Liu Deli, Takhar Mandeep, Alshalalfa Mohammed, Erho Nicholas, Shoag Jonathan, Jenkins Robert B, Karnes R Jeffrey, Ross Ashley E, Schaeffer Edward M, Rubin Mark A, Trock Bruce, Klein Eric A, Den Robert B, Tomlins Scott A, Spratt Daniel E, Davicioni Elai, Sboner Andrea, Barbieri Christopher E
Weill Cornell Medicine.
GenomeDx Bioscience, Vancouver, British Columbia, Canada.
JCO Precis Oncol. 2018;2018. doi: 10.1200/PO.18.00036. Epub 2018 Jul 24.
Molecular characterization of prostate cancer, including The Cancer Genome Atlas, has revealed distinct subtypes with underlying genomic alterations. One of these core subtypes, (speckle-type POZ protein) mutant prostate cancer, has previously only been identifiable via DNA sequencing, which has made the impact on prognosis and routinely used risk stratification parameters unclear.
We have developed a novel gene expression signature, classifier (Subclass Predictor Based on Transcriptional Data), and decision tree to predict the mutant subclass from RNA gene expression data and classify common prostate cancer molecular subtypes. We then validated and further interrogated the association of prostate cancer molecular subtypes with pathologic and clinical outcomes in retrospective and prospective cohorts of 8,158 patients.
The subclass predictor based on transcriptional data model showed high sensitivity and specificity in multiple cohorts across both RNA sequencing and microarray gene expression platforms. We predicted approximately 8% to 9% of cases to be mutant from both retrospective and prospective cohorts. We found that the mutant subclass was associated with lower frequency of positive margins, extraprostatic extension, and seminal vesicle invasion at prostatectomy; however, mutant cancers were associated with higher pretreatment serum prostate-specific antigen (PSA). The association between mutant status and higher PSA level was validated in three independent cohorts. Despite high pretreatment PSA, the SPOP mutant subtype was associated with a favorable prognosis with improved metastasis-free survival, particularly in patients with high-risk preoperative PSA levels.
Using a novel gene expression model and a decision tree algorithm to define prostate cancer molecular subclasses, we found that the mutant subclass is associated with higher preoperative PSA, less adverse pathologic features, and favorable prognosis. These findings suggest a paradigm in which the interpretation of common risk stratification parameters, particularly PSA, may be influenced by the underlying molecular subtype of prostate cancer.
包括癌症基因组图谱在内的前列腺癌分子特征研究揭示了具有潜在基因组改变的不同亚型。这些核心亚型之一，即（斑点型POZ蛋白）突变型前列腺癌，此前仅能通过DNA测序识别，这使得其对预后和常规使用的风险分层参数的影响尚不清楚。
我们开发了一种新的基因表达特征、分类器（基于转录数据的亚类预测器）和决策树，以从RNA基因表达数据预测SPOP突变亚类并对常见的前列腺癌分子亚型进行分类。然后，我们在8158例患者的回顾性和前瞻性队列中验证并进一步探究了前列腺癌分子亚型与病理和临床结果之间的关联。
基于转录数据模型的亚类预测器在RNA测序和微阵列基因表达平台的多个队列中均显示出高敏感性和特异性。我们从回顾性和前瞻性队列中预测约8%至9%的病例为SPOP突变型。我们发现，SPOP突变亚类与前列腺切除术中切缘阳性、前列腺外侵犯和精囊侵犯的频率较低相关；然而，SPOP突变型癌症与更高的术前血清前列腺特异性抗原（PSA）相关。SPOP突变状态与更高PSA水平之间的关联在三个独立队列中得到验证。尽管术前PSA水平较高，但SPOP突变亚型与无转移生存期改善的良好预后相关，尤其是在术前PSA水平高风险的患者中。
使用一种新的基因表达模型和决策树算法来定义前列腺癌分子亚类，我们发现SPOP突变亚类与更高的术前PSA、更少的不良病理特征和良好的预后相关。这些发现提示了一种模式，即常见风险分层参数，尤其是PSA的解释，可能受前列腺癌潜在分子亚型的影响。
