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 FOS

Gene summary

Gene Symbol	FOS
Gene ID	2353
Gene name	Fos proto-oncogene, AP-1 transcription factor subunit
Synonyms	AP-1;C-FOS
Type of gene	protein_coding
UniProtAcc	P01100

Gene ontology (Biological Process only)

GO ID	GO term
GO:0006355	regulation of DNA-templated transcription
GO:0006357	regulation of transcription by RNA polymerase II
GO:0006954	inflammatory response
GO:0045893	positive regulation of DNA-templated transcription
GO:0006366	transcription by RNA polymerase II
GO:1902895	positive regulation of miRNA transcription
GO:0045944	positive regulation of transcription by RNA polymerase II
GO:0034614	cellular response to reactive oxygen species
GO:0071276	cellular response to cadmium ion
GO:0060395	SMAD protein signal transduction
GO:0140467	integrated stress response signaling
GO:0007179	transforming growth factor beta receptor signaling pathway
GO:0001661	conditioned taste aversion
GO:0006306	DNA methylation
GO:0007399	nervous system development
GO:0007565	female pregnancy
GO:0009410	response to xenobiotic stimulus
GO:0009416	response to light stimulus
GO:0009612	response to mechanical stimulus
GO:0009629	response to gravity
GO:0009636	response to toxic substance
GO:0010468	regulation of gene expression
GO:0014070	response to organic cyclic compound
GO:0014823	response to activity
GO:0014856	skeletal muscle cell proliferation
GO:0030316	osteoclast differentiation
GO:0031668	cellular response to extracellular stimulus
GO:0032496	response to lipopolysaccharide
GO:0032570	response to progesterone
GO:0032868	response to insulin
GO:0032870	cellular response to hormone stimulus
GO:0034097	response to cytokine
GO:0034224	cellular response to zinc ion starvation
GO:0035902	response to immobilization stress
GO:0035914	skeletal muscle cell differentiation
GO:0035994	response to muscle stretch
GO:0045471	response to ethanol
GO:0045672	positive regulation of osteoclast differentiation
GO:0048545	response to steroid hormone
GO:0051412	response to corticosterone
GO:0051450	myoblast proliferation
GO:0051591	response to cAMP
GO:0071277	cellular response to calcium ion
GO:0071356	cellular response to tumor necrosis factor
GO:0071364	cellular response to epidermal growth factor stimulus
GO:0071374	cellular response to parathyroid hormone stimulus
GO:0071456	cellular response to hypoxia
GO:0071560	cellular response to transforming growth factor beta stimulus
GO:1903131	mononuclear cell differentiation
GO:1904628	cellular response to phorbol 13-acetate 12-myristate
GO:1990646	cellular response to prolactin

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Literatures describing the association of FOS and immune escape mechanisms

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

Icon	PMID	Cancer Type	Mechanism	Evidence Sentences
	38528546	Non-small cell lung cancer	Inhibiting recruitment of dendritic cells	Finally, the experimental validation results indicated that C. sinensis significantly decreased the VEGF and Ki67 expression, downregulated RhoA, Raf-1, and c-fos expression, which are related to cell migration and invasion, increased the serum concentration of hematopoietic factors (EPO and GM-CSF), and improved the percentage of immune cells (natural killer cells, dendritic cells, and CD4+ and CD8+ lymphocytes), which enhanced immune function.

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Comparison of the FOS 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
BRCA	-2.57e+00	4.10e-62	1.27e-60
LIHC	-3.11e+00	8.01e-38	7.95e-36
LUSC	-2.46e+00	1.67e-36	2.29e-35
UCEC	-2.75e+00	8.81e-26	2.37e-24
LUAD	-1.69e+00	2.77e-21	3.21e-20
BLCA	-3.12e+00	7.14e-21	1.11e-18
KICH	-2.63e+00	2.11e-19	3.02e-18
THCA	-1.46e+00	1.21e-14	1.08e-13
HNSC	-1.61e+00	1.09e-14	1.38e-13
STAD	-1.47e+00	3.71e-11	5.29e-10
KIRP	-1.59e+00	5.86e-07	2.77e-06
CHOL	-2.24e+00	3.82e-05	1.85e-04
READ	-1.49e+00	1.76e-04	8.54e-04

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Comparison of the FOS 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 FOS 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).

Cancer type	Mean tumor	Mean normal	Tumor minus normal	P value	Adjusted P value
COAD	5.81e-01	7.05e-01	-1.25e-01	6.24e-24	3.98e-23
PRAD	7.54e-01	6.35e-01	1.19e-01	1.18e-21	1.02e-20
BLCA	4.78e-01	5.83e-01	-1.04e-01	5.45e-09	1.79e-08
READ	5.73e-01	6.87e-01	-1.15e-01	5.41e-08	2.77e-07
CHOL	7.05e-01	8.38e-01	-1.33e-01	5.40e-07	5.93e-06
PAAD	5.09e-01	6.73e-01	-1.64e-01	9.08e-05	6.86e-04

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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
ENSG00000142408	CACNG8	3.67e+00	1.79e-04	4.13e-03
ENSG00000166387	PPFIBP2	-2.39e+00	1.80e-04	4.14e-03
ENSG00000280096	RP11-294N21.2	5.67e+00	1.80e-04	4.14e-03
ENSG00000243766	HOTTIP	-3.63e+00	1.80e-04	4.15e-03
ENSG00000270390	RP11-470B22.1	7.06e+00	1.82e-04	4.18e-03
ENSG00000124875	CXCL6	-4.20e+00	1.83e-04	4.21e-03
ENSG00000242876	RN7SL812P	2.83e+00	1.83e-04	4.21e-03
ENSG00000104936	DMPK	-1.32e+00	1.84e-04	4.21e-03
ENSG00000096006	CRISP3	-5.08e+00	1.84e-04	4.21e-03
ENSG00000136869	TLR4	2.10e+00	1.84e-04	4.22e-03
ENSG00000285658	NA	4.18e+00	1.84e-04	4.22e-03
ENSG00000203804	ADAMTSL4-AS1	2.62e+00	1.84e-04	4.22e-03
ENSG00000248208	RP11-153M7.1	2.42e+00	1.85e-04	4.23e-03
ENSG00000211942	IGHV3-13	-5.21e+00	1.85e-04	4.24e-03
ENSG00000105443	CYTH2	-1.04e+00	1.87e-04	4.27e-03
ENSG00000236088	COX10-AS1	1.12e+00	1.88e-04	4.29e-03
ENSG00000271392	RP1-161P9.5	-4.98e+00	1.89e-04	4.30e-03
ENSG00000207022	SNORA51	3.75e+00	1.89e-04	4.31e-03
ENSG00000130988	RGN	-3.35e+00	1.90e-04	4.32e-03
ENSG00000134317	GRHL1	-2.29e+00	1.90e-04	4.32e-03

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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_3	2.05e+00	6.90e-04	Lung cancer	Anti-PD-1/PD-L1	8	19	32762727

Mutation

No significant differences were found in FOS 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 FOS 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 FOS and diseases related to FOS.

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 FOS 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.