4.6 Article

A Novel Prostate Cell Type-Specific Gene Signature to Interrogate Prostate Tumor Differentiation Status and Monitor Therapeutic Response (Running Title: Phenotypic Classification of Prostate Tumors)

期刊

CANCERS
卷 12, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/cancers12010176

关键词

prostate cancer; tumor classification; predictive biomarkers; gene signature; gene classifier

类别

资金

  1. Swiss National Science Foundation [310030-169942, IZLSZ3-170898, IZK0Z3-144637]
  2. Swiss Cancer League [KLS-3872-02-2016, KLS-4569-08-2018]
  3. Fondazione Fidinam
  4. Fondazione Ticinese per la Ricerca sul Cancro
  5. FEDER [PI18/00263, CB16/12/00228]
  6. [KFS3243-08-2013/Swiss Bridge]
  7. Swiss National Science Foundation (SNF) [310030_169942, IZK0Z3_144637, IZLSZ3_170898] Funding Source: Swiss National Science Foundation (SNF)

向作者/读者索取更多资源

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 (LumE(low)). LumE(low) 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 also an association of LumE score with tumor phenotype 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据