Single cell analysis reveals intra-tumour heterogeneity, microenvironment and potential diagnosis markers for clear cell renal cell carcinoma.
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ABSTRACT:
SUBMITTER: Zhang M
PROVIDER: S-EPMC9126499 | biostudies-literature | 2022 May
REPOSITORIES: biostudies-literature
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Single cell analysis reveals intra-tumour heterogeneity, microenvironment and potential diagnosis markers for clear cell renal cell carcinoma.
Zhang Man M Zhai Wei W Miao Juju J Cheng Xiaomu X Luo Wenqin W Song Weichen W Wang Jia J Gao Wei-Qiang WQ
Clinical and translational medicine 20220501 5
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Project description:BackgroundIntratumoral heterogeneity (ITH) is a hallmark of clear cell renal cell carcinoma (ccRCC) that reflects the trajectory of evolution and influences clinical prognosis. Here, we seek to elucidate how ITH and tumor evolution during immune checkpoint inhibitor (ICI) treatment can lead to therapy resistance.MethodsHere, we completed a single-arm pilot study to examine the safety and feasibility of neoadjuvant nivolumab in patients with localized RCC. Primary endpoints were safety and feasibility of neoadjuvant nivolumab. Then, we spatiotemporally profiled the genomic and immunophenotypic characteristics of 29 ccRCC patients, including pre- and post-therapy samples from 17 ICI-treated patients. Deep multi-regional whole-exome and transcriptome sequencing were performed on 29 patients at different time points before and after ICI therapy. T cell repertoire was also monitored from tissue and peripheral blood collected from a subset of patients to study T cell clonal expansion during ICI therapy.ResultsAngiogenesis, lymphocytic infiltration, and myeloid infiltration varied significantly across regions of the same patient, potentially confounding their utility as biomarkers of ICI response. Elevated ITH associated with a constellation of both genomic features (HLA LOH, CDKN2A/B loss) and microenvironmental features, including elevated myeloid expression, reduced peripheral T cell receptor (TCR) diversity, and putative neoantigen depletion. Hypothesizing that ITH may itself play a role in shaping ICI response, we derived a transcriptomic signature associated with neoantigen depletion that strongly associated with response to ICI and targeted therapy treatment in several independent clinical trial cohorts.ConclusionsThese results argue that genetic and immune heterogeneity jointly co-evolve and influence response to ICI in ccRCC. Our findings have implications for future biomarker development for ICI response across ccRCC and other solid tumors and highlight important features of tumor evolution under ICI treatment.Trial registrationThe study was registered on ClinicalTrial.gov (NCT02595918) on November 4, 2015.
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Project description:Here we performed 3’ transcriptome single cell sequencing on 7 ccRCC patients’ tumor and matched 5 adjacent normal samples. We identified two different functional subgroups in ccRCC tumor epithelial cells and two different gene transcriptional programs that were associated with survival across a TCGA cohort. We found important transcriptional factor regulons that played roles in ccRCC progression. We identified macrophage and T cell clusters whose higher infiltration in tumor was associated with overall survival. We also discovered some potentially targetable inhibitory interactions among tumor epithelial cells, the infiltrating T cell and myeloid compartments. Collectively, our data provided cell clusters and gene sets for predict clinical outcome and potential targets for ccRCC therapy.
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Project description:Inter- and intra-tumour molecular heterogeneity is increasingly recognized in clear cell renal cell carcinoma (ccRCC). It may partially explain the diversity of responses to targeted therapies and the various clinical outcomes. In this study, a 56-year-old male ccRCC patient with multiple metastases received radical nephrectomy and resection of the metastatic tumour in chest wall. The surgical specimens were implanted into nude mice to establish patient-derived xenograft (PDX) models with KI2367 model derived from the primary tumour and KI2368 model from the metastastic tumour. The two modles were treated with Sorafenib, Sunitinib, Axitinib, combined Sorafenib/Sunitinib, or alternating therapy of Sorafenib and Sunitinib. Significant anti-tumour activity was found in KI2367 treated with Sorafenib/Sunitinib monotherapy, combined Sorafenib/Sunitinib, and alternating therapy of Sorafenib/Sunitinib (P<0.05) but not in that treated with Axitinib monotherapy. In contrast, KI2368 was significantly responsive to Sunitinib monotherapy, combined Sorafenib/Sunitinib therapy and alternating therapy of Sorafenib/Sunitinib but not responsive to Sorafenib and Axitinib monotherapy (P<0.05). RNAseq of the two models demonstrated that the expression levels of 1,725 genes including the drug targeted genes of PDGFA, PDGFB and PDGFRA were >5-fold higher in KI2367 than in KI2368 and the expression levels of 994 genes were > 5-fold higher in KI2368 than in KI2367. These results suggest the presence of intra-tumour molecular heterogeneity in this patient. This heterogeneity may influence the response to targeted therapies. Multiple biopsy, liquid biopsy and genomic analysis of intra- tumour molecular heterogeneity may help guide a more precise and effective plan in selecting targeted therapies for ccRCC patients.
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Project description:Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer and has strong immunogenicity. A systematically investigation of the tumor microenvironment (TME) in ccRCC could contribute to help clinicians develop personalized treatment and facilitate clinical decision-making. In this study, we analyzed the immune-related subtype of ccRCC on the basis of immune-related gene expression data in The Cancer Genome Atlas (TCGA, N = 512) and E-MTAB-1980 (N = 101) dataset, respectively. As a result, two subtypes (C1 and C2) were identified by performing non-negative matrix factorization clustering. Subtype C1 was characterized by increased advance ccRCC cases and immune-related pathways. A higher immune score, stromal score, TMB value, Tumor Immune Dysfunction and Exclusion (TIDE) prediction score, and immune checkpoint genes expression level were also observed in C1. In addition, the C1 subtype might benefit from chemotherapy and immunotherapy. The patients in subtype C2 had more metabolism-related pathways, higher tumor purity, and a better prognosis. Moreover, some small molecular compounds for the treatment of ccRCC were identified between the two subtypes by using the Connectivity Map (CMap) database. Finally, we constructed and validated an immune-related (IR) score to evaluate immune modification individually. A high IR score corresponded to a favorable prognosis compared to a low IR score, while more advanced tumor stage and grade cases were enriched in the low IR score group. The two IR score groups also showed a distinct divergence among immune status, TME, and chemotherapy. The external validation dataset (E-MTAB-1980) and another immunotherapy cohort (IMvigor 210) demonstrated that patients in the high IR score group had a significantly prolonged survival time and clinical benefits compared to the low IR score group. Together, characterization of molecular heterogeneity and IR signature may help develop new insights into the TME of ccRCC and provide new strategies for personalized treatment.
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Project description:BackgroundThe tumor microenvironment (TME) performs a crucial function in the tumorigenesis and response to immunotherapies of clear cell renal cell carcinoma (ccRCC). However, a lack of recognized pre-clinical TME-based risk models poses a great challenge to investigating the risk factors correlated with prognosis and treatment responses for patients with ccRCC.MethodsStromal and immune contexture were assessed to calculate the TMErisk score of a large sample of patients with ccRCC from public and real-world cohorts using machine-learning algorithms. Next, analyses for prognostic efficacy, correlations with clinicopathological features, functional enrichment, immune cell distributions, DNA variations, immune response, and heterogeneity were performed and validated.ResultsClinical hub genes, including INAFM2, SRPX, DPYSL3, VSIG4, APLNR, FHL5, A2M, SLFN11, ADAMTS4, IFITM1, NOD2, CCR4, HLA-DQB2, and PLAUR, were identified and incorporated to develop the TMErisk signature. Patients in the TMEhigh risk group (category) exhibited a considerably grim prognosis, and the TMErisk model was shown to independently function as a risk indicator for the overall survival (OS) of ccRCC patients. Expression levels of immune checkpoint genes were substantially increased in TMEhigh risk group, while those of the human leukocyte antigen (HLA) family genes were prominently decreased. In addition, tumors in the TMEhigh group showed significantly high infiltration levels of tumor-infiltrated lymphocytes, including M2 macrophages, CD8+ T cells, B cells, and CD4+ T cells. In heterogeneity analysis, more frequent somatic mutations, including pro-tumorigenic BAP1 and PBRM1, were observed in the TMEhigh group. Importantly, 19.3% of patients receiving immunotherapies in the TMEhigh group achieved complete or partial response compared with those with immune tolerance in the TMElow group, suggesting that TMErisk prominently differentiates prognosis and responses to immunotherapy for patients with ccRCC.ConclusionsWe first established the TMErisk score of ccRCC using machine-learning algorithms based on a large-scale population. The TMErisk score can be utilized as an innovative independent prognosis predictive marker with high sensitivity and accuracy. Our discovery also predicted the efficacy of immunotherapy in ccRCC patients, indicating the intimate link between tumor immune microenvironment and intratumoral heterogeneity.
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Project description:BackgroundIncreasing evidence suggests a link between the enzyme cathepsin S (CTSS) and tumour development. However, the potential involvement and molecular functions of CTSS in clear cell renal cell carcinoma (ccRCC) remain unclear.MethodsWe downloaded original data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and integrated them using R. Kaplan-Meier plots of integrated expression scores were used to analyse survival outcomes. Additionally, we investigated mRNA expression, clinicopathological features, immune infiltrates, and single-cell sequencing analysis of CTSS in ccRCC. In vitro experiments were conducted with qRT-PCR and IHC staining.ResultsCTSS transcriptomic and proteomic levels were higher in ccRCC than in para-cancerous tissues. Low CTSS expression was correlated with poor prognosis in patients with ccRCC. Our data demonstrated that the expression of CTSS was strongly correlated with immune cell infiltration levels and gene markers of immune cells, chemokines, and receptors. Single-cell sequencing analysis demonstrated that CTSS expression was detectable in monocytes/macrophages. Finally, certain chemicals were confirmed to affect CTSS expression.ConclusionOur findings indicate that CTSS offers promise as a prognostic biomarker and novel immune-related therapeutic target for ccRCC.
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Project description:Clear cell renal cell carcinoma (ccRCC), a prevalent kidney cancer form characterised by its invasiveness and heterogeneity, presents challenges in late-stage prognosis and treatment outcomes. Programmed cell death mechanisms, crucial in eliminating cancer cells, offer substantial insights into malignant tumour diagnosis, treatment and prognosis. This study aims to provide a model based on 15 types of Programmed Cell Death-Related Genes (PCDRGs) for evaluating immune microenvironment and prognosis in ccRCC patients. ccRCC patients from the TCGA and arrayexpress cohorts were grouped based on PCDRGs. A combination model using Lasso and SuperPC was constructed to identify prognostic gene features. The arrayexpress cohort validated the model, confirming its robustness. Immune microenvironment analysis, facilitated by PCDRGs, employed various methods, including CIBERSORT. Drug sensitivity analysis guided clinical treatment decisions. Single-cell data enabled Programmed Cell Death-Related scoring, subsequent pseudo-temporal and cell-cell communication analyses. A PCDRGs signature was established using TCGA-KIRC data. External validation in the arrayexpress cohort underscored the model's superiority over traditional clinical features. Furthermore, our single-cell analysis unveiled the roles of PCDRG-based single-cell subgroups in ccRCC, both in pseudo-temporal progression and intercellular communication. Finally, we performed CCK-8 assay and other experiments to investigate csf2. In conclusion, these findings reveal that csf2 inhibit the growth, infiltration and movement of cells associated with renal clear cell carcinoma. This study introduces a PCDRGs prognostic model benefiting ccRCC patients while shedding light on the pivotal role of programmed cell death genes in shaping the immune microenvironment of ccRCC patients.
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Project description:PurposeWe aimed to develop and externally validate a CT-based deep learning radiomics model for predicting overall survival (OS) in clear cell renal cell carcinoma (ccRCC) patients, and investigate the association of radiomics with tumor heterogeneity and microenvironment.MethodsThe clinicopathological data and contrast-enhanced CT images of 512 ccRCC patients from three institutions were collected. A total of 3566 deep learning radiomics features were extracted from 3D regions of interest. We generated the deep learning radiomics score (DLRS), and validated this score using an external cohort from TCIA. Patients were divided into high and low-score groups by the DLRS. Sequencing data from the corresponding TCGA cohort were used to reveal the differences of tumor heterogeneity and microenvironment between different radiomics score groups. What's more, univariate and multivariate Cox regression were used to identify independent risk factors of poor OS after operation. A combined model was developed by incorporating the DLRS and clinicopathological features. The SHapley Additive exPlanation method was used for interpretation of predictive results.ResultsAt multivariate Cox regression analysis, the DLRS was identified as an independent risk factor of poor OS. The genomic landscape of different radiomics score groups was investigated. The heterogeneity of tumor cell and tumor microenvironment significantly varied between both groups. In the test cohort, the combined model had a great predictive performance, with AUCs (95%CI) for 1, 3 and 5-year OS of 0.879(0.868-0.931), 0.854(0.819-0.899) and 0.831(0.813-0.868), respectively. There was a significant difference in survival time between different groups stratified by the combined model. This model showed great discrimination and calibration, outperforming the existing prognostic models (all p values < 0.05).ConclusionThe combined model allowed for the prognostic prediction of ccRCC patients by incorporating the DLRS and significant clinicopathologic features. The radiomics features could reflect the tumor heterogeneity and microenvironment.
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