A computational pipeline for identifying gene targets and signalling pathways in cancer cells to improve lymphocyte infiltration and immune checkpoint therapy efficacy
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Updated: Jun 25, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Sahar Nasr1, Lin Li2, Mohammad Asad1
1Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, AB, T2N 1N4, Canada.
Background:
Tumour-infiltrating lymphocytes (TILs) are crucial for effective immune checkpoint blockade (ICB) therapy in solid tumours. However, ∼70% of these tumours exhibit poor lymphocyte infiltration, rendering ICB therapies less effective.
Methods:
We developed a bioinformatics pipeline integrating multiple previously unconsidered factors or datasets, including tumour cell immune-related pathways, copy number variation (CNV), and single tumour cell sequencing data, as well as tumour mRNA-seq data and patient survival data, to identify targets that can potentially improve T cell infiltration and enhance ICB efficacy. Furthermore, we conducted wet-lab experiments and successfully validated one of the top-identified genes.
Findings:
We applied this pipeline in solid tumours of the Cancer Genome Atlas (TCGA) and identified a set of genes in 18 cancer types that might potentially improve lymphocyte infiltration and ICB efficacy, providing a valuable drug target resource to be further explored. Importantly, we experimentally validated SUN1, which had not been linked to T cell infiltration and ICB therapy previously, but was one of the top-identified gene targets among 3 cancer types based on the pipeline, in a mouse colon cancer syngeneic model. We showed that Sun1 KO could significantly enhance antigen presentation, increase T-cell infiltration, and improve anti-PD1 treatment efficacy. Moreover, with a single-cell multiome analysis, we identified subgene regulatory networks (sub-GRNs) showing Stat proteins play important roles in enhancing the immune-related pathways in Sun1-KO cancer cells.
Interpretation:
This study not only established a computational pipeline for discovering new gene targets and signalling pathways in cancer cells that block T-cell infiltration, but also provided a gene target pool for further exploration in improving lymphocyte infiltration and ICB efficacy in solid tumours.
Funding:
A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
Insights
Researchers developed a bioinformatics pipeline to identify genes that improve T cell infiltration for better cancer immunotherapy. They validated SUN1, showing its deficiency enhances anti-PD1 treatment efficacy by boosting antigen presentation and T cell infiltration.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Tumour-infiltrating lymphocytes (TILs) are critical for effective immune checkpoint blockade (ICB) therapy in solid tumours.
- However, approximately 70% of solid tumours show poor lymphocyte infiltration, limiting ICB efficacy.
Purpose of the Study:
- To develop a bioinformatics pipeline to identify novel gene targets for enhancing T cell infiltration and ICB efficacy.
- To experimentally validate identified gene targets in preclinical cancer models.
Main Methods:
- Integrated multiple datasets: tumour immune-related pathways, copy number variation (CNV), single tumour cell sequencing, mRNA-seq, and patient survival data.
- Developed a computational pipeline to identify potential therapeutic targets.
- Conducted wet-lab experiments, including gene knockout (KO) and mouse syngeneic models, with single-cell multiome analysis.
Main Results:
- Identified a set of candidate genes across 18 cancer types in TCGA data that may improve lymphocyte infiltration and ICB efficacy.
- Experimentally validated SUN1, demonstrating that Sun1 KO significantly enhances antigen presentation, increases T-cell infiltration, and improves anti-PD1 treatment efficacy in a mouse colon cancer model.
- Discovered subgene regulatory networks (sub-GRNs) involving Stat proteins that enhance immune-related pathways in Sun1-KO cancer cells.
Conclusions:
- Established a computational pipeline for discovering novel gene targets and signaling pathways that impede T-cell infiltration in cancer.
- Provided a valuable resource of potential gene targets for improving lymphocyte infiltration and ICB efficacy in solid tumours.
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