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Patient-derived Orthotopic Xenograft Models for Human Urothelial Cell Carcinoma and Colorectal Cancer Tumor Growth and Spontaneous Metastasis
Published on: May 12, 2019
A molecular taxonomy for urothelial carcinoma
Gottfrid Sjödahl1, Martin Lauss, Kristina Lövgren
1Department of Oncology, Clinical Sciences, Lund University Hospital, Lund University, Lund, Sweden.
This study introduces a molecular taxonomy for urothelial carcinoma, identifying five subtypes based on integrated genomics. This new classification offers distinct clinical outcomes and potential for targeted therapies, improving upon traditional methods.
Area of Science:
- Oncology
- Genomics
- Molecular Biology
Background:
- Urothelial carcinoma is a common male cancer with limited treatment advancements.
- Accurate tumor assessment is challenging in clinical practice, necessitating molecular biomarkers.
- Classifying tumors based on molecular features is an emerging strategy.
Purpose of the Study:
- To develop a molecular taxonomy for urothelial carcinoma using integrated genomics.
- To define distinct molecular subtypes of urothelial carcinoma.
- To explore the potential of molecular classification for improved patient outcomes and treatment selection.
Main Methods:
- Analysis of gene expression profiles from 308 urothelial carcinoma cases.
- Definition and validation of five major molecular subtypes using independent datasets.
- Validation of key gene expression at the protein level via immunohistochemistry.
Main Results:
- Five urothelial carcinoma subtypes identified: urobasal A, genomically unstable, urobasal B, squamous cell carcinoma like, and infiltrated.
- Subtypes exhibit distinct clinical outcomes and molecular profiles (gene expression, mutation frequency).
- Molecular subtypes are intrinsic tumor properties, independent of pathologic classification, and correlate with drug susceptibility.
Conclusions:
- The proposed molecular taxonomy provides a novel framework for classifying urothelial carcinoma.
- This classification has the potential to guide future clinical investigations and personalize treatment strategies.
- Molecular stratification may offer superior prediction of drug response compared to traditional pathologic classification.
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