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Transcriptomic Analysis Of Micropapillary High Grade T1 Urothelial Bladder Cancer

ABSTRACT: Management of high-grade T1 (HGT1) micropapillary bladder cancer (MPBC) is controversial. An accurate genomic characterization might help predict outcome. We analyze the transcriptomic profile of MPBC in a clinically annotated HGT1 cohort establishing correlation with clinical outcome.A retrospective analysis of prospectively clinically followed series of HGT1; 23 patients with MPBC and 64 without MPBC (reference set). We identified a set of genes differentially expressed (DE). Associations between; clinical variables and gene expression (GE) levels (Wilcoxon’s rank sum); GE and time to progression (TTP) in univariate analysis (Log rank); GE levels adjusting for clinical variables (Cox regression) were evaluated.We identified a 26-gene signature that characterized MPBC in HGT1. Several genes including CD36, FAPB3 and RAETE1 were significantly associated with TTP. High expression of FABP3 and CD36 were associated with shorter TTP (P=0.045 and P=0.08 (NS)) as was low expression of RAET1E (P=0.01). Limitations include the lack of specific quantification of the percentage of MPBC tumor >10%.

ORGANISM(S): Homo sapiens

PROVIDER: GSE136401 | GEO | 2021/01/31

REPOSITORIES: GEO

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Project description:Background: Accurate survival stratification in early-stage NSCLC could inform the use of adjuvant therapy. We developed a clinically-implementable mortality risk score incorporating distinct tumor microenvironmental gene expression signatures and clinical variables. Methods: Gene expression profiles from 1106 non-squamous NSCLCs were used for generation and internal validation of a 9-gene molecular prognostic index (MPI). Expression of the MPI genes was determined within sorted tumor cell subpopulations. A quantitative PCR (qPCR) assay was developed and validated on an independent cohort of FFPE tissues. A prognostic score using clinical variables was generated using Surveillance Epidemiology and End Results (SEER) data and combined with the MPI. Results: The MPI stratified stage I patients into prognostic categories in four independent validation datasets, including three microarray and one FFPE qPCR cohorts (HR=2.4, 95% CI, 1.8-3.3, P=7x10-9 in the largest microarray cohort; and HR=2.5, 95% CI 1.1-6.0, P=.03 in stage I patients of the qPCR validation cohort). Prognostic genes were expressed in distinct tumor cell subpopulations and expression of genes implicated in cellular proliferation and stem cells portended poor outcomes, while expression of genes involved in normal lung differentiation and immune infiltration was associated with superior survival. Integrating the MPI with clinical variables conferred greatest prognostic power (HR=3.3, 95% CI 2.4-4.6; P=2x10-15 in the largest microarray cohort; and HR=3.6, 95% CI 1.5-8.8, P=.003 in stage I patients of the qPCR validation cohort). Finally, the MPI was prognostic irrespective of somatic alterations in EGFR, KRAS, TP53, and ALK. Conclusion: The MPI incorporates genes expressed in the tumor and its microenvironment, and designates risk of death for patients with early-stage non-squamous NSCLC. The MPI can be implemented clinically using qPCR assays on FFPE tissues and a composite model integrating the MPI with clinical variables provides the most accurate risk stratification.

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Transcriptomic Analysis Of Micropapillary High Grade T1 Urothelial Bladder Cancer

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