Verification of Molecular Subtyping of Bladder Cancer in the GUSTO Clinical Trial
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PROVIDER: PRJNA1066227 | ENA |
REPOSITORIES: ENA
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Project description:The GUSTO clinical trial (Gene expression subtypes of Urothelial carcinoma: Stratified Treatment and Oncological outcomes) uses molecular subtypes to guide neoadjuvant therapies in participants with muscle-invasive bladder cancer (MIBC). Before commencing the GUSTO trial, we needed to determine the reliability of a commercial subtyping platform (Decipher Bladder; Veracyte) when performed in an external trial laboratory as this has not been done previously. Here we report our pre-trial verification of the TCGA molecular subtyping model using gene expression profiling. Formalin fixed paraffin embedded tissue blocks of MIBC were used for gene expression subtyping by gene expression microarrays. Intra- and inter-laboratory technical reproducibility, together with quality control of laboratory and bioinformatics processes were assessed. Eighteen samples underwent analysis. RNA of sufficient quality and quantity was successfully extracted from all samples. All subtypes were represented in the cohort. Each sample was subtyped twice in our laboratory and once in a separate reference laboratory. No clinically significant discordance in subtype occurred between intra- or inter-laboratory replicates. Examination of sample histopathology showed variability of morphological appearances within and between subtypes. Overall, these results show that molecular subtyping by gene expression profiling is reproducible, robust, and suitable for use in the GUSTO clinical trial.
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Project description:Bladder cancer exhibits molecular heterogeneity that complicates early diagnosis and prognosis, and drives confounding clinical outcomes. Non–muscle invasive and muscle-invasive subtypes, especially for intermediate to high grade, carry a 25 – 50% progression-free survival rate, underscoring the need for high precision prognostic strategy. Urinary extracellular vesicles (uEVs) are promising carriers of tumor-derived RNAs and proteins. However, significant challenges in studying uEVs arise from the diverse cellular origin of uEVs associated with the dynamic composition of urine, which presents roadblocks for developing the clinical utility of uEVs. We introduced an AI-driven EV liquid biopsy pipeline that integrates (1) standardized EV isolation via NanoPom magnetic beads, (2) transcriptomic profiling for molecular subtyping, and (3) prognostic scoring algorithm. In a discovery cohort of 16 bladder cancer patients including both MIBC and NMIBC, we compared NanoPom isolated uEVs with ExoEasy and Fujifilm MagCapture isolated uEVs, for identifying bladder cancer subtype-specific gene signatures, and externally validated them using UCSC Xena. The result outperformed currently reported bladder cancer diagnostic biomarkers from assays including Galeas, CxBladder, and Xpert. In a validation cohort of matched 7 patient plasma samples, we confirmed with plasma derived EVs for correlating with urinary EV biomarkers from NGS sequencing. The prognostic score stratified patients into low-, intermediate-, and high-grade risk groups based on Xena's bladder cancer survival outcomes. Our AI-driven uEV liquid biopsy pipeline proves the concept for high precision bladder cancer subtyping and prognosis, which could potentially facilitate treatment decision and lead to advanced profiling of bladder tumor biology using uEV liquid biopsy.
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Project description:RNASeq profiling of bladder carcinomas from different histologies (urothelial carcinomas or squamous cell carcinomas) which were collected at different moments of the Neodurvarib-related clinical procedure (pretreatment ("SCR") or post-treatment ("Q")). The main goals of the screening were 1) to define potential predictive/prognostic biomarkers (genes or molecular pathways) through the comparison of diagnostic samples ("SCR") according to the clinical response observed in the patients under study; ii) to evaluate the molecular changes observed in the tumors under study by comparing the profiles of diagnostic samples ("SCR") versus cystectomies ("Q"), which would be a direct consequence of the neoadjuvant therapy used in this clinical trial.
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Project description:Molecular subtyping is expected to enable bladder cancer (BC) precise treatment. However, reliable subtyping strategies for clinical application remains defective and controversial. Given the significance of tumor immune dysfunction and exclusion (TIDE) in tumor immune escape and immunotherapy, we aimed to develop a novel TIDE-based subtyping method to facilitate personalized management. Transcriptome data of BC was used to evaluate the heterogeneity and the status of TIDE patterns. We identified 69 TIDE biomarker genes and classified BC samples into three subtypes using consensus clustering. Subtype I showed the lowest TIDE status and malignancy with the best prognosis and highest sensitivity to immune checkpoint blockade (ICB) treatment, which was enriched of metabolic related signaling pathways. Subtype III represented the highest TIDE status and malignancy with the poorest prognosis and resistance to ICB treatment, resulting from its inhibitory immune microenvironment and T cell terminal exhaustion. Subtype II was in a transitional state with intermediate TIDE level, malignancy, and prognosis. We further confirmed the existence and characteristics of our novel TIDE subtypes using real-world BC samples. This subtyping method was proved to be more efficient than previous known methods in identifying non-responders to immunotherapy. We also propose that combining our TIDE subtypes with known biomarkers can potentially improve the sensitivity and specificity of these biomarkers. Moreover, besides guiding ICB treatment, this classification approach can assist in selecting the frontline or recommended drugs. Finally, we confirmed that the TIDE subtypes are conserved across the pan-tumors. In conclusion, our novel TIDE-based subtyping method can serve as a powerful clinical tool for BC and pan-cancer patients, and potentially guiding personalized therapy decisions for selecting potential beneficiaries and excluding resistant patients of ICB therapy.
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