New publication by Giuseppina Carbone’s Prostate Cancer Biology group

A new study, published in the journal Cancers describes a novel approach to dissect the intrinsic heterogeneity of prostate tumors and provide predictive information on clinical outcome and treatment response in clinical samples and experimental models. This prostate-specific metagene approach can improve treatment selection and clinical management of prostate cancer patients.

Sarah N. Mapelli, Domenico Albino, Maurizia Mello-Grand, Dheeraj Shinde, Manuel Scimeca, Rita Bonfiglio, Elena Bonanno, Giovanna Chiorino, Ramon Garcia-Escudero, Carlo V. Catapano and Giuseppina M. Carbone. A Novel Prostate Cell Type-Specific Gene Signature to Interrogate Prostate Tumor Differentiation Status and Monitor Therapeutic Response. Cancers, 2010 DOI: 10.3390/cancers12010176

Abstract:

In this study, we extracted prostate cell-specific gene sets (metagenes) to define the epithelial differentiation status of prostate cancers and, using a deconvolution-based strategy, interrogated thousands of primary and metastatic tumors in public gene profiling datasets. We identified a subgroup of primary prostate tumors with low luminal epithelial enrichment (LumElow). LumElow tumors were associated with higher Gleason score and mutational burden, reduced relapse-free and overall survival, and were more likely to progress to castration-resistant prostate cancer (CRPC). Using discriminant function analysis, we generate a predictive 10-gene classifier for clinical implementation. This mini-classifier predicted with high accuracy the luminal status in both primary tumors and CRPCs. Immunohistochemistry for COL4A1, a low-luminal marker, sustained the association of attenuated luminal phenotype with metastatic disease. We found an association of LumE score with tumor phenotype also in genetically engineered mouse models (GEMMs) of prostate cancer. Notably, the metagene approach led to the discovery of drugs that could revert the low luminal status in prostate cell lines and mouse models. This study describes a novel tool to dissect the intrinsic heterogeneity of prostate tumors and provide predictive information on clinical outcome and treatment response in experimental and clinical samples.