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Computers in Biology and Medicine
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Computers in Biology and Medicine An International Journal
A multi-omics analysis of effector and resting treg cells in pan-cancer
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Anna-Maria Chalepaki a,b, Marios Gkoris a,b, Irene Chondrou ª, Malamati Kourti aD, Ilias Georgakopoulos-Soares CD, Apostolos Zaravinos İD
a Department of Life Sciences, School of Sciences, European University Cyprus, Nicosia, Cyprus
b Cancer Genetics, Genomics and Systems Biology Laboratory, Basic and Translational Cancer Research Center (BTCRC), Nicosia, Cyprus
” Institute for Personalized Medicine, Department of Biochemistry and Molecular Biology, The Pennsylvania State University College of Medicine, Hershey, PA, USA
ARTICLE INFO
Keywords:
Regulatory T cells (tregs) Effector tregs
Resting tregs
FOXP3 IL2RA CTLA-4
CCR8 TNFRSF9 Pan-cancer analysis Immune infiltration Gene expression Tumor microenvironment Drug sensitivity
ABSTRACT
Regulatory T cells (Tregs) are critical for maintaining the stability of the immune system and facilitating tumor escape through various mechanisms. Resting T cells are involved in cell-mediated immunity and remain in a resting state until stimulated, while effector T cells promote immune responses. Here, we investigated the roles of two gene signatures, one for resting Tregs (FOXP3 and IL2RA) and another for effector Tregs (FOXP3, CTLA-4, CCR8 and TNFRSF9) in pan-cancer. Using data from The Cancer Genome Atlas (TCGA), The Cancer Proteome Atlas (TCPA) and Gene Expression Omnibus (GEO), we focused on the expression profile of the two signatures, the existence of single nucleotide variants (SNVs) and copy number variants (CNVs), methylation, infiltration of immune cells in the tumor and sensitivity to different drugs. Our analysis revealed that both signatures are differentially expressed across different cancer types, and correlate with patient survival. Furthermore, both types of Tregs influence important pathways in cancer development and progression, like apoptosis, epithelial-to- mesenchymal transition (EMT) and the DNA damage pathway. Moreover, a positive correlation was highlighted between the expression of gene markers in both resting and effector Tregs and immune cell infiltration in adrenocortical carcinoma, while mutations in both signatures correlated with enrichment of specific immune cells, mainly in skin melanoma and endometrial cancer. In addition, we reveal the existence of widespread CNVs and hypomethylation affecting both Treg signatures in most cancer types. Last, we identified a few correlations between the expression of CCR8 and TNFRSF9 and sensitivity to several drugs, including COL-3, Chlorambucil and GSK1070916, in pan-cancer. Overall, these findings highlight new evidence that both Treg signatures are crucial regulators of cancer progression, providing potential clinical outcomes for cancer therapy.
1. Introduction
Regulatory T cells (Tregs), a subset of CD4+ T cells, play a crucial role in maintaining immunological tolerance [1,2]. To ensure immune sys- tem balance, Tregs inhibit immune responses driven by cytotoxic T lymphocytes (CTLs), being essential for preserving immune self-tolerance and facilitating immunosuppression [3].
Resting T cells are T lymphocytes that have a main role in cell- mediated immunity maintaining their resting state most of their life- time, but changing to the proliferating state once stimulated [4]. On the other hand, effector T cells are responsible for steering the immune re- sponses to promote immune functions [5].
The identification of resting Tregs, characterized by the expression of the transcription factor forkhead box protein 3 (FOXP3) and interleukin-
2 receptor subunit alpha (IL2RA), alongside effector Tregs, which are distinguished for expressing FOXP3, CC chemokine receptor 8 (CCR8), cytotoxic T Lymphocyte-associated antigen-4 (CTLA-4) and TNF recep- tor superfamily member 9 (TNFRSF9), has provided significant insights into the functional diversity and regulatory mechanisms within the Treg population [3]. These Treg markers along with key immune checkpoints such as PD-1, TIM-3, and LAG-3, play crucial roles in shaping the immunosuppressive landscape of the tumor microenvironment (TME) by promoting Treg-mediated immune evasion, dampening effector T-cell responses, and fostering an immune-permissive niche that facili- tates tumor progression [6,7].
CCR8 is a chemokine receptor expressed on Tregs that facilitates their migration to sites of inflammation and tumors. It plays a role in establishing an immunosuppressive microenvironment favorable for
* Corresponding author. Department of Life Sciences, School of Sciences, European University Cyprus, Nicosia, Cyprus.
https://doi.org/10.1016/j.compbiomed.2025.110021
tumor progression. High expression of CCR8 on Tregs has been associ- ated with increased infiltration into tumors and poor prognosis in various cancers [8-10].
CTLA-4 is a critical immune checkpoint receptor expressed on Tregs that regulates T cell activation and immune responses. It plays a key role in suppressing effector T cell responses, thereby promoting immune tolerance and inhibiting anti-tumor immunity. CTLA-4 blockade has been successfully used in cancer immunotherapy (e.g., ipilimumab) by enhancing T cell activation and overcoming Treg-mediated immuno- suppression [11-13].
TNFRSF9, also known as 4-1BB, is a co-stimulatory receptor expressed on Tregs and other immune cells. It enhances Treg survival and suppressive functions, influencing immune responses in cancer. Activation of TNFRSF9 signaling has been linked to enhanced Treg function and inhibition of anti-tumor immunity in various cancer types [14-16].
Most studies examine the role of these markers in specific tumors, lacking a comprehensive analysis in pan-cancer. Recent advancements have provided access to extensive bioinformatics data samples across different cancer types and normal tissues. This availability allows broader and more representative bioinformatics analysis and enables the identification of new research directions through pan-cancer approaches.
Understanding the molecular signatures and the dynamic interplay between the two subsets of Tregs, resting and effector, is essential for elucidating their contribution to immune regulation and their potential therapeutic applications in cancer and other immune-mediated disorders.
To delve into the resting (FOXP3 and IL2RA) and effector Treg markers (FOXP3, CCR8, CTLA-4 and TNFRSF9), we investigated their expression and mutation profiles, but also their link with immune infiltration and drug resistance in pan-cancer. To this end, we extracted data from more than 33 types of cancer, available in publicly available datasets, such as The Cancer Genome Atlas (TCGA) and The Cancer Proteome Atlas (TCPA), focusing on new and innovative aspects. We have also explored the different Treg states and subpopulations across different tumor types, using spatial transcriptomics data. Our study provides a more focused analysis of both Treg signatures across multiple cancer types. While pan-cancer analyses have been performed, very few studies focus specifically on the unique roles and differential regulation of Treg subtypes.
2. Materials and methods
2.1. Differential expression
The differential expression of resting Tregs (FOXP3 and IL2RA) and effector Tregs (FOXP3, CTLA-4, CCR8 and TNFRSF9) was examined in pan-cancer, as recently described [17]. The investigated Treg signatures correspond to the cancer tissues, since all data were retieved from The Cancer Genome Atlas (TCGA). To conduct comprehensive gene set enrichment analyses we utilized the Gene Set Cancer Analysis (GSCA) platform [18].
The RNA-Seq by Expectation-Maximization (RSEM) method was selected for RNA-seq analysis [19]. mRNA expression data were down- loaded from harmonized TCGA PanCan Atlas data in UCSC Xena. RNA-seq data were normalized using log2(TPM + 1) to standardize expression values while preserving biological variability and batch corrected prior to their analysis. Based on the normalized and batch corrected RSEM mRNA expression, we calculated the fold change by mean(Tumor)/mean(Normal). A fold change (log2FC ≥|0.6|) threshold was applied in differential expression analysis, following a HTSeq TCGA-related protocol. This threshold was selected to ensure the robustness of our findings while considering the limitations of our dataset and the specific context of cancer-related markers. The p-value was estimated by t-test and was further adjusted by FDR. Furthermore,
we studied differences in gene expression across different molecular subtypes, using Wilcoxon and ANOVA tests. To analyze gene expression according to the tumor stage, we focused on 4 different types of stage, as recently described in detail [17]. All data analyzed stem from non-treated cancer patients from the TCGA.
2.2. Patient suvival
To assess patient survival based on the two signatures, we used GSCAlite [20]. Tumor samples were divided into high- and low-expressing subgroups using the median value. Patient survival [overall survival (OS), progression-free survival (PFS), disease-specific survival (DSS), and disease-free interval (DFI)] were assessed with sur- vival in R using Cox proportional-hazards and the log-rank test. Overall survival and disease-free survival maps for both signatures were built using GEPIA2 [21], with an FDR-adjusted p ≤ 0.05 as level of signifi- cance and the median value of expression used as cutoff. In addition, we used PrognoScan [22], exploring survival in different cancer types in the Gene Expression Omnibus (GEO) repository.
2.3. Pathway activity
We explored the activating or inhibitory activities of the two Treg signatures across 10 cancer-related pathways in pan-cancer, using reverse phase protein array (RPPA) data from the TCPA database (htt ps://www.tcpaportal.org/tcpa/accessed on June 15, 2023) [23]. We divided samples into 2 groups, according to their (high or low) gene expression, and assessed differences between them using the student’s t-test with FDR-adjusted p-values.
2.4. Gene mutations
We investigated the presence of single nucleotide variations (SNVs) and copy number variations (CNVs) for the two Treg signatures in pan- cancer. Genomic regions affected by significant amplifications or de- letions were explored using GISTIC2.0 [24]. The correlation between CNVs and the expression profile of the two Treg signatures was assessed using Spearman’s correlation [25].
2.5. Immune infiltration
The abundance of 24 immune cells was explored using Immune Cell Abundance Identifier (ImmuCellAI) (https://guolab.wchscu. cn/ImmuCellAI/#!//) [26,27]. In addition, immune infiltration was correlated with gene mutations in the two Treg signatures, and differ- ences between mutant (MUT) and wild-type (WT) samples were assessed using the Wilcoxon test The expression and mutations in each Treg signature were correlated with the immune cells’ infiltrates using Spearman’s test (corrr package in R).
2.6. Differential methylation
To assess differential methylation of the two Treg signatures in tumor and normal samples, we used Illumina Human Methylation 450K (level 3) data. The p-value was estimated by t-test and adjusted by FDR, with statistically significant values ≤ 0.01. Clinical data were downloaded from TCGA and the study by Liu et al. [28]. Gene expression and methylation were correlated using Spearman’s test.
2.7. Drug sensitivity
We collected the IC50 of a large number of small molecules across different cell lines and the corresponding mRNA levels for each Treg signatures, using the Genomic of Drug Sensitivity in Cancer (GDSC; 265 small molecules in 860 cell lines; Release 8.5; October 2023) [29-31] and Genomics of Therapeutics Response Portal (CTRP v2; 481 small
molecules in 1001 cell lines; December 2023) [32-34]. We estimated the relationship between the two Treg signatures and drug IC50, using Pearson’s correlation test. P-values were adjusted by FDR.
2.8. In-vitro validation
For in-vitro validation purposes, three compounds were obtained from the following sources: Vorinostat was purchased from Sigma- Aldrich (St. Louis, MO, USA), while Methotrexate (MTX) (25 mg/mL, Teva) and 5-Fluorouracil (5-FU) (50 mg/mL, Accord) were obtained as solutions ready for injection from a local pharmacy. All stocks were kept protected from direct light according to the instructions.
The HT29 and H460 cells were routinely cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10 % heat- inactivated fetal bovine serum (FBS) and 1 % antibiotic-antimycotic cocktail. MCF7 cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) with L-Glutamine, supplemented with 10 % heat- inactivated FBS and 1 % antibiotic-antimycotic solution. All cells were grown to confluence in loosely capped 25 cm3 or 75 cm3 cell culture flasks (Greiner Bio-One Ltd., Gloucestershire, UK) under standard con- ditions of 37 ℃, 5 % CO2, and 95 % humidity. Cell cultures were allowed to reach the desired confluence before being used in experiments.
The MTT assay was utilized to evaluate the cytotoxic effects of compounds on various cell lines [35]. Cells (10,000/well) were seeded in 96-well plates and incubated overnight to facilitate attachment in their respective culture media (DMEM or RPMI). Following this, the cells were treated with increasing concentrations of the test compounds (Vorinostat, 5-FU, MTX) or vehicle control (DMSO for Vorinostat and PBS for 5-FU and MTX, further diluted in culture media) for 72 h. At the end of the treatment period, fresh media containing 15 uL of MTT so- lution (5 mg/mL in PBS) was added to each well and incubated for 4 h at 37 ℃. The formazan crystals formed were solubilized using 100 µL of DMSO, and absorbance was measured at 570 nm using a microplate reader. The IC50 values for each compound were calculated using non-linear regression analysis in GraphPad Prism. Each experiment was performed at least in triplicate, and results were normalized to vehicle-treated controls.
2.9. Gene Ontology (GO) and pathway enrichment analysis
The biological properties (BP), molecular function (MF) and cellular components (CC) of the resting Tregs markers (FOXP3 and IL2RA) and the effector Tregs markers (FOXP3, CTLA-4, CCR8 and TNFRSF9) were investigated using GO and KEGG pathway enrichment analysis, using EnrichR (https://maayanlab.cloud/Enrichr/) [36]. The adjusted p-value, odds ratio and combined scores were used for pathway evaluation.
2.10. Protein-Protein Interaction (PPI) network
We constructed a Protein-Protein Interaction (PPI) Network for CCR8 and is ligands and assessed their functional roles in immune regulation and tumor biology using the STRING database. Known and predicted PPI data for CCR8 were retrieved focusing on high-confidence interactions (interaction score >0.7). We also used the GDSC [30] and DrugBank (v5.1.13) databases to explore potential drug candidates targeting CCR8 and related pathways.
2.11. External validation
We also validated the mutation, structural variants and copy-number alterations using 4 external cohorts: the MSK-IMPACT Clinical sequencing cohort, containing 10,945 samples [37], China Pan-cancer (containing 10,194 samples) [38], MSK MetTropism (containing 25, 775 samples) [39] and the Pan-cancer analysis of whole genomes (2658 whole-cancer genomes across 38 tumor types) [40], using cBioPortal
[41]. Annotation was performed with OncoKB™M and Cancer Hotspots. In total external validation was performed in 49,836 samples across 48, 888 patients.
Subsequently, we explored the expression of the two Treg gene sig- natures in colorectal, bladder, breast, esophageal, kidney and non-small cell lung cancers, using six independent scRNA-seq databases (10x Ge- nomics platform) to explore how Treg markers are distributed across tumor regions, focusing on the TME (CRC_GSE139555 [42] containing 10,112 cells, BLCA_GSE130001 [43] containing 4129 cells, BRCA_GSE110686 [44] containing 6035 cells, ESCA_GSE160269 [45] containing 208,658 cells, KIPAN_GSE154763 [46] containing 28,930 cells, and NSCLC EMTAB6149 [47] containing 40,218 cells. The anal- ysis was performed in treatment-naive patients, using Tumor Immune Single-cell Hub 2 (TISCH2) [48]. The global-scaling normalization method (‘NormalizeData’ function) in Seurat was used to scale the raw counts (UMI) in each cell to 10,000, and to log-transform the results. For each collected dataset, the MAESTRO analysis pipeline was adopted to perform quality control (QC), clustering and cell-type annotation (QC: cell number per dataset, >1000; UMI count per cell, >1000; gene number per cell, >500; Data pre-processing: batch effect removal, cell clustering, differential expression). After the streamlined processing, we curated the cell-type annotation of all datasets at three levels: malig- nancy, major-lineage and minor-lineage. The gene expression levels were calculated in log2(TPM+1) values and displayed using UMAP.
2.12. Post-translational modifications (PTMs)
We analyzed phosphorylation and ubiquitination sites of key Treg markers (e.g., FOXP3 and CCR8) using data from PhosphoSitePlus [49] and UniProt [50] databases. We focused on PTMs that may regulate protein stability, localization, or activity. The identified modifications were cross-referenced with published literature to assess their potential functional impact on Treg biology.
2.13. Spatially defined immune infiltration
We also explored spatial transcriptomics data in non-small cell lung cancer (NSCLC), breast cancer (BRCA), pan-kidney cancer (KIPAN) and colorectal cancer (CRC). For NSCLC, we analyzed a proprietary FFPE neuroendocrine carcinoma dataset from 10x Genomics (Dataset ID: V52Y10-286), featuring 6195 spots with a median of 66,268 UMI counts and 10,087 genes per spot. For BRCA, the fresh-frozen invasive ductal carcinoma dataset (GSE250163) included 4898 spots with a median of 9720 UMI counts and 3654 genes per spot. Publicly available datasets for KIPAN (GSE226997) and CRC (GSE242521) were also used to pro- vide comparative insights into spatial and cellular heterogeneity. Un- fortunately, no publicly available spatial transcriptomics datasets for bladder cancer (BLCA) or esophageal cancer (ESCA) were identified at the time of analysis.
To process raw data and perform quality control, scRNA-seq data was studied using a uniform original processing method and normalization process, according to Monocle 3 protocol. In specific we obtained a unique molecular identifier (UMI) matrix for each sample and per- formed normalization using the Seurat package (version 3.2.2) in R. All normalized data were log2 transformed. Three quality control measures were adopted, with exclusion criteria as follows: (1) <200 expressed genes or >2500 expressed genes, (2) mitochondrial genes >20 % of UMIs, (3) nCount_RNA >10000 UMIs. The Harmony algorithm was executed to eliminate batch effects.
2.14. Pseudotime trajectories analysis
Pseudotime trajectories of Tregs were constructed using Monocle 3 in R to infer their differentiation states. The raw count matrices, feature annotation files, and cell barcodes were loaded for each dataset using load_mm_data(), and all individual datasets were combined into a single
cell dataset (CDS) using combine_cds(). Preprocessing included prin- cipal component analysis (PCA) with 100 components, variance explained analysis, and dimensionality reduction using UMAP, followed by batch effect correction with align_cds(). Cells were clustered using the Louvain algorithm with a resolution of 1e-5, and marker gene expression analysis identified distinct subpopulations. Regulatory T cell subsets were defined based on known marker genes, including General Tregs (CD4, CD25, FOXP3, CTLA4, GITR, CD127), Naïve Tregs (CD45RA, CCR7, CD62L), Effector Tregs (CD45RO, HLA-DR, ICOS, PD1, CCR4, CCR8, CXCR3, Helios), and Exhausted Tregs (PD1, TIM3, LAG3, TIGIT, CD39, CD73, FOXP3). Subsets were clustered separately and visualized using plot_cells(). Trajectory analysis was performed on General Tregs using Monocle3’s pseudotime inference, where the trajectory graph was learned with learn graph(), and root cells were selected based on FOXP3-low expression. Cells were then ordered using order_cells() and visualized in a pseudotime trajectory with plot_cells(). Data analysis and visualization were conducted in R using ggplot2, patchwork, and Monocle3, ensuring robust identification of Treg subpopulations, clus- tering, and trajectory inference to understand Treg heterogeneity across various cancers.
A detailed flow diagram summarizing the analytical pipeline of the study is shown in Fig. S1.
3. Results
3.1. Differential expression of resting and effector treg markers
Initially, we examined the mRNA expression of the resting and effector Treg markers in pan-cancer. Regarding the first, we noticed higher FOXP3 levels in colon adenocarcinoma (COAD), head and neck squamous cell carcinoma (HNSC), stomach adenocarcinoma (STAD), breast cancer (BRCA), lung cancers (LUSC and LUAD) and kidney renal clear cell carcinoma (KIRC). In contrast, IL2RA levels were lower across HNSC and LUAD (Fig. 1a, b and Table S1). As for effector Tregs, CCR8 expression was higher in STAD, ESCA, BRCA and KIRC. On the other hand, high mRNA levels of TNFRSF9 and CTLA-4 were detected in KIRC, compared to the rest of the cancer types (Fig. 1c, d and Table S1).
Moreover, FOXP3 levels correlated with higher pathological stages in kidney renal papillary cell carcinoma (KIRP), rectum adenocarcinoma (READ), adrenocortical carcinoma (ACC), bladder cancer (BLCA), KIRC, thyroid cancer (THCA) and STAD, but with lower in HNSC, COAD, LUAD and LUSC. Trend plots were constructed to depict a rising tendency of IL2RA expression in different stages of liver hepatocellular carcinoma (LICH), uveal melanoma (UVM), pancreatic adenocarcinoma (PAAD), esophageal carcinoma (ESCA), BLCA, thyroid cancer (THCA) and STAD, but a lower tendency in HNSC and LUAD (Fig. S1). Additionally, we noticed high expression of both FOXP3 and IL2RA in stage I THCA, KIRC and SKCM (Fig. 1e). In addition, CTLA-4 high levels correlated only with KIRC stages. TNFRSF9 expression was higher in high-stage PAAD, me- sothelioma (MESO) and KIRC, but in low-stage HNSC, COAD and
a.
Resting Treg cells
C.
Effector Treg cells
CTLA4 FOXP3 TNFRSF9 CCR8
FDR
FOXP3
⇐ 0.06
>0.05
FDR
0 0.05
0.01
0.001
⇐ 0.0001
IL2RA
log2(FC)
5
0
2
0 -1
BLCA
KICH-
KIRP
LIHC
PRAD
THCA
COAD
HNSG
ESCA
STAD
BRCA
LUSG
LUAD
KIRC
BLCĄ
KICH
KIRP
LIHC
PRAD
THCA
COAD
HNSC
ESCA
STAD
BRCA
LUSC
LUAĐ
KIRG
b.
d.
Expression log2(RSEM)
IL2RA (LUAD)
FOXP3 (HNSC) FDR =5e-04
FOXP3 (LUAD)
IL2RA (HNSC)
Expression Jog2(RSEM)
TNFRSF9 (BRCA)
FOXP3 (BRCA) FDR = 5.7e-14
18.0
CTLA4 (BRCA)
CCR8 (BRCA) FDR = 2.1e-13
FDR = 1.8e-06
FDR =4e-10
FDR = 2.1e-04
FDR = 5.4e-05
FDR = 1.8e-03
.
·
#
*%
7.8
.
5.0
.
4
10
3
2.5
3
28
*
·
Normal
Tumor
Normal
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Normal
Tumor
Normal
Tumor
Normal
Tumor
Normal
Tumor
Normal
Tumor
ta
Normal
Tumor
e.
Expression log2(RSEM)
IL2RA mRNA expression in pathologic_stage of SKCM
Expression log2(RSEM)
FOXP3 mRNA expression in pathologic_stage of KIRC
f.
Expression log2(RSEM)
TNFRSF9 mRNA expression in pathologic_stage of KIRC
Expression log2(RSEM)
FOXP3 mRNA expression in pathologic_stage of KIRC
15
15
NS
15
NS
NS
Stage
Stage
15
NS
NS.
NS
Stage
Stage
10
Stage I, n=77
10
Stage I, n=267
10
Stage I, n=267
10
Stage I, n=267
Stage II, n=140
Stage II, n=57
Stage II, n=57
Stage II, n=57
5
Stage III, n=170
5
Stage III, n=123
5
Stage III, n=123
Stage IV, n=23
Stage IV, n=84
5
Stage III, n=123
Stage IV, n=84
Stage IV, n=84
0
0
0
0
Stage I
Stage II
Stage
Stage III
Stage IV
Stage I
Stage II
Stage
Stage III
Stage IV
Stage I
Stage II
Stage
Stage III
Stage IV
Stage I
Stage II
Stage
Stage III
Stage IV
Expression log2(RSEM)
IL2RA mRNA expression in pathologic_stage of THCA
Expression log2(RSEM)
FOXP3 mRNA expression in pathologic_stage of THCA
CTLA4 mRNA expression in pathologic_stage of KIRC
Expression log2(RSEM)
CCR8 mRNA expression in pathologic_stage of KIRC
15
NS
15
NS
Expression log2(RSEM)
15
10.0
NS
Stage
Stage
10
Stage
7.5
Stage
Stage I, n=283
10
Stage I, n=283
10
Stage I, n=267
Stage II, n=51
Stage II, n=51
Stage I, n=267
5.0
Stage II, n=57
5
Stage III, n=110
Stage IV, n=55
5
Stage III, n=110
Stage II, n=57
Stage IV, n=55
5
Stage III, n=123
Stage III, n=123
Stage IV, n=84
2,5
Stage IV, n=84
0
2
0
0.0
Stage I
Stage II
Stage III
0
Stage
Stage IV
Stage I
Stage II
Stage III Stage
Stage
Stage III
Stage IV
Stage I
Stage II
Stage
Stage III
Stage IV
Stage I
Stage II
Stage IV
testicular germ cell tumors (TGCT). CCR8 levels correlated with path- ological stages in KIRP and KIRC, but presented a low tendency in HNSC, LUAD and LUSC. Statistically significant results of effector Tregs showed high expression of the pathological stages of all four gene markers (CCR8, CTLA-4, FOXP3, TNFRSF9) in stage I in KIRC (Fig. 1f).
We also validated the low expression of three Treg markers (CCD8, FOXP3 and IL2RA) at the protein level, using immunohistochemistry (IHC) staining data from the Human Protein Atlas. Overall, we examined IHC-staining data from 12 patients from each tumor type and noticed negative staining for all three Treg markers in colorectal, breast, pros- tate, lung and liver cancers (Fig. 2).
Furthermore, FOXP3 mRNA levels differed across the molecular subtypes in breast cancer (basal, Her2, luminal A, luminal B and normal- like; p = 7.54E-09), glioblastoma multiforme (classical, G-CIMP, mesenchymal, neural and proneural; p < 0.001), kidney renal clear cell carcinoma (subtypes 1-4 [51]; p = 4.31E-13), lung adenocarcinoma (subtypes 1-6; p = 3.79E-13), lung squamous cell carcinoma (basal, classical, primitive, secretory; p = 5.08E-07) and stomach adenocarci- noma (CIN, EBV, GS and MSI; p = 0.002174). Similarly, IL2RA expres- sion differed across molecular subtypes in BLCA (non-papillary vs papillary; p < 0.001), BRCA (p = 1.9E-09), GBM (p=4.42E-06), KIRC (p < 0.0001), LUAD (p = 2.15E-09) and STAD (p<0.0001). TNFRSF9 mRNA expression differed across molecular subtypes in LUAD (p = 1.44E-13), BLCA (p=0.0001), BRCA (p=3.46E-08), LUSC (p=0.007), GBM (p = 3.79E-05) and STAD (p=0.0001).
In specific, FOXP3 and IL2RA mRNA levels were higher in basal and Her2+ breast cancers, compared to luminal A/B and normal-like tu- mors. The similar expression pattern was also exhibited by TNFRSF9, CCR8 and CTLA-4 (Fig. 3a and b). Moreover, the expression of all markers was higher in non-papillary bladder cancers, compared to papillary ones (Fig. 3c and d). In addition, mesenchymal and neural GBM tumors had higher levels of all Treg markers, compared to classical, G-CIMP and proneural tumors (Fig. 3e and f). In KIRC, the expression of the Treg markers was higher in subtypes 3 and 4 (Fig. 3g and h).
3.2. Correlations between FOXP3/IL2RA and FOXP3/CCR8/TNFRSF9/ CTLA-4 mRNA expression and patient survival
High FOXP3 expression levels shifted towards worse survival (OS,
PFS and DSS; HR > 1) in KIRC and GBM, among other tumors. In contrast, in HNSC, increased FOXP3 levels correlated with better progression-free survival (HR < 1). In addition, high IL2RA levels correlated with worse PFS and OS in GMB and acute myeloid leukemia (LAML) respectively, but with better survival in cholangiocarcinoma (CHOL; PFS) and skin melanoma (SKCM; OS, PFS, DSS) (Fig. 4a and Table S2).
On the other hand, regarding the effector Tregs, lower CCR8 levels correlated with better survival in glioblastoma multiforme (GBM; OS, PFS) and in HNSC (OS, PFS, DSS). Furthermore, high TNFRSF9 levels correlated to worse PFS survival in GBM. Finally, higher CTLA-4 levels associated with better survival results in HNSC (OS, PFS, DSS), while higher expression was shown to increase the risk in KIRC (OS, PFS, DSS) (Table S2).
Additionally, we created survival maps of the two Treg signatures using GEPIA2. In ACC, GBM, KIRP and THYM, the OS of high-FOXP3- expressing tumors was significantly (p < 0.01) higher than that of the low-expressing ones. Furthermore, the DFS of the high-FOXP3 expres- sion group was significantly higher in ACC, GBM, KICH, KIRC, KIRP, LAML and PCPG. In contrast, in READ and SKCM, the OS of the high- IL2RA expressing tumors was significantly lower. Additionally, in DLBC, GBM, LAML and PCPG, high IL2RA expression related to better DFS (Fig. 4b).
For the effector Treg signature, the OS of the high-TNFRSF9 group, was significantly higher in KIRP, MESO, pheochromocytoma and para- ganglioma (PCPG) and UVM. In addition, the DFS of high-TNFRSF9 expressing tumors was significantly higher in diffuse-large B cell lym- phoma (DLBC), KIRP, PCPG and UVM. Moreover, the OS of the high- FOXP3 expression group was higher level in ACC, THYM and UVM, while the DFS was higher in GBM, KIRC, KIRP, LAML and PCPG. In addition, the OS of the high-CTLA-4 expression group was better in THYM and UVM. In contrast, the DFS of the high-CTLA-4 expressing tumors was significantly lower than that of the high-expressing ones, in ACHOL and THYM. Furthermore, in OS and DFS was lower in the high- CCR8 expression group in CHOL, HNSC and SKCM.
Analyzing various GEO datasets using Prognoscan, we found that FOXP3 is an adverse prognostic factor in gliomas and ovarian cancer (p < 0.01, HR > 0) and a protective prognostic factor in colorectal cancer, uveal melanoma and lung adenocarcinoma (p < 0.01, HR < 0). In
Colorectal cancer
Breast cancer
Prostate cancer
Lung cancer
Liver cancer
CCR8
FOXP3
1
IL2RA
a.
Subtype difference between high and low gene expression
b.
Expression log2(RSEM)
IL2RA (BRCA)
Subtype difference between high and low gene expression
NS
Expression log2(RSEM)
TNFRSF9 (BRCA)
20
20
15
-Log(10) FDR
15
FOXP3
O ☐
☒
☒
☒
☐
☒ ☐
☐
10
13
10
FOXP3
☐
☒
☐
☒
☒
5
☐
☒
☐
☐
3.0
5
7.0
0
0
10.0
Basal
Her2
LumA
LumBNormal_like
TNFRSF90
☒
☒
☐
☒
☐
☒
☐
Basal
Her2
LumA
LumBNormal_like
FDR
CCR8 (BRCA)
☐ ⇐ 0.05
FOXP3 (BRCA)
Expression log2(RSEM)
NS
☐
>0.05
Expression log2(RSEM)
20
NS.
NE
CTLA4
O ☐
☒ ☒
☒
15
☐
☒ ☐
☐
FDR
NG
15
10
IL2RA
☐
☐
☐ 0.05
☐
0.01
10
5
0.001
⇐ 0.0001
5
CCR8 ☐
☒
☒
☐
0
Basal
Her2
LumA
LumBNormal_like
Basal
Her2
LumA
LumBNormal_like
BLCA
BRCA
GBM
KIRO
LUAD
STAD
LUSC
COAD
HNSC
BRCA LUAD
Subtypes
BLCA
LUSC
GBM
KIRC
STAD
COAD
HNSC
CTLA4 (BRCA)
Basal, n=139
Expression log2(RSEM)
20
* NS
Her2, n=67
15
LumA, n=417
10
LumB, n=191
Normal_like, n=23
5
0
C.
d.
Basal
Her2
LumA
LumB Normal_like
Expression log2(RSEM)
IL2RA (BLCA)
CCR8 (BLCA)
Expression log2(RSEM)
TNFRSF9 (BLCA)
12.5
Expression log2(RSEM)
10.0
10.0
6
7.5
7.5
4
5.0
5.0
2.5
2
2.5
0.0
0
0.0
Non-Papillary
Papillary
Non-Papillary
Papillary
Non-Papillary
Papillary
Expression log2(RSEM)
FOXP3 (BLCA)
Expression log2(RSEM)
CTLA4 (BLCA)
9
9
2.
6
6
3
3
0
e.
Non-Papillary
Papillary
f.
Non-Papillary
Papillary
Expression log2(RSEM)
IL2RA (GBM)
Expression log2(RSEM)
CCR8 (GBM)
Expression log2(RSEM)
TNFRSF9 (GBM)
20
8
0.053
00036
NS
0.02
31
10
0.00091
011
Subtypes
15
N6
6
0330 91
001 0.18
Classical, n=40
NS
0.64
5e
1033
0.19
06
G-CIMP, n=8
10
4.
5
Mesenchymal, n=52
5
2
Neural, n=28
Proneural, n=30
0
Classical
G-CIMP
Mesenchymal
Neural
Proneural
C
Mesenchymal
0
Classical
G-CIMP
Neural
Proneural
Classical
G-CIMP
Mesenchymal
Neural
Proneural
Expression log2(RSEM)
FOXP3 (GBM)
Expression log2(RSEM)
CTLA4 (GBM)
0,029
S
20
10
0 86
0.02c
10
NS
NS
15
0.89”
0012
1.51
NS
10
5.
5
0
Classical
G-CIMP
Mesenchymal
Neural
Proneural
Classical
G-CIMP
Mesenchymal
Neural
Proneural
g.
h.
Expression log2(RSEM)
CCR8 (KIRC)
20
IL2RA (KIRC)
Expression log2(RSEM)
10.0
0.013
Expression log2(RSEM)
TNFRSF9 (KIRC)
NS
0.75
15
0.55
0.79
15
NS
7.5
0.18
0.0001
0.17
0.0078
Subtypes
10
10
1, n=147
5.0
E 2, n=90
5
2.5
5
3, n=94
4, n=86
0
0.0
1
0
1
2
3
4
1
2
3
4
1
2
3
4
Expression log2(RSEM)
FOXP3 (KIRC)
15
NS
NS
Expression log2(RSEM)
CTLA4 (KIRC)
15
0.12
0.96
0.0002
0.00013
10
10
70-08
5
5
1:
0
1
2
3
4
1
2
3
4
a.
OS of FOXP3 expression in KIRC
PFS of FOXP3 expression in KIRC
DSS of FOXP3 expression in KIRC
1.00-
Logrank P value = 6e-06
1.00-
1.00
Logrank P value = 1.4e-08
Logrank P value = 2.1e-05
OS probability
0.75
PFS probability
0.75
DSS probability
0.75
60.50
5.0.50
5.0.50
0.25
-Higher expr., n=266
0.25-
0.25
Higher expr., n=235
Higher expr., n=265
+Lower expr., n=267
Lower expr., n=232
0.00
0.00
ower expr., n=266
0.00
0
50
100
150
0
Time (month)
50
100
0
50
100
Time (month)
Time (month)
150
OS of FOXP3 expression in GBM
PFS of FOXP3 expression in GBM
DSS of FOXP3 expression in GBM
1.00-
1.00-
1.00
Logrank P value = 4.1e-05
Logrank P value = 7.1e-05
≥0.75
OS probability
PFS probability
0.75
DSS probability
0.75
Logrank P value = 3.6e-05
+ Higher expr., n=79
Higher expr., n=80
+ Higher expr., n=71
0.50
Lower expr., n=80
0.50
Lower expr., n=80
5.0.50
Lower expr., n=73
0.25
Q. 0.25
0.25
0.00
0.00
0.00
0
20
40
60
80
0
10
30
40
50
0
20
60
80
Time (month)
20
Time (month)
40
Time(month)
PFS of FOXP3 expression in HNSC
PFS of IL2RA expression in GBM
OS of IL2RA expression in LAML
1.00
Logrank P value = 0.0035
1.00
1.00-
Logrank P value = 0.0067
Logrank P value = 0.00058
PFS probability
0.75
PFS probability
0.75
OS probability
£0.75
+ Higher expr., n=81
+ Higher expr., n=80
Lower expr., n=82
.50
+ Higher expr., n=259
0.50
Lower expr., n=80
0.50
Lower expr., n=260
0.25
0.25
0.25
0.00
0.00
0.00
0
50
100
Time (month)
150
200
0
10
20
30
40
50
Time (month)
0
25
50
Time (month)
75
100
PFS of IL2RA expression in CHOL
OS of IL2RA expr. in SKCM
PFS of IL2RA expr. in SKCM
1.00
Logrank P value = 0.0019
1.00-
Logrank P value = 2.6e-07
1.00-
-Higher expr., n=227
PFS probability
0.75
~0.75
OS probability
Higher expr., n=230
≥0.75
PFS probability
+Lower expr., n=228
Logrank P value = 0.0015
5.0.50
Lower expr., n=230
0.50
0.50
Higher expr., n=18
0.25
Lower expr., n=18
0.25
0.25
0.00
0.00
0.00
0
Time (month)
20
40
60
0
100
200
300
0
100
200
300
b.
Time (month)
Time (month)
log10(HR)
1.0
OS
IL2RA
0.5
FOXP3
0.0
-0.5
DFS
IL2RA
-1.0
FOXP3
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
CTLA4
OS
TNFRSF9
CCR8
FOXP3
CTLA4
DFS
TNFRSF9
CCR8
FOXP3
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
addition, IL2RA had an adverse prognostic factor role in lung cancer and AML and a protective prognostic role in ovarian cancer, uveal mela- noma, multiple myelomas, and glioblastoma (Table S3).
Regarding the effector Treg signature, our findings showed that CCR8 is an adverse prognostic factor in breast cancer (p < 0.01, HR > 0) although it does not appear a protective aspect according to significantly correct results. Additionally, CTLA-4 is an adverse prognostic factor in skin melanoma, colorectal cancer, brain cancer, bladder cancer (tran- sitional cell carcinoma), blood cancer (AML), lung adenocarcinoma and breast cancer (p < 0.01, HR > 0) and a protective prognostic factor in colorectal cancer, lung cancer, blood cancer and uveal melanoma. FOXP3 is an adverse protective factor in ovarian cancer, brain cancer, uveal melanoma, lung cancer, colorectal cancer, and a protective prognostic factor in colorectal cancer as well, ovarian cancer and lung cancer. In addition, TNFRSF9 is an adverse prognostic factor in lung cancer and a protective prognostic factor in colorectal cancer, breast cancer and lung cancer (Table S3).
3.3. Pathway activity
Next, we investigated the activity of the two Treg signatures across 10 cancer-related pathways, and compared it between signatures with
high and low expression. Regarding the resting Treg signature we found that increased FOXP3 mRNA levels might have a strong activation on apoptosis (34 %), epithelial-mesenchymal transition (EMT) (22 %) and hormone ER (19 %) pathways in pan-cancer. FOXP3 expression could also have an inhibitory effect on the receptor tyrosine kinase (RTK) (16 %), the hormone androgen receptor (AR) pathway (16 %), the DNA damage response pathway (12 %) and the phosphoinositide 3-kinase (PI3K)/protein kinase B (PKB or AKT) (12 %) pathway in pan-cancer. Additionally, we found that high IL2RA mRNA levels could have an activating effect on apoptosis (38 %), EMT (34 %) and the hormone ER pathway (25 %). In contrast IL2RA mRNA levels might have an inhibi- tory effect on the DNA damage response (19 %), hormone AR (16 %) and tuberous sclerosis protein complex/mechanistic target of rapamycin (TSC/mTOR) (12 %), PI3K/AKT (9 %) and RTK (9 %) pathways (Fig. 5a and Table S4).
As for the effector Treg signature, TNFRSF9 revealed a putative activating effect on EMT (38 %), apoptosis (31 %) and hormone ER (19 %) pathways. On the other hand, TNFRSF9 could have an inhibitory effect on the RTK (16 %), DNA damage response (12 %), hormone AR (12 %) and PI3K/AKT (12 %) pathways. As for the resting Treg T cells signature, FOXP3 presented similar percentages of activating and inhibitory effects on these pathways. CTLA-4 mRNA expression could
a.
Resting Treg cells
b.
Effector Treg cells
TNFRSF9
31
0
3
3
3
12
38
0
0
12
19
6
3
12
3
3
3
16
0
6
IL2RA
38
0
6
6
3
19
34
0
3
16
25
6
3
9
9
6
9
9
3
12
FOXP3
34
0
6
9
3
12
22
0
6
16
19
6
3
12
0 6
3
16
3
6
CTLA4
50
0
6
0
6
12
31
0
6
12
25
3
3
3
3
6
3
16
6
0
FOXP3
34
0
6
9
3
12
22
0
6
16
19
6
3
12
0
6
3
16
3
6
CCR8
16
0
3
12
16
3
0
12
19
3
3
6
3
9
6
0
Apoptosis_A
Apoptosis_I
CellCycle_A
CellCycle_1
DNADamage_A
DNADamage_I
EMT_A
EMT_J
Hormone AR_A
Hormone AR_
Hormone ER_A
Hormone ER_I
PI3KAKT_A
PI3KAKT_
RASMAPK_A
RASMAPK_J
RTK_A
RTK_J
TSCmTOR_A
TSCmTOR_J
Apoptosis_A
Apoptosis_J
CellCycle_A
CellCycle_
DNADamage_A
DNADamage_I
EMT_A
EMT_I
Hormone AR_A
Hormone AR_J
Hormone ER_A
Hormone ER_I
PI3KAKT_A
PI3KAKT_1
RASMAPK_A
RASMAPK_J
RTK_A
RTK I
TSCmTOR_A
TSCmTOR_I
Percent 19 Inhibit
0
38
Activate
C.
d.
Activity of EMT pathway between high and low IL2RA expression groups in BLCA
Activity of EMT pathway between high and low CTLA4 expression groups in BLCA
10
10
Pathway activity
FDR = 7.4e-18
score
Pathway activity
FDR = 6.4e-13
5
score
5
0
0
-5
-5
Higher expr.
Lower expr.
Higher expr.
Lower expr.
Activity of DNA Damage pathway between high and low IL2RA expression groups in BLCA
Activity of DNA Damage pathway between high and low CTLA4 expression groups in BLCA
Pathway activity
FDR = 3.5e-04
FDR = 1.6e-03
2.5
Pathway activity
2.5
score
0.0
score
0.0
-2.5
-2.5
-5.0
-5.0
Higher expr.
Lower expr.
Higher expr.
Lower expr.
Activity of Apoptosis pathway between high and low IL2RA expression groups in BLCA
Activity of Apoptosis pathway between high and low CTLA4 expression groups in BLCA
6
FDR = 6.3e-12
6
FDR = 1e-16
Pathway activity
4
4
score
Pathway activity
2
score
2
0
0
-2.
-2
-4
Higher expr.
Lower expr.
-4
Higher expr.
Lower expr.
have a strong activating effect on apoptosis (50 %), EMT (31 %) and hormone ER (25 %) pathways in pan-cancer. In contrast CTLA-4 could have an inhibitory effect on RTK (16 %), DNA damage response (12 %) and hormone AR (12 %) pathways (Fig. 5b and Table S4). Indicative examples of the pathway activity scores in different pathways, between high and low IL2RA (or CTLA-4) expression groups in bladder cancer, are shown in Fig. 5c and d.
Moreover, CCR8 expression presented a putative activating effect on the hormone ER (19 %), apoptosis (16 %) and EMT (16 %) pathways, and a putative inhibitory effect on the DNA damage response (12 %), hormone AR (12 %) and RTK (9 %) pathways in pan-cancer (Table S4).
For instance, examining the resting Treg signature in bladder cancer, high FOXP3-expressing tumors had significantly higher activity scores in the pathways of apoptosis (FDR = 4.6 x 10-10) and EMT (FDR = 1.8 x 10-11), compared to low expressing tumors. However, low-expressing FOXP3 tumors in BLCA presented higher pathway activity scores in
the hormone AR (FDR = 3.7 x 10-5) and RTK (6.8 x 10-6) pathways. Furthermore, BLCA tumors with high and low IL2RA expression, had the same results for the corresponding pathways as the FOXP3 gene. Addi- tionally, BLCA tumors with high IL2RA expression, revealed a reverse pattern in apoptosis compared to the DNA damage response pathway, similar to EMT compared to the hormone AR pathway. On the other hand, BLCA tumors with high FOXP3 expression, presented a reverse pattern in apoptosis compared to the RTK pathway (Table S4).
Furthermore, we noticed that high TNRFSF9-expressing bladder tu- mors had higher PAS in apoptosis (FDR = 5.86E-12), while those with lower TNRFSF9 expression had higher PAS in DNA damage response (FDR = 0.0001). Analyzing the results of FOXP3 and CTLA-4, we found that high levels of both genes revealed higher PAS in EMT (for FOXP3, FDR = 1.84E-11; for CTLA-4, FDR = 6.43E-13), while their low expression was correlated with higher activity scores in hormone AR (for FOXP3, FDR = 3.74E-05; for CTLA-4, FDR = 1.14E-08), in contrast to
a.
Resting Treg cells
b.
Effector Treg cells
Correlation between expression and immune infiltrates in ACC
Correlation between expression and immune infiltrates in ACC
Correlation
CTLA4
0
-0.8
IL2RA
0.0
CCR8
o
0
0
0.8
FDR
⇐ 0.05
>0.05
TNFRSF9
0
0
0
FOXP3
FDR
C
C
0
0.05
0.01
FOXP3
O
o
0
0
0.001
⇐ 0.0001
InfiltrationScore
iTreg
CD4_T
CD8_T
Cytotoxic Exhausted
MAIT
Macrophage
NK
NKT
Tth
Th2
Central_memory
DC
Effector_memory
Gamma_delta
Monocyte
Th17
Tr1
nireg
Bcell
CD4_naive
CD8_naive
Neutrophil
InfiltrationScore
ITreg
CD4_T
CDB_T
Cytotoxic Exhausted
h
MAIT
Macrophage
NK
NKT
Tm
Thị
ThZ
Central_memory
DC
Effector_memory
Gamma_delta
Monocyte
Th17
Trt
nireg
Bcell
CD4_naive
CD8_naive
Neutrophil
C.
IL2RA mRNA expression
ACC
Cor. = 0.69
.800
IL2RA mRNA expression
ACC
Cor. =- 0.57
IL2RA mRNA expression
800
FDR = 2.1e-06
ACC
Cor. = 0.62
800
siol
ACC
Cor. = 0.53
FOR = 2.5e-10
FDR = 8.3e-08
FOR = 1.60-05
$00
5DO
600
press
f500
00
400
00
NAe
00
00
DO
00
200
IL2RA
0
—
0
Q
0
0.0
Cytotoxic infiltrate score”
0.6
“Bcell infiltrate score (ImmüCellAI)
0.0
CD8 T infiltrate score
0.1
0.2
0.3
Infiltration Score infiltrate ‘Score
0.9
IL2RA mRNA expression
800
ACC
·
Cor. = 0.38
-800
IL2RA mRNA expression
ACC
Cor. = 0.45
800
.
FDR = 1.1e-03
IL2RA mRNA expression
ACC
Cor. =- 0.43
800
IL2RA mRNA expression
FDR = 6.6e-03
FOR = 4.5e-03
ACC
Cor. = 0.43
600
600
FDR = 2.1e-03
00
000
800
100
200
3
00
..
500
0
0
-
0
0
0.0
MÅIT infiltrate score03
0.0
d.
Macrophage infiltrate score
0.00
CD8 naive infiltrate score
O.OS
0.10
0.15
0.20
0.00
Exhausted infiltrate score
0.20
.40
CCR8 mRNA expression
ACC
Cor. = 0.37
FDR = 5.36-03
CCR8 mRNA expression
ACC
Cor. = 0.58
FDR = 6.4e-06
CCR8 mRNA expression
ACC
Cor. = 0.36
-40
FOR = 7.40-03
ACC
Cor. = 0.49
FDR = 1.1e-04
So
mRNA express
30
30
20
O
NO
0
0
10
10
H
CCR8
0
0
0
Cytotoxic infiltrate score
0.00
0.05
iTreg infiltrate score
0.10
0.15
0.20
0.25
0.0
CD8
T infiltrate score
03
0.0
Tfh infiltrate score
02
0.3
0.4
CCR8 mRNA expression
ACC
Cor. = - 0.4
CCR8 mRNA expression
FDR = 1.9c-03
ACC
Cor. = 0.53
CCR8 mRNA expression
120
·
ACC
Cor. =- 0.71
CCR8 mRNA expression
120
Cor. =- 0.66
FDR = 1.3e-05
FDR = 1.8c-09
ACC
FDR = 6.1e-09
·
30
30
80
00
0
20
4,0
:
40
0
10
Q-
·
0
0
0.00
Neutrophil infiltrate score
0.0
Th1 infiltrate score
0.1
0.2
0.3
0.4
0.000
CD4 “naive infiltrate score
0.00
Bcell infiltrate score
005
110
0.15
low expressing tumors. High CCR8 expression levels were associated with high PAS in EMT (FDR = 3.49E-12), while low CCR8-expressing tumors presented a higher activity score in the DNA damage response pathway (FDR = 0.008) (Table S4).
Using Enrichr we identified several key pathways associated with FOXP3 and IL2RA (resting Tregs) as well as with FOXP3, CTLA-4, CCR8 and TNFRSF9 (effector Tregs), including cytokine-cytokine receptor interaction (effector Tregs) and Th17 cell differentiation (resting Tregs) (KEGG 2021 Human). We then constructed a Protein-Protein Interaction (PPI) Network for CCR8 and is ligands and assessed their functional roles in immune regulation and tumor biology using the STRING database. Known and predicted PPI data for CCR8 were retrieved focusing on high- confidence interactions (interaction score >0.7) (Fig. S2). To further understand the biological pathways associated with CCR8 and its interaction partners, we found that the nodes of the afore-mentioned network participate mainly in chemokine receptor bind chemokines, peptide ligand-binding receptor, class A1 (Rhodopsin-like receptors), GPCR ligand binding, signaling by GPCR, and interleukin-10 signaling, among others.
As regards the Gene Ontology enrichment, these genes were involved in the regulation of T cell tolerance induction (GO Biological Process), cytokine receptor activity, G protein-coupled chemoattractant receptor activity, histone acetyltransferase binding, chemokine receptor activity, NF-kappaB binding and histone deacetylase binding (GO Molecular Function).
3.4. mRNA expression and immune infiltration
We then investigated the correlation between FOXP3 and IL2RA expression and infiltration of 24 immune cells in pan-cancer. We found significant correlations (p < 0.01 and FDR<0.01) between IL2RA expression and infiltration of iTreg, CD8_T, cytotoxic T cells, NK, NKT, Tfh, Th1 and Th2 cells, as well as the infiltration score in ACC (Fig. 6a). On the other hand, IL2RA expression correlated negatively with B cells, CD4 naïve and neutrophils in this tumor type. Compared with IL2RA results, FOXP3 expression was significantly correlated (FDR<0.01) with infiltration of central memory, CD4 T, CD8 T, exhausted cells and macrophages, among others, while it was negatively correlated with CD4 naïve, CD8 naïve, neutrophils and Th17 in bladder cancer (Table S5).
We used the same method to examine the correlation between FOXP3, CCR8, CTLA-4 and TNFRSF9 expression and immune infiltration in pan-cancer. We observed significant positive correlations between CTLA-4 mRNA levels and tumor infiltration of iTreg, NKT, Tfh, Th1, nTreg, CD8 T, cytotoxic, Th2, exhausted cells and NK cells and effector memory; while we found negative correlations with Th17, B cell, CD4 naive and neutrophil infiltration in ACC (Fig. 6b and c). On the other hand, CCR8 mRNA expression was positively correlated with iTreg, Tfh and Th1 infiltration and negatively correlated with infiltration of B cells and neutrophils. TNFRSF9 mRNA expression was correlated with iTreg infiltration in ACC. The correlation between FOXP3 expression and immune infiltration was similar both in resting and effector Tregs (Table S5).
In addition, we explored the expression of all genes in six indepen- dent single-cell RNA-seq datasets (colorectal, breast, bladder, esopha- geal, kidney and lung cancers) (Fig. 7a and Table S6). Both in breast (BRCA_GSE110686) and colorectal cancers (CRC_GSE139555) FOXP3, TNFRSF9, CTLA-4, CCR8 and IL2RA were mainly expressed in Tregs, monocytes, plasma cells and secondarily in CD8Tex and T proliferation cells (Fig. 7b, c and Table S6). In bladder cancer (BLCA_GSE130001) IL2RA, FOXP3, CCR8 and CTLA-4 were not expressed by any type of immune cell; while TNFRSF9 was expressed lowly in epithelial (stromal) cells (Fig. 7d and Table S6). In esophageal tumors, all markers were mainly expressed in Treg cells, but TNFRSF9 and CTLA-4 were also expressed in CD8 Tex cells (Fig. 7e). In kidney cancer IL2RA and CTLA-4 were expressed in DCs and TNFRSF9 in mast cells. CCR8 and FOXP3
were not expressed either in DCs, mast cells or mono/macro cells (Fig. 7f and Table S1). Last, in non-small cell lung cancer TNFRSF9 and CTLA-4 were expressed in CD8 Tex cells, while IL2RA, FOXP3 and CTLA-4 in Tregs. On the other hand, CCR8 was not expressed in any type of im- mune cells (Fig. 7g and Table S6).
Subsequently, we investigated the expression of Treg-associated genes (FOXP3, CTLA-4, IL2RA, CCR8 and TNFRSF9) in breast cancer (BRCA_GSE110686), colorectal cancer (CRC_GSE139555), kidney can- cer (KIPAN_GSE226997) and non-small cell lung cancer (NSCLC_EM- TAB6149) using spatial transcriptomics analysis. Overall, our analysis revealed distinct spatial and cellular patterns. In specific, in breast cancer we noted scattered gene expression, with FOXP3 having the broadest range but low levels overall. CTLA-4 and IL2RA showed the highest expression, peaking at 0.7, with CTLA-4 displaying slightly broader expression. CCR8 was largely absent, except for a single high- expression cell (Fig. 8a). In colorectal cancer, FOXP3, CTLA-4, IL2RA, and CCR8 were highly expressed in very few cells (2-5 per gene), whereas TNFRSF9 was expressed at lower levels across many cells, peaking at around 0.7 (Fig. 8b). In kidney cancer, FOXP3 and CCR8 were absent, CTLA-4 exhibited consistent expression around 0.5, IL2RA had a smaller range but higher levels than CTLA-4, and TNFRSF9 displayed the greatest range, peaking around 1.0 (Fig. 8c). Last, in NSCLC FOXP3 was expressed at low levels in most cells, but showed occasional peaks (1.5-2.0). CTLA-4 demonstrated broader expression, with many cells expressing around 0.5-1.0, while IL2RA was concentrated in specific regions, varying from high to low levels. TNFRSF9 was predominantly low, and CCR8 exhibited the widest spatial expression across the tissue, albeit at moderate levels (Fig. 8d).
In addition, we analyzed Treg subpopulations and their develop- mental trajectories in breast cancer. Nevertheless, we encountered substantial challenges due to limitations in the datasets, particularly the insufficient resolution and utility of key markers required to reliably distinguish distinct Treg subgroups. CD45RA, typically associated with naive Tregs, and ICOS, indicative of effector Tregs, lacked the resolution necessary to define these subpopulations with confidence. Similarly, CCR8, a marker considered to be specific for effector Tregs, showed inconsistent expression across different cancer types, reducing its utility for reliably distinguishing effector Tregs. Furthermore, while IL2RA, FOXP3 and CTLA-4 were among the most commonly co-expressed markers, they did not show clear differences between the identified subgroups, limiting their capacity to distinguish functional or develop- mental heterogeneity within the Treg population. Additionally, LAG3, which is a defining marker of exhausted Tregs, exhibited little to no expression, further complicating efforts to characterize terminally differentiated or exhausted Tregs. These issues resulted in inconsistent and fragmented clustering patterns across all scenarios, complicating the identification of biologically distinct subgroups and meaningful trajectories (Fig. S3).
3.5. SNVs and immune infiltration
Initially, we investigated the IL2RA and FOXP3 mutation rates across various cancer types, and found that 15 % of skin melanomas (SKCM), 9 % of uterine corpus endometrial carcinomas (UCEC), and 6 % of lung adenocarcinomas (LUAD) had IL2RA SNVs. The FOXP3 mutation rate was even higher in UCEC (18 %) and lower in GBM and BRCA tumors (5 % each). These SNVs affect both the activation and repression of the IL2RA gene locus, while activation is affected in a higher level by FOXP3 SNVs (Fig. 9a and b).
In the effector Treg T cells signature, we found mutations affecting the activation and inhibition TNFRSF9 and CTLA-4 respectively; how- ever, the activation was stronger in CCR8 and FOXP3 for specific cancer types. For example, the mutation rate of CCR8 was 17 % in SKCM and 16 % in UCEC. In addition, 12 % of UCEC and 11 % of STAD tumors bared mutations in CCR8. A significant percentage of UCECs had mu- tations in FOXP3 (18 %) and CTLA-4 (12 %) (Fig. 9c and d). In general,
a.
CRC GSE139555
BRCA_GSE110686
BLCA GSE130001
ESCA GSE160269
KIPAN GSE154763
NSCLC EMTAB6149
Celltype (major-lineage)
Celltype (major-lineage)
CDITcom
COST
Bodothal
.
Alveolar
Trog
Plasma
Celltype (major-lineage)
CDTOOT
Tcom
MonoMacro
DC
Tprol
COST
Epithelial
Plasma
Siasma
Tprolt ca
İTprbir
c/Macro
fibroblasts Celltype (major-lineage)
Mono/Macm
B
COSTex
CDOTe
CDATcon
ECDET
Endothelial
ono/Macro
Fibroblasts!
fast
9
Fibroblasts
Malignant
Celtype (major-lineage)
Mast
CQ4Toony
Myofibroblasts
opdat
MonoIN
acro
Celltype (major-lineage)
Maut
CD47com
Endothelial
proif
Endothelal
MonoMacro Plasma
COST
COBTAR
Myofibroblasts
Fitwobilanits
Mast
Myofibroblas’s
CD4Too
Mast
CDBTex
an
gehdothelial
·Treg
lignant
Mono/Macro
Mfana/Macro
Biano Macro
Viast
Myofibroblasts
Mono/Macro
8Tex
Fibroblasts
NC
indothelial
Atteolar
Toroll
Treg
· Treg
b.
CCR8
TNFRSF9
IL2RA
FOXP3
CTLA4
35
40
4D
35
-25
-as
-30
-35
30
30
-30
-20
25
-25
25
-75
-15
-70
-20
20
-20
-1.5
1.5
-10
-1.5
-15
-10
-19
-10
L.D
-05
-05
-05
-05
-05
-00
-OD
-05
-00
OD
C.
CCR8
TNFRSF9
IL2RA
FOXP3
CTLA4
4.0
4.0
30
40
20
-35
-35
-35
-25
-30
-35
-30
-1.5
-25
-25
-20
-25
-20
-20
-1.0
-15
20
1.5
-13
-1.5
10
-0.5
-1.0
1.0
LD
-0.5
-0.5
-05
-0.5
-0.0
-0.0
-0.0
-00
-OLD
d
CCR8 is NOT detected in the dataset
TNFRSF9
IL2RA is NOT detected in the dataset
FOXP3 is NOT detected in the dataset
CTLA4 is NOT detected in the dataset
1.0
1.0
1.0
1.0
2.00
-0.8
1.75
-0.8
0.8
-0.8
-1.50
0.6
1.25
-0.6
-0.6
0.6
1.00
-04
-0.4
-0.4
0.4
-a.75
0.2
-5.50
-0.2
-0.2
-0.2
0.25
0.0
-5.00
0.0
-0.0
-0.0
e.
CCR8
TNFRSF9
IL2RA
FOXP3
CTLA4
4.0
-3.0
4.0
4.0
4.0
3.5
-3.5
-35
-2.5
-3.5
3.0
-3.0
-3.0
-30
-2.0
2.5
2.5
2.5
-2.5
-1.5
-2.0
-2.0
-2.0
2.0
1.5
-1.5
-1.5
-1.0
-1.5
-10
-1.0
-1.0
-10
0.5
-0.5
0.5
-0.5
-0,5
f.
0.0
0.0
0.0
0.0
0.0
CCR8
TNFRSF9
IL2RA
FOXP3
CTLA4
1.75
2.5
2.00
$2.5
2.5
1.50
-1.75
-2.0
2.0
1.25
-2.0
-1.50
-1.00
-1.5
-1.25
-1.5
-1.5
-1.00
0.75
-1.0
-10
-0.75
-10
-0.50
-0.5
-0.50
-0.5
0.5
-0.25
-0.25
0.00
-0.0
-0.0
-0.00
-0.0
g.
CCR8
TNFRSF9
IL2RA
FOXP3
CTLA4
25
-3.5
40
-30
-30
-35
4
-20
30
-25
-25
-15
-25
-20
20
3
20
-15
-15
=15
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15
=10
10
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-05
-1
.
-DS
-65
03
.
-
-0.0
-0.0
-00
-0.0
·
a.
ident
nCount_Spatial
nFeature_Spatial
FOXP3
0.0
0.5
1.0
CTLA4
0.00 0 25 0 50 0.75 1
LZRA
0.0
0.5
1.0
·
0
8000
5
3
11
40000
4
O
nCount_Spatial
6000
nFeature_Spatial
@
5
60000
8000
umap_2
4
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10000
NO06
0000
4000
[
4000
10
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20000
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spatial_CCRa
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INFRSF9
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2
15
2000
3
14
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15
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0
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-5
18
10
-5
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10
umap_1
Identity
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b.
nCount_Spatial
nFeature_Spatial
FOXP3
CTLA4
30 02 04 0 00 02
0.4 0
LZRA
DI
02 04 0.8
ident
60000
3
…
0
7500
1
nCount_Spatial
nFeature_Spašal
2
40000
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10000
5000
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5000
umap_2
O
5
20000
20000
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CCRB
00 02 04
TNFRSF9
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-3
E
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0
0
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-6
Identity
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umap_1
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nFeature_Spatial
FOXP3
CTLA4
LZRA
3+
¢
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1.0
0.00 0.25/0.50 0.75 1/00
20000
6000
2
nCount_Spatial
nFeature_Spatial
ident
15000
8000
1
1
20000
…
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0
16000
4000
10000
10000
4000
umap_2
0
3
2
5000
2000
2000
0
CCR8
· 4
5000
TNFRSF9
00 04 00 1.2 1.6
-1
=
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-8
-4
0
4
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umap_1
nCount_Spatial
nFeature_Spatial
FOXP3
00 0.5 1.0 1.5 1
CTLA4
0.0 0.5
ILZRA
ident
00 05 10 15 20 25
…
0
150000
10000
4
nCount_Spatial
nFeature_Spatial
100000
150000
12500
0
100000
10000
TOco
umap_2
0
60000
5000
5000
50000
2000
CCRB
OD 0.4 08 12
INFRSF9
00 05 1.0 1.5 20 25
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-4
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10
Identity
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umap_1
the low mutation rates of both gene signatures (<1-2 %), as well as the mutual exclusivity in the genes, was validated in 4 independent cohorts (Fig. S4).
Furthermore, we investigated the infiltration of immune cells in mutant and wild-type FOXP3 or IL2RA tumors. We found high levels of infiltrated Th1 cells in FOXP3-mutant (logFC = 0.615, p = 0.017) and IL2RA-mutant UCECs (logFC = 0.711, p = 0.017), beyond an increased level of Th17 infiltrated cells in wild-type IL2RA UCECs (logFC = -1,562, p = 0.002). Th1 cells can kill tumor cells via the release of cytokines that activate death receptors on the surface of the tumor cells [52]. Th17 cells present plasticity potential, which allows them to change into Th17/Tregs and Th17/Th1 T cells. Th17 cells set pathways and interact with other cells in the TME and as a result, Th17 cells prevail over tumors [53].
Moreover, we noticed a significant enrichment of exhausted T cells both in FOXP3-mutant (p = 0.011) and IL2RA-mutant UCECs (p= 0.01), as well as an increased level of cytotoxic cells in IL2RA-mutant UCECs (p = 0.01). The exhaustion of T cells prevents optimal control against tu- mors and infection. The pathways involved in exhaustion are not totally defined; however, the molecular delineation of T cell exhaustion present promising therapeutic opportunities [54]. Cytotoxic T cells kill the cancer cells and eliminate the tumor. Their lethal function is based on the release of two cytotoxic proteins, the granzymes and the pore-forming perforin [55-59]. The above findings suggest that FOXP3 and IL2RA mutations is correlated with the infiltration of specific
immune cells in UCECs.
As for the effector Tregs, we observed a significant decreased level of CD4 naive T cells in TNFRSF9-WT UCEC tumors (p = 7.73E-06), as well as in CCR8-WT ovarian cancers (p = 1.09E-06). Naïve CD4 T cells can be activated and proliferate rapidly into effector T cells, avoiding apoptosis, and continuing as quiescent memory cells [60].
3.6. CNVs and immune infiltration
First, we explored the number of CNVs (heterozygous and homozy- gous) affecting the FOXP3 and IL2RA loci in pan-cancer (Table S7). Our findings revealed the existence of many heterozygous CNVs affecting both genes, mainly in UCS, OV, GBM, BLCA and BRCA tumors. Furthermore, a high percentage of FOXP3 and IL2RA amplifications and deletions characterized several tumors, such us ACC, UCS, BLCA, UCEC, BRCA, OV, STAD, LUAD, LIHC, SARC and ESCA tumors.
Additionally, we investigated the corresponding percentage of CNVs in effector Tregs, and found a high number of heterozygous CNVs correlating with all the genes in this signature in pan-cancer (Table S7). Most CNVs were detected in USC, SKCM, OV and ESCA, referring to heterozygous amplifications in FOXP3, CCR8, TNFRSF9 and CTLA-4. On the other hand, high percentage of heterozygous deletions of TNFRSF9 were observed in BRCA, LGG, UVM, ESCA, MESO, LUSC, PCPG, CHOL and KICH. We also detected a high percentage of heterozygous deletions affecting CCR8 in UVM, ESCA, MESO, KIRC, HNSC, LUSC PCPG and
a.
Resting Treg cells
b. Mutation freq. (%)
Effector Treg cells
UCEC (n=531)
SKCM (n=468)
DLBC (n=37)
READ (n=149)
COAD (n=407)
LUAD (n=587)
UCS (n=57)
GBM (0=403)
STAD (n=439)
PAAD (n=178)
ESCA (n=185)
CESC (n=291) OV (nm412)
SARC (n=239) BLCA (n=411)
BRCA (n= 1026)
HNSC (n=509)
LINC (n=365)
LUSC (n=485)
KIRP (n=282) KIRC (n=370)
THCA (n=500) ACC (n=92)
KICH (n=66)
LGG (n=526) TGCT
UCEC (n=531)
SKCM (n=468)
STAD (n=439)
COAD (n=407)
LUAD (n=567)
READ (n=149)
SARC (n=239) GBM (0=403)
OV (n=412)
ACC (n=92)
LUSC (n=485)
BLCA (n=411)
UCS (n=57)
HNSC (n=509)
PHAD (n=178)
BRCA (n=1026)
CESC (n=291)
LINC (n=365)
LGG (n=526)
ESCA (n=185)
KIRP (n=282)
KIRC (n=370)
THCA (n=500)
PRAD (n=498)
TGCT (n=151)
CCR8
16
17
5
4
6
3
3
5
4
1
0
1
1
1
1
O
IL2RA
9
15
1
1
4
S
1
2
3
1
1
3
2
2
2
₹
1
2
1
0
0
TNFRSF9
12
&
11
6
7
1
2
1
2
3
2
4
1
2
1
1
1
FOXP3
18
3
2
4
4
2
2
5
2
0
1
1
1
5
0
1
1
1
1
0
FOXP3
18
3
2
4
4
5
2
1
1
0
2
2
1
5
1
1
1
1
0
0
CTLA4
12
6
7
3
2
1
2
1
2
2
2
1
2
0
3
1
1
1
0
0
C.
d.
2
5
I
0
59
0
68
0
0
CCR8
32%
IL2RA
54%
TNFR$F9
29%
FOXP G
25%
FOXP3
50%
CTLA4
23%
Cancer_type
Cancer_type
Missense_Mutation
Frame_Shift_Ins
Cancer_type
Splice_Site
In_Frame_Del
Cancer_type
Frame_Shift_Del
· Multi_Hit
BLCA
DLBC
KIRC
LUSC
SARC
UCEC
Nonsense_Mutation
. BRCA
ESCA
KIRP
OV
SKCM
UCS
ACC
COAD
KIRC
LUAD
READ
THCA
M
CESC
GBM
LIHC
PAAD
STAD
BLCA
ESCA
KIRP
LUSC
SARC
UCEC
. COAD
HNSC
LUAD
READ
THCA
BRCA
GBM
LGG
.
OV
SKCM
UCS
. CESC
HNSC
LIHC
PAAD
STAD
CHOL. Our data presented a significant difference between the per- centage of heterozygous amplifications/deletions and homozygous amplifications/deletions; while homozygous CNVs affected to a lower level the Treg genes in pan-cancer.
To assess the mechanisms responsible for the deregulated expression of FOXP3 and IL2RA, we explored the existence of CNVs in pan-cancer. In BRCA we observed significant correlations between IL2RA CNVs and the gene’s expression, mainly in exhausted T cells (corr. = 0.14, p = 19.95x10-6, FDR =1.18x10-5), as well as in Th1 cells (corr. = 0.14, p = 2.07x10-6, FDR = 1.67x10-5), B cells (corr. = 0.22, p = 4.08x10-13, FDR = 8.86x10-12), cytotoxic T cells (corr. = 0.11, p = 0.0004, FDR = 0.002), dendritic cells (DC) (corr. = 0.14, p =4.29x10-6, FDR<0.0001) and the infiltration score (corr. = 0.15, p = 7.01x10-7, FDR = 1.1x10-5). CNVs affecting IL2RA were negatively correlated with its expression in CD4 T cells (corr. =- 0.10, p=0.0007, FDR =0.002), CD8 naive cells (corr. = - 0.15, p = 5.12x10-7, FDR = 1.81x10-6) and mucosal-associated invariant T (MAIT) cells (corr. = - 0.12, p = 4.52×10-5, FDR = 0.002) in BRCA.
We then followed the same method to explore the correlations be- tween CNVs affecting effector Treg markers and immune infiltrates. More specifically, we found various correlations between them in ACC, BLCA, BRCA and HNSC, among other tumors. For example, in BLCA CCR8-affecting CNVs correlated with the gene’s expression in B cells (corr. = 0.2, p = 4.12x10-5, FDR = 0.004), CD4 T cells (corr. = 0.22, p = 8,22x10-6, FDR = 0,002) and CD8 naive cells (corr. = 0.23, p = 2.65x10-6, FDR = 4.44x10-5). However, CCR8-affecting CNVs were negatively correlated with its expression in cytotoxic T cells (corr. = -0.19, p = 8.45x10-5, FDR =0.001), DCs (corr. =- 0.20, p= 3.66x10-5, FDR = 0.002), exhausted T cells (corr .=- 0.17, p<0.001, FDR=0.008) and Th1 cells (corr. = - 0.19, p = 7.11 x10-5, FDR = 0.001) (Table S7).
Dendritic cells originate from progenitors in the bone marrow and play a significant role in tumorigenesis and the orchestration of immune response by T cells. Normally, they function as antigen presenting cells, activating T and B cells [61]. Generally, tumor-associated immune cells can process tumor-antagonizing or tumor-promoting functions to target and kill the cancer cells in the preliminary stages of tumorigenesis. In addition, through a variety of mechanisms, they present the ability to
inhibit the cytotoxic functions of tumor-antagonizing immune cells [61, 62].
Naïve CD8 T cells are found mainly in the bloodstream, the spleen and the lymph nodes, where they explore the entire body for antigen- presenting DCs [63]. The quantification of the immune cell infiltration within tumors, allows us to predict the disease prognosis. For example, high levels of immune infiltrates are associated with better clinical outcomes in BRCA patients [64,65].
Overall, the above findings suggest that CNVs affecting the genes in the resting and effector Treg’ signatures, correlate with the infiltration of various immune cells in different types of cancer.
3.7. Methylation and immune infiltration
We then investigated the methylation levels of the two Treg signa- tures across different tumors. We found that FOXP3 is either non- methylated or hypomethylated in most cancer types. Similarly, IL2RA presented low or null methylation levels (beta values) in KIRC, BRCA and other tumors. The methylation difference of CCR8 between cancer and normal tissue, appeared to be zero in PAAD and COAD, while negative levels were observed in HNSC and KIRC. Likewise, the differ- ence for CTLA-4 methylation was negative in LUSC and LIHC compared to the normal tissues. Lastly, TNFRSF9 revealed zero or negative methylation difference in KIRC, UCEC and BRCA, among other tumors (Fig. 10a-d and Table S8). High IL2RA methylation was associated with better OS and DSS in KIRC (cg11733245 tag) (Fig. 10e), as well as in LAML (cg26316423), LGG (cg16949914) and STAD (cg26316423). In contrast, high IL2RA methylation was associated with worse survival in SKCM (cg26316423). High FOXP3 methylation was also associated with worse DSS in UCEC (cg06767008), but with better OS and DSS in UVM (cg04920616) (log-rank p < 0.001) (Table S9).
We then explored the association between methylation in the resting Treg markers (FOXP3/IL2RA) and infiltration of immune cells, across different tumors (Table S10). We found several correlations; for example, FOXP3 methylation was negatively correlated with central memory, and infiltration of CD8+ cytotoxic T cells, and other immune cells in THCA and SKCM. On the other hand, IL2RA methylation was
a.
Resting Treg cells
b. Effector Treg cells
Methylation difference in each cancer
Methylation difference in each cancer
FDR
o ⇐ 0.05
CCR8
☐
☐
☐
☐ ☐
☐
☐
☐ ☐
☐
☐
IL2RA
☐
☐
☐
☐
☐
☐
☐
☐
FDR
☐ 0.001
CTLA4-
☐ ☐
☐
☐
☐
☐
☐
☐
☐
☐
☐
⇐ 0.0001
Methy. diff(T-N)
TNFRSF9
☐
☐
☐
☐ ☐
☐
☐
-1
☐
☐
☐
☐
☐
FOXP3
☐
☐
— ☐
☐
☐
☐ ☐
☐
☐ ☐
0
FOXP3-
☐
☐ ☐
☐
☐ ☐
☐ ☐
COAD
PAAD
PRAD
KIRP
LUAD
BLCA
HNSC
UCEC
LUSC
LIHC
KIRC
1
BRCA
Types
THCA
PAAD
PRAD
KIRP
ESCA
COAD
BLCA
LUAD
UCEC
BRCA
LIHC
HNSC
LUSC
KIRC
Normal
C.
IL2RA methylation across TCGA cancer types
Tumor
1.00-
e.
V S Methylation (Beta value)
OS of IL2RA methylation in KIRC
75-
1.00
:50-
OS probability
0.75
25-
:
0.50
-
0.00-
-
1
0
-
-
-
-
-
-
ACC 3LC/ BRC/ SESC CHOI OAI LBC ESC/ GBN INS( <ICH ORC <IRF .AMI LGG LIHC .UAL .USC IES( OV MAL ‘CP( PRAI TEAL JAR( IKCI STAL “GC”HC/”HYM ICE( UCS UVM
Logrank P value = 0.0012
FOXP3 methylation across TCGA cancer types
0.25
Methylation (Beta,value).
1.00-
+ Higher meth., in=160
HE
Lower meth., n=161
75-
0.00
0
50
100
150
Time (month)
50-
DSS of IL2RA methylation in KIRC
25-
1.00
0.00-
-
ACC 3LC/ IRC/ SESC CHOI OAI LBC ESCA SBM INS( <ICH (RC <IRF .AMI LGG LIHC .UAL .USC IES( OV MAAL ‘CP( PRAI REAL IAR( IKCI STAL “GC” “HC/”HYN ICE( UCS UVM
Methylation (Beta value)
DSS probability
d.
0.75
CCR8 methylation across TCGA cancer types
1.00-
20.50
75-
Logrank P value = 0.00033
0.25
50-
+Higher meth., n=142
:
25-
0.00
+Lower meth., n=142
0
50
100
Time (month)
150
0.00-
ACC 3LC/ IRC/ SESC CHOI SOAI LBC ESC/ GBM INS( <ICH- CIRC <IRF .AMI LGG LIHC .UAL .USC MES( OV MAAL CP( ‘RAI REAL IAR( KCN STAL “GO” THC/ “HYN JCEC UCS UVM
CTLA4 methylation across TCGA cancer types
1.00-
Methylation (Beta value)
-
15
0
50
25-
-
-
-
-
0.00
ACC 3LC/ BRC/ SESC CHOIXOAI OLBC ESC/ GBN INS( <ICH (RC <IRF .AMI LGG _IHC UAL USC IES( OV MAL ‘CP( “RAI REAL IAR( IKCA STAL “GC”HC/ “HYN JCE UCS LIVM
TNFRSF9 methylation across TCGA cancer types
1.00-
Methylation (Beta value)
☐
L
75-
50-
0%
=
7
3
-
-7
0.00-
ACC 3LC/ SRC/ JESC CHOI SOAI LBC ESC/ GBM INS( <ICH- CIRC <IRF AMI LGG LIHC ,UAL .USC MES( OV MAAL CP( PRAI REAL SAR( IKCA STAL “GC”HC/”HYN JCE( UCS UVM
anti-correlated with the infiltration of most immune cells in THCA, BRCA, THYM, TGCT, LUSC, LUAD and CESC tumors (Table S10).
As for the effector Tregs, CCR8 methylation significantly correlated with the infiltration of B cells, CD4 T naïve, CD4 T, CD8 naïve, cytotoxic, DCs, exhausted, gamma delta (y8) T cells, macrophage, monocytes, NK, NKT, Th1, Th17, Th2 and iTreg T cells, as well as with the infiltration score in BLCA. Furthermore, TNFRSF9 methylation correlated signifi- cantly with infiltration of B cells, CD8 T cells, cytotoxic, NK, neutrophils, Th1, Th2 and iTreg T cells in ACC. In addition, CTLA-4 methylation was correlated with infiltration of CD8-naïve, central memory cells, cyto- toxic, DC, exhausted T cells, the infiltration score, MAIT, macrophage, NK, neutrophil, Tfh, Th1, Th17, Th2 and iTreg T cells, in BLCA (Table S10).
Overall, these findings highlight the correlation between methyl- ation of the Treg signature genes and immune cell infiltration in various cancer types.
Apart from methylation, we further explored other post-translational modifications, including phosphorylation and ubiquitination of FOXP3, CCR8 and IL2RA (Fig. S5). We found FOXP3 phosphorylation in Ser25, Ser33, Thr44, Thr115, Thr138, Ser142, Ser270, Ser274, Ser275, Tyr342, Ser41 and Ser422 residues, which are implicated in regulating the protein’s activity as a DNA-binding transcription factor. Phosphoryla- tion at Ser418 in the C-terminal DNA-binding domain of FOXP3 was previously shown to be dephosphorylated by protein phosphatase 1 (PP1) leading to impaired Treg cell function, which is associated with rheumatoid arthritis. [66]. On the other, key kinases such as GSK36 (Ser270-p, Ser274-p), Lck (Tyr342-p) and Pim1 (Ser422-p) have been shown to phosphorylate FOXP3 in-vitro, modulating its interactions with other proteins and influencing Treg differentiation and function [67]. FOXP3 stability can also be regulated by ubiquitination and acet- ylation at certain lysine residues leading to its degradation. We detected ubiquitination at Lys31, Lys200, Lys263, Lys250, Lys268, Lys356, Lys382, Lys393 and Lys416; as well as acetylation at Lys179, Lys252, Lys263 and Lys268. Of these, Lys250-Ub, Lys252-Ub, Lys263-Ub and Lys268-Ub within the protein’s leucine zipper domain were previously shown to affect FOXP3 DNA binding, transcriptional activation, and
proteasomal degradation [68].
Similarly, we found CCR8 phosphorylation at residues Tyr132, Ser334, Ser338, Ser339 and Ser340, which can influence receptor desensitization and internalization, both being crucial for regulating immune responses through calcium- and chemokine-mediated signaling [69]. In addition, we detected two downstream phosphorylation at residues Ser268 and Thr271 of IL2RA. Apart from phosphorylation, we did not detect any other PTMs affecting the function of CCR8 and IL2RA.
3.8. Drug sensitivity
Overall, we gathered the IC50 values of drugs from various cancer cell lines using the GDSC and CTRP databases, and correlated them with the corresponding mRNA expression of markers in the resting and effector Treg T cells.
We found that FOXP3 and IL2RA mRNA expression negatively cor- relates with sensitivity in Methotrexate and Pandacostat. Moreover, FOXP3 expression negatively correlates with all drugs in GDSC (MP470, YM201636, 5-Fluorouracil, AR-42, AZD7762, AZD8055, BHG712, BIX02189, Belinostat, CAY 10603, CEP-701, CUDC-101, I-BET-762, KIN001-244, KIN001-260, KIN001-270, Methotrexate, NG-25, Phen- formin, QL-XI-92, T0901317, THZ-2-49, TL-1-85, TL-2-105, TPCA-1, Tubastatin A, Vorinostat, XMD13-2 and Y-39983). Similarly, IL2RA mRNA expression was anti-correlated with sensitivity in 5-Fluorouracil, AZD7762, KIN001-260, Methotrexate, QL-XI-92, Vorinostat and XMD 13-2, among other drugs (Fig. 11a).
Regarding the markers of the effector Treg T cells, CCR8 expression was correlated with sensitivity in chemically modified Tetracycline (COL-3) and Chlorambucil. On the other hand, CTLA-4 expression was negatively correlated with sensitivity in COL-3. In contrast, FOXP3 and TNFRSF9 mRNA levels did not correlate with any of the above- mentioned drugs (Fig. 11b).
As regards sensitivity in the GDSC drugs, CCR8 expression was anti- correlated with sensitivity in all types of the investigated drugs, while TNFRSF9 mRNA levels were positively correlated with sensitivity in GSK1070916 and TL-2-105. In addition, CTLA-4 mRNA expression was
a.
Resting Treg cells
b.
Effector Treg cells
Correlation between CTRP drug sensitivity and mRNA expression
Correlation between CTRP drug sensitivity and mRNA expression
FDR
CCR8
FDR
> ⇐ 0.05
0
0.05
IL2RA
>0.05
0.01
FDR
0.001
0.05
TNFRSF9
0.01
Correlation
-0.2
0.001
⇐ 0.0001
0.0
Correlation
FOXP3
-0.3
0.2
FOXP3
FDR
0.0
o ⇐ 0.05
0.2
CTLA4
0 >0.05
ABT-737
methotrexate
pandacostat
COL-3
chlorambucil
Correlation between GDSC drug sensitivity and mRNA expression
Correlation between GDSC drug sensitivity and mRNA expression
TNFRSF9
O
O
0
O 0
0
0
O
C
O
O
O
C
O
O
0
0
C
C
0
O
0
0
FDR
O ⇐ 0.05
O >0.05
IL2RA
0
O
O
O
0
C
2
O
O
0
O
0
0
FDR
CTLA4
9
O
O
C
C
0
C
C
0
C
C
Man
0
O
C
D
C
5
O
0.05
0.01
0.001
⇐ 0.0001
FOXP3
0
D
0
O
0
O
O
C
O
0
C
0
0
0
0
C
C
C
0
Correlation
-0.3
FOXP3
O
0
3
C
0
0
2
0
0.0
CCR8
C
O
C
O
O
02
CI-1040
MP470
YM201636
5-Fluorouracu
AR-42
AZD7762
AZD8055
BHG712
BIX02189
Belinostat
CAY 10603
CEP-701
CUDC-101
-BET-762
KING01-244
KINDD1-260
KIN001-270 Methotrexate
NG-25
Phenformis
QL-XI-92
10901317
THZ-2-49
11-1-85
11-2-105
IPCA-
Tubastatin A
vorinostat
AMD 13-2
Y-39983
GSK1070916
418-Xg
CP466722
Genentech Cpd 10
KU-55933
11-2-105
ZM-447439
AT-7519
Ispinesib Mesylate
KIND01-102
KIND01-236
Methotrexate
NPK76-ITZ-
Naviociax
STF-82247
IL-1-85
Tubastatin
WZ.3105
BHG712 BMS34554
CAL-101
NG-25 CO-Xid
Phenformin
TAK-715
THZ-2-102-
IPCA-
Vorinostat
XMD13-2
ZSTK474
negatively corelated with sensitivity in Phenformin, THZ-2-102-1, TPCA-1 and Vorinostat. Finally, FOXP3 mRNA levels were negatively correlated with sensitivity in BX-912, TL-2-105, KIN001-102, Metho- trexate, TL-1-85, Tubastatin A, WZ3105, BHG712, NG-25, PIK-93, Phenformin, TAK-715, THZ-2-102-1, TPCA-1, Vorinostat and XMD13-2 (Fig. 11b).
Using AutoDock Vina, we docked natural ligands (CCL1, CCL22) and small-molecule inhibitors from PubChem. The docking analysis revealed strong binding affinities, with AZD8797 showing the highest binding energy (-9.3 kcal/mol). The interactions were characterized by hydrogen bonding with key residues (e.g., Glu32, Asp87, Phe170) and hydrophobic interactions.
3.9. Cytotoxicity assays
Vorinostat exhibited potent activity across all tested cell lines. The calculated IC50 values were 2.956 µM (R2 = 0.9871) for HT29 cells, 3.650 µM (R2 = 0.9416) for MCF7 cells, and 8.270 µM (R2 = 0.9664) for H460 cells. These findings are consistent with prior reports indicating Vorinostat’s efficacy in NCI-H460 cells [70], HT29 cells [71] and MCF7 cells [72-74] (Fig. 12a).
5-Fluorouracil showed variable cytotoxicity depending on the cell line. The IC50 was 7.072 µM (R2 = 0.8806) for HT29 cells and 72.23 µM (R2 = 0.8328) for H460 cells, whereas the IC50 was not converged in MCF7 cells. These findings align with data from Huang et al. [75] for H460 cells, and Mans et al. [76], Briffa et al. [77], and Bálintová et al. [71] for HT29 cells. Resistance to 5-Fluorouracil in cancer cells has been reported in studies, potentially due to p53-independent survival mech- anisms [78] or durable translational reprogramming that sustains cell plasticity [79], even though in other studies [80] an IC50 value could be calculated (Fig. 12b).
Methotrexate did not produce converged IC50 values within the tested concentration range in any of the cell lines, as the percentage of cell viability remained consistent across all tested concentrations. This suggests a lack of dose-dependent response, indicating possible resis- tance to MTX under the conditions employed in this study. This is consistent with literature suggesting adaptation and resistance in certain cell types, including HT29 cells [81,82] and low efficacy in breast cancer cells such as MCF7 [83,84]. This adaptation and resistance could be due to their differentiation into columnar absorptive and mucus-secreting cells [81] or overexpression of folate binding protein [82] among other mechanisms. These findings are also in line with the observations of Ferreira-Teixeira et al. [85], who reported similar results in bladder cancer cell lines HT-1376 and UM-UC3. Their study demonstrated uni- form viability percentages across varying MTX concentrations, high- lighting a resistance mechanism potentially mediated by the presence of cancer stem-like cells (Fig. 12c).
4. Discussion
Regulatory T cells are a main subgroup of CD4+ T cells that control peripheral existence to self- and allo-antigens [86]. Their presence is identified mainly from the expression of IL2RA (CD25) and FOXP3 genes [87]. Through their suppressive role, Treg cells play a decisive role in the escape of cancer cells from the anti-tumor effector T cells [88-92]. Dysregulation of resting and effector Treg cells has been recently noticed across different cancers.
Treg activation might not always correlate directly with prognosis but is still relevant for understanding tumor immunology. Although it might be more relevant in cancers with extensive immune infiltration, understanding the baseline activation state of Tregs, particularly through our comprehensive multi-omics approach, offers valuable
a.
HT29 Vorinostat
MCF7 Vorinostat
H460 Vorinostat
150
150
150
125
125
T#25
.
%Viability
100-
%Viability
100
% Viability
100-
75-
75-
75-
50
50
50
25-
25
25
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
log Concentration (IM)
log Concentration (UM)
log Concentration (UM)
b.
HT29 5FU
MCF7 5FU
H460 5FU
150
150
100
125
125
80%
%Viability
100
% Viability
100
% Viability
60
75-
75-
50-
50
40-
25-
25
20-
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
log Concentration (uM)
log Concentration (UM)
log Concentration (UM)
C.
HT29 MTX
MCF7 MTX
H460 MTX
100-
100
100-
75-
.75-
80-
% Viability
% Viability
% Viability
60
50
50
40-
25.
25
20-
0
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
log Concentration (UM)
log Concentration (UM)
log Concentration (UM)
insights into immune regulation even in tumors with lower infiltration.
Here we explored the expression profiles, mutations (SNVs and CNVs), methylation profiles, infiltration of immune cells and sensitivity in various drugs of two signatures, characterizing the resting and the effector Treg cells, respectively. In both types of Treg cells, FOXP3 is a main marker. Although other markers can distinguish Tregs from con- ventional T cells, such as CD127 [93,94], which is lowly expressed on Tregs. Here, we selected FOXP3 based on its broad use and significance in prior studies of Treg function in cancer. It is expressed in tumors of epithelial and mesenchymal origin, such as melanoma, pancreatic, breast, prostate, lung, gastric and thyroid cancers [95-98]. Our results show that FOXP3 is overexpressed in COAD, HNSC, STAD, BRCA, KIRC and lung tumors [99-101].
In T cell malignancies, the induction of FOXP3 activates the TGF- ß/Smad signaling pathway, promoting the development of pathogenic Treg-like cells, which can escape the host’s immune system [102]. In addition, FOXP3 inhibits the transcription of SKP2 and HER-2 [102]. FOXP3 can reduce invasion and metastasis of cancer cells by controlling the expression of tumor-associated proteins [102-104].
Although suppressive in most tumor types, in some, high FOXP3 expression helps tumor development and results in poor prognosis [102]. In breast cancer, FOXP3 inhibits growth by SK2 and HER2 repression [105,106], as well as migration [107] and prevents DNA damage repair [108]. FOXP3 also induces apoptosis in hepatocellular, ovarian and prostate cancers [109-111]. In addition, it acts as an oncogene in specific cancer types, promoting cell growth, migration, and invasion [95,99,112,113]. In breast cancer, FOXP3 associates with better prognosis, suggesting that it might be considered both as a prognostic and diagnostic marker [100].
IL-2 is the main growth factor for the activation and stimulation of the maturation of T-lymphocytes. It binds to different receptor com- plexes, the IL-2Ralpha (IL2RA), beta (IL2RB) and gamma (IL2RG) chains. IL2RA expression is associated with a variety of cancers, including leukemia, lymphoma, lung, and breast cancer [114]. Ac- cording to recent studies, high expression of IL2RA correlates with poor cancer patient prognosis [114]. In addition, high IL2RA expression was used as a prognostic factor in PDAC [115]. In our study, we showed that IL2RA is significantly downregulated in BRCA and LUSC and upregu- lated in HNSC and LUAD, which agrees with the findings of previous studies [116-118]. Overexpression of IL2RA has been previously observed in lung [119], skin [120], prostate [121], esophageal [122] and leukemic cancers [123-125]. Notably, IL2RA expression can cause increased proliferation, as well as anti-apoptotic protein expression and drug resistance in human head and neck cancer [114].
Overall, IL2RA plays a role in multiple cellular functions such as proliferation, differentiation, cell survival and apoptosis [126]. Decreased levels of IL2RA in activated circulating immune cells and Tregs have been used by IL-2 immunotherapies in tumors and autoim- mune diseases. In addition, various polymorphisms have been reported in IL2RA on breast and ovarian cancers [127]. Importantly, FOXP3 and IL2RA may affect each other’s activity in cancer [128,129].
While IL2RA expression is induced upon Treg activation, our analysis aimed to highlight its role specifically in the context of resting Tregs. IL- 2RA, alongside FOXP3, represents a key marker for the resting state of Tregs before activation. We acknowledge that its expression is also seen in activated Tregs, but our results suggest that IL2RA has a distinctive expression profile in resting Tregs in the datasets we analyzed.
Furthermore, we examined the correlation of both Treg signatures with patient survival. We noticed that FOXP3 expression associates with adverse prognosis in gliomas and ovarian cancer, while its protective prognosis appears to be high in colorectal cancer, uveal melanoma and lung adenocarcinoma. Two recent meta-analyses by Saleh et al. [103] and Xu et al. [130] agree with our findings. Our findings showing that IL2RA expression is an independent and adverse prognostic factor in AML and lung adenocarcinoma, agree with previous reports [131,132].
On the other hand, CCR8, CTLA-4, FOXP3 and TNFRSF9 are markers
for effector Treg cells. CCR8 is a cell surface receptor of Tregs that be- longs to the G protein-coupled receptor (GPCR) family [133]. It plays a vital role in recruiting cells to the tumor site, providing an immuno- suppressive environment that helps tumor escape. The inhibition of CCR8 can confuse the recruitment of Tregs, leading to antitumor im- mune responses and the suppression of the tumor growth [134].
CCR8 recruits Tregs to the tumor site and creates an immunosup- pressive environment that helps tumor escape. Similar to CTLA-4, therapeutic results using anti-CCR8 mabs have been observed in different cancers [135]. Anti-CCR8 mabs reduce the accumulation of Tregs in the tumor and change their immunosuppressive function [136]. T-cell inhibition using anti-CTLA-4 or anti-PD-1 blockade is still limited to a small part of cancer patients, so future promise lies in the parallel use with this therapy for immune cell stimulation by agonistic agents exerting their function on costimulatory receptors, like TNFRSF9 (CD137) [137]. Regarding CCR8, our findings suggest that it is an adverse prognostic factor in breast cancer. According to Plitas et al., CCR8 is expressed by human breast cancer-infiltrating Treg cells. The deficiency of CCR8 significantly reduces primary tumor development and metastases without any overt immunopathology [138]. Our elabo- ration on effector Treg cells revealed a double role for TNFRSF9, as it seems to act both protectively, but also as an adverse factor in the prognosis of lung cancer. Apart from lung cancer however, TNFRSF9 has a protective prognostic factor in breast and colorectal cancer [139]. Liu et al., examined its upregulation in breast cancer, which was associated to PAX6 downregulation and the prevention of tumor growth, suggest- ing that an agonistic antibody for TNFRSF9 might help in the treatment of breast cancer [139].
Our investigation revealed significant upregulation of CCR8 in STAD, BRCA and KIRC tumors, being associated with the tumor’s grade, nodal metastasis and overall survival [118], as well as a significant upregulation of CTLA-4 in high-risk KIRC and downregulation of TNSFRF9 in breast cancer. The latter agrees with the recent report by Xu et al. [139], showing that this is related to metastasis.
Another important feature of FOXP3+ Treg cells, apart from the constitutive expression of IL2RA, is that of the co-inhibitory molecule, CTLA-4 [140-144]. According to our results, low CTLA-4 expression associates with better overall survival in HNSC, while high CTLA-4 expression increases the risk in KIRC. Most studies have investigated the prognostic role of CTLA-4 in kidney cancer and other types of tumor. Clinical studies that have used Ipilimumab (anti-CTLA-4) to block its action, have yielded in very promising results in skin melanoma and other types of cancer [145-147]. Although this type of immunotherapy presented high frequency of immune-related toxicity, it was the first to show a clear survival benefit for patients with advanced skin melanoma [148].
As regards TNFRSF9, it belongs to the TNFR superfamily, which promotes clonal expansion, differentiation and helps the survival of CD4+ and CD8+ T cells. TNFRSF9 is also a biomarker for tumor- infiltrating lymphocytes (TILs) and enhances lymphocyte activation and infiltration in the tumor [149,150]. This also promotes proliferation in peripheral monocytes and enhances T cell apoptosis [151].
In addition, we investigated the pathway activity scores of resting and effector Treg cells. Our findings indicate an activating effect of FOXP3 expression on apoptosis, EMT and hormone ER pathways in pan- cancer. On the other hand, FOXP3 expression had an inhibitory effect on the RTK, hormone AR, DNA damage response and PI3K/AKT pathways. Similar to FOXP3, IL2RA had an activating effect on apoptosis, EMT and the hormone ER pathway. The above findings are in strong agreement with previous studies, examining FOXP3 function in apoptosis. Analyt- ically, FOXP3 can inhibit cellular proliferation and induce apoptosis in gastric cancer [109]. However, in some tumors like thyroid cancer, its inhibition promotes apoptosis. In fact, FOXP3 expression has a negative impact on thyroid cancer and does not correlate with high apoptotic rates [95]. On the other hand, according to Yang et al., FOXP3 acts as a co-activator of the Wnt-b-catenin signaling pathway and induces EMT,
as well as neoplasia and metastasis in non-small cell lung cancer [99].
In contrast to our findings, other studies showed that IL2RA expression inhibits apoptosis and cell differentiation in AML [152]. IL2RA resistance to apoptosis is also supported by Kuhn et al. [153,154].
As regards the pathway activity of effector Treg markers, we found that TNFRSF9 has an activating effect on EMT, apoptosis and hormone ER pathway. The inhibitory function of TNFRSF9 was associated with the RTK, DNA damage response, hormone AR and PI3K/AKT pathways. Additionally, we found that CTLA-4 expression strongly activates apoptosis, EMT and hormone ER pathways in pan-cancer. We also showed that the inhibitory role of CTLA-4 and TNFRSF9 had a similar impact on the RTK, DNA damage response and hormone AR pathways.
Finally, we explored the activating function of CCR8 expression, and found that it correlates with hormone ER, apoptosis and EMT. On the other hand, we found that CCR8, TNFRSF9 and CTLA-4 expression had an inhibiting effect on the DNA damage response and hormone AR pathways. The effector Treg signature showed a high activation asso- ciated with EMT. During EMT epithelial cells lose their cell identity and obtain a mesenchymal phenotype. Nevertheless, this prosses can be controlled by cancer cells and in many cases, it is related to resistance to apoptosis, accession to tissue invasiveness, cancer stem cells character- istics, and increased resistance to tumor therapies [155]. Resistance to apoptosis is a crucial factor for tumor growth. CTLA-4 has not been well described in apoptosis and the data are somehow controversial [156, 157]. Our further investigation of the CCR8 pathway activity, revealed lower apoptotic rates in contrast to CTLA-4, FOXP3, IL2RA and TNFRSF9 [134]. On the other hand, TNFRSF9 has been mentioned to enhance T cell apoptosis [151], which agrees with our findings.
Another significant point in our analysis was the detection of the correlations between the expression of FOXP3, IL2RA, CTLA-4, CCR8 and TNFRSF9 and immune infiltration across different cancers. Our re- sults agree with those in a recent study, were CD20+ B cells, CD68+, CD8+ cytotoxic T cells and tumor-associated macrophages were signif- icantly increased in bladder cancer and were also related to poor prognosis. Another study presented that bladder cancers with a high tumor mutation burden (TMB) have higher fractions of CD8 and CD4 T cells, as well as NK cells, while in low TMB tumors, there is a higher fraction of mast cells [158]. Importantly, high infiltration of T lym- phocytes in tumors, is correlated with bladder cancer patient survival [159].
There is a variety of conflicted data about the CTLA-4 pathway and its function on Treg suppression [143,144,160,161]. Taking into consideration recent data, we showed that in some studies anti-CTLA-4 mabs failed to interrupt the suppression of human Tregs [162], whereas in others, the suppression was CTLA-4-dependent with minor help from TGF$ [163]. Furthermore, it has been reported that the exhaustion of CD25+ abolishes the ability of anti-CTLA-4 to increase human T cell proliferation [164,165].
We recognize that gene expression and tumorigenesis are balanced by genetic alterations in specific genes. Here, we found multiple asso- ciations between FOXP3, IL2RA, CTLA-4, CCR8 and TNFRSF9 expression and genetic hits (SNVs and CNVs) in pan-cancer. We noted a high mu- tation rate of IL2RA in skin melanoma, uterine corpus endometrial carcinoma and lung cancers, as well as of FOXP3 mutations in uterine corpus endometrial carcinoma, glioblastoma and breast cancer. In the past, polymorphisms in FOXP3 were linked with an increased risk of NSCLC [166]. The increased instability caused by FOXP3 CNVs, seems to result in modulation of Treg activity in breast cancer [167]. According to Zhao et al., type I UCEC is associated with gene mutations in PTEN, KRAS, ARID1A, PIK3CA and CTNNB1 and is characterized by micro- satellite instability; while type II UCECs are characterized by mutations in TP53 and Her2 overexpression [168].
Furthermore, our analysis shows that the Treg signature is not hypermethylated in pan-cancer. An interesting finding however, is the correlation of their low (or minimal) methylation levels with gene expression and immune cell infiltration in kidney, breast and other
cancer types. Other studies have shown that FOXP3 hypomethylation can lead to increased expression, resulting in favorable prognosis and tumor suppressor activity in breast cancer [100]. Comparable in- vestigations are absent for the remaining genes in breast cancer. Nevertheless, in other tumor types, such as cervical cancer (TNFRSF9) [169], skin melanoma (CTLA-4) [170], and head and neck squamous cell carcinoma (CTLA-4) [171], methylation shows correlation with gene expression.
The spatial transcriptomics data add a novel spatial dimension to the traditional gene expression data from dissociated cells. In our study, we integrated the two histopathological imaging and sequencing fields across 6 types of cancer, with the ultimate aim to discover novel biology and to improve histopathological diagnosis in a quantitative and auto- mated way. Of course, different cancers have unique TMEs, which can impact Treg recruitment, activation and suppressive functions. In addition, variability in Treg marker expression (e.g., FOXP3, CCR8) might result from differences in tumor immune evasion mechanisms or stromal-immune interactions. In some cancers, Tregs are highly abun- dant; for example, in ovarian and colorectal cancers [172], as opposed to glioblastoma, which has a lower Treg infiltration [173,174].
The observed expression patterns underscore the diversity of Treg- associated gene activity across different cancer types and microenvi- ronments. In NSCLC, the high variability in FOXP3 and IL2RA expres- sion, coupled with the broad spatial distribution of CCR8, suggests a heterogeneous immune landscape influenced by Tregs. KIPAN presented limited activity for most genes, with TNFRSF9 standing out as the most variable, potentially reflecting its role in localized immune regulation. BRCA showed scattered gene expression, with CTLA-4 and IL2RA emerging as key contributors, potentially signifying their involvement in modulating immune checkpoint pathways in this cancer type. In contrast, CRC highlighted a unique pattern, with very high expression of FOXP3, CTLA-4, IL2RA, and CCR8 restricted to a few cells, while TNFRSF9 exhibited moderate, widespread expression, suggesting its more consistent role in shaping the immune microenvironment. These findings emphasize the context-specific roles of Treg-associated genes, with TNFRSF9 and CTLA-4 consistently emerging as key players across multiple cancers.
Therefore, tumor heterogeneity is related to the efficacy of Treg- targeting therapies. In specific, tumors with high Treg infiltration may respond better to Treg-depleting therapies (e.g., CCR8 inhibitors) than those with minimal Treg presence [175]. High Treg infiltration corre- lates with poor prognosis in breast cancer, but may have limited impact in lung cancer due to differences in TME composition [176,177].
Furthermore, differences in cytokine profiles (e.g., TGF-6, IL-10) across cancers may affect Treg suppressive function and their target- ing by immunotherapy [178]. In hepatocellular carcinoma, Treg expansion is linked to high levels of IL-10; while in melanoma, Tregs are primarily regulated by TGF-ß signaling [179]. Last, we need to consider immune checkpoint inhibitors as a context where Treg presence corre- lates variably with response depending on the tumor type [180].
We finally explored the IC50 of drugs across different cancer cell lines and their correlation with the two Treg signatures. We observed that both FOXP3 and IL2RA were negatively correlated with sensitivity in Methotrexate, while FOXP3 correlated with sensitivity in Pandaco- stat, as well. According to Tohyama et al., Methotrexate, 5-Fluoracil and Arsenic trioxide are likely to decrease cancer cell survival and prolifer- ation, while they can increase CD4+CD25+FOXP3+ T cell frequency in peripheral-blood after mitogen activation in vitro. The above results suggested that various types of anticancer agents reduce T cell-mediated immunity as a response to malignancies by inhibiting the proliferative response and elevating the frequency of Treg cells, as well as leading to the suppressive function of antitumor effector T cells [181]. Our data showed a positive correlation between CCR8 and sensitivity in COL-3, as well as in Chlorambucil; however, there is a lack of studies relating anticancer agents to CCR8 expression, as chemokine receptor remains a promising target for cancer immunotherapy for many tumor types,
including lung cancer [182].
On the other hand, CTLA-4 mRNA levels were negatively correlated with both therapies, while TNFRSF9 mRNA levels were positively correlated with sensitivity in COL-3. Furthermore, we found negative correlations between FOXP3, IL2RA and CCR8 expression and sensitivity in Methotrexate treatment, among other therapies. We also found negative correlation between FOXP3, IL2RA and CTLA-4 expression and sensitivity in Vorinostat.
In addition, another study revealed that Vorinostat, when combined with checkpoint inhibitors, reduced the overall tumor growth and pro- longed survival. In several cases the drug led to a complete eradication of the tumor. The study suggested that the beneficial therapeutic result of Vorinostat induced an increase in cancer cells immune function through the increase of HLA-DR, which relates to T cell function. In this case, T cells were activated due to co-inhibition of PD-1 and CTLA-4 [183]. Therefore, although CTLA-4 is correlated negatively with Vor- inostat, its blockade combined with Vorinostat could be significantly fundamental for cancer immunotherapy.
Positive correlation was finally observed between TNFRSF9 expres- sion and sensitivity in GSK1070916 and Tk-2-105 drugs treatment. TNFRSF9 correlates positively with COL-3, which has anti-cancer func- tion in specific genes, while it presents anti-proliferative effects [184]. In contrast, the correlation between TNFRSF9 and GSK1070916 has not been mentioned before. The anticancer activity of GSK1070916 has been demonstrated in various tumor models [185,186]. An agonistic anti-TNFRSF9 antibody regulating the TNFRSF9/P38/PAX6 pathway, can have a potential role in breast cancer immunotherapy [139].
The correlations observed between Treg markers and drug sensitivity have important implications for the development of personalized ther- apeutic strategies. Given the role of Tregs in immune suppression and their influence on tumor progression, targeting Treg-associated path- ways could be a promising strategy to enhance the efficacy of cancer therapies. Specifically, the gene markers we identified may serve as predictive biomarkers for patient selection in clinical settings, allowing for more tailored treatment regimens. For example, patients with high expression of certain Treg markers could benefit from therapies designed to deplete or reprogram Tregs, potentially improving the response to immune checkpoint inhibitors or other immune-modulating therapies.
Moreover, combining Treg-targeted strategies with conventional treatments could enhance drug sensitivity, overcoming immune evasion mechanisms and improving patient outcomes [187,188]. For example, Cyclophosphamide can suppress Tregs and allow more effective induc- tion of antitumor immune responses [189]. In addition, Treg suppres- sion when combined with ICI therapies coould provide a potentially effective option in cancer immunotherapy [180]. For example, Moga- mulizumab (a Treg-depleting anti-CCR4 antibody), when combined with Nivolumab (anti-PD1) could decrease the population of effector Tregs (CD4+CD45RA-FoxP3high) in pancreatic adenocarcinoma patients [190]. CAR-Treg cell therapies have also been proposed as a potential therapeutic strategy [191]. Future clinical trials should aim to validate these markers as part of precision medicine approaches, identifying subgroups of patients who may benefit from Treg-targeted interventions alongside existing therapies.
Finally, we would like to acknowledge a few limitations in using extensive datasets in our study. As these come from diverse sources and platforms, they may introduce variability in sequencing protocols and sample processing. To mitigate this, we applied consistent pre- processing methods and performed batch effect correction where applicable. In addition, we need to acknowledge that differences in patient demographics, sample sizes, and clinical annotations across cancer types may introduce cohort biases. For example, the over- representation of certain cancer types or demographic groups might limit the generalizability of our pan-cancer findings to underrepresented populations. Furthermore, the lack of detailed treatment data in some cohorts restricted our ability to correlate Treg markers with specific
therapeutic responses comprehensively. Despite these limitations, we validated key findings using external datasets to ensure robustness.
5. Conclusions
In this study, we comprehensively characterized the role of resting and effector Treg cells in tumor progression by integrating their mo- lecular expression, methylation landscape, genetic alterations, and tumor infiltration across multiple cancer types. We further examined their impact on cancer immunity and therapeutic responses, identifying potential vulnerabilities to specific drugs. These findings provide a detailed understanding of Treg-mediated immune regulation in cancer and offer a foundation for developing targeted immunotherapies.
CRediT authorship contribution statement
Anna-Maria Chalepaki: Writing - original draft, Investigation, Formal analysis. Marios Gkoris: Formal analysis, Visualization. Irene Chondrou: Investigation, Methodology. Malamati Kourti: Data cura- tion, Methodology, Writing - review & editing. Ilias Georgakopoulos- Soares: Visualization, Resources, Methodology. Apostolos Zaravinos: Writing - review & editing, Supervision, Software, Resources, Project administration, Methodology, Conceptualization.
Ethics statement
All authors confirm that all procedures were performed in compli- ance with relevant laws and institutional guidelines. No informed con- sent was required for this study. This research does not need an Ethics statement as data were retrieved from publicly available datasets.
Declaration of generative AI and AI-assisted technologies in the writing process
During the preparation of this work the authors used ChatGPT to improve the readability and language of the manuscript. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.
Funding sources
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Declaration of competing interest
The authors declare the following financial interests/personal re- lationships which may be considered as potential competing interests: Apostolos Zaravinos reports article publishing charges was provided by European University Cyprus. Apostolos Zaravinos reports a relationship with European University Cyprus that includes: employment. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi. org/10.1016/j.compbiomed.2025.110021.
References
[1] J. Schlöder, F. Shahneh, F .- J. Schneider, B. Wieschendorf, Boosting regulatory T cell function for the treatment of autoimmune diseases - that’s only half the battle, Front. Immunol. 13 (2022) 973813, https://doi.org/10.3389/ fimmu.2022.973813.
[2] Z. Liu, J. Zhou, S. Wu, Z. Chen, S. Wu, L. Chen, X. Zhu, Z. Li, Why Treg should be the focus of cancer immunotherapy: the latest thought, Biomed. Pharmacother. 168 (2023) 115142, https://doi.org/10.1016/j.biopha.2023.115142.
[3] J. Shi, M. Ge, S. Lu, X. Li, Y. Shao, J. Huang, Z. Huang, J. Zhang, N. Nie, Y. Zheng, Intrinsic impairment of CD4+CD25+ regulatory T cells in acquired aplastic anemia, Blood 120 (2012) 1624-1632, https://doi.org/10.1182/blood-2011-11- 390708.
[4] Q. Hu, Y. Xie, Y. Ge, X. Nie, J. Tao, Y. Zhao, Resting T cells are hypersensitive to DNA damage due to defective DNA repair pathway, Cell Death Dis. 9 (2018) 662, https://doi.org/10.1038/s41419-018-0649-z.
[5] Y.Y. Wan, R.A. Flavell, How diverse-CD4 effector T cells and their functions, J. Mol. Cell Biol. 1 (2009) 20-36, https://doi.org/10.1093/jmcb/mjp001.
[6] P. Sharma, J.P. Allison, The future of immune checkpoint therapy, Science 348 (2015) 56-61, https://doi.org/10.1126/science.aaa8172.
[7] Y. Togashi, K. Shitara, H. Nishikawa, Regulatory T cells in cancer immunosuppression - implications for anticancer therapy, Nat. Rev. Clin. Oncol. 16 (2019) 356-371, https://doi.org/10.1038/s41571-019-0175-7.
[8] M. Haruna, A. Ueyama, Y. Yamamoto, M. Hirata, K. Goto, H. Yoshida, N. Higuchi, T. Yoshida, Y. Kidani, Y. Nakamura, M. Nagira, A. Kawashima, K. Iwahori, Y. Shintani, N. Ohkura, H. Wada, The impact of CCR8+ regulatory T cells on cytotoxic T cell function in human lung cancer, Sci. Rep. 12 (2022) 5377, https:// doi.org/10.1038/s41598-022-09458-5.
[9] Y. Takeuchi, H. Nishikawa, Roles of regulatory T cells in cancer immunity, Int. Immunol. 28 (2016) 401-409, https://doi.org/10.1093/intimm/dxw025.
[10] A. Martinez-Perez, C. Aguilar-Garcia, S. Gonzalez, The emerging role of NK cells in immune checkpoint blockade, Cancers 14 (2022) 6005, https://doi.org/ 10.3390/cancers14236005.
[11] F.S. Hodi, S.J. O’Day, D.F. McDermott, R.W. Weber, J.A. Sosman, J.B. Haanen, R. Gonzalez, C. Robert, D. Schadendorf, J.C. Hassel, W. Akerley, A.J.M. van den Eertwegh, J. Lutzky, P. Lorigan, J.M. Vaubel, G.P. Linette, D. Hogg, C. H. Ottensmeier, C. Lebbé, C. Peschel, I. Quirt, J.I. Clark, J.D. Wolchok, J. S. Weber, J. Tian, M.J. Yellin, G.M. Nichol, A. Hoos, W.J. Urba, Improved survival with ipilimumab in patients with metastatic melanoma, N. Engl. J. Med. 363 (2010) 711-723, https://doi.org/10.1056/NEJMoa1003466.
[12] M.J. Selby, J.J. Engelhardt, M. Quigley, K.A. Henning, T. Chen, M. Srinivasan, A. J. Korman, Anti-CTLA-4 antibodies of IgG2a isotype enhance antitumor activity through reduction of intratumoral regulatory T cells, Cancer Immunol. Res. 1 (2013) 32-42, https://doi.org/10.1158/2326-6066.CIR-13-0013.
[13] T.R. Simpson, F. Li, W. Montalvo-Ortiz, M.A. Sepulveda, K. Bergerhoff, F. Arce, C. Roddie, J.Y. Henry, H. Yagita, J.D. Wolchok, K.S. Peggs, J.V. Ravetch, J. P. Allison, S.A. Quezada, Fc-dependent depletion of tumor-infiltrating regulatory T cells co-defines the efficacy of anti-CTLA-4 therapy against melanoma, J. Exp. Med. 210 (2013) 1695-1710, https://doi.org/10.1084/jem.20130579.
[14] H .- W. Lee, S .- J. Park, B.K. Choi, H.H. Kim, K .- O. Nam, B.S. Kwon, 4-1BB promotes the survival of CD8+ T lymphocytes by increasing expression of Bcl-xL and Bfl-1, J. Immunol. 169 (2002) 4882-4888, https://doi.org/10.4049/ jimmunol.169.9.4882.
[15] D.S. Vinay, E.P. Ryan, G. Pawelec, W.H. Talib, J. Stagg, E. Elkord, T. Lichtor, W. K. Decker, R.L. Whelan, H.M.C.S. Kumara, E. Signori, K. Honoki, A.
G. Georgakilas, A. Amin, W.G. Helferich, C.S. Boosani, G. Guha, M.R. Ciriolo, S. Chen, S.I. Mohammed, A.S. Azmi, W.N. Keith, A. Bilsland, D. Bhakta, D. Halicka, H. Fujii, K. Aquilano, S.S. Ashraf, S. Nowsheen, X. Yang, B.K. Choi, B. S. Kwon, Immune evasion in cancer: mechanistic basis and therapeutic strategies, Semin. Cancer Biol. 35 (Suppl) (2015) S185-S198, https://doi.org/10.1016/j. semcancer.2015.03.004.
[16] C. Chester, S. Ambulkar, H.E. Kohrt, 4-1BB agonism: adding the accelerator to cancer immunotherapy, Cancer Immunol. Immunother. 65 (2016) 1243-1248, https://doi.org/10.1007/s00262-016-1829-2.
[17] S. Baritaki, A. Zaravinos, Cross-talks between RKIP and YY1 through a Multilevel bioinformatics pan-cancer analysis, Cancers 15 (2023) 4932, https://doi.org/ 10.3390/cancers15204932.
[18] C .- J. Liu, F .- F. Hu, G .- Y. Xie, Y .- R. Miao, X .- W. Li, Y. Zeng, A .- Y. Guo, GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels, Brief Bioinform 24 (2023) bbac558, https://doi.org/ 10.1093/bib/bbac558.
[19] B. Li, C.N. Dewey, RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome, BMC Bioinf. 12 (2011) 323, https://doi.org/ 10.1186/1471-2105-12-323.
[20] C .- J. Liu, F .- F. Hu, M .- X. Xia, L. Han, Q. Zhang, A .- Y. Guo, GSCALite: a web server for gene set cancer analysis, Bioinformatics 34 (2018) 3771-3772, https://doi. org/10.1093/bioinformatics/bty411.
[21] Z. Tang, B. Kang, C. Li, T. Chen, Z. Zhang, GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis, Nucleic Acids Res. 47 (2019) W556-W560, https://doi.org/10.1093/nar/gkz430.
[22] H. Mizuno, K. Kitada, K. Nakai, A. Sarai, PrognoScan: a new database for meta- analysis of the prognostic value of genes, BMC Med Genomics 2 (2009) 18, https://doi.org/10.1186/1755-8794-2-18.
[23] R. Akbani, P.K.S. Ng, H.M.J. Werner, M. Shahmoradgoli, F. Zhang, Z. Ju, W. Liu, J .- Y. Yang, K. Yoshihara, J. Li, S. Ling, E.G. Seviour, P.T. Ram, J.D. Minna, L. Diao, P. Tong, J.V. Heymach, S.M. Hill, F. Dondelinger, N. Stadler, L.A. Byers, F. Meric-Bernstam, J.N. Weinstein, B.M. Broom, R.G.W. Verhaak, H. Liang, S. Mukherjee, Y. Lu, G.B. Mills, A pan-cancer proteomic perspective on the Cancer Genome Atlas, Nat. Commun. 5 (2014) 3887, https://doi.org/10.1038/ ncomms4887.
[24] C.H. Mermel, S.E. Schumacher, B. Hill, M.L. Meyerson, R. Beroukhim, G. Getz, GISTIC2.0 facilitates sensitive and confident localization of the targets of focal
somatic copy-number alteration in human cancers, Genome Biol. 12 (2011) R41, https://doi.org/10.1186/gb-2011-12-4-r41.
[25] A. Schlattl, S. Anders, S.M. Waszak, W. Huber, J.O. Korbel, Relating CNVs to transcriptome data at fine resolution: assessment of the effect of variant size, type, and overlap with functional regions, Genome Res. 21 (2011) 2004-2013, https:// doi.org/10.1101/gr.122614.111.
[26] Y .- R. Miao, M. Xia, M. Luo, T. Luo, M. Yang, A .- Y. Guo, ImmuCellAI-mouse: a tool for comprehensive prediction of mouse immune cell abundance and immune microenvironment depiction, Bioinformatics 38 (2022) 785-791, https://doi. org/10.1093/bioinformatics/btab711.
[27] Y. Miao, Q. Zhang, Q. Lei, M. Luo, G. Xie, H. Wang, A. Guo, ImmuCellAI: a unique method for comprehensive T-cell subsets abundance prediction and its application in cancer immunotherapy, Adv. Sci. 7 (2020) 1902880, https://doi. org/10.1002/advs.201902880.
[28] J. Liu, T. Lichtenberg, K.A. Hoadley, L.M. Poisson, A.J. Lazar, A.D. Cherniack, A. J. Kovatich, C.C. Benz, D.A. Levine, A.V. Lee, L. Omberg, D.M. Wolf, C.D. Shriver, V. Thorsson, H. Hu, S.J. Caesar-Johnson, J.A. Demchok, I. Felau, M. Kasapi, M. L. Ferguson, C.M. Hutter, H.J. Sofia, R. Tarnuzzer, Z. Wang, L. Yang, J.
C. Zenklusen, J. Julia Zhang, S. Chudamani, J. Liu, L. Lolla, R. Naresh, T. Pihl, Q. Sun, Y. Wan, Y. Wu, J. Cho, T. DeFreitas, S. Frazer, N. Gehlenborg, G. Getz, D. I. Heiman, J. Kim, M.S. Lawrence, P. Lin, S. Meier, M.S. Noble, G. Saksena,
D. Voet, H. Zhang, B. Bernard, N. Chambwe, V. Dhankani, T. Knijnenburg, R. Kramer, K. Leinonen, Y. Liu, M. Miller, S. Reynolds, I. Shmulevich, V. Thorsson, W. Zhang, R. Akbani, B.M. Broom, A.M. Hegde, Z. Ju, R.S. Kanchi, A. Korkut, J. Li, H. Liang, S. Ling, W. Liu, Y. Lu, G.B. Mills, K .- S. Ng, A. Rao, M. Ryan, J. Wang, J.N. Weinstein, J. Zhang, A. Abeshouse, J. Armenia,
D. Chakravarty, W.K. Chatila, I. De Bruijn, J. Gao, B.E. Gross, Z.J. Heins, R. Kundra, K. La, M. Ladanyi, A. Luna, M.G. Nissan, A. Ochoa, S.M. Phillips, E. Reznik, F. Sanchez-Vega, C. Sander, N. Schultz, R. Sheridan, S.O. Sumer,
Y. Sun, B.S. Taylor, J. Wang, H. Zhang, P. Anur, M. Peto, P. Spellman, C. Benz, J. M. Stuart, C.K. Wong, C. Yau, D.N. Hayes, J.S. Parker, M.D. Wilkerson, A. Ally, M. Balasundaram, R. Bowlby, D. Brooks, R. Carlsen, E. Chuah, N. Dhalla, R. Holt, S.J.M. Jones, K. Kasaian, D. Lee, Y. Ma, M.A. Marra, M. Mayo, R.A. Moore, A. J. Mungall, K. Mungall, A.G. Robertson, S. Sadeghi, J.E. Schein, P. Sipahimalani, A. Tam, N. Thiessen, K. Tse, T. Wong, A.C. Berger, R. Beroukhim, A.D. Cherniack, C. Cibulskis, S.B. Gabriel, G.F. Gao, G. Ha, M. Meyerson, S.E. Schumacher, J. Shih, M.H. Kucherlapati, R.S. Kucherlapati, S. Baylin, L. Cope, L. Danilova, M.
S. Bootwalla, P.H. Lai, D.T. Maglinte, D.J. Van Den Berg, D.J. Weisenberger, J. T. Auman, S. Balu, T. Bodenheimer, C. Fan, K.A. Hoadley, A.P. Hoyle, S. R. Jefferys, C.D. Jones, S. Meng, P.A. Mieczkowski, L.E. Mose, A.H. Perou, C. M. Perou, J. Roach, Y. Shi, J.V. Simons, T. Skelly, M.G. Soloway, D. Tan, U. Veluvolu, H. Fan, T. Hinoue, P.W. Laird, H. Shen, W. Zhou, M. Bellair,
K. Chang, K. Covington, C.J. Creighton, H. Dinh, H. Doddapaneni, L. A. Donehower, J. Drummond, R.A. Gibbs, R. Glenn, W. Hale, Y. Han, J. Hu, V. Korchina, S. Lee, L. Lewis, W. Li, X. Liu, M. Morgan, D. Morton, D. Muzny, J. Santibanez, M. Sheth, E. Shinbro, L. Wang, M. Wang, D.A. Wheeler, L. Xi,
F. Zhao, J. Hess, E.L. Appelbaum, M. Bailey, M.G. Cordes, L. Ding, C.C. Fronick, L. A. Fulton, R.S. Fulton, C. Kandoth, E.R. Mardis, M.D. Mclellan, C.A. Miller, H. K. Schmidt, R.K. Wilson, D. Crain, E. Curley, J. Gardner, K. Lau, D. Mallery, S. Morris, J. Paulauskis, R. Penny, C. Shelton, T. Shelton, M. Sherman,
E. Thompson, P. Yena, J. Bowen, J.M. Gastier-Foster, M. Gerken, K.M. Leraas, T. M. Lichtenberg, N.C. Ramirez, L. Wise, E. Zmuda, N. Corcoran, T. Costello, C. Hovens, A.L. Carvalho, A.C. De Carvalho, J.H. Fregnani, A. Longatto-Filho, R. M. Reis, C. Scapulatempo-Neto, H.C.S. Silveira, D.O. Vidal, A. Burnette,
J. Eschbacher, B. Hermes, A. Noss, R. Singh, M.L. Anderson, P.D. Castro, M. Ittmann, D. Huntsman, B. Kohl, X. Le, R. Thorp, C. Andry, E.R. Duffy, V. Lyadov, O. Paklina, G. Setdikova, A. Shabunin, M. Tavobilov, C. McPherson, R. Warnick, R. Berkowitz, D. Cramer, C. Feltmate, N. Horowitz, A. Kibel, M. Muto, C.P. Raut, A. Malykh, J.S. Barnholtz-Sloan, W. Barrett, K. Devine, J. Fulop, Q. T. Ostrom, K. Shimmel, Y. Wolinsky, A.E. Sloan, A. De Rose, F. Giuliante, M. Goodman, B.Y. Karlan, C.H. Hagedorn, J. Eckman, J. Harr, J. Myers,
K. Tucker, L.A. Zach, B. Deyarmin, H. Hu, L. Kvecher, C. Larson, R.J. Mural, S. Somiari, A. Vicha, T. Zelinka, J. Bennett, M. Iacocca, B. Rabeno, P. Swanson, M. Latour, L. Lacombe, B. Têtu, A. Bergeron, M. McGraw, S.M. Staugaitis, J. Chabot, H. Hibshoosh, A. Sepulveda, T. Su, T. Wang, O. Potapova, O. Voronina, L. Desjardins, O. Mariani, S. Roman-Roman, X. Sastre, M .- H. Stern, F. Cheng, S. Signoretti, A. Berchuck, D. Bigner, E. Lipp, J. Marks, S. McCall, R. Mclendon, A. Secord, A. Sharp, M. Behera, D.J. Brat, A. Chen, K. Delman, S. Force, F. Khuri, K. Magliocca, S. Maithel, J.J. Olson, T. Owonikoko, A. Pickens, S. Ramalingam, D. M. Shin, G. Sica, E.G. Van Meir, H. Zhang, W. Eijckenboom, A. Gillis, E. Korpershoek, L. Looijenga, W. Oosterhuis, H. Stoop, K.E. Van Kessel, E. C. Zwarthoff, C. Calatozzolo, L. Cuppini, S. Cuzzubbo, F. DiMeco, G. Finocchiaro,
L. Mattei, A. Perin, B. Pollo, C. Chen, J. Houck, P. Lohavanichbutr, A. Hartmann, C. Stoehr, R. Stoehr, H. Taubert, S. Wach, B. Wullich, W. Kycler, D. Murawa, M. Wiznerowicz, K. Chung, W.J. Edenfield, J. Martin, E. Baudin, G. Bubley, R. Bueno, A. De Rienzo, W.G. Richards, S. Kalkanis, T. Mikkelsen, H. Noushmehr, L. Scarpace, N. Girard, M. Aymerich, E. Campo, E. Giné, A.L. Guillermo, N. Van Bang, P.T. Hanh, B.D. Phu, Y. Tang, H. Colman, K. Evason, P.R. Dottino, J. A. Martignetti, H. Gabra, H. Juhl, T. Akeredolu, S. Stepa, D. Hoon, K. Ahn, K. J. Kang, F. Beuschlein, A. Breggia, M. Birrer, D. Bell, M. Borad, A.H. Bryce, E. Castle, V. Chandan, J. Cheville, J.A. Copland, M. Farnell, T. Flotte, N. Giama, T. Ho, M. Kendrick, J .- P. Kocher, K. Kopp, C. Moser, D. Nagorney, D. O’Brien, B. P. O’Neill, T. Patel, G. Petersen, F. Que, M. Rivera, L. Roberts, R. Smallridge, T. Smyrk, M. Stanton, R.H. Thompson, M. Torbenson, J.D. Yang, L. Zhang,
F. Brimo, J.A. Ajani, A.M. Angulo Gonzalez, C. Behrens, J. Bondaruk,
R. Broaddus, B. Czerniak, B. Esmaeli, J. Fujimoto, J. Gershenwald, C. Guo, A.
J. Lazar, C. Logothetis, F. Meric-Bernstam, C. Moran, L. Ramondetta, D. Rice, A. Sood, P. Tamboli, T. Thompson, P. Troncoso, A. Tsao, I. Wistuba, C. Carter, L. Haydu, P. Hersey, V. Jakrot, H. Kakavand, R. Kefford, K. Lee, G. Long, G. Mann, M. Quinn, R. Saw, R. Scolyer, K. Shannon, A. Spillane, J. Stretch, M. Synott, J. Thompson, J. Wilmott, H. Al-Ahmadie, T.A. Chan, R. Ghossein, A. Gopalan, D. A. Levine, V. Reuter, S. Singer, B. Singh, N.V. Tien, T. Broudy, C. Mirsaidi, P. Nair, P. Drwiega, J. Miller, J. Smith, H. Zaren, J .- W. Park, N.P. Hung, E. Kebebew, W. M. Linehan, A.R. Metwalli, K. Pacak, P.A. Pinto, M. Schiffman, L.S. Schmidt, C. D. Vocke, N. Wentzensen, R. Worrell, H. Yang, M. Moncrieff, C. Goparaju, J. Melamed, H. Pass, N. Botnariuc, I. Caraman, M. Cernat, I. Chemencedji, A. Clipca, S. Doruc, G. Gorincioi, S. Mura, M. Pirtac, I. Stancul, D. Tcaciuc, M. Albert, I. Alexopoulou, A. Arnaout, J. Bartlett, J. Engel, S. Gilbert, J. Parfitt, H. Sekhon, G. Thomas, D.M. Rassl, R.C. Rintoul, C. Bifulco, R. Tamakawa, W. Urba, N. Hayward, H. Timmers, A. Antenucci, F. Facciolo, G. Grazi, M. Marino, R. Merola, R. De Krijger, A .- P. Gimenez-Roqueplo, A. Piché, S. Chevalier, G. McKercher, K. Birsoy, G. Barnett, C. Brewer, C. Farver, T. Naska, N.A. Pennell, D. Raymond, C. Schilero, K. Smolenski, F. Williams, C. Morrison, J. A. Borgia, M.J. Liptay, M. Pool, C.W. Seder, K. Junker, L. Omberg, M. Dinkin, G. Manikhas, D. Alvaro, M.C. Bragazzi, V. Cardinale, G. Carpino, E. Gaudio, D. Chesla, S. Cottingham, M. Dubina, F. Moiseenko, R. Dhanasekaran, K .- F. Becker, K .- P. Janssen, J. Slotta-Huspenina, M.H. Abdel-Rahman, D. Aziz, S. Bell, C.M. Cebulla, A. Davis, R. Duell, J.B. Elder, J. Hilty, B. Kumar, J. Lang, N. L. Lehman, R. Mandt, P. Nguyen, R. Pilarski, K. Rai, L. Schoenfield, K. Senecal, P. Wakely, P. Hansen, R. Lechan, J. Powers, A. Tischler, W.E. Grizzle, K.C. Sexton, A. Kastl, J. Henderson, S. Porten, J. Waldmann, M. Fassnacht, S.L. Asa, D. Schadendorf, M. Couce, M. Graefen, H. Huland, G. Sauter, T. Schlomm, R. Simon, P. Tennstedt, O. Olabode, M. Nelson, O. Bathe, P.R. Carroll, J.M. Chan, P. Disaia, P. Glenn, R.K. Kelley, C.N. Landen, J. Phillips, M. Prados, J. Simko, K. Smith-McCune, S. VandenBerg, K. Roggin, A. Fehrenbach, A. Kendler, S. Sifri, R. Steele, A. Jimeno, F. Carey, I. Forgie, M. Mannelli, M. Carney, B. Hernandez, B. Campos, C. Herold-Mende, C. Jungk, A. Unterberg, A. Von Deimling, A. Bossler, J. Galbraith, L. Jacobus, M. Knudson, T. Knutson, D. Ma, M. Milhem, R. Sigmund, A.K. Godwin, R. Madan, H.G. Rosenthal, C. Adebamowo, S. N. Adebamowo, A. Boussioutas, D. Beer, T. Giordano, A .- M. Mes-Masson, F. Saad, T. Bocklage, L. Landrum, R. Mannel, K. Moore, K. Moxley, R. Postier, J. Walker, R. Zuna, M. Feldman, F. Valdivieso, R. Dhir, J. Luketich, E.M. Mora Pinero, M. Quintero-Aguilo, C.G. Carlotti Jr., J.S. Dos Santos, R. Kemp, A. Sankarankuty, D. Tirapelli, J. Catto, K. Agnew, E. Swisher, J. Creaney, B. Robinson, C.S. Shelley, E.M. Godwin, S. Kendall, C. Shipman, C. Bradford, T. Carey, A. Haddad, J. Moyer, L. Peterson, M. Prince, L. Rozek, G. Wolf, R. Bowman, K.M. Fong, I. Yang,
R. Korst, W.K. Rathmell, J.L. Fantacone-Campbell, J.A. Hooke, A.J. Kovatich, C. D. Shriver, J. DiPersio, B. Drake, R. Govindan, S. Heath, T. Ley, B. Van Tine, P. Westervelt, M.A. Rubin, J.I. Lee, N.D. Aredes, A. Mariamidze, An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics, Cell 173 (2018) 400-416.e11, https://doi.org/10.1016/j. cell.2018.02.052.
[29] Y. Ye, Y. Xiang, F.M. Ozguc, Y. Kim, C .- J. Liu, P.K. Park, Q. Hu, L. Diao, Y. Lou, C. Lin, A .- Y. Guo, B. Zhou, L. Wang, Z. Chen, J.S. Takahashi, G.B. Mills, S .- H. Yoo, L. Han, The genomic landscape and pharmacogenomic interactions of clock genes in cancer chronotherapy, Cell Systems 6 (2018) 314-328.e2, https://doi.org/ 10.1016/j.cels.2018.01.013.
[30] W. Yang, J. Soares, P. Greninger, E.J. Edelman, H. Lightfoot, S. Forbes, N. Bindal, D. Beare, J.A. Smith, I.R. Thompson, S. Ramaswamy, P.A. Futreal, D.A. Haber, M. R. Stratton, C. Benes, U. McDermott, M.J. Garnett, Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells, Nucleic Acids Res. 41 (2012) D955-D961, https://doi.org/10.1093/nar/gks1111.
[31] F. Iorio, T.A. Knijnenburg, D.J. Vis, G.R. Bignell, M.P. Menden, M. Schubert, N. Aben, E. Gonçalves, S. Barthorpe, H. Lightfoot, T. Cokelaer, P. Greninger, E. Van Dyk, H. Chang, H. De Silva, H. Heyn, X. Deng, R.K. Egan, Q. Liu,
T. Mironenko, X. Mitropoulos, L. Richardson, J. Wang, T. Zhang, S. Moran, S. Sayols, M. Soleimani, D. Tamborero, N. Lopez-Bigas, P. Ross-Macdonald, M. Esteller, N.S. Gray, D.A. Haber, M.R. Stratton, C.H. Benes, L.F.A. Wessels, J. Saez-Rodriguez, U. McDermott, M.J. Garnett, A landscape of pharmacogenomic interactions in cancer, Cell 166 (2016) 740-754, https://doi.org/10.1016/j. cell.2016.06.017.
[32] M.G. Rees, B. Seashore-Ludlow, J.H. Cheah, D.J. Adams, E.V. Price, S. Gill, S. Javaid, M.E. Coletti, V.L. Jones, N.E. Bodycombe, C.K. Soule, B. Alexander, A. Li, P. Montgomery, J.D. Kotz, C.S .- Y. Hon, B. Munoz, T. Liefeld, V. Dančík, D. A. Haber, C.B. Clish, J.A. Bittker, M. Palmer, B.K. Wagner, P.A. Clemons, A. F. Shamji, S.L. Schreiber, Correlating chemical sensitivity and basal gene expression reveals mechanism of action, Nat. Chem. Biol. 12 (2016) 109-116, https://doi.org/10.1038/nchembio.1986.
[33] B. Seashore-Ludlow, M.G. Rees, J.H. Cheah, M. Cokol, E.V. Price, M.E. Coletti, V. Jones, N.E. Bodycombe, C.K. Soule, J. Gould, B. Alexander, A. Li, P. Montgomery, M.J. Wawer, N. Kuru, J.D. Kotz, C.S .- Y. Hon, B. Munoz, T. Liefeld, V. Dančík, J.A. Bittker, M. Palmer, J.E. Bradner, A.F. Shamji, P. A. Clemons, S.L. Schreiber, Harnessing connectivity in a large-scale small- molecule sensitivity dataset, Cancer Discov. 5 (2015) 1210-1223, https://doi. org/10.1158/2159-8290.CD-15-0235.
[34] A. Basu, N.E. Bodycombe, J.H. Cheah, E.V. Price, K. Liu, G.I. Schaefer, R. Y. Ebright, M.L. Stewart, D. Ito, S. Wang, A.L. Bracha, T. Liefeld, M. Wawer, J. C. Gilbert, A.J. Wilson, N. Stransky, G.V. Kryukov, V. Dancik, J. Barretina, L. A. Garraway, C.S .- Y. Hon, B. Munoz, J.A. Bittker, B.R. Stockwell, D. Khabele, A. M. Stern, P.A. Clemons, A.F. Shamji, S.L. Schreiber, An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules, Cell 154 (2013) 1151-1161, https://doi.org/10.1016/j.cell.2013.08.003.
[35] M. Kourti, A. Westwell, W. Jiang, J. Cai, Repurposing old carbon monoxide- releasing molecules towards the anti-angiogenic therapy of triple-negative breast cancer, Oncotarget 10 (2019) 1132-1148, https://doi.org/10.18632/ oncotarget.26638.
[36] M.V. Kuleshov, M.R. Jones, A.D. Rouillard, N.F. Fernandez, Q. Duan, Z. Wang, S. Koplev, S.L. Jenkins, K.M. Jagodnik, A. Lachmann, M.G. McDermott, C. D. Monteiro, G.W. Gundersen, A. Ma’ayan, Enrichr: a comprehensive gene set enrichment analysis web server 2016 update, Nucleic Acids Res. 44 (2016) W90-W97, https://doi.org/10.1093/nar/gkw377.
[37] A. Zehir, R. Benayed, R.H. Shah, A. Syed, S. Middha, H.R. Kim, P. Srinivasan, J. Gao, D. Chakravarty, S.M. Devlin, M.D. Hellmann, D.A. Barron, A.M. Schram, M. Hameed, S. Dogan, D.S. Ross, J.F. Hechtman, D.F. DeLair, J. Yao, D. L. Mandelker, D.T. Cheng, R. Chandramohan, A.S. Mohanty, R.N. Ptashkin, G. Jayakumaran, M. Prasad, M.H. Syed, A.B. Rema, Z.Y. Liu, K. Nafa, L. Borsu, J. Sadowska, J. Casanova, R. Bacares, I.J. Kiecka, A. Razumova, J.B. Son, L. Stewart, T. Baldi, K.A. Mullaney, H. Al-Ahmadie, E. Vakiani, A.A. Abeshouse, A.V. Penson, P. Jonsson, N. Camacho, M.T. Chang, H.H. Won, B.E. Gross, R. Kundra, Z.J. Heins, H .- W. Chen, S. Phillips, H. Zhang, J. Wang, A. Ochoa, J. Wills, M. Eubank, S.B. Thomas, S.M. Gardos, D.N. Reales, J. Galle, R. Durany, R. Cambria, W. Abida, A. Cercek, D.R. Feldman, M.M. Gounder, A.A. Hakimi, J. J. Harding, G. Iyer, Y.Y. Janjigian, E.J. Jordan, C.M. Kelly, M.A. Lowery, L.G. T. Morris, A.M. Omuro, N. Raj, P. Razavi, A.N. Shoushtari, N. Shukla, T. E. Soumerai, A.M. Varghese, R. Yaeger, J. Coleman, B. Bochner, G.J. Riely, L. B. Saltz, H.I. Scher, P.J. Sabbatini, M.E. Robson, D.S. Klimstra, B.S. Taylor, J. Baselga, N. Schultz, D.M. Hyman, M.E. Arcila, D.B. Solit, M. Ladanyi, M. F. Berger, Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients, Nat Med 23 (2017) 703-713, https://doi. org/10.1038/nm.4333.
[38] L. Wu, H. Yao, H. Chen, A. Wang, K. Guo, W. Gou, Y. Yu, X. Li, M. Yao, S. Yuan, F. Pang, J. Hu, L. Chen, W. Liu, J. Yao, S. Zhang, X. Dong, W. Wang, J. Hu, Q. Ling, S. Ding, Y. Wei, Q. Li, W. Cao, S. Wang, Y. Di, F. Feng, G. Zhao, J. Zhang, L. Huang, J. Xu, W. Yan, Z. Tong, D. Jiang, T. Ji, Q. Li, L. Xu, H. He, L. Shang, J. Liu, K. Wang, D. Wu, J. Shen, Y. Liu, T. Zhang, C. Liang, Y. Wang, Y. Shang, J. Guo, G. Liang, S. Xu, J. Liu, K. Wang, M. Wang, Landscape of somatic alterations in large-scale solid tumors from an Asian population, Nat. Commun. 13 (2022) 4264, https://doi.org/10.1038/s41467-022-31780-9.
[39] B. Nguyen, C. Fong, A. Luthra, S.A. Smith, R.G. DiNatale, S. Nandakumar, H. Walch, W.K. Chatila, R. Madupuri, R. Kundra, C.M. Bielski, B. Mastrogiacomo, M.T.A. Donoghue, A. Boire, S. Chandarlapaty, K. Ganesh, J.J. Harding, C. A. Iacobuzio-Donahue, P. Razavi, E. Reznik, C.M. Rudin, D. Zamarin, W. Abida, G.K. Abou-Alfa, C. Aghajanian, A. Cercek, P. Chi, D. Feldman, A.L. Ho, G. Iyer, Y. Y. Janjigian, M. Morris, R.J. Motzer, E.M. O’Reilly, M.A. Postow, N.P. Raj, G. J. Riely, M.E. Robson, J.E. Rosenberg, A. Safonov, A.N. Shoushtari, W. Tap, M. Y. Teo, A.M. Varghese, M. Voss, R. Yaeger, M.G. Zauderer, N. Abu-Rustum, J. Garcia-Aguilar, B. Bochner, A. Hakimi, W.R. Jarnagin, D.R. Jones, D. Molena, L. Morris, E. Rios-Doria, P. Russo, S. Singer, V.E. Strong, D. Chakravarty, L. H. Ellenson, A. Gopalan, J.S. Reis-Filho, B. Weigelt, M. Ladanyi, M. Gonen, S. P. Shah, J. Massague, J. Gao, A. Zehir, M.F. Berger, D.B. Solit, S.F. Bakhoum, F. Sanchez-Vega, N. Schultz, Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients, Cell 185 (2022) 563-575.e11, https://doi.org/10.1016/j.cell.2022.01.003.
[40] The ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium, L. A. Aaltonen, F. Abascal, A. Abeshouse, H. Aburatani, D.J. Adams, N. Agrawal, K. S. Ahn, S .- M. Ahn, H. Aikata, R. Akbani, K.C. Akdemir, H. Al-Ahmadie, S.T. Al- Sedairy, F. Al-Shahrour, M. Alawi, M. Albert, K. Aldape, L.B. Alexandrov, A. Ally, K. Alsop, E.G. Alvarez, F. Amary, S.B. Amin, B. Aminou, O. Ammerpohl, M. J. Anderson, Y. Ang, D. Antonello, P. Anur, S. Aparicio, E.L. Appelbaum, Y. Arai, A. Aretz, K. Arihiro, S. Ariizumi, J. Armenia, L. Arnould, S. Asa, Y. Assenov, G. Atwal, S. Aukema, J.T. Auman, M.R.R. Aure, P. Awadalla, M. Aymerich, G. D. Bader, A. Baez-Ortega, M.H. Bailey, P.J. Bailey, M. Balasundaram, S. Balu, P. Bandopadhayay, R.E. Banks, S. Barbi, A.P. Barbour, J. Barenboim, J. Barnholtz- Sloan, H. Barr, E. Barrera, J. Bartlett, J. Bartolome, C. Bassi, O.F. Bathe, D. Baumhoer, P. Bavi, S.B. Baylin, W. Bazant, D. Beardsmore, T.A. Beck, S. Behjati, A. Behren, B. Niu, C. Bell, S. Beltran, C. Benz, A. Berchuck, A. K. Bergmann, E.N. Bergstrom, B.P. Berman, D.M. Berney, S.H. Bernhart, R. Beroukhim, M. Berrios, S. Bersani, J. Bertl, M. Betancourt, V. Bhandari, S. G. Bhosle, A.V. Biankin, M. Bieg, D. Bigner, H. Binder, E. Birney, M. Birrer, N. K. Biswas, B. Bjerkehagen, T. Bodenheimer, L. Boice, G. Bonizzato, J.S. De Bono,
A. Boot, M.S. Bootwalla, A. Borg, A. Borkhardt, K.A. Boroevich, I. Borozan, C. Borst, M. Bosenberg, M. Bosio, J. Boultwood, G. Bourque, P.C. Boutros, G. S. Bova, D.T. Bowen, R. Bowlby, D.D.L. Bowtell, S. Boyault, R. Boyce, J. Boyd, A. Brazma, P. Brennan, D.S. Brewer, A.B. Brinkman, R.G. Bristow, R.R. Broaddus, J.E. Brock, M. Brock, A. Broeks, A.N. Brooks, D. Brooks, B. Brors, S. Brunak, T.J. C. Bruxner, A.L. Bruzos, A. Buchanan, I. Buchhalter, C. Buchholz, S. Bullman, H. Burke, B. Burkhardt, K.H. Burns, J. Busanovich, C.D. Bustamante, A.P. Butler, A.J. Butte, N.J. Byrne, A .- L. Børresen-Dale, S.J. Caesar-Johnson, A. Cafferkey, D. Cahill, C. Calabrese, C. Caldas, F. Calvo, N. Camacho, P.J. Campbell, E. Campo, C. Cantù, S. Cao, T.E. Carey, J. Carlevaro-Fita, R. Carlsen, I. Cataldo, M. Cazzola, J. Cebon, R. Cerfolio, D.E. Chadwick, D. Chakravarty, D. Chalmers, C.W.Y. Chan, K. Chan, M. Chan-Seng-Yue, V.S. Chandan, D.K. Chang, S.J. Chanock, L. A. Chantrill, A. Chateigner, N. Chatterjee, K. Chayama, H .- W. Chen, J. Chen, K. Chen, Y. Chen, Z. Chen, A.D. Cherniack, J. Chien, Y .- E. Chiew, S .- F. Chin, J. Cho, S. Cho, J.K. Choi, W. Choi, C. Chomienne, Z. Chong, S.P. Choo, A. Chou, A. N. Christ, E.L. Christie, E. Chuah, C. Cibulskis, K. Cibulskis, S. Cingarlini, P. Clapham, A. Claviez, S. Cleary, N. Cloonan, M. Cmero, C.C. Collins, A. A. Connor, S.L. Cooke, C.S. Cooper, L. Cope, V. Corbo, M.G. Cordes, S.M. Cordner,
I. Cortés-Ciriano, K. Covington, P.A. Cowin, B. Craft, D. Craft, C.J. Creighton, Y. Cun, E. Curley, I. Cutcutache, K. Czajka, B. Czerniak, R.A. Dagg, L. Danilova, M.V. Davi, N.R. Davidson, H. Davies, I.J. Davis, B.N. Davis-Dusenbery, K. J. Dawson, F.M. De La Vega, R. De Paoli-Iseppi, T. Defreitas, A.P.D. Tos, O. Delaneau, J.A. Demchok, J. Demeulemeester, G.M. Demidov, D. Demircioğlu, N.M. Dennis, R.E. Denroche, S.C. Dentro, N. Desai, V. Deshpande, A.G. Deshwar, C. Desmedt, J. Deu-Pons, N. Dhalla, N.C. Dhani, P. Dhingra, R. Dhir, A. DiBiase, K. Diamanti, L. Ding, S. Ding, H.Q. Dinh, L. Dirix, H. Doddapaneni, N. Donmez, M.T. Dow, R. Drapkin, O. Drechsel, R.M. Drews, S. Serge, T. Dudderidge, A. Dueso-Barroso, A.J. Dunford, M. Dunn, L.J. Dursi, F.R. Duthie, K. Dutton- Regester, J. Eagles, D.F. Easton, S. Edmonds, P.A. Edwards, S.E. Edwards, R. A. Eeles, A. Ehinger, J. Eils, R. Eils, A. El-Naggar, M. Eldridge, K. Ellrott, S. Erkek, G. Escaramis, S.M.G. Espiritu, X. Estivill, D. Etemadmoghadam, J.E. Eyfjord, B. M. Faltas, D. Fan, Y. Fan, W.C. Faquin, C. Farcas, M. Fassan, A. Fatima, F. Favero, N. Fayzullaev, I. Felau, S. Fereday, M.L. Ferguson, V. Ferretti, L. Feuerbach, M. A. Field, J.L. Fink, G. Finocchiaro, C. Fisher, M.W. Fittall, A. Fitzgerald, R. C. Fitzgerald, A.M. Flanagan, N.E. Fleshner, P. Flicek, J.A. Foekens, K.M. Fong, N. A. Fonseca, C.S. Foster, N.S. Fox, M. Fraser, S. Frazer, M. Frenkel-Morgenstern, W. Friedman, J. Frigola, C.C. Fronick, A. Fujimoto, M. Fujita, M. Fukayama, L. A. Fulton, R.S. Fulton, M. Furuta, P.A. Futreal, A. Füllgrabe, S.B. Gabriel, S. Gallinger, C. Gambacorti-Passerini, J. Gao, S. Gao, L. Garraway, Ø. Garred, E. Garrison, D.W. Garsed, N. Gehlenborg, J.L.L. Gelpi, J. George, D.S. Gerhard, C. Gerhauser, J.E. Gershenwald, M. Gerstein, M. Gerstung, G. Getz, M. Ghori, R. Ghossein, N.H. Giama, R.A. Gibbs, B. Gibson, A.J. Gill, P. Gill, D.D. Giri, D. Glodzik, V.J. Gnanapragasam, M.E. Goebler, M.J. Goldman, C. Gomez, S. Gonzalez, A. Gonzalez-Perez, D.A. Gordenin, J. Gossage, K. Gotoh, R. Govindan, D. Grabau, J.S. Graham, R.C. Grant, A.R. Green, E. Green, L. Greger, N. Grehan, S. Grimaldi, S.M. Grimmond, R.L. Grossman, A. Grundhoff, G. Gundem, Q. Guo, M. Gupta, S. Gupta, I.G. Gut, M. Gut, J. Göke, G. Ha, A. Haake, D. Haan, S. Haas, K. Haase, J.E. Haber, N. Habermann, F. Hach, S. Haider, N. Hama, F.C. Hamdy, A. Hamilton, M.P. Hamilton, L. Han, G. B. Hanna, M. Hansmann, N.J. Haradhvala, O. Harismendy, I. Harliwong, A. O. Harmanci, E. Harrington, T. Hasegawa, D. Haussler, S. Hawkins, S. Hayami, S. Hayashi, D.N. Hayes, S.J. Hayes, N.K. Hayward, S. Hazell, Y. He, A.P. Heath, S. C. Heath, D. Hedley, A.M. Hegde, D.I. Heiman, M.C. Heinold, Z. Heins, L. E. Heisler, E. Hellstrom-Lindberg, M. Helmy, S.G. Heo, A.J. Hepperla, J. M. Heredia-Genestar, C. Herrmann, P. Hersey, J.M. Hess, H. Hilmarsdottir, J. Hinton, S. Hirano, N. Hiraoka, K.A. Hoadley, A. Hobolth, E. Hodzic, J.I. Hoell, S. Hoffmann, O. Hofmann, A. Holbrook, A.Z. Holik, M.A. Hollingsworth, O. Holmes, R.A. Holt, C. Hong, E.P. Hong, J.H. Hong, G.K. Hooijer, H. Hornshøj, F. Hosoda, Y. Hou, V. Hovestadt, W. Howat, A.P. Hoyle, R.H. Hruban, J. Hu, T. Hu, X. Hua, K. Huang, M. Huang, M.N. Huang, V. Huang, Y. Huang, W. Huber, T.J. Hudson, M. Hummel, J.A. Hung, D. Huntsman, T.R. Hupp, J. Huse, M. R. Huska, B. Hutter, C.M. Hutter, D. Hübschmann, C.A. Iacobuzio-Donahue, C. D. Imbusch, M. Imielinski, S. Imoto, W.B. Isaacs, K. Isaev, S. Ishikawa, M. Iskar, S. M.A. Islam, M. Ittmann, S. Ivkovic, J.M.G. Izarzugaza, J. Jacquemier, V. Jakrot, N.B. Jamieson, G.H. Jang, S.J. Jang, J.C. Jayaseelan, R. Jayasinghe, S.R. Jefferys, K. Jegalian, J.L. Jennings, S .- H. Jeon, L. Jerman, Y. Ji, W. Jiao, P.A. Johansson, A. L. Johns, J. Johns, R. Johnson, T.A. Johnson, C. Jolly, Y. Joly, J.G. Jonasson, C. D. Jones, D.R. Jones, D.T.W. Jones, N. Jones, S.J.M. Jones, J. Jonkers, Y.S. Ju, H. Juhl, J. Jung, M. Juul, R.I. Juul, S. Juul, N. Jäger, R. Kabbe, A. Kahles, A. Kahraman, V.B. Kaiser, H. Kakavand, S. Kalimuthu, C. Von Kalle, K.J. Kang, K. Karaszi, B. Karlan, R. Karlić, D. Karsch, K. Kasaian, K.S. Kassahn, H. Katai, M. Kato, H. Katoh, Y. Kawakami, J.D. Kay, S.H. Kazakoff, M.D. Kazanov, M. Keays, E. Kebebew, R.F. Kefford, M. Kellis, J.G. Kench, C.J. Kennedy, J.N. A. Kerssemakers, D. Khoo, V. Khoo, N. Khuntikeo, E. Khurana, H. Kilpinen, H. K. Kim, H .- L. Kim, H .- Y. Kim, H. Kim, J. Kim, J. Kim, J.K. Kim, Y. Kim, T.A. King, W. Klapper, K. Kleinheinz, L.J. Klimczak, S. Knappskog, M. Kneba, B. M. Knoppers, Y. Koh, J. Komorowski, D. Komura, M. Komura, G. Kong, M. Kool, J. O. Korbel, V. Korchina, A. Korshunov, M. Koscher, R. Koster, Z. Kote-Jarai, A. Koures, M. Kovacevic, B. Kremeyer, H. Kretzmer, M. Kreuz, S. Krishnamurthy, D. Kube, K. Kumar, P. Kumar, S. Kumar, Y. Kumar, R. Kundra, K. Kübler, R. Küppers, J. Lagergren, P.H. Lai, P.W. Laird, S.R. Lakhani, C.M. Lalansingh, E. Lalonde, F.C. Lamaze, A. Lambert, E. Lander, P. Landgraf, L. Landoni, A. Langerød, A. Lanzós, D. Larsimont, E. Larsson, M. Lathrop, L.M.S. Lau, C. Lawerenz, R.T. Lawlor, M.S. Lawrence, A.J. Lazar, A.M. Lazic, X. Le, D. Lee, D. Lee, E.A. Lee, H.J. Lee, J.J .- K. Lee, J .- Y. Lee, J. Lee, M.T.M. Lee, H. Lee-Six, K .- V. Lehmann, H. Lehrach, D. Lenze, C.R. Leonard, D.A. Leongamornlert, I. Leshchiner, L. Letourneau, I. Letunic, D.A. Levine, L. Lewis, T. Ley, C. Li, C. H. Li, H.I. Li, J. Li, L. Li, S. Li, S. Li, X. Li, X. Li, X. Li, Y. Li, H. Liang, S .- B. Liang, P. Lichter, P. Lin, Z. Lin, W.M. Linehan, O.C. Lingjærde, D. Liu, E.M. Liu, F .- F. F. Liu, F. Liu, J. Liu, X. Liu, J. Livingstone, D. Livitz, N. Livni, L. Lochovsky, M. Loeffler, G.V. Long, A. Lopez-Guillermo, S. Lou, D.N. Louis, L.B. Lovat, Y. Lu, Y .- J. Lu, Y. Lu, C. Luchini, I. Lungu, X. Luo, H.J. Luxton, A.G. Lynch, L. Lype, C. López, C. López-Otín, E.Z. Ma, Y. Ma, G. MacGrogan, S. MacRae, G. Macintyre, T. Madsen, K. Maejima, A. Mafficini, D.T. Maglinte, A. Maitra, P.P. Majumder, L. Malcovati, S. Malikic, G. Malleo, G.J. Mann, L. Mantovani-Löffler, K. Marchal, G. Marchegiani, E.R. Mardis, A.A. Margolin, M.G. Marin, F. Markowetz, J. Markowski, J. Marks, T. Marques-Bonet, M.A. Marra, L. Marsden, J.W. M. Martens, S. Martin, J.I. Martin-Subero, I. Martincorena, A. Martinez- Fundichely, Y.E. Maruvka, R.J. Mashl, C.E. Massie, T.J. Matthew, L. Matthews, E. Mayer, S. Mayes, M. Mayo, F. Mbabaali, K. McCune, U. McDermott, P. D. McGillivray, M.D. Mclellan, J.D. McPherson, J.R. McPherson, T.A. McPherson, S.R. Meier, A. Meng, S. Meng, A. Menzies, N.D. Merrett, S. Merson, M. Meyerson, W. Meyerson, P.A. Mieczkowski, G.L. Mihaiescu, S. Mijalkovic, T. Mikkelsen, M. Milella, L. Mileshkin, C.A. Miller, D.K. Miller, J.K. Miller, G.B. Mills,
A. Milovanovic, S. Minner, M. Miotto, G.M. Arnau, L. Mirabello, C. Mitchell, T. J. Mitchell, S. Miyano, N. Miyoshi, S. Mizuno, F. Molnár-Gábor, M.J. Moore, R. A. Moore, S. Morganella, Q.D. Morris, C. Morrison, L.E. Mose, C.D. Moser, F. Muiños, L. Mularoni, A.J. Mungall, K. Mungall, E.A. Musgrove, V. Mustonen, D. Mutch, F. Muyas, D.M. Muzny, A. Muñoz, J. Myers, O. Myklebost, P. Möller, G. Nagae, A.M. Nagrial, H.K. Nahal-Bose, H. Nakagama, H. Nakagawa, H. Nakamura, T. Nakamura, K. Nakano, T. Nandi, J. Nangalia, M. Nastic, A. Navarro, F.C.P. Navarro, D.E. Neal, G. Nettekoven, F. Newell, S.J. Newhouse, Y. Newton, A.W.T. Ng, A. Ng, J. Nicholson, D. Nicol, Y. Nie, G.P. Nielsen, M. M. Nielsen, S. Nik-Zainal, M.S. Noble, K. Nones, P.A. Northcott, F. Notta, B. D. O’Connor, P. O’Donnell, M. O’Donovan, S. O’Meara, B.P. O’Neill, J.R. O’Neill, D. Ocana, A. Ochoa, L. Oesper, C. Ogden, H. Ohdan, K. Ohi, L. Ohno-Machado, K. A. Oien, A.I. Ojesina, H. Ojima, T. Okusaka, L. Omberg, C.K. Ong, S. Ossowski, G. Ott, B.F.F. Ouellette, C. P’ng, M. Paczkowska, S. Paiella, C. Pairojkul, M. Pajic, Q. Pan-Hammarström, E. Papaemmanuil, I. Papatheodorou, N. Paramasivam, J. W. Park, J .- W. Park, K. Park, K. Park, P.J. Park, J.S. Parker, S.L. Parsons, H. Pass, D. Pasternack, A. Pastore, A .- M. Patch, I. Pauporté, A. Pea, J.V. Pearson, C. S. Pedamallu, J.S. Pedersen, P. Pederzoli, M. Peifer, N.A. Pennell, C.M. Perou, M. D. Perry, G.M. Petersen, M. Peto, N. Petrelli, R. Petryszak, S.M. Pfister, M. Phillips, O. Pich, H.A. Pickett, T.D. Pihl, N. Pillay, S. Pinder, M. Pinese, A. V. Pinho, E. Pitkänen, X. Pivot, E. Piñeiro-Yáñez, L. Planko, C. Plass, P. Polak, T. Pons, I. Popescu, O. Potapova, A. Prasad, S.R. Preston, M. Prinz, A.L. Pritchard, S.D. Prokopec, E. Provenzano, X.S. Puente, S. Puig, M. Puiggròs, S. Pulido- Tamayo, G.M. Pupo, C.A. Purdie, M.C. Quinn, R. Rabionet, J.S. Rader, B. Radlwimmer, P. Radovic, B. Raeder, K.M. Raine, M. Ramakrishna, K. Ramakrishnan, S. Ramalingam, B.J. Raphael, W.K. Rathmell, T. Rausch, G. Reifenberger, J. Reimand, J. Reis-Filho, V. Reuter, I. Reyes-Salazar, M. A. Reyna, S.M. Reynolds, E. Rheinbay, Y. Riazalhosseini, A.L. Richardson, J. Richter, M. Ringel, M. Ringnér, Y. Rino, K. Rippe, J. Roach, L.R. Roberts, N. D. Roberts, S.A. Roberts, A.G. Robertson, A.J. Robertson, J.B. Rodriguez, B. Rodriguez-Martin, F.G. Rodríguez-González, M.H.A. Roehrl, M. Rohde, H. Rokutan, G. Romieu, I. Rooman, T. Roques, D. Rosebrock, M. Rosenberg, P. C. Rosenstiel, A. Rosenwald, E.W. Rowe, R. Royo, S.G. Rozen, Y. Rubanova, M. A. Rubin, C. Rubio-Perez, V.A. Rudneva, B.C. Rusev, A. Ruzzenente, G. Rätsch, R. Sabarinathan, V.Y. Sabelnykova, S. Sadeghi, S.C. Sahinalp, N. Saini, M. Saito- Adachi, G. Saksena, A. Salcedo, R. Salgado, L. Salichos, R. Sallari, C. Saller, R. Salvia, M. Sam, J.S. Samra, F. Sanchez-Vega, C. Sander, G. Sanders, R. Sarin, I. Sarrafi, A. Sasaki-Oku, T. Sauer, G. Sauter, R.P.M. Saw, M. Scardoni, C. J. Scarlett, A. Scarpa, G. Scelo, D. Schadendorf, J.E. Schein, M.B. Schilhabel, M. Schlesner, T. Schlomm, H.K. Schmidt, S .- J. Schramm, S. Schreiber, N. Schultz, S.E. Schumacher, R.F. Schwarz, R.A. Scolyer, D. Scott, R. Scully, R. Seethala, A. V. Segre, I. Selander, C.A. Semple, Y. Senbabaoglu, S. Sengupta, E. Sereni, S. Serra, D.C. Sgroi, M. Shackleton, N.C. Shah, S. Shahabi, C.A. Shang, P. Shang, O. Shapira, T. Shelton, C. Shen, H. Shen, R. Shepherd, R. Shi, Y. Shi, Y .- J. Shiah, T. Shibata, J. Shih, E. Shimizu, K. Shimizu, S.J. Shin, Y. Shiraishi, T. Shmaya, I. Shmulevich, S.I. Shorser, C. Short, R. Shrestha, S.S. Shringarpure, C. Shriver, S. Shuai, N. Sidiropoulos, R. Siebert, A.M. Sieuwerts, L. Sieverling, S. Signoretti, K.O. Sikora, M. Simbolo, R. Simon, J.V. Simons, J.T. Simpson, P.T. Simpson, S. Singer, N. Sinnott-Armstrong, P. Sipahimalani, T.J. Skelly, M. Smid, J. Smith, K. Smith-McCune, N.D. Socci, H.J. Sofia, M.G. Soloway, L. Song, A.K. Sood, S. Sothi, C. Sotiriou, C.M. Soulette, P.N. Span, P.T. Spellman, N. Sperandio, A. J. Spillane, O. Spiro, J. Spring, J. Staaf, P.F. Stadler, P. Staib, S.G. Stark, L. Stebbings, Ó.A. Stefánsson, O. Stegle, L.D. Stein, A. Stenhouse, C. Stewart, S. Stilgenbauer, M.D. Stobbe, M.R. Stratton, J.R. Stretch, A.J. Struck, J.M. Stuart, H.G. Stunnenberg, H. Su, X. Su, R.X. Sun, S. Sungalee, H. Susak, A. Suzuki, F. Sweep, M. Szczepanowski, H. Sültmann, T. Yugawa, A. Tam, D. Tamborero, B. K.T. Tan, D. Tan, P. Tan, H. Tanaka, H. Taniguchi, T.J. Tanskanen, M. Tarabichi, R. Tarnuzzer, P. Tarpey, M.L. Taschuk, K. Tatsuno, S. Tavaré, D.F. Taylor, A. Taylor-Weiner, J.W. Teague, B.T. Teh, V. Tembe, J. Temes, K. Thai, S. P. Thayer, N. Thiessen, G. Thomas, S. Thomas, A. Thompson, A.M. Thompson, J. F.F. Thompson, R.H. Thompson, H. Thorne, L.B. Thorne, A. Thorogood, G. Tiao, N. Tijanic, L.E. Timms, R. Tirabosco, M. Tojo, S. Tommasi, C.W. Toon, U. H. Toprak, D. Torrents, G. Tortora, J. Tost, Y. Totoki, D. Townend, N. Traficante, I. Treilleux, J .- R. Trotta, L.H.P. Trumper, M. Tsao, T. Tsunoda, J.M.C. Tubio, O. Tucker, R. Turkington, D.J. Turner, A. Tutt, M. Ueno, N.T. Ueno, C. Umbricht, H.M. Umer, T.J. Underwood, L. Urban, T. Urushidate, T. Ushiku, L. Uusküla- Reimand, A. Valencia, D.J. Van Den Berg, S. Van Laere, P. Van Loo, E.G. Van Meir, G.G. Van Den Eynden, T. Van Der Kwast, N. Vasudev, M. Vazquez, R. Vedururu, U. Veluvolu, S. Vembu, L.P.C. Verbeke, P. Vermeulen, C. Verrill, A. Viari, D. Vicente, C. Vicentini, K. VijayRaghavan, J. Viksna, R.E. Vilain, I. Villasante, A. Vincent-Salomon, T. Visakorpi, D. Voet, P. Vyas, I. Vázquez- García, N.M. Waddell, N. Waddell, C. Wadelius, L. Wadi, R. Wagener, J.A. Wala, J. Wang, J. Wang, L. Wang, Q. Wang, W. Wang, Y. Wang, Z. Wang, P.M. Waring, H .- J. Warnatz, J. Warrell, A.Y. Warren, S.M. Waszak, D.C. Wedge, D. Weichenhan, P. Weinberger, J.N. Weinstein, J. Weischenfeldt, D.
J. Weisenberger, I. Welch, M.C. Wendl, J. Werner, J.P. Whalley, D.A. Wheeler, H. C. Whitaker, D. Wigle, M.D. Wilkerson, A. Williams, J.S. Wilmott, G.W. Wilson, J. M. Wilson, R.K. Wilson, B. Winterhoff, J.A. Wintersinger, M. Wiznerowicz, S. Wolf, B.H. Wong, T. Wong, W. Wong, Y. Woo, S. Wood, B.G. Wouters, A.
J. Wright, D.W. Wright, M.H. Wright, C .- L. Wu, D .- Y. Wu, G. Wu, J. Wu, K. Wu, Y. Wu, Z. Wu, L. Xi, T. Xia, Q. Xiang, X. Xiao, R. Xing, H. Xiong, Q. Xu, Y. Xu, H. Xue, S. Yachida, S. Yakneen, R. Yamaguchi, T.N. Yamaguchi, M. Yamamoto, S. Yamamoto, H. Yamaue, F. Yang, H. Yang, J.Y. Yang, L. Yang, L. Yang, S. Yang, T .- P. Yang, Y. Yang, X. Yao, M .- L. Yaspo, L. Yates, C. Yau, C. Ye, K. Ye, V.
D. Yellapantula, C.J. Yoon, S .- S. Yoon, F. Yousif, J. Yu, K. Yu, W. Yu, Y. Yu,
K. Yuan, Y. Yuan, D. Yuen, C.K. Yung, O. Zaikova, J. Zamora, M. Zapatka, J.
C. Zenklusen, T. Zenz, N. Zeps, C .- Z. Zhang, F. Zhang, H. Zhang, H. Zhang, H. Zhang, J. Zhang, J. Zhang, J. Zhang, X. Zhang, X. Zhang, Y. Zhang, Z. Zhang, Z. Zhao, L. Zheng, X. Zheng, W. Zhou, Y. Zhou, B. Zhu, H. Zhu, J. Zhu, S. Zhu, L. Zou, X. Zou, A. deFazio, N. Van As, C.H.M. Van Deurzen, M.J. Van De Vijver, L. Van’T Veer, C. Von Mering, Pan-cancer analysis of whole genomes, Nature 578 (2020) 82-93, https://doi.org/10.1038/s41586-020-1969-6.
[41] I. De Bruijn, R. Kundra, B. Mastrogiacomo, T.N. Tran, L. Sikina, T. Mazor, X. Li, A. Ochoa, G. Zhao, B. Lai, A. Abeshouse, D. Baiceanu, E. Ciftci, U. Dogrusoz, A. Dufilie, Z. Erkoc, E. Garcia Lara, Z. Fu, B. Gross, C. Haynes, A. Heath, D. Higgins, P. Jagannathan, K. Kalletla, P. Kumari, J. Lindsay, A. Lisman, B. Leenknegt, P. Lukasse, D. Madela, R. Madupuri, P. Van Nierop, O. Plantalech, J. Quach, A.C. Resnick, S.Y.A. Rodenburg, B.A. Satravada, F. Schaeffer, R. Sheridan, J. Singh, R. Sirohi, S.O. Sumer, S. Van Hagen, A. Wang, M. Wilson, H. Zhang, K. Zhu, N. Rusk, S. Brown, J.A. Lavery, K.S. Panageas, J.E. Rudolph, M. L. LeNoue-Newton, J.L. Warner, X. Guo, H. Hunter-Zinck, T.V. Yu, S. Pilai, C. Nichols, S.M. Gardos, J. Philip, Aacr Project Genie Bpc Core Team, Aacr Project Genie Consortium, K.L. Kehl, G.J. Riely, D. Schrag, J. Lee, M.V. Fiandalo, S. M. Sweeney, T.J. Pugh, C. Sander, E. Cerami, J. Gao, N. Schultz, Analysis and visualization of longitudinal genomic and clinical data from the AACR Project GENIE biopharma collaborative in cBioPortal, Cancer Res. 83 (2023) 3861-3867, https://doi.org/10.1158/0008-5472.CAN-23-0816.
[42] T.D. Wu, S. Madireddi, P.E. De Almeida, R. Banchereau, Y .- J.J. Chen, A.S. Chitre, E.Y. Chiang, H. Iftikhar, W.E. O’Gorman, A. Au-Yeung, C. Takahashi, L. D. Goldstein, C. Poon, S. Keerthivasan, D.E. De Almeida Nagata, X. Du, H .- M. Lee, K.L. Banta, S. Mariathasan, M. Das Thakur, M.A. Huseni, M. Ballinger, I. Estay, P. Caplazi, Z. Modrusan, L. Delamarre, I. Mellman, R. Bourgon, J.L. Grogan, Peripheral T cell expansion predicts tumour infiltration and clinical response, Nature 579 (2020) 274-278, https://doi.org/10.1038/s41586-020-2056-8.
[43] L. Wang, R.P. Sebra, J.P. Sfakianos, K. Allette, W. Wang, S. Yoo, N. Bhardwaj, E. E. Schadt, X. Yao, M.D. Galsky, J. Zhu, A reference profile-free deconvolution method to infer cancer cell-intrinsic subtypes and tumor-type-specific stromal profiles, Genome Med. 12 (2020) 24, https://doi.org/10.1186/s13073-020-0720- 0.
[44] Kathleen Cuningham Foundation Consortium for Research into Familial Breast Cancer (kConFab), P. Savas, B. Virassamy, C. Ye, A. Salim, C.P. Mintoff, F. Caramia, R. Salgado, D.J. Byrne, Z.L. Teo, S. Dushyanthen, A. Byrne, L. Wein, S. J. Luen, C. Poliness, S.S. Nightingale, A.S. Skandarajah, D.E. Gyorki, C. M. Thornton, P.A. Beavis, S.B. Fox, P.K. Darcy, T.P. Speed, L.K. Mackay, P. J. Neeson, S. Loi, Single-cell profiling of breast cancer T cells reveals a tissue- resident memory subset associated with improved prognosis, Nat Med 24 (2018) 986-993, https://doi.org/10.1038/s41591-018-0078-7.
[45] X. Zhang, L. Peng, Y. Luo, S. Zhang, Y. Pu, Y. Chen, W. Guo, J. Yao, M. Shao, W. Fan, Q. Cui, Y. Xi, Y. Sun, X. Niu, X. Zhao, L. Chen, Y. Wang, Y. Liu, X. Yang, C. Wang, C. Zhong, W. Tan, J. Wang, C. Wu, D. Lin, Dissecting esophageal squamous-cell carcinoma ecosystem by single-cell transcriptomic analysis, Nat. Commun. 12 (2021) 5291, https://doi.org/10.1038/s41467-021-25539-x.
[46] S. Cheng, Z. Li, R. Gao, B. Xing, Y. Gao, Y. Yang, S. Qin, L. Zhang, H. Ouyang, P. Du, L. Jiang, B. Zhang, Y. Yang, X. Wang, X. Ren, J .- X. Bei, X. Hu, Z. Bu, J. Ji, Z. Zhang, A pan-cancer single-cell transcriptional atlas of tumor infiltrating myeloid cells, Cell 184 (2021) 792-809.e23, https://doi.org/10.1016/j. cell.2021.01.010.
[47] D. Lambrechts, E. Wauters, B. Boeckx, S. Aibar, D. Nittner, O. Burton, A. Bassez, H. Decaluwé, A. Pircher, K. Van den Eynde, B. Weynand, E. Verbeken, P. De Leyn, A. Liston, J. Vansteenkiste, P. Carmeliet, S. Aerts, B. Thienpont, Phenotype molding of stromal cells in the lung tumor microenvironment, Nat Med 24 (2018) 1277-1289, https://doi.org/10.1038/s41591-018-0096-5.
[48] Y. Han, Y. Wang, X. Dong, D. Sun, Z. Liu, J. Yue, H. Wang, T. Li, C. Wang, TISCH2: expanded datasets and new tools for single-cell transcriptome analyses of the tumor microenvironment, Nucleic Acids Res. 51 (2023) D1425-D1431, https:// doi.org/10.1093/nar/gkac959.
[49] P.V. Hornbeck, B. Zhang, B. Murray, J.M. Kornhauser, V. Latham, E. Skrzypek, PhosphoSitePlus, 2014: mutations, PTMs and recalibrations, Nucleic Acids Res. 43 (2015) D512-D520, https://doi.org/10.1093/nar/gku1267.
[50] UniProt Consortium, UniProt: the universal protein knowledgebase in 2025, Nucleic Acids Res. 53 (2025) D609-D617, https://doi.org/10.1093/nar/ gkae1010.
[51] A. Verbiest, G. Couchy, S. Job, J. Zucman-Rossi, L. Caruana, E. Lerut, R. Oyen, A. De Reyniès, B. Laguerre, N. Rioux-Leclercq, A. Wozniak, S. Joniau, H. Van Poppel, K. Van Den Eynde, B. Beuselinck, Molecular subtypes of clear cell renal cell carcinoma are associated with outcome during pazopanib therapy in the metastatic setting, Clin. Genitourin. Cancer 16 (2018) e605-e612, https://doi. org/10.1016/j.clgc.2017.10.017.
[52] K.L. Knutson, M.L. Disis, Tumor antigen-specific T helper cells in cancer immunity and immunotherapy, Cancer Immunol. Immunother. 54 (2005) 721-728, https://doi.org/10.1007/s00262-004-0653-2.
[53] H.S. Marques, B.B. De Brito, F.A.F. Da Silva, M.L.C. Santos, J.C.B. De Souza, T.M. L. Correia, L.W. Lopes, N.S.D.M. Neres, R.S.D.M. Dórea, A.C.S. Dantas, L.L. B. Morbeck, I.S. Lima, A.A. De Almeida, M.R.D.J. Dias, F.F. De Melo, Relationship between Th17 immune response and cancer, WJCO 12 (2021) 845-867, https:// doi.org/10.5306/wjco.v12.i10.845.
[54] E.J. Wherry, T cell exhaustion, Nat. Immunol. 12 (2011) 492-499, https://doi. org/10.1038/ni.2035.
[55] L. Martínez-Lostao, A. Anel, J. Pardo, How do cytotoxic lymphocytes kill cancer cells? Clin. Cancer Res. 21 (2015) 5047-5056, https://doi.org/10.1158/1078- 0432.CCR-15-0685.
[56] S. Agioti, A. Zaravinos, Immune cytolytic activity and strategies for therapeutic treatment, Int. J. Mol. Sci. 25 (2024) 3624, https://doi.org/10.3390/ ijms25073624.
[57] C. Roufas, I. Georgakopoulos-Soares, A. Zaravinos, Molecular correlates of immune cytolytic subgroups in colorectal cancer by integrated genomics analysis, NAR Cancer 3 (2021) zcab005, https://doi.org/10.1093/narcan/zcab005.
[58] C. Roufas, I. Georgakopoulos-Soares, A. Zaravinos, Distinct genomic features across cytolytic subgroups in skin melanoma, Cancer Immunol. Immunother. 70 (2021) 3137-3154, https://doi.org/10.1007/s00262-021-02918-3.
[59] C. Roufas, D. Chasiotis, A. Makris, C. Efstathiades, C. Dimopoulos, A. Zaravinos, The expression and prognostic impact of immune cytolytic activity-related markers in human malignancies: a comprehensive meta-analysis, Front. Oncol. 8 (2018) 27, https://doi.org/10.3389/fonc.2018.00027.
[60] D.J. Gasper, M.M. Tejera, M. Suresh, CD4 T-cell memory generation and maintenance, Crit. Rev. Immunol. 34 (2014) 121-146, https://doi.org/10.1615/ CritRevImmunol.2014010373.
[61] S. Puhr, J. Lee, E. Zvezdova, Y.J. Zhou, K. Liu, Dendritic cell development-history, advances, and open questions, Semin. Immunol. 27 (2015) 388-396, https://doi.org/10.1016/j.smim.2016.03.012.
[62] X. Lei, Y. Lei, J .- K. Li, W .- X. Du, R .- G. Li, J. Yang, J. Li, F. Li, H .- B. Tan, Immune cells within the tumor microenvironment: biological functions and roles in cancer immunotherapy, Cancer Lett. 470 (2020) 126-133, https://doi.org/10.1016/j. canlet.2019.11.009.
[63] J.C. Nolz, G.R. Starbeck-Miller, J.T. Harty, Naive, effector and memory CD8 T-cell trafficking: parallels and distinctions, Immunotherapy 3 (2011) 1223-1233, https://doi.org/10.2217/imt.11.100.
[64] M. Allam, T. Hu, J. Lee, J. Aldrich, S.S. Badve, Y. Gökmen-Polar, M. Bhave, S. S. Ramalingam, F. Schneider, A.F. Coskun, Spatially variant immune infiltration scoring in human cancer tissues, Npj Precis. Onc. 6 (2022) 60, https://doi.org/ 10.1038/s41698-022-00305-4.
[65] S. Sui, X. An, C. Xu, Z. Li, Y. Hua, G. Huang, S. Sui, Q. Long, Y. Sui, Y. Xiong, M. Ntim, W. Guo, M. Chen, M. Li, X. Xiao, W. Deng, An immune cell infiltration- based immune score model predicts prognosis and chemotherapy effects in breast cancer, Theranostics 10 (2020) 11938-11949, https://doi.org/10.7150/ thno.49451.
[66] H. Nie, Y. Zheng, R. Li, T.B. Guo, D. He, L. Fang, X. Liu, L. Xiao, X. Chen, B. Wan, Y.E. Chin, J.Z. Zhang, Phosphorylation of FOXP3 controls regulatory T cell function and is inhibited by TNF-a in rheumatoid arthritis, Nat Med 19 (2013) 322-328, https://doi.org/10.1038/nm.3085.
[67] Z. Li, F. Lin, C. Zhuo, G. Deng, Z. Chen, S. Yin, Z. Gao, M. Piccioni, A. Tsun, S. Cai, S.G. Zheng, Y. Zhang, B. Li, PIM1 kinase phosphorylates the human transcription factor FOXP3 at serine 422 to negatively regulate its activity under inflammation, J. Biol. Chem. 289 (2014) 26872-26881, https://doi.org/10.1074/jbc. M114.586651.
[68] J. van Loosdregt, P.J. Coffer, Post-translational modification networks regulating FOXP3 function, Trends Immunol. 35 (2014) 368-378, https://doi.org/10.1016/ j.it.2014.06.005.
[69] N.F. Neel, E. Schutyser, J. Sai, G .- H. Fan, A. Richmond, Chemokine receptor internalization and intracellular trafficking, Cytokine Growth Factor Rev. 16 (2005) 637-658, https://doi.org/10.1016/j.cytogfr.2005.05.008.
[70] Y. Zhao, D. Yu, H. Wu, H. Liu, H. Zhou, R. Gu, R. Zhang, S. Zhang, G. Wu, Anticancer activity of SAHA, a potent histone deacetylase inhibitor, in NCI-H460 human large-cell lung carcinoma cells in vitro and in vivo, Int. J. Oncol. 44 (2014) 451-458, https://doi.org/10.3892/ijo.2013.2193.
[71] L. Bálintová, M. Matúšková, A. Gábelová, The evaluation of the efficacy and potential genotoxic hazard of combined SAHA and 5-FU treatment in the chemoresistant colorectal cancer cell lines, Mutat. Res. Genet. Toxicol. Environ. Mutagen 874-875 (2022) 503445, https://doi.org/10.1016/j. mrgentox.2022.503445.
[72] Y. Shi, Z. Li, X. Han, J. Yi, Z. Wang, J. Hou, C. Feng, Q. Fang, H. Wang, P. Zhang, F. Wang, J. Shen, P. Wang, The histone deacetylase inhibitor suberoylanilide hydroxamic acid induces growth inhibition and enhances taxol-induced cell death in breast cancer, Cancer Chemother. Pharmacol. 66 (2010) 1131-1140, https:// doi.org/10.1007/s00280-010-1455-1.
[73] A. Wawruszak, J.J. Luszczki, A. Grabarska, E. Gumbarewicz, M. Dmoszynska- Graniczka, K. Polberg, A. Stepulak, Assessment of interactions between cisplatin and two histone deacetylase inhibitors in MCF7, T47D and MDA-MB-231 human breast cancer cell lines - an isobolographic analysis, PLoS One 10 (2015) e0143013, https://doi.org/10.1371/journal.pone.0143013.
[74] E.A. Mohamed, I.I. Abu Hashim, R.M. Yusif, G.M. Suddek, A.A.A. Shaaban, F.A. E. Badria, Enhanced in vitro cytotoxicity and anti-tumor activity of vorinostat- loaded pluronic micelles with prolonged release and reduced hepatic and renal toxicities, Eur J Pharm Sci 96 (2017) 232-242, https://doi.org/10.1016/j. ejps.2016.09.029.
[75] X .- C. Huang, M. Wang, Y .- M. Pan, X .- Y. Tian, H .- S. Wang, Y. Zhang, Synthesis and antitumor activities of novel «-aminophosphonates dehydroabietic acid derivatives, Bioorg Med Chem Lett 23 (2013) 5283-5289, https://doi.org/ 10.1016/j.bmcl.2013.08.005.
[76] D.R. Mans, I. Grivicich, G.J. Peters, G. Schwartsmann, Sequence-dependent growth inhibition and DNA damage formation by the irinotecan-5-fluorouracil combination in human colon carcinoma cell lines, Eur. J. Cancer 35 (1999) 1851-1861, https://doi.org/10.1016/s0959-8049(99)00222-1.
[77] R. Briffa, I. Um, D. Faratian, Y. Zhou, A.K. Turnbull, S.P. Langdon, D.J. Harrison, Multi-scale genomic, transcriptomic and proteomic analysis of colorectal cancer cell lines to identify novel biomarkers, PLoS One 10 (2015) e0144708, https:// doi.org/10.1371/journal.pone.0144708.
[78] R. Mazrouei, E. Raeisi, Y. Lemoigne, E. Heidarian, Activation of p53 gene expression and synergistic antiproliferative effects of 5-fluorouracil and ß-escin on MCF7 cells, J Med Signals Sens 9 (2019) 196-203, https://doi.org/10.4103/ jmss.JMSS_44_18.
[79] M. Chalabi-Dchar, O. Villeronce, J. Ripoll, A. Vincent, T. Fenouil, R. Khoueiry, J. Kucharczak, L. Jentschel, F. Catez, A. Vigneron, J. Tréguier, C. Mandier, C. Bouclier, J. Vitre, L. Lagerqvist, A. Choquet, Z. Herceg, C. Machon, J. Guitton, A. David, E. Solary, D. Bernard, N. Martin, E. Rivals, N.D. Venezia, J. Pannequin, J .- J. Diaz, Translational control of cell plasticity drives 5-FU tolerance, bioRxiv (2024), https://doi.org/10.1101/2024.07.03.601826, 2024.07.03.601826.
[80] D. Klopotowska, J. Matuszyk, VDR agonists increase sensitivity of MCF-7 and BT- 474 breast cancer cells to 5 FU, Anticancer Res. 40 (2020) 837-840, https://doi. org/10.21873/anticanres.14015.
[81] T. Lesuffleur, A. Barbat, E. Dussaulx, A. Zweibaum, Growth adaptation to methotrexate of HT-29 human colon carcinoma cells is associated with their ability to differentiate into columnar absorptive and mucus-secreting cells, Cancer Res. 50 (1990) 6334-6343.
[82] M. de Nonancourt-Didion, J.L. Guéant, C. Adjalla, C. Chéry, R. Hatier, F. Namour, Overexpression of folate binding protein alpha is one of the mechanism explaining the adaptation of HT29 cells to high concentration of methotrexate, Cancer Lett. 171 (2001) 139-145, https://doi.org/10.1016/s0304-3835(01) 00552-3.
[83] M. Barani, M. Reza Hajinezhad, S. Sargazi, M. Zeeshan, A. Rahdar, S. Pandey, M. Khatami, F. Zargari, Simulation, in vitro, and in vivo cytotoxicity assessments of methotrexate-loaded pH-responsive nanocarriers, Polymers 13 (2021) 3153, https://doi.org/10.3390/polym13183153.
[84] H.M. Kaplan, P. Pazarci, Antiproliferative and apoptotic effects of tempol, methotrexate, and their combinations on the MCF7 breast cancer cell line, ACS Omega 9 (2024) 6658-6662, https://doi.org/10.1021/acsomega.3c07624.
[85] M. Ferreira-Teixeira, B. Parada, P. Rodrigues-Santos, V. Alves, J.S. Ramalho, F. Caramelo, V. Sousa, F. Reis, C.M. Gomes, Functional and molecular characterization of cancer stem-like cells in bladder cancer: a potential signature for muscle-invasive tumors, Oncotarget 6 (2015) 36185-36201, https://doi.org/ 10.18632/oncotarget.5517.
[86] S. Sakaguchi, N. Sakaguchi, M. Asano, M. Itoh, M. Toda, Immunologic self- tolerance maintained by activated T cells expressing IL-2 receptor alpha-chains (CD25). Breakdown of a single mechanism of self-tolerance causes various autoimmune diseases, J. Immunol. 155 (1995) 1151-1164.
[87] 7] J.D. Fontenot, M.A. Gavin, A.Y. Rudensky, Foxp3 programs the development and function of CD4+CD25+ regulatory T cells, Nat. Immunol. 4 (2003) 330-336, https://doi.org/10.1038/ni904.
[88] P.B. Olkhanud, B. Damdinsuren, M. Bodogai, R.E. Gress, R. Sen, K. Wejksza, E. Malchinkhuu, R.P. Wersto, A. Biragyn, Tumor-evoked regulatory B cells promote breast cancer metastasis by converting resting CD4+ T cells to T- regulatory cells, Cancer Res. 71 (2011) 3505-3515, https://doi.org/10.1158/ 0008-5472.CAN-10-4316.
[89] M. Ammirante, J .- L. Luo, S. Grivennikov, S. Nedospasov, M. Karin, B-cell-derived lymphotoxin promotes castration-resistant prostate cancer, Nature 464 (2010) 302-305, https://doi.org/10.1038/nature08782.
[90] M. Rafei, J. Hsieh, S. Zehntner, M. Li, K. Forner, E. Birman, M .- N. Boivin, Y. K. Young, C. Perreault, J. Galipeau, A granulocyte-macrophage colony-stimulating factor and interleukin-15 fusokine induces a regulatory B cell population with immune suppressive properties, Nat Med 15 (2009) 1038-1045, https://doi.org/10.1038/nm.2003.
[91] T. Ito, S. Ishikawa, T. Sato, K. Akadegawa, H. Yurino, M. Kitabatake, S. Hontsu, T. Ezaki, H. Kimura, K. Matsushima, Defective B1 cell homing to the peritoneal cavity and preferential recruitment of B1 cells in the target organs in a murine model for systemic lupus erythematosus, J. Immunol. 172 (2004) 3628-3634, https://doi.org/10.4049/jimmunol.172.6.3628.
[92] T. Matsushita, K. Yanaba, J .- D. Bouaziz, M. Fujimoto, T.F. Tedder, Regulatory B cells inhibit EAE initiation in mice while other B cells promote disease progression, J. Clin. Investig. (2008) JCI36030, https://doi.org/10.1172/ JCI36030.
[93] N. Yu, X. Li, W. Song, D. Li, D. Yu, X. Zeng, M. Li, X. Leng, X. Li, CD4+CD25+ CD127low/- T cells: a more specific Treg population in human peripheral blood, Inflammation 35 (2012) 1773-1780, https://doi.org/10.1007/s10753-012-9496- 8.
[94] F. Simonetta, A. Chiali, C. Cordier, A. Urrutia, I. Girault, S. Bloquet, C. Tanchot, C. Bourgeois, Increased CD127 expression on activated FOXP3+CD4+ regulatory T cells, Eur. J. Immunol. 40 (2010) 2528-2538, https://doi.org/10.1002/ eji.201040531.
[95] R. Chu, S.Y.W. Liu, A.C. Vlantis, C.A. Van Hasselt, E.K.W. Ng, M.D. Fan, S.K. Ng, A.B.W. Chan, J. Du, W. Wei, X. Liu, Z. Liu, G.G. Chen, Inhibition of Foxp3 in cancer cells induces apoptosis of thyroid cancer cells, Mol. Cell. Endocrinol. 399 (2015) 228-234, https://doi.org/10.1016/j.mce.2014.10.006.
[96] L.M. Ebert, B.S. Tan, J. Browning, S. Svobodova, S.E. Russell, N. Kirkpatrick, C. Gedye, D. Moss, S.P. Ng, D. MacGregor, I.D. Davis, J. Cebon, W. Chen, The regulatory T cell-associated transcription factor FoxP3 is expressed by tumor cells, Cancer Res. 68 (2008) 3001-3009, https://doi.org/10.1158/0008-5472. CAN-07-5664.
[97] R. Liu, C. Liu, D. Chen, W .- H. Yang, X. Liu, C .- G. Liu, C.M. Dugas, F. Tang, P. Zheng, Y. Liu, L. Wang, FOXP3 controls an miR-146/NF-KB negative feedback loop that inhibits apoptosis in breast cancer cells, Cancer Res. 75 (2015) 1703-1713, https://doi.org/10.1158/0008-5472.CAN-14-2108.
[98] G .- F. Ma, Q. Miao, Y .- M. Liu, H. Gao, J .- J. Lian, Y .- N. Wang, X .- Q. Zeng, T .- C. Luo, L .- L. Ma, Z .- B. Shen, Y .- H. Sun, S .- Y. Chen, High FoxP3 expression in tumour cells
predicts better survival in gastric cancer and its role in tumour microenvironment, Br. J. Cancer 110 (2014) 1552-1560, https://doi.org/ 10.1038/bjc.2014.47.
[99] S. Yang, Y. Liu, M .- Y. Li, C.S.H. Ng, S. Yang, S. Wang, C. Zou, Y. Dong, J. Du, X. Long, L .- Z. Liu, I.Y.P. Wan, T. Mok, M.J. Underwood, G.G. Chen, FOXP3 promotes tumor growth and metastasis by activating Wnt/ß-catenin signaling pathway and EMT in non-small cell lung cancer, Mol. Cancer 16 (2017) 124, https://doi.org/10.1186/s12943-017-0700-1.
[100] J. Li, X. Zhang, B. Liu, C. Shi, X. Ma, S. Ren, X. Zhao, Y. Liu, The expression landscape of FOXP3 and its prognostic value in breast cancer, Ann. Transl. Med. 10 (2022) 801, https://doi.org/10.21037/atm-22-3080, 801.
[101] S. Liu, Z. Wang, Interferon regulatory factor family genes: at the crossroads between immunity and head and neck squamous carcinoma, Dis. Markers 2022 (2022) 1-19, https://doi.org/10.1155/2022/2561673.
[102] H. Jia, H. Qi, Z. Gong, S. Yang, J. Ren, Y. Liu, M .- Y. Li, G.G. Chen, The expression of FOXP3 and its role in human cancers, Biochim. Biophys. Acta Rev. Canc 1871 (2019) 170-178, https://doi.org/10.1016/j.bbcan.2018.12.004.
[103] R. Saleh, E. Elkord, FoxP3+ T regulatory cells in cancer: prognostic biomarkers and therapeutic targets, Cancer Lett. 490 (2020) 174-185, https://doi.org/ 10.1016/j.canlet.2020.07.022.
[104] J. Wang, R. Gong, C. Zhao, K. Lei, X. Sun, H. Ren, Human FOXP3 and tumour microenvironment, Immunology 168 (2023) 248-255, https://doi.org/10.1111/ imm.13520.
[105] T. Zuo, R. Liu, H. Zhang, X. Chang, Y. Liu, L. Wang, P. Zheng, Y. Liu, FOXP3 is a novel transcriptional repressor for the breast cancer oncogene SKP2, J. Clin. Investig. (2007) JCI32538, https://doi.org/10.1172/JCI32538.
[106] T. Zuo, L. Wang, C. Morrison, X. Chang, H. Zhang, W. Li, Y. Liu, Y. Wang, X. Liu, M.W.Y. Chan, J .- Q. Liu, R. Love, C. Liu, V. Godfrey, R. Shen, T.H .- M. Huang, T. Yang, B.K. Park, C .- Y. Wang, P. Zheng, Y. Liu, FOXP3 is an X-linked breast cancer suppressor gene and an important repressor of the HER-2/ErbB2 oncogene, Cell 129 (2007) 1275-1286, https://doi.org/10.1016/j. cell.2007.04.034.
[107] S. Douglass, A.P. Meeson, D. Overbeck-Zubrzycka, J.G. Brain, M.R. Bennett, C. A. Lamb, T.W. Lennard, D. Browell, S. Ali, J.A. Kirby, Breast cancer metastasis: demonstration that FOXP3 regulates CXCR4 expression and the response to CXCL12, J. Pathol. 234 (2014) 74-85, https://doi.org/10.1002/path.4381.
[108] W. Li, H. Katoh, L. Wang, X. Yu, Z. Du, X. Yan, P. Zheng, Y. Liu, FOXP3 regulates sensitivity of cancer cells to irradiation by transcriptional repression of BRCA1, Cancer Res. 73 (2013) 2170-2180, https://doi.org/10.1158/0008-5472.CAN-12- 2481.
[109] G .- F. Ma, S .- Y. Chen, Z .- R. Sun, Q. Miao, Y .- M. Liu, X .- Q. Zeng, T .- C. Luo, L .- L. Ma, J .- J. Lian, D .- L. Song, FoxP3 inhibits proliferation and induces apoptosis of gastric cancer cells by activating the apoptotic signaling pathway, Biochem. Biophys. Res. Commun. 430 (2013) 804-809, https://doi.org/10.1016/j. bbrc.2012.11.065.
[110] J .- Y. Shi, L .- J. Ma, J .- W. Zhang, M. Duan, Z .- B. Ding, L .- X. Yang, Y. Cao, J. Zhou, J. Fan, X. Zhang, Y .- J. Zhao, X .- Y. Wang, Q. Gao, FOXP3 Is a HCC suppressor gene and Acts through regulating the TGF-B/Smad2/3 signaling pathway, BMC Cancer 17 (2017) 648, https://doi.org/10.1186/s12885-017-3633-6.
[111] H .- Y. Zhang, H. Sun, Up-regulation of Foxp3 inhibits cell proliferation, migration and invasion in epithelial ovarian cancer, Cancer Lett. 287 (2010) 91-97, https:// doi.org/10.1016/j.canlet.2009.06.001.
[112] Q. Luo, S. Zhang, H. Wei, X. Pang, H. Zhang, Roles of Foxp3 in the occurrence and development of cervical cancer, Int. J. Clin. Exp. Pathol. 8 (2015) 8717-8730.
[113] Y. Li, D. Li, W. Yang, H. Fu, Y. Liu, Y. Li, Overexpression of the transcription factor FOXP3 in lung adenocarcinoma sustains malignant character by promoting G1/S transition gene CCND1, Tumor Biol. 37 (2016) 7395-7404, https://doi.org/ 10.1007/s13277-015-4616-3.
[114] D. Kuhn J., The role of interleukin-2 receptor alpha in cancer, Front. Biosci. 10 (2005) 1462, https://doi.org/10.2741/1631.
[115] L. Fan, X. Wang, Q. Chang, Y. Wang, W. Yang, L. Liu, IL2RA is a prognostic indicator and correlated with immune characteristics of pancreatic ductal adenocarcinoma, Medicine 101 (2022) e30966, https://doi.org/10.1097/ MD.0000000000030966.
[116] B. Shang, Y. Liu, S. Jiang, Y. Liu, Prognostic value of tumor-infiltrating FoxP3+ regulatory T cells in cancers: a systematic review and meta-analysis, Sci. Rep. 5 (2015) 15179, https://doi.org/10.1038/srep15179.
[17] S. Murakami, A. Satomi, K. Ishida, H. Murai, Y. Okamura, Serum soluble interleukin-2 receptor in colorectal cancer, Acta Oncologica 33 (1994) 19-21, https://doi.org/10.3109/02841869409098369.
[118] Z. Zhang, G. Wang, X. Shao, H. Wu, X. Su, L. Zhu, Z. Ji, A novel prognostic biomarker CCR8 for gastric cancer and anti-CCR8 blockade attenuate the immunosuppressive capacity of Tregs in vitro, Cancer Biother. Rad. 38 (2023) 415-424, https://doi.org/10.1089/cbr.2022.0095.
[119] A.L. McDoniels-Silvers, G.D. Stonert, R.A. Lubett, M. You, Differential expression of critical cellular genes in human lung adenocarcinomas and squamous cell carcinomas in comparison to normal lung tissues, Neoplasia 4 (2002) 141-150, https://doi.org/10.1038/sj.neo.7900217.
[120] D. Rimoldi, S. Salvi, F. Hartmann, M. Schreyer, S. Blum, L. Zografos, S. Plaisance, B. Azzarone, S. Carrel, Expression of IL-2 receptors in human melanoma cells, Anticancer Res. 13 (1993) 555-564.
[121] M. Royuela, M.P. De Miguel, F.R. Bethencourt, B. Fraile, M.I. Arenas, R. Paniagua, IL-2, its receptors, and bcl-2 and bax genes in normal, hyperplastic and carcinomatous human prostates: immunohistochemical comparative analysis, Growth Factors 18 (2000) 135-146, https://doi.org/10.3109/ 08977190009003239.
[122] L.S. Wang, K.C. Chow, W.Y. Li, C.C. Liu, Y.C. Wu, M.H. Huang, Clinical significance of serum soluble interleukin 2 receptor-alpha in esophageal squamous cell carcinoma, Clin. Cancer Res. 6 (2000) 1445-1451.
[123] K. Araki, K. Harada, K. Nakamoto, M. Shiroma, T. Miyakuni, Clinical significance of serum soluble IL-2R levels in patients with adult T cell leukaemia (ATL) and HTLV-1 carriers, Clin. Exp. Immunol. 119 (2001) 259-263, https://doi.org/ 10.1046/j.1365-2249.2000.01136.x.
[124] C.H. Pui, S.H. Ip, S. Iflah, F.G. Behm, B.H. Grose, R.K. Dodge, W.M. Crist, W. L. Furman, S.B. Murphy, G.K. Rivera, Serum interleukin 2 receptor levels in childhood acute lymphoblastic leukemia, Blood 71 (1988) 1135-1137.
[125] G. Semenzato, R. Foa, C. Agostini, R. Zambello, L. Trentin, F. Vinante, F. Benedetti, M. Chilosi, G. Pizzolo, High serum levels of soluble interleukin 2 receptor in patients with B chronic lymphocytic leukemia, Blood 70 (1987) 396-400.
[126] W.A. Comrie, M.J. Lenardo, Molecular classification of primary immunodeficiencies of T lymphocytes, in: Advances in Immunology, Elsevier, 2018, pp. 99-193, https://doi.org/10.1016/bs.ai.2018.02.003.
[127] Z. Jia, Z. Zhang, Q. Yang, C. Deng, D. Li, L. Ren, Effect of IL2RA and IL2RB gene polymorphisms on lung cancer risk, Int. Immunopharmacol. 74 (2019) 105716, https://doi.org/10.1016/j.intimp.2019.105716.
[128] A. Raugh, D. Allard, M. Bettini, Nature vs. nurture: FOXP3, genetics, and tissue environment shape Treg function, Front. Immunol. 13 (2022) 911151, https:// doi.org/10.3389/fimmu.2022.911151.
[129] T. Chinen, A.K. Kannan, A.G. Levine, X. Fan, U. Klein, Y. Zheng, G. Gasteiger, Y. Feng, J.D. Fontenot, A.Y. Rudensky, An essential role for the IL-2 receptor in Treg cell function, Nat. Immunol. 17 (2016) 1322-1333, https://doi.org/ 10.1038/ni.3540.
[130] W. Xu, H. Liu, J. Song, H .- X. Fu, L. Qiu, B .- F. Zhang, H .- Z. Li, J. Bai, J .- N. Zheng, The appearance of Tregs in cancer nest is a promising independent risk factor in colon cancer, J. Cancer Res. Clin. Oncol. 139 (2013) 1845-1852, https://doi.org/ 10.1007/s00432-013-1500-7.
[131] W. Du, J. He, W. Zhou, S. Shu, J. Li, W. Liu, Y. Deng, C. Lu, S. Lin, Y. Ma, Y. He, J. Zheng, J. Zhu, L. Bai, X. Li, J. Yao, D. Hu, S. Gu, H. Li, A. Guo, S. Huang, X. Feng, D. Hu, High IL2RA mRNA expression is an independent adverse prognostic biomarker in core binding factor and intermediate-risk acute myeloid leukemia, J. Transl. Med. 17 (2019) 191, https://doi.org/10.1186/s12967-019- 1926-z.
[132] Y. Hou, B. Xiang, Z. Yang, J. Liu, D. Xu, L. Geng, M. Zhan, Y. Xu, B. Zhang, Down- regulation of interleukin-2 predicts poor prognosis and associated with immune escape in lung adenocarcinoma, Int. J. Immunopathol. Pharmacol. 37 (2023) 03946320231202748, https://doi.org/10.1177/03946320231202748.
[133] C. Qu, E.W. Edwards, F. Tacke, V. Angeli, J. Llodrá, G. Sanchez-Schmitz, A. Garin, N.S. Haque, W. Peters, N. Van Rooijen, C. Sanchez-Torres, J. Bromberg, I. F. Charo, S. Jung, S.A. Lira, G.J. Randolph, Role of CCR8 and other chemokine pathways in the migration of monocyte-derived dendritic cells to lymph nodes, J. Exp. Med. 200 (2004) 1231-1241, https://doi.org/10.1084/jem.20032152.
[134] Y. Kidani, W. Nogami, Y. Yasumizu, A. Kawashima, A. Tanaka, Y. Sonoda, Y. Tona, K. Nashiki, R. Matsumoto, M. Hagiwara, M. Osaki, K. Dohi, T. Kanazawa, A. Ueyama, M. Yoshikawa, T. Yoshida, M. Matsumoto, K. Hojo, S. Shinonome, H. Yoshida, M. Hirata, M. Haruna, Y. Nakamura, D. Motooka, D. Okuzaki, Y. Sugiyama, M. Kinoshita, T. Okuno, T. Kato, K. Hatano, M. Uemura, R. Imamura, K. Yokoi, A. Tanemura, Y. Shintani, T. Kimura, N. Nonomura, H. Wada, M. Mori, Y. Doki, N. Ohkura, S. Sakaguchi, CCR8-targeted specific depletion of clonally expanded Treg cells in tumor tissues evokes potent tumor immunity with long-lasting memory, Proc. Natl. Acad. Sci. USA. 119 (2022) e2114282119, https://doi.org/10.1073/pnas.2114282119.
[135] S.K. Whiteside, F.M. Grant, D.S. Gyori, A.G. Conti, C.J. Imianowski, P. Kuo, R. Nasrallah, F. Sadiyah, S.A. Lira, F. Tacke, R.L. Eil, O.T. Burton, J. Dooley, A. Liston, K. Okkenhaug, J. Yang, R. Roychoudhuri, CCR8 marks highly suppressive Treg cells within tumours but is dispensable for their accumulation and suppressive function, Immunology 163 (2021) 512-520, https://doi.org/ 10.1111/imm.13337.
[136] N. Kim, M .- H. Kim, J. Pyo, S .- M. Lee, J .- S. Jang, D .- W. Lee, K.W. Kim, CCR8 as a therapeutic novel target: omics-integrated comprehensive analysis for systematically prioritizing indications, Biomedicines 11 (2023) 2910, https://doi. org/10.3390/biomedicines11112910.
[137] A. Ribas, J.D. Wolchok, Cancer immunotherapy using checkpoint blockade, Science 359 (2018) 1350-1355, https://doi.org/10.1126/science.aar4060.
[138] G. Plitas, C. Konopacki, K. Wu, B. Paula, M. Morrow, A. Rudensky, Abstract P4- 04-11: preferential expression of the chemokine receptor 8 (CCR8) on regulatory T cells (Treg) infiltrating human breast cancers represents a novel immunotherapeutic target, Cancer Res. 76 (2016), https://doi.org/10.1158/ 1538-7445.SABCS15-P4-04-11. P4-04-11-P4-04-11.
[139] X. Liu, Y. Zhou, C. Qin, X. Zhu, TNFRSF9 suppressed the progression of breast cancer via the p38MAPK/PAX6 signaling pathway, Journal of Oncology 2022 (2022) 1-16, https://doi.org/10.1155/2022/8549781.
[140] Y. Zhao, W. Yang, Y. Huang, R. Cui, X. Li, B. Li, Evolving roles for targeting CTLA- 4 in cancer immunotherapy, Cell. Physiol. Biochem. 47 (2018) 721-734, https:// doi.org/10.1159/000490025.
[141] D.R. Leach, M.F. Krummel, J.P. Allison, Enhancement of antitumor immunity by CTLA-4 blockade, Science 271 (1996) 1734-1736, https://doi.org/10.1126/ science.271.5256.1734.
[142] B. Salomon, D.J. Lenschow, L. Rhee, N. Ashourian, B. Singh, A. Sharpe, J. A. Bluestone, B7/CD28 costimulation is essential for the homeostasis of the CD4+ CD25+ immunoregulatory T cells that control autoimmune diabetes, Immunity 12 (2000) 431-440, https://doi.org/10.1016/S1074-7613(00)80195-8.
[143] T. Takahashi, T. Tagami, S. Yamazaki, T. Uede, J. Shimizu, N. Sakaguchi, T. W. Mak, S. Sakaguchi, Immunologic self-tolerance maintained by Cd25+Cd4+ Regulatory T cells constitutively expressing cytotoxic T lymphocyte-associated antigen 4, J. Exp. Med. 192 (2000) 303-310, https://doi.org/10.1084/ jem.192.2.303.
[144] S. Read, V. Malmström, F. Powrie, Cytotoxic T lymphocyte-associated antigen 4 plays an essential role in the function of Cd25+Cd4+ regulatory cells that control intestinal inflammation, J. Exp. Med. 192 (2000) 295-302, https://doi.org/ 10.1084/jem.192.2.295.
[145] B. Szostak, F. Machaj, J. Rosik, A. Pawlik, CTLA4 antagonists in phase I and phase II clinical trials, current status and future perspectives for cancer therapy, Expet Opin. Invest. Drugs 28 (2019) 149-159, https://doi.org/10.1080/ 13543784.2019.1559297.
[146] L. Lisi, P.M. Lacal, M. Martire, P. Navarra, G. Graziani, Clinical experience with CTLA-4 blockade for cancer immunotherapy: from the monospecific monoclonal antibody ipilimumab to probodies and bispecific molecules targeting the tumor microenvironment, Pharmacol. Res. 175 (2022) 105997, https://doi.org/ 10.1016/j.phrs.2021.105997.
[147] 7] M.K. Callahan, J.D. Wolchok, Clinical activity, toxicity, biomarkers, and future development of CTLA-4 checkpoint antagonists, Semin. Oncol. 42 (2015) 573-586, https://doi.org/10.1053/j.seminoncol.2015.05.008.
[148] D.M. Pardoll, The blockade of immune checkpoints in cancer immunotherapy, Nat. Rev. Cancer 12 (2012) 252-264, https://doi.org/10.1038/nrc3239.
[149] A. Fröhlich, S. Loick, E.G. Bawden, S. Fietz, J. Dietrich, E. Diekmann, G. Saavedra, H. Fröhlich, D. Niebel, J. Sirokay, R. Zarbl, G.H. Gielen, G. Kristiansen, F. Bootz, J. Landsberg, D. Dietrich, Comprehensive analysis of tumor necrosis factor receptor TNFRSF9 (4-1BB) DNA methylation with regard to molecular and clinicopathological features, immune infiltrates, and response prediction to immunotherapy in melanoma, EBioMedicine 52 (2020) 102647, https://doi.org/ 10.1016/j.ebiom.2020.102647.
[150] T.J. Monberg, T.H. Borch, I.M. Svane, M. Donia, TIL therapy: facts and hopes, Clin. Cancer Res. 29 (2023) 3275-3283, https://doi.org/10.1158/1078-0432. CCR-22-2428.
[151] T. So, N. Ishii, The TNF-TNFR family of Co-signal molecules, in: M. Azuma, H. Yagita (Eds.), Co-Signal Molecules in T Cell Activation, Springer Singapore, Singapore, 2019, pp. 53-84, https://doi.org/10.1007/978-981-32-9717-3_3.
[152] C.H. Nguyen, A. Schlerka, A.M. Grandits, E. Koller, E. Van Der Kouwe, G. S. Vassiliou, P.B. Staber, G. Heller, R. Wieser, IL2RA promotes aggressiveness and stem cell-related properties of acute myeloid leukemia, Cancer Res. 80 (2020) 4527-4539, https://doi.org/10.1158/0008-5472.CAN-20-0531.
[153] D.J. Kuhn, D.M. Smith, S. Pross, T.L. Whiteside, Q.P. Dou, Overexpression of interleukin-2 receptor « in a human squamous cell carcinoma of the head and neck cell line is associated with increased proliferation, drug resistance, and transforming ability, J of Cellular Biochemistry 89 (2003) 824-836, https://doi. org/10.1002/jcb.10557.
[154] D.J. Kuhn, Q.P. Dou, Direct inhibition of interleukin-2 receptor a-mediated signaling pathway induces G 1 arrest and apoptosis in human head-and-neck cancer cells, J of Cellular Biochemistry 95 (2005) 379-390, https://doi.org/ 10.1002/jcb.20446.
[155] I. Georgakopoulos-Soares, D.V. Chartoumpekis, V. Kyriazopoulou, A. Zaravinos, EMT factors and metabolic pathways in cancer, Front. Oncol. 10 (2020) 499, https://doi.org/10.3389/fonc.2020.00499.
[156] J.G. Gribben, G.J. Freeman, V.A. Boussiotis, P. Rennert, C.L. Jellis, E. Greenfield, M. Barber, V.A. Restivo, X. Ke, G.S. Gray, CTLA4 mediates antigen-specific apoptosis of human T cells, Proc. Natl. Acad. Sci. USA. 92 (1995) 811-815, https://doi.org/10.1073/pnas.92.3.811.
[157] M.F. Krummel, J.P. Allison, CTLA-4 engagement inhibits IL-2 accumulation and cell cycle progression upon activation of resting T cells, J. Exp. Med. 183 (1996) 2533-2540, https://doi.org/10.1084/jem.183.6.2533.
[158] Z. Wu, M. Wang, Q. Liu, Y. Liu, K. Zhu, L. Chen, H. Guo, Y. Li, B. Shi, Identification of gene expression profiles and immune cell infiltration signatures between low and high tumor mutation burden groups in bladder cancer, Int. J. Med. Sci. 17 (2020) 89-96, https://doi.org/10.7150/ijms.39056.
[159] M.E. Winerdal, P. Marits, M. Winerdal, M. Hasan, R. Rosenblatt, A. Tolf, K. Selling, A. Sherif, O. Winqvist, FOXP3 and survival in urinary bladder cancer, BJU Int. 108 (2011) 1672-1678, https://doi.org/10.1111/j.1464- 410X.2010.10020.x.
[160] A.M. Thornton, E.M. Shevach, CD4+CD25+ immunoregulatory T cells suppress polyclonal T cell activation in vitro by inhibiting interleukin 2 production, J. Exp. Med. 188 (1998) 287-296, https://doi.org/10.1084/jem.188.2.287.
[161] J .- G. Chai, J.Y.S. Tsang, R. Lechler, E. Simpson, J. Dyson, D. Scott, CD4+CD25+ T cells as immunoregulatory T cells in vitro, Eur. J. Immunol. 32 (2002) 2365, https://doi.org/10.1002/1521-4141(200208)32:8<2365 :: AID-IMMU2365>3.0. CO;2-2.
[162] H. Jonuleit, E. Schmitt, M. Stassen, A. Tuettenberg, J. Knop, A.H. Enk, Identification and functional characterization of human Cd4+Cd25+ T cells with regulatory properties isolated from peripheral blood, J. Exp. Med. 193 (2001) 1285-1294, https://doi.org/10.1084/jem.193.11.1285.
[163] F. Annunziato, L. Cosmi, F. Liotta, E. Lazzeri, R. Manetti, V. Vanini, P. Romagnani, E. Maggi, S. Romagnani, Phenotype, localization, and mechanism of suppression of CD4 + CD25 + human thymocytes, J. Exp. Med. 196 (2002) 379-387, https://doi.org/10.1084/jem.20020110.
[164] C.N. Manzotti, H. Tipping, L.C.A. Perry, K.I. Mead, P.J. Blair, Y. Zheng, D. M. Sansom, Inhibition of human T cell proliferation by CTLA-4 utilizes CD80 and requires CD25+ regulatory T cells, Eur. J. Immunol. 32 (2002) 2888-2896,
https://doi.org/10.1002/1521-4141(2002010)32:10<2888 :: AID- IMMU2888>3.0.CO;2-F.
[165] L.S.K. Walker, Treg and CTLA-4: two intertwining pathways to immune tolerance, J. Autoimmun. 45 (2013) 49-57, https://doi.org/10.1016/j.jaut.2013.06.006.
[166] Y .- Q. He, Q. Bo, W. Yong, Z .- X. Qiu, Y .- L. Li, W .- M. Li, FoxP3 genetic variants and risk of non-small cell lung cancer in the Chinese Han population, Gene 531 (2013) 422-425, https://doi.org/10.1016/j.gene.2013.08.066.
[167] S. Boussios, E. Rassy, M. Moschetta, A. Ghose, S. Adeleke, E. Sanchez, M. Sheriff, C. Chargari, N. Pavlidis, BRCA mutations in ovarian and prostate cancer: bench to bedside, Cancers 14 (2022) 3888, https://doi.org/10.3390/cancers14163888.
[168] M. Zhao, W. Li, Metabolism-associated molecular classification of uterine corpus endometrial carcinoma, Front. Genet. 14 (2023) 955466, https://doi.org/ 10.3389/fgene.2023.955466.
[169] Y. Ma, X. Zhang, J. Yang, Y. Jin, Y. Xu, J. Qiu, Comprehensive molecular analyses of a TNF family-based gene signature as a potentially novel prognostic biomarker for cervical cancer, Front. Oncol. 12 (2022) 854615, https://doi.org/10.3389/ fonc.2022.854615.
[170] D. Goltz, H. Gevensleben, T.J. Vogt, J. Dietrich, C. Golletz, F. Bootz, G. Kristiansen, J. Landsberg, D. Dietrich, CTLA4 methylation predicts response to anti-PD-1 and anti-CTLA-4 immunotherapy in melanoma patients, JCI Insight 3 (2018) e96793, https://doi.org/10.1172/jci.insight.96793.
[171] F. Hoffmann, A. Franzen, L. De Vos, L. Wuest, Z. Kulcsár, S. Fietz, A.P. Maas, S. Hollick, M.Y. Diop, J. Gabrielpillai, T. Vogt, P. Kuster, R. Zarbl, J. Dietrich, G. Kristiansen, P. Brossart, J. Landsberg, S. Strieth, D. Dietrich, CTLA4 DNA methylation is associated with CTLA-4 expression and predicts response to immunotherapy in head and neck squamous cell carcinoma, Clin. Epigenet. 15 (2023) 112, https://doi.org/10.1186/s13148-023-01525-6.
[172] W.H. Fridman, L. Zitvogel, C. Sautès-Fridman, G. Kroemer, The immune contexture in cancer prognosis and treatment, Nat. Rev. Clin. Oncol. 14 (2017) 717-734, https://doi.org/10.1038/nrclinonc.2017.101.
[173] Y. Li, B. Liu, Y. Cao, L. Cai, Y. Zhou, W. Yang, T. Sun, Metformin-induced reduction of CCR8 enhances the anti-tumor immune response of PD-1 immunotherapy in glioblastoma, Eur. J. Pharmacol. 964 (2024) 176274, https:// doi.org/10.1016/j.ejphar.2023.176274.
[174] Z. Amoozgar, J. Kloepper, J. Ren, R.E. Tay, S.W. Kazer, E. Kiner, S. Krishnan, J. M. Posada, M. Ghosh, E. Mamessier, C. Wong, G.B. Ferraro, A. Batista, N. Wang, M. Badeaux, S. Roberge, L. Xu, P. Huang, A.K. Shalek, D. Fukumura, H .- J. Kim, R. K. Jain, Targeting Treg cells with GITR activation alleviates resistance to immunotherapy in murine glioblastomas, Nat. Commun. 12 (2021) 2582, https:// doi.org/10.1038/s41467-021-22885-8.
[175] A. Tanaka, S. Sakaguchi, Targeting Treg cells in cancer immunotherapy, Eur. J. Immunol. 49 (2019) 1140-1146, https://doi.org/10.1002/eji.201847659.
[176] Y. Zhou, N. Shao, N. Aierken, C. Xie, R. Ye, X. Qian, Z. Hu, J. Zhang, Y. Lin, Prognostic value of tumor-infiltrating Foxp3+ regulatory T cells in patients with breast cancer: a meta-analysis, J. Cancer 8 (2017) 4098-4105, https://doi.org/ 10.7150/jca.21030.
[177] R.P. Petersen, M.J. Campa, J. Sperlazza, D. Conlon, M .- B. Joshi, D.H. Harpole, E. F. Patz, Tumor infiltrating Foxp3+ regulatory T-cells are associated with recurrence in pathologic stage I NSCLC patients, Cancer 107 (2006) 2866-2872, https://doi.org/10.1002/cncr.22282.
[178] S. Bhattacharya, G. Paraskar, M. Jha, G.L. Gupta, B.G. Prajapati, Deciphering regulatory T-cell dynamics in cancer immunotherapy: mechanisms, implications,
and therapeutic innovations, ACS Pharmacol. Transl. Sci. 7 (2024) 2215-2236, https://doi.org/10.1021/acsptsci.4c00156.
[179] Y. Shen, Y. Wei, Z. Wang, Y. Jing, H. He, J. Yuan, R. Li, Q. Zhao, L. Wei, T. Yang, J. Lu, TGF-ß regulates hepatocellular carcinoma progression by inducing Treg cell polarization, Cell. Physiol. Biochem. 35 (2015) 1623-1632, https://doi.org/ 10.1159/000373976.
[180] T.L. Whiteside, FOXP3+ Treg as a therapeutic target for promoting anti-tumor immunity, Expert Opin. Ther. Targets 22 (2018) 353-363, https://doi.org/ 10.1080/14728222.2018.1451514.
[181] N. Tohyama, S. Tanaka, K. Onda, K. Sugiyama, T. Hirano, Influence of anticancer agents on cell survival, proliferation, and CD4+CD25+Foxp3+ regulatory T cell- frequency in human peripheral-blood mononuclear cells activated by T cell- mitogen, Int. Immunopharmacol. 15 (2013) 160-166, https://doi.org/10.1016/j. intimp.2012.11.008.
[182] B. Moser, Chemokine receptor-targeted therapies: special case for CCR8, Cancers 14 (2022) 511, https://doi.org/10.3390/cancers14030511.
[183] M. Terranova-Barberio, S. Thomas, N. Ali, N. Pawlowska, J. Park, G. Krings, M. D. Rosenblum, A. Budillon, P.N. Munster, HDAC inhibition potentiates immunotherapy in triple negative breast cancer, Oncotarget 8 (2017) 114156-114172, https://doi.org/10.18632/oncotarget.23169.
[184] R. El-Tabba, P. Mathew, W. Masocha, M. Khajah, COL-3 enhances the anti- proliferative and pro-apoptotic effects of paclitaxel in breast cancer cells, Oncol. Rep. (2018), https://doi.org/10.3892/or.2018.6815.
[185] Z .- X. Wu, Q. Mai, Y. Yang, J .- Q. Wang, H. Ma, L. Zeng, Z .- S. Chen, Y. Pan, Overexpression of human ATP-binding cassette transporter ABCG2 contributes to reducing the cytotoxicity of GSK1070916 in cancer cells, Biomed. Pharmacother. 136 (2021) 111223, https://doi.org/10.1016/j.biopha.2021.111223.
[186] M.A. Hardwicke, C.A. Oleykowski, R. Plant, J. Wang, Q. Liao, K. Moss, K. Newlander, J.L. Adams, D. Dhanak, J. Yang, Z. Lai, D. Sutton, D. Patrick, GSK1070916, a potent Aurora B/C kinase inhibitor with broad antitumor activity in tissue culture cells and human tumor xenograft models, Mol. Cancer Therapeut. 8 (2009) 1808-1817, https://doi.org/10.1158/1535-7163.MCT-09-0041.
[187] H. von Boehmer, C. Daniel, Therapeutic opportunities for manipulating T(Reg) cells in autoimmunity and cancer, Nat. Rev. Drug Discov. 12 (2013) 51-63, https://doi.org/10.1038/nrd3683.
[188] A. Spence, J.E. Klementowicz, J.A. Bluestone, Q. Tang, Targeting Treg signaling for the treatment of autoimmune diseases, Curr. Opin. Immunol. 37 (2015) 11-20, https://doi.org/10.1016/j.coi.2015.09.002.
[189] D.T. Le, E.M. Jaffee, Regulatory T-cell modulation using cyclophosphamide in vaccine approaches: a current perspective, Cancer Res. 72 (2012) 3439-3444, https://doi.org/10.1158/0008-5472.CAN-11-3912.
[190] T. Doi, K. Muro, H. Ishii, T. Kato, T. Tsushima, M. Takenoyama, S. Oizumi, K. Gemmoto, H. Suna, K. Enokitani, T. Kawakami, H. Nishikawa, N. Yamamoto, A phase I study of the anti-CC chemokine receptor 4 antibody, Mogamulizumab, in combination with Nivolumab in patients with advanced or metastatic solid tumors, Clin. Cancer Res. 25 (2019) 6614-6622, https://doi.org/10.1158/1078- 0432.CCR-19-1090.
[191] A.R. Abraham, P. Maghsoudlou, D.A. Copland, L.B. Nicholson, A.D. Dick, CAR- Treg cell therapies and their future potential in treating ocular autoimmune conditions, Front Ophthalmol (Lausanne) 3 (2023) 1184937, https://doi.org/ 10.3389/fopht.2023.1184937.