that we found when comparing particle size
00:11:24.06 is that we did find differences in particle size
00:11:26.22 between the gliomas with the worst prognosis,
00:11:29.23 which are the wild type ones,
00:11:31.21 and normal donors.
00:11:33.29 And even more interesting,
00:11:35.19 we found differences in total number of particles
00:11:38.13 between gliomas with the worst prognosis,
00:11:41.04 or the wild type gliomas,
00:11:42.26 and normal donors.
00:11:44.22 Our hypothesis is that because these tumors
00:11:48.03 tend to be more aggressive,
00:11:50.28 they might release more particles into the bloodstream
00:11:54.14 to contribute to shut down the immune response
00:11:56.14 and successfully eliminate the cancer from the body.
00:11:59.22 In summary, for this first part,
00:12:01.16 we can efficiently enrich for small EVs,
00:12:04.14 and small EVs in GBM patients
00:12:06.29 are different from normal donors
00:12:09.10 in that, for the worst prognoses,
00:12:12.09 they’re slightly smaller but more frequent.
00:12:14.22 So, the next thing that we wanted to address
00:12:16.29 was inflammatory signals.
00:12:19.14 How can we decode other messages coming from the…
00:12:24.02 from the immunological part of the disease?
00:12:26.29 Since the immune system of these patients
00:12:29.26 is shut down,
00:12:31.04 what information can we obtain from that?
00:12:33.25 EVs are known to be immune mediators
00:12:36.08 in normal and in tumor conditions,
00:12:38.14 because like I said,
00:12:39.27 healthy cells can also release EVs
00:12:42.27 into the bloodstream.
00:12:44.07 And this function is very important
00:12:47.07 to mediate cell-to-cell communication in normal conditions,
00:12:50.17 as well as other important processes in the body.
00:12:54.14 But they are very important immune mediators,
00:12:57.24 and they can just shut down any immune response
00:13:01.02 elicited towards the tumor
00:13:02.21 and prevent important immunological processes,
00:13:05.09 when these EVs are released from tumor cells
00:13:08.24 and are uptaken by immune cells
00:13:11.12 and by other cells in the body.
00:13:13.23 Our immune system is like an army.
00:13:15.24 We have patrols, we have messages,
00:13:18.07 and we have soldiers.
00:13:19.23 The patrols are the receptors,
00:13:22.03 and they are in charge of surveillance,
00:13:24.04 of just checking that everything is under control
00:13:27.01 and alerting the rest of the immune system
00:13:30.16 if there is a virus, if there is something going on,
00:13:33.23 in this case a cancer.
00:13:35.18 We also have messages,
00:13:37.27 that are cytokines or proteins,
00:13:40.06 that can alert immune cells,
00:13:42.27 which are the soldiers,
00:13:45.00 to do their job,
00:13:46.24 to just be called into duty
00:13:49.00 and just understand that there is something going on,
00:13:50.29 something wrong going on in the body
00:13:53.01 and they need to become activated.
00:13:55.14 But there are some problems with the army
00:13:57.16 in brain tumor patients,
00:13:59.04 specifically with the messages
00:14:01.21 that alert the soldier or the immune cells.
00:14:03.23 These messages normally go down in the patients.
00:14:06.22 What causes this is that the immune cells
00:14:10.00 don’t become alerted,
00:14:12.00 don’t become activated in a successful way to get rid of the cancer.
00:14:15.18 For that reason, we were interested
00:14:18.05 in studying the expression of these cytokines, or proteins,
00:14:21.29 in the EVs derived from the patients.
00:14:25.12 And in these graphs, what we’re studying
00:14:27.18 is the concentration in the y-axis,
00:14:29.19 which is picograms per mL
00:14:32.17 in different two-sample cohorts —
00:14:35.26 in the normal donors and also in the patients.
00:14:37.24 And as you may see here,
00:14:39.16 very important inflammatory proteins
00:14:41.06 that are in charge of activating the immune response
00:14:44.04 and activating the immune cells
00:14:46.04 are all decreased, are all reduced,
00:14:49.10 in the patients in comparison to normal donors,
00:14:53.05 which makes sense because it is well known in the literature
00:14:55.19 that these patients’ immune systems are shut down.
00:14:59.15 Also, the immune cells, or the soldiers,
00:15:02.07 need some activation, some boost,
00:15:04.08 some energy boost.
00:15:05.28 It’s like a co-stimulatory molecule,
00:15:08.13 and we were interested in seeing,
00:15:10.03 what is the expression of these co-stimulatory molecules
00:15:13.18 in the patients, in comparison to normal donors?
00:15:16.27 And we found that these co-stimulatory molecules
00:15:20.04 were all decreased across the board
00:15:22.28 in patients versus normal donors,
00:15:24.18 again validating our point that the immune system of these patients
00:15:29.12 is shut down
00:15:31.29 and they cannot elicit an effective immune response
00:15:34.01 to get rid of the cancer.
00:15:35.23 In summary, we also demonstrated
00:15:38.03 that these patients have an immunosuppressed profile
00:15:41.05 and that they have a decreased co-stimulatory profile
00:15:43.26 that doesn’t allow the patients
00:15:45.26 to respond effectively to the cancer.
00:15:49.20 Something that I want to emphasize
00:15:51.24 is that all these results come from pooled EVs
00:15:54.04 without sorting into specific tumor EVs.
00:15:57.25 However, we are still seeing a signature that is
00:16:00.26 very specific to the behavior of the tumor.
00:16:04.13 So, now that we know their profile
00:16:06.12 and what they’re capable of doing,
00:16:08.01 what’s next?
00:16:09.26 We wanted to take a look at other types of molecules
00:16:12.21 that are involved in brain cancer,
00:16:14.22 and it is well known that protein- and microRNA-based signatures
00:16:18.14 could be diagnostic biomarkers for glioblastoma
00:16:21.00 and for other types of cancers.
00:16:24.07 What are microRNAs?
00:16:25.15 MicroRNAs are just molecules that help cells
00:16:28.11 to control the kind and the amount of proteins they make.
00:16:32.22 They’re basically gene expression controllers.
00:16:36.13 And it is known, for GBM and for other cancers,
00:16:39.17 that these EV proteins and RNA molecules
00:16:43.01 are important mediators of cell-to-cell communication
00:16:46.11 during the disease,
00:16:47.21 and that also, they could predict clinical outcomes.
00:16:50.18 And this is true for other types of cancer as well:
00:16:53.00 for melanoma, for colon cancer,
00:16:54.21 for prostate cancer, among others.
00:16:58.10 And in GBM patients, it is known that there are specific proteins
00:17:01.26 and microRNA signatures
00:17:03.23 that have been associated with poor outcomes in patients.
00:17:06.25 What we want to do next is investigate
00:17:09.03 if there are any GBM-associated microRNA signals
00:17:12.29 in bulk EVs, in circulating EVs,
00:17:15.10 and in tumor cell-derived EVs.
00:17:18.03 So, first we’re going to study
00:17:20.17 these same circulating EVs that we studied before,
00:17:22.17 for the profiling part,
00:17:24.02 and we’re going to perform short non-coding RNA sequencing,
00:17:27.22 including microRNA sequencing,
00:17:29.26 on them to understand the signatures,
00:17:31.29 to understand from where they’re coming.
00:17:34.22 We are developing a diagnostics test…
00:17:37.18 a diagnostic test is currently under underway
00:17:40.07 to detect cancer in a non-invasive way,
00:17:43.01 and this is one of the best tools that we have so far.
00:17:45.19 Also, with the new tools provided,
00:17:47.08 we can actually investigate the origin of the particle.
00:17:51.21 Is it really coming from the tumor,
00:17:53.21 or is it coming from other cells in the body?
00:17:55.29 For this purpose, we can use a fluorescent compound
00:17:59.21 that is called 5-ALA
00:18:02.01 that is used for better resection of tumors in the OR
00:18:04.19 when neurosurgeons are operating on patients.
00:18:08.13 And it fluoresces in a pink-red spectrum.
00:18:12.11 And our goal is to track down
00:18:15.11 EVs that specifically come from tumors
00:18:17.01 and that fluoresce in the same color.
00:18:19.16 In that way, we can also perform
00:18:22.05 short non-coding RNA sequencing
00:18:23.27 and understand the signature
00:18:26.09 and the origin of these particles.
00:18:28.17 In summary, I hope I convinced you
00:18:31.10 that we developed a non-invasive technique
00:18:33.20 to diagnose and monitor patients with brain tumors;
00:18:37.17 that also we can enrich, or augment,
00:18:40.09 small particles that are different in patients
00:18:42.29 in comparison to healthy controls,
00:18:45.06 and we demonstrate this by comparing the size,
00:18:48.12 also the expression of cytokines and proteins
00:18:52.11 and co-stimulatory molecules;
00:18:54.15 and that these particles in patients
00:18:56.24 may reflect the brain tumor composition
00:18:58.29 by analyzing RNA and other proteins
00:19:02.08 inside of them as cargo;
00:19:04.18 and that we could detect and know the origin of these particles
00:19:08.03 using fluorescence.
00:19:10.24 Thank you.

This Talk
Speaker: Luz Cumba García
Audience:
  • General Public
  • Educators of H. School / Intro Undergrad
  • Student
  • Educators of Adv. Undergrad / Grad
  • Researcher
  • Educators
Recorded: December 2020
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Talk Overview

Solid tumors, such as those in the brain, can be deadly when they expand or spread to other parts of the body. Before they spread, tumors send out molecular messages within small membranous structures known as extracellular vesicles (EVs). The proteins and small non-coding RNAs inside EVs can help tumors evade the immune system and seed new sites of growth. In this Share Your Research talk, Dr. Luz Cumba García describes her thesis research on extracellular vesicles released by aggressive brain cancers called glioblastoma. Dr. Cumba García’s work helps scientists understand the EV profiles of different glioma subtypes, as well as how the messages inside EVs help them shut off the immune system.  

Speaker Bio

Luz Cumba García

Cumba Garcia

Dr. Luz M. Cumba García is an immunologist with 13 years of experience working at the intersection of immunology, public policy, and diplomacy. Luz is currently a policy advisor at the government relations firm Lewis-Burke, LLC, where she leverages her biomedical research background to bolster issue expertise of the National Institutes of Health (NIH) and… Continue Reading

More Talks in Human Disease

Related Resources

Cumba Garcia, Luz M., et al. “Isolation and analysis of plasma-derived exosomes in patients with glioma.” Frontiers in oncology 9 (2019): 651.

Puffer, Ross C., et al. “Plasma extracellular vesicles as a source of biomarkers in traumatic brain injury.” Journal of Neurosurgery 134.6 (2020): 1921-1928.

Himes, Benjamin T., et al. “The role of extracellular vesicles and PD-L1 in glioblastoma-mediated immunosuppressive monocyte induction.” Neuro-oncology 22.7 (2020): 967-978.

Credits

Brittany Anderton (iBiology): Producer
Eric Kornblum (iBiology): Videographer
Rebecca Ellsworth (iBiology): Editor
Jiefei Yuan (iBiology): Editor
Chris George (iBiology): Graphics
Maggie Hubbard (iBiology): Graphics

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