MARKERS SELECTIVELY DEREGULATED IN TUMOR-INFILTRATING REGULATORY T CELLS

Abstract

The present invention discloses a number of markers selectively deregulated in tumor-infiltrating regulatory T cells. The invention relates to molecules able to modulate the expression and/or function of at least one such marker for use in the prevention and/or treatment of the tumor. Preferably the molecule specifically binds to the marker and induces antibody-dependent cell-mediated cytotoxicity (ADCC). The invention further relates to a molecule able to modulate the expression and/or function of at least one such marker for use in a method for in vivo depleting tumor-infiltrating regulatory T cell in a subject, or for use in a method to enhance tumor immunity in a subject. Corresponding pharmaceutical compositions are also contemplated.

Claims

1.-25. (canceled)

26. A method for identifying an antibody acting as an anti-tumoral agent that depletes tumor-infiltrating regulatory T cells, comprising the steps of: a) assaying candidate antibodies for their binding specificity to CCR8; b) selecting antibodies having a specific binding activity to CCR8; c) testing the specific binding antibodies in a cell system comprising tumor infiltrating regulatory T cells for their capacity of inhibiting proliferation and/or inducing an apoptotic response of the tumor infiltrating regulatory T cells, thereby identifying an antibody that depletes tumor-infiltrating regulatory T cells.

27. The method of claim 26, wherein the depletion of tumor-infiltrating regulatory T cells comprises inducing antibody-dependent cell-mediated cytotoxicity (ADCC).

28. An in vitro method for monitoring the efficacy of a therapeutic treatment in a subject of a solid tumor which is a non-small cell lung cancer or a metastasis derived therefrom, said method comprising the steps of: a) obtaining an isolated biological sample containing tumor infiltrating T regulatory cells from the subject; b) detecting CCR8 in said biological sample; c) comparing the detected CCR8 to a control selected from a biological sample obtained from the same subject before initiation of the therapeutic treatment or taken at a time during the course of the therapeutic treatment, wherein a lower amount of CCR8 in the biological sample than in the control indicates effective treatment of the tumor.

Description

[0185] The invention will be illustrated by means of non-limiting examples in reference to the following figures.

[0186] FIG. 1. Purification, functional characterization and expression of immune checkpoints in tumor infiltrating cells.

[0187] (A) Representation of the sorting strategy of Treg cells infiltrating tumor or normal tissue.

[0188] (B) Representative flow cytometry plots showing suppressive activity of Treg cells isolated from tumor (NSCLC or CRC), normal lung and blood of the same patient. 410.sup.5 carboxyfluorescein diacetate succinimidyl ester (CFSE)-labeled CD4+ nave T cells from healthy donors were cocultured with an equal number of Treg cells for 4 days with a CD3-specific mAb and CD1c+CD11c+ dendritic cells. Percentage of proliferating cells are indicated. Data are representative of three independent experiments.

[0189] (C) Z-score normalized RNA-seq expression values of immunecheckpoints genes are represented as a heatmap. Cell populations are reported in the upper part of the graph, while gene names have been assigned to heatmap rows. Hierarchical clustering results are shown as a dendrogram drawn on the left side of the matrix. Colon tissues are indicated as C, lung tissues as L and peripheral blood as B. See also FIG. 6.

[0190] FIG. 2. Differential expression analysis identifies co-regulated genes in tumor infiltrating Treg cells

[0191] Z-score normalized expression values of genes that are preferentially expressed in tumor-infiltrating Tregs (Wilcoxon Mann Whitney test p<2.21016) over the listed cell subsets are represented as boxed plots. Colon tissues are indicated as C, lung tissues as L and peripheral blood as B.

[0192] FIG. 3. Single cell analysis of tumor infiltrating Treg cells

[0193] (A) Schematic representation of the experimental workflow. Experiments were performed on Treg cells infiltrating CRC, NSCLC, or isolated from peripheral blood of healthy donors (PB); five samples were collected for each tissue.

[0194] (B) Percentage of co-expression of signature genes with FOXP3 and IL2RA is depicted.

[0195] (C) Expression levels of the signature genes classified by the percentage of co-expression are represented as box plot.

[0196] (D) Expression distribution (violin plots) in Treg cells infiltrating CRC, NSCLC or PB. Plots representing the ontology classes of receptors, signaling and enzymatic activity, cytokine activity and transcription factors are shown (Wilcoxon Mann Whitney test p<0.05). Gray scale gradient indicates the percentage of cells expressing each gene in Treg cells isolated from the three compartments.

[0197] (E) Gene expression analysis of tumor Treg signature genes in different tumor types. Expression values are expressed as log 2 (2{circumflex over ()}DCt).

[0198] FIG. 4. Expression of tumor-infiltrating Treg cells protein signatures in CRC and NSCLC samples.

[0199] (A and B) Representative flow cytometry plots for tumor normal tissue infiltrating Treg cells and peripheral blood Treg cells analyzed for the expression of the indicated proteins.

[0200] FIG. 5. Prognostic value of signature transcripts of tumor infiltrating Treg cells.

[0201] (A) Kaplan-Meier survival curve comparing the high and low expression of the tumor Treg signature transcripts (CCR8, MAGEH1, LAYN) normalized to the CD3G for the CRC (n=177) and NSCLC(n=263) studies. Univariate analysis confirmed a significant difference in overall survival curve comparing patients with high and low expression. Statistical significance was determined by the log-rank test. (CRC: p=0.05 for CCR8, p=1.48103 for MAGEH1, p=2.1104 for LAYN; NSCLC: p=0.0125 for CCR8, p=0.035 for MAGEH1, p=0.0131 for LAYN) Each table depicts the Kaplan Meier estimates at the specified time points. (B) Expression distributions of CCR8, MAGEH1 and LAYN according to tumor staging at the time of surgery in the cohort of CRC patients. See also FIG. 9.

[0202] FIG. 6 related to FIG. 1. Transcriptome analysis of tumor infiltrating lymphocytes.

[0203] (A) Representation of the sorting strategy of Treg cells infiltrating colorectal tumor or normal tissue.

[0204] (B) RNA-seq expression values (normalized counts) of FOXP3, TBX21 and RORC in CD4+ Th1, Th17 and Treg cells from CRC (C), NSCLC (L) or peripheral blood (PB) of healthy donors.

[0205] (C) RNA-seq normalized counts data for selected immune checkpoints and their ligands are shown as histogram plot. Cell population names are reported in the lower part of each graph, while gene names are shown in the upper part.

[0206] FIG. 7 related to FIG. 3. Single-cell analysis of tumor infiltrating Treg cells.

[0207] Assessment of CD4+ Treg, Th1, Th17, Th2, CD8+ T cells and B cell markers expression (percentage of expressing cells) in single Treg cells purified from NSCLC and CRC.

[0208] FIG. 8 related to FIG. 4. Comparison of BATF expression in CD4+ Treg vs Th17 cells.

[0209] BATF expression levels (RNA-seq normalized counts data) in CD4+ Treg and Th17 subsets isolated from tumor tissue or peripheral blood

[0210] FIG. 9 related to FIG. 5. Expression levels of tumour-infiltrating Treg signature genes.

[0211] RNA-seq normalized counts data of three tumour-infiltrating Treg signature genes (MAGEH1 (panel A), LAYN (panel B) and CCR8 (panel C)) across listed cell populations.

[0212] FIG. 10. Results of RT-PCR analysis done on cDNA from Tumor infiltrating Treg cells (L=NSCLC, C=CRC, =ntc) with specific primers able to discriminate the different transcript isoforms annotated for SIRPG.

DETAILED DESCRIPTION OF THE INVENTION

Experimental Procedures

Human Primary Tissues

[0213] Primary human lung or colorectal tumors and non-neoplastic counterparts were obtained respectively from fifteen and fourteen patients who underwent surgery for therapeutic purposes at Fondazione IRCCS Ca' Granda, Policlinico or San Gerardo Hospitals (Italy).

[0214] Records were available for all cases and included patients' age at diagnosis, gender, smoking habit (for lung cancer patients), clinicopathological staging (Sobin et al., 2009), tumor histotype and grade (Table II). No patient received palliative surgery or neoadjuvant chemo- and/or radiotherapy. Informed consent was obtained from all patients, and the study was approved by the Institutional Review Board of the Fondazione IRCCS Ca' Granda (approval n. 30/2014).

[0215] Non-small-cell lung cancer (NSCLC) were cut into pieces and single-cell suspensions were prepared by using the Tumor Dissociation Kit, human and the gentleMACS Dissociator (Miltenyi Biotech cat. 130-095-929) according to the accompanying standard protocol. Cell suspensions were than isolated by ficoll-hypaque density-gradient centrifugation (Amersham Bioscience). Colorectal cancer (CRC) specimens were cut into pieces and incubated in DTT 0.1 mM (Sigma-Aldrich) for 10 min, then extensively washed in HBSS (Thermo Scientific) and incubated in 1 mM EDTA (Sigma-Aldrich) for 50 min at 37 C. in the presence of 5% CO2. They were then washed and incubated in type D collagenase solution 0.5 mg/mL (Roche Diagnostic) for 4 h at 37 C. Supernatants containing tumor infiltrating lymphocytes were filtered through 100 m cell strainer, centrifuged and fractionated 1800g for 30 min at 4 C. on a four-step gradient consisting of 100%, 60%, and 40% and 30% Percoll solutions (Pharmacia). The T cell fraction was recovered from the inter-face between the 60% and 40% Percoll layers.

[0216] CD4 T cell subsets were purified by FACS sorting using the following fluorochrome conjugated antibodies: anti-CD4 APC/Cy7 (Biolegend clone OKT4), anti-CD27 Pacific Blue (Biolegend, clone M-T271), anti-IL7R PE (Milteniy, clone MB15-18C9), anti-CD25 PE/Cy7 (eBioscience, clone BC96), anti-CXCR3 PE/Cy5 (BD, clone 1C6/CXCR3), anti-CCR6 APC (Biolegend, clone G034E3) and anti-CCR5 FITC (Biolegend, clone j418F1) using a FACSAria II (BD).

Flow Cytometry

[0217] To validate surface marker expression cells were directly stained with the following fluorochrome-conjugated antibodies and analyzed by flow cytometry: anti-CD4 (Biolegend, clone OKT4); anti-PD-L2 (Biolegend, Clone CL24F.10C12); anti-CD127 (eBioscience, clone RDR5); anti-BATF (eBioscience, clone MBM7C7), anti-GITR (eBioscience, clone eBIOAITR), anti-CD25 (Miltenyi, clone 4E3) and anti 4-1BB (eBioscience clone 4B4) anti CCR8(Biolegend clone L263G8) anti CD30 (eBioscience, clone Ber-H2) anti PD-L1 (Biolegend clone 29E.2A3) anti TIGIT (eBioscience, clone MBSA43) anti IL1 R2 (R and D clone 34141) IL21R (Biolegend clone 2G1-K12) anti OX40 (Biolegend clone Ber-ACT35). Intracellular staining was performed using eBioscience Foxp3 staining kit according to the manufactured's protocol (eBioscience cat 00-5523-00). Briefly cells were harvested and fixed for 30 min in fixation/permeabilization buffer at 4 C., and than stained with anti-FOXP3 antibody (eBioscience, clone 236A/E7) and anti-BATF (eBioscience clone MBM7C7) in permeabilisation buffer for 30 min at 4 C. Cells were then washed two times, resuspended in FACS washing buffer and analyzed by flow cytometry.

Suppression Assay.

[0218] 410.sup.4 carboxyfluorescein diacetate succinimidyl ester (CFSE)-labeled (1 M) responders Naive.sup.+ T cells from healthy donors were cocultured with different E/T ratio with unlabeled CD127.sup.CD25.sup.lowCD4.sup.+ T cells sorted from TILs or PBMCs of patients with CRC or NSCLC, using FACS Aria II (BD Biosciences), in the presence of CD11c.sup.+CD1c.sup.+dentritic cells as antigen-presenting cells and 0.5 mg/ml anti-CD3 (OKT3) mAb. Proliferation of CFSE-labeled cells was assessed by flow cytometry after 96 hr culture. RNA Isolation and RNA Sequencing

[0219] RNA from tumor-infiltrating lymphocytes was isolated using mirVana Isolation Kit.

[0220] Residual contaminating genomic DNA was removed from the total RNA fraction using Turbo DNA-free (Thermo Fisher). The RNA yields were quantified using the QuantiFluor RNA System (Promega) and the RNA quality was assessed by the Agilent 2100 Bioanalyzer (Agilent). Libraries for Illumina sequencing were constructed from 50 ng of total RNA with the Illumina TruSeq RNA Sample Preparation Kit v2 (Set A). The generated libraries were loaded on to the cBot (Illumina) for clustering on a HiSeq Flow Cell v3. The flow cell was then sequenced using a HiSeq 2500 in High Output mode (Illumina). A paired-end (2125) run was performed.

RNA-Seq Data Analysis

[0221] Raw .fastq files were analyzed using FastQC v0.11.3, and adapter removal was performed using cutadapt 1.8. Cutadapt is run both for reverse and forward sequences with default parameters [-anywhere <adapter1>-anywhere <adapter2>-overlap 10-times 2-mask-adapter]. Adapter sequences used for libraries preparation are

TABLE-US-00004 Adapter1: (SEQIDNO:710) AGATCGGAAGAGCACACGTCTGAACTCCAGTCACNNNNNNATCTCGTATG CCGTCTTCTGCTTG Adapter2: (SEQIDNO:711) AGATCGGAAGAGCGTCGTGTAGGGAAAGAGTGTAGATCTCGGTGGTCGCC GTATCATT

[0222] Trimming was performed on raw reads using Trimmomatic (Bolger et al., 2014): standard parameters for phred33 encoding were used: ILLUMINACLIP (LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15), MINLEN parameter was set to 50.

[0223] Mapping and quantification: reads mapping to the reference genome (GRCh38) was performed on quality-checked and trimmed reads using STAR 2.4.1c: [STAR-genomeDir <index_star>-runThreadN <cpu_number>-readFilesin <trimmed>_R1.fastq.gz<trimmed>_R2_P.fastq.gz-readFilesCommand zcat]. The reference annotation is Ensembl v80. The overlap of reads with annotation features found in the reference .gtf was calculated using HT-seq v0.6.1. The output computed for each sample (raw read counts) was then used as input for DESeq2 analysis. Raw counts were normalized using DESeq2's function rlog, and normalized counts were used to perform and visualize Principal Component Analysis (PCA) results (using DESeq2's plotPCA function).

[0224] Differential expression analysis: differential expression analyses of tumor-infiltrating CD4+ Treg/Th1/Th17 subsets vs. CD4+ Treg/Th1/Th17 from PBMC were performed using DESeq2. Upregulated/downregulated genes were selected for subsequent analyses if their expression values were found to exceed the threshold of 0.05 FDR (Benjamini-Hochberg correction).

Capturing of Single Cells, Preparation of cDNA and Single-Cell PCR

[0225] Treg cells from 5 CRC and 5 NSCLC specimens were isolated as previously described (See also Table II).Single cells were captured on a microfluidic chip on the C1 System (Fluidigm) and whole-transcriptome amplified. cDNA was prepared on chip using the SMARTer Ultra Low RNA kit (Clontech). Cells were loaded onto the chip at a concentration of 3-5E5 cells/ml, stained for viability (LIVE/DEAD cell viability assay; Thermo Fisher) and imaged by phase-contrast and fluorescence microscopy to assess the number and viability of cells per capture site. Only single, live cells were included in the analysis. For qPCR experiments, harvested cDNA was pre-amplified using a 0.2 pool of primers prepared from the same gene expression assays to be used for qPCR.

[0226] Pre-amplification allows for multiplex sequence-specific amplification 78 targets. In detail, a 1.25 l aliquot of single cell cDNA was pre-amplified in a final volume of 5 l using 1 l of PreAmp Master Mix (Fluidigm) and 1.25 l pooled TaqMan assay mix (0.2). cDNA went through amplification by denaturing at 95 C. for 15 s, and annealing and amplification at 60 C. for 4 min for 20 cycles. After cycling, pre-amplified cDNA was diluted 1:5 by adding l TE Buffer to the final 5 l reaction volume for a total volume of 25 l. Single-cell gene expression experiments were performed using the 9696 quantitative PCR (qPCR) DynamicArray microfluidic chips (Fluidigm). A 2.25 l aliquot of amplified cDNA was mixed with 2.5 l of TaqMan Fast Adavanced Master Mix (Thermo Fisher) and 0.25 l of Fluidigm's sample loading agent, then inserted into one of the chip sample inlets. A 2.5 l aliquot of each 20TaqMan assay was mixed with 2.5 l of Fluidigm's assay loading agent and individually inserted into one of the chip assay inlets. Samples and probes were loaded into 9696 chips using an IFC Controller HX (Fluidigm), then transferred to a BioMark real-time PCR reader (Fluidigm) following manufacturer's instructions. A list of the 78 TaqMan assays used in this study is provided below.

TABLE-US-00005 TABLE V Related to FIG. 3. List of TaqMan Probes and assay number used in RT-qPCR single-cell experiments Taqman Assays Numbers Assay Gene Name Number Gene Name Assay Number BCL2L1 Hs00235329_m1 ACP5 Hs00356261_m1 EOS Hs00223842_m1 BATF Hs00232390_m1 AHCYL1 Hs00198382_m1 SLC35F2 Hs00233850_m1 NFE2L3 Hs00852569_m1 LAX1 Hs00214948_m1 IL12RB2 Hs00155486_m1 CCR8 Hs00174764_m1 CD177 Hs00360669_m1 ADPRH Hs00153890_m1 OX40 HS00937194_g1 IKZF2 Hs00212361_m1 METTL7A Hs00204042_m1 C5F2RB Hs00166144_m1 ENTPD1 HS00969339_m1 NDFIP2 Hs00324851_m1 NFAT5 Hs00232437_m1 CADM1 Hs00942508_m1 CT9C Hs00175188_m1 ICOS Hs00359999_m1 SSH1 Hs00368014_m1 COL9A2 Hs00156712_m1 TMEM184C Hs00217311_m1 LTA Hs00236874_m1 HTATIP2 Hs03091727_m1 MAGEH1 Hs00371974_s1 HSDL2 Hs00953689_m1 IL21R Hs00222310_m1 FOXP3 Hs01085834_m1 S6TR3 Hs01066399_m1 IL2RA Hs00907778_m1 RNF145 Hs01066399_m1 LIMA1 Hs01033646_m1 LAPTM4B Hs00363282_m1 NAB1 Hs00428619_m1 GRSF1 Hs00909877_m1 ACSL4 Hs00244871_m1 ANKRD10 Hs00214321_m1 ERI1 Hs00405251_m1 NPTN Hs01033353_m1 FKEP1A Hs00356621_g1 HS3ST3B1 Hs00797512_s1 LEPROT Hs00956627_s1 TRAF3 Hs00936781_m1 NETO2 Hs00983152_m1 RRAGB Hs01099787_m1 VDR Hs00172113_m1 ZBT3S Hs00257315_s1 CSF1 Hs00174164_m1 TIGIT Hs00545087_m1 GITR Hs00188346_m1 TFRC Hs00951083_m1 IL1R2 Hs01030384_m1 JAK1 Hs01026982_m1 IL1R1 Hs00991010_m1 KSR1 Hs00300134_m1 LAYN Hs00379511_m1 ZNF202 Hs00411965_m1 THADA Hs00736554_m1 PTPRJ Hs01119326_m1 CTLA4 Hs00175480_m1 CHRNA6 Hs02563909_s1 CHST2 Hs01921028_s1 IL2RB Hs01081597_m1 CHST7 Hs00219871_m1 TBX21 Hs00203436_m1 LRBA Hs01032231_m1 RORC Hs01076112_m1 ETV7 Hs00903229_m1 CXCR5 Hs00540548_s1 LY75 Hs00982383_m1 CD8A Hs00233520_m1 ADAT2 Hs00699339_m1 CD8B Hs00174762_m1 GCNT1 Hs00155243_m1 PTGDR2 Hs00173717_m1 CASP1 Hs00354836_m1 CD19 Hs01047410_g1

[0227] Single-cell data analysis: The Quality Threshold in the BioMark Analysis software is a qualitative tool designed to measure the quality of each amplification curve. Basically, each amplification curve is compared to an ideal exponential curve and as the quality score approaches 1 the closer it is to ideal. The further the curve is from ideal, its quality score approaches 0. The default cutoff of 0.65 is an arbitrary value set by Fluidigm. Any curve above 0.65 passes. Any curve below, fails. Baseline correction was set on Linear (Derivative)[default]. Ct Threshold Method was set on Auto (Detectors). This method independently calculates a threshold for each detector on a chip. For clustering and downstream analysis, raw Cts have been converted to Log 2Exp by using a Limit of Detection (LOD) of 35, which corresponds to the last PCR cycle. Co-expression analysis has been performed by considering both CRC and NSCLC samples on those genes for which both FOXP3 and IL2RA were co-expressed at least to 2%. Gene's levels above the background were depicted as violin plots after log 2 scale transformation by ggplot2 (v. 2.1.10). The violin color gradient is the percentage of cells that are expressing the gene of interest and the upper bound of the color scale is the maximum percentage of cells that express a gene of the whole geneset.

[0228] Procedure for the removal of transcripts whose expression values are affected by the dropout effect. Single-cell qPCR data are inherently noisy, and due the limitations of current technologies the expression patterns of a certain number of genes may be affected by the dropout effect. Inventors performed a gene selection procedure in order to take into account this dropout effect and discard those genes whose expression values cannot be reliably used in a binary comparison (tumor-peripheral vs blood). Inventors fitted a number of parametric distributions to the ratios of detected genes on the total number of tumor cells (both NSCLC and CRC) and selected the reciprocal inverse Gaussian continuous random variable as best fit.

[0229] Inventors then calculated the median value of the fitted distribution and discarded those genes whose detection ratio is less than this threshold value (at least 8.4% of detection). Inventors reasoned that these genes are more likely to be affected by the dropout effect. With this threshold inventors selected 45 genes for which a non-parametric T-test (Wilcoxon Mann Whitney test p<0.05) has been performed (by comparing tumor vs. peripheral blood samples).

Meta Analysis Kaplan-Meier and Stage Correlation

[0230] Statistical analysis was performed by using the R survival package (Therneau T. 2013). Survival times were calculated as the number of days from initial pathological diagnosis to death, or the number of days from initial pathological diagnosis to the last time the patient was reported to be alive. The Kaplan-Meier (KM) was used to compare the high and low expression levels of the tumor-Treg cell signature transcripts in either CRC (GSE17536) and NSCLC (GSE41271) patients. For both studies annotation was normalized to four tumor stages (1,2,3,4). For study GSE41271 five patients were excluded due to incomplete or inaccurate annotation (GSM1012883, GSM1012884, GSM1012885, GSM1013100, GSM1012888), retaining a total of two hundred and sixty three patients. Patients from both studies were labeled as High Low whether or not their relative expression values exceeded a decision boundary (mean of the samples). Inventors define {umlaut over (x)}.sub.ij to denote the relative expression of the gene i for the n samples of the study normalized to the CD3 level:

[00001]$\begin{array}{ccc}{\tilde{x}}_{i,j}=\frac{x_{i,j}}{x_{\mathrm{CD}3G,j}};& i=\left(\mathrm{CCR}8,\mathrm{MAGEH}1,\mathrm{LAYN}\right)& j=1,2,.Math.,n\mathrm{samples}\end{array}$

[0231] To classify a patient, a threshold on the {umlaut over (x)}.sub.ij is required and defined as

[00002]$T_{\left(\mathrm{Upper}\mathrm{Lower}\right)}=\mathrm{median}\left({\tilde{x}}_{i,j}\right)\frac{\left({\tilde{x}}_{i,j}\right)}{10}$

where T.sub.(Upper,Lower) represent the upper and lower extreme of the decision boundary:

[00003]$\{\begin{array}{c}{\tilde{x}}_{i,j}>T_{\mathrm{Upper}}\mathrm{High}\\ {\tilde{x}}_{i,j}<T_{\mathrm{Lower}}\mathrm{Low}\\ T_{\mathrm{Upper}}{\tilde{x}}_{i,j}{T}_{\mathrm{Lower}}\mathrm{excluded}\end{array}$

[0232] Inventors examined the prognostic significance of tumor Treg cells transcripts by using log-rank statistics; a p-value of less than 0.05 was considered statistically significant. Since the log-rank test resulted in a p-value of less than 0.05, a post stage comparison by means of box plot representation was performed in order to evaluate the correlation degree between the expression level of the transcripts and tumor stages in the cohort of CRC patients. The annotation was normalized to four tumor stages (1,2,3,4).

ACCESSION NUMBERS

[0233] The accession numbers for the present data are as follows: ENA: PRJEB11844 for RNA-seq tumor and tissue infiltrating lymphocytes; ArrayExpress: E-MTAB-2319 for RNA-seq human lymphocytes datasets; ArrayExpress: E-MTAB-513 for Illumina Human BodyMap 2.0 project; GEO: GSE50760 for RNA-seq datasets CRC; GEO: GSE40419 for RNA-seq datasets NSCLC; GEO: GSE17536 for CRC expression profiling by array; and GEO: GSE41271 for NSCLC expression profiling by array.

Prediction of Surface-Exposed and Membrane-Associated Proteins

[0234] The probability of surface exposure of the proteins encoded by the genes of interest was determined by a combination of four different cell localization prediction algorithms: Yloc (Briesemeister et al, 2010), TMHMM (http://www.cbs.dtu.dk/services/TMHMM/), SignalP (http://www.cbs.dtu.dk/services/SignalP/) and Phobius (KAII et. al., 2007). In particular Yloc is a interpretable system offering multiple predictive models in animal version; inventors used both YLoc-LowRes predicting into 4 location (nucleus, cytoplasm, mitochodrion, secretory pathway) and Yloc-HighRes predicting into 9 locations (extracellular space, plasma membrane, nucleus, cytoplasm, mitochodrion, endoplasmic reticulum, peroxisome, Golgi apparatus, and lysosome).

[0235] TMHMM and SignalP were developed by the bioinformatic unit of the technical University of Denmark for the prediction of transmembrane helices and the presence and location of signal peptide cleavage sites in amino acid sequences, respectively. Phobius is a combined transmembrane topology and signal peptide predictor.

RT-PCR Analysis of Transcript Isoforms Expressed by Tumor-Infiltrating Regulatory T Cells (Treg Cells)

[0236] Total RNA was extracted from tumor Treg cells (NSCLC or CRC) using miRCURY RNA isolation kit (Exiqon) and 1 g was reverse transcribed with iScript reverse transcription supermix (BIORAD). Afterwards, 25 ng of cDNA were amplified with DreamTaq Green PCR Master Mix (ThermoScientific) using multiple gene-specific primers able to discriminate the different isoforms. PCR products were run on agarose gel. The expression of specific transcripts was assessed based on the expected band size.

Results

Tumor Infiltrating Tregs Cells Upregulate Immune Checkpoints and are Highly Suppressive

[0237] To assess the gene expression landscape of tumor infiltrating CD4.sup.+ T cells, the inventors isolated different CD4.sup.+ lymphocytes subsets from two different tumors, NSCLC and CRC, from the adjacent normal tissues, and from peripheral blood samples. From all these tissues, the inventors purified by flow cytometry (FIGS. 1A and 6A and 6B) CD4.sup.+ Treg (36 samples from 18 individuals), Th1 (30 samples from 21 individuals) and Th17 (22 samples from 14 individuals) cells (Table I and Table II).

TABLE-US-00006 TABLE 1 Purification and RNA-Sequencing of Human Primary Lymphocyte Subsets Mapped Sorting Number of Reads Tissue Subset Phenotype Samples (M) NSCLC CD4+ Treg CD4.sup.+ CD127.sup. CD25.sup.+ 8 587 CD4.sup.+ Th1 CD4.sup.+ CXCR3.sup.+ CCR6.sup. 8 409 CD4.sup.+ Th17 CD4.sup.+ CCR6.sup.+ CXCR3.sup. 6 206 CRC CD4.sup.+ Treg CD4.sup.+ CD127.sup. CD25.sup.+ 7 488 CD4.sup.+ Th1 CD4.sup.+ CXCR3.sup.+ CCR6.sup. 5 266 CD4.sup.+ Th17 CD4.sup.+ CCR6.sup.+ CXCR3 5 308 Lung CD4.sup.+ Treg CD4.sup.+ CD127.sup. CD25.sup.+ 1 (pool 73 (normal tissue) of 6) CD4.sup.+ Th1 CD4.sup.+ CXCR3.sup.+ CCR6.sup. 1 (pool 76 of 6) Colon CD4.sup.+ Treg CD4.sup.+ CD127.sup. CD25.sup.+ 7 404 (normal tissue) CD4.sup.+ Th1 CD4.sup.+ CXCR3.sup.+ CCR6.sup. 6 352 CD4.sup.+ Th17 CD4.sup.+ CCR6.sup.+ CXCR3.sup. 6 284 PB (healthy donor) CD4.sup.+ Treg CD4.sup.+ CD127.sup. CD25.sup.+ 8 259 CD4.sup.+ Th1 CD4.sup.+ CXCR3.sup.+ CCR6.sup. 5 70 CD4.sup.+ Th17 CD4.sup.+ CCR6.sup.+ CXCR3.sup. 5 77 For each cell subsets profiled by RNA-sequencing tissue of origin, surface marker combinations used for sorting, number of profiled samples, as well as number of mapped sequencing reads are indicated. M, million; CRC, colorectal cancer; NSCLC, non-small cell lung cancer; PB, peripheral blood.

TABLE-US-00007 TABLE II Table II related to Table I. Patients information and histological analysis. For each cell subset profiled by RNA-sequencing, patient records are shown including: age at diagnosis, gender, smoking habit (for lung cancer patients), clinicopathological staging (TNM classification) tumor histotype and grade. For Treg cell isolated for qPCR experiment the same information are available, including also the number of live cells captured from each tumor and available for single-cell analysis. NSCLC PATIENTS LIST (RNA SMOKE SEQUENCING) (T)Th1 (T)Th17 (T)Treg (H)Th1 (H)Th17 (H)Treg HABIT STATUS GENDER PATIENT1 SQ_0342 PREVIOUS ALIVE M SMOKER >15 y PATIENT2 SQ_0339 PREVIOUS ALIVE M SMOKER <15 y PATIENT3 SQ_0365 SQ_0375 PREVIOUS ALIVE M SMOKER <15 y PATIENT4 SQ_0366 SQ_0374 SQ_0341 PREVIOUS ALIVE M SMOKER >15 y PATIENT5 SQ_0364 SQ_0373 SQ_0350 SQ_0351 SMOKER DEAD M PATIENT6 SQ_0358 SQ_0336 SQ_0350 SQ_0351 PREVIOUS ALIVE M SMOKER <15 y PATIENT7 SQ_0363 SQ_0376 SQ_0334 SQ_0350 SQ_0351 PREVIOUS ALIVE M SMOKER <15 y PATIENT8 SQ_0357 SQ_0337 SQ_0350 SQ_0351 PREVIOUS ALIVE M SMOKER WITH <15 y RELAPSE PATIENT9 SQ_0404 SQ_0408 SQ_0398 SQ_0350 SQ_0351 SMOKER ALIVE F PATIENT10 SQ_0403 SQ_0407 SQ_0396 SQ_0350 SQ_0351 PREVIOUS ALIVE F SMOKER >15 y NSCLC ADCA PATIENTS HISTO- SUBTYPE LIST (RNA TYPE (PRE- pTNM: pTNM: pTNM: SEQUENCING) AGE(y) MAJOR DOMINANT) GRADE T N M STAGE PATIENT1 84 SCC G3 2b 0 0 IIA PATIENT2 83 SCC G3 2a 0 0 IB PATIENT3 72 SCC G3 2 2 0 IIIA PATIENT4 79 SCC G3 2a 0 0 IB PATIENT5 66 SCC G3 3 2 0 IIIA PATIENT6 71 SCC G3 4 1 0 IIIA PATIENT7 78 SCC G3 2b 1 0 IB PATIENT8 77 ADCA SOLID G3 2 2 0 IIIA PATIENT9 69 ADCA SOLID G3 1a 0 0 IA PATIENT10 77 ADCA ACINAR G3 1a 0 0 IA NSLC = Non Small Cell Lung Cancer ADC = Adenocarcinoma SCC = Squamous Cell Carcinoma (T) = Tumor Sample (H) = Healthy Tissue TUMOR INFILTRATING TREG FROM NSCLC HISTO- (SINGLE SMOKE TYPE ADCA SUBTYPE CELL qPCR) HABIT STATUS GENDER AGE(y) MAJOR PREDOMINANT PATIENT1 NEVER ALIVE F 65 ADCA ACINAR and SMOKER PAPILLARY PATIENT2 PREVIOUS ALIVE M 62 ADCA SOLID SMOKER <15 y PATIENT3 NEVER ALIVE F 63 ADCA ACINAR SMOKER PATIENT4 SMOKER ALIVE M 66 SCC PATIENT5 SMOKER ALIVE M 68 SCC TUMOR INFILTRATING TREG FROM NSCLC (SINGLE pTNM: pTNM: pTNM: CAPTURED CELL qPCR GRADE T N M STAGE SINGLE CELLS PATIENT1 G2 2a 0 0 IB 71 PATIENT2 G2 1b 0 0 IA 61 PATIENT3 G1 1a 0 0 IA 44 PATIENT4 G2 2a 0 0 IB 55 PATIENT5 G3 1b 0 0 IA 55 NSLC = Non Small Cell Lung Cancer ADC = Adenocarcinoma SCC = Squamous Cell Carcinoma (T) = Tumor Sample (H) = Healthy Tissue CRC PATIENTS LIST (RNA SEQUENCING) (T) Th1 (T) Th17 (T) Treg (H) Th1 (H) Th17 (H) Treg GENDER PATIENT1 SQ_0389 SQ_0386 SQ_0387 SQ_0388 M PATIENT2 SQ_0427 SQ_0434 SQ_0418 F PATIENT3 SQ_0423 SQ_0436 SQ_0411 M PATIENT4 SQ_0426 SQ_0437 SQ_0413 SQ_0428 SQ_0439 SQ_0417 M PATIENT5 SQ_0425 SQ_0412 SQ_0429 SQ_0441 SQ_0422 M PATIENT6 SQ_0424 SQ_0415 SQ_0431 SQ_0442 SQ_0421 M PATIENT7 SQ_0435 SQ_0416 SQ_0432 SQ_0438 SQ_0420 F PATIENT8 SQ_0414 F PATIENT9 SQ_0433 SQ_0430 SQ_0440 SQ_0419 M CRC PATIENTS HISTO- LIST (RNA TYPE pTNM: pTNM: pTNM: SEQUENCING) AGE(y) MAJOR GRADE T N M STAGE PATIENT1 76 ADC G2 3 1A 0 IIIB PATIENT2 68 ADC G2 3 0 0 IIA PATIENT3 80 ADC G2 4B 1B 0 IIIB PATIENT4 79 ADC G2 3 1A 0 IIIB PATIENT5 78 ADC G2 3 0 0 IIA PATIENT6 69 MUC 3 1B 0 IIIB ADC PATIENT7 84 ADC G2 4B 0 0 IIC PATIENT8 75 MUC 3 1C 0 IIIB ADC PATIENT9 54 ADC G2 2 0 0 I ADC = Adenocarcinoma MUC ADC = Mucinous Adenocarcinoma CRIB ADC = Cribrous Adenocarcinoma (T) = Subsets purified from Tumor Sample (H) = Subsets purified from Healthy Tissue TUMOR INFILTRATING TREG FROM HISTO- CRC (SINGLE TYPE pTNM: CELL qPCR) GENDER AGE(y) MAJOR GRADE T PATIENT1 M 64 ADC 2 3 PATIENT2 M 59 CRIB 3 ADC PATIENT3 F 75 MUC 4A ADC PATIENT4 M 71 ADC 1 3 PATIENT5 M 64 ADC 2 3 TUMOR INFILTRATING TREG FROM CRC pTNM: pTNM: CAPTURED (SINGLE CELL qPCR) N M STAGE SINGLE CELLS PATIENT1 0 0 IIA 62 PATIENT2 0 0 IIA 66 PATIENT3 2B 0 IIIC 65 PATIENT4 0 0 IIA 63 PATIENT5 0 0 IIA 64 ADC = Adenocarcinoma MUC ADC = Mucinous Adenocarcinoma CRIB ADC = Cribrous Adenocarcinoma (T) = Subsets purified from Tumor Sample (H) = Subsets purified from Healthy Tissue CRC: colorectal cancer; NSCLC: non-small cell lung cancer; (T): Tumor Sample; (H): Healthy Tissue; ADC: Adenocarcinoma; SCC: Squamous Cell Carcinoma; MUC ADC: Mucinous Adenocarcinoma.

[0238] To assess Treg cell function, inventors tested their suppressor activity and showed that Treg cells infiltrating either type of tumor tissues have a remarkably stronger suppressive activity in vitro compared to Treg cells isolated from the adjacent normal tissue and peripheral blood of the same patients (FIG. 1B).

[0239] The polyadenylated RNA fraction extracted from the sorted CD4+ Treg, Th1, and Th17 cells was then analyzed by pair-end RNA sequencing obtaining about 4 billion mapped reads (Table 1). First, inventors interrogated RNA-sequencing data of CD4+ T cells infiltrating both CRC and NSCLC and their matched normal tissues, to quantitate mRNA expression of known immune checkpoints and their ligands. Second, inventors analyzed RNA-seq data of CRC and NSCLC, as well as of normal colon and lung samples.

[0240] Inventors found that several immune checkpoints and their ligands transcripts were strikingly upregulated in tumor infiltrating Treg cells compared to both normal tissue and peripheral blood-derived Treg cells, as well as to T and B lymphocyte subsets purified from peripheral blood mononuclear cells (PBMCs) (FIGS. 10C and 60 and Table Ill).

TABLE-US-00008 Treg_Tumor_ Treg_Tumor_ Treg_Tissue_ Treg_Tissue_ Treg healthy GENE Infiltrating Infiltrating Inflitrating Inflitrating Peripheral NAME CRC NSCLC Colon Lung Blood ADORA2A 14.69 24.06 17.97 44.84 18.52 BTLA 554.04 742.11 389.51 208.76 108.2 BTNL2 0 0.14 0.29 0 0.75 (BTLN2) C10orf54 779.38 872.36 555.47 1405.63 1111.37 (VISTA) CD160 58.39 38.24 51.87 34.54 36.55 CD200 268.39 283.21 282.05 104.64 99.59 CD200R1 95.89 136.08 81.36 349.99 59.03 CD244 34.46 31.21 29.59 128.35 47.8 CD27 710.13 1068.55 583.58 496.38 468.93 CD274 1050.94 645.66 576.59 390.71 120.19 (PD-L1) CD276 16.85 72.3 10.44 65.98 3.61 CD28 4770.41 4585.17 5446.29 3687.01 5179.32 CD40 112.04 161.29 80.64 93.3 34.71 CD40LG 135.51 143.07 360.09 418.55 104.22 CD44 13049.36 8518.98 13513.69 19851 16013.71 CD48 346.61 489.78 494.58 594.83 1523.63 CD70 426.35 269.38 318.97 249.48 101.67 CD80 632.12 483.34 318.48 269.06 114.41 CD86 29.52 78.86 52.72 278.86 3.87 CTLA4 6798.82 10378.3 4810.74 5340.06 4806.23 HAVCR2 577.57 633.27 265.84 487.62 49.81 (TIM-3) HHLA2 3.41 3.66 4.47 9.28 12.7 ICOS 6830.94 7339.08 4119.2 5211.71 3398.28 ICOSLG 58.02 8.86 59.13 33.5 76.5 (B7RP1) IDO1 3.86 83.81 9.51 5.15 2.36 IDO2 0.22 2.25 1.41 5.15 1.58 KIR3DL1 0.38 0.43 0.28 4.64 0.9 (KIR) LAG3 705.14 1956.22 2181.52 1505.63 127.02 LAIR1 277.06 194.09 551.94 874.72 346.22 LGALS9 1175.81 1530.47 1160.89 1593.26 592.56 (Galectin-9) NRP1 7.38 36.24 8.89 106.7 8.59 PDCD1LG2 214.51 223.04 61.89 25.77 12.12 (PD-L2) PDCD1 467.22 496.56 405.01 676.27 111.26 (PD1) TIGIT 14821.45 14747.79 10986.74 4901.41 4611.14 TMIGD2 28.38 16.64 78.3 75.77 71.27 TNFRSF14 2230.85 2677.32 2297.43 2675.7 2274.82 (HVEM) TNFRSF18 4038.86 4078.14 2871.78 3071.57 333.36 (GITR) TNFRSF25 5236.86 4188.61 4986.56 5111.71 3587.58 TNFRSF4 4222.16 4642.56 2873.16 2992.18 400.56 (OX40) TNFRSF8 155.59 430.23 115.57 208.24 30.89 (CD30) TNFRSF9 2921.72 3128.82 898.69 1739.13 502.86 (4-1BB) TNFSF14 148.57 183.77 223.49 421.12 105.12 (LIGHT) TNFSF15 1.58 3.75 0.89 25.77 1.23 TNFSF18 0.4 1.11 0.53 0 0.45 TNFSF4 110.82 136.82 100.95 98.97 16.33 (OX40LG) TNFSF9 26.79 19.48 19.72 29.9 7.41 (CD137L) VTCN1 1.12 4.49 1.48 1.55 2.65 (B7-H4)

[0241] RNA-seq normalized counts data for selected immune checkpoints genes and their ligands in all the subsets analyzed.

[0242] These findings highlight the specific expression patterns of immune checkpoints and their ligands in tumor infiltrating Treg and effector cells and suggest that their functional relevance should be investigated directly at tumor sites.

Tumor-Infiltrating Treg Cells Express a Specific Gene Signature

[0243] The inventors then asked whether tumor infiltrating Treg cells could be defined by specific gene expression patterns.

[0244] To identify signature transcripts of tumor-infiltrating Treg cells, the inventors included in the expression pattern analyses the transcriptome dataset they previously obtained from different T and B lymphocyte subsets purified from PBMCs (Ranzani et al., 2015). In so doing, the inventors obtained a signature of 328 transcripts whose expression is higher in tumor infiltrating Treg cells (Wilcoxon Mann Whitney test p<2.21016) (FIG. 2, and Table IV compared to the other lymphocyte subsets purified from non-tumoral tissues and from PBMCs of healthy or neoplastic patients.

TABLE-US-00009 Treg_Tumor_ Treg_Tumor_ Treg_Tissue_ Treg_Tissue_ Treg healthy Gene Infiltrating Infiltrating Inflitrating Inflitrating Peripheral Name CRC NSCLC Colon Lung Blood AC019206.1 15.41 8.72 12.89 12.04 29.46 ACAA2 305.76 499.02 497.41 526.58 614.28 ACOT9 918.3 803.71 1361.82 2180.66 1272.07 ACOX3 183.48 384.73 469.06 506.97 439.27 ACP5 267.7 837.72 859.77 1872.29 1483.27 ACSL4 1154.87 1384.88 1903.56 2170.94 2043.91 ACTA2 86.65 270.74 108.76 234.86 232.15 ACTG2 10.69 6.16 22.68 21.11 36.14 ADAM10 2378.26 3051.7 2545.29 3600.38 3167.56 ADAT2 927.45 1272.17 1214.4 2094.25 3103.21 ADPRH 136.34 460.61 352.57 836.7 718.74 AHCYL1 914.19 1271.5 1269.55 1835.94 1711.94 AHCYL2 305.15 570.67 525.24 790.1 856.25 AKAP5 174.24 264 358.75 709.28 535.97 AKIP1 261.47 273.85 225.25 436.84 360.48 ANKRD10 2251.92 3433.73 2805.08 4192.8 4672.81 ARHGEF12 1371.05 2064.05 1536.04 3069.77 2637.79 ARHGEF4 19.42 71.47 28.87 195.02 252.84 ARL6IP5 3008.69 4385.74 4051.43 4983.16 4712.48 ARNTL2 20.4 201.3 281.95 560.77 445.13 ATP13A3 3776.14 4020.7 4688.02 6688.94 6967.94 ATP2C1 1491.87 1399.81 1553.57 2029.41 1819.78 AURKA 24.56 50.12 79.89 66.37 87.07 BATF 820.97 3325.93 1698.92 5052.64 2727.65 BCL2L1 212.64 478.8 537.61 554.11 892.28 BIRC5 14.74 20.27 20.62 25.03 44.99 C17orf96 19 174.31 159.79 239.88 377.03 C5orf63 146.45 201.44 112.88 228.2 357.09 CABLES1 59.04 196.68 125.77 473.94 386.73 CACNB2 67.43 50.49 40.21 169.83 105.62 CADM1 113.76 602.72 115.46 1766.12 901.32 CALM3 2474.48 2829.3 2675.18 2954.03 4107.03 CARD16 370.31 696.36 493.29 1220.7 823.89 CARD17 41.87 96.94 54.12 101.19 132.95 CASP1 925.29 1453.84 1521.09 2028.95 1980.45 CASQ1 52.11 31.21 24.74 135.08 174.95 CCNB2 18.28 27.62 34.02 51.57 58.08 CCR8 255.66 578.27 1355.63 3127.33 2069.11 CD177 2.36 204.74 299.99 718.58 470.27 CD27 468.93 583.58 496.38 710.13 1068.55 CD274 120.19 576.59 390.71 1050.94 645.66 CD7 1622.12 6900.01 2829.82 9053.96 6919.59 CDCA2 19.24 35.09 49.48 68.21 49.95 CDH24 57.67 57.11 89.69 148.93 105.02 CDK6 602.97 2175.36 2463.85 3580.4 3238.58 CEACAM1 360.01 340.84 326.28 381.79 732.86 CENPM 43,72 39.12 61.85 72.94 61.32 CEP55 56.18 88.17 223.71 220.17 273.64 CGA 1.08 13.59 22.68 334.28 9.73 CHRNA6 14.46 218.49 67.52 336.38 504.28 CHST11 1822.7 2085.92 2806.11 2790.19 2535.23 CHST2 75.46 218.75 156.7 458.24 604.97 CHST7 141.3 341.87 426.79 1087.21 333.3 CIT 89.25 105.13 155.15 150.2 262.67 CLNK 153.06 288.36 248.96 340.12 528.54 CNIH1 1028.31 1005.46 935.03 2336.95 1101.87 COL9A2 149.87 278.77 357.72 889.47 805.72 CORO1B 481.34 667.37 861.83 774.65 1040.47 COX10 305.31 399.33 397.93 447.17 612.29 CRADD 77.04 155.66 277.31 394.31 306.61 CREB3L2 739.04 1289.66 1415.94 2984.54 2590.37 CSF1 313.09 1629.13 1609.75 2204.79 3288.67 CSF2RB 1069.75 1275.49 1290.69 2036.76 2531.99 CTLA4 4806.23 4810.74 5340.06 6798.82 10378.3 CTSC 1026.76 2196.93 2514.88 3030.74 2767.27 CTTNBP2NL 85 200.53 248.45 500.75 267.16 CX3CR1 9.57 63.99 123.71 341.79 293.28 CXCL13 1.07 255.23 1145.33 1270.98 11433.26 CYB5B 714.26 1129.39 947.4 1156.4 1221.22 CYP7B1 9.83 210.33 29.38 186.99 161.17 DCPS 153.25 210.26 210.82 191.31 271.71 DFNB31 561.87 1636.56 1727.79 4251.83 2526.15 DIRAS3 1.9 4.59 3.61 26.01 35.64 DLGAP5 7.89 14.46 20.62 27.41 49.7 DNPH1 160.15 650.05 321.13 683.55 576.77 DOC2B 10.47 3.42 5.15 14.23 238.86 DPYSL2 208.98 189.08 580.4 591.32 618.42 EBI3 7.47 103.59 56.7 148.96 200.74 ECEL1 3.7 150.7 34.02 199.17 794.51 EGLN1 977.29 969.32 1021.11 1381.2 1271.06 EML2 861.51 1601.25 1643.25 2156.04 1957.43 ENTPD1 752.88 2078.17 1447.38 4321.79 4162.57 ERI1 354.33 862.86 932.45 1200.06 1070.15 ETFA 414.08 586.15 534.01 615.35 689.14 ETV7 93.62 511.26 361.85 728.85 1111.55 EVA1B 21.39 35.63 26.8 42.86 47.36 F5 2343.39 2346.94 2499.41 4868.41 4729.97 FAAH2 244.19 431.76 209.27 737.44 699.42 FAIM2 15.05 33.47 57.21 69.26 117.28 FAM184A 192.41 742.47 525.24 706.33 891.02 FAM19A2 311.38 204.56 302.57 264.46 748.09 FAM98B 314.26 664.69 491.22 698.92 657.42 FAS 2337.14 5167.46 2712.81 5982.39 3656.21 FBXO45 460.56 783.06 631.43 964.13 894.23 FCRL3 1161.64 1997.02 938.63 3281.36 2699.01 FKBP1A 733.83 1240.62 1174.19 1377.67 1578.09 FLNB 1671.04 1363.04 1394.81 3395.38 2307.44 FLVCR2 69.84 579.55 388.13 744.8 528.01 FNDC3B 377.47 501.27 506.17 1111.07 531.12 FOXA1 2.7 11.87 17.01 70.68 18.22 FOXM1 56.39 74.94 108.24 88.16 125.31 FOXP3 6586.98 10713.12 6060.66 13483.77 11472.41 FUCA2 107.56 175.46 160.82 249.54 315.45 GADD45A 745.14 1431.9 884.51 3681.24 1396.98 GCNT1 99.22 632.16 608.75 1133.62 845.83 GK 637.31 1994.73 2430.34 5200.55 2065.35 GLB1 563.96 819.22 873.17 1077.84 854.94 GLCCI1 1557.57 3211.73 1753.04 3189.77 2909.06 GLDC 19.25 20.56 25.26 31.21 74.61 GLRX 1213.06 1251.64 1512.85 1764.61 1872 GNG4 5.08 79.18 64.43 197.1 343.93 GNG8 11.94 63.28 10.82 67.63 175.16 GRSF1 1277.4 1725.67 1397.9 2899.76 2343.4 GSK3B 1099.5 1267.18 1208.73 1333.16 1454.67 GTF3C6 313.17 579.04 445.86 617.48 597.55 GTSF1L 13.67 20.36 15.46 44.6 99.03 HADHB 1179.61 1207.14 1287.59 1396.89 1521.16 HAP1 92.39 180.51 74.22 292.97 577 HAVCR2 49.81 265.84 487.62 577.57 633.27 HECW2 17.63 98.93 38.66 111.21 177.5 HIBCH 124.32 290.04 226.8 348.34 332.88 HIVEP3 358.34 649.68 893.27 1091.96 1316.89 HJURP 8.55 18.52 15.98 27.13 39.99 HOXA1 16.66 15.22 14.95 25.57 44.75 HPRT1 442.58 532.66 542.25 811.75 724.15 HPSE 248.88 676.54 515.45 674.09 754.04 HS3ST3B1 1222.43 1930.88 1980.87 2609.49 2431.83 HSDL2 242.56 611.72 285.56 785.27 921.97 HTATIP2 567.61 1439.29 997.4 3285.86 1576.24 ICA1 94.65 371.57 113.91 487.68 411.64 ICOS 3398.28 4119.2 5211.71 6830.94 7339.08 IGFLR1 67.43 78.13 92.78 108.12 185.13 IKZF2 6061.48 6317.6 4919.45 9983.52 8551.49 IKZF4 1422.66 2362.49 1258.21 3745.25 3958.19 IL12RB2 120.8 369.84 509.78 835.92 877.51 IL17REL 9.74 23.21 34.02 52.62 57.04 IL1R1 506.51 9670.81 2766.42 7852.18 5585.89 IL1R2 41.72 1225.4 526.79 2117.34 1793.21 IL1RL1 17.37 135.26 44.33 715.42 71.67 IL1RL2 8.65 76.53 28.35 74.81 59.47 IL21R 708.61 1355.83 1715.93 3092.3 3514.36 IL2RA 5244.31 9685.38 5627.68 11454.42 12731.31 IL2RB 6716.4 14249.6 12502.75 17733 18564.35 IL32 4332.08 13202.73 9755.92 11766.98 13883.45 IL7 117.66 230.78 165.97 257.71 178.1 INPP1 124.25 497.01 312.88 458.2 487.93 INPP5F 787.92 2172.55 830.9 2189.48 1549.46 ISOC1 233.44 329.49 400.5 514.43 335.93 ITFG1 313.34 324.11 402.05 396.94 511.86 JAK1 10779.78 11919.66 10072.4 17755.9 11521.32 JAKMIP1 291.14 387.49 1063.89 756.36 953.47 KAT2B 3145.05 3910.01 4756.57 5520.88 4632.76 KIF14 20.18 25.43 31.96 36.73 59.61 KIF15 20.64 29.67 51.03 41.9 68.63 KIF20A 9.84 14.93 7.22 20.97 32.72 KLHDC7B 131.39 211.42 188.65 245.3 394.73 KSR1 837.87 1569.86 1176.77 2241.36 1847.72 LAPTM4B 86.42 369.78 181.44 938.88 738.38 LAX1 1135.24 1155.91 1406.15 1721.7 1854.78 LAYN 441.73 796.76 859.25 2650.24 1681.25 LEPR 58.77 130.22 129.38 137.47 237.88 LEPROT 614.73 860.55 676.79 1044.66 1296.13 LHFP 1.58 10.38 9.79 18.09 63.16 LIMA1 404.55 727.57 1017.5 1064.46 1570.15 LMCD1 115.76 104.74 112.37 257.92 404.7 LOC388813 7.42 45.99 28.87 86.3 60.63 LRG1 17.67 61.54 46.39 71.6 78.3 LRRC61 98.78 291.45 138.66 292.51 314.79 LTA 214.07 516.57 270.61 351.26 747.01 LXN 67.37 91.06 75.77 114.23 133.43 LY75 249.92 970.85 680.91 1302.79 1624.82 MAGEH1 461.13 1349.51 448.96 2800.36 3719.29 MALT1 3362.14 3568.46 2743.74 5892.86 4776.24 MAP1LC3A 70,92 110,44 119,07 272,07 169,3 MAP3K5 1865.12 2189.99 1787.06 2822.55 2265.54 MAST4 1053.08 2239.36 2198.39 3373.36 1855.42 MAT2B 2305.62 4050.5 2959.2 4435.41 4159.25 MCCC2 737.75 875.78 873.69 1018.1 1245.79 MELK 28.77 50.08 83.5 72.28 83.06 METTL7A 280.99 442.99 385.04 845.09 1671.74 METTL8 318.99 882.21 377.82 880.99 1413.12 MGME1 236.76 332.08 342.77 400.19 552.69 MGST2 54.22 87.18 69.59 147.04 148.13 MICAL2 354.6 1601.79 1813.35 1910.22 3188.92 MINPP1 85.19 204.32 211.85 243.22 290.02 MKI67 192.68 206.77 518.03 372.61 650.04 MREG 120.75 119.91 226.28 229.41 325.33 MYL6B 122.13 182.71 107.73 174.22 252.52 MYO5C 95.68 122.36 157.21 130.81 347.49 NAB1 508.21 973.74 1261.31 1831.77 1227.51 NCALD 111.73 163.32 272.67 283.43 370.26 NCAM1 7.88 58.27 39.69 207.45 213.23 NCF4 509.63 630.55 880.39 894.67 1176.84 NCOA1 2088.38 2062.57 1941.7 2367.54 2618.11 NDFIP2 77.99 529.73 618.54 829.53 987.25 NEMP2 382.56 478.4 475.76 565.18 634.41 NETO2 145.84 559.95 773.69 1490.82 1137.73 NEURL3 4.04 29.74 12.37 24.02 35.49 NFAT5 2075.17 3880.92 3923.6 4786.04 5295.06 NFE2L3 279.28 590.19 560.29 743.24 1114.26 NFYC 588.49 713.51 756.16 733.52 798.27 NHS 7.27 18.73 55.15 60.16 159.44 NPTN 525.86 838.02 897.91 1007.87 969.1 NTNG2 117.04 296.81 534.52 669.43 1001.58 NTRK1 20.85 27.9 155.15 88.29 161.78 NUSAP1 199.28 266.11 445.86 635.51 365.17 NXT2 221.6 263.39 226.8 285.15 302.01 OSBP2 111.03 89.82 127.83 195.47 244.93 PAK2 4621.62 6173.86 5024.6 7194.78 6376.28 PAM 582.52 904.05 1069.56 1365.03 1631.64 PANX2 3.7 76.02 15.46 97.12 71.72 PAQR4 16.99 46.54 62.37 92.6 65.27 PARD6G 55.86 172.18 249.99 546.52 182.4 PARK7 1271.06 1563.96 1283.47 1764.8 1764.91 PCTP 49.2 173.47 163.4 253.27 270.62 PDCD1LG2 12.12 61.89 25.77 214.51 223.04 PDGFA 6.19 38.74 159.79 154.17 153.03 PEX3 179.31 239.78 205.66 326.61 291.17 PGM2 316.91 419.51 454.63 471.89 487.85 PHKA1 8.59 19.98 28.87 107.79 109.7 PIGU 147.54 205.18 184.53 220.25 265.12 PLA2G4C 22.16 128.81 65.98 245.65 159.6 PPM1G 1974.96 2324.16 2563.85 2751.69 2598.5 PRDX3 466.56 854.12 745.34 890.58 1052.67 PRKCDBP 4.45 6.8 19.07 28.51 27.92 PROB1 53.7 140.39 109.79 177.19 272.89 PTGIR 96.17 147.61 107.21 214.61 449.25 PTP4A3 134.06 262.63 463.39 340.08 667.84 PTPRJ 2654.92 3999.84 5584.38 6101.63 7239.3 PTTG1 211.97 198.56 236.59 302.53 335.68 RAB15 160.6 470.25 302.05 420.06 519.4 RAD51AP1 29.89 46.33 40.21 49.23 51.73 RASAL1 18.87 53.37 50 87.38 238.78 RBKS 67.62 56.45 133.5 141.16 85.46 RCBTB1 1154.33 1312.01 1131.41 1960.76 1384.84 RDH10 194.04 311.58 467.51 658.5 1448.57 REXO2 487.9 832.35 648.44 852.58 987.43 RFK 378.31 396.91 292.26 460.78 452.8 RGS1 16547.6 15176.27 18057.75 23425.18 17168.17 RHOC 78.07 230.17 207.21 317.85 290.86 RMI2 19.46 76.58 39.69 70.44 73.47 RNF145 1625.11 3074.78 2117.47 4417.29 3266.94 RNF207 41.75 469.3 314.94 723.56 765.87 RRAGB 281.49 274.98 196.9 384.81 506.1 RYBP 1861.27 2273.72 2496.32 3178.31 2818.02 SEC14L6 6.42 86.23 27.32 179.47 274.97 SEC24A 718 917.25 1157.7 1259.04 1062.95 SECTM1 69.01 1347.35 725.75 2354.1 1511.04 SEPT3 15.6 59.23 49.48 149.11 244.4 SGPP2 428.14 656.73 364.94 1001.71 809.92 SH3RF2 20.9 18.3 65.98 98.4 196.34 SIRPG 433.99 605.49 317 575.41 1245.12 SLC16A1 947.47 1385.08 1532.43 2050.74 1460.73 SLC25A12 246.72 323.6 423.18 406.15 498.91 SLC35E3 385.3 451.16 370.09 582.86 653.13 SLC35F2 378.22 795.55 688.64 1130.81 880.5 SLC41A1 1194.29 1119.86 1164.92 1401.41 1630.88 SLC41A2 13.45 356.73 114.95 482.48 395.27 SMAD1 15.34 53.93 30.41 63.54 87.46 SMS 565.6 760.65 719.57 818.12 735.99 SNAP47 310.71 503.77 577.82 690.31 696.18 SOCS2 245.77 405.76 463.39 605.25 611.78 SOX4 128.76 244.57 218.04 1205.78 715.01 SPATA24 38.86 77.02 36.6 66.43 94.41 SPATC1 7.97 10.96 19.59 61.51 55.84 SPATS2L 366.98 891.61 1172.13 1430.11 1531.61 SSH1 1890.01 3432.55 2771.06 4390.36 4552.26 SSTR3 230.28 248.12 341.74 240.77 901.25 STAC 11.63 48.36 39.69 75.94 71.4 STARD7 2415.01 3185.95 3024.66 3809.46 3445.47 STRIP2 103.39 1002.96 540.19 716.49 1192.77 SYT11 1078.51 1733.37 2080.36 2110.18 2818.39 TADA3 677.14 893.74 852.04 880.43 1189.01 TBC1D8 53.89 374.1 265.97 817.36 1087.39 TDRD3 461.34 383.25 520.09 584.64 643.84 TFRC 3608.04 4612.18 5640.05 8107.35 10082.21 THADA 1102.51 1505.13 1467.48 3472.21 3171.99 TIGIT 4611.14 10986.74 4901.41 14821.45 14747.79 TM9SF2 2048.03 2689.14 2665.91 2935.98 3358.4 TMA16 172.88 180.92 137.11 304.24 192.53 TMEM140 273.98 640.28 574.73 917.16 691 TMEM184C 520.19 508.83 599.98 1170.37 519.43 TMOD1 14.75 72.22 32.47 150.93 89.62 TMPRSS3 70.84 352.78 321.64 540.8 1106.85 TMPRSS6 113.53 548.87 265.97 698.41 985.34 TNFRSF18 333.36 2871.78 3071.57 4038.86 4078.14 TNFRSF4 400.56 2873.16 2992.18 4222.16 4642.56 TNFRSF8 30.89 115.57 208.24 155.59 430.23 TNFRSF9 502.86 898.69 1739.13 2921.72 3128.82 TNIP3 28.73 485.83 213.91 324.53 419.8 TOR4A 141.27 291.3 346.9 358.98 326.51 TOX2 237.46 860.48 490.71 861.08 1264.13 TP73 7.86 31.27 39.69 78.27 93.99 TPMT 357.13 354.93 305.66 480.15 519.82 TPP1 2589.92 6024.92 4380.81 7164.96 6236.83 TPX2 106.25 89.08 184.02 150.35 202.77 TRAF3 1140.85 3231.25 2706.11 4078.84 3554.01 TRIB1 927.27 1820.64 1482.95 2402.58 1469.85 TRIM16 160.05 115.2 121.13 240.55 210.13 TSPAN17 709.59 1721.26 1322.64 1685.38 1865.69 TSPAN5 372.4 1167.46 723.69 1230.67 1398.7 TST 3.8 26.32 26.8 39.78 41.65 TTBK1 13.41 164.27 99.48 380.69 460.64 TTC22 237.9 386.91 323.19 483.96 451.61 TWIST1 4.21 94.46 21.65 95.32 195.78 UGP2 1950.41 3283.79 2562.82 3399.18 2864.71 USP51 48.1 133.95 28.87 233.48 291.46 UXS1 1661.1 2156.16 1600.47 2614.66 1914.74 VANGL1 97.19 192.58 248.96 263.46 289.05 VDR 123 992.41 1771.6 2616.68 3656.18 VWA5A 426.29 550.67 373.7 604.53 739.57 WDHD1 101.74 126.37 140.2 136.76 193.58 WDTC1 1220.3 3855.35 2029.33 4398.54 3774.61 WSB1 2837.49 3876.77 4697.29 5090.18 5383.33 XKRX 16.06 71.84 90.2 115.05 101.81 YIPF1 310.29 351.68 285.04 354.44 456.27 YIPF6 342.01 687.07 705.14 1078.09 793.2 ZBED2 87.53 94.86 522.15 230.51 1238.63 ZBTB38 1986.89 5405.41 3134.97 6174.05 4680.43 ZC3H12C 123.76 159.39 518.54 1191.95 985.54 ZG16B 3.42 17.03 15.46 32.31 32.59 ZMAT3 529.91 925.46 822.66 1077.17 1234.3 ZMYND8 585.94 675.31 711.84 850.29 1131.01 ZNF280C 181.86 444.81 326.28 635.21 467.78 ZNF280D 698.54 973.93 616.48 1061.55 1290.04 ZNF282 374.36 1273.4 2253.55 2562.43 3165.99 ZNF334 6.95 26.52 17.53 40.03 100.33 ZWINT 60.55 73.28 101.03 87.1 105.4

[0245] Altogether, the data show that Treg cells display the most pronounced differences in transcripts expression among CD4+ T cell subsets infiltrating normal and tumor tissues.

[0246] The inventors defined a subset of signature genes that describe the specific gene expression profile of tumor infiltrating Treg cells.

Gene Signature of Tumor-Infiltrating Treg Cells is Present in Primary and Metastatic Human Tumors

[0247] The inventors then looked at the single cell level for the differential expression profile of signature genes of tumor infiltrating Treg cells. The inventors isolated CD4+ T cells from CRC and 5 NSCLC tumor samples as well as from 5 PBMCs of healthy individuals (Table II), purified Treg cells, and using an automated microfluidic system (Cl Fluidigm) captured single cells (a total of 858 Treg cells: 320 from CRC and 286 from NSCLC; 252 from PBMCs of healthy individuals). The inventors then assessed by high throughput RT-qPCR (Biomark HD, Fluidigm) the expression of 79 genes selected among the highly expressed (>10 FKPM) tumor Treg cell signature genes (FIGS. 3A, 3C and 7). Notably, it was found that the vast majority (75 over 79; 95%) of the tumor-infiltrating Treg cell signatures were co-expressed with bona fide Treg cell markers (i.e., FOXP3+ and IL2RA) (FIG. 3B). The percentage of co-expression between these Treg cell markers and the 79 genes selected among the tumor-infiltrating-Treg-cell signature genes ranged between 81% of TIGIT and 0.59% of CGA (FIG. 3B). The expression of Treg signature genes in the RNA-seq of the whole Treg cell population correlated with the percentage of single cells expressing the different genes (FIG. 3C). In order to reduce the drop-out effect of the single cell data (i.e., events in which a transcript is detected in one cell but not in another one because the transcript is missed during the reverse-transcription step) (Kharchenko et al., 2014), a threshold (median value t=8.4%) was defined based on the expression distribution for each transcript and discarded genes below this threshold. The forty-five signature transcripts of tumor infiltrating Treg cells detected above this threshold were in most cases significantly over-expressed in Treg cells from both tumors (39 over 45, 87%; Wilcoxon Mann Whitney test p<0.05) or in one tumor type (43 over 45, 96%; FIG. 3D). Homogeneity of the purified tissue infiltrating Treg cells can be affected by the carry-over of cells from other lymphocyte subsets. To quantitate this possible contamination, the single cell RT-qPCR analyses of Treg cells was performed including markers specific for other lymphocytes subsets (i.e., Th1, Th2, Th17, Tfh, CD8 T cells, B cells) (FIG. 7). Our data showed that only a very low fraction of the purified single cells displayed markers of lymphocytes subsets different from Treg cells (FIG. 7).

[0248] The overlap between the signature genes in the CRC and NSCLC infiltrating Treg cells (FIG. 2) prompted us to assess whether this signature were also enriched in Treg cells infiltrating other tumors. RNA was thus extracted from Treg cells infiltrating breast cancer, gastric cancer, brain metastasis of NSCLC, and liver metastasis of CRC. It was found by RT-qPCR that tumor infiltrating Treg signatures genes were mostly upregulated also in these tumors (FIG. 3E).

[0249] Overall these data show that the tumor-infiltrating Treg cell signature genes are co-expressed at single cell level with FOXP3 and IL2RA and that several primary and metastatic human tumors express the tumor-infiltrating Treg cell signature.

Gene Signature of Tumor Infiltrating Treg Cells is Translated in a Protein Signature

[0250] The inventors then assessed at the single cell level by flow cytometry the protein expression of ten representative signature genes present in CRC and NSCLC infiltrating Treg cells, adjacent normal tissues, and patients PBMCs. Of the ten proteins, two are proteins (OX40 and TIGIT) whose relevance for Treg cells biology has been demonstrated (Joller et al., 2014; Voo et al., 2013), seven are proteins (BATF, CCR8, CD30, IL-1 R2, IL-21R, PDL-1 and PDL-2) whose expression has never been described in tumor-infiltrating Treg cells, and one protein, 4-1BB, is a co-stimulatory receptor expressed on several hematopoietic cells, whose expression on Treg cells has been shown to mark antigen-activated cells (Schoenbrunn et al., 2012). Our findings showed that all these proteins were upregulated (FIGS. 4A and 4B), at different extent, in tumor infiltrating Treg cells compared to the Treg cells resident in normal tissues.

[0251] Altogether, our data show there is a molecular signature of tumor infiltrating Treg cells, which can be detected both at the mRNA and at the protein levels.

Expression of Tumor Treg Signature Genes is Negatively Correlated with Patients Survival

[0252] In an attempt to correlate our findings with clinical outcome, the inventors asked whether the expression of the tumor-Treg signature transcripts correlated with disease prognosis in CRC and NSCLC patients. The inventors therefore interrogated for expression of Treg signature genes transcriptomic datasets obtained from resected tumor tissues of a cohort of 177 CRC patients (GSE17536 (Smith et al., 2010) and of a cohort of 263 NSCLC patients (GSE41271(Sato et al., 2013), and correlated high and low gene expression levels with the 5-years survival data. Among those genes whose expression is highly enriched in tumor infiltrating Treg cells, LAYN, MAGEH1 and CCR8 were selected as they are the three genes more selectively expressed (FIG. 9A-C). To normalize for differences in T cell densities within the resected tumor tissues, the inventors used the ratio between expression of the selected signature genes and CD3G. Remarkably, it was found that high expression of the three signature genes is in all cases correlated with a significantly reduced survival (FIG. 5A). Interestingly, it was also observed that expressions of the three signature genes increased with tumor staging of CRC patients (FIG. 5B).

[0253] In conclusion, high expression in the whole tumor samples of three genes (LAYN, MAGEH1 and CCR8) that are specifically and highly expressed in tumor infiltrating Treg cells, correlates with a poor prognosis in both NSCLC and CRC patients.

Selection of Potential Targets Specifically Over-Expressed on the Surface of Tumor-Infiltrating Treg

[0254] All annotated protein isoforms encoded by the 328 genes and retrievable in the public database EnsEMBL (http://www.ensembl.org) were simultaneously analysed with the four prediction algorithms and genes encoding at least one isoform predicted to be surface exposed were considered as potential targets.

[0255] Out of 328 genes, 193 encode for at least one potential cell surface protein isoform on the basis of at least one of the four predictors. The list of protein isoforms predicted to be membrane-associated is reported in Table VI.

TABLE-US-00010 TABLE VI SEQ ID No of the aa sequence of the Gene ENSG ID protein name Description release87 ENST ID ENSP ID isoform LAYN Layilin ENSG00000204381 ENST00000375614 ENSP00000364764 1 ENST00000375615 ENSP00000364765 2 ENST00000436913 ENSP00000392942 3 ENST00000525126 ENSP00000434328 4 ENST00000525866 ENSP00000434300 5 ENST00000528924 ENSP00000486561 6 ENST00000530962 ENSP00000431627 7 ENST00000533265 ENSP00000434972 8 ENST00000533999 ENSP00000432434 9 CCR8 CC chemokine receptor ENSG00000179934 ENST00000326306 ENSP00000326432 10 type 8 ENST00000414803 ENSP00000390104 11 IL21R Interleukin-21 receptor ENSG00000103522 ENST00000337929 ENSP00000338010 12 ENST00000395754 ENSP00000379103 13 ENST00000564089 ENSP00000456707 14 FUCA2 Plasma alpha-L- ENSG00000001036 ENST00000002165 ENSP00000002165 15 fucosidase ENST00000451668 ENSP00000398119 16 ICA1 Islet cell autoantigen 1 ENSG00000003147 ENST00000407906 ENSP00000386021 17 COX10 Protoheme IX ENSG00000006695 ENST00000261643 ENSP00000261643 18 farnesyltransferase, mit. IL32 Interleukin-32 ENSG00000008517 ENST00000008180 ENSP00000008180 19 ENST00000396890 ENSP00000380099 20 ENST00000525228 ENSP00000431740 21 ENST00000525377 ENSP00000433866 22 ENST00000530890 ENSP00000433747 23 ENST00000534507 ENSP00000431775 24 ENST00000548246 ENSP00000447979 25 ENST00000548476 ENSP00000449483 26 ENST00000548807 ENSP00000448354 27 ENST00000551513 ENSP00000449147 28 ENST00000552356 ENSP00000446978 29 ENST00000552936 ENSP00000447033 30 ETV7 Transcription factor ENSG00000010030 ENST00000339796 ENSP00000342260 31 ETV7 ENST00000627426 ENSP00000486712 32 ATP2C1 Calcium-transporting ENSG00000017260 ENST00000328560 ENSP00000329664 33 ATPase type 2C member 1 ENST00000359644 ENSP00000352665 34 ENST00000422190 ENSP00000402677 35 ENST00000428331 ENSP00000395809 36 ENST00000504381 ENSP00000425320 37 ENST00000504571 ENSP00000422489 38 ENST00000504612 ENSP00000425228 39 ENST00000504948 ENSP00000423330 40 ENST00000505072 ENSP00000427625 41 ENST00000505330 ENSP00000423774 42 ENST00000507194 ENSP00000427087 43 ENST00000507488 ENSP00000421326 44 ENST00000508297 ENSP00000421261 45 ENST00000508532 ENSP00000424783 46 ENST00000508660 ENSP00000424930 47 ENST00000509662 ENSP00000426849 48 ENST00000510168 ENSP00000427461 49 ENST00000513801 ENSP00000422872 50 ENST00000515854 ENSP00000422890 51 ENST00000533801 ENSP00000432956 52 FAS Fatty acid synthase ENSG00000026103 ENST00000352159 ENSP00000345601 53 ENST00000355279 ENSP00000347426 54 ENST00000355740 ENSP00000347979 55 ENST00000357339 ENSP00000349896 56 ENST00000479522 ENSP00000424113 57 ENST00000484444 ENSP00000420975 58 ENST00000488877 ENSP00000425159 59 ENST00000492756 ENSP00000422453 60 ENST00000494410 ENSP00000423755 61 ENST00000612663 ENSP00000477997 62 PEX3 Peroxisomal biogenesis ENSG00000034693 ENST00000367591 ENSP00000356563 63 factor 3 ENST00000367592 ENSP00000356564 64 TSPAN17 Tetraspanin-17 ENSG00000048140 ENST00000298564 ENSP00000298564 65 ENST00000310032 ENSP00000309036 66 ENST00000503030 ENSP00000425975 67 ENST00000503045 ENSP00000425212 68 ENST00000504168 ENSP00000423957 69 ENST00000507471 ENSP00000423610 70 ENST00000508164 ENSP00000422053 71 ENST00000515708 ENSP00000426650 72 COL9A2 Collagen alpha-2(IX) ENSG00000049089 ENST00000372736 ENSP00000361821 73 chain ENST00000372748 ENSP00000361834 74 ENST00000417105 ENSP00000388493 75 NFE2L3 Nuclear factor erythroid ENSG00000050344 ENST00000056233 ENSP00000056233 76 2-related factor 3 TNIP3 TNFAIP3-interacting ENSG00000050730 ENST00000515036 ENSP00000424284 77 prot.3 LY75 Lymphocyte antigen 75 ENSG00000054219 ENST00000263636 ENSP00000263636 78 YIPF1 Protein YIPF1 ENSG00000058799 ENST00000072644 ENSP00000072644 79 ENST00000371399 ENSP00000360452 80 ENST00000412288 ENSP00000416507 81 ENST00000464950 ENSP00000432266 82 ISOC1 Isochorismatase domain- ENSG00000066583 ENST00000173527 ENSP00000173527 83 containing protein 1 ENST00000514194 ENSP00000421273 84 ACSL4 Long-chain-fatty-acid-- ENSG00000068366 ENST00000340800 ENSP00000339787 85 CoA ligase 4 ENST00000469796 ENSP00000419171 86 ENST00000469857 ENSP00000423077 87 ENST00000502391 ENSP00000425408 88 ENST00000504980 ENSP00000421425 89 ENST00000508092 ENSP00000425378 90 MAST4 Microtubule-assoc.serine/ ENSG00000069020 ENST00000434115 ENSP00000396765 91 threonine-protein kinase 4 LMCD1 LIM and cysteine-rich ENSG00000071282 ENST00000456506 ENSP00000405049 92 domains protein 1 TFRC Transferrin receptor ENSG00000072274 ENST00000360110 ENSP00000353224 93 protein 1 ENST00000392396 ENSP00000376197 94 ENST00000421258 ENSP00000402839 95 ENST00000426789 ENSP00000414015 96 PANX2 Pannexin-2 ENSG00000073150 ENST00000159647 ENSP00000159647 97 ENST00000395842 ENSP00000379183 98 ENST00000402472 ENSP00000384148 99 FNDC3B Fibronectin type III ENSG00000075420 ENST00000336824 ENSP00000338523 100 domain-containing ENST00000415807 ENSP00000411242 101 protein 3B ENST00000416957 ENSP00000389094 102 ENST00000421757 ENSP00000408496 103 ENST00000423424 ENSP00000392471 104 IL12RB2 Interleukin-12 receptor ENSG00000081985 ENST00000262345 ENSP00000262345 105 subunit beta-2 ENST00000371000 ENSP00000360039 106 ENST00000441640 ENSP00000400959 107 ENST00000541374 ENSP00000445276 108 ENST00000544434 ENSP00000442443 109 STARD7 StAR-related lipid ENSG00000084090 ENST00000337288 ENSP00000338030 110 transfer protein 7, mitochondrial SSH1 Protein phosphatase ENSG00000084112 ENST00000546697 ENSP00000446652 111 Slingshot homolog 1 ENST00000548522 ENSP00000448586 112 MGST2 Microsomal glutathione ENSG00000085871 ENST00000265498 ENSP00000265498 113 S-transferase 2 ENST00000503816 ENSP00000423008 114 ENST00000506797 ENSP00000424278 115 ENST00000616265 ENSP00000482639 116 ACOX3 Peroxisomal acyl- ENSG00000087008 ENST00000514423 ENSP00000427321 117 coenzyme A oxidase 3 ANKRD10 Ankyrin repeat domain- ENSG00000088448 ENST00000603993 ENSP00000474638 118 containing protein 10 FKBP1A Peptidyl-prolyl cis-trans ENSG00000088832 ENST00000612074 ENSP00000480846 119 isomerase FKBP1A ENST00000614856 ENSP00000482758 120 ENST00000618612 ENSP00000478093 121 SIRPG Signal-regulatory protein ENSG00000089012 ENST00000216927 ENSP00000216927 122 gamma ENST00000303415 ENSP00000305529 123 ENST00000344103 ENSP00000342759 124 ENST00000381580 ENSP00000370992 125 ENST00000381583 ENSP00000370995 126 WHRN Whirlin ENSG00000095397 ENST00000374059 ENSP00000363172 127 CENPM Centromere protein M ENSG00000100162 ENST00000215980 ENSP00000215980 128 ENST00000402338 ENSP00000384731 129 ENST00000402420 ENSP00000384132 130 ENST00000404067 ENSP00000384814 131 ENST00000407253 ENSP00000384743 132 NCF4 Neutrophil cytosol factor 4 ENSG00000100365 ENST00000447071 ENSP00000414958 133 CSF2RB Cytokine receptor ENSG00000100368 ENST00000262825 ENSP00000262825 134 common subunit beta ENST00000403662 ENSP00000384053 135 ENST00000406230 ENSP00000385271 136 ENST00000421539 ENSP00000393585 137 CNIH1 Protein cornichon ENSG00000100528 ENST00000216416 ENSP00000216416 138 homolog 1 ENST00000395573 ENSP00000378940 139 ENST00000553660 ENSP00000452457 140 ENST00000554683 ENSP00000452466 141 ENST00000556113 ENSP00000451142 142 ENST00000557659 ENSP00000451640 143 ENST00000557690 ENSP00000451852 144 PIGU Phosphatidylinositol ENSG00000101464 ENST00000217446 ENSP00000217446 145 glycan anchor ENST00000374820 ENSP00000363953 146 biosynthesis class U ENST00000438215 ENSP00000395755 147 protein NDFIP2 NEDD4 family- ENSG00000102471 ENST00000218652 ENSP00000218652 148 interacting protein 2 ENST00000487865 ENSP00000419200 149 ENST00000612570 ENSP00000480798 150 ENST00000620924 ENSP00000480881 151 ACP5 Tartrate-resistant acid ENSG00000102575 ENST00000218758 ENSP00000218758 152 phosphatase type 5 ENST00000412435 ENSP00000392374 153 ENST00000433365 ENSP00000413456 154 ENST00000589792 ENSP00000468685 155 ENST00000590420 ENSP00000468509 156 ENST00000590832 ENSP00000465127 157 ENST00000591319 ENSP00000464831 158 ENST00000592828 ENSP00000468767 159 NFAT5 Nuclear factor of ENSG00000102908 ENST00000567990 ENSP00000455115 160 activated T-cells 5 CYB5B Cytochrome b5 type B ENSG00000103018 ENST00000307892 ENSP00000308430 161 ENST00000512062 ENSP00000423679 162 ENST00000568237 ENSP00000464102 163 LAPTM4B Lysosomal-associated ENSG00000104341 ENST00000445593 ENSP00000402301 164 transmembrane protein 4B ENST00000517924 ENSP00000429868 165 ENST00000521545 ENSP00000428409 166 ENST00000619747 ENSP00000482533 167 IL7 Interleukin-7 ENSG00000104432 ENST00000263851 ENSP00000263851 168 ENST00000379113 ENSP00000368408 169 ENST00000518982 ENSP00000430272 170 ENST00000520215 ENSP00000428364 171 ENST00000520269 ENSP00000427750 172 ENST00000520317 ENSP00000427800 173 ENST00000541183 ENSP00000438922 174 EBI3 Interleukin-27 subunit ENSG00000105246 ENST00000221847 ENSP00000221847 175 beta PLA2G4C Cytosolic phospholipase ENSG00000105499 ENST00000595161 ENSP00000469528 176 A2 gamma ENST00000595487 ENSP00000471328 177 ENST00000596352 ENSP00000471759 178 ENST00000598488 ENSP00000468972 179 GLCCI1 Glucocorticoid-induced ENSG00000106415 ENST00000430798 ENSP00000396171 180 transcript 1 protein MINPP1 Multiple inositol ENSG00000107789 ENST00000371994 ENSP00000361062 181 polyphosphate ENST00000371996 ENSP00000361064 182 phosphatase 1 ENST00000536010 ENSP00000437823 183 WSB1 WD repeat and SOCS ENSG00000109046 ENST00000581440 ENSP00000462737 184 box-containing protein 1 ENST00000582208 ENSP00000463621 185 ENST00000583193 ENSP00000462595 186 ENST00000583742 ENSP00000462365 187 HTATIP2 Oxidoreductase HTATIP2 ENSG00000109854 ENST00000419348 ENSP00000392985 188 ENST00000530266 ENSP00000436548 189 ENST00000532081 ENSP00000432107 190 ENST00000532505 ENSP00000432338 191 CTSC Dipeptidyl peptidase 1 ENSG00000109861 ENST00000227266 ENSP00000227266 192 ENST00000524463 ENSP00000432541 193 ENST00000527018 ENSP00000432556 194 ENST00000528020 ENSP00000433229 195 ENST00000529974 ENSP00000433539 196 VWA5A von Willebrand factor A ENSG00000110002 ENST00000392744 ENSP00000376501 197 domain-containing ENST00000392748 ENSP00000376504 198 protein 5A ENST00000456829 ENSP00000407726 199 SLC35F2 Solute carrier family 35 ENSG00000110660 ENST00000375682 ENSP00000364834 200 member F2 ENST00000525071 ENSP00000434307 201 ENST00000525815 ENSP00000436785 202 ENST00000532513 ENSP00000433783 203 VDR Vitamin D3 receptor ENSG00000111424 ENST00000547065 ENSP00000449074 204 SEC24A Protein transport protein ENSG00000113615 ENST00000398844 ENSP00000381823 205 Sec24A IL1R2 Interleukin-1 receptor ENSG00000115590 ENST00000332549 ENSP00000330959 206 type 2 ENST00000393414 ENSP00000377066 207 ENST00000441002 ENSP00000414611 208 ENST00000457817 ENSP00000408415 209 IL1R1 Interleukin-1 receptor ENSG00000115594 ENST00000409288 ENSP00000386478 210 type 1 ENST00000409329 ENSP00000387131 211 ENST00000409589 ENSP00000386555 212 ENST00000409929 ENSP00000386776 213 ENST00000410023 ENSP00000386380 214 ENST00000413623 ENSP00000407017 215 ENST00000422532 ENSP00000390349 216 ENST00000424272 ENSP00000415366 217 ENST00000428279 ENSP00000410461 218 ENST00000430171 ENSP00000408101 219 ENST00000442590 ENSP00000393296 220 ENST00000450319 ENSP00000411627 221 ENST00000452403 ENSP00000401646 222 IL1RL2 Interleukin-1 receptor- ENSG00000115598 ENST00000264257 ENSP00000264257 223 like 2 ENST00000421464 ENSP00000387611 224 ENST00000441515 ENSP00000413348 225 IL1RL1 Interleukin-1 receptor- ENSG00000115602 ENST00000233954 ENSP00000233954 226 like 1 ENST00000311734 ENSP00000310371 227 ENST00000404917 ENSP00000384822 228 ENST00000409584 ENSP00000386618 229 ENST00000427077 ENSP00000391120 230 ENST00000447231 ENSP00000409437 231 UXS1 UDP-glucuronic acid ENSG00000115652 ENST00000283148 ENSP00000283148 232 decarboxylase 1 ENST00000409501 ENSP00000387019 233 ENST00000441952 ENSP00000416656 234 ENST00000457835 ENSP00000399316 235 SLC25A12 Calcium-binding ENSG00000115840 ENST00000426896 ENSP00000413968 236 mitochondrial carrier protein Aralar1 THADA Thyroid adenoma- ENSG00000115970 ENST00000403856 ENSP00000385469 237 associated protein LEPR Leptin receptor ENSG00000116678 ENST00000344610 ENSP00000340884 238 ENST00000349533 ENSP00000330393 239 ENST00000371058 ENSP00000360097 240 ENST00000371059 ENSP00000360098 241 ENST00000371060 ENSP00000360099 242 ENST00000406510 ENSP00000384025 243 ENST00000616738 ENSP00000483390 244 MREG Melanoregulin ENSG00000118242 ENST00000263268 ENSP00000263268 245 ENST00000620139 ENSP00000484331 246 FLVCR2 Feline leukemia virus ENSG00000119686 ENST00000238667 ENSP00000238667 247 subgroup C receptor- ENST00000539311 ENSP00000443439 248 related protein 2 ENST00000553341 ENSP00000452584 249 ENST00000553587 ENSP00000451603 250 ENST00000554580 ENSP00000451781 251 ENST00000555027 ENSP00000452453 252 ENST00000555058 ENSP00000451104 253 ENST00000556856 ENSP00000452468 254 SOCS2 Suppressor of cytokine ENSG00000120833 ENST00000548537 ENSP00000448709 255 signaling 2 ENST00000549510 ENSP00000474888 256 RDH10 Retinol dehydrogenase 10 ENSG00000121039 ENST00000240285 ENSP00000240285 257 ENST00000519380 ENSP00000428132 258 ENST00000521928 ENSP00000429727 259 LAX1 Lymphocyte ENSG00000122188 ENST00000367217 ENSP00000356186 260 transmembrane adapter 1 ENST00000442561 ENSP00000406970 261 ZWINT ZW10 interactor ENSG00000122952 ENST00000489649 ENSP00000473330 262 ACOT9 Acyl-coenzyme A ENSG00000123130 ENST00000336430 ENSP00000336580 263 thioesterase 9, ENST00000379303 ENSP00000368605 264 mitochondrial ENST00000494361 ENSP00000420238 265 TM9SF2 Transmembrane 9 ENSG00000125304 ENST00000376387 ENSP00000365567 266 superfamily member 2 HS3ST3B1 Heparan sulfate ENSG00000125430 ENST00000360954 ENSP00000354213 267 glucosamine 3-O- ENST00000466596 ENSP00000436078 268 sulfotransferase 3B1 EML2 Echinoderm ENSG00000125746 ENST00000245925 ENSP00000245925 269 microtubule-associated ENST00000586195 ENSP00000465339 270 protein-like 2 ENST00000586405 ENSP00000465885 271 ENST00000586770 ENSP00000465786 272 ENST00000587152 ENSP00000468312 273 ENST00000587484 ENSP00000465994 274 ENST00000588272 ENSP00000466100 275 ENST00000588308 ENSP00000468329 276 ENST00000589876 ENSP00000464789 277 ENST00000590018 ENSP00000468373 278 ENST00000590043 ENSP00000464804 279 ENST00000590819 ENSP00000464950 280 ENST00000591721 ENSP00000468470 281 ENST00000592853 ENSP00000468383 282 ENST00000593255 ENSP00000467941 283 MGME1 Mitochondrial genome ENSG00000125871 ENST00000377704 ENSP00000366933 284 maintenance ENST00000377709 ENSP00000366938 285 exonuclease 1 ENST00000377710 ENSP00000366939 286 IGFLR1 IGF-like family receptor 1 ENSG00000126246 ENST00000246532 ENSP00000246532 287 ENST00000588018 ENSP00000468545 288 ENST00000588992 ENSP00000465962 289 ENST00000591277 ENSP00000468644 290 ENST00000591748 ENSP00000476009 291 ENST00000592537 ENSP00000466181 292 ENST00000592693 ENSP00000474913 293 ENST00000592889 ENSP00000467750 294 MYO5C Unconventional myosin- ENSG00000128833 ENST00000261839 ENSP00000261839 295 Vc ITFG1 T-cell ENSG00000129636 ENST00000320640 ENSP00000319918 296 immunomodulatory ENST00000544001 ENSP00000441062 297 protein ENST00000563730 ENSP00000455630 298 ENST00000565262 ENSP00000457665 299 ENST00000565940 ENSP00000459192 300 SYT11 Synaptotagmin-11 ENSG00000132718 ENST00000368324 ENSP00000357307 301 SLC41A1 Solute carrier family 41 ENSG00000133065 ENST00000367137 ENSP00000356105 302 member 1 ATP13A3 Probable cation- ENSG00000133657 ENST00000256031 ENSP00000256031 303 transporting ATPase ENST00000429136 ENSP00000402550 304 13A3 ENST00000439040 ENSP00000416508 305 ENST00000446356 ENSP00000410767 306 ENST00000457986 ENSP00000406234 307 ENST00000619199 ENSP00000482200 308 MICAL2 Protein-methionine ENSG00000133816 ENST00000379612 ENSP00000368932 309 sulfoxide oxidase MICAL2 CABLES1 CDK5 and ABL1 enzyme ENSG00000134508 ENST00000256925 ENSP00000256925 310 substrate 1 ENST00000579963 ENSP00000464435 311 HAVCR2 Hepatitis A virus cellular ENSG00000135077 ENST00000307851 ENSP00000312002 312 receptor 2 ENST00000522593 ENSP00000430873 313 CGA Chromogranin-A ENSG00000135346 ENST00000369582 ENSP00000358595 314 ENST00000610310 ENSP00000482232 315 ENST00000625577 ENSP00000486666 316 ENST00000627148 ENSP00000486024 317 ENST00000630630 ENSP00000487300 318 FAIM2 Protein lifeguard 2 ENSG00000135472 ENST00000320634 ENSP00000321951 319 ENST00000547871 ENSP00000449360 320 ENST00000550195 ENSP00000447715 321 ENST00000550635 ENSP00000449711 322 ENST00000550890 ENSP00000450132 323 ENST00000552669 ENSP00000446771 324 ENST00000552863 ENSP00000449957 325 ARHGEF4 Rho guanine nucleotide ENSG00000136002 ENST00000392953 ENSP00000376680 326 exchange factor 4 SLC41A2 Solute carrier family 41 ENSG00000136052 ENST00000258538 ENSP00000258538 327 member 2 ENST00000437220 ENSP00000391377 328 NUSAP1 Nucleolar and spindle- ENSG00000137804 ENST00000557840 ENSP00000453428 329 associated protein 1 ENST00000559046 ENSP00000452725 330 ADAM10 Disintegrin and ENSG00000137845 ENST00000260408 ENSP00000260408 331 metalloproteinase ENST00000396136 ENSP00000456542 332 domain-containing ENST00000402627 ENSP00000386056 333 protein 10 ENST00000439637 ENSP00000391930 334 ENST00000461408 ENSP00000481779 335 ENST00000558004 ENSP00000452704 336 ENST00000559053 ENSP00000453952 337 ENST00000561288 ENSP00000452639 338 HADHB Trifunctional enzyme ENSG00000138029 ENST00000545822 ENSP00000442665 339 subunit beta, mitochondrial CD27 CD27 antigen ENSG00000139193 ENST00000266557 ENSP00000266557 340 CDH24 Cadherin-24 ENSG00000139880 ENST00000267383 ENSP00000267383 341 ENST00000397359 ENSP00000380517 342 ENST00000487137 ENSP00000434821 343 ENST00000554034 ENSP00000452493 344 ENST00000610348 ENSP00000478078 345 ETFA Electron transfer ENSG00000140374 ENST00000560044 ENSP00000452942 346 alpha, mitochondrial ENST00000560309 ENSP00000453753 347 flavoprotein subunit KSR1 Kinase suppressor of Ras 1 ENSG00000141068 ENST00000580163 ENSP00000463204 348 SECTM1 Secreted and ENSG00000141574 ENST00000269389 ENSP00000269389 349 transmembrane protein 1 ENST00000580437 ENSP00000463904 350 ENST00000581691 ENSP00000463114 351 ENST00000581864 ENSP00000464111 352 ENST00000581954 ENSP00000464385 353 ENST00000582290 ENSP00000462294 354 ENST00000582563 ENSP00000463120 355 ENST00000583093 ENSP00000462563 356 EVA1B Protein eva-1 homolog B ENSG00000142694 ENST00000270824 ENSP00000270824 357 CTTNBP2NL CTTNBP2 N-terminal-like ENSG00000143079 ENST00000271277 ENSP00000271277 358 protein ENST00000441739 ENSP00000390976 359 CASQ1 Calsequestrin-1 ENSG00000143318 ENST00000368078 ENSP00000357057 360 ARL6IP5 PRA1 family protein 3 ENSG00000144746 ENST00000273258 ENSP00000273258 361 ENST00000478935 ENSP00000420138 362 ENST00000484921 ENSP00000419374 363 ENST00000485444 ENSP00000419021 364 ADPRH [Protein ADP- ENSG00000144843 ENST00000357003 ENSP00000349496 365 ribosylarginine] ENST00000465513 ENSP00000417430 366 hydrolase ENST00000478399 ENSP00000420200 367 ENST00000478927 ENSP00000417528 368 ENST00000481816 ENSP00000419703 369 PAM Peptidyl-glycine alpha- ENSG00000145730 ENST00000304400 ENSP00000306100 370 amidating ENST00000345721 ENSP00000302544 371 monooxygenase ENST00000346918 ENSP00000282992 372 ENST00000348126 ENSP00000314638 373 ENST00000438793 ENSP00000396493 374 ENST00000455264 ENSP00000403461 375 ENST00000504691 ENSP00000424203 376 ENST00000505654 ENSP00000421569 377 ENST00000506006 ENSP00000423611 378 ENST00000509832 ENSP00000423763 379 ENST00000511477 ENSP00000421823 380 ENST00000511839 ENSP00000426448 381 ENST00000512073 ENSP00000420851 382 RNF145 RING finger protein 145 ENSG00000145860 ENST00000274542 ENSP00000274542 383 ENST00000424310 ENSP00000409064 384 ENST00000518802 ENSP00000430955 385 ENST00000519865 ENSP00000430397 386 ENST00000520638 ENSP00000429071 387 ENST00000521606 ENSP00000430753 388 ENST00000611185 ENSP00000482720 389 TMEM140 Transmembrane protein ENSG00000146859 ENST00000275767 ENSP00000275767 390 140 CHST7 Carbohydrate ENSG00000147119 ENST00000276055 ENSP00000276055 391 sulfotransferase 7 CHRNA6 Neuronal acetylcholine ENSG00000147434 ENST00000276410 ENSP00000276410 392 receptor subunit alpha-6 ENST00000533810 ENSP00000434659 393 ENST00000534622 ENSP00000433871 394 PTPRJ Receptor-type tyrosine- ENSG00000149177 ENST00000418331 ENSP00000400010 395 protein phosphatase eta ENST00000440289 ENSP00000409733 396 ENST00000527952 ENSP00000435618 397 ENST00000534219 ENSP00000432686 398 ENST00000613246 ENSP00000477933 399 ENST00000615445 ENSP00000479342 400 NCAM1 Neural cell adhesion ENSG00000149294 ENST00000316851 ENSP00000318472 401 molecule 1 ENST00000401611 ENSP00000384055 402 ENST00000524916 ENSP00000478072 403 ENST00000526322 ENSP00000479687 404 ENST00000528158 ENSP00000486241 405 ENST00000528590 ENSP00000480269 406 ENST00000529356 ENSP00000482205 407 ENST00000531044 ENSP00000484943 408 ENST00000531817 ENSP00000475074 409 ENST00000533073 ENSP00000486406 410 ENST00000613217 ENSP00000479353 411 ENST00000615112 ENSP00000480797 412 ENST00000615285 ENSP00000479241 413 ENST00000618266 ENSP00000477835 414 ENST00000619839 ENSP00000480132 415 ENST00000620046 ENSP00000482852 416 ENST00000621128 ENSP00000481083 417 ENST00000621518 ENSP00000477808 418 ENST00000621850 ENSP00000480774 419 INPP1 Inositol polyphosphate ENSG00000151689 ENST00000413239 ENSP00000391415 420 1-phosphatase ENST00000444194 ENSP00000404732 421 ENST00000451089 ENSP00000410662 422 ENST00000458193 ENSP00000412119 423 JAKMIP1 Janus kinase and ENSG00000152969 ENST00000409021 ENSP00000386711 424 protein 1 ENST00000409371 ENSP00000387042 425 microtubule-interacting RHOC Rho-related GTP-binding ENSG00000155366 ENST00000468093 ENSP00000431392 426 protein RhoC ENST00000484280 ENSP00000434310 427 ENST00000528831 ENSP00000432209 428 SLC16A1 Monocarboxylate ENSG00000155380 ENST00000369626 ENSP00000358640 429 transporter 1 ENST00000429288 ENSP00000397106 430 ENST00000443580 ENSP00000399104 431 ENST00000458229 ENSP00000416167 432 ENST00000538576 ENSP00000441065 433 CXCL13 CXC motif chemokine ENSG00000156234 ENST00000286758 ENSP00000286758 434 13 SH3RF2 Putative E3 ubiquitin- ENSG00000156463 ENST00000359120 ENSP00000352028 435 protein ligase SH3RF2 ENST00000511217 ENSP00000424497 436 NPTN Neuroplastin ENSG00000156642 ENST00000345330 ENSP00000290401 437 ENST00000351217 ENSP00000342958 438 ENST00000562924 ENSP00000456349 439 ENST00000563691 ENSP00000457028 440 ENST00000565325 ENSP00000457470 441 AHCYL2 Adenosylhomocysteinase 3 ENSG00000158467 ENST00000466924 ENSP00000419346 442 PTGIR Prostacyclin receptor ENSG00000160013 ENST00000291294 ENSP00000291294 443 ENST00000594275 ENSP00000469408 444 ENST00000596260 ENSP00000468970 445 ENST00000597185 ENSP00000470566 446 ENST00000598865 ENSP00000470799 447 TMPRSS3 Transmembrane ENSG00000160183 ENST00000291532 ENSP00000291532 448 protease serine 4 ENST00000398397 ENSP00000381434 449 ENST00000398405 ENSP00000381442 450 ENST00000433957 ENSP00000411013 451 FCRL3 Fc receptor-like protein 3 ENSG00000160856 ENST00000368184 ENSP00000357167 452 ENST00000368186 ENSP00000357169 453 ENST00000477837 ENSP00000433430 454 ENST00000485028 ENSP00000434331 455 ENST00000492769 ENSP00000435487 456 ENST00000496769 ENSP00000473680 457 PAQR4 Progestin and adipoQ ENSG00000162073 ENST00000293978 ENSP00000293978 458 receptor family member 4 ENST00000318782 ENSP00000321804 459 ENST00000572687 ENSP00000459418 460 ENST00000574988 ENSP00000458683 461 ENST00000576565 ENSP00000460326 462 ZG16B Zymogen granule ENSG00000162078 ENST00000382280 ENSP00000371715 463 protein 16 homolog B ENST00000570670 ENSP00000460793 464 ENST00000571723 ENSP00000458847 465 ENST00000572863 ENSP00000461740 466 SGPP2 Sphingosine-1- ENSG00000163082 ENST00000321276 ENSP00000315137 467 phosphate phosphatase 2 NEURL3 E3 ubiquitin-protein ENSG00000163121 ENST00000310865 ENSP00000479456 468 ligase NEURL1B ENST00000435380 ENSP00000480933 469 KIF15 Kinesin-like protein ENSG00000163808 ENST00000438321 ENSP00000406939 470 KIF15 TMEM184C Transmembrane protein ENSG00000164168 ENST00000296582 ENSP00000296582 471 184C ENST00000505999 ENSP00000421159 472 ENST00000508208 ENSP00000425940 473 C5ORF63 Glutaredoxin-like protein ENSG00000164241 ENST00000296662 ENSP00000453964 474 C5orf63 ENST00000508527 ENSP00000475157 475 ENST00000509733 ENSP00000475415 476 ENST00000535381 ENSP00000454153 477 ENST00000606042 ENSP00000475733 478 ENST00000606937 ENSP00000475810 479 ENST00000607731 ENSP00000476160 480 MELK Maternal embryonic ENSG00000165304 ENST00000495529 ENSP00000487536 481 leucine zipper kinase ENST00000536329 ENSP00000443550 482 ENST00000536987 ENSP00000439184 483 ENST00000543751 ENSP00000441596 484 ENST00000626154 ENSP00000486558 485 FAAH2 Fatty-acid amide ENSG00000165591 ENST00000374900 ENSP00000364035 486 hydrolase 2 TPP1 Alpha-tocopherol ENSG00000166340 ENST00000299427 ENSP00000299427 487 transfer protein ENST00000436873 ENSP00000398136 488 ENST00000528571 ENSP00000434647 489 ENST00000528657 ENSP00000435001 490 CX3CR1 CX3C chemokine ENSG00000168329 ENST00000358309 ENSP00000351059 491 receptor 1 ENST00000399220 ENSP00000382166 492 ENST00000412814 ENSP00000408835 493 ENST00000435290 ENSP00000394960 494 ENST00000541347 ENSP00000439140 495 ENST00000542107 ENSP00000444928 496 TSPAN5 Tetraspanin-5 ENSG00000168785 ENST00000305798 ENSP00000307701 497 ENST00000505184 ENSP00000423916 498 ENST00000508798 ENSP00000421808 499 ENST00000511651 ENSP00000426248 500 ENST00000511800 ENSP00000422548 501 ENST00000515287 ENSP00000423504 502 ENST00000515440 ENSP00000422351 503 UGP2 UTP--glucose-1- ENSG00000169764 ENST00000467999 ENSP00000418642 504 uridylyltransferase ENST00000496334 ENSP00000420760 505 phosphate GLB1 Beta-galactosidase ENSG00000170266 ENST00000307363 ENSP00000306920 506 ENST00000307377 ENSP00000305920 507 ENST00000399402 ENSP00000382333 508 ENST00000415454 ENSP00000411813 509 ENST00000436768 ENSP00000387989 510 ENST00000438227 ENSP00000401250 511 ENST00000440656 ENSP00000411769 512 ENST00000446732 ENSP00000407365 513 ENST00000450835 ENSP00000403264 514 SPATA24 Spermatogenesis- ENSG00000170469 ENST00000514983 ENSP00000423424 515 associated protein 24 RBKS Ribokinase ENSG00000171174 ENST00000449378 ENSP00000413789 516 NETO2 Neuropilin and tolloid- ENSG00000171208 ENST00000303155 ENSP00000306726 517 like protein 2 ENST00000562435 ENSP00000455169 518 ENST00000562559 ENSP00000454213 519 ENST00000563078 ENSP00000456818 520 ENST00000564667 ENSP00000457133 521 LRG1 Leucine-rich alpha-2- ENSG00000171236 ENST00000306390 ENSP00000302621 522 glycoprotein FAM98B Protein FAM98B ENSG00000171262 ENST00000491535 ENSP00000453166 523 ENST00000559431 ENSP00000453926 524 CHST11 Carbohydrate ENSG00000171310 ENST00000303694 ENSP00000305725 525 sulfotransferase 11 ENST00000546689 ENSP00000448678 526 ENST00000547956 ENSP00000449093 527 ENST00000549260 ENSP00000450004 528 ECEL1 Endothelin-converting ENSG00000171551 ENST00000304546 ENSP00000302051 529 enzyme-like 1 ENST00000409941 ENSP00000386333 530 BCL2L1 Bcl-2-like protein 1 ENSG00000171552 ENST00000307677 ENSP00000302564 531 ENST00000376055 ENSP00000365223 532 ENST00000376062 ENSP00000365230 533 MALT1 Mucosa-associated ENSG00000172175 ENST00000345724 ENSP00000304161 534 lymphoid tissue ENST00000348428 ENSP00000319279 535 lymphoma translocation ENST00000591792 ENSP00000467222 536 protein 1 CYP7B1 25-hydroxycholesterol 7- ENSG00000172817 ENST00000310193 ENSP00000310721 537 alpha-hydroxylase HPSE Heparanase ENSG00000173083 ENST00000311412 ENSP00000308107 538 ENST00000405413 ENSP00000384262 539 ENST00000507150 ENSP00000426139 540 ENST00000508891 ENSP00000421827 541 ENST00000509906 ENSP00000421038 542 ENST00000512196 ENSP00000423265 543 ENST00000513463 ENSP00000421365 544 VANGL1 Vang-like protein 1 ENSG00000173218 ENST00000310260 ENSP00000310800 545 ENST00000355485 ENSP00000347672 546 ENST00000369509 ENSP00000358522 547 ENST00000369510 ENSP00000358523 548 CD7 T-cell antigen CD7 ENSG00000173762 ENST00000312648 ENSP00000312027 549 ENST00000578509 ENSP00000464565 550 ENST00000581434 ENSP00000464546 551 ENST00000582480 ENSP00000464182 552 ENST00000583376 ENSP00000463489 553 ENST00000584284 ENSP00000463612 554 HAP1 Huntingtin-associated ENSG00000173805 ENST00000455021 ENSP00000397242 555 protein 1 FBXO45 F-box/SPRY domain- ENSG00000174013 ENST00000440469 ENSP00000389868 556 containing protein 1 CHST2 Carbohydrate ENSG00000175040 ENST00000309575 ENSP00000307911 557 sulfotransferase 2 RM12 RecQ-mediated genome ENSG00000175643 ENST00000572173 ENSP00000461206 558 instability protein 2 SLC35E3 Solute carrier family 35 ENSG00000175782 ENST00000398004 ENSP00000381089 559 member E3 ENST00000431174 ENSP00000403769 560 ZBTB38 Zinc finger and BTB ENSG00000177311 ENST00000503809 ENSP00000422051 561 domain-containing protein 38 YIPF6 Protein YIPF6 ENSG00000181704 ENST00000374622 ENSP00000363751 562 ENST00000451537 ENSP00000401799 563 ENST00000462683 ENSP00000417573 564 CREB3L2 Cyclic AMP-responsive ENSG00000182158 ENST00000330387 ENSP00000329140 565 element-binding protein ENST00000420629 ENSP00000402889 566 3-like protein 2 ENST00000456390 ENSP00000403550 567 XKRX XK-related protein 2 ENSG00000182489 ENST00000372956 ENSP00000362047 568 ENST00000468904 ENSP00000419884 569 CADM1 Cell adhesion molecule 1 ENSG00000182985 ENST00000331581 ENSP00000329797 570 ENST00000452722 ENSP00000395359 571 ENST00000536727 ENSP00000440322 572 ENST00000537058 ENSP00000439817 573 ENST00000540951 ENSP00000445375 574 ENST00000542447 ENSP00000439176 575 ENST00000542450 ENSP00000442001 576 ENST00000543540 ENSP00000439847 577 ENST00000545380 ENSP00000442387 578 ENST00000612235 ENSP00000483648 579 ENST00000612471 ENSP00000483793 580 ENST00000616271 ENSP00000484516 581 ENST00000621043 ENSP00000482840 582 ENST00000621709 ENSP00000482924 583 LHFP Lipoma HMGIC fusion ENSG00000183722 ENST00000379589 ENSP00000368908 584 partner CSF1 Macrophage colony- ENSG00000184371 ENST00000329608 ENSP00000327513 585 stimulating factor 1 ENST00000357302 ENSP00000349854 586 ENST00000369801 ENSP00000358816 587 ENST00000369802 ENSP00000358817 588 ENST00000420111 ENSP00000407317 589 ENST00000488198 ENSP00000433837 590 ENST00000525659 ENSP00000431547 591 ENST00000527192 ENSP00000434527 592 PTP4A3 Protein tyrosine ENSG00000184489 ENST00000329397 ENSP00000332274 593 phosphatase type IVA 3 ENST00000349124 ENSP00000331730 594 ENST00000520105 ENSP00000428758 595 ENST00000521578 ENSP00000428976 596 ENST00000523147 ENSP00000428725 597 ENST00000524028 ENSP00000430332 598 OSBP2 Oxysterol-binding ENSG00000184792 ENST00000445781 ENSP00000411497 599 protein 2 METTL7A Methyltransferase-like ENSG00000185432 ENST00000332160 ENSP00000331787 600 protein 7A ENST00000547104 ENSP00000447542 601 ENST00000548553 ENSP00000448785 602 ENST00000550097 ENSP00000448286 603 ENST00000550502 ENSP00000450239 604 TMPRSS6 Transmembrane ENSG00000187045 ENST00000346753 ENSP00000334962 605 protease serine 6 ENST00000381792 ENSP00000371211 606 ENST00000406725 ENSP00000385453 607 ENST00000406856 ENSP00000384964 608 ENST00000423761 ENSP00000400317 609 ENST00000429068 ENSP00000392433 610 ENST00000442782 ENSP00000397691 611 GCNT1 Beta-1,3-galactosyl-O- ENSG00000187210 ENST00000376730 ENSP00000365920 612 glycosyl-glycoprotein ENST00000442371 ENSP00000415454 613 beta-1,6-N- ENST00000444201 ENSP00000390703 614 acetylglucosaminyltransferase MAGEH1 Melanoma-associated ENSG00000187601 ENST00000342972 ENSP00000343706 615 antigen H1 NEMP2 Nuclear envelope ENSG00000189362 ENST00000343105 ENSP00000340087 616 integral membrane ENST00000409150 ENSP00000386292 617 protein 2 ENST00000414176 ENSP00000404283 618 ENST00000421038 ENSP00000410306 619 ENST00000444545 ENSP00000403867 620 NTNG2 Netrin-G2 ENSG00000196358 ENST00000372179 ENSP00000361252 621 ENST00000393229 ENSP00000376921 622 PDGFA Platelet-derived growth ENSG00000197461 ENST00000354513 ENSP00000346508 623 factor subunit A ENST00000400761 ENSP00000383572 624 ENST00000402802 ENSP00000383889 625 ENST00000405692 ENSP00000384673 626 PDCD1LG2 Programmed cell death 1 ENSG00000197646 ENST00000397747 ENSP00000380855 627 ligand 2 TOR4A Torsin-4A ENSG00000198113 ENST00000357503 ENSP00000350102 628 HIBCH 3-hydroxyisobutyryl-CoA ENSG00000198130 ENST00000392333 ENSP00000376145 629 hydrolase, mitochondrial ENST00000414928 ENSP00000414820 630 NTRK1 High affinity nerve ENSG00000198400 ENST00000358660 ENSP00000351486 631 growth factor receptor ENST00000368196 ENSP00000357179 632 ENST00000392302 ENSP00000376120 633 ENST00000497019 ENSP00000436804 634 ENST00000524377 ENSP00000431418 635 FAM19A2 Protein FAM19A2 ENSG00000198673 ENST00000416284 ENSP00000393987 636 ENST00000548780 ENSP00000449310 637 ENST00000549379 ENSP00000447584 638 ENST00000549958 ENSP00000447280 639 ENST00000550003 ENSP00000449457 640 ENST00000551449 ENSP00000449632 641 ENST00000551619 ENSP00000447305 642 ENST00000552075 ENSP00000449516 643 F5 Coagulation factor V ENSG00000198734 ENST00000367796 ENSP00000356770 644 ENST00000367797 ENSP00000356771 645 GK Glycerol kinase ENSG00000198814 ENST00000378943 ENSP00000368226 646 ENST00000427190 ENSP00000401720 647 ENST00000488296 ENSP00000419771 648 INPP5F Phosphatidylinositide ENSG00000198825 ENST00000490818 ENSP00000487706 649 phosphatase SAC2 ENST00000631572 ENSP00000488726 650 CD177 CD177 antigen ENSG00000204936 ENST00000378012 ENSP00000367251 651 ENST00000607855 ENSP00000483817 652 ENST00000618265 ENSP00000479536 653 LEPROT Leptin Receptor ENSG00000213625 ENST00000371065 ENSP00000360104 654 Overlapping Transcrip ENST00000613538 ENSP00000483521 655 TRIM16 Tripartite motif- ENSG00000221926 ENST00000579219 ENSP00000463639 656 containing protein 16 LTA Lymphotoxin-alpha ENSG00000226979 ENST00000418386 ENSP00000413450 657 ENST00000454783 ENSP00000403495 658 PROB1 Proline-rich basic protein 1 ENSG00000228672 ENST00000434752 ENSP00000416033 659 SSTR3 Somatostatin receptor ENSG00000278195 ENST00000610913 ENSP00000480971 660 type 3 ENST00000617123 ENSP00000481325 661 CEACAM1 Carcinoembryonic ENSG00000079385 ENST00000161559 ENSP00000161559 662 antigen-related cell ENST00000352591 ENSP00000244291 663 adhesion molecule 1 ENST00000358394 ENSP00000351165 664 ENST00000403444 ENSP00000384709 665 ENST00000403461 ENSP00000384083 666 ENST00000471298 ENSP00000472633 667 ENST00000599389 ENSP00000471918 668 ENST00000600172 ENSP00000471566 669 CTLA4 Cytotoxic T-lymphocyte ENSG00000163599 ENST00000295854 ENSP00000295854 670 protein 4 ENST00000302823 ENSP00000303939 671 ENST00000427473 ENSP00000409707 672 ENST00000472206 ENSP00000417779 673 TIGIT T-cell immunoreceptor ENSG00000181847 ENST00000383671 ENSP00000373167 674 with Ig and ITIM ENST00000461158 ENSP00000418917 675 domains ENST00000481065 ENSP00000420552 676 ENST00000484319 ENSP00000419706 677 ENST00000486257 ENSP00000419085 678 IL2RA Interleukin-2 receptor ENSG00000134460 ENST00000256876 ENSP00000256876 679 subunit alpha ENST00000379954 ENSP00000369287 680 ENST00000379959 ENSP00000369293 681 ENTPD1 Ectonucleoside ENSG00000138185 ENST00000371205 ENSP00000360248 682 triphosphate ENST00000371207 ENSP00000360250 683 diphosphohydrolase 1 ENST00000453258 ENSP00000390955 684 ENST00000483213 ENSP00000489333 685 ENST00000543964 ENSP00000442968 686 ENST00000635076 ENSP00000489250 687 ICOS Inducible T-cell ENSG00000163600 ENST00000316386 ENSP00000319476 688 costimulator ENST00000435193 ENSP00000415951 689 TNFRSF4 Tumor necrosis factor ENSG00000186827 ENST00000379236 ENSP00000368538 690 receptor superfamily member 4 TNFRSF18 Tumor necrosis factor ENSG00000186891 ENST00000328596 ENSP00000328207 691 receptor superfamily ENST00000379265 ENSP00000368567 692 member 18 ENST00000379268 ENSP00000368570 693 ENST00000486728 ENSP00000462735 694 TNFRSF8 Tumor necrosis factor ENSG00000120949 ENST00000263932 ENSP00000263932 695 receptor superfamily ENST00000417814 ENSP00000390650 696 member 8 ENST00000514649 ENSP00000421938 697 CD274 Programmed cell death 1 ENSG00000120217 ENST00000381573 ENSP00000370985 698 ligand 1 ENST00000381577 ENSP00000370989 699 IL2RB Interleukin-2 receptor ENSG00000100385 ENST00000216223 ENSP00000216223 700 subunit beta ENST00000429622 ENSP00000402685 701 ENST00000445595 ENSP00000401020 702 ENST00000453962 ENSP00000403731 703 TNFRSF9 Tumor necrosis factor ENSG00000049249 ENST00000377507 ENSP00000366729 704 receptor superfamily ENST00000474475 ENSP00000465272 705 member 9 ENST00000615230 ENSP00000478699 706 IKZF2 Zinc finger protein Helios ENSG00000030419 ENST00000442445 ENSP00000390045 707

[0256] Genes of table VI are characterized by their Ensembl Gene accession number (ENSG), retrievable in the public database EnsEMBL (http://www.ensembl.org). Each related protein isoform is characterized by an Ensembl transcript accession number (ENST) and an Ensembl protein accession number (ENSP).

Identification of Transcript Isoforms Expressed by Tumor-Treg Cells

[0257] An important aspect to be verified in the selection of potential targets of tumor-T reg is that the protein isoforms predicted to be surface exposed/membrane associated by the cell localization algorithms are indeed expressed in tumor Treg cells. Thus, total RNA was extracted from tumor Treg cells isolated from NSCLC or CRC samples and subjected to RT-PCR using specific primer pairs able to discriminate the different isoforms annotated for each gene. Exemplificative results of protein isoforms predicted to be surface exposed and detected in tumor T reg cells is reported in Table VII. Moreover, an example of RT-PCR analysis carried out for SIRPG is reported in FIG. 10.

TABLE-US-00011 TABLE VII Representative examples of transcripts detected in tumor-infiltrating Treg cells GENE SYMBOL Surface predicted isoform detected in Tumor Treg cells CCR8 ENST00000326306 LAYN ENST00000375614 and/or ENST00000533265 and/or ENST00000375615 and/or ENST00000525126 CD7 ENST00000312648 and/or ENST00000584284 CXCL13 ENST00000286758 FCRL3 ENST00000492769 and/or ENST00000368184 and/or ENST00000368186 and/or ENST00000485028 IL1R2 ENST00000332549 and/or ENST00000393414 IL21R ENST00000337929 and/or ENST00000395754 and/or ENST00000564089 NTNG2 ENST00000393229 SIRPG ENST00000303415 and/or ENST00000216927 and/or ENST00000344103 and/or ENST00000381580 and/or ENST00000381583 TSPAN5 ENST00000305798 and/or ENST00000505184 TMPRSS3 ENST00000291532 TMPRSS6 ENST00000406725 and/or ENST00000406856 NDFIP2 ENST00000218652

Discussion

[0258] Diversity of tumor infiltrating Treg cells should be fully elucidated to understand their functional relevance and prognostic significance in different types of cancer, and to possibly improve the therapeutic efficacy of Treg cell modulation through the selective depletion of tumor infiltrating Treg cells. The transcriptome analysis performed on CRC- and NSCLC-infiltrating T cells showed that tumor-infiltrating Treg cells are different from both circulating and normal tissue-infiltrating Tregs, suggesting that the tumor microenvironment influences specific gene expression in Treg cells. Our findings further support the view that Treg cells from different tissues are instructed by environmental factors to display different gene expression profiles (Panduro et al., 2016). Indeed the list of signature genes includes a number of molecules that are consistently upregulated in tumor infiltrating Treg cells isolated from different tumor types, and these signature genes would have not been identified if the inventors had not profiled specifically tumor infiltrating Treg cells. It was found tumor-infiltrating-Treg signature genes are not only largely shared between CRC and NSCLC infiltrating cells, but are also conserved in breast and gastric cancers as well as in CRC and NSCLC metastatic tumors (in liver and brain respectively) suggesting that expression of these genes is a common feature of tumor infiltrating Treg cells that may correlate with Treg cells specific function within the tumor microenvironment. Although our knowledge on the function of immune checkpoints on lymphocytes is still incomplete, agonist or antagonist monoclonal antibodies targeting checkpoints are in clinical development. Interestingly, it has been found that some of these checkpoints (such as GITR, OX40, TIGIT, LAG-3 and TIM-3) and some of their ligands (such as OX40LG, Galectin-9, CD70) are upregulated also in tumor infiltrating Treg cells, and this fact should be taken into account in interpreting clinical results with checkpoint inhibitors. Indeed, it is likely that assessment of the expression of checkpoints and of their ligands on the various subsets of tumor infiltrating lymphocytes will help to elucidate conflicting results and provide the rationale for combination therapies. Therefore, expression pattern of checkpoints should be evaluated both in tumor infiltrating lymphocytes and in tumor cells. Single-cell analysis on selected tumor Treg signature genes confirmed the whole transcriptomic data and provided information on the expression frequency of these genes. Tumor infiltrating Treg cells express with high frequency genes that are associated with increased suppressor activity, such as the well characterized OX40, CTLA4 and GITR. Moreover, there are a number of interesting and less expected genes the specific expression of which was validated also at the protein level. For example, IL-1 R2 upregulation could be another mechanism that tumor resident Treg cells employ to dampen anti-tumor immune responses through the neutralization of IL-1 function on effector cells. PD-L1 and PD-L2 expression has been recently reported on activated T cells or APCs (Boussiotis et al., 2014; Lesterhuis et al., 2011; Messal et al., 2011) but, to the best of our knowledge, neither PD-L2 nor PD-L1 expression has ever been reported in Treg cells, and our finding that they are overexpressed in tumor infiltrating Treg cells adds an additional level of complexity to the PD1/PD-Ls immunomodulatory axis within the tumor microenvironment. BATF is a transcription factor that has been mainly associated to Th17 development and CD8.sup.+ T cells differentiation (Murphy et al., 2013). Our findings show that BATF transcript is upregulated in tumor infiltrating Treg cells more than in tumor infiltrating Th17 cells (FIG. 8). Interestingly, expression of BATF in CD8.sup.+ T cells is induced by IL-21 (Xin et al., 2015), and it was found that IL21R is highly expressed in tumor-infiltrating Treg cells (FIG. 4).

[0259] It was showed that tumor infiltrating Treg cells express high amounts of 4-1 BB (CD137) a marker of TcR mediated activation (Schoenbrunn et al., 2012) and have shown they display very high suppressor function on effector T cell proliferation. It could be that expression of the signature genes correlated with the enhanced suppressive ability and so contributed to the establishment of a strong immunosuppressive environment at tumor sites. A corollary to our findings would have that increased number of Treg cells in the tumor environment should associate with a worst clinical outcome. In fact, when LAYN, MAGEH1 and CCR8 (which represent three of the most enriched genes in tumor infiltrating Treg cells) are highly detected in whole tumor samples there is a significant worsening of the 5 years survival of both CRC and NSCLC patients. Although, the functional roles in Treg cells of LAYN, a transmembrane protein with homology to c-type lectin (Borowsky and Hynes, 1998), and of MAGEH1, a member of the Melanoma Antigen Gene family (Weon and Potts, 2015) are unknown, the high expression of the chemokine receptor CCR8 is instead intriguing. Indeed CCL18, the ligand of CCR8 (Islam et al., 2013), is highly expressed in different tumors including NSCLC (Chen et al., 2011; Schutyser et al., 2005). The high specificity of CCR8 expression on tumor infiltrating Treg cells suggests it could be a new interesting therapeutic target to inhibit Treg cells trafficking to tumor sites, without disturbing recruitment of other effector T cells that do not express CCR8. Considerable efforts have been recently put in the development of sophisticated bioinformatics approaches that exploit lymphocyte gene expression data to understand the immune-modulatory networks at tumor sites, to predict clinical responses to immune-therapies, and to define novel therapeutic targets (Bindea et al., 2013a; Bindea et al., 2013b; Gentles et al., 2015). The data here presented represent the first comprehensive RNA-sequencing analysis performed on tumor-infiltrating human CD4.sup.+ Treg, Th1 and Th17 cells. Our findings highlight the relevance of assessing gene expression patterns of lymphocyte at tumor-sites and suggest that generation of more transcriptomic data of tumor-infiltrating lymphocyte subsets purified from different cancer types may contribute to a better understanding of the dynamics underlying immune modulation in the tumor microenvironment. Moreover, our data represent a resource to generate and validate novel hypotheses that will increase our knowledge on tumor infiltrating Treg cell biology and should lead to the identification of new therapeutic targets.

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