Novel Gene Expression Signature Predictive of Clinical Recurrence After Radical Prostatectomy in Early Stage Prostate Cancer Patients.
Novel Gene Expression Signature Predictive of Clinical Recurrence After Radical Prostatectomy in Early Stage Prostate Cancer Patients.
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DOI：
10.1002/pros.23211
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发表时间：
2016-10
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目前的临床工具在区分有复发风险的局限性前列腺癌患者与惰性疾病患者方面的准确性有限。我们的目的是确定一个基因表达特征，结合临床变量可以提高对T2期PCa患者RP后临床复发的预测。研究人群包括在PSA时代（1988-2008年）在南加州大学接受根治性耻骨后直肠切除术（RP）和双侧盆腔淋巴结清扫术的知情同意患者。我们对187例器官受限患者（pT 2N 0 M0）进行了巢式病例对照研究：154例无复发（“对照”），33例临床复发（“病例”）。从激光捕获显微切割的恶性腺体获得RNA，代表每个患者的总体Gleason评分。使用全基因组DASL HT平台（Illumina，Inc）获得全基因组基因表达谱（29，000个转录物）。PCa临床复发的基因表达特征使用稳定性选择和弹性网络正则化逻辑回归来鉴定。使用Affytron Human Exon 1.0 ST阵列生成的三个现有数据集进行验证：马约诊所（MC，n = 545）、纪念斯隆-凯特琳癌症中心（SKCC，n = 150）和伊拉斯谟医学中心（EMC，n = 48）。使用重复五重交叉验证获得ROC曲线下面积（AUC）。鉴定了与关键临床变量（年龄、Gleason评分、术前PSA水平和手术年份）联合预测临床复发的28个基因表达特征（仅临床变量的AUC为0.67，临床变量和28个基因特征的AUC为0.99）。在每个外部数据集中与临床变量拟合的该基因签名的AUC为0.75（0.72-0.77）（MC），0.90（0.86-0.94）（MSKCC）和0.82（0.74-0.91）（EMC），而每个数据集中仅临床变量的AUC分别为0.72（0.70-0.74）、0.86（0.82-0.91）和0.76（0.67-0.85）。我们报告了一种新的基于基因表达的分类器，该分类器使用来自全基因组表达谱的不可知方法来识别，该方法可以提高临床指标的准确性，从而对RP后有临床复发风险的早期局部患者进行分层。
Current clinical tools have limited accuracy in differentiating patients with localized prostate cancer who are at risk of recurrence from patients with indolent disease. We aimed to identify a gene expression signature that jointly with clinical variables could improve upon the prediction of clinical recurrence after RP for patients with stage T2 PCa. The study population includes consented patients who underwent a radical retropubic prostatectomy (RP) and bilateral pelvic lymph node dissection at the University of Southern California in the PSA-era (1988–2008). We used a nested case-control study of 187 organ-confined patients (pT2N0M0): 154 with no recurrence (“controls”) and 33 with clinical recurrence (“cases”). RNA was obtained from laser capture microdissected malignant glands representative of the overall Gleason score of each patient. Whole genome gene expression profiles (29,000 transcripts) were obtained using the Whole Genome DASL HT platform (Illumina, Inc). A gene expression signature of PCa clinical recurrence was identified using stability selection with elastic net regularized logistic regression. Three existing datasets generated with the Affymetrix Human Exon 1.0ST array were used for validation: Mayo Clinic (MC, n = 545), Memorial Sloan Kettering Cancer Center (SKCC, n = 150), and Erasmus Medical Center (EMC, n = 48). The areas under the ROC curve (AUCs) were obtained using repeated fivefold cross-validation. A 28-gene expression signature was identified that jointly with key clinical variables (age, Gleason score, pre-operative PSA level, and operation year) was predictive of clinical recurrence (AUC of clinical variables only was 0.67, AUC of clinical variables, and 28-gene signature was 0.99). The AUC of this gene signature fitted in each of the external datasets jointly with clinical variables was 0.75 (0.72–0.77) (MC), 0.90 (0.86–0.94) (MSKCC), and 0.82 (0.74–0.91) (EMC), whereas the AUC for clinical variables only in each dataset was 0.72 (0.70–0.74), 0.86 (0.82–0.91), and 0.76 (0.67–0.85), respectively. We report a novel gene-expression based classifier identified using agnostic approaches from whole genome expression profiles that can improve upon the accuracy of clinical indicators to stratify early stage localized patients at risk of clinical recurrence after RP.