Dataset Information

Identification of an endogenous retroviral signature to predict anti-PD1 response in advanced clear cell renal cell carcinoma: an integrated analysis of three clinical trials.

ABSTRACT:

Background

Endogenous retrovirus (ERV) elements are genomic footprints of ancestral retroviral infections within the human genome. Previous studies have demonstrated that dysregulated ERV transcription level is associated with immune cell infiltration in cancers, but the association between ERV expression and programmed cell death protein 1 (PD-1) blockade response is currently unraveled for solid cancers, such as advanced clear cell renal cell carcinoma (ccRCC).

Methods

ERV mRNA profiles were obtained from three clinical trials of ccRCC where the patients were treated with anti-PD-1 (CM-009, CM-010, CM-025, and TCGA-KIRC data). Patients treated with nivolumab were divided into training and test cohort, while the TCGA-KIRC cohort was used as an external validation. Univariate Cox regression analysis and least absolute shrinkage and selection operator regression were used to establish the signature. Immune cell infiltration analysis and gene set enrichment analysis were performed to explore potential biological mechanisms.

Results

An ERV signature was established based on nine ERV expression patterns. In the training cohort, the median overall survival in the low- and high-risk group was 45.2 and 19.6 months [hazard ratio (HR) = 0.49, 0.32-0.75, p < 0.001], respectively. The results were confirmed in the test (HR = 0.41, 0.20-0.83, p = 0.013), and in the TCGA-KIRC cohort (HR = 0.55, 0.34-0.90, p = 0.017). Moreover, in the CM-025 cohort, the low-risk group that received nivolumab had a more favorable survival compared with those that received the mTOR inhibitor everolimus, while no significant differences were observed in the high-risk group. CD8+ T cells were enriched in the low-risk group, while immune suppressive pathways were suppressed.

Conclusion

The newly identified ERV signature is not only a prognostic, but also a predictive biomarker for advanced ccRCC patients who received anti-PD-1 therapy, which can guide personalized treatment in cancer patients in the future.

SUBMITTER: Zhou JG

PROVIDER: S-EPMC10442641 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Identification of an endogenous retroviral signature to predict anti-PD1 response in advanced clear cell renal cell carcinoma: an integrated analysis of three clinical trials.

Zhou Jian-Guo JG Zeng Yu Y Wang Haitao H Jin Su-Han SH Wang Yun-Jia YJ He Sisi S Frey Benjamin B Fietkau Rainer R Hecht Markus M Ma Hu H Zhang Wenchuan W Gaipl Udo S US

Therapeutic advances in medical oncology 20220924

<h4>Background</h4>Endogenous retrovirus (ERV) elements are genomic footprints of ancestral retroviral infections within the human genome. Previous studies have demonstrated that dysregulated ERV transcription level is associated with immune cell infiltration in cancers, but the association between ERV expression and programmed cell death protein 1 (PD-1) blockade response is currently unraveled for solid cancers, such as advanced clear cell renal cell carcinoma (ccRCC).<h4>Methods</h4>ERV mRNA ...[more]

PMID: 37614979

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Project description:Background: Amino acid metabolism (AAM) deregulation, an emerging metabolic hallmark of malignancy, plays an essential role in tumour proliferation, invasion, and metastasis. However, the expression of AAM-related genes and their correlation with prognosis in clear cell renal cell carcinoma (ccRCC) remain elusive. This study aims to develop a novel consensus signature based on the AAM-related genes. Methods: The RNA-seq expression data and clinical information for ccRCC were downloaded from the TCGA (KIRC as training dataset) and ArrayExpress (E-MTAB-1980 as validation dataset) databases. The AAM-related differentially expressed genes were screened via the "limma" package in TCGA cohorts for further analysis. The machine learning algorithms (Lasso and stepwise Cox (direction = both)) were then utilised to establish a novel consensus signature in TCGA cohorts, which was validated by the E-MTAB-1980 cohorts. The optimal cutoff value determined by the "survminer" package was used to categorise patients into two risk categories. The Kaplan-Meier curve, the receiver operating characteristic (ROC) curve, and multivariate Cox regression were utilised to evaluate the prognostic value. The nomogram based on the gene signature was constructed, and its performance was analysed using ROC and calibration curves. Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis were conducted on its potential mechanisms. The relationship between the gene signature and key immune checkpoint, N6-methyladenosine (m6A)-related genes, and sensitivity to chemotherapy was assessed. Results: A novel consensus AMM-related gene signature consisting of IYD, NNMT, ACADSB, GLDC, and PSAT1 is developed to predict prognosis in TCGA cohorts. Kaplan-Meier survival shows that overall survival in the high-risk group was more dismal than in the low-risk group in the TCGA cohort, validated by the E-MTAB-1980 cohort. Multivariate regression analysis also demonstrates that the gene signature is an independent predictor of ccRCC. Immune infiltration analysis highlighted that the high-risk group indicates an immunosuppressive microenvironment. It is also closely related to the level of key immune checkpoints, m6A modification, and sensitivity to chemotherapy drugs. Conclusion: In this study, a novel consensus AAM-related gene signature is developed and validated as an independent predictor to robustly predict the overall survival from ccRCC, which would further improve the clinical outcomes.

Project description:Background Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancers. As cuproptosis, a new cell death mechanism proposed recently, differs from all other known mechanisms regulating cell death, we aimed to create prognostic markers using cuproptosis-related long non-coding ribonucleic acids (RNAs; lncRNAs) and elucidate the molecular mechanism. Methods Data from transcriptome RNA sequencing of ccRCC samples and the relevant clinical data were downloaded from The Cancer Genome Atlas, and Pearson's correlation analysis was implemented to obtain the cuproptosis-related lncRNAs. Then, univariate Cox, multivariate Cox, and Least Absolute Shrinkage and Selection Operator Cox analyses were performed to construct the risk signatures. The cuproptosis-related lncRNAs predictive signature was evaluated with receiver operating characteristic curves and subgroup analysis. Finally, Gene Set Enrichment Analysis (GSEA), single-sample GSEA (ssGSEA), tumor immune microenvironment (TIME), and immune checkpoints were performed to explore the relationship between immunity and patient prognosis. Results Five cuproptosis-related lncRNAs, including FOXD2-AS1, LINC00460, AC091212.1, AC007365.1, and AC026401.3, were used to construct the signature. In the training and test sets, low-risk groups (as identified by a risk score lower than the median) demonstrated a better prognosis with an area under the curve for 1-, 3-, and 5-year survival being 0.793, 0.716, and 0.719, respectively. GSEA analysis suggested significant enrichment of the tricarboxylic acid cycle and metabolism-related pathways in the low-risk group. Besides, both ssGSEA and TIME suggested that the high-risk group exhibited more active immune infiltration. Conclusion We proposed a cuproptosis-related lncRNAs signature, which had the potential for prognoses and prediction. Our findings might contribute to elucidating potential genomic biomarkers and targets for future therapies in the cuproptosis-related signaling pathways.

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Dataset Information

Identification of an endogenous retroviral signature to predict anti-PD1 response in advanced clear cell renal cell carcinoma: an integrated analysis of three clinical trials.

Background

Methods

Results

Conclusion

Publications

Identification of an endogenous retroviral signature to predict anti-PD1 response in advanced clear cell renal cell carcinoma: an integrated analysis of three clinical trials.

Similar Datasets

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