IRIS: Immunotherapy Resistance cell-cell Interaction Scanner

Last Updated: 09/24/2024

Overview

We developed Immunotherapy Resistance cell-cell Interaction Scanner (IRIS), an R package specifically designed to identify immune checkpoint blockade (ICB) resistance relevant ligand-receptor interactions in the tumor microenvironment (TME), given a patients cohort including tumor bulk expression data and ICB treatment response data. The gene expression data is deconvolved using CODEFACS such that the input to IRIS in a given patients cohort is comprised of two components: 1. Literature-curated cell-type-specific ligand-receptor interaction activity profiles (denoting either activation: 1 or inactivation: 0) in each tumor sample, which is inferred using LIRICS from the deconvolved expression – an interaction is considered as activated if the (deconvolved) expression of both its ligand and receptor genes is above their median expression values across the cohort samples, and inactivated otherwise; 2. The corresponding ICB response outcome for each patient.

IRIS consists of two steps: Step I uses a Fisher’s test to identify differentially activated ligand-receptor interactions in the pre-treatment and non-responder post-treatment samples. These interactions are categorized as either resistant downregulated interactions (RDI) or resistant upregulated interactions (RUI) based on their differential activity state in the post-treatment vs. the pre-treatment state; that is, RDIs are downregulated in post-treatment resistant patients and vice versa for RUIs. Step II employs a hill climbing aggregative feature selection algorithm to choose the optimal set of RDIs or RUIs for classifying responders and non-responders in pre-treatment samples. The final output of IRIS is a selected set of RDIs and RUIs hypothesized to facilitate in ICB resistance, that can be used to predict ICB therapy response in a new ICB cohort.

Installation

install.packages('devtools')
library(devtools)
devtools::install_github("KWangLab/IRIS")

Tutorials

• To reproduce the Nat. Comms. results from Sahni et al., see: https://github.com/KWangLab/IRIS/blob/main/vignettes/IRIS.Rmd
• To implement IRIS in your own work, see: https://github.com/KWangLab/IRIS/blob/main/vignettes/IRIS.Rmd

Sample data availability

Sample CODEFACS and LIRICS data can be found at https://zenodo.org/records/13172848.

System requirements

IRIS was developed on R (v4.4.1) using R packages: dplyr (v1.1.4), magrittr (v2.0.3), parallel (v4.4.1), pROC (v1.18.5), rBayesianOptimization (v1.2.1), tidyr (v1.3.1). All analyses were done on R (v4.4.1).

Citation

If using IRIS, please cite:

Sahni, S., Wang, B., Wu, D. et al. A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade. Nat Commun 15, 8867 (2024). https://doi.org/10.1038/s41467-024-52555-4

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Acknowledgement(s)

Lead Developer(s)

1. Sahil Sahni
2. Kun Wang (kwang222@illinois.edu)^
3. Eytan Ruppin (eytan.ruppin@nih.gov)^

^equally-contributing corresponding author(s)

Acknowledgement(s)

IRIS figure was created with BioRender.com.