YSM Issue 93.2

Computational Biology
FOCUS
into a sample often creates a detectable
perturbation in the variant allele frequency
distribution. “Once you introduce a
driver into a population that grows, now
the population starts growing faster, and
that has an impact on the frequency of
the mutations that happen before that,”
Salichos said.
Driving Towards More Accurate Cancer
Prognosis
“Traditionally, the way people find these
drivers is they look at cohorts of cancer
patients at the same time,” Gerstein said.
Salichos also highlighted that over a thousand
samples are often needed in traditional
methods, since large numbers are required to
ground observations that something deviates
from the normal. At least computationally,
this is how scientists normally validate a
suspicion that a specific mutation is important
for the development of a tumor.
However, the need to examine a whole
cohort can serve as a limitation in the study
of cancer genomics. If several samples
are required every time physicians want
to understand the role of a driver in a
tumor’s progression within a particular
patient, individualized assessments of how
specific growths will develop become more
complicated to attain. In that regard, this
is where their model adds something new.
“This method doesn’t require a cohort, but
only one tumor to be very deeply sequenced,”
Gerstein said. The approach incorporates
ultra-deep sequencing, a method that
entails the sequencing of the same location
in the genome several times to identify rare
variations, into their analysis. “The novelty
of this method was, instead of looking
into many different samples, we actually
harnessed the frequency of the mutations
based on growth models and analyzed both
the mutations and their frequency in the
population to try to make an assessment of
which of them mattered and which did not,
all within that individual sample,” Salichos
said.
This model could enable scientists to
account for how cancer heterogeneity
results in no two tumors ever being
completely alike. While reliance upon
averages is often important when looking
at growths that behave differently
depending on their genetic make-up,
as well as the context in which they are
www.yalescientific.org
inserted, every tumor—even ones of the
same kind—will behave differently. “With
this kind of model, you can look into
an individual’s tumor in a more direct
way… you don’t have to think about a
cohort or a database very much,” Gerstein
said. Considering how this framework’s
applicability does not require more than a
single tumor, it could lay the foundation
for more specialized evaluations that take
only the characteristics of the studied
tumor into account, making more specific
assessments possible.
Testing the Model’s Efficacy
In order to test the model’s effectiveness,
simulations were run to see if it could,
in fact, predict the presence, time of
occurrence, and effect of a driver mutation.
In addition to testing the algorithmic
function upon which the model relied
by applying it under different growth
models, such as exponential growth and
logistic growth, the group also sought to
demonstrate the framework’s efficacy on
real samples. To that end, the model was
applied to 993 tumors obtained from the
Pan-Cancer Analysis of Whole Genomes
Consortium—an online database that
provides information obtained through
whole genome sequencing and integrative
analysis data of over 2,600 tumors across
thirty-eight diverse types of tumor.
After observing that the identified
drivers were correlated with periods of
positive growth in the samples examined,
ABOUT THE AUTHOR
the group sought to further consolidate
their framework by applying it to a sample
of an Acute Myeloid Leukemia (AML)
tumor. According to Gerstein, this tumor
was chosen due to its history of having
been deeply sequenced in the past. For
AML, the growth patterns they predicted
showed significant similarities with those
exhibited by the tumor.
The promising evidence surrounding the
model’s effectiveness provides reasons to
be optimistic about its future applications.
This novel way to look into tumors could
make a big difference in the future of cancer
treatments. Instead of just relying on broad
data, this could allow doctors to tailor their
evaluations of a patient’s prognosis to what
their specific tumor sample shows. In this
way, this kind of personalized assessment
could herald a new era in cancer genomics. ■
MARIA FERNANDA PACHECO
MARIA FERNANDA PACHECO
YSM
Yale Global Health ReviewYale Daily News
FURTHER READING
Deep Sequencing.
Oncogene
IMAGE COURTESY OF PIXABAY
Three-dimensional illustration of DNA.
September 2020 Yale Scientific Magazine 15