Research Papers:

A four gene signature predictive of recurrent prostate cancer

Justin Komisarof _, Matthew McCall, Laurel Newman, Wiam Bshara, James L. Mohler, Carl Morrison and Hartmut Land

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Oncotarget. 2017; 8:3430-3440. https://doi.org/10.18632/oncotarget.13837

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Justin Komisarof1, Matthew McCall2, Laurel Newman1, Wiam Bshara3, James L. Mohler4, Carl Morrison3, Hartmut Land1,5

1Departments of Biomedical Genetics, University of Rochester Medical Center, Rochester NY, 14642, USA

2Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester NY, 14642, USA

3Department of Pathology, Roswell Park Cancer Institute, Buffalo, NY, 14623, USA

4Department of Urology, Roswell Park Cancer Institute, Buffalo, NY, 14623, USA

5Wilmot Cancer Institute, University of Rochester Medical Center, Rochester NY, 14642, USA

Correspondence to:

Hartmut Land, email: [email protected]

Keywords: prostate cancer, biochemical recurrence, cooperation response genes, radical prostatectomy, algorithmic prediction

Received: January 19, 2016    Accepted: November 21, 2016    Published: December 09, 2016


Prostate cancer is the most common form of non-dermatological cancer among US men, with an increasing incidence due to the aging population. Patients diagnosed with clinically localized disease identified as intermediate or high-risk are often treated by radical prostatectomy. Approximately 33% of these patients will suffer recurrence after surgery. Identifying patients likely to experience recurrence after radical prostatectomy would lead to improved clinical outcomes, as these patients could receive adjuvant radiotherapy. Here, we report a new tool for prediction of prostate cancer recurrence based on the expression pattern of a small set of cooperation response genes (CRGs). CRGs are a group of genes downstream of cooperating oncogenic mutations previously identified in a colon cancer model that are critical to the cancer phenotype. We show that systemic dysregulation of CRGs is also found in prostate cancer, including a 4-gene signature (HBEGF, HOXC13, IGFBP2, and SATB1) capable of differentiating recurrent from non-recurrent prostate cancer. To develop a suitable diagnostic tool to predict disease outcomes in individual patients, multiple algorithms and data handling strategies were evaluated on a training set using leave-one-out cross-validation (LOOCV). The best-performing algorithm, when used in combination with a predictive nomogram based on clinical staging, predicted recurrent and non-recurrent disease outcomes in a blinded validation set with 83% accuracy, outperforming previous methods. Disease-free survival times between the cohort of prostate cancers predicted to recur and predicted not to recur differed significantly (p = 1.38x10-6). Therefore, this test allows us to accurately identify prostate cancer patients likely to experience future recurrent disease immediately following removal of the primary tumor.

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