Expression changes in the stroma of prostate cancer predict subsequent relapse.
Expression changes in the stroma of prostate cancer predict subsequent relapse.
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DOI：
10.1371/journal.pone.0041371
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Mercola D
中科院分区：
文献类型：
--
作者：
Jia Z;Rahmatpanah FB;Chen X;Lernhardt W;Wang Y;Xia XQ;Sawyers A;Sutton M;McClelland M;Mercola D
需要生物标志物来解决大多数前列腺癌患者发生的过度治疗，这些患者不会死于这种疾病，但会接受根治性治疗。生物标志物发现的一个可能障碍可能是前列腺肿瘤的多克隆/多灶性性质以及患者样本之间的细胞类型异质性。肿瘤邻近间质（肿瘤微环境）受遗传改变的影响较小，因此可能产生更一致的生物标志物以响应肿瘤侵袭性。为此，我们比较了肿瘤附近间质中的Affyrin基因表达谱，并鉴定了一组115个探针组，其表达水平与复发时间显著相关。我们还比较了膀胱癌切除术后不久（<1年）化学复发的患者和膀胱癌切除术后前4年内未复发的患者。我们确定了131差异表达的微阵列探针集之间的这两个类别。19个探针组（15个基因在两个基因列表之间重叠，p<0.0001）。我们通过在含有肿瘤附近基质的样品上训练开发了基于PAM的分类器：9个快速复发患者样品和9个惰性患者样品。然后，我们在47个不同的样本上测试了分类器，其中包含90%或更多的基质。该分类器预测患者的风险状态，平均准确率为87%。这是第一个基于肿瘤微环境的预后分类器。这些结果表明，前列腺癌微环境表现出可重复的变化，可用于预测患者的预后。
Biomarkers are needed to address overtreatment that occurs for the majority of prostate cancer patients that would not die of the disease but receive radical treatment. A possible barrier to biomarker discovery may be the polyclonal/multifocal nature of prostate tumors as well as cell-type heterogeneity between patient samples. Tumor-adjacent stroma (tumor microenvironment) is less affected by genetic alteration and might therefore yield more consistent biomarkers in response to tumor aggressiveness. To this end we compared Affymetrix gene expression profiles in stroma near tumor and identified a set of 115 probe sets for which the expression levels were significantly correlated with time-to-relapse. We also compared patients that chemically relapsed shortly after prostatectomy (<1 year), and patients that did not relapse in the first four years after prostatectomy. We identified 131 differentially expressed microarray probe sets between these two categories. 19 probe sets (15 genes overlapped between the two gene lists with p<0.0001). We developed a PAM-based classifier by training on samples containing stroma near tumor: 9 rapid relapse patient samples and 9 indolent patient samples. We then tested the classifier on 47 different samples, containing 90% or more stroma. The classifier predicted the risk status of patients with an average accuracy of 87%. This is the first general tumor microenvironment-based prognostic classifier. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for predicting outcomes for patients.
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