Discovery of a biomarker signature that predicts upgrading or upstaging in patients with low-risk prostate cancer

• Published in: Journal of clinical oncology Vol. 30; no. 30_suppl; p. 37
• Main Authors: Erho, Nicholas, Vergara, Ismael A., Buerki, Christine, Ghadessi, Mercedeh, Crisan, Anamaria, Sierocinski, Thomas, Haddad, Zaid, Zimmermann, Benedikt, Harko, Sebastian, Sosu-Sedzorme, Worlanyo, Triche, Timothy J., Davicioni, Elai
• Format: Journal Article
• Language: English
• Published: 20.10.2012
• ISSN: 0732-183X; 1527-7755
• DOI: 10.1200/jco.2012.30.30_suppl.37

Abstract only 37 Background: More than 90% of patients diagnosed with organ-confined prostate cancer (PCa) choose upfront definitive treatment (e.g., radical surgery) even though many are excellent candidates for delayed therapy (i.e., active surveillance [AS]). Therefore, patients may suffer from the adverse effects of treatment without gaining any benefit. Biomarker signatures that predict tumour aggressiveness are promising tools for identification of patients suited for AS. In this study, we use a transcriptome-wide assay to develop a biomarker signature for patients assessed as low risk at diagnosis who are upgraded or upstaged following radical prostatectomy (RP). Methods: Gene expression data of 56 RP samples from the Memorial Sloan Kettering Oncogenome Project (GSE21034) which met the low risk criteria (i.e., biopsy Gleason score (GS) ≤ 6, clinical stage T1 or T2A, and pre-operative PSA (pre-op PSA) ≤ 10 ng/ml) were used to develop the signature. Of these tumors, 31 underwent upgrading or upstaging (defined by pathological GS ≥ 7 or a pathological tumor stage > T3A). In the training set (n = 29) a median fold difference filter (MFD > 1.4) was applied to select features. The top 16 t-test ranked features were modelled with a K-nearest-neighbor (KNN) classifier (k = 3) which predicts upgrading/upstaging events. Results: The KNN was applied to the test set (n = 27) and achieved an area under the receiver operating characteristic curve (AUC) of 0.93, significantly better discrimination than pre-op PSA (AUC = 0.52) or tumor stage (AUC = 0.63). Compared to the null model’s accuracy of 56%, the KNN correctly predicts 81% (p-value < 0.005) of the upgrading/upstaging events. In multivariable analysis with pre-op PSA, tumor stage, and age at diagnosis, the KNN remained the only significant (p < 0.05) factor with an odds ratio of 2.7. Conclusions: A 16 marker signature was identified from RP specimens and shown to accurately segregate true low risk patients from those which transitioned to higher risk. Validation studies of this signature in prospectively designed cohorts of active surveillance candidates are underway to determine if the molecular signature can improve treatment and management decisions for low risk PCa patients.