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 CTNNB1

Gene summary

Gene Symbol	CTNNB1
Gene ID	1499
Gene name	catenin beta 1
Synonyms	BETA-CATENIN;CTNNB;ARMADILLO
Type of gene	protein_coding
UniProtAcc	P35222

Gene ontology (Biological Process only)

GO ID	GO term
GO:0007155	cell adhesion
GO:0016055	Wnt signaling pathway
GO:0007399	nervous system development
GO:0045893	positive regulation of DNA-templated transcription
GO:0045944	positive regulation of transcription by RNA polymerase II
GO:0043065	positive regulation of apoptotic process
GO:0008285	negative regulation of cell population proliferation
GO:0045892	negative regulation of DNA-templated transcription
GO:0043161	proteasome-mediated ubiquitin-dependent protein catabolic process
GO:0060070	canonical Wnt signaling pathway
GO:0008284	positive regulation of cell population proliferation
GO:0043066	negative regulation of apoptotic process
GO:0000209	protein polyubiquitination
GO:0098609	cell-cell adhesion
GO:0045597	positive regulation of cell differentiation
GO:0032355	response to estradiol
GO:0003151	outflow tract morphogenesis
GO:0001569	branching involved in blood vessel morphogenesis
GO:1904948	midbrain dopaminergic neuron differentiation
GO:0016525	negative regulation of angiogenesis
GO:0034333	adherens junction assembly
GO:0035995	detection of muscle stretch
GO:0010718	positive regulation of epithelial to mesenchymal transition
GO:0071363	cellular response to growth factor stimulus
GO:0045765	regulation of angiogenesis
GO:0019827	stem cell population maintenance
GO:0035315	hair cell differentiation
GO:0009410	response to xenobiotic stimulus
GO:0090279	regulation of calcium ion import
GO:2000008	regulation of protein localization to cell surface
GO:0050767	regulation of neurogenesis
GO:0071681	cellular response to indole-3-methanol
GO:0001837	epithelial to mesenchymal transition
GO:0034394	protein localization to cell surface
GO:0033234	negative regulation of protein sumoylation
GO:0072182	regulation of nephron tubule epithelial cell differentiation
GO:0032212	positive regulation of telomere maintenance via telomerase
GO:0001649	osteoblast differentiation
GO:0002052	positive regulation of neuroblast proliferation
GO:0010909	positive regulation of heparan sulfate proteoglycan biosynthetic process
GO:0030997	regulation of centriole-centriole cohesion
GO:0031396	regulation of protein ubiquitination
GO:0036023	embryonic skeletal limb joint morphogenesis
GO:0043525	positive regulation of neuron apoptotic process
GO:0045976	negative regulation of mitotic cell cycle, embryonic
GO:0048145	regulation of fibroblast proliferation
GO:0048660	regulation of smooth muscle cell proliferation
GO:0061154	endothelial tube morphogenesis
GO:0061549	sympathetic ganglion development
GO:0070602	regulation of centromeric sister chromatid cohesion
GO:0072497	mesenchymal stem cell differentiation
GO:1990138	neuron projection extension
GO:0000122	negative regulation of transcription by RNA polymerase II
GO:0000165	MAPK cascade
GO:0000578	embryonic axis specification
GO:0000902	cell morphogenesis
GO:0001501	skeletal system development
GO:0001570	vasculogenesis
GO:0001658	branching involved in ureteric bud morphogenesis
GO:0001701	in utero embryonic development
GO:0001702	gastrulation with mouth forming second
GO:0001706	endoderm formation
GO:0001708	cell fate specification
GO:0001711	endodermal cell fate commitment
GO:0001764	neuron migration
GO:0001822	kidney development
GO:0001840	neural plate development
GO:0001894	tissue homeostasis
GO:0001944	vasculature development
GO:0002053	positive regulation of mesenchymal cell proliferation
GO:0002062	chondrocyte differentiation
GO:0002067	glandular epithelial cell differentiation
GO:0002089	lens morphogenesis in camera-type eye
GO:0003266	regulation of secondary heart field cardioblast proliferation
GO:0003338	metanephros morphogenesis
GO:0003340	negative regulation of mesenchymal to epithelial transition involved in metanephros morphogenesis
GO:0006351	DNA-templated transcription
GO:0006357	regulation of transcription by RNA polymerase II
GO:0006366	transcription by RNA polymerase II
GO:0007160	cell-matrix adhesion
GO:0007268	chemical synaptic transmission
GO:0007398	ectoderm development
GO:0007403	glial cell fate determination
GO:0007405	neuroblast proliferation
GO:0007507	heart development
GO:0008104	protein localization
GO:0008283	cell population proliferation
GO:0008543	fibroblast growth factor receptor signaling pathway
GO:0009948	anterior/posterior axis specification
GO:0009950	dorsal/ventral axis specification
GO:0009953	dorsal/ventral pattern formation
GO:0009954	proximal/distal pattern formation
GO:0010463	mesenchymal cell proliferation
GO:0010467	gene expression
GO:0010468	regulation of gene expression
GO:0010628	positive regulation of gene expression
GO:0010629	negative regulation of gene expression
GO:0016331	morphogenesis of embryonic epithelium
GO:0021819	layer formation in cerebral cortex
GO:0021854	hypothalamus development
GO:0022009	central nervous system vasculogenesis
GO:0022405	hair cycle process
GO:0030097	hemopoiesis
GO:0030154	cell differentiation
GO:0030182	neuron differentiation
GO:0030217	T cell differentiation
GO:0030316	osteoclast differentiation
GO:0030324	lung development
GO:0030539	male genitalia development
GO:0030856	regulation of epithelial cell differentiation
GO:0030858	positive regulation of epithelial cell differentiation
GO:0030900	forebrain development
GO:0030901	midbrain development
GO:0030902	hindbrain development
GO:0031016	pancreas development
GO:0031069	hair follicle morphogenesis
GO:0031641	regulation of myelination
GO:0032331	negative regulation of chondrocyte differentiation
GO:0032968	positive regulation of transcription elongation by RNA polymerase II
GO:0033077	T cell differentiation in thymus
GO:0034332	adherens junction organization
GO:0035050	embryonic heart tube development
GO:0035112	genitalia morphogenesis
GO:0035115	embryonic forelimb morphogenesis
GO:0035116	embryonic hindlimb morphogenesis
GO:0036520	astrocyte-dopaminergic neuron signaling
GO:0042127	regulation of cell population proliferation
GO:0042129	regulation of T cell proliferation
GO:0042475	odontogenesis of dentin-containing tooth
GO:0042733	embryonic digit morphogenesis
GO:0042981	regulation of apoptotic process
GO:0043410	positive regulation of MAPK cascade
GO:0043524	negative regulation of neuron apoptotic process
GO:0043588	skin development
GO:0044338	canonical Wnt signaling pathway involved in mesenchymal stem cell differentiation
GO:0045453	bone resorption
GO:0045595	regulation of cell differentiation
GO:0045596	negative regulation of cell differentiation
GO:0045603	positive regulation of endothelial cell differentiation
GO:0045667	regulation of osteoblast differentiation
GO:0045669	positive regulation of osteoblast differentiation
GO:0045670	regulation of osteoclast differentiation
GO:0045671	negative regulation of osteoclast differentiation
GO:0045743	positive regulation of fibroblast growth factor receptor signaling pathway
GO:0048469	cell maturation
GO:0048489	synaptic vesicle transport
GO:0048513	animal organ development
GO:0048538	thymus development
GO:0048568	embryonic organ development
GO:0048599	oocyte development
GO:0048617	embryonic foregut morphogenesis
GO:0048643	positive regulation of skeletal muscle tissue development
GO:0048664	neuron fate determination
GO:0048709	oligodendrocyte differentiation
GO:0048715	negative regulation of oligodendrocyte differentiation
GO:0050768	negative regulation of neurogenesis
GO:0050808	synapse organization
GO:0051145	smooth muscle cell differentiation
GO:0051450	myoblast proliferation
GO:0051884	regulation of timing of anagen
GO:0051963	regulation of synapse assembly
GO:0060066	oviduct development
GO:0060173	limb development
GO:0060439	trachea morphogenesis
GO:0060440	trachea formation
GO:0060441	epithelial tube branching involved in lung morphogenesis
GO:0060479	lung cell differentiation
GO:0060484	lung-associated mesenchyme development
GO:0060487	lung epithelial cell differentiation
GO:0060492	lung induction
GO:0060742	epithelial cell differentiation involved in prostate gland development
GO:0060767	epithelial cell proliferation involved in prostate gland development
GO:0060769	positive regulation of epithelial cell proliferation involved in prostate gland development
GO:0060789	hair follicle placode formation
GO:0060856	establishment of blood-brain barrier
GO:0060916	mesenchymal cell proliferation involved in lung development
GO:0061047	positive regulation of branching involved in lung morphogenesis
GO:0061198	fungiform papilla formation
GO:0061550	cranial ganglion development
GO:0072033	renal vesicle formation
GO:0072053	renal inner medulla development
GO:0072054	renal outer medulla development
GO:0072079	nephron tubule formation
GO:0072089	stem cell proliferation
GO:0090090	negative regulation of canonical Wnt signaling pathway
GO:0090425	acinar cell differentiation
GO:0097091	synaptic vesicle clustering
GO:0097190	apoptotic signaling pathway
GO:1901331	positive regulation of odontoblast differentiation
GO:1903377	negative regulation of oxidative stress-induced neuron intrinsic apoptotic signaling pathway
GO:1904888	cranial skeletal system development
GO:1990403	embryonic brain development
GO:1990791	dorsal root ganglion development
GO:1990963	establishment of blood-retinal barrier
GO:2000017	positive regulation of determination of dorsal identity
GO:2000179	positive regulation of neural precursor cell proliferation
GO:2000288	positive regulation of myoblast proliferation
GO:2000648	positive regulation of stem cell proliferation
GO:2001234	negative regulation of apoptotic signaling pathway

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Literatures describing the association of CTNNB1 and immune escape mechanisms

The table presents literature evidences demonstrating the involvement of CTNNB1 in cancer immune escape mechanisms.

PMID	Cancer Type	Mechanism	Evidence Sentences
32332013	Hepatocellular carcinoma	Inhibiting recruitment of dendritic cells	In support of this, melanoma cell intrinsic secretion of CCL4 can attract cDC1, although this can be blocked by activated β-catenin signaling. Studies of hepatocellular carcinoma have also connected the β-catenin pathway with cDC1 tumor infiltration and anti-PD-1 response.
38280119	Review, pan-cancer	Inhibiting recruitment of dendritic cells	Overactive β-catenin signaling hampers dendritic cell (DC) recruitment, promotes CD8+ T cell exclusion and increases the population of regulatory T cells (Tregs).
33574942	Melanoma	Inhibiting recruitment of dendritic cells	β-catenin expression by tumor cells suppressed dendritic cell recruitment to the tumor microenvironment in a melanoma model, resulting in fewer tumor-infiltrating lymphocytes.
38335272	Breast cancer	Metabolic barrier	Mechanistically, MIF increased c-MYC-mediated transcriptional upregulation of the glycolytic enzyme aldolase C by activating WNT/β-catenin signaling. Targeting MIF attenuated glycolysis and impaired xenograft growth and metastasis. MIF depletion in breast cancer cells also augmented intratumoral cytolytic CD8+ T cells and proinflammatory macrophages while decreasing regulatory T cells and tumor-associated neutrophils in the tumor microenvironment.
38321204	Acute myeloid leukemia	Metabolic barrier	Mechanistically, SHP-1 inhibition leads to the upregulation of phosphofructokinase platelet (PFKP) through the AKT-β-catenin pathway. The increase in PFKP elevates energy metabolic activities and, as a consequence, enhances the sensitivity of LSCs to chemotherapeutic agents.

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Comparison of the CTNNB1 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).

No significant differences were found in CTNNB1 expression.

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Comparison of the CTNNB1 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 CTNNB1 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 CTNNB1 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
ENSG00000019169	MARCO	-2.48e+00	2.07e-03	2.69e-02
ENSG00000128606	LRRC17	1.83e+00	2.07e-03	2.70e-02
ENSG00000196091	MYBPC1	-2.07e+00	2.08e-03	2.70e-02
ENSG00000139679	LPAR6	-1.05e+00	2.08e-03	2.70e-02
ENSG00000186074	CD300LF	-1.54e+00	2.08e-03	2.70e-02
ENSG00000137473	TTC29	-3.14e+00	2.09e-03	2.71e-02
ENSG00000137821	LRRC49	1.06e+00	2.09e-03	2.71e-02
ENSG00000287568	NA	-5.98e+00	2.09e-03	2.71e-02
ENSG00000183715	OPCML	2.18e+00	2.10e-03	2.72e-02
ENSG00000144410	CPO	-1.53e+00	2.10e-03	2.72e-02
ENSG00000171051	FPR1	-1.89e+00	2.10e-03	2.72e-02
ENSG00000115884	SDC1	1.21e+00	2.11e-03	2.72e-02
ENSG00000184500	PROS1	-1.07e+00	2.11e-03	2.72e-02
ENSG00000186994	KANK3	-1.14e+00	2.11e-03	2.72e-02
ENSG00000129451	KLK10	-2.49e+00	2.11e-03	2.72e-02
ENSG00000114251	WNT5A	1.58e+00	2.12e-03	2.74e-02
ENSG00000241684	ADAMTS9-AS2	1.26e+00	2.13e-03	2.74e-02
ENSG00000116824	CD2	-1.99e+00	2.13e-03	2.75e-02
ENSG00000184261	KCNK12	-1.73e+00	2.13e-03	2.75e-02
ENSG00000188850	RP11-159F24.2	1.83e+00	2.13e-03	2.75e-02

Page: 1 2 ... 87 88 89 90 91 ... 129 130

Up-regulated GOBP pathways

Up-regulated Hallmark pathways

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

No significant differences were found in CTNNB1 expression.

Mutation

No significant differences were found in CTNNB1 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 CTNNB1 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 CTNNB1 and diseases related to CTNNB1.

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 CTNNB1 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.