DR. HADDAD: It's a pleasure to have a global leader in medical oncology
participate in our course, Dr. John Marshall,
the director of the Ruesch Center for the Cure of GI Cancers at Georgetown
University, and the Chief of Hematology Oncology
at Georgetown University Medical Center.
Dr. Marshall has tremendous experience in treating patients and conducting
clinical trials all meant for the improvement of the care of cancer
patients.
He will share with us his experience.
He will let us know how we can take advantage of big data
to improve patient care, now and in the future.
It's really a treat to have Dr. Marshall join our course.
And Dr. Marshall, thank you so much for making the time for us.
DR. MARSHALL: Bassem, it's just an honor to be here.
Thanks for inviting me.
DR. HADDAD: We would like to start by asking about the use of big data in precision
medicine.
Specifically, we generate tremendous amounts of data,
as you know, from clinical labs and from the research lab.
Do you have the tools, and are you using this information
in your treatment of patients?
Are you tailoring treatment for individual patients,
or are we still using a one-size-fits-all approach to treating
patients?
DR. MARSHALL: Bassem, this is such an important question.
And I think there's a major disconnect about what people think we're doing
and what we're really doing.
We generate, as you say, lots of data, whether it's
physicians entering into electronic medical records, reports, laboratory
data, genetic data, radiology data.
All of this comes to us, and our job now is to sort of synthesize this.
And we think that our computers and our analytics
and the Watsons of the world out there are constantly
churning on this information to spit out just the right answer, just
the right precision recommendation.
And the reality is, we're not there yet.
There's so much variability on the input.
Some data is images.
Some data is numbers.
Some data is text.
We haven't really figured out how to simplify this data so that it can
be used collaboratively, if you will.
Now, on the other side, though, we are doing more precision medicine.
It's less of a one-size-fits-all than it was just five years ago.
Almost everyone with cancer has some additional genetic test done
that helps me decide do I go this direction or this direction,
whether that's prognosis or a prediction of what treatments will work or not.
But I don't think we're to the place where
I can do a genetic test on some patient, or some other molecular test,
and say to that patient, this medicine will definitely work
or will not definitely work.
What we're doing is improving what I call the gray scale.
We've made it more likely that it will work, or less likely that it'll work.
So, we still have miles to go on incorporating all of this data
into our daily decision-making.
So, the fundamental answer to your question
is, no, we really aren't using all of this big data
in a way that reaches to an individual patient
and has a dramatic input and output to decision-making and their outcomes.
Are we going there?
Do we need to go there?
Absolutely.
Do we understand that precision medicine is the way of the future?
Absolutely.
Will big data be a critical element in how we go there,
will it be a critical tool?
Absolutely.
And what we've got to do now is figure out
how to make that data available, usable, translatable
to the patient in front of us.
DR. HADDAD: This is great.
And part of what we're doing by developing
this course and similar courses is exactly to help you.
So do you see us helping you by what we're doing, or do you need more?
DR. MARSHALL: This course is incredibly important for all of us to move this forward.
We need to understand what we're doing right, what we're not doing right yet,
and how to bridge that gap.
So, lots of us have the concept of where we want to go.
Lots of us have maybe the tools in front of us to get there.
But we don't use those tools in an appropriate way,
or to their best advantage.
So I think this course is fundamental to helping
to bridge the gap to get us to the use of big data for precision medicine.
DR. HADDAD: Again, we're producing a lot of data in the research labs.
You're requesting a lot of data as a clinician.
These data are there in the charts of the patients or in the research
lab databases.
How can we take advantage of the data not for the specific patient only,
but also for every patient?
Are we sharing the data?
Is there enough sharing?
Do we have the tools?
And how do you foresee sharing data?
Are the electronic medical records our way to sharing data, or we need more?
And if so, what do we need?
DR. MARSHALL: Data sharing is an incredible obstacle to our success right now.
Our data is in-- our data collections are in silos.
I think about just my practice of medicine here at Georgetown University.
I have some of the patients' records in one program,
warehoused in some location.
I've got other in a different place.
And then some of my patients, of course, get their care
at other institutions, other hospitals.
And so that's not necessarily in the same warehouse, if you will.
So the data is there, but it's stored in different places,
and they don't necessarily talk, or even speak the same language.
And then you couple that to genetic data,
and that's housed in another place altogether, research data, or even
the clinical trial data.
These are all protected.
And there's actually laws in the United States
which prevent us from connecting these.
So-called HIPAA laws, or health care protection privacy laws actually
prevent us from easily connecting these data points.
And so I think one of the major barriers we
have out there is that we all are operating
with different programs, different systems, different warehouses of data,
but in addition we can't just-- as may be appropriate,
we can't just send patient data from one bank to another bank.
There have to be particular protocols and approvals that are done.
So on some level, yes, we are sharing data when appropriate, when approved.
But are we realizing the full potential of all of that data?
No.
And I do want to point out again the common elements.
What I wish for is really a simplified set of common data elements
that we could agree on as scientists and health care providers
not only in the United States, but around the world.
We have ratcheted up the complexity of the data
that we're required to collect, whether that's for medical legal purposes
or for billing purposes.
And so we get all hung up in collecting data points that really are not
fundamental to our understanding of disease and to outcome,
but have to respond to whether it's an insurance
company or the threat of getting sued or, frankly, the FDA and auditing
of our clinical records.
I wish that we could simplify our data elements
and find a common data set that we could share,
that we could collect accurately and bring together.
And I believe one of our major hurdles that we must overcome
is simplifying our complex data sets.
DR. HADDAD: Are electronic medical records one way to getting to where you wish to get to?
DR. MARSHALL: So, electronic medical records are a very good thing.
They're much better than our old paper records,
being old enough to remember the days before and the days after.
But it's back to data quality in versus out.
So, many of our electronic medical records
are merely places where we can type in text, although most of them
have the so-called pulldown, so that we can use discoverable data elements.
If I have a bunch of free text in my EMR, it's not a very useful thing.
The pulldown elements, the numerical or pre-certified elements, those
are the ones that are so valuable.
But they cost us all time.
And some people are very good documenters,
and some people are not very good documenters.
So just because you have a tool called an electronic medical record
doesn't guarantee that what's in there is right or is
of the same quality of, you know, entry.
And so, yes, EMRs are the way forward.
I wish we could have agreed not to just have EMRs,
but to have one EMR in the United States,
that we were all using the same tool.
And that really, I think, would have been a major positive for health care
science and the ultimate cure of cancer.
DR. HADDAD: I want to go back on what you said about agreeing on a certain set of data
that is sharable, if you will.
Is there any effort being done in that direction?
DR. MARSHALL: Yeah.
So, pushing back against the complexity of data is not easy.
The complex data sets are very powerful lobbies, if you will.
It's the health insurance industry, it's the FDA and others.
So we have to prove that these simple data elements reflect the patient,
reflect what's happening to the patient, or to the science.
And so, yes, there are many efforts going on
to create a common data set, a simplified common data set
that we would be very good at entering, that would be easily shared,
and that reflect that.
We just published at our recent cancer meetings
in 2016 an abstract where it basically uses the simple data that
can be collected easily and accurately in an electronic medical record
to determine the benefit of a patient.
Instead of diving deep into a CAT scan or into the text of a given doctor's
note, this looks at changing treatment from one type to another,
something we call time to next treatment,
or TNT, as a very simple, very easy, very accurate discoverable data
set that would depict for us what the real impact of a given treatment
is for a patient.
DR. HADDAD: Glad to hear it.
We're on the right track.
DR. MARSHALL: We hope.
We hope.
DR. HADDAD: We hope.
Dr. Marshall, you talked about the quality of the information entered
onto electronic medical records.
And that's great.
But I want to talk about the quality of the data generated,
the quality of the big data itself.
We are generating data from research labs,
from clinical labs when physicians ask for certain studies on patients.
How do we make sure that that quality is pristine,
is good enough for us to use to make decisions in the future, or to share?
It's one thing to share, but if we're sharing data of poor quality,
we're really not doing a service to anybody.
We want to talk about quality.
Of course, anyone can-- almost anyone can generate big data,
whether in their lab or they can outsource it.
How do we make sure that we are working with pristine data?
How can we trust big data?
Now, big data is a big word, but can we trust the data that we're looking at?
What are mechanisms that we can use to make sure that what we are looking at
is good big data?
DR. MARSHALL: When we think about big data, many of us think
that this is a number that's a very precise thing that's been generated
and any-- it's the truth, right, that whatever that number in the box is
is the truth.
And it goes into this big pot, and then we can analyze the truth,
and get more truths out of it.
Health care big data is different.
So, there are some of our tests that we run on patients
which are probably the truth.
The accuracy of the test is very, very good, very reproducible.
So the number in the box is the truth.
But there are many tests that we run-- could be genetic, genomic data,
could be protein data, tissue-based data, blood-based data,
where the assays are evolving over time, that the quality
of the antibody in an immunohistochemical
assay we take as truth, but maybe the lot's different.
And so you get different tests back, or results back.
The more we're doing molecular profiling,
the more we're recognizing that we get different answers depending
on which lab we send the result to.
And we know, for example, in cancer that if you take a ball of cancer,
a single ball from a single patient, and you biopsy it north,
south, east, and west, and do molecular profiling on that,
you get four different answers.
Same patient, same cancer, four different.
Which one is the truth?
Which one is the accurate reflection?
All of that information goes into a database.
And it's such cool science.
We think of it as the truth.
We work with a group out of Germany here at Georgetown called Indivumed.
And they have shown us, or convinced us, that even
removing the cancer from a patient, the technique
that one uses to remove the cancer from the patient
changes the genetic outcome.
So if one doesn't control for what we call ischemia time,
then you get different answers on the big data
that we're generating off of this.
We as physicians are humans, and our data entry is not the truth
all the time.
We do our best assessment, whether it's the stage
of a patient or some assessment, a benefit or not for a patient.
So, all of this variable truth goes into big pots.
And then we analyze that.
We must understand the high-quality elements
and what they bring to the table, and then those that have variable quality.
And we have to know that if we're going to make
any sense of the results that come out.
Many of us feel that there's safety in numbers.
The value of big data is that there are a lot of data points,
and that even though there is variability,
the truth still comes out because the trends can be detected.
And I believe that's, in large part, true.
But we have to always be cautious of these large analyses that
are going on in health care that spit out answers that, in the end,
might not be the medical truth because of the poor quality.
DR. HADDAD: We want to talk about clinical trials.
Of course, you've been conducting clinical trials for many years.
You led over 150 clinical trials.
Are the ways used to conduct clinical trials changing
because of the availability of data?
How is the data influencing the way you conduct clinical trials?
DR. MARSHALL: Yeah, it's been fascinating to watch the evolution of clinical research
over the last really decade or so, and I think
in large part because of our concepts around precision medicine,
the ability to have big data to pick the right patient for the right treatment.
In the old days, not too long ago, one size did fit all.
Every colon cancer patient was eligible for every clinical trial.
We shoved everybody through the same grinder and see if it worked
or it didn't work.
What we learned, interestingly, is that now
that we know more about the different kind of colon cancer patients
that went through the grinder, we learned that some patients were helped,
but some patients were hurt.
And so our curves that were apart by a month or two or three,
that improved curve was being pushed down, as well, by the treatment that
was hurting some patients.
And so yes, it was being pushed up by the new treatment,
but it was being also pushed down.
And that difference was then a set amount of time, the average of that.
Now that we're beginning to incorporate more molecular profiling,
more big data, what we're doing is removing the patients that
were not being helped, in fact hurting.
And so our benefit rate is going up and up and up,
but for a smaller group of patients.
So, not everybody is eligible for clinical trials
anymore, because only patients with certain genetic types
are eligible for that treatment or that treatment,
which means that clinical trials take either much longer because it takes
a longer time to find all of those patients, but what I'm pleased to see
is that what's actually happening is that clinical trials are
getting much smaller.
And they're being done much quicker, because when
you find the right treatment for the right patient, even if it's only 10%
of a very large pot, you can show dramatic improvement very quickly
in a very small number of patients.
And so the approval for new medicines is going much more quickly.
Not for every patient, though.
You have to recognize this.
We are carving cancer up into its variable molecular types.
And each one has treatments specific to it.
And so this is the promise and the hope, and now
the realization of precision medicine.
And we are clearly seeing it in the clinical trial world.
We can't really run a clinical trial portfolio anymore without big data.
DR. HADDAD: I would like to end with your vision of the future.
In January 2016, President Obama in his State of the Union
discussed, or proposed, the Moonshot Initiative for cancer.
45 years before, President Nixon declared the war on cancer.
And both presidents proposed to cure cancer.
But here we are.
We haven't cured cancer yet.
You still have patients coming to your clinic.
Do you foresee us curing cancer in the near future, in the far future?
Or is this just a dream?
And how do we get closer to the reality?
DR. MARSHALL: So to start with, we have cured some cancers.
OK?
That's no question.
We cure many leukemias, most lymphomas, testicular cancers.
Surgery cures the majority of patients with breast cancer and other cancers.
So we do cure cancer.
Let's not be-- let's not miss that point.
But, we have lots of people still dying of metastatic cancer.
And our understanding about cancer has changed.
It's not one disease, it's lots of diseases.
And so what we're doing is we're picking off cancers that we are curing,
or that we are having dramatic positive impact on.
The goal of getting them all, of course, is where we want to get.
And the way to get there is, I believe, through big data,
through data sharing, through enriched prospective clinical trials.
So I do think we're on the right track.
The concept of the original war on cancer
was we could just pour enough chemotherapy or some other toxin
onto patients.
We would fix their cancers, and cure them.
And we understand that that's not going to work.
The most exciting new angle around the cure for cancer
is, of course, the immune system in immunotherapy.
But even there, even with that success, we
will still be left with many patients who will need our help.
Can we convert cancer from a chronic disease to a cured disease?
That is still my dream, although many in our field
are not sure that we will cure cancer, but that we will convert it
to a long-lasting chronic disease.
So even within our own field, we have that debate.
But I do believe that the new initiative that's being brought forward
by the Obama administration and by the Biden effort around the Moonshot
has a chance of further kickstarting, further stimulating the work ahead.
It is around big data.
It is around data sharing.
It is around common data elements, because we
know that that's how cancer is.
We're smarter than we were before.
And so I am optimistic and hopeful as the director of this center
to cure GI cancers is that we will, in fact, realize that dream.
DR. HADDAD: Dr. Marshall, thank you very much for providing us
with a lot of hope for the future.
You've talked before about fighting a smarter war on cancer.
And it seems to me, and it's clear to me,
that using the smart use of big data is a major part
of this war against cancer.
Thank you very much for your time, and for sharing what I believe
is an incredible information with all of us.
Thank you, Dr. Marshall.
DR. MARSHALL: Bassem, thank you.
And thank you all for listening in.