YSM Issue 93.2

FOCUS
Computational Biology
How the Model Works
PHOTOGRAPH COURTESY OF WIKIMEDIA COMMONS
Photograph of a driving wheel, symbolizing how some mutations drive tumor progression.
The word “tumor” is laced with
terrifying potential. Possibilities of
anarchical growth, silent spread, and
rapid lethality render these neoplasms’
behavior difficult to predict. In an
effort to circumvent this uncertainty, a
group of Yale researchers led by Mark
Gerstein, Albert L. Williams Professor
of Biomedical Informatics and Professor
of Molecular Biophysics & Biochemistry,
Computer Science, and Statistics & Data
Science, published a paper in February
reporting a mathematical model they
developed. The model looks at a specific
kind of mutation called driver mutations
to estimate a tumor’s growth pattern.
to detect because those are the ones that
actually play a role in tumor progression.”
Conversely, mutations identified as nonsignificant
in terms of tumor development
are dubbed passenger mutations.
According to Gerstein, driver mutations
can be defined as the “few mutations that
accumulate in the cell and drive its growth
forward.” In the paper, the authors discuss
different means through which these
mutations can trigger the formation of
tumors, including hindering the ability of
tumor-suppressor genes from impeding
tumor growth and enhancing the level of
expression of oncogenes, which are genes
that can cause cancer.
When tumors are biopsied, a sample is
often extracted and sequenced to reveal
its genetic composition. According to
Salichos, the number of times a specific
position in the genome is sequenced is
very important. The deeper the sequencing,
the more accurate you can expect the
measurement of a mutation’s frequency
within a population-to-be. At the end of
the process, you have acquired a run-down
that details all of the mutations detected as
well as their respective frequencies, which
paints a clear picture of their expressivity
within the cell’s genetic code.
“Based on the frequency, you can already
make an assessment of whether that
mutation happened early or late in the tumor,
because, if it happened early in the tumor
progression, we are expecting it to have a
higher frequency at the end,” Salichos said.
Therefore, ordering mutations from those
that appear most to least provides insight
into the order in which they occurred. This
information contextualizes what mutations
might have stimulated tumor growth and
which ones occurred as collateral damage,
helping frame their relevance with respect
to tumor progression.
Salichos explained that, based on this
idea, he developed a mathematical model
that uses the frequency of some of the
mutations that happened exactly before
the driver mutation to detect presence of
the driver and estimate tumor growth at
the precise moment when it first emerged.
This examination allowed the group to
gauge the impact of this phenomenon,
since the introduction of a driver mutation
Driver Mutations
The development of cancer is an
evolutionary process, punctuated by
mutations. Historically, several theories
have been put forward as to how researchers
can study such genetic alterations, but, most
recently, mutations have been increasingly
labeled as either drivers or passengers to
categorize them according to their relevance
in tumor progression. Leonidas Salichos,
a postdoctoral associate and first author
of the paper, explained that “we have a lot
of mutations in every tumor, sometimes
thousands of mutations, and a few of them
are what we call drivers, … which we try
With this kind of model, you
can look into an individual’s
tumor in a more direct way.
14 Yale Scientific Magazine September 2020 www.yalescientific.org