Functional enrichment analysis of LYSET and identification of related hub gene signatures as novel biomarkers to predict prognosis and immune infiltration status of clear cell renal cell carcinoma.
The latest research shows that the lysosomal enzyme trafficking factor (LYSET) encoded by TMEM251 is a key regulator of the amino acid metabolism reprogramming (AAMR) and related pathways significantly correlate with the progression of some tumors. The purpose of this study was to explore the potential pathways of the TMEM251 in clear cell renal cell carcinoma (ccRCC) and establish related predictive models based on the hub genes in these pathways for prognosis and tumor immune microenvironment (TIME).
We obtained mRNA expression data and clinical information of ccRCC samples from The Cancer Genome Atlas (TCGA), E-MATE-1980, and immunotherapy cohorts. Single-cell sequencing data (GSE152938) were downloaded from the Gene Expression Omnibus (GEO) database. We explored biological pathways of the LYSET by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of TMEM251-coexpression genes. The correlation of LYSET-related pathways with the prognosis was conducted by Gene Set Variation Analysis (GSVA) and unsupervised cluster analysis. The least absolute shrinkage and selection operator (LASSO) and Cox regression were used to identify hub prognostic genes and construct the risk score. Immune infiltration analysis was conducted by CIBERSORTx and Tumor Immune Estimation Resource (TIMER) databases. The predictive value of the risk score and hub prognostic genes on immunotherapy responsiveness was analyzed through the tumor mutation burden (TMB) score, immune checkpoint expression, and survival analysis. Immunohistochemistry (IHC) was finally used to verify the expressions of hub prognostic genes.
The TMEM251 was found to be significantly correlated with some AAMR pathways. AAGAB, ENTR1, SCYL2, and WDR72 in LYSET-related pathways were finally identified to construct a risk score model. Immune infiltration analysis showed that LYSET-related gene signatures significantly influenced the infiltration of some vital immune cells such as CD4 + cells, NK cells, M2 macrophages, and so on. In addition, the constructed risk score was found to be positively correlated with TMB and some common immune checkpoint expressions. Different predictive values of these signatures for Nivolumab therapy responsiveness were also uncovered in immunotherapy cohorts. Finally, based on single-cell sequencing analysis, the TMEM251 and the hub gene signatures were found to be expressed in tumor cells and some immune cells. Interestingly, IHC verification showed a potential dual role of four hub genes in ccRCC progression.
The novel predictive biomarkers we built may benefit clinical decision-making for ccRCC. Our study may provide some evidence that LYSET-related gene signatures could be novel potential targets for treating ccRCC and improving immunotherapy efficacy. Our nomogram might be beneficial to clinical choices, but the results need more experimental verifications in the future.
Chen Y
，He J
，Jin T
，Zhang Y
，Ou Y
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The Significance of PARP1 as a biomarker for Predicting the Response to PD-L1 Blockade in Patients with PBRM1-mutated Clear Cell Renal Cell Carcinoma.
Immune checkpoint inhibitors (ICIs) have become key agents in the management of clear cell renal cell carcinoma (ccRCC), but their benefits are limited and responders remain unidentified. We investigated the significance of PARP1 in ccRCC using RNA sequencing data for 311 tumors from patients enrolled in prospective clinical trials of PD-1 blockade. Among patients treated with nivolumab (n = 181), overall survival (OS) was significantly higher in the PARP1-low group than in the PARP1-high group (p = 0.006), and PARP1 status was significantly associated with OS (hazard ratio [HR] 1.7; p = 0.007). By contrast, for patients treated with everolimus (n = 130) there was no significant difference by PARP1 status for progression-free survival (PFS; p = 0.9) or OS (p = 0.38). In subgroup analysis for PBRM1-mutated ccRCC, PFS (p = 0.016) and OS (p = 0.004) were significantly longer in the group with PARP1-low status and PBRM1 mutation in comparison to the other groups. In addition, PARP1 status was significantly associated with PFS (HR 2.6; p = 0.007) and OS (HR 3.5; p = 0.016) among patients with PBRM1-mutated ccRCC treated with nivolumab. Our study suggests that PARP1 can be used as a biomarker for predicting response to ICI treatment for patients with PBRM1-mutated ccRCC. PATIENT SUMMARY: Immune checkpoint inhibitors (ICIs) are key agents in the treatment of multiple cancers. We found that expression of the PARP1 protein was associated with survival after ICI treatment and with the response to ICI treatment in patients with clear cell kidney cancer who have a mutation of the PBRM1 gene.
Hagiwara M
，Fushimi A
，Matsumoto K
，Oya M
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Genomically annotated risk model for advanced renal-cell carcinoma: a retrospective cohort study.
The Memorial Sloan Kettering Cancer Center (MSKCC) risk model is an established prognostic tool for metastatic renal-cell carcinoma that integrates clinical and laboratory data, but is agnostic to tumour genomics. Several mutations, including BAP1 and PBRM1, have prognostic value in renal-cell carcinoma. Using two independent clinical trial datasets of patients with metastatic renal-cell carcinoma, we aimed to study whether the addition of the mutation status for several candidate prognostic genes to the MSKCC model could improve the model's prognostic performance.
In this retrospective cohort study, we used available formalin-fixed paraffin-embedded tumour tissue and clinical outcome data from patients with metastatic renal-cell carcinoma assigned to treatment with tyrosine kinase inhibitors in the COMPARZ trial (training cohort; n=357) and RECORD-3 trial (validation cohort; n=258). Eligible patients in both trials were treatment-naive; had histologically confirmed, advanced, or metastatic renal-cell carcinoma; and a Karnofsky performance status score of at least 70. For each cohort, data from patients in all treatment groups (sunitinib and pazopanib in the training cohort, and everolimus and sunitinib in the validation cohort) were pooled for this analysis. In the training cohort, tumour tissue was used to evaluate somatic mutations by next-generation sequencing, and the association between cancer-specific outcomes (overall survival, progression-free survival, and overall response) and the mutation status of six genes of interest (BAP1, PBRM1, TP53, TERT, KDM5C, and SETD2) was tested. Only those genes with prognostic value in this setting were added to the MSKCC risk model to create a genomically annotated version. The validation cohort was used to independently test the prognostic value of the annotated model compared with the original MSKCC risk model.
357 (32%) of 1110 patients assigned to protocol treatment in the COMPARZ study between August, 2008, and September, 2011, were evaluable for mutation status and clinical outcomes in the training cohort. The independent validation cohort included 258 (55%) of 471 evaluable patients, enrolled between October, 2009, and June, 2011, on the RECORD-3 study. In the training cohort, the presence of any mutation in BAP1 or TP53, or both, and absence of any mutation in PBRM1 were prognostic in terms of overall survival (TP53wt/BAP1mut, TP53mut/BAP1wt o TP53mut/BAP1mut vs TP53wt/BAP1wt hazard ratio [HR] 1·57, 95% CI 1·21-2·04; p=0·0008; PBRM1wt vs PBRMmut, HR 1·58, 1·16-2·14; p=0·0035). The mutation status for these three prognostic genes were added to the original MSKCC risk model to create a genomically annotated version. Distribution of participants in the training cohort into the three risk groups of the original MSKCC model changed from 87 (24%) of 357 patients deemed at favourable risk, 217 (61%) at intermediate risk, and 53 (15%) at poor risk, to distribution across four risk groups in the genomically annotated risk model, with 36 (10%) of 357 deemed at favourable risk, 77 (22%) at good risk, 108 (30%) at intermediate risk, and 136 (38%) at poor risk. Addition of genomic information improved model performance for predicting overall survival (C-index: original model, 0·595 [95% CI 0·557-0·634] vs new model, 0·637 [0·595-0·679]) and progression-free survival (0·567 [95% CI 0·529-0·604] vs 0·602 [0·560-0·643]) with adequate discrimination of the proportion of patients who achieved an objective response (Cochran-Armitage one-sided p=0·0014). Analyses in the validation cohort confirmed the superiority of the genomically annotated risk model over the original version.
The mutation status of BAP1, PBRM1, and TP53 has independent prognostic value in patients with advanced or metastatic renal-cell carcinoma treated with first-line tyrosine kinase inhibitors. Improved stratification of patients across risk groups by use of a genomically annotated model including the mutational status of these three genes warrants further investigation in prospective trials and could be of use as a model to stratify patients with metastatic renal-cell carcinoma in clinical trials.
Novartis Pharmaceuticals Corporation, MSKCC Support Grant/Core Grant, and the J Randall & Kathleen L MacDonald Research Fund.
Voss MH
，Reising A
，Cheng Y
，Patel P
，Marker M
，Kuo F
，Chan TA
，Choueiri TK
，Hsieh JJ
，Hakimi AA
，Motzer RJ
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