Molecular Characterization of Neuroendocrine-like Bladder Cancer

José Batista Da Costa, Ewan A. Gibb, Trinity J. Bivalacqua, Yang Liu, Htoo Zarni Oo, David T. Miyamoto, Mohammed Alshalalfa, Elai Davicioni, Jonathan Wright, Marc A. Dall’era, James Douglas, Joost L. Boormans, Michiel S. Van Der Heijden, Chin-lee Wu, Bas W.g. Van Rhijn, Shilpa Gupta, Petros Grivas, Kent W. Mouw, Paari Murugan, Ladan FazliSeong Ra, Badrinath R. Konety, Roland Seiler, Siamak Daneshmand, Omar Y. Mian, Jason A. Efstathiou, Yair Lotan, Peter C. Black

Research output: Contribution to journalArticlepeer-review

77 Scopus citations

Abstract

Purpose: Neuroendocrine (NE) bladder carcinoma is a rare and aggressive variant. Molecular subtyping studies have found that 5-15% of muscle-invasive bladder cancer (MIBC) have transcriptomic patterns consistent with NE bladder cancer in the absence of NE histology. The clinical implications of this NE-like subtype have not been explored in depth. Experimental design: Transcriptome-wide expression profiles were generated for MIBC collected from seven institutions and clinical-use of Decipher Bladder. Using unsupervised clustering, we generated a clustering solution on a prospective training cohort (PTC; n=175), developed single-sample classifiers to predict NE tumors, and evaluated the resultant models on a testing radical cystectomy (RC) cohort (n=225). A random forest model was finalized and applied to five validation cohorts (n=1302). Uni- and multivariable survival analyses were used to characterize clinical outcomes. Results: In the training cohort (PTC), hierarchical clustering using an 84 gene panel showed a cluster of eight patients (4.6%) with highly heterogeneous expression of NE markers in the absence of basal or luminal marker expression. NE-like tumors were identified in 1% to 7.1% of cases in validation cohorts. Patients with NE-like tumors had significantly worse 1-year progression free survival (65% NE-like vs 82% overall; p=0.046) and, after adjusting for clinical and pathological factors, had a 6.4-fold increased risk of all-cause mortality (p=0.001). IHC confirmed the neuronal character of these tumors. Conclusion: A single patient classifier was developed that identifies patients with histological urothelial cancer harboring a NE transcriptomic profile. These tumors represent a high-risk subgroup of MIBC which may require different treatment.
Original languageEnglish (US)
Pages (from-to)3908-3920
Number of pages13
JournalClinical Cancer Research
Volume25
Issue number13
DOIs
StatePublished - Jul 1 2019

Bibliographical note

Funding Information:
E. Davicioni has ownership interests (including patents) at GenomeDx. J.L. Wright reports receiving speakers bureau honoraria from Onc Live and Seattle Cancer Care Alliance, reports receiving commercial research grants from Merck, Altor Biosciences, Nucleix, Movember, and receives royalties from UpToDate. J.L. Boormans is a consultant/advisory board member for Janssen Pharmaceuticals, Roche, and Merck, and reports receiving commercial research grants from GenomeDx. B.W.G. van Rhijn is a consultant/advisory board member for Ferring and Astra Zeneca. P. Grivas reports receiving speakers bureau honoraria from Genentech and Bristol-Myers Squibb, is a consultant/ advisory board member for Merck, Genentech, Dendreon, Bristol-Myers Squibb, Astra Zeneca, Pfizer, EMD Serono, Clovis Oncology, Biocept, Jansssen, QED Therapeutics, Heron Therapeutics, Driver Inc., Seattle Genetics, Foundation Medicine, Exelixis, and reports receiving commercial research support from Pfizer, Clovis Oncology, Bavarian Nordic and Immunomedics. K.W. Mouw is a consultant/advisory board member for Pfizer, EMD Serono, and reports receiving commercial research support from Pfizer. R. Seiler has ownership interests

Funding Information:
J. Batista da Costa's salary was partially funded by the Association Franc¸aise d'Urologie. We are grateful for the insightful comments provided by A. Gordon Robertson on this manuscript.

Publisher Copyright:
© 2019 American Association for Cancer Research.

PubMed: MeSH publication types

  • Research Support, Non-U.S. Gov't
  • Journal Article

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