This web page was produced as an assignment for an undergraduate course at Davidson College.
Genomic analysis used to predict immunotherapy success
1) What was the research project?
Cancer
immunotherapies
are a rapidly expanding set of cancer treatments which use the
bodies own immune system to fight cancer. Unfortunately, only a small
number of
patients respond to immunotherapies, and the factors which determine why
those
patients respond are still being explored. The Trajanoski group used a
genomic database—The
Cancer Genome Atlas (TCGA)—to examine which immune cell types were
present in various
cancers. They then used this information on the immune systems of the
patients
to create a system which allowed them to predict a cancer patient’s
likelihood
of responding to immunotherapy.
2) Were they testing a hypothesis or doing
discovery science?
The
group
was doing discovery science, using previously available data to
characterize tumors and examine their interaction with the immune
system. The
group did not have a specific hypothesis, but instead wanted to
examine a
complex interaction.
3) What genomic technology was used in the
project?
The
primary
genomic technology used in this paper was gene set enrichment
analysis (GSEA).
GSEA relies on gene expression data, which indicates which genes are
being most
actively used. Related genes are then grouped into gene sets, and it
is
determined which gene sets are being over or under used. By grouping
related
genes and examining them together, researchers can see changes in
sample
composition that might have been missed by single gene analysis.
The Trajanoski group used GSEA to assess which cell types were present in cancer samples, looking for the increased usage of different immune-related gene sets. Enrichment or depletion of immune-related genes gives researchers clues about which immune cell types were in the tumor. Using these data, the group was able to create immunophenotypes—profiles of what immune cell types were present in the tumor.
4) What was the take home message?
The groups was able to use tumor sample genomic data to assess the presence of immune cells in various cancer types, using these data to create an immunophenotype. This immunophenotype was then used to predict the patient’s response to cancer immunotherapy, hopefully creating a clinical tool to help doctor’s decide treatment plans.
5) What is your evaluation of the project?
The
project is an interesting, novel, and needed examination of the
interaction
between the immune system and cancer. The ability to predict the
success of
immunotherapies is an important clinical tool. The method used could
also be a
source of insight into the mechanisms of immunotherapy. However, the
paper
itself was confusing at times, with a complex design and busy, vague
figures.
One important and worrying note is the fact that there is no
indication of when
the samples were collected. If samples were collected during or after
treatment
then the diagnostic value of the study would be almost eliminated.
Despite
these ambiguities, the paper used a common resource in a novel way,
and opened
a path for many similar immune studies.
References:
Charoentong, P., Finotello, F., Angelova, M., Mayer, C., Efremova,
M., Rieder, D., Hackl, H., and Trajanoski, Z. (2017). Pan-cancer
Immunogenomic
Analyses Reveal Genotype-Immunophenotype Relationships and Predictors
of
Response to Checkpoint Blockade. Cell Rep. 18, 248–262.
Ben
Whitfield Home Page
Biology Home Page
Email Questions or Comments: bewhitfield@davidson.edu
© Copyright 2017 Department of Biology, Davidson College, Davidson, NC 28035