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Genomic analysis used to predict immunotherapy success

Article: "Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade."

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.

Immune Identification

Figure 1. Expression profiles created from purified immune cells, normal cells, and cancer cell lines were used to create immune cell specific profiles. These were then paired with gene set enrichment analysis (discussed below) to profile immune presence in various tumors (Charoenton et al., 2017).

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.

Figure 2. Basic schematic of GSEA. From:

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.


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.



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