Development of an m6A subtype classifier to guide precision therapy for patients with bladder cancer
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ABSTRACT:
SUBMITTER: Zhang G
PROVIDER: S-EPMC11375535 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundBladder cancer (BLCA) is one of the most prevalent urinary system malignancies, with high mortality and recurrence. The present study aimed to identify potential tumor antigens for mRNA vaccines in BLCA and patient subtypes suitable for different immunotherapy.MethodsGene expression profiles, mutation data, methylation data, and corresponding clinical information were obtained from the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress databases. Immunohistochemical staining of microarrays was performed to assess protein expression levels of IGF2BP2 and MMP9. Differential gene analysis, survival analysis, correlation analysis, consensus clustering analysis, and immune cell infiltration analysis were conducted using R software. Finally, the R package "immcluster" was used based on Combat and eXtreme Gradient Boosting algorithms to predict immune clusters of BLCA samples.ResultsTwo mutated, amplified, and over-expressed tumor antigens, IGF2BP2 and MMP9, were found to be associated with clinical outcomes and the abundance of antigen-presenting cells (APCs). Subsequently, three immune subtypes (BIS1, BIS2, and BIS3) were defined in the BLCA cohort. BIS3 subtype exhibited an "active" immune phenotype, while BIS1 and BIS2 subtypes have a "suppressive" immune phenotype. Patients in BIS1 and BIS2 had a poor prognosis compared to BIS3. BIS3 had a higher score in checkpoints or immunomodulators (CP) and immunophenoscore (IPS), while BIS1 and BIS2 scored higher in major histocompatibility complex-related molecules (MHC molecules). Meanwhile, BIS2 and BIS3 had a significantly higher tumor mutational burden (TMB) compared to patients with BIS1. Finally, the "immcluster" package was applied to the dataset, which has been shown to accurately predict the immune subtypes of BLCA samples in many cohorts.ConclusionsIGF2BP2 and MMP9 were potential antigens for developing mRNA vaccines against BLCA. The results in the present study suggested that immunotherapy targeting these two antigens would be suitable for patients falling under the BIS2 subtype. R package "immcluster" could assist in screening suitable BLCA patients for antitumor therapy.
| S-EPMC9769457 | biostudies-literature
Project description:BackgroundRecent molecular characterization of urothelial cancer (UC) has suggested potential pathways in which to direct treatment, leading to a host of targeted therapies in development for UC. In parallel, gene expression profiling has demonstrated that high-grade UC is a heterogeneous disease. Prognostic basal-like and luminal-like subtypes have been identified and an accurate transcriptome BASE47 classifier has been developed. However, these phenotypes cannot be broadly investigated due to the lack of a clinically viable diagnostic assay. We sought to develop and evaluate a diagnostic classifier of UC subtype with the goal of accurate classification from clinically available specimens.MethodsTumor samples from 52 patients with high-grade UC were profiled for BASE47 genes concurrently by RNAseq as well as NanoString. After design and technical validation of a BASE47 NanoString probeset, results from the RNAseq and NanoString were used to translate diagnostic criteria to the Nanostring platform. Evaluation of repeatability and accuracy was performed to derive a final Nanostring based classifier. Diagnostic classification resulting from the NanoString BASE47 classifier was validated on an independent dataset (n = 30). The training and validation datasets accurately classified 87% and 93% of samples, respectively.ResultsHere we have derived a NanoString-platform BASE47 classifier that accurately predicts basal-like and luminal-like subtypes in high grade urothelial cancer. We have further validated our new NanoString BASE47 classifier on an independent dataset and confirmed high accuracy when compared with our original Transcriptome BASE47 classifier.ConclusionsThe NanoString BASE47 classifier provides a faster turnaround time, a lower cost per sample to process, and maintains the accuracy of the original subtype classifier for better clinical implementation.
| S-EPMC7745986 | biostudies-literature
Project description:Purpose: We sought to develop and evaluate a diagnostic classifier of UC subtype with the goal of accurate classification from clinically available specimens. Methods: Tumor samples from 52 patients with high-grade UC were profiled for BASE47 genes concurrently by RNAseq as well as NanoString. After design and technical validation of a BASE47 NanoString probeset, results from the RNAseq and NanoString were used to translate diagnostic criteria to the Nanostring platform. Evaluation of repeatability and accuracy was performed to derive a final Nanostring based classifier. Diagnostic classification resulting from the NanoString BASE47 classifier was validated on an independent dataset (n=63). The training and validation datasets accurately classified 87% and 93% of samples, respectively. Results: We have derived a NanoString-platform BASE47 classifier that accurately predicts basal-like and luminal-like subtypes in high grade urothelial cancer. We have further validated our new NanoString BASE47 classifier on an independent dataset and confirmed high accuracy when compared with our original Transcriptome BASE47 classifier. Conclusions: The NanoString BASE47 classifier provides a faster turnaround time, a lower cost per sample to process, and maintains the accuracy of the original subtype classifier for better clinical implementation.
2020-11-03 | GSE160693 | GEO
Project description:BackgroundBladder cancer is one of the most common malignancies of the urinary system with an unfavorable prognosis. More and more studies have suggested that lipid metabolism could influence the progression and treatment of tumors. However, there are few studies exploring the relationship between lipid metabolism and bladder cancer. This study aimed to explore the roles that lipid metabolism-related genes play in patients with bladder cancer.MethodsTCGA_BLCA cohort and GSE13507 cohort were included in this study, and transcriptional and somatic mutation profiles of 309 lipid metabolism-related genes were analyzed to discover the critical lipid metabolism-related genes in the incurrence and progression of bladder cancer. Furthermore, the TCGA_BLCA cohort was randomly divided into training set and validation set, and the GSE13507 cohort was served as an external independent validation set. We performed the LASSO regression and multivariate Cox regression in training set to develop a prognostic signature and further verified this signature in TCGA_BLCA validation set and GSE13507 external validation set. Finally, we systematically investigated the association between this signature and tumor microenvironment, drug response, and potential functions and then verified the differential expression status of signature genes in the protein level by immunohistochemistry.ResultsA novel 6-lipidmetabolism-related gene signature was identified and validated, and this risk score model could predict the prognosis of patients with bladder cancer. In addition, the prognostic model was tightly related to immune cell infiltration and tumor mutation burden. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) showed that mTOR signaling pathway, G2M checkpoint, fatty acid metabolism, and hypoxia were enriched in patients in the high-risk score groups. Furthermore, 3 therapies specific for bladder cancer patients in different risk scores were identified.Conclusions. In conclusion, we investigated the lipid metabolism-related genes in bladder cancer through comprehensive bioinformatic analysis. A novel 6-gene signature associated with lipid metabolism for predicting the outcomes of patients with bladder cancer was conducted and validated. Furthermore, the risk score model could be utilized to indicate the choice of therapy in bladder cancer.
| S-EPMC9484922 | biostudies-literature
Project description:BackgroundBladder cancer (BLCA) is notably associated with advanced age, characterized by its high incidence and mortality among the elderly. Despite promising advancements in models that amalgamate molecular subtypes with treatment and prognostic outcomes, the considerable heterogeneity in BLCA poses challenges to their universal applicability. Consequently, there is an urgent need to develop a new molecular subtyping system focusing on a critical clinical feature of BLCA: senescence.MethodsUtilizing unsupervised clustering on the Cancer Genome Atlas Program (TCGA)-BLCA cohort, we crafted a senescence-associated molecular classification and precision quantification system (Senescore). This method underwent systematic validation against established molecular subtypes, treatment strategies, clinical outcomes, the immune tumor microenvironment (TME), relevance to immune checkpoints, and identification of potential therapeutic targets.ResultsExternal validations were conducted using the Xiangya cohort, IMvigor210 cohort, and meta-cohort, with multiplex immunofluorescence confirming the correlation between Senescore, immune infiltration, and cellular senescence. Notably, patients categorized within higher Senescore group were predisposed to the basal subtype, showcased augmented immune infiltration, harbored elevated driver gene mutations, and exhibited increased senescence-associated secretory phenotype (SASP) factors expression in the transcriptome. Despite poorer prognoses, these patients revealed greater responsiveness to immunotherapy and neoadjuvant chemotherapy.ConclusionsOur molecular subtyping and Senescore, informed by age-related clinical features, accurately depict age-associated biological traits and its clinical application potential in BLCA. Moreover, this personalized assessment framework is poised to identify senolysis targets unique to BLCA, furthering the integration of aging research into therapeutic strategies.
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Project description:There are no predictive biomarkers in clinical use for the neoadjuvant treatment of bladder cancer. Here we report on a recent randomized phase 2 trial validating the identification of predictive biomarkers using cell lines in the absence of patient response data.
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Project description:The hallmark of precision medicine involves tailoring the treatment to the patient and/or tumor-specific biomarkers. Candidate biomarkers in bladder cancer are abundant, but few have been validated in clinical practice. Significant obstacles to precision medicine in bladder cancer include the limited predictive value of candidate biomarkers, lack of standardization in biomarker assessment, heterogeneity in biomarker expression and function, and limited insight into the biologic factors that influence biomarker expression and predictive capacity. This review summarizes key biomarkers explored in bladder cancer and outlines innovative trial designs to approach these obstacles.
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Project description:BackgroundDepicting the heterogeneity and functional characteristics of the tumor microenvironment (TME) is necessary to achieve precision medicine for bladder cancer (BLCA). Although classical molecular subtypes effectively reflect TME heterogeneity and characteristics, their clinical application is limited by several issues.MethodsIn this study, we integrated the Xiangya cohort and multiple external BLCA cohorts to develop a novel 5-methylcytosine (5mC) regulator-mediated molecular subtype system and a corresponding quantitative indicator, the 5mC score. Unsupervised clustering was performed to identify novel 5mC regulator-mediated molecular subtypes. The principal component analysis was applied to calculate the 5mC score. Then, we correlated the 5mC clusters (5mC score) with classical molecular subtypes, immunophenotypes, clinical outcomes, and therapeutic opportunities in BLCA. Finally, we performed pancancer analyses on the 5mC score.ResultsTwo 5mC clusters, including 5mC cluster 1 and cluster 2, were identified. These novel 5mC clusters (5mC score) could accurately predict classical molecular subtypes, immunophenotypes, prognosis, and therapeutic opportunities of BLCA. 5mC cluster 1 (high 5mC score) indicated a luminal subtype and noninflamed phenotype, characterized by lower anticancer immunity but better prognosis. Moreover, 5mC cluster 1 (high 5mC score) predicted low sensitivity to cancer immunotherapy, neoadjuvant chemotherapy, and radiotherapy, but high sensitivity to antiangiogenic therapy and targeted therapies, such as blocking the β-catenin, FGFR3, and PPAR-γ pathways.ConclusionsThe novel 5mC regulator-based subtype system reflects many aspects of BLCA biology and provides new insights into precision medicine in BLCA. Furthermore, the 5mC score may be a generalizable predictor of immunotherapy response and prognosis in pancancers.
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Project description:We developed preclinical PDX models, recapitulating the molecular heterogeneity of MIBCs and UTUC, which will represent an essential tool in therapy development. Pharmacological characterization of the PDXs suggested that upper urinary tract and bladder cancers (UCC/ SCC) with similar molecular characteristics could benefit from the same treatments, and showed a benefit for combined FGFR/EGFR inhibition in FGFR3-mutant PDXs, compared to FGFR inhibition alone.
2022-08-31 | GSE181962 | GEO
Project description:Transcriptional profiling of muscle-invasive bladder cancer (MIBC) using RNA sequencing (RNA-seq) technology has demonstrated the existence of intrinsic basal and luminal molecular subtypes that vary in their prognosis and response to therapy. However, routine use of RNA-seq in a clinical setting is restricted by cost and technical difficulties. Herein, we provide a single-sample NanoString-based seven-gene (KRT5, KRT6C, SERPINB13, UPK1A, UPK2, UPK3A and KRT20) MIBC molecular classifier that assigns a luminal and basal molecular subtype. The classifier was developed in a series of 138 chemotherapy naïve MIBCs split into training (70%) and testing (30%) datasets. Further, we validated the previously published CK5/6 and GATA3 immunohistochemical classifier which showed high concordance of 96.9% with the NanoString-based gene expression classifier. Immunohistochemistry-based molecular subtypes significantly correlated with recurrence-free survival (RFS) and disease-specific survival (DSS) in univariable (p = 0.006 and p = 0.011, respectively) and multivariate cox regression analysis for DSS (p = 0.032). Used sequentially, the immunohistochemical- and NanoString-based classifiers provide faster turnaround time, lower cost per sample and simpler data analysis for ease of clinical implementation in routine diagnostics.
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