METHODS AND COMPOSITIONS FOR PREVENTING AND TREATING A CANCER

Abstract

Inventors have shown that CD70 and CD27 are highly expressed in ccRCC and correlates with poor survival. Multiplex IF demonstrated that CD27+T cells interact with CD70+ tumor cells in tumor microenvironment (TME). CD27+T cells are more apoptotic than CD27−T cells in ccRCC. Elevated levels of plasma sCD27 is observed in ccRCC patients and correlates with CD27−CD70 interaction in situ. Their study demonstrates that CD27−CD70 interaction contributes to the release of sCD27 in peripheral blood in ccRCC, indicating that sCD27 is a potential biomarker. The apoptosis of CD27+T cells suggests the deleterious effect of CD27−CD70 interaction in T cell response. Therefore, CD27/CD70 is a promising therapeutic target in ccRCC. Accordingly, the invention relates to a method for determining the interaction between CD27 and CD70 by determining the level of soluble CD27 (sCD27) in a biological sample and to method of targeting CD27/CD70 interaction to treat a cancer or metastatic cancer.

Claims

1. (canceled)

2. (canceled)

3. A method for predicting whether a subject suffers from or is susceptible to suffer from a cancer and/or metastatic cancer expressing CD70 and treating the subject, comprising: i) determining the level of soluble CD27 (sCD27) in a biological sample from the subject; ii) determining that the level of sCD27 in the biological sample is higher than a corresponding predetermined reference value; and iii) treating the subject determined to have a level of sCD27 that is higher than its corresponding predetermined reference value with a therapeutically effective amount of an inhibitor of interaction of CD27/CD70.

4. The method of claim 3, wherein the biological sample is a plasma sample.

5. A method for treating cancer and/or metastatic cancer in a subject in need thereof comprising administering to said subject a therapeutically effective amount of an inhibitor of interaction of CD27/CD70.

6. The method according to claim 5 further comprising, before the step of administering, a step of determining the interaction between CD27 and CD70 by i) determining the level of soluble CD27 (sCD27) in a biological sample from the subject and ii) determining that the level of sCD27 is higher than a corresponding predetermined reference value.

7. The method according to claim 5, wherein said inhibitor of interaction of CD27/CD70 is an inhibitor of CD27.

8. The method according to claim 5, wherein said inhibitor of interaction of CD27/CD70 is an anti-CD27 neutralizing monoclonal antibody.

9. The method according to claim 5, wherein said inhibitor of interaction of CD27/CD70 is an inhibitor of CD70.

10. The method according to claim 5, wherein said inhibitor of interaction of CD27/CD70 is an anti-CD70 neutralizing monoclonal antibody.

11. A method of preventing and/or treating cancer and/or metastatic cancer in a subject in need thereof, comprising, administering to the subject a therapeutically effective amount of an inhibitor of interaction of CD27/CD70 and a classical cancer treatment.

12. The method according to claim 11, wherein the classical cancer treatment is an immune checkpoint inhibitor.

13. The method according to claim 12, wherein the immune checkpoint inhibitor is an anti-PD-1 antibody, an anti-PD-L1 antibody or an anti-PD-L2 antibody.

14. A kit or device for performing the method of claim 1, comprising means for determining the level of the soluble CD27 in a biological sample.

Description

FIGURES

[0145] FIG. 1: Overexpression of CD27 and CD70 gene expression in ccRCC. CD27 (A) and CD70 (B) mRNA expression were compared in normal kidney tissues from non-cancer individuals (n=28), tumor tissues (n=530) and normal tissues adjacent to tumors (NAT) (n=72) from ccRCC patients. Data was presented as the mean with SD. Unpaired t test was used to determine the significance. (C) Correlation analysis (n=530) between CD27 and CD70 mRNA expression in ccRCC. Pearson correlation coefficient (r) and significance levels (P value) were shown. (D-F) Kaplan-Meier plot of the overall survival of ccRCC patients (n=376). Patients were divided into 2 groups (high and low) based on the median of CD27 (8.12) and CD70 (10.265) mRNA expression. “CD27 high CD70 high” group (n=124) referred to patients with high expression of CD27 and CD70 while “CD27 low and CD70 low” group (n=124) referred to patients with low expression of CD27 and CD70. Significance was determined using the log-rank Mantel-Cox test. Values of mRNA data were in log 2 scale for clarity. Values of P<0.05 were considered statistically significant. * P<0.05, **** P<0.0001.

[0146] FIG. 2: CD27−CD70 interaction in ccRCC demonstrated by multiplex IHC. Quantification of interacted CD27 and interacted CD70 in ccRCC (n=25). Data were presented with dots and mean with SD.

[0147] FIG. 3: CD27+T cells in tumors express cleaved caspase 3. Cleaved caspase 3 percentage in CD27+/−T cells (n=2).

[0148] FIG. 4: sCD27 in the plasma correlate with expression of intratumoral CD27 and the levels of interaction of CD27 with CD70. (A) Concentrations of plasma sCD27 from ccRCC patients (n=44) and healthy donors (n=15) were determined by ELISA. Significance was determined by unpaired t test. Data are presented with dots and mean with SD. sCD27 plotted against CD27+ cell numbers per field (B) and numbers of interacted CD27+ cells (CD27 within 30 μm of CD70) per field (C) in tumors (n=25). Pearson correlation coefficient (r) and significant levels (P value) are presented. Values of P<0.05 were considered statistically significant. ** P<0.01, *** P<0.001.

EXAMPLE

Material & Methods

Patients and Samples

[0149] Two cohorts from prospective studies were enrolled at the Hôpital Européen Georges Pompidou (HEGP) (Paris, France). Patients from cohort Colcheckpoint (CPP Ile-de-France 2015 Aug. 4 MS2) were diagnosed with metastatic ccRCC and were treated by anti-PD-1/PD-L1. Patients from cohort ExhauCRF (CPP Ile-de-France 2016 Jul. 8) were diagnosed with localized ccRCC. For ELISA, 26 plasma samples from Colcheckpoint before anti-PD-1/PD-L1 treatment and 18 plasma samples from ExhauCRF at the time of diagnosis were collected. Plasma of 15 healthy donors from Etablissement Français du Sang were selected as control. For multiplex immunofluorescence (mIF), 7 formalin-fixed, paraffin-embedded (FFPE) tumor tissues from Colcheckpoint collected before immunotherapy and 18 from ExhauCRF cohort were selected. For flow cytometry, 2 fresh tumors were collected from patients confirmed with ccRCC on the day of surgery. Clinical characteristics, such as the histopathology, the performance status scale (ECOG), the TNM stage and the survival data for each patient were collected prospectively.

Multiplex Immunofluorescence (mIF) Staining

[0150] We developed 2 mIF panels, which were CD4 panel (CD4−CD27−CD70-PAX8) and CD8 panel (CD8−CD27−CD70-PAX8). Pax8 is a transcriptional factor with strong nuclear expression in most neoplastic cells in all histologic types of human primary or metastatic RCC. mIF panel composed of these different markers was developed manually and then applied to all FFPE tumor tissues automatically on the LEICA Bond RX with the same protocol. Slides from FFPE tissue were heated at 57° C. for 2 h before staining. Residual paraffin was removed in three successive Bioclear New dewaxing solution (Biognost, Zagreb, Croatia) for 3 minutes each. After the tissue rehydration using three serially diluted ethanol (100%, 75%, 50%) to distilled water for 2 minutes each, tissues were then fixed for 15 minutes in Formaldehyde fixation neutral buffer (NB) (Biognost) and washed with distilled water. Subsequently, antigen retrieval was performed using pH9 Target Retrieval (Agilent, California, United States) and microwave treatment for 45 seconds at 1000 watts followed by 30 minutes at 100 watts. Blocking was performed with blocking buffer devoid of animal-derived proteins (Cell Signaling Technology (CST), Massachusetts, United States) for 15 minutes, then slides were incubated with the primary antibody diluted in SignalStain® Antibody Diluent (CST) for 30 minutes. After washing in Tris-Buffered Saline, 0.1% Tween® 20 Detergent (TBST) (Agilent), incubation with secondary antibody coupled with horseradish peroxidase (HRP) (ImmunoReagents, North Carolina, United States) was performed for 15 minutes. The slides were washed in TBST and the CF® Dye Tyramide (Biotium, California, United States) was applied for 10 minutes. Slides were then microwaved to strip the primary and secondary antibodies, washed, and blocked again using blocking solution. The process was repeated until the fourth marker was labeled (FIG. 1). At the last step, staining with DAPI (PerkinElmer, Massachusetts, United States) was applied and then washed in distilled water. Slides were mounted using EverBrite Mounting Medium (Biotium). Finally, the slides were read on a Vectra Polaris fluorescence microscope (Akoya Biosciences, California, United States). Antibodies used in mIF panel are listed in Table 1.

TABLE-US-00007 TABLE 1 Antibody information of multiplex IHC panel Position Primary Ab Concentration Secondary Ab TSA-Dye 1 CD70 1:500 Anti-Mouse 488A 1:450 R&D MAB2738 GAMHRP-050 92171 2 CD4 1:500 Anti-Rabbit CF680R 1:450 AB133616 GARHRP-050 92196 2 CD8 1:500 Anti-Mouse CF680R 1:450 CST 70306S GAMHRP-050 92196 3 CD27  1:12500 Anti-Rabbit 543 1:450 AB131254 GARHRP-050 92172 4 PAX8  1:1000 Anti-Rabbit 594 1:450 AB191870 GARHRP-050 92174

Multispectral Imaging, Phenotyping and Spatial Analysis

[0151] Multiplex stained slides were imaged using the Vectra® Polaris™ Automated Quantitative Pathology Imaging system version 2 (Akoya). Regions of interest were selected and 10 representative images per patient were used for analysis. Using multispectral images obtained from single stained slides for each marker, a spectral library containing fluorophores emitting spectral peaks was created with inForm (version 2.4.6) image analysis software (PerkinElmer). This spectral library was then used to separate each multispectral image into its individual components, which allows for the color-based identification of all 5 markers in a single image using inForm software.

[0152] Selected images were exported as component data with inForm for phenotyping and spatial analysis in HALO software (Indica labs, New Mexico, United States). Highplex FL module in HALO was used for cellular phenotyping. Cells were segmented according to DAPI (nucleus) staining. Phenotyping was realized by setting an appropriate threshold to each marker according to the fluorescent intensity, and the same algorithm was applied to all images for homogeneity. To determine the cellular interaction, spatial analysis was realized based on cellular phenotypes with HALO. Cellular distance is calculated from the center of each cell. CD27 positive cells within 30 μm of CD70 positive cells were nominated as “interacted CD70 and center CD70 positive cells with interacted CD27 as “interacted CD70”. Interacted CD27 and interacted CD70 were calculated.

Flow Cytometry on Fresh Tumors for the Characterization of TILs in ccRCC

[0153] Two fresh tumors of ccRCC patients were collected. For digestion, fresh tumor was first cut into pieces then incubated with 25 mL Hank's Balance Salt Solution (HBSS) with calcium and magnesium (Lonza, Basel, Switzerland), lmg/ml final concentration collagenase (Roche, Basel, Switzerland) and 15 mg/ml DNAase (Roche) at 37° C. for 1 hour. Tumor tissues were filtered and washed with 200 μl EDTA (Sigma-Aldrich, Missouri, United States) and HBSS at 1000 r for 10 minutes. After removal of supernatant, residual cells were resuspended with HBSS. Cell numbers were counted with Trypan Blue.

[0154] 1 million cells per tube were used for staining. Cells were first stained with Zombie Nir (Biolegend, California, United States) for 30 minutes to distinguish living cells from dead cells. After washing with cell staining buffer (Biolegend), cells were then stained with the following monoclonal antibodies directly labeled with fluorophore (Table 2) for 30 minutes in dark. After the staining, cells were washed again and resuspended in 200 uL cell staining buffer. Samples were acquired in cytometer (Navios 10 colors, Beckman Coulter) and data were analyzed with software Kaluza (version 1.2).

TABLE-US-00008 TABLE 2 Antibody information for flow cytometry Antibody Isotype Company Catalog CD3 BV510 Mouse IgG1 k Biolegend 300448 CD4 BV421 Mouse IgG1 k Biolegned 300532 CD8 PE Mouse IgG1 k BD 345773 IgG1 APC Mouse IgG1 k Biolegend 400122 CD27 APC Mouse IgG1 k Biolegend 302810 Caspase 3 — Biotium 10403 substrate AF488

Determination of sCD27 in Plasma

[0155] 10 uL plasma from ccRCC patients and healthy donors were used to analyze sCD27 concentration by CD27 (Soluble) Human Instant ELISA Kit (ThermoFisher Scientific, Massachusetts, United States) according to the manufacturer's instructions. Data were acquired with MRX Revelation Microplate Reader (DYNEX Technologies, Virginia, United States).

Gene Expression Analysis from TCGA Database

[0156] Gene expression analysis was performed using UC Santa Cruz Cancer Genomics Browser (http://xena.ucsc.edu/). To compare CD27 and CD70 gene expression across 31 types of human solid tumors, TCGA PanCan Study was created using normalized gene-level RNA-seq data (n=9575) downloaded from The Cancer Genome Atlas (TCGA) and Pan-Cancer Atlas database. To compare CD27 and CD70 gene expression in tumor and normal tissue, normalized mRNA data from ccRCC tumor tissue (n=530), normal kidney tissue (n=28) and normal tissue adjacent to tumors (NAT) (n=72) were downloaded from the TCGA-PanCan Study and GTEX Study. Tumor tissues and NAT tissues were derived from ccRCC patients and normal kidney tissues from individuals who do not have cancers. Corresponding clinical data (n=376) such as overall survival was also downloaded for Kaplan-Meier survival analysis.

Statistical Analysis

[0157] One-way Anova was performed with UCSC Xena tool for the comparison of CD27 and CD70 gene expression across different types of solid tumors. Other statistical analyses were performed using GraphPad Prism software version 8. Two-tailed unpaired t test was used to evaluate the quantifications between two groups as appropriate. For the correlation analyses, the Pearson's correlation coefficient (r) and P value was calculated. For survival analyses, Kaplan-Meier plots were depicted, and statistical differences were evaluated using the log-rank Mantel-Cox test. A P value <0.05 was considered statistically significant.

Tissue Processing RNA Extraction, Single-cell RNA Sequencing

[0158] Fresh tumors were collected as described above. After tumor dissociation, cells were stained with a fixable viability stain FVS 520 (eBioscience), an anti-CD8a labeled with APC fluorophore (Biolegend) and an anti-NKP46 labeled with BV421 (Biolegend) in order to eliminate natural killer cells from the analysis during the cell sorting FACS-enriched T cells were loaded on a 10× Chromium (10× Genomics) and libraries were prepared using a Single Cell 5′ Reagent Kit (V1 chemistry, 10× Genomics) according to the manufacturer's protocol, targeting 3000 recovered cells per sample. Single cells were partitioned and barcoded into droplets together with gel beads coated with oligos harbouring unique barcodes, molecular identifiers (UMI), and template switch oligo (TSO) sequences, followed by in droplet reverse transcription to generate barcoded full-length cDNA. cDNA was subsequently recovered from droplets, then cleaned up with DynaBeads MyOne Silane Beads (Thermo Fisher Scientific), then amplified with the following protocol: 98° C.—45 s; 12× (98° C.—20 s, 67° C.—30 s, 72° C.—1 min), 72° C.—1 min; held at 4° C. Amplified cDNA product was cleaned up using the SPRI select Reagent Kit (Beckman Coulter). Gene expression libraries were constructed following these steps: (1) fragmentation, end repair and A-tailing; (2) size selection with SPRI select beads; (3) adaptor ligation; (4) post-ligation cleanup with SPRI select beads; (5) sample index PCR with the following protocol: 98° C.—45 s; 12× (98° C.—20 s, 54° C.—30 s, 72° C.—20 s), 72° C.—1 min; held at 4° C. and final cleanup with SPRI select beads. Libraries quality was assessed using a dsDNA High Sensitivity Assay Kit and Bioanalyzer Agilent 2100 System. Libraries were quantified with dsDNA HS kit and Qubit 2.0. Indexed libraries were pooled and sequenced on an Illumina HiSeqX using paired-end 150 bp as sequencing mode, targeting at least 50 000 reads per cell.

scRNA-Seq Data Analysis

[0159] All scRNA-seq data were processed with the Cellranger pipeline (version 2.1.1). This step included demultiplexing of raw base call (BCL) files into FASTQ files, reads alignment on human genome assembly GRCh38-3.0.0 using STAR, and counting of unique molecular identifier (UMI). The Seurat (v3.1.1) workflow was used to read the Raw data in R (3.6.1). Raw counts of each donor were normalized with the Seurat SCTransform function using the default parameters and integrated in a single object using the Seurat CCA integration algorithm.As a quality-control step, we first filtered out low-quality cells: cells with less than 200 genes detected and more than 5,000 genes detected. Cells with more than 5,000 mitochondrial reads were removed as well as cells for which the ratio of reads over mitochondrial reads exceeded 6.5. Reads aligning to mitochondrial genes or ribosomal proteins were removed from the analysis. Following these quality-control criteria, 13,389 T CD8 (patient P1=2,006 cells; patient P2=7,002 cells; patient P3=1,654 cells; patient P4=2,727 cells) were finally conserved. For each patient, single cell were selected as “CD27 positive” (nUMI corresponding to CD27 gene>0) or “CD27 negative”. The differentially expressed genes between CD27 positive cells and CD27 negative cells were identified using Student t-test and Benjamini-Hochberg p-value. Genes were considered differentially expressed if p value corrected was ≤0.05 and log FC≥0.4). 412 genes were up-regulated in CD27 negative cells and 330 genes were up-regulated in CD27 positive cells. Morpheus tool (https://software.broadinstitute.org/morpheus/) was used for Heatmap visualization. scRNA-seq data sorted from renal cell carcinoma samples are available on NCBI's Gene Expression Omnibus (GEO) Archive platform (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE160243.

Results

Distinct Expression Pattern of CD70 and CD27 in ccRCC Correlates with Patient Survival

[0160] To validate the frequent expression of CD70 in ccRCC (Adam et al., 2006; Jilaveanu et al., 2012), CD70 expression at mRNA levels, as well as its ligand CD27, were compared across 31 types of human solid tumors from cohort TCGA and Pan-Can Atlas (n=9575). As result, CD70 is the most frequently expressed in kidney renal clear cell carcinoma (KIRC) (data not shown). Similarly, CD27 expression in KIRC is also at high prevalence compared to other solid tumors (data not shown). When we focused on ccRCC, we found that CD27 and CD70 expression were significantly higher in tumors than in normal kidney tissues and in normal tissues adjacent to tumor (NAT) (FIG. 1A, 1B). Besides, CD27 is significantly correlated with CD70 in tumor (FIG. 1C). To study the impact of this distinct expression pattern of CD27 and CD70 on clinical outcomes, overall survival analysis was performed using data from TCGA-PanCan study (n=530). Patients were divided into 2 groups (high and low) based on the median of mRNA data at log 2 scale. The median threshold is 8.12 for CD27 and 10.265 for CD70. CD27 or CD70 alone had no impact on patient overall survival (FIG. 1D, 1E). Given the correlation of CD27 and CD70 in tumors, we combined 2 markers and divided patients into 2 groups, which were “CD27 high CD70 high” (n=124) and “CD27 low CD70 low” (n=124) respectively. Interestingly, we observed a significantly poor OS in “CD27 high CD70 high” group (FIG. 1F), suggesting that a synergic role of CD27 and CD70 has a negative impact on ccRCC patient prognosis.

Interaction of CD70.SUP.+ .Tumor Cells and CD27.SUP.+.T cells in ccRCC

[0161] To better understand the synergic role of CD27 and CD70 in ccRCC tumor microenvironment (TME), we performed 5-color mIF on FFPE tumor tissues from 25 patients. Two staining panels were designed: CD4 panel (CD4, red; CD27, yellow; CD70, green; PAX8, orange; DAPI (nuclear stain), blue) and CD8 panel (CD8, red; CD27, yellow; CD70, green; PAX8, orange; DAPI, white). PAX8 was used to identify tumor cells. Composite images with 5 markers after multispectral imaging and corresponding single stained images were presented (data not shown). After cell segmentation of the nucleus, cytoplasm and membrane and measurement of the fluorescent intensity of each compartment, cells were phenotyped and counted by HALO software (data not shown). Algorithm composed of appropriate intensity threshold of each marker was applied to all patients for homogeneity. Flow cytometry allows for accurate phenotyping of cells, but fails to capture the spatial relationships that can better reflect the cellular interaction. To examine this, after phenotyping, each cell was assigned coordinates that could be used to determine the intracellular distances. Cell numbers within certain distance of another cell population can be also calculated.

[0162] As result, we observed that CD27 was expressed on CD4.sup.+T cells and CD8.sup.+T cells while CD70 was expressed on tumor cells (PAX8). More importantly, interactions of CD27.sup.+T cells and CD70.sup.+ tumor cells can be seen in TME (data not shown). After phenotyping, CD27.sup.+ cells are composed of 65% CD4.sup.+CD27.sup.+T cells, 19% CD8.sup.+CD27.sup.+T cells and 16% other cells (data based on 7 samples) (data not shown), suggesting that CD27 is mostly expressed on T cells. To better measure the CD27−CD70 intracellular interaction, we did the spatial analysis by HALO software (data not shown). We defined CD27.sup.+ cells within 30 μm of CD70.sup.+ cells as “interacted CD27”, CD27.sup.+ cells and CD70.sup.+ cells with CD27.sup.+ cells around in a radius of 30 μm as “interacted CD70” (data not shown). Quantitative result of interacted CD27 and interacted CD70 was presented in FIG. 2. These results suggest that significant interaction exist between CD27 and CD70 positive cells. CD70.sup.+ cells seem to interact with more CD27.sup.+ cells than CD27.sup.+ cells with CD70.sup.+ cells. Since CD70.sup.+ is expressed by tumor cells, and CD27 is mainly expressed by T cells, the larger surface area occupied by the tumour cell compared to T cells may explain this result.

Apoptosis of CD27.SUP.+.T Cells in ccRCC

[0163] As the role of CD27−CD70 interaction is unclear in ccRCC, we next studied the phenotypic characteristic of CD27.sup.+T cells in tumors. Previous reports indicated that CD27-induced apoptosis was mediated by exposure to CD70 in ccRCC in vitro (Diegmann et al., 2006). We then asked whether CD70 highly expressing ccRCC induced the apoptosis of CD27.sup.+T cells in human. To confirm our hypothesis, we performed flow cytometry analysis on fresh tumors from 2 ccRCC patients. CD27 is expressed on CD4.sup.+ and CD8.sup.+T cells (data not shown). Caspase 3 is associated with cell apoptosis. Cleaved caspase 3 remains intact during apoptosis and can be detecting using substrate. In our study, cleaved caspase 3 is observed in TILs (data not shown). More importantly, we found a higher percentage of cleaved caspase 3 in CD27.sup.+T cells compared to CD27.sup.−T cells: CD4.sup.+CD27.sup.+T cells (28%) vs CD4.sup.+CD27.sup.−T cells (13%); CD8.sup.+CD27.sup.+T cells (15.5%) vs CD8.sup.+CD27.sup.− T cells (9%) (FIG. 3). Despite the limited samples, it still suggests that CD27.sup.+T cells are more apoptotic than CD27.sup.−T cells, which may result from the CD27−CD70 interaction in tumor. However, this result has to be confirmed in a larger series of patients.

CD27−CD70 Interaction In Situ Correlates with Elevated Levels of sCD27 in Peripheral Blood of ccRCC Patients

[0164] CD27 is known to be cleaved by a protease into its soluble form sCD27 on activated T cells upon CD27−CD70 interaction (Hintzen et al., 1991; Loenen et al., 1992a). Previous studies indicated that CD70-expressing glioma and RCC cell lines could induce the release of sCD27 from PBMCs (Ruf et al., 2015; Wischhusen et al., 2002). To investigate whether CD27−CD70 interaction in situ induced the release of sCD27, we first performed ELISA to measure plasma sCD27 from ccRCC patients. Elevated levels of sCD27 were observed in ccRCC patients (n=44) compared to healthy donors (n=15) (FIG. 4A). We then did the correlation analysis of sCD27 and CD27 cell counts in situ. sCD27 in peripheral significantly correlated with CD27.sup.+ cells in situ (FIG. 4B). More importantly, significant correlation was observed in interacted CD27.sup.+ cells in situ and sCD27 in peripheral (FIG. 4C), suggesting that elevated levels of sCD27 in the peripheral might come from the CD27−CD70 interaction in ccRCC.

Apoptosis and Dysfunction of CD27+T cells in RCC

[0165] To confirm the phenotype of CD27+T cells, we sorted CD27+ and CD27−CD8+T cells and performed single-cell RNA sequencing (scRNA-seq) analysis from 4 renal tumor samples. CD8+CD27+ TILs had an expression profile of enriched genes associated with apoptosis from the Gene Ontology database composed of BAX, FASLG, BCL2L11, CYCS, FBXO32, LGALS1, PIK3R1, TERF1, TXNIP, CDKN2A. In addition, we confirmed the TRM phenotype of CD27+CD8+ T cells, as they more frequently express the ITGAE gene (CD103) and other transcription factors (PRDM1, RBPJ, ZNF683) associated with the TRM phenotype. Furthermore, single-cell RNAseq analysis also confirmed that CD27+CD8+T cells express markers of exhaustion (PDCD1, CTLA4, HAVCR2, LAG3, TIGIT, TNFRSF9, SIRPG, ICOS, LAYN, CXCL13, CD38, TOX) (data not shown). An increase of cytotoxicity-associated transcripts (GZMA, GZMB, GZMH, CTSC) was also observed in CD27+CD8+T cells. Finally, we showed that CD27−CD8+T cells more frequently express IL-2 and IL-7R associated with naive, central memory phenotype.

[0166] In conclusion, by using TCGA data, inventors found that the frequent expression pattern of CD27 and CD70 in ccRCC correlates with patient survival. They attempt to understand the role of CD27 and CD70 in ccRCC. In their study cohort, they observed the interaction of CD27+T cells and CD70+ tumor cells interaction in situ. The outcome of CD27−CD70 interaction may lead to CD27+ T cells apoptosis, which is suggested in the analysis of TILs by flow cytometry. Besides, plasma levels of sCD27 are elevated in ccRCC and correlate with CD27−CD70 interaction in situ. Our study demonstrate that CD27−CD70 interaction results in T cell dysfunction and the release of sCD27.

REFERENCES

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