Abstract
In the age of personalized medicine stratifying tumors into molecularly defined subtypes associated with distinctive clinical behaviors and predictable responses to therapies holds tremendous value. Towards this end, we developed a custom microfluidics-based bladder cancer gene expression panel for characterization of archival clinical samples. In silico analysis indicated that the content of our panel was capable of accurately segregating bladder cancers from several public datasets into the clinically relevant basal and luminal subtypes. On a technical level, our bladder cancer panel yielded robust and reproducible results when analyzing formalin-fixed, paraffin-embedded (FFPE) tissues. We applied our panel in the analysis of a novel set of 204 FFPE samples that included non-muscle invasive bladder cancers (NMIBCs), muscle invasive disease (MIBCs), and bladder cancer metastases (METs). We found NMIBCs to be mostly luminal-like, MIBCs to include both luminal- and basal-like types, and METs to be predominantly of a basal-like transcriptional profile. Mutational analysis confirmed the expected enrichment of FGFR3 mutations in luminal samples, and, consistently, FGFR3 IHC showed high protein expression levels of the receptor in these tumors. Our bladder cancer panel enables basal/luminal characterization of FFPE tissues and with further development could be used for stratification of bladder cancer samples in the clinic.
Highlights
Bladder cancer is the fifth most common malignancy worldwide, with close to 400,000 newly diagnosed cases and ~150,000 associated deaths per year [1, 2]
We introduce a microfluidics-based bladder cancer gene expression panel that is optimized for the analysis of FFPE tissues
We used samples from four public data sets that were designated as basal or luminal by Choi et al [14] or through BASE47 scoring by Damrauer, et al [10] and hierarchically clustered samples based on the expression of probes corresponding to genes on our panel (Fig 1B)
Summary
Bladder cancer is the fifth most common malignancy worldwide, with close to 400,000 newly diagnosed cases and ~150,000 associated deaths per year [1, 2]. JI is an employee of Alternative Allele Consulting. Alternative Allele Consulting provided support in the form of salary for this author but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific role of this author is articulated in the ‘author contributions’ section
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