EDITOR’S NOTE: Unfortunately, this weekend, I
was forced to get my slides together for the upcoming SBM Conference,
plus editing a manuscript for resubmission, plus working on a manuscript
that I should have submitted six months ago, plus reading over some
grants, plus…well, you get the idea. What this means is that, alas, I
didn’t have any time to prepare one of the new, long posts that you’ve
come to love (or hate). Fortunately, there are a lot of other things
I’ve written out there that can be rapidly adapted to SBM. For instance,
what I am about to present now. Since I wrote this, I’ve thought of a
couple of things that I should have said the first time (and was kicking
myself for not having done so); so publishing an updated version here
allows me to rectify those omissions.
A couple of weeks ago, there was a lot of hype about a study that hadn’t been released yet. Indeed, there was a story in
Wired entitled
To Survive Cancer, Live With It and an editorial by the study’s lead author in
Nature entitled
A change in strategy in the war on cancer.
Not bad for a study that hadn’t been released yet. Intrepid medical and
science blogger that I am, I waited until the actual study was
published a week ago the June 1 episode of
Cancer Research. It’s a clever study, but the hype over it was a bit overblown. For example:
For all the weapons deployed in the war on cancer, from
chemicals to radiation to nanotechnology, the underlying strategy has
remained the same: Detect and destroy, with no compromise given to the
killer. But Robert Gatenby wants to strike a peace.
A mathematical oncologist at the Moffitt Cancer Center, Gatenby is
part of a new generation of researchers who conceive of cancer as a
dynamic, evolutionary system. According to his models, trying to wipe
cancer out altogether actually makes it stronger by helping
drug-resistant cells flourish. Rather than fighting cancer by trying to
eradicate its every last cell, he suggests doctors might fare better by
intentionally keeping tumors in a long-term stalemate.
Maybe I’m being a bit picky, but what annoys me about the news
reports on this study is that the concept of turning cancer into a
manageable chronic disease like diabetes or hypertension is not by any
means a new idea. Remember, one of my major research interests is the
inhibition of tumor angiogenesis. Consequently, I know that the late,
great
Judah Folkman first proposed the concept of using antiangiogenic therapy to
turn cancer into a chronic disease
at least as early as the mid-1990′s. The only difference is the
strategy that he proposed. The idea had also been floating around for
quite a while before that, although I honestly do not know who first
came up with it.
But let’s see what Dr. Gatenby proposes. What makes it interesting is
that his study actually looks at how scientists have applied
evolutionary principles to cancer until recently, argues that we’ve been
doing it wrong. He then proposes a way to use the evolutionary dynamics
of applied ecology. He may well be on to something. First, here’s
the problem:
The German Nobel laureate Paul Ehrlich introduced the
concept of ‘magic bullets’ more than 100 years ago: compounds that could
be engineered to selectively target and kill tumour cells or
disease-causing organisms without affecting the normal cells in the
body. The success of antibiotics 50 years later seemed to be a strong
validation of Ehrlich’s idea. Indeed, so influential and enduring was
medicine’s triumph over bacteria that the ‘war on cancer’ continues to
be driven by the implicit assumption that magic bullets will one day be
found for the disease.
Yet lessons learned in dealing with exotic species, combined with
recent mathematical models of the evolutionary dynamics of tumours,
indicate that eradicating most disseminated cancers may be impossible.
And, more importantly, trying to do so could make the problem worse.
Traditionally, cytotoxic chemotherapy has been given in either as
fixed doses close to the maximum tolerated dose or, as has been common
more recently, a regimen known as “dose dense.” Basically, the fixed
dose schedules involve giving as much chemotherapy as the patient can
tolerate up to what is known as the “maximum tolerated” dose and giving
it over as short a period of time as possible, while “dose-dense”
therapies try to target chemotherapy doses to the time of maximal tumor
growth, when tumors are maximally sensitive to chemotherapy. This
strategy is based on what is called the Norton-Simon model. One key
assumption behind such therapies is that chemotherapy fails because of
the evolution of resistant cells after chemotherapy has begun. The idea
behind this strategy is to hit the tumor cells as hard as possible as
fast as possible to kill as many cells as possible and minimize the
opportunity to develop resistance. Dose-dense chemotherapy has
definitely resulted in improvements in survival in multiple tumors but
rarely results in cure, at least in the common “solid” malignancies that
kill so many, such as breast, prostate, lung, and colon cancer.
However, that improvement sometimes comes at a price: Increased toxicity
and side effects.
Based on Judah Folkman’s work, around the turn of the century (the
21st century, not the 20th century) Robert Kerbel proposed a new regimen
known as metronomic chemotherapy. Metronomic therapy involves giving
chemotherapy either continuously or at frequent dosing but at a much
lower dose, the idea being that, because blood vessels are lined by
genetically stable endothelial cells, they do not evolve resistance, and
chemotherapy can be antiangiogenic. The idea was to deliver the same
total dose of chemotherapy but without all the toxicity, meanwhile
keeping the tumor in check or shrinking it by the effect the
chemotherapy has on the tumor blood vessels. The drawback is that long
periods of therapy may be required and the cumulative doses may end up
being actually higher than more standard therapies. On the other hand,
this latter aspect may not be a drawback because metronomic chemotherapy
may allow a greater cumulative dose, with a concurrent greater
cumulative effect. Metronomic chemotherapy is a promising concept, but
thus far clinical trials in humans have been fairly disappointing.
One aspect that is shared among both of these therapy modalities is
that they generally both involve fixed schedules and fixed doses. What
Dr. Gatenby proposes to get around this is to apply what he calls
“adaptive therapy.” This therapy is based on population ecology and the
observation that the development of resistance does not come free.
Indeed, resistant cells need to expend energy in order to do what cells
do to overcome chemotherapy; for exmple, to repair DNA faster, pump the
chemotherapy out of the cell, bypass intracellular signaling pathways
blocked by new targeted therapies, or crank out enough peptides that
induce the ingrowth of new blood vessels in order to overcome therapies
that block these factors. In other words resistant cells tend to have a
lower fitness under normal conditions. It is only the selective pressure
of chemotherapy that allows resistant cells to proliferate faster than
normal cells, and, indeed, resistant cells tend to lose their resistance
when the selective pressure is removed.
Given this concept, Gatenby likens adaptive therapy to
controling invasive species:
Gatenby: How people treat invasive
species can provide an analogy for thinking about cancer therapy. In
treating a field for a pest, for example, you might treat three-quarters
of it with a pesticide, and leave the other quarter untreated.
Pesticide-sensitive pests remain there, and they spread out into the
field after treatment, preventing pesticide resistance from becoming
dominant.
Using pesticides on an entire field is like what we’re doing with
cancer now. And we all agree that we’d rather get rid of the pests
altogether, but if you can’t do it, if every time you have an
infestation you treat it and get resistance, then you try a different
strategy. The alternative is to try to reduce the pest population so
that it doesn’t damage your crop, and accept the fact that they’re going
to be there. That’s what I’m talking about with cancer.
Wired.com: What type of treatment would that involve?
Gatenby: Instead of fixing the dose of the drugs,
you fix the size of the tumor. Your whole goal is to keep the tumor
stable. You continuously alter the drug, the dose, the timing of the
dose, with that goal in mind.
And:
Our models show that in the absence of therapy, cancer
cells that haven’t evolved resistance will proliferate at the expense of
the less-fit resistant ones. And, when a large number of the sensitive
cells are killed, for instance by aggressive therapies, the resistant
types are able to proliferate unconstrained. This means that high doses
of chemotherapy might actually increase the likelihood of a tumour
becoming unresponsive to further therapy.
So, just as the judicious use of pesticides can be used to
successfully control invasive species, a therapeutic strategy explicitly
designed to maintain a stable, tolerable tumour volume could increase a
patient’s survival by allowing sensitive cells to suppress the growth
of resistant ones.
It’s a fascinating concept. The idea is to keep from killing off too
many of the sensitive cancer cells, so that they can grow to a certain
point and keep the resistant cells in check. But can it work?
The
Cancer Research
paper published a week ago presents evidence that, at least in mouse
models, it might be able to. I will admit that a lot of the mathematics
in the paper are beyond me. There was a time when I was in college and
taking all sorts of calculus and differential equations when these
equations wouldn’t make my brain hurt to look at them, but if you don’t
use it you lose it, and lost it I have (mostly). Suffice it to say that
the model takes into account estimates of variability of fitness in
tumor cells making up the population, dosing, differential uptake with
tumor size, and other critical parameters. The concept of adaptive
therapy requires that chemotherapy doses be adjusted to maintain
constant tumor volume, increasing dosage if the tumor grows and
decreasing it if the tumor shrinks. First, the mathematical model:
The graphs above represent modeling of dose dense/maximum tolerated
dose (MTD) therapy, adaptive therapy, adaptive therapy (ADAP), and three
varieties of metrnomic therapy, continuous infusion, high frequency,
and low frequency. Four combinations of mixed cell populations were
tested, including:
- FR with high free-field fitness and high sensitivity to therapy
- R with lower fitness and low sensitivity to therapy
- S with low fitness and high sensitivity
- ER with high intrinsic sensitivity and fitness but in an environment that restricts proliferation and response.
Combinations that were modeled included: (a) ”FS and R,” (b) ”S and
FR,” (c) ”FS and R and ER,” and (d) ”FS and ER.” Strikingly, by day
1,500 of tumor growth (1,100 days after therapy was started), the tumor
treated using the MTD strategy had grown to be the largest whereas those
treated with metronomic therapy were smallest. When the simulations
were run out to many thousands more days, until the tumor burden
achieved the lethal threshold, all patients in the MTD and metronomic
therapies eventually succumbed to their disease. In this model the
tumors treated with adaptive therapy remained stable even after a period
exceeding 10,000 days. In other words, tumors treated with MTD had the
best initial response rate to therapy but tended to develop resistance
rapidly, while tumors treated with metronomic chemotherapy remained
stable and did not grow appreciably for much longer but nonetheless
eventually developed resistance to the point where the tumor escaped
therapy and killed the host. In contrast, tumors treated with adaptive
therapy remained stable for a very long time.
Mathematical models are all well and good, but does adaptive
chemotherapy work for real? To test that, Gatenby designed an adaptive
therapy protocol for a mouse model of ovarian cancer. It was a tricky
experiment to do, as his group had to measure the tumor burden every
three days and then adjust the chemotherapy dose according to their
behavior, decreasing the dose for each mouse if its tumor shrank and
increasing the dose if it grew. All of this was done fore each and every
mouse, meaning that there could be as many doses of chemotherapy as
there were mice in the adaptive therapy group. Here’s the method:
The adaptive group received an initial dose of 50 mg/kg
and thereafter the tumors were evaluated every 3 days and the dose was
adjusted to maintain a stable tumor volume. The algorithm for dosing
basically represented “a shot in the dark” because no prior experience
was available to parameterize the models. Drug doses were established in
increments of 10 mg/kg starting at the starting dose of 50 mg/kg. A
treatment decision was made at the time of each measurement. If the
tumor remained stable (defined as the no more than a 10% change from the
prior volume using caliper measurements), no drug would be
administered. If the tumor diminished in size or remained stable for two
or more measurements, the next dose would be decreased by one 10 mg/kg
decrement. If the tumor increased in size greater than 10%, the same
dose of drug would be administered. If the tumor again increased in
size, the dose would be increased to the next higher level.
As you can see, this is a pretty labor intensive regimen. No doubt
Gatenby will be able to refine his method and develop a protocol that
isn’t in essence a reasonable guess, but for now there isn’t a lot to
guide scientists in developing such adaptive protocols.
Here’s the result:
In the mice, the adaptive regimen using carboplatin clearly worked
better than the standard carboplatin regimen, suggesting that adaptive
therapy can work. As Gatenby puts it:
Our analysis shows that, in the absence of therapy, the
fitter, chemosensitive cells actually suppress the growth of the less
fit but resistant population. Therapies designed to kill maximum numbers
of cancer cells produce an environment in which the resistant cells
both survive and are unopposed by the fitter, chemosensitive
populations. This permits rapid regrowth of a therapy-resistant cancer.
Alternatively, if therapy is limited to allow a significant number of
chemosensitive cells to survive, they will, in turn, suppress the growth
of the resistant population. We hypothesized that under these
circumstances, adaptive therapy should be designed to maintain a normal
cohort of surviving sensitive cells.
Another interesting aspect of this study is that it’s been known for
some time that using metronomic chemotherapy allows a larger total dose
of chemotherapy given over a longer period of time with lower toxicity.
It works well in mice, but unfortunately is less stunningly effective in
humans (much like antiangiogenic therapy, alas). The larger total dose
of chemotherapy that can be delivered is one reason that has been
postulated as an explanation for why metronomic chemotherapy can be more
effective than dose dense chemotherapy. There’s no reason to think that
adaptive chemotherapy wouldn’t behave similarly and allow for a larger
total dose. But, in this model at least, it went beyond that. The
adaptive chemotherapy group the dose required to maintain tumor
stability decreased with time from 50 mg/kg to 10 mg/kg. In the
experiment I showed, the individtual doses were 50, 40, 40, 30, 30, 20,
20, 10, 10, 10, 10, 10, 10, 10, 10, 10 mg/kg. This observation is
consistent with a stabilization of the tumor cell population consistent
with the evolutionary and ecological model used to test the study
hypothesis.
The power of evolutionary principles is that they apply to more than
just populations of organisms. They can equally apply to populations of
cells within an organism, like cancer. In other words, evolution acts at
both the organism level and the cellular leve. Tumors, given their
genetic instability, enormous heterogeneity, and subpopulations of cells
with different fitness and sensitivity to selective pressures are a
perfect system to apply the principles of evolutionary ecology to.
What’s fascinating about this study is that it appears that using
evolutionary principles in a savvier way than we have in the past can
work. In theory and in at least one animal model, it can produce more
effective chemotherapeutic regimens. Indeed, one fascinating observation
is that, the longer the tumors were treated with adaptive therapy, the
less chemotherapy was required and the longer the intervals between
doses that were needed to maintain a constant volume. Like the concept
of antiangiogenic therapy proposed by Judah Folkman, however, applying
evolution to cancer may require a rethinking of how we deal with cancer.
Unfortunately, I don’t see an obvious or immediate application of
adaptive chemtherapy in humans. The reason is that it would be very
cumbersome, labor-intensive, and expensive. Tumor measurements far more
frequent than what we routinely do now would be required, as would
frequent adjustments in chemotherapy dosing. As a strictly practical
matter, it would be very hard to implement. Indeed, this model was very
simplistic in that it tested adaptive chemotherapy using one drug. In
reality, very few chemotherapy regimens in common use involve only one
drug, and any truly adaptive therapy would have to adjust multiple
drugs, with a concomitant exponential increase in complexity
administrating it. Also, from a strictly clinical standpoint, tumors
that are large or advanced would need to be shrunk because their size
causes serious symptoms. How that would be integrated into an adaptive
regimen remains to be seen.
Another potential problem is one that has been seen by scientists
trying to use a similar approach to control HIV infection: compensatory
mutations. In antibacterial therapy, the long term removal of
antibiotics has not thus far resulted in the disappearance of resistant
strains, and this is due to compensatory mutations that can restore the
fitness of these strains. This phenomenon has only been described in
viruses and bacteria, but it would not be surprising if they also
occurred in cancer cells.
Even so, there is one potential use that I can envision for this sort
of ecological approach to produce adaptive chemotherapy. This would be
as a means of treating tumors that have well-validated serum tumor
markers that correlate well with tumor burden in individual patients;
for example, colorectal cancer (tumor marker: carcinoembryonic antigen,
or CEA) or prostate cancer (tumor marker: prostate-specific antigen, or
PSA). One can imagine an implantable pump that could measure the levels
of these tumor markers and then, according to algorithms developed based
on ecological and evolutionary principles, continuously adjust the dose
of metronomic chemotherapy to keep a patient’s tumors in check.
Finally, as Gatenby himself points out, these sorts of approaches
will not render the search for cures unnecessary. After all, consider
other chronic diseases. Diabetes, for instance, can be managed quite
well on a chronic basis, but what patient with type I diabetes wouldn’t
want to be cured and thus able to throw away his insulin syringes? In
the case of cancer, cures remain preferable, but, like the case of
diabetes, sometimes settling for chronic management is the best we can
do.
The challenge is to apply these methods to other drugs in cancer medicine
