YSM Issue 90.4

FOCUS
cell biology
You lost your keys again. Without any idea as to where they went, you dart from room to room,
pursuing each lead for just a moment before giving up and trying something else. As you search,
you piece it together and suddenly remember where they are: the refrigerator. Turning away from your
hamper, you make a beeline for the kitchen, and the keys are recovered within moments. The way you
went about finding your keys—the indiscriminate giving way to the direct—is not unique to forgetful
humans. All living things—dogs, bees, cancer cells—devise these strategies as they explore the world
around them. In the last case, however, this probing can do more harm than good. The more cancer cells
move in a persistent beeline, the more efficiently they will spread and the more lethal the cancer will be.
A team at the Yale Systems Biology Institute
led by professor Andre Levchenko
and postdoctorate researcher JinSeok Park
wanted to more comprehensively understand
how cancer cells find their proverbial
keys. To start, it is important to understand
how cells interact with their direct
outside environment, a network of proteins
called the extracellular matrix (ECM) that
cells secrete themselves. Coming into contact
with a neighboring cell’s ECM triggers
two signaling pathways that mediate cell
movement. The balance between the different
behaviors that these two pathways
trigger—the cell’s polarity, so to speak—
influences cells’ migration behavior, their
switch from frenzy to beeline. Previous research
implied that cell contact with the
ECM was responsible for these different
patterns, but how the cells went about this
was poorly understood. Levchenko and
Park derived a mathematical model to predict
a given cell’s behavior based on the activity
of the two principal signaling pathways,
which both cements the link between
cell-ECM contact and migration and, more
importantly, clarifies a new avenue for cancer
treatment.
Migration
The type of cancer called melanoma
rarely kills at its origin, the skin, but rather
takes lives when it attacks other organ
systems, or metastasizes. Therefore, it is a
disease that relies heavily on movement,
a trait that made it a useful model for the
team to study. The critical moment in melanoma
progression happens when the cancer
stops expanding across the skin and begins
tunneling downward. At that point,
it is only a matter of time until melanoma
cells find their way to a blood or lymphatic
vessel, the anatomical highways necessary
for metastasis. But first, these cells have to
navigate the fibers of the dermis, the layer
of tissue right below the skin.
These tissue fibers are like ropes that cells
can pull to move around. There are three
types of cell movements. When the cells
rapidly pull ropes in different directions,
their behavior is random; when cells alternate
pulling forward and backward on the
same rope, their behavior is oscillatory;
and when cells consistently pull one rope in
one direction, their behavior is persistent.
The oscillatory pattern is a cell-specific behavior
and complicates the model—once
a target has been established for the cell,
it continues to travel back and forth in the
vicinity instead of heading straight there.
Levchenko likes to think of oscillation as
a cell overshooting its target and doubling
back repeatedly. Regardless, if a cell is to
reach a destination, persistent behavior is
the most efficient, which, in the context of
cancer, means a faster progression towards
metastasis.
Migration patterns do not happen by
chance. Cells’ direct contact with the fibers
of the ECM sets off two sets of signals
that influence cell movement and so
determine whether cells will display random,
oscillatory, or persistent behavior.
One of these signals is characterized by a
protein called Rac1 and the other by a protein
called RhoA. The two sets of signals
work towards the same goal—to mediate
cell movement—but perform opposite
functions, as Rac1 acts as an accelerator to
RhoA’s brakes. To complicate matters further
RhoA functions to halt cell migration,
its presence is also required for migration
in the first place. This somewhat paradoxical
mechanism only highlights the complexity
of these networks. Research in oncology
typically has focused on how cancer
cells grow and replicate, but these convoluted
and poorly understood processes of
cell migration pushed Levchenko and Park
to establish a coherent model and reconcile
the three migration patterns.
Polarization
The model the researchers derives uses
advanced mathematics to integrate these
three behaviors into the same framework,
an unprecedented feat. The model shows
how cells’ contact with the ECM changes
the balance between the levels of activity
of the Rac1 and RhoA pathways, which
in turn impacts migration. By plotting the
two activation rates on the same axes, the
researchers were able to establish discrete
regions for each migration pattern. For example,
if a cell has a high Rac1 activation
rate and a low RhoA activation rate—a lot
of accelerator and little brakes—it will display
persistent behavior.
The model accounts for not only the balance,
but also the location of this pathway
activation within a given cell. By tracking
sites of high signaling activity, the researchers
were able to visualize the spatial
distribution of these two pathways. In
randomly migrating cells, signaling was
sporadic; in oscillating cells, hotspots of
activity alternated between the front and
the back; and in persistently moving cells,
activity was high on one side and inhibited
on the other. These signals are consistent
with the aforementioned “ropes” of the
ECM and helped Levchenko and Park affirm
that their model accurately represented
cell movement.
This model is unique because Levchenko
and Park were able to map specific populations
of cells onto diagrams that were generated
using the model. Within the same
cancer or even the same tumor, different
cells will display different behaviors, and
the model accounts for these varied responses.
“[Cells] all get the same piece of
information, but they interpret that information
in very different ways,” Levchenko
said. Understanding how real cells move
within and correspond to the rigid math-
16 Yale Scientific Magazine October 2017 www.yalescientific.org