Prostate cancer is heterogeneous in both cellular composition and patient outcome, and development of biomarker signatures to distinguish indolent from aggressive tumours is a high priority. Stroma plays an important role during prostate cancer progression and undergoes histological and transcriptional changes associated with disease. However, identification and validation of stromal markers is limited by a lack of datasets with defined stromal/tumour ratio. We have developed a prostate-selective signature to estimate the stromal content in cancer samples of mixed cellular composition. We identified stromal-specific markers from transcriptomic datasets of developmental prostate mesenchyme and prostate cancer stroma. These were experimentally validated in cell lines, datasets of known stromal content, and by immunohistochemistry in tissue samples to verify stromal-specific expression. Linear models based upon six transcripts were able to infer the stromal content and estimate stromal composition in mixed tissues. The best model had a coefficient of determination R2 of 0.67. Application of our stromal content estimation model in various prostate cancer datasets led to improved performance of stromal predictive signatures for disease progression and metastasis. The stromal content of prostate tumours varies considerably; consequently, deconvolution of stromal proportion may yield better results than tumour cell deconvolution. We suggest that adjusting expression data for cell composition will improve stromal signature performance and lead to better prognosis and stratification of men with prostate cancer. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

中文翻译：

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开发前列腺癌样品中基质含量的预测模型以改善签名性能。

前列腺癌在细胞组成和患者预后方面都是异质的，因此将生物标志物的特征区分开来区分惰性和侵袭性肿瘤是当务之急。基质在前列腺癌的进展过程中起着重要作用，并经历与疾病相关的组织学和转录变化。然而，基质标记物的鉴定和验证受到缺乏具有确定的基质/肿瘤比率的数据集的限制。我们已经开发出一种前列腺选择性标记来评估混合细胞组成的癌症样品中的基质含量。我们从发育性前列腺间充质和前列腺癌基质的转录组数据集中确定了基质特异性标志物。这些已在细胞系，已知基质含量的数据集中进行了实验验证，并通过免疫组织化学检测组织样本中的基质特异性表达。基于六个转录本的线性模型能够推断基质含量并估计混合组织中的基质组成。最佳模型的确定系数R2为0.67。我们的基质含量估算模型在各种前列腺癌数据集中的应用导致疾病进展和转移的基质预测特征的性能得到改善。前列腺肿瘤的基质含量差异很大。因此，基质部分的反卷积比肿瘤细胞的反卷积可产生更好的结果。我们建议调整表达数据的细胞组成将改善基质签名的性能，并导致前列腺癌男性更好的预后和分层。©2019作者。