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Purpose: Immune checkpoint inhibitors (ICI) have revolutionized the treatment of patients with clear-cell renal cell carcinomas (ccRCC). Although analyses of transcriptome, genetic alterations, and the tumor microenvironment (TME) have shed light into mechanisms of response and resistance to these agents, the role of epigenetic alterations in this process remains fully unknown. Experimental Design: We investigated the methylome of six ccRCC cohorts as well as one cell line dataset. Of note, we took advantage of the BIONIKK trial aiming to tailor treatments according to Paris Descartes 4-gene expression subgroups, and performed Illumina EPIC profiling for 46 samples related to patients treated with ipilimumab plus nivolumab, and 17 samples related to patients treated with sunitinib. Results: A group of tumors associated with enhancer demethylation was discovered, namely TED. TED was associated with tumors with sarcomatoid differentiation and poor clinical outcome. TED harbored TET1 promoter demethylation, activated the gene expression signature of epithelial–mesenchymal transition and IL6/JAK/STAT3 pathways, and displayed a TME characterized by both immune activation and suppressive populations, fibroblast infiltration, and endothelial depletion. In addition, TED was a predictive factor of resistance to the combination of first-line ipilimumab-nivolumab in the BIONIKK clinical trial. Finally, TED was associated with activation of specific regulons, which we also found to be predictive of resistance to immunotherapy in an independent cohort. Conclusions: We report on the discovery of a novel epigenetic phenotype associated with resistance to ICIs that may pave the way to better personalizing patients’ treatments. See related commentary by Zhou and Kim, p. 1170

The omics era made possible the quest for efficient markers for cancer progression and revealed that macrophage populations are much more complex than just the M1/M2 dichotomy. Complement C1q pops up as a marker of a tolerogenic and immunosuppressive macrophage populations in both healthy and tumor tissues, but the specific role of C1q+ tumor-associated macrophages (TAM) is poorly understood. C1q is co-expressed in healthy and tumor macrophages with human leukocyte antigen DR (HLA-DR), Apolipoprotein E (APOE), and mannose receptor C-type 1 (MRC1) (CD206), suggesting a resident origin of this population. TAM expressing C1q correlate with T cell exhaustion and poor prognosis in numerous cancers. Herein, we discuss the plural roles of C1q in these macrophages and how it could drive cancer progression.

Purpose:Immune checkpoint inhibitors (ICI) have revolutionized the treatment of patients with clear-cell renal cell carcinomas (ccRCC). Although analyses of transcriptome, genetic alterations, and the tumor microenvironment (TME) have shed light into mechanisms of response and resistance to these agents, the role of epigenetic alterations in this process remains fully unknown.Experimental Design:We investigated the methylome of six ccRCC cohorts as well as one cell line dataset. Of note, we took advantage of the BIONIKK trial aiming to tailor treatments according to Paris Descartes 4-gene expression subgroups, and performed Illumina EPIC profiling for 46 samples related to patients treated with ipilimumab plus nivolumab, and 17 samples related to patients treated with sunitinib.Results:A group of tumors associated with enhancer demethylation was discovered, namely TED. TED was associated with tumors with sarcomatoid differentiation and poor clinical outcome. TED harbored TET1 promoter demethylation, activated the gene expression signature of epithelial–mesenchymal transition and IL6/JAK/STAT3 pathways, and displayed a TME characterized by both immune activation and suppressive populations, fibroblast infiltration, and endothelial depletion. In addition, TED was a predictive factor of resistance to the combination of first-line ipilimumab-nivolumab in the BIONIKK clinical trial. Finally, TED was associated with activation of specific regulons, which we also found to be predictive of resistance to immunotherapy in an independent cohort.Conclusions:We report on the discovery of a novel epigenetic phenotype associated with resistance to ICIs that may pave the way to better personalizing patients’ treatments.See related commentary by Zhou and Kim, p. 1170

Figure S2. Focal copy number deletion identified by the GISTIC algorithm for a) 𝑇𝐸𝐷+ and b) 𝑇𝐸𝐷−, respectively, in the TCGA-KIRC cohort.

Supplementary Tables S1-S23

Supplemental Table 4-6. Table S4: Detail for each sample of the PBRM1 and VHL-mutation status. NA stands for Not Available. Table S5: For each data type, clinical, transcriptome, methylome or SNP array, sensitity and specificity of the two predictors: PD vs (PR+SD) and PR vs (PD+SD) in the training and validation sets. The 'N()' columns indicate the numbers of each responder type. Table S6: List of the 27 genes firstly used by the classifier to predict the subtypes ccrcc2, ccrcc3 and the joint subtype ccrcc14 and list of the 8 genes used by the second classifier to assign the ccrcc14 samples to the two subtypes ccrcc1 and ccrcc4.

Supplemental Figure 7-10. Figure S7: Summary of the results obtained on the TCGA data; Figure S8: Survival analysis of the patients included in the TCGA according to our classification ccrcc 1 to 4; Figure S9: Illustration of the gradient assumption defined by subtype order

PDF file - 83K, Baseline characteristics of 52 patients with RCC lung metastasis. * determined by Heng et al.

PDF file - 90K, Baseline characteristics of 140 patients with CRC lung metastasis. The stage was determined by pathological examination at the time of diagnosis.

Purpose: Selecting patients with metastatic clear-cell renal cell carcinoma (m-ccRCC) who might benefit from treatment with targeted tyrosine kinase inhibitors (TKI) is a challenge. Our aim was to identify molecular markers associated with outcome in patients with m-ccRCC treated with sunitinib.Experimental Design: We performed global transcriptome analyses on 53 primary resected ccRCC tumors from patients who developed metastatic disease and were treated with first-line sunitinib. We also determined chromosome copy-number aberrations, methylation status, and gene mutations in von Hippel–Lindau and PBRM1. Molecular data were analyzed in relation with response rate (RR), progression-free survival (PFS), and overall survival (OS). Validation was performed in 47 additional ccRCC samples treated in first-line metastatic setting with sunitinib.Results: Unsupervised transcriptome analysis identified 4 robust ccRCC subtypes (ccrcc1 to 4) related to previous molecular classifications that were associated with different responses to sunitinib treatment. ccrcc1/ccrcc4 tumors had a lower RR (P = 0.005) and a shorter PFS and OS than ccrcc2/ccrcc3 tumors (P = 0.001 and 0.0003, respectively). These subtypes were the only significant covariate in the multivariate Cox model for PFS and OS (P = 0.017 and 0.006, respectively). ccrcc1/ccrcc4 tumors were characterized by a stem-cell polycomb signature and CpG hypermethylation, whereas ccrcc3 tumors, sensitive to sunitinib, did not exhibit cellular response to hypoxia. Moreover, ccrcc4 tumors exhibited sarcomatoid differentiation with a strong inflammatory, Th1-oriented but suppressive immune microenvironment, with high expression of PDCD1 (PD-1) and its ligands.Conclusions: ccRCC molecular subtypes are predictive of sunitinib response in metastatic patients, and could be used for personalized mRCC treatment with TKIs, demethylating or immunomodulatory drugs. Clin Cancer Res; 21(6); 1329–39. ©2015 AACR.

Purpose: If immune cells are involved in tumor surveillance and have a prognostic impact in most primary tumors, little is known about their significance in metastases. Because patients' survival is heterogeneous, even at metastatic stages, we hypothesized that immune cells may be involved in the control of metastases. We therefore characterized the tumor immune microenvironment and its prognostic value in colorectal and renal cell carcinoma (RCC) metastases, and compared it to primary tumors.Experimental Design: We analyzed by immunohistochemistry (n = 192) and qPCR (n = 32) the immune environments of colorectal carcinoma and RCC lung metastases.Results: Metastases from colorectal carcinoma and RCC have different immune infiltrates. Higher densities of DC-LAMP+ mature dendritic cells (P < 0.0001) and lower densities of NKp46+ NK cells (P < 0.0001) were observed in colorectal carcinoma as compared to RCC metastases, whereas densities of T cells were similar. High densities of CD8+ and DC-LAMP+ cells correlated with longer overall survival (OS) in colorectal carcinoma (P = 0.008) and shorter OS in RCC (P < 0.0001). High NK-cell densities were associated with improved survival in RCC (P = 0.002) but not in colorectal carcinoma. Densities of immune cells correlated significantly from primary to relapsing metastases for the same patient. A TH1 orientation was found in colorectal carcinoma metastases, whereas a heterogeneous immune gene expression was found in RCC metastases.Conclusions: Our results show a major prognostic value of the immune pattern (CD8+/DC-LAMP+ cell densities) in colorectal carcinoma and RCC, reproducible from primary to metastatic tumors, although with opposite clinical impacts, and highlight the role of the tumor cell in shaping its immune environment. Clin Cancer Res; 19(15); 4079–91. ©2013 AACR.

PDF file - 101K, Influence of pre-operative patient treatments on the distribution of CD8+, DC-LAMP+ and NKp46+ immune cells in CRC (a, b and c) and RCC lung metastases (d, e and f). 63/140 patients with CRC lung metastases have been treated with neo-adjuvant chemotherapy and 9/52 patients with RCC lung metastases have been treated with IL2/IFN. Whiskers length represents 10-90 percentile. ns, not significant (Mann-Whitney test).

Purpose: The tumor microenvironment is formed by many distinct and interacting cell populations, and its composition may predict patients' prognosis and response to therapies. Colorectal cancer is a heterogeneous disease in which immune classifications and four consensus molecular subgroups (CMS) have been described. Our aim was to integrate the composition of the tumor microenvironment with the consensus molecular classification of colorectal cancer.Experimental Design: We retrospectively analyzed the composition and the functional orientation of the immune, fibroblastic, and angiogenic microenvironment of 1,388 colorectal cancer tumors from three independent cohorts using transcriptomics. We validated our findings using immunohistochemistry.Results: We report that colorectal cancer molecular subgroups and microenvironmental signatures are highly correlated. Out of the four molecular subgroups, two highly express immune-specific genes. The good-prognosis microsatellite instable–enriched subgroup (CMS1) is characterized by overexpression of genes specific to cytotoxic lymphocytes. In contrast, the poor-prognosis mesenchymal subgroup (CMS4) expresses markers of lymphocytes and of cells of monocytic origin. The mesenchymal subgroup also displays an angiogenic, inflammatory, and immunosuppressive signature, a coordinated pattern that we also found in breast (n = 254), ovarian (n = 97), lung (n = 80), and kidney (n = 143) cancers. Pathologic examination revealed that the mesenchymal subtype is characterized by a high density of fibroblasts that likely produce the chemokines and cytokines that favor tumor-associated inflammation and support angiogenesis, resulting in a poor prognosis. In contrast, the canonical (CMS2) and metabolic (CMS3) subtypes with intermediate prognosis exhibit low immune and inflammatory signatures.Conclusions: The distinct immune orientations of the colorectal cancer molecular subtypes pave the way for tailored immunotherapies. Clin Cancer Res; 22(16); 4057–66. ©2016 AACR.

Antibodies used for immunohistochemical analyses

Supplemental Figure 1-3. Figure S1: Expression of immune modulators among the 4 molecular subgroups and normal Samples; Figure S2: Expression of cell-type specific metagenes among the different subgroups and normal samples; Figure S3: CD8+ cell infiltration according to the molecular ccRCC subtype classification.

Immune, and other stromal samples analyzed

PDF file - 88K, Gene expression in lung metastases from CRC and RCC. Expression of genes related to (a) immune cell populations, (b) Th1/Th2 orientations, (c) inflammation and angiogenesis, (d) immunosuppression, (e) cytotoxicity, (f) chemokines/chemokine receptors in lung metastases from CRC (grey bars) and RCC (white bars). Expression levels of genes were determined using threshold cycle (Ct) values normalized to actin B ACTB (ΔCt). Whiskers length represents 10-90 percentile. ns, not significant; *P

Legends for supplementary figure and trables

PDF file - 107K, Kaplan-Meier curves for the duration of OS according to the presence of CD8+ T cells in the center of the tumor (CT) (a), in the invasive margin (IM) (b) and in combined regions (CT+IM) (c) of CRC lung metastases. Statistical comparison was performed by the log-rank test.

Supplementary Figure 1 : Design and use of the Microenvironment Cell Populations (MCP)-counter algorithm a) Heatmap representation of the expression of selected transcriptomic markers underlying MCP-counter scores in transcriptomic profiles corresponding to microenvironment cell populations. These scores have been quantitatively validated for their ability to predict the abundance of the corresponding cell population from the gene expression profile of a cellularly heterogeneous sample. b) MCP-counter is implemented as an R package, which takes as input transcriptomic profiles of cellularly heterogeneous tissues, such as tumors. It first maps the previously-identified transcriptomic markers to the assayed gene-expression features (as illustrated here for the 'discovery' CIT CRC cohort, and summarize them to output a score per cell population per sample. These scores can then be used to compare samples against each-others and to determine whether a group of samples is more highly infiltrated by a given cell type than another group of samples.

Supplementary Figure and Table Legends. Legends of all supplemental Figures and Tables

PDF file - 56K, P values corresponding to different cutoff (minimum P value, first, second and third quartile) in CRC and RCC lung metastases. {section sign}P values were corrected by the formula proposed by Altman et al.

Consensus Molecular Subgroup-level statistics on each MCP-counter abundance estimates

Supplemental Figure 4-6. Figure S4: Characterisation of the ccRCC molecular subtypes: methylation status and Benporath polycomb signature; Figure S5: Cytogenetic characterization of the ccRCC molecular subtypes; Figure S6: Characterization of the ccRCC molecular subtypes: Myc methylation and (target) amplification

Supplemental Table 7-10. Table S7: Area under the ROC curve or c-index for the predictive and prognostic markers presented in Table 1 and Figures 3A, 3B and 3C. Table S8: P-values of the pathway enrichment analysis for the genes differentially expressed, the genes differentially methylated and the genes differentially methylated and anti-correlated to expression data in the four unsupervised subgroups. Table S9: List of the recurrent chromosomal aberrations characterizing ccrcc4-samples with a sensitivity and a specificity greater than 0.65. Table S10: Correlation of the centroids of our classification in four subtypes with the Brannon classification in three subgroups ccA, ccB and cluster_3 and the subtypes predicted in the TCGA cohort

PDF file - 148K, Kaplan-Meier curves for the duration of OS according to the presence of CD8+ T cells in the center of the tumor (CT) (a), in the invasive margin (IM) (b) and in combined regions (CT+IM) (c) of RCC lung metastases. Representation of the Kaplan-Meier curves for the duration of OS according to the presence of NKp46+ cells in the CT (d), in the IM (e) and in combined region (CT+IM) (f) of RCC lung metastases. Statistical comparison was performed by the log-rank test.

Colorectal cancer samples constituting the 3 cohorts analyzed, along with their molecular subgroups

Levels of expression of genes related to inflammation, angiogenesis and immunomodulation in CRC cohorts

PDF file - 81K

Samples from the Cancer Cell Line Encyclopedia

PDF file - 95K, Comparison of different methods used for the stratification of patients according to the density of mature DC, stromal CD8+ T cells or tumor nest CD8+ T cells.

PDF file - 308K, Characterization of immune cells in and out TLS.

Supplementary Methods, Figures 4-6 from Coordination of Intratumoral Immune Reaction and Human Colorectal Cancer Recurrence

Supplementary Table 1 from Coordination of Intratumoral Immune Reaction and Human Colorectal Cancer Recurrence

Toll-like receptors (TLR) recognize pathogen molecules and danger-associated signals that stimulate inflammatory processes. TLRs have been studied mainly in antigen-presenting cells, where they exert important immune regulatory functions, but they are also expressed by epithelial tumor cells, where they have been implicated in tumor progression. In this study, we demonstrate that the injection of TLR7 agonist in NOD/SCID mice, in C57BL/6 wild-type, and TLR7-deficient mice grafted with lung adenocarcinoma tumor cells leads to increased tumor progression and chemotherapeutic resistance. In patients with non–small cell lung cancer, expression analyses revealed that high TLR7 expression was strongly associated with resistance to neoadjuvant chemotherapy and poor clinical outcomes. Our findings delineate a crucial role for TLR7 in lung cancer physiopathology. Cancer Res; 74(18); 5008–18. ©2014 AACR.

PDF file - 131K, Clinical characteristic of NSCLC patients with DC-Lamp High versus DC-Lamp Low tumors.

PDF file - 65KB, Appendix A5.

PDF file - 123K, CD62L- T cells represent the main T cell population in human lung tumors.

PDF file - 380K, Gene expression levels related to immune populations, TLS, Th-orientation, cytotoxicity, T-cell activation, immuno-suppression, inflammation and angiogenesis according to the high and low density of mature DC.

Supplementary Table 3 from Coordination of Intratumoral Immune Reaction and Human Colorectal Cancer Recurrence

PDF file - 200K, Twelve-color analysis of the immune cell populations in freshly resected tumors.

Tumor-infiltrating T cells, particularly CD45RO+CD8+ memory T cells, confer a positive prognostic value in human cancers. However, the mechanisms that promote a protective T-cell response in the tumor microenvironment remain unclear. In chronic inflammatory settings such as the tumor microenvironment, lymphoid neogenesis can occur to create local lymph node–like structures known as tertiary lymphoid structures (TLS). These structures can exacerbate a local immune response, such that TLS formation in tumors may help promote an efficacious immune contexture. However, the role of TLS in tumors has yet to be investigated carefully. In lung tumors, mature dendritic cells (DC) present in tumor-associated TLS can provide a specific marker of these structures. In this study, we evaluated the influence of TLS on the characteristics of the immune infiltrate in cohorts of prospective and retrospective human primary lung tumors (n = 458). We found that a high density of mature DC correlated closely to a strong infiltration of T cells that are predominantly of the effector–memory phenotype. Moreover, mature DC density correlated with expression of genes related to T-cell activation, T-helper 1 (Th1) phenotype, and cytotoxic orientation. Lastly, a high density of TLS-associated DC correlated with long-term survival, which also allowed a distinction of patients with high CD8+ T-cell infiltration but a high risk of death. Taken together, our results show how tumors infiltrated by TLS-associated mature DC generate a specific immune contexture characterized by a strong Th1 and cytotoxic orientation that confers the lowest risk of death. Furthermore, our findings highlight the pivotal function of TLS in shaping the immune character of the tumor microenvironment, in promoting a protective immune response mediated by T cells against cancer. Cancer Res; 74(3); 705–15. ©2013 AACR.