ImmunEscpMap: a Knowledgebase of Cancer Immune Escape Mechanisms

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	Gene summary
	Literatures describing the association of the gene and immune escape mechanisms
	Comparison of the expression level between tumor and normal groups
	Comparison of the methylation level between tumor and normal groups
	Summary of the copy number in TCGA tumor samples
	The differentially expressed genes (DEGs) and enrichment analysis between mutated and wild type groups
	Expression and mutation differences between non-responders and responders after immunotherapy
	Correlation between the gene expression, copy number, methylation and tumor infiltrating lymphocytes
	The association between gene expression and immune subtypes/status
	Drug-gene interaction and disease-gene association
	Survival analysis based on gene expression

Gene summary for IL7

Gene summary

Gene Symbol	IL7
Gene ID	3574
Gene name	interleukin 7
Synonyms	IL-7
Type of gene	protein_coding
UniProtAcc	P13232

Gene ontology (Biological Process only)

GO ID	GO term
GO:0006955	immune response
GO:0043066	negative regulation of apoptotic process
GO:0050896	response to stimulus
GO:0019221	cytokine-mediated signaling pathway
GO:0007267	cell-cell signaling
GO:0009887	animal organ morphogenesis
GO:0045453	bone resorption
GO:0032722	positive regulation of chemokine production
GO:0008284	positive regulation of cell population proliferation
GO:0030890	positive regulation of B cell proliferation
GO:0006959	humoral immune response
GO:0045582	positive regulation of T cell differentiation
GO:0001961	positive regulation of cytokine-mediated signaling pathway
GO:0002360	T cell lineage commitment
GO:0010468	regulation of gene expression
GO:0010628	positive regulation of gene expression
GO:0035265	organ growth
GO:0042100	B cell proliferation
GO:0045579	positive regulation of B cell differentiation
GO:0046622	positive regulation of organ growth
GO:0048873	homeostasis of number of cells within a tissue
GO:0050730	regulation of peptidyl-tyrosine phosphorylation
GO:0097191	extrinsic apoptotic signaling pathway
GO:2001237	negative regulation of extrinsic apoptotic signaling pathway
GO:2001240	negative regulation of extrinsic apoptotic signaling pathway in absence of ligand
GO:0007165	signal transduction

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Literatures describing the association of IL7 and immune escape mechanisms

The table presents literature evidences demonstrating the involvement of IL7 in cancer immune escape mechanisms.

Icon	PMID	Cancer Type	Mechanism	Evidence Sentences
	37790917	Melanoma	Inhibiting recruitment of dendritic cells	Herein, we engineered attenuated Salmonella typhimurium VNP20009 with gene circuits to synthetize granulocyte-macrophage colony-stimulating factor (GM-CSF) and interleukin 7 (IL-7) within tumors, which recruited dendritic cells (DCs) and enhanced T cell priming to elicit anti-tumor response.

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Comparison of the IL7 expression level between tumor and normal groups

Gene expression level in TCGA (Tumor vs Normal). The threshold for adjusted p-value is shown as : ***, padj < 0.001; **: 0.001 < padj < 0.01; 0.01 < padj < 0.05; NS: padj > 0.05. (Click on the image to enlarge it in a new window.)

The table shows the significant results for TCGA cancers with (adjusted P value < 0.05) and (|logFC| > 1).

Cancer type	Log2FoldChange	P value	Adjusted P value
KIRC	1.12e+00	4.47e-36	6.57e-35
GBM	2.43e+00	4.10e-07	3.98e-06

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Comparison of the IL7 methylation level between tumor and normal groups

The boxplot shows the mean beta value in normal and tumor group, and the dotplot shows the correlation between methylation level and expression level. Methylation level of the promoter region using TCGA data. The promoter regions were defined as the genomic regions spanning 2000 base pairs upstream and 500 base pairs downstream of the transcription start sites of genes. The average methylation value across all CpG sites within its promoter region was calculated to obtain the gene-level methylation value.

The table shows the significant results for TCGA cancers with (adjusted P value < 0.05) and (|diff_beta| > 0.1).

No significant differences were found in IL7 methylation in promoter region.

Methylation level of the genebody region using TCGA data. The genebody regions were defined from 500 base pairs downstream of the transcription start site to the transcription end site.

The table shows the significant results for TCGA cancers with (adjusted P value < 0.05) and (|diff_beta| > 0.1).

No significant differences were found in IL7 methylation in genebody region.

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Summary of the copy number in TCGA tumor samples

The gene level copy number is annotated as: 0: homozygous deletion; 1: deletion leading to LOH; 2: wild type, including copy-neutral LOH; 3/4: minor gain; 5-8: moderate gain; >=9: high-level amplification, as referenced in [1].

The violin plot shows the correlation between copy number and expression level.

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DEGs and the enrichment analysis between the mutated and wild type groups

For each cancer type in TCGA, if samples in the mutated group are no less than 5, we performed the differential gene expression analysis. The table shows DEGs with (adjusted p-value < 0.05) and (|logFC| > 1). Then we performed the KEGG, GOBP and Hallmark enrichment analysis for up-regulated and down-regulated DEGs separately (logFC > 1 means the gene is upregulated in the mutated group).

Gene ID	Symbol	Log2 Fold Change	P-value	Adjusted P-value
ENSG00000276916	RP11-230F18.6	2.44e+00	1.88e-06	3.66e-04
ENSG00000213204	RP3-382I10.7	2.60e+00	1.90e-06	3.69e-04
ENSG00000254463	RP11-484D2.3	3.55e+00	1.91e-06	3.69e-04
ENSG00000280916	FOXCUT	-4.82e+00	1.93e-06	3.70e-04
ENSG00000261546	CTD-2555A7.3	2.91e+00	1.98e-06	3.79e-04
ENSG00000203724	C1orf53	-1.78e+00	2.13e-06	4.05e-04
ENSG00000231831	MTHFD1P1	2.78e+00	2.22e-06	4.19e-04
ENSG00000287945	NA	4.60e+00	2.21e-06	4.19e-04
ENSG00000177989	ODF3B	-2.80e+00	2.26e-06	4.24e-04
ENSG00000219395	HSPA8P15	2.37e+00	2.29e-06	4.28e-04
ENSG00000259314	CTD-3065B20.3	2.68e+00	2.34e-06	4.36e-04
ENSG00000100626	GALNT16	-3.51e+00	2.36e-06	4.37e-04
ENSG00000244052	RPL5P24	1.84e+00	2.38e-06	4.39e-04
ENSG00000232648	RP11-367N14.2	1.53e+00	2.48e-06	4.55e-04
ENSG00000200090	Y_RNA	2.60e+00	2.52e-06	4.60e-04
ENSG00000207445	SNORD15B	4.18e+00	2.56e-06	4.65e-04
ENSG00000253915	MAPRE1P1	3.79e+00	2.60e-06	4.71e-04
ENSG00000232027	RP11-275F13.3	3.02e+00	2.63e-06	4.75e-04
ENSG00000223928	RP11-183E9.2	6.26e+00	2.69e-06	4.79e-04
ENSG00000228808	HMGB3P4	3.45e+00	2.70e-06	4.79e-04

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Down-regulated KEGG pathways

Down-regulated GOBP pathways

Down-regulated Hallmark pathways

Gene expression and mutation differences between non-responders and responders after immunotherapy

In ImmunEscpMap, we integrated 10 pre-calculated transcriptomic datasets, and 6 genomic datasets to study the response after immunotherapy [2, 3]. The figure shows the logFC and the -log10(p value) between non-responders and responders in different datasets. The significant results (p value < 0.05 and |logFC| > 1), including the detailed information of datasets are presented in the table.

Expression	Mutation

Expression

ImmunEscpMap id	Log2FC	P value	Cancer type	Regimen	Num Resp	Num NonResp	PMID
Exp_2	-1.05e+00	4.79e-02	Melanoma	Nivolumab	10	39	29033130

Mutation

No significant differences were found in IL7 mutation.

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Correlation between the composition of TIL and gene expression, methylation and CNV

The clustered correlation matrix shows the association between the abundance of TIL subpopulations [4] and gene expression, methylation and copy number level. Users can check if the pattern is similar across cancer types or TIL subtypes. The value of each cell represents the spearman correlation of gene expression and an immune cell subtype within one TCGA cancer type. Only cells with p-values < 0.05 were colored, while non-significant correlations were set to NA and displayed as white cells.

Expression	Copy number variation

Promoter methylation	Genebody methylation

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The association between IL7 expression and immune subtypes/status

Thorsson et al identified six immune subtypes: Wound Healing, IFN-gamma Dominant, Inflammatory, Lymphocyte Depleted, Immunologically Quiet, and TGF-b Dominant [5]. Zapata et al classified tumors as immune edited when antigenic mutations were removed by negative selection and immune escaped when antigenicity was covered up by aberrant immune modulation. In addition, they used immune dN/dS, the ratio of nonsynonymous to synonymous mutations in the immunopeptidome, to measure immune selection [6]. Cortes-Ciriano et al investigated the microsatellite instability (MSI) status, which is related to the antitumour immune responses [7]. The expression level was compared among immune subtype/status.

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Drugs targeting IL7 and diseases related to IL7.

The drug-gene interactions are extracted from the Drug-Gene Interaction Database (DGIdb, https://dgidb.org) 5.0 [8], while the disease-gene associations are obtained from Diseases 2.0 (https://diseases.jensenlab.org/Search), which collects disease-gene associations from curated databases, genome-wide association studies (GWAS) and automatic text mining of the biomedical literature [9].

Drug-gene interaction	Disease-gene association

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Survival analysis based on IL7 expression

The Kaplan-Meir curves reflects the association of gene expression with overall survival across different cancers. The significance (p value) is assessed by log-rank test.

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Reference

[1] Steele CD, Abbasi A, Islam SMA, et al. Signatures of copy number alterations in human cancer. Nature. 2022 Jun;606(7916):984-991. doi: 10.1038/s41586-022-04738-6. Epub 2022 Jun 15. PMID: 35705804; PMCID: PMC9242861.
[2] Beibei Ru, Ching Ngar Wong, Yin Tong, et al. TISIDB: an integrated repository portal for tumor–immune system interactions, Bioinformatics, Volume 35, Issue 20, October 2019, Pages 4200–4202, https://doi.org/10.1093/bioinformatics/btz210.
[3] Zhongyang Liu, Jiale Liu, Xinyue Liu, et al. CTR–DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response, Nucleic Acids Research, Volume 50, Issue D1, 7 January 2022, Pages D1184–D1199, https://doi.org/10.1093/nar/gkab860.
[4] Charoentong P, Finotello F, Angelova M, et al. Pan-cancer Immunogenomic Analyses Reveal Genotype-Immunophenotype Relationships and Predictors of Response to Checkpoint Blockade. Cell Rep. 2017 Jan 3;18(1):248–262. doi: 10.1016/j.celrep.2016.12.019. PMID: 28052254.
[5] Thorsson V, Gibbs DL, Brown SD, et al. The Immune Landscape of Cancer. Immunity. 2018 Apr 17;48(4):812-830.e14. doi: 10.1016/j.immuni.2018.03.023. Epub 2018 Apr 5. Erratum in: Immunity. 2019 Aug 20;51(2):411-412. doi: 10.1016/j.immuni.2019.08.004. PMID: 29628290; PMCID: PMC5982584.
[6] Zapata L, Caravagna G, Williams MJ, et al. Immune selection determines tumor antigenicity and influences response to checkpoint inhibitors. Nat Genet. 2023 Mar;55(3):451-460. doi: 10.1038/s41588-023-01313-1. Epub 2023 Mar 9. PMID: 36894710; PMCID: PMC10011129.
[7] Cortes-Ciriano I, Lee S, Park WY, et al. A molecular portrait of microsatellite instability across multiple cancers. Nat Commun. 2017 Jun 6;8:15180. doi: 10.1038/ncomms15180. PMID: 28585546; PMCID: PMC5467167.
[8] Cannon M, Stevenson J, Stahl K, et al. DGIdb 5.0: rebuilding the drug-gene interaction database for precision medicine and drug discovery platforms. Nucleic Acids Res. 2024 Jan 5;52(D1):D1227-D1235. doi: 10.1093/nar/gkad1040. PMID: 37953380; PMCID: PMC10767982.
[9] Grissa D, Junge A, Oprea TI, Jensen LJ. Diseases 2.0: a weekly updated database of disease-gene associations from text mining and data integration. Database (Oxford). 2022 Mar 28;2022:baac019. doi: 10.1093/database/baac019. PMID: 35348648; PMCID: PMC9216524.