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Good afternoon. My name is Rahul Aggarwal,
the GEO of medical oncologist at UCSF.
I'd like to thank the organizers
for inviting me to speak today,
and a special thanks to
Dean Bailey for helping to organize the event.
Over the next few minutes,
I will give a brief introduction to clinical trials,
and we should hopefully have a few minutes
for questions at the end.
I'm going to briefly touch upon three topics.
An overview of phase 1,
2, and 3 trials,
how to interpret clinical trial results,
and addressing a couple of the common questions that we
receive about clinical trial participation.
The three phases of clinical trials
refer to the different phases of testing
for a given drug or device that's being
used to diagnose or treat prostate cancer.
In the first phase,
phase 1 clinical trials,
we aim to determine whether a drug is safe,
what is the right dose to use?
Does the body adequately absorb and metabolize the drug?
Is there preliminary evidence of the anti-tumor activity?
In the second phase,
we aim to test whether there is
sufficient evidence of a positive treatment effect,
and increasingly critical as we think
about personalized care in cancer,
we also try to identify
the subgroups of patients that are
most likely to benefit within a phase 2 study.
In phase 3, we aim to determine,
as compared to the current standard
of care for that disease setting,
does the new treatment
improve clinically meaningful outcomes?
It's worth saying that this is
a somewhat traditional schema of drug development.
Increasingly, in order to speed up
timelines and enable patient access to drugs sooner,
phase 1 trials have become larger and more
focused on determination of effectiveness.
In select cases, especially if there are
limited standard of care options
for a particular cancer type,
FDA approval may even be based on
expanded phase 1 or 2 trial results,
a type of approval called accelerated approval.
A good example of this would be the approval of
pembrolizumab or KEYTRUDA in multiple cancer types,
including microsatellite high prostate cancer.
Increasingly, many of
our prostate cancer trials are geared
towards specific subgroups of patients based on clinical,
genetic, and other factors
rather than treating all comers.
The advantage of this approach is that it can potentially
maximize the likelihood of
benefit with a particular treatment.
However, there are trade-offs,
including potentially slower patient enrollment
and potentially excluding
patients who may benefit from the treatment
due to the use of imperfect selection factors.
Some of the notable examples of
precision oncology trials that we've
seen in prostate cancer over the last few years
include trials of PARP inhibitors
in prostate cancer patients
with mutations in their DNA repair pathway genes
such as BRCA2,
as well as the use of the decipher score in
localized prostate cancer to decide potentially who
should get hormone therapy in combination with radiation.
This slide shows an example of
such a prostate cancer trial that is
geared towards subgroups of
patients with a particular genetic profile.
This is the initial study of Olaparib,
an oral PARP inhibitor medication for
patients with metastatic
hormone-resistant prostate cancer.
Each column represented in
the figure represents an individual patient.
Shaded in tan on the left are
all the patients whose cancer responded to treatment,
and the individual rows with
the colored squares show whether
the patient's tumor had a mutation in
a particular gene within the DNA repair pathway.
As you can see clearly indicated with the red circle,
nearly all of the cancer that responded to
the PARP inhibitor had
a mutation in one of these DNA repair genes.
This type of selected trial results
led to a phase 3 trial of Olaparib
which only enrolled those patients
whose tumors harbored a mutation in one of these genes,
and ultimately led to FDA approval of this medication.
I want to spend a minute discussing
interpretation of survival results amongst
the [inaudible] trials as this is
often a pertinent topic for discussion.
We all, including those of us
who conduct the clinical research,
have a tendency to focus too much on the median
or average survival estimates of a particular treatment.
In fact, it is actually important to
look at the actual survival curves,
what we call KAPLAN-MEIER curves,
which estimate the probability of
a treatment outcome starting from
when a patient enrolls on this trial.
The median or average survival is
the time estimate of when
half the patients have met the outcome of interest.
On the left side of the screen,
you see an example where
the median survival estimates are nearly identical.
Yet clearly there is
an overall benefit with
the green curve compared to the red curve,
you will see a clear separation in the curves over time,
especially towards the right side.
On the right side of the screen,
you see the opposite,
example where the median estimates are far
apart and yet the curves
come together with longer follow-up.
The better statistic of how well
a particular drug does is what's called the hazard ratio,
which is roughly speaking the area between the curves,
the amount of space we can see between the two curves,
which is also weighted for longer-term follow-up.
Lastly, I want to address some of
the great questions we commonly
receive from patients who are
considering enrolling on clinical trials.
Hopefully, we'll have time for
a few more questions at the end.
Number one a question I often get,
is it true that clinical trials are only for
patients who have been treated with
all standard of care treatments?
In fact, the majority of cancer clinical trials,
including phase 1 studies,
do not fall into this category.
In clinical practice, a patient may be
treated with the standard of care therapy,
then enroll on a clinical trial,
and then go back to standard of care.
At each treatment decision point,
it's important to evaluate
all the potential treatment options,
both those as part of a clinical trial,
as well as those that are
FDA-approved standard treatment options.
Whether a particular trial is
right for you at the current junction and
time will depend on many factors
that you'll be able to discuss with your clinician.
A second question that I commonly get is,
how much is the study going to cost?
This is a great question
that I hear from patients all the
time and I think it's a really important consideration,
especially when you factor in not only
dollar cost but time cost,
the amount of time it takes to come to
a center to participate on a clinical trial,
and that often can be not trivial.
The good news is that we try very hard to
make sure commercial insurance is only billed for
those items that are considered
standard of care and everything
else including the trial medications
is covered by the study.
I'm also hopeful that one of
the important insights COVID-19 has taught us is that
we can vastly expand the use of
telemedicine for our clinical trial participants,
making it much more accessible for them to participate
and cut down on the amount of
travel associated costs to come to UCSF.
Hopefully, this pattern that we've
seen emerge during the pandemic with
clinical trial participation will
continue well beyond when it's over.
With that, I'll wrap up.
Thank you very much for your attention.
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