The unique genomic landscape and prognostic mutational signature of Chinese clear cell renal cell carcinoma

Background:The genomic background affects the occurrence and metastasis of cancers,including clear cell renal cell carcinoma(ccRCC).However,reports focusing on the prognostic mutational signature of Chinese ccRCC are lacking.Methods:Overall,929 patients,including a training cohort with Chinese patients(n=201),a testing cohort with Caucasian patients(n=274),and a validation cohort(n=454)were analyzed for the genomic landscape of ccRCC.Then,machine-learning algorithms were used to identify and evaluate the genomic mutational signature(GMS)in ccRCC.Analyses for prognosis,immune microenvironment,association with independent clinicopathological features,and predictive responses for immune checkpoint therapies(ICTs)were performed.Results:The DNA variation data of 929 patients with ccRCC suggested markedly differential genomic mutational frequency of the most frequent genes,such as VHL,PBRM1,BAP1,SETD2,and KDM5C between the Chinese and Caucasian populations.PBRM1 showed significant co-occurrence with VHL and SETD2.We then successfully iden-tified a seven-gene mutational signature(GMS^(Mut))that included mutations in FBN1,SHPRH,CELSR1,COL6A6,DST,ABCA13,and BAP1.The GMS^(Mut)significantly predicted progressive progression(P<0.0001,HR=2.81)and poor prognosis(P<0.0001,HR=3.89)in the Chinese training cohort.Moreover,ccRCC patients with the GMS^(Mut)had poor survival rates in the testing cohort(P=0.020)and poor outcomes were predicted for those treated with ICTs in the validation cohort(P=0.036).Interestingly,a favorable clinical response to ICTs,ele-vated expression of immune checkpoints,and increased abundance of tumor-infiltrated lymphocytes,specifically CD8^(+)T cells,Tregs,and macrophages,were observed in the GMS^(Mut)cluster.Conclusions:This study described the pro-tumorigenic GMS^(Mut)cluster that improved the prognostic accuracy in Chinese patients with ccRCC.Our discovery of the novel independent prognostic signature highlights the relationship between tumor phenotype and genomic mutational characteristics of ccRCC.

Tumor microenvironment-based signatures distinguish intratumoral heterogeneity, prognosis, and immunogenomic features of clear cell renal cell carcinoma

Background:The 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.Methods:Stromal 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 distribu-tions,DNA variations,immune response,and heterogeneity were performed and validated.Results:Clinical 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 TME high 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 TME high risk group,while those of the human leukocyte antigen(HLA)family genes were prominently decreased.In addition,tumors in the TME high 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 TME high group.Importantly,19.3%of patients receiving immunotherapies in the TME high group achieved complete or partial response compared with those with immune tolerance in the TME low group,suggesting that TMErisk prominently differentiates prognosis and responses to immunotherapy for patients with ccRCC.Conclusions:We 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.

Systematic Genome-Wide Profiles Reveal Alternative Splicing Landscape and Implications of Splicing Regulator DExD-Box Helicase 21 in Aggressive Progression of Adrenocortical Carcinoma

Alternative splicing(AS)in the tumor biological process has provided a novel perspective on carcinogenesis.However,the clinical significance of individual AS patterns of adrenocortical carcinoma(ACC)has been underestimated,and in-depth investigations are lacking.We selected 76 ACC samples from the Cancer Genome Atlas(TCGA)SpliceSeq and SpliceAid2 databases,and 39 ACC samples from Fudan University Shanghai Cancer Center(FUSCC).Prognosis-related AS events(PASEs)and survival analysis were evaluated based on prediction models constructed by machine-learning algorithm.In total,23,984 AS events and 3,614 PASEs were detected in the patients with ACC.The predicted risk score of each patient suggested that eight PASEs groups were significantly correlated with the clinical outcomes of these patients(p<0.001).Prognostic models produced AUC values of 0.907 in all PASEs’groups.Eight splicing factors(SFs),including BAG2,CXorf56,DExD-Box Helicase 21(DDX21),HSPB1,MBNL3,MSI1,RBMXL2,and SEC31B,were identified in regulatory networks of ACC.DDX21 was identified and validated as a novel clinical promoter and therapeutic target in 115 patients with ACC from TCGA and FUSCC cohorts.In conclusion,the strict standards used in this study ensured the systematic discovery of profiles of AS events using genome-wide cohorts.Our findings contribute to a comprehensive understanding of the landscape and underlying mechanism of AS,providing valuable insights into the potential usages of DDX21 for predict-ing prognosis for patients with ACC.