What if I told you that a machine shaped like a box
and operated by a woman
was crucial to end World War II?
A machine far less sophisticated
than any of our smartphones was instrumental
in decoding Nazi communications,
which led to their defeat and to the end of the war.
This is interesting, but there is another war going on,
a war that is taking more than 16,000 lives every year,
that we have been fighting longer than World War I and World War II combined,
and the principle behind this machine can help us win it.
I’m talking about the war against brain cancer.
Like the woman scientists that back in 1940
worked to decode Nazi communications
and end World War II,
I developed a technique to intercept and decode messages
that come from the tumor,
to find out whether it’s cancer or not
and whether the treatment administered to the patient is working.
Also, tumors make codes that could help them
to shut down immune responses.
I’m grateful to have a wonderful team working by my side
to use the same principles as they used back in 1940
to break the brain tumor code
and play my part in the war against brain cancer.
What happens when you get a brain tumor?
Brain tumors are typically diagnosed through tissue histopathology and MRI.
The diagnosis and resection of these tumors
normally involve painful and very invasive surgeries
and brain biopsies.
For the time of diagnosis through surgery to rehabilitation,
and sometimes even hospice cares,
the total cost runs around $600,000-700,000.
Due to inflammation and cell death caused by treatment,
MRIs are sometimes not as reliable
and not as effective to assess response to treatment.
My mentor is also a neurosurgeon
that operates on multiple patients every week.
On these two MRI scans,
there is a real tumor and one that is not real.
Can you guess which one is which?
Us, either.
And both of them underwent surgery regardless,
which is a big problem to have.
This patient didn’t have to undergo surgery.
This scan is just showing inflammation
caused by treatment.
But unfortunately, MRIs are not that smart to distinguish that.
The real tumor is this scan.
How would you feel if you or a loved one underwent surgery or treatment
without having to?
This is why an alternative method to diagnose and monitor treatment outcomes
is so necessary,
since it will prevent unnecessary interventions to patients.
Also, imagine not getting very painful biopsies and surgery,
and just a blood draw instead.
What if we can study the code from brain tumors using this…
these blood draws.
Wouldn’t that be cool?
And before we address this non-invasive technique,
I will explain what type of brain tumor we’re working on.
We’re working with glioblastomas, or GBMs,
that are the most aggressive and lethal of brain tumors.
The standard of care is surgery, chemotherapy, and radiotherapy.
And there are more than 18,000 newly diagnosed patients
every year in the United States.
Despite all of the aggressive treatment that these patients receive,
it is very fatal and very lethal,
this disease.
And also, the patient’s immune system
shuts down almost completely.
For my thesis project, I focus on grade 4’s,
that are the more malignant and the most lethal ones.
And we propose using extracellular vesicles,
or EVs,
as a non-invasive tool for GBM diagnosis
and treatment monitoring.
EVs are teeny tiny particles
that are shed from tumors and also healthy cells.
They come in different flavors and varying sizes.
We have small EVs that range from 50-100 nanometers in size,
large EVs that range from 100-1000 nanometers in size,
and apoptotic bodies that are the biggest ones,
from 1000-5000 nanometers.
They are released from GBMs into the tumor microenvironment,
as you may see in the picture,
and they contain proteins that are very specific to tumor cells,
and we can measure the amount of these proteins
that are inside of the vesicles as a cargo.
As well, we can characterize other proteins
coming from tumors and their sizes,
so we are able to distinguish what is really an EV
from other types of particles.
And like I said, it’s very important to note that
these particles could be released from healthy cells
and healthy tissues as well,
not only from tumors.
Tumors need the perfect environment
to grow and disseminate throughout the body.
Therefore, they need to control nearby cells and tissues
to create the conditions that enable their growth
and dissemination throughout the body.
For this purpose, they send encoded messages
inside of the extracellular vesicles
to make healthy cells do their bidding.
Cracking the tumor code inside of these particles could allow us…
and empower doctors with useful techniques
for early cancer diagnosis
and also successful treatment monitoring
for these patients.
It will be a liquid biopsy extracted from the blood.
And my research aims to break the EV code
inside of these particles
and devise the counterattack in this war.
But before breaking any code,
the first thing that we were interested in finding out
was the profile of these particles.
And the first question that we posed was,
can we efficiently isolate small EVs in GBM patients?
Small vesicles are enriched for proteins,
so that means that there is an increase of proteins
in the smallest vesicles that there are,
that… these ones range from 50-100 nanometers in size.
And these kinds of particles
are the focus of my thesis project because of this reason.
So, the first thing that we wanted to demonstrate
was to validate our technique,
the technique that we developed in our lab,
and test if we can enrich, or augment,
the amount of small EVs in both patients and normal donors.
In this graph, in the y-axis,
you can see the concentration — that equals particles per mL —
and in the x-axis, you can see the size,
in nanometers, of these particles.
So, for patients, this graph…
what it’s showing is that we are successfully able
to enrich for particles that range from 50-100 nanometers in size,
and the same thing is true for normal donors,
as you may see in this next peak.
We can efficiently isolate particles
that range from 50-100 nanometers in size
and, most importantly, that are enriched for proteins,
the tumor proteins that we want to study.
Also, to further validate our technique,
we were interested in testing the expression,
the protein expression —
the presence of proteins that are characteristic from EVs.
All these markers are typical EV markers,
and we just wanted to find out if we can find them
in the samples derived from patients and normal donors.
And the answer is yes —
we can see the presence of these proteins
in both patients and normal donors,
again validating our technique.
There are a number of ways that gliomas are stratified,
and we try to divide this stratification
by different categories
to compare the profile of the EVs derived.
And the first question that we wanted to address is that…
are there any differences in GBM patients’ small EVs
based on tumor type?
And the first stratification that we tried
was based on tumor grades.
We have grade 2 gliomas that are low risk —
less likely to grow and spread.
We have grade 3 gliomas that are higher risk than grade 2,
and they can grow quickly.
And finally, we have grade 4 GBMs,
which are the scope of my thesis project.
They are the most lethal ones,
and they can grow and spread fastest throughout the body.
These are all different types of grades.
And we were interested, again,
in seeing if there were any differences in the profiling of the EVs
derived from these different tumor grades.
And the first thing that we wanted to address
is if there were any differences whatsoever in particle size.
Again, we’re measuring particle size in nanometers,
and comparing normal donors, or healthy controls,
to the different grades of gliomas.
And we found that there were no differences whatsoever
in particle size between these tumor grades
and normal donors.
And also, we were interested
in finding out the concentration,
the amount of these particles in the blood
in these different groups,
and we didn’t find any differences
in the total number of particles in the blood
amongst these different glioma grades.
So, the take-home message from this graph is that
when we stratify the tumor by grades,
we don’t see any differences,
and this was very, very helpful to know.
The next stratification of tumors that we tried
was comparing new versus recurrent tumors.
The newly diagnosed tumors are…
they have better chances of survival, of course,
because it’s the first time that they have been diagnosed,
in comparison to the recurrent tumors,
that are simply tumors that are coming back for a second, for a third time,
and these tumors are normally more aggressive,
and they have less…
and the patient has less chances of survival.
So, we wanted to compare the EVs, the extracellular vesicles,
derived from these two types of tumors
as well as healthy controls…
comparing the EVs derived from healthy controls.
And again, we compared particle size,
and we didn’t find any differences
between new and recurrent tumors and normal donors,
nor did we find any differences
in the total number of particles
between new and recurrent tumors.
So, that’s the take-home message here:
no differences in this stratification.
The next stratification that we wanted to study
was tumor mutations.
And it is well known that in glioblastoma,
and in other types of cancers,
these mutations can predict survival.
In this curve, in the y-axis,
there is the probability of survival in a percentage.
Here, we’re comparing for people with mutations
versus no mutations in general.
And what this graph is showing us is that,
for glioblastoma specifically,
patients that have mutations tend to do better
and have a better prognosis in comparison to patients
that have wild type tumors.
And this is interesting because
for most cancers and other diseases,
mutations are normally associated
with worse prognosis or worse chances of survival.
And in glioblastoma, it’s quite the opposite.
And here… something very interesting
that we found when comparing particle size
is that we did find differences in particle size
between the gliomas with the worst prognosis,
which are the wild type ones,
and normal donors.
And even more interesting,
we found differences in total number of particles
between gliomas with the worst prognosis,
or the wild type gliomas,
and normal donors.
Our hypothesis is that because these tumors
tend to be more aggressive,
they might release more particles into the bloodstream
to contribute to shut down the immune response
and successfully eliminate the cancer from the body.
In summary, for this first part,
we can efficiently enrich for small EVs,
and small EVs in GBM patients
are different from normal donors
in that, for the worst prognoses,
they’re slightly smaller but more frequent.
So, the next thing that we wanted to address
was inflammatory signals.
How can we decode other messages coming from the…
from the immunological part of the disease?
Since the immune system of these patients
is shut down,
what information can we obtain from that?
EVs are known to be immune mediators
in normal and in tumor conditions,
because like I said,
healthy cells can also release EVs
into the bloodstream.
And this function is very important
to mediate cell-to-cell communication in normal conditions,
as well as other important processes in the body.
But they are very important immune mediators,
and they can just shut down any immune response
elicited towards the tumor
and prevent important immunological processes,
when these EVs are released from tumor cells
and are uptaken by immune cells
and by other cells in the body.
Our immune system is like an army.
We have patrols, we have messages,
and we have soldiers.
The patrols are the receptors,
and they are in charge of surveillance,
of just checking that everything is under control
and alerting the rest of the immune system
if there is a virus, if there is something going on,
in this case a cancer.
We also have messages,
that are cytokines or proteins,
that can alert immune cells,
which are the soldiers,
to do their job,
to just be called into duty
and just understand that there is something going on,
something wrong going on in the body
and they need to become activated.
But there are some problems with the army
in brain tumor patients,
specifically with the messages
that alert the soldier or the immune cells.
These messages normally go down in the patients.
What causes this is that the immune cells
don’t become alerted,
don’t become activated in a successful way to get rid of the cancer.
For that reason, we were interested
in studying the expression of these cytokines, or proteins,
in the EVs derived from the patients.
And in these graphs, what we’re studying
is the concentration in the y-axis,
which is picograms per mL
in different two-sample cohorts —
in the normal donors and also in the patients.
And as you may see here,
very important inflammatory proteins
that are in charge of activating the immune response
and activating the immune cells
are all decreased, are all reduced,
in the patients in comparison to normal donors,
which makes sense because it is well known in the literature
that these patients’ immune systems are shut down.
Also, the immune cells, or the soldiers,
need some activation, some boost,
some energy boost.
It’s like a co-stimulatory molecule,
and we were interested in seeing,
what is the expression of these co-stimulatory molecules
in the patients, in comparison to normal donors?
And we found that these co-stimulatory molecules
were all decreased across the board
in patients versus normal donors,
again validating our point that the immune system of these patients
is shut down
and they cannot elicit an effective immune response
to get rid of the cancer.
In summary, we also demonstrated
that these patients have an immunosuppressed profile
and that they have a decreased co-stimulatory profile
that doesn’t allow the patients
to respond effectively to the cancer.
Something that I want to emphasize
is that all these results come from pooled EVs
without sorting into specific tumor EVs.
However, we are still seeing a signature that is
very specific to the behavior of the tumor.
So, now that we know their profile
and what they’re capable of doing,
what’s next?
We wanted to take a look at other types of molecules
that are involved in brain cancer,
and it is well known that protein- and microRNA-based signatures
could be diagnostic biomarkers for glioblastoma
and for other types of cancers.
What are microRNAs?
MicroRNAs are just molecules that help cells
to control the kind and the amount of proteins they make.
They’re basically gene expression controllers.
And it is known, for GBM and for other cancers,
that these EV proteins and RNA molecules
are important mediators of cell-to-cell communication
during the disease,
and that also, they could predict clinical outcomes.
And this is true for other types of cancer as well:
for melanoma, for colon cancer,
for prostate cancer, among others.
And in GBM patients, it is known that there are specific proteins
and microRNA signatures
that have been associated with poor outcomes in patients.
What we want to do next is investigate
if there are any GBM-associated microRNA signals
in bulk EVs, in circulating EVs,
and in tumor cell-derived EVs.
So, first we’re going to study
these same circulating EVs that we studied before,
for the profiling part,
and we’re going to perform short non-coding RNA sequencing,
including microRNA sequencing,
on them to understand the signatures,
to understand from where they’re coming.
We are developing a diagnostics test…
a diagnostic test is currently under underway
to detect cancer in a non-invasive way,
and this is one of the best tools that we have so far.
Also, with the new tools provided,
we can actually investigate the origin of the particle.
Is it really coming from the tumor,
or is it coming from other cells in the body?
For this purpose, we can use a fluorescent compound
that is called 5-ALA
that is used for better resection of tumors in the OR
when neurosurgeons are operating on patients.
And it fluoresces in a pink-red spectrum.
And our goal is to track down
EVs that specifically come from tumors
and that fluoresce in the same color.
In that way, we can also perform
short non-coding RNA sequencing
and understand the signature
and the origin of these particles.
In summary, I hope I convinced you
that we developed a non-invasive technique
to diagnose and monitor patients with brain tumors;
that also we can enrich, or augment,
small particles that are different in patients
in comparison to healthy controls,
and we demonstrate this by comparing the size,
also the expression of cytokines and proteins
and co-stimulatory molecules;
and that these particles in patients
may reflect the brain tumor composition
by analyzing RNA and other proteins
inside of them as cargo;
and that we could detect and know the origin of these particles
using fluorescence.
Thank you.