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Genomic Landscape of Young-Onset Bladder Cancer and Its Prognostic Implication on Adult Bladder Cancer

ABSTRACT: Genomic Landscape of Young-Onset Bladder Cancer and Its Prognostic Implication on Adult Bladder Cancer

PROVIDER: PRJNA550320 | ENA |

REPOSITORIES: ENA

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Genomic Landscape of Young-Onset Bladder Cancer and Its Prognostic Implication on Adult Bladder Cancer

Project description:Bladder cancer is common in elderly men, and it is a highly mutated tumor with frequent TP53 and FGFR3 mutation. Young-onset bladder cancer (YBC) is extremely rare; therefore, its genomic characteristics are unknown and appropriate management strategy is yet to be defined. Through a nation-wide search for bladder cancer diagnosed at 20 years or younger, 29 cases of biopsy-proven YBCs were collected. Whole exome sequencing and RNA sequencing were performed on 21 and 11 cases, respectively, and the results were compared with those of adult bladder cancer (ABC) retrieved from public database. Almost all YBCs were low grade, non-invasive papillary tumors. YBC had low mutation burden and less complex copy number alterations. Driver mutations were found in all cases. Common mutations included HRAS mutations (10 cases) with a preference in exon 5 and FGFR3 gene fusions (7 cases) with various partner genes, which occurred in a mutually exclusive manner. Other alterations included KRAS mutations (2 cases), arm-level deletion of chromosomes 4p and 10q (1 case), and ERCC2 mutation (1 case). TP53 and FGFR3 point mutations were not found. The gene expression profiles of YBC corresponded to those of the good prognostic group of ABC. No YBC cases had progressed to muscle-invasive tumor during follow-up. In addition, ABCs with YBC-like mutations also showed no progression to muscle-invasive tumor. In conclusion, YBC had distinct driver genetic alterations such as HRAS mutation and FGFR3 gene fusion and showed good prognosis. YBCs and ABCs with YBC-like mutations may be managed with less aggressive surveillance.

2020-03-30 | GSE133192 | GEO

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Comprehensive molecular classification of bladder cancer reveals distinct prognostic subgroups with different sensitivities to immunotherapy

Project description:Previous studies successfully revealed molecular characteristics of bladder cancers, dealing with non-muscle invasive bladder cancer and muscle invasive bladder cancer, separately. At the molecular level, however, there is a great need to aggregate these subtypes, which may share biological characteristics. This study aimed to identify distinct molecular subtypes of BC and the clinical and/or biological characteristics of each subtype. We used seven gene expression data sets for bladder cancer, which included data from 118 primary bladder cancer samples and 27 recurrent bladder tumor tissues from the Yonsei University Severance Hospital. Hierarchical clustering revealed four molecular subtypes of BC with different clinical outcomes: class 1 with low-grade NMIBC and the best prognosis; class 2 characterized by active FGFR3 and inhibited immune response pathways; class 3 with high-grade NMIBC and the worst progression-free survival; and class 4 mainly comprised of MIBC along with EMT activation. By applying the classifier based on these characteristics, we stratified all BC samples into newly identified molecular subtypes. When comparing previously reported subtypes, our subtypes well agreed with their molecular characteristics regardless of breast cancer-based biology, and showed a strong prognostic relevance in class 3. Integrative analysis of mutation and gene expression suggested that class 3 may have the potential benefit from anti-PD-L1 immunotherapy. Our classifier, constructed by NMIBC and MIBC integration, successfully stratified BC patients into distinct subtypes with different clinical outcomes and a possible treatment option.

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Benchmarking pathway analysis methods offer novel prognostic cancer biomarkers and therapeutics: application in bladder cancer

Project description:The pathogenesis of cancer is typically driven by alterations in multiple cellular pathways that are challenging to identify and target. However, it is unclear how suitable the existing pathway analysis tools are for unbiased discovery and ranking of dysregulated and/or cancer-specific pathways. Here, we created a new platform called Benchmark to evaluate the potential of pathway analysis tools for discovery under experimental conditions. Unexpectedly, we found that despite wide-spread success in confirming hypothesized dysregulated pathways, common pathway analysis tools are less than ideal for unbiased discovery. Nevertheless, our pathway ensemble tool (PET) that combines the rank statistics from the exisiting methods significantly enhanced discovery. We applied PET to transcriptomics data from 12 independent tumor types to identify prognostic pathways. We showed that the genes from prognostic pathways are excellent biomarkers and can define cancer molecular subtypes. Moreover, drug prediction to normalize genes from prognostic pathways identified effective known and novel drugs. Finally, in vitro and/or a xenograft models of bladder cancer treated with the top predicted drug significantly restricted tumor cells. We anticipate that our unbiased approach for pathway discovery will have tangible impacts on cancer management.

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