LANCE MILLER, MD/CANCER BIOLOGY: Personalized Medicine is about the tailoring of a very
specific therapy according to the unique attributes of the patient and the patient's disease.
Those unique attributes could have something to do with the genetics of the patient or
the genetics of the disease or other physiological properties that can be measured.
It's always been the goal of the clinician to provide the best therapy for a patient
and to use what information is known about the patient and the disease to give the best
therapy.
But it's only been in recent years that we've had technologies that allow us to really go
deep into characterizing the moleular makeup of people and their diseases.
So Personalized Medicine is about taking this wealth of information that we can now begin
to measure from the patient and the disease and to understand what combinations of different
properties predict for a very good therapeutic outcome for the patients.
In my work, it's really about characterizing the genome using these new genomic technologies
that we have available to us in ways that weren't possible before and trying to understand
how these combinations of alterations predict for the aggressiveness of the cancer.
There are various technologies that we have at Wake Forest for doing this.
We have Next Generation Sequencing as it's called, which allows us to sequence genes-
tens of thousands at a time.
But we also have technologies called Microarray Technologies that help us to ask a different
kind of question but similar.
That is: How are genes turned on and off?
For example in a patient's tumor.
And so we can profile human tumors that have been collected in surgery using this microarray
technology to look at how their tens of thousands of genes are turn on or off.
And then, when you have hundreds of patients that have gone through this process whereby
they've given a tumor sample, we've profiled it.
If we know the outcomes of those patients- how they responded to the drug- did the cancer
come back?
These types of questions.
We can look at how genes- or combinations of genes- can be used to predict for those
outcomes.
Now what we find is very encouraging.
We find that compared to conventional wisdom- which are the markers that are used today
by pathologists to predict outcomes of patients- that we can sometimes do better or we can
find gene combinations that are very complementary to those markers that are currently used.
And the concept then is to take that information into the clinic where it can impact on clinical
decision-making so that oncologists can make better decisions for their patients.
My main area of research is in breast cancer biology and clinical aspects of breast cancer.
So using these genomic technologies such as DNA Microarray Technology, we're able to look
at how expression patterns of genes can be used to classify human breast cancer in ways
that haven't been considered before.
So historically, breast cancer's been considered maybe two diseases- what's called estrogen
receptor positive and estrogen receptor negative.
But in more recent years, we've discovered there's another way to classify and so there's
a protein called HER2- we talk about HER2 positive breast cancer- and there are specific
clinical treatments for each of these three types of breast cancer.
But with the advent of these genomic technologies, we now know that no two cancers are alike.
That there is heterogeneity across the board- and that what makes one cancer tick will not
make another cancer tick.
And so it is an opportunity then to use these technologies to better define the molecular
sub-types of cancer.
So by better defining the diagnostic sets- the particular type of cancer- the closer
we get towards the goal of personalized treatment for that individual.
And within those sub-types we can begin to ask very specific questions about what makes
this sub-type of cancer tick?
And once we understand that, then we can come in now with ideas about therapeutic intervention:
Are there particular mechanisms active in this certain sub-type of cancer?
Such as for example what's called Triple Negative Breast Cancer which is one of the more aggressive
breast cancer sub-types- and say: Here is the best therapy for this particular type
of Triple Negative Breast Cancer.
These are some of concepts that we deal with in the lab.
So Biomarker Discovery Research has the potential to identify unique combinations of genes or
proteins or any other type of genomic or molecular markers that in combination can make better
predictions about the outcomes of patients than anything that we have currently.
So you can imagine that having a combination of genes that can be studied in the context
of a diagnostic assay or test, would be an important contribution to the molecular toolkit
that a clinician has with which to decide the best treatment for a patient.
So it's our hope that this type of research is going to further Personalized Medicine
at Wake Forest by enhancing our ability to make the right decisions for how to treat
patients.
Selecting the right drugs.
The drugs that the patient's unique attributes of their disease are going to be most responsive
to.