The efficacy of PD-1 checkpoint blockade as adjuvant therapy in localized clear cell renal cell carcinoma (ccRCC) is currently unknown. The identification of tumor microenvironment (TME) prognostic biomarkers in this setting may help define which patients could benefit from checkpoint blockade and uncover new therapeutic targets. We performed multiparametric flow cytometric immunophenotypic analysis of T cells isolated from tumor tissue [tumor-infiltrating lymphocytes (TIL)], adjacent non-malignant renal tissue [renal-infiltrating lymphocytes (RIL)], and peripheral blood lymphocytes (PBL), in a cohort of patients ( = 40) with localized ccRCC. Immunophenotypic data were integrated with prognostic and histopathologic variables, T-cell receptor (TCR) repertoire analysis of sorted CD8PD-1 TILs, tumor mRNA expression, and digital quantitative immunohistochemistry. On the basis of TIL phenotypic characterization, we identified three dominant immune profiles in localized ccRCC: (i) immune-regulated, characterized by polyclonal/poorly cytotoxic CD8PD-1Tim-3Lag-3 TILs and CD4ICOS cells with a Treg phenotype (CD25CD127Foxp3/HeliosGITR), that developed in inflamed tumors with prominent infiltrations by dysfunctional dendritic cells and high PD-L1 expression; (ii) immune-activated, enriched in oligoclonal/cytotoxic CD8PD-1Tim-3 TILs, that represented 22% of the tumors; and (iii) immune-silent, enriched in TILs exhibiting RIL-like phenotype, that represented 56% of patients in the cohort. Only immune-regulated tumors displayed aggressive histologic features, high risk of disease progression in the year following nephrectomy, and a CD8PD-1Tim-3 and CD4ICOS PBL phenotypic signature. In localized ccRCC, the infiltration with CD8PD-1Tim-3Lag-3 exhausted TILs and ICOS Treg identifies the patients with deleterious prognosis who could benefit from adjuvant therapy with TME-modulating agents and checkpoint blockade. This work also provides PBL phenotypic markers that could allow their identification. .

Newly discovered anti-cancer immunotherapies, such as immune checkpoint inhibitors and chimeric antigen receptor T cells, focus on spurring the anti-tumor effector T cell (Teff) response. Although such strategies have already demonstrated a sustained beneficial effect in certain malignancies, a substantial proportion of treated patients does not respond. CD4+FOXP3+ regulatory T cells (Tregs), a suppressive subset of T cells, can impair anti-tumor responses and reduce the efficacy of currently available immunotherapies. An alternative view that has emerged over the last decade proposes to tackle this immune brake by targeting the suppressive action of Tregs on the anti-tumoral response. It was recently demonstrated that the tumor necrosis factor alpha (TNF-α) tumor necrosis factor receptor 2 (TNFR2) is critical for the phenotypic stabilization and suppressive function of human and mouse Tregs. The broad non-specific effects of TNF-α infusion in patients initially led clinicians to abandon this signaling pathway as first-line therapy against neoplasms. Previously unrecognized, TNFR2 has emerged recently as a legitimate target for anti-cancer immune checkpoint therapy. Considering the accumulation of pre-clinical data on the role of TNFR2 and clinical reports of TNFR2+ Tregs and tumor cells in cancer patients, it is now clear that a TNFR2-centered approach could be a viable strategy, once again making the TNF-α pathway a promising anti-cancer target. Here, we review the role of the TNFR2 signaling pathway in tolerance and the equilibrium of T cell responses and its connections with oncogenesis. We analyze recent discoveries concerning the targeting of TNFR2 in cancer, as well as the advantages, limitations, and perspectives of such a strategy.

BackgroundTargeting immune checkpoints that inhibit antitumor immune responses has emerged as a powerful new approach to treat cancer. We recently showed that blocking the tumor necrosis factor receptor-type 2 (TNFR2) pathway induces the complete loss of the protective function of regulatory T cells (Tregs) in a model of graft-versus-host disease (GVHD) prevention that relies on Treg-based cell therapy. Here, we tested the possibility of amplifying the antitumor response by targeting TNFR2 in a model of tumor relapse following hematopoietic stem-cell transplantation, a clinical situation for which the need for efficient therapeutic options is still unmet.MethodWe developed appropriate experimental conditions that mimic patients that relapsed from their initial hematological malignancy after hematopoietic stem-cell transplantation. This consisted of defining in allogeneic bone marrow transplantation models developed in mice, the maximum number of required tumor cells and T cells to infuse into recipient mice to develop a model of tumor relapse without inducing GVHD. We next evaluated whether anti-TNFR2 treatment could trigger alloreactivity and consequently antitumor immune response. In parallel, we also studied the differential expression of TNFR2 on T cells including Treg from patients in post-transplant leukemia relapse and in patients developing GVHD.ResultsUsing experimental conditions in which neither donor T cells nor TNFR2-blocking antibody per se have any effect on tumor relapse, we observed that the coadministration of a suboptimal number of T cells and an anti-TNFR2 treatment can trigger alloreactivity and subsequently induce a significant antitumor effect. This was associated with a reduced percentage of activated CD4+ and CD8+ Tregs. Importantly, human Tregs over-expressed TNFR2 relative to conventional T cells in healthy donors and in patients experiencing leukemia relapse or cortico-resistant GVHD after hematopoietic stem cell transplantation.ConclusionsThese results highlight TNFR2 as a new target molecule for the development of immunotherapies to treat blood malignancy relapse, used either directly in grafted patients or to enhance donor lymphocyte infusion strategies. More widely, they open the door for new perspectives to amplify antitumor responses against solid cancers by directly targeting Tregs through their TNFR2 expression.

<p>Table S1. Demographic and clinical characteristics of the analyzed patients; Table S2. Antibodies and conditions used for the IHC studies; Table S3. List of immune-related genes analyzed by Low Density Array; Figure S1. Gating and data analysis strategy; Figure S2. CD4+ and CD8+ T-cell differentiation in ccRCC TIL, and autologous PBL and RIL; Figure S3. Gap statistics according to the possible number of clusters of TIL phenotype. Optimal cut-off according to firstSEmax method (R package: cluster) is displayed (dotted line); Figure S4. PCA analysis including RIL and TIL phenotype. TIL clusters are displayed; Figure S5. Tumor size according to TIL clusters; Figure S6. Percentages of CD4+TIL expressing AM and InR according to tumor clusters. C1, Cluster1; C2, Cluster2; C3, Cluster3; Figure S7. Percentages of CD8+TIL expressing AM and InR according to tumor clusters. C1, Cluster1; C2, Cluster2; C3, Cluster3; Figure S8. Clonality Index and frequency of top 15 clonotypes in CD8+PD-1+ TIL according to tumor clusters. C1, Cluster1; C2, Cluster2; C3, Cluster3; Figure S9. Corrected P values for the differential gene expression between TIL clusters; Figure S10. Correlation matrix including TLS-related genes and immune cells densities in Immune-activated and Immune-regulated tumors; Figure S11. Percentages of CD4+PBL expressing differentiation markers, AM and InR in healthy controls (HC) and ccRCC-bearing patients; Figure S12. Percentages of CD8+PBL expressing differentiation markers, AM and InR in HC and ccRCC-bearing patients; Figure S13. Percentages of CD4+PBL expressing differentiation markers, AM and InR according to PBL Clusters; Figure S14. Percentages of CD8+PBL expressing differentiation markers, AM and InR according to PBL Clusters.</p>

<div>Abstract<p><b>Purpose:</b> The efficacy of PD-1 checkpoint blockade as adjuvant therapy in localized clear cell renal cell carcinoma (ccRCC) is currently unknown. The identification of tumor microenvironment (TME) prognostic biomarkers in this setting may help define which patients could benefit from checkpoint blockade and uncover new therapeutic targets.</p><p><b>Experimental Design:</b> We performed multiparametric flow cytometric immunophenotypic analysis of T cells isolated from tumor tissue [tumor-infiltrating lymphocytes (TIL)], adjacent non-malignant renal tissue [renal-infiltrating lymphocytes (RIL)], and peripheral blood lymphocytes (PBL), in a cohort of patients (<i>n</i> = 40) with localized ccRCC. Immunophenotypic data were integrated with prognostic and histopathologic variables, T-cell receptor (TCR) repertoire analysis of sorted CD8⁺PD-1⁺ TILs, tumor mRNA expression, and digital quantitative immunohistochemistry.</p><p><b>Results:</b> On the basis of TIL phenotypic characterization, we identified three dominant immune profiles in localized ccRCC: (i) immune-regulated, characterized by polyclonal/poorly cytotoxic CD8⁺PD-1⁺Tim-3⁺Lag-3⁺ TILs and CD4⁺ICOS⁺ cells with a Treg phenotype (CD25⁺CD127⁻Foxp3⁺/Helios⁺GITR⁺), that developed in inflamed tumors with prominent infiltrations by dysfunctional dendritic cells and high PD-L1 expression; (ii) immune-activated, enriched in oligoclonal/cytotoxic CD8⁺PD-1⁺Tim-3⁺ TILs, that represented 22% of the tumors; and (iii) immune-silent, enriched in TILs exhibiting RIL-like phenotype, that represented 56% of patients in the cohort. Only immune-regulated tumors displayed aggressive histologic features, high risk of disease progression in the year following nephrectomy, and a CD8⁺PD-1⁺Tim-3⁺ and CD4⁺ICOS⁺ PBL phenotypic signature.</p><p><b>Conclusions:</b> In localized ccRCC, the infiltration with CD8⁺PD-1⁺Tim-3⁺Lag-3⁺ exhausted TILs and ICOS⁺ Treg identifies the patients with deleterious prognosis who could benefit from adjuvant therapy with TME-modulating agents and checkpoint blockade. This work also provides PBL phenotypic markers that could allow their identification. <i>Clin Cancer Res; 23(15); 4416–28. ©2017 AACR</i>.</p></div>

<div>Abstract<p><b>Purpose:</b> The efficacy of PD-1 checkpoint blockade as adjuvant therapy in localized clear cell renal cell carcinoma (ccRCC) is currently unknown. The identification of tumor microenvironment (TME) prognostic biomarkers in this setting may help define which patients could benefit from checkpoint blockade and uncover new therapeutic targets.</p><p><b>Experimental Design:</b> We performed multiparametric flow cytometric immunophenotypic analysis of T cells isolated from tumor tissue [tumor-infiltrating lymphocytes (TIL)], adjacent non-malignant renal tissue [renal-infiltrating lymphocytes (RIL)], and peripheral blood lymphocytes (PBL), in a cohort of patients (<i>n</i> = 40) with localized ccRCC. Immunophenotypic data were integrated with prognostic and histopathologic variables, T-cell receptor (TCR) repertoire analysis of sorted CD8⁺PD-1⁺ TILs, tumor mRNA expression, and digital quantitative immunohistochemistry.</p><p><b>Results:</b> On the basis of TIL phenotypic characterization, we identified three dominant immune profiles in localized ccRCC: (i) immune-regulated, characterized by polyclonal/poorly cytotoxic CD8⁺PD-1⁺Tim-3⁺Lag-3⁺ TILs and CD4⁺ICOS⁺ cells with a Treg phenotype (CD25⁺CD127⁻Foxp3⁺/Helios⁺GITR⁺), that developed in inflamed tumors with prominent infiltrations by dysfunctional dendritic cells and high PD-L1 expression; (ii) immune-activated, enriched in oligoclonal/cytotoxic CD8⁺PD-1⁺Tim-3⁺ TILs, that represented 22% of the tumors; and (iii) immune-silent, enriched in TILs exhibiting RIL-like phenotype, that represented 56% of patients in the cohort. Only immune-regulated tumors displayed aggressive histologic features, high risk of disease progression in the year following nephrectomy, and a CD8⁺PD-1⁺Tim-3⁺ and CD4⁺ICOS⁺ PBL phenotypic signature.</p><p><b>Conclusions:</b> In localized ccRCC, the infiltration with CD8⁺PD-1⁺Tim-3⁺Lag-3⁺ exhausted TILs and ICOS⁺ Treg identifies the patients with deleterious prognosis who could benefit from adjuvant therapy with TME-modulating agents and checkpoint blockade. This work also provides PBL phenotypic markers that could allow their identification. <i>Clin Cancer Res; 23(15); 4416–28. ©2017 AACR</i>.</p></div>

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<p>Table S1. Demographic and clinical characteristics of the analyzed patients; Table S2. Antibodies and conditions used for the IHC studies; Table S3. List of immune-related genes analyzed by Low Density Array; Figure S1. Gating and data analysis strategy; Figure S2. CD4+ and CD8+ T-cell differentiation in ccRCC TIL, and autologous PBL and RIL; Figure S3. Gap statistics according to the possible number of clusters of TIL phenotype. Optimal cut-off according to firstSEmax method (R package: cluster) is displayed (dotted line); Figure S4. PCA analysis including RIL and TIL phenotype. TIL clusters are displayed; Figure S5. Tumor size according to TIL clusters; Figure S6. Percentages of CD4+TIL expressing AM and InR according to tumor clusters. C1, Cluster1; C2, Cluster2; C3, Cluster3; Figure S7. Percentages of CD8+TIL expressing AM and InR according to tumor clusters. C1, Cluster1; C2, Cluster2; C3, Cluster3; Figure S8. Clonality Index and frequency of top 15 clonotypes in CD8+PD-1+ TIL according to tumor clusters. C1, Cluster1; C2, Cluster2; C3, Cluster3; Figure S9. Corrected P values for the differential gene expression between TIL clusters; Figure S10. Correlation matrix including TLS-related genes and immune cells densities in Immune-activated and Immune-regulated tumors; Figure S11. Percentages of CD4+PBL expressing differentiation markers, AM and InR in healthy controls (HC) and ccRCC-bearing patients; Figure S12. Percentages of CD8+PBL expressing differentiation markers, AM and InR in HC and ccRCC-bearing patients; Figure S13. Percentages of CD4+PBL expressing differentiation markers, AM and InR according to PBL Clusters; Figure S14. Percentages of CD8+PBL expressing differentiation markers, AM and InR according to PBL Clusters.</p>