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Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma

Abstract

Immune checkpoint inhibitor (ICI) treatments produce clinical benefit in many patients. However, better pretreatment predictive biomarkers for ICI are still needed to help match individual patients to the treatment most likely to be of benefit. Existing gene expression profiling (GEP)-based biomarkers for ICI are primarily focused on measuring a T cell-inflamed tumour microenvironment that contributes positively to the response to ICI. Here, we identified an immunosuppression signature (IMS) through analysing RNA sequencing data from a combined discovery cohort (n = 120) consisting of three publicly available melanoma datasets. Using the ratio of an established IFN-γ signature and IMS led to consistently better prediction of the ICI therapy outcome compared to a collection of nine published GEP signatures from the literature on a newly generated internal validation cohort (n = 55) and three published datasets of metastatic melanoma treated with anti- PD-1 (n = 54) and anti-CTLA-4 (n = 42) as well as in patients with gastric cancer treated with anti-PD-1 (n = 45), demonstrating the potential utility of IMS as a predictive/prognostic biomarker that complements existing GEP signatures for immunotherapy.

Result

fig4 Ratio of IFN-γ signature and IMS predicts response to ICI immunotherapy on the PUCH cohort. A, The ORR of patients from IFN-γ/IMS-high vs IFN-γ/IMS-low and B IFN-γ-high vs IFN-γ-low on the PUCH cohort. The cutoff points were decided by the Youden index for IFN-γ/IMS and IFN-γ scores, respectively. C, IFN-γ signature and IMS scores of individual patients in the PUCH cohort. The red and blue dashed lines indicate the cutoff points for IFN-γ/IMS ratio and IFN-γ, respectively. D, Waterfall plots of IFN-γ/IMS versus patients with different clinical responses to anti-PD-1 therapy in the PUCH cohort. E, ROC curve of the sensitivity versus 1-specificity of the predictive performance of IFN-γ/IMS. Patients with SD were not included in AUC calculation. F, Comparison of the AUC of IFN-γ/IMS with nine GEP signatures in predicting response to ICI. G, Kaplan-Meier plots of OS segregated by IFN-γ/IMS ratio with cutoff points selected according to the Youden index.

Dataset

Dataset Expression data Clinical data Survival data
Riaz17 mel_bms038_exp_data.csv mel_bms038_cli_data.csv mel_bms038_survival_data.csv
Gide19 mel_gide19_exp_data.csv mel_gide19_cli_data.csv mel_gide19_survival_data.csv
Hugo16 mel_hugo16_exp_data.csv mel_hugo16_cli_data.csv mel_hugo16_survival_data.csv
VanAllen15 mel_van_exp_data.csv mel_van_cli_data.csv mel_van_survival_data.csv
Liu19 mel_liu19_exp_data.csv mel_liu19_cli_data.csv mel_liu_survival_data.csv
PUCH mel_puch_exp_data.csv mel_puch_cli_data.csv mel_puch_survival_data.csv
Kim18 gas_korean_exp_data.csv gas_korean_cli_data.csv

Note: for survival data, 1 means responder, 0 means nonresponder, -1 represents patients with stable disease.

Repository Structure

├── data
│   ├── gas_korean_cli_data.csv
│   ├── gas_korean_exp_data.csv
│   ├── mel_bms038_cli_data.csv
│   ├── mel_bms038_exp_data.csv
│   ├── mel_bms038_survival_data.csv
│   ├── mel_gide19_cli_data.csv
│   ├── mel_gide19_exp_data.csv
│   ├── mel_gide19_survival_data.csv
│   ├── mel_hugo16_cli_data.csv
│   ├── mel_hugo16_exp_data.csv
│   ├── mel_hugo16_survival_data.csv
│   ├── mel_liu19_cli_data.csv
│   ├── mel_liu19_exp_data.csv
│   ├── mel_liu_survival_data.csv
│   ├── mel_puch_cli_data.csv
│   ├── mel_puch_exp_data.csv
│   ├── mel_puch_survival_data.csv
│   ├── mel_van_cli_data.csv
│   ├── mel_van_exp_data.csv
│   ├── mel_van_survival_data.csv
│   └── NanoString_gene_list.txt
├── README.md
├── requirements.txt
└── src
    ├── calculate_performance.py
    ├── calculate_ratio_score.py
    ├── load_data.py
    ├── main.py
    └── survival.py
  • Language Version Python 3.7.3.
  • requirements.txt: list of version of packages used in this software.
  • data: All the expression data and clinical data of the datasets
  • NanoString_gene_list.txt: List of cancer immune-related genes curated in Nanostring’s IO 360 panel.
  • calculate_ratio_score.py: Python code for identification of IMS genes and calculation of the ratio of IFN-γ signature score and IMS score.
  • calculate_performance.py: Python code for calculation of the area under the operational curve (AUC) score.
  • main.py: Python code for calculation of AUC of IFN-γ/IMS ratio for all datasets
  • survival.py: Python code for generation of survival curve plot of each dataset.

Usage

Install packages

pip install -r requirements.txt

Identification of the IMS genes from the combined discovery dataset (Riaz17, Gide19, Hugo16)

cd ./src
python calculate_ratio_score.py
# output: IMS genes list

Calculate AUC of IFN-γ/IMS ratio score for all datasets

python main.py
# output: auc of IFN-γ/IMS ratio score for all datasets

Draw survival curve

python survival.py
# output: ./result/{dataset}_survival_OS.pdf

Citation

If any part of this code is used, please give appropriate citation to our paper.

Ratio of the interferon-γ signature to the immunosuppression signature predicts anti-PD-1 therapy response in melanoma