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 RB1

Gene summary

Gene Symbol	RB1
Gene ID	5925
Gene name	RB transcriptional corepressor 1
Synonyms	OSRC;RB;PPP1R130
Type of gene	protein_coding
UniProtAcc	P06400

Gene ontology (Biological Process only)

GO ID	GO term
GO:0006357	regulation of transcription by RNA polymerase II
GO:0051726	regulation of cell cycle
GO:0007049	cell cycle
GO:0080090	regulation of primary metabolic process
GO:0006325	chromatin organization
GO:0060255	regulation of macromolecule metabolic process
GO:0051172	negative regulation of nitrogen compound metabolic process
GO:0006355	regulation of DNA-templated transcription
GO:0030154	cell differentiation
GO:0045892	negative regulation of DNA-templated transcription
GO:0030308	negative regulation of cell growth
GO:0031175	neuron projection development
GO:0120163	negative regulation of cold-induced thermogenesis
GO:0000122	negative regulation of transcription by RNA polymerase II
GO:0043433	negative regulation of DNA-binding transcription factor activity
GO:0006338	chromatin remodeling
GO:0006469	negative regulation of protein kinase activity
GO:0050728	negative regulation of inflammatory response
GO:2000134	negative regulation of G1/S transition of mitotic cell cycle
GO:0003180	aortic valve morphogenesis
GO:0031507	heterochromatin formation
GO:0007265	Ras protein signal transduction
GO:0010629	negative regulation of gene expression
GO:2001234	negative regulation of apoptotic signaling pathway
GO:1902948	negative regulation of tau-protein kinase activity
GO:0045445	myoblast differentiation
GO:2000679	positive regulation of transcription regulatory region DNA binding
GO:0000082	G1/S transition of mitotic cell cycle
GO:0001558	regulation of cell growth
GO:0001894	tissue homeostasis
GO:0002062	chondrocyte differentiation
GO:0006366	transcription by RNA polymerase II
GO:0006915	apoptotic process
GO:0007224	smoothened signaling pathway
GO:0007283	spermatogenesis
GO:0007346	regulation of mitotic cell cycle
GO:0008283	cell population proliferation
GO:0008285	negative regulation of cell population proliferation
GO:0014009	glial cell proliferation
GO:0030182	neuron differentiation
GO:0031134	sister chromatid biorientation
GO:0032869	cellular response to insulin stimulus
GO:0034088	maintenance of mitotic sister chromatid cohesion
GO:0034349	glial cell apoptotic process
GO:0035914	skeletal muscle cell differentiation
GO:0042551	neuron maturation
GO:0043353	enucleate erythrocyte differentiation
GO:0043550	regulation of lipid kinase activity
GO:0045651	positive regulation of macrophage differentiation
GO:0045786	negative regulation of cell cycle
GO:0045842	positive regulation of mitotic metaphase/anaphase transition
GO:0045879	negative regulation of smoothened signaling pathway
GO:0045930	negative regulation of mitotic cell cycle
GO:0045944	positive regulation of transcription by RNA polymerase II
GO:0048565	digestive tract development
GO:0048667	cell morphogenesis involved in neuron differentiation
GO:0050673	epithelial cell proliferation
GO:0050680	negative regulation of epithelial cell proliferation
GO:0051146	striated muscle cell differentiation
GO:0051276	chromosome organization
GO:0051301	cell division
GO:0051402	neuron apoptotic process
GO:0060253	negative regulation of glial cell proliferation
GO:0071459	protein localization to chromosome, centromeric region
GO:0071466	cellular response to xenobiotic stimulus
GO:0071901	negative regulation of protein serine/threonine kinase activity
GO:0090230	regulation of centromere complex assembly
GO:0097284	hepatocyte apoptotic process
GO:1903055	positive regulation of extracellular matrix organization
GO:1903944	negative regulation of hepatocyte apoptotic process
GO:1904028	positive regulation of collagen fibril organization
GO:1904761	negative regulation of myofibroblast differentiation
GO:2000045	regulation of G1/S transition of mitotic cell cycle
GO:0008150	biological_process

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Literatures describing the association of RB1 and immune escape mechanisms

The table presents literature evidences demonstrating the involvement of RB1 in cancer immune escape mechanisms.

Icon	PMID	Cancer Type	Mechanism	Evidence Sentences
	38383412	Small-cell lung carcinoma	Inhibiting target recognition	Intriguingly, RB1 inactivation emerged as a factor influencing TIME heterogeneity in cSCLC, possibly through neoantigen depletion.

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Comparison of the RB1 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
GBM	1.06e+00	3.06e-04	1.41e-03

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Comparison of the RB1 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 RB1 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 RB1 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
ENSG00000251675	CTC-458I2.2	-1.03e+00	2.55e-06	4.74e-05
ENSG00000257302	FAHD2P1	-1.90e+00	1.53e-07	4.74e-06
ENSG00000223850	MYCNUT	-2.45e+00	2.55e-06	4.74e-05
ENSG00000142606	MMEL1	-1.01e+00	2.56e-06	4.76e-05
ENSG00000277440	RP11-295D4.5	-1.26e+00	3.44e-11	4.76e-09
ENSG00000105695	MAG	1.54e+00	2.59e-06	4.79e-05
ENSG00000196534	OR9K1P	-1.99e+00	2.59e-06	4.79e-05
ENSG00000200795	RNU4-1	-1.56e+00	2.59e-06	4.79e-05
ENSG00000254266	PKIA-AS1	-1.44e+00	5.62e-10	4.80e-08
ENSG00000165383	LRRC18	-1.48e+00	2.60e-06	4.81e-05
ENSG00000138115	CYP2C8	-1.37e+00	1.56e-07	4.82e-06
ENSG00000144834	TAGLN3	2.24e+00	3.53e-11	4.84e-09
ENSG00000257906	RP1-90J4.1	-1.10e+00	3.53e-11	4.84e-09
ENSG00000129951	PLPPR3	-1.91e+00	9.22e-09	4.86e-07
ENSG00000287313	NA	1.85e+00	2.64e-06	4.86e-05
ENSG00000269514	RP11-370I10.12	-1.48e+00	3.56e-11	4.88e-09
ENSG00000174827	PDZK1	1.39e+00	1.58e-07	4.89e-06
ENSG00000286538	NA	-1.62e+00	2.65e-06	4.89e-05
ENSG00000260337	RP11-386M24.6	-1.74e+00	1.59e-07	4.90e-06
ENSG00000261730	RP4-668J24.2	-1.28e+00	2.66e-06	4.90e-05

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Up-regulated Hallmark pathways

Down-regulated KEGG pathways

Down-regulated GOBP 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

No significant differences were found in RB1 expression.

Mutation

No significant differences were found in RB1 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 RB1 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 RB1 and diseases related to RB1.

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 RB1 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.

Select Cancer Type:

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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.