Abstract B46: Identification of biomarkers for stratification of Gleason score 7 patients using whole-transcriptome expression profiling
Authors: Nicholas Erho, Thomas Sierocinski, Elai Davicioni, Robert B. Jenkins, Mercedeh Gadhessi, Christine Buerki, Ismael A. Vergara, Anamaria Crisan, Zaid Haddad, Benedikt Zimmermann, Anirban Mitra, Timothy J. Triche
Journal: Cancer Research (2012)

Abstract Motivations: Gleason score is the most prognostic variable in prostate cancer pathology. Patients with high Gleason (8+) are the most likely to experience biochemical recurrence (BCR), clinical progression (CP) and prostate-cancer specific mortality (PCSM), whereas patients with Gleason ≤6 are expected to have excellent long-term outcomes. However, the majority of prostate cancer patients are diagnosed with Gleason score 7, a heterogeneous group in terms of patient outcomes. In the absence of adverse pathological factors (APF, such as preoperative PSA, SM+, ECE and SVI) most Gleason 7 patients will have excellent outcomes. In contrast, Gleason 7 patients with APF are considered at high-risk for recurrence and therefore are candidates for adjuvant therapy. In this study, we have investigated whether a classifier based on differentially expressed genomic features could be used to improve prognostication of Gleason 7 patients, independent of APF. Methods: Two whole-transcriptome (1.4 million feature) oligonucleotide microarray expression datasets from radical prostatectomy specimens were analyzed. The first one (GEO accession: GSE21032) consisted of 125 fresh frozen (FF) samples from the MSKCC Oncogenome Project and the second dataset had 545 formalin-fixed-paraffin- embedded (FFPE) samples from the Mayo Clinic tumor registry. For genomic feature selection, differential expression in the FF dataset between Gleason ≤6 (n=40) and ≥8 (n=15) was assessed with false discovery rate adjusted t-tests (p&amp;lt;0.05). Next, a k-nearest neighbor (KNN) classifier was trained and tuned using the selected features to predict clinical progression (i.e., positive bone or CT scans after BCR) in the FFPE dataset, again using samples with Gleason score ≤6 (n=63) and ≥8 (n=211). The KNN classifier was evaluated on Gleason score 7 (n=209) FFPE samples (not used for training) using multivariable logistic regression. Results: The KNN classifier segregated the Gleason 7 samples into high and low risk CP groups. The KNN odds ratios were 2.9 (p&amp;lt;0.005), 2.1 (p&amp;lt;0.05) and 2.8 (p&amp;lt;0.05) for BCR, CP and PCSM end-points, respectively when comparing high and low risk groups. Multivariable analysis revealed that the KNN classifier was independently prognostic. Conclusions: A KNN classifier developed using differentially expressed genomic features significantly stratified Gleason score 7 patients for several clinically relevant endpoints. Future validation studies will address the utility of this classifier in predicting pathological Gleason score from biopsy specimens, which may be particularly useful for evaluation of biopsy Gleason 6 cases. In these patients, deferring prostatectomy in favour of active surveillance is an appealing treatment option unless the ‘true’ Gleason of the index lesion is in fact a Gleason 7 or higher, where immediate surgery is likely a life-saving procedure. Citation Format: Nicholas Erho, Thomas Sierocinski, Elai Davicioni, Robert B. Jenkins, Mercedeh Gadhessi, Christine Buerki, Ismael A. Vergara, Anamaria Crisan, Zaid Haddad, Benedikt Zimmermann, Anirban Mitra, Timothy J. Triche. Identification of biomarkers for stratification of Gleason score 7 patients using whole-transcriptome expression profiling [abstract]. In: Proceedings of the AACR Special Conference on Advances in Prostate Cancer Research; 2012 Feb 6-9; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2012;72(4 Suppl):Abstract nr B46.