336 - Examination of Decipher Prostate Genomic Classifier in Patients with De Novo Metastatic Disease from a Large Scale Real-World Clinical and Transcriptomic Data Linkage

S. Moningi¹, J. Ho², J. Proudfoot³, P. Sutera⁴, M. P. Deek⁵, R. Phillips⁶, Z. H. Rana⁷, J. K. Molitoris⁸, Y. Kwok⁸, M. V. Mishra⁷, C. S. Spina⁹, A. Engelman¹⁰, A. E. Ross¹¹, Y. Liu², E. Davicioni¹², A. Y. Jia¹³, D. E. Spratt¹⁴, M. S. Leapman¹⁵, and P. T. Tran¹⁶; ¹Department of Radiation Oncology, Brigham and Women’s Hospital/Dana-Farber Cancer Institute, Boston, MA, ²Veracyte, San Diego, CA, ³Decipher Biosciences, Vancouver, Canada, ⁴Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, Baltimore, MD, ⁵Rutgers Cancer Institute of New Jersey, Department of Radiation Oncology, New Brunswick, NJ, ⁶Department of Radiation Oncology, Mayo Clinic, Rochester, MN, ⁷Maryland Proton Treatment Center, Baltimore, MD, ⁸Department of Radiation Oncology, University of Maryland School of Medicine, Baltimore, MD, ⁹Columbia University College of Physicians and Surgeons, New York, NY, ¹⁰Florida Urology Partners, Tampa, FL, ¹¹Northwestern Medicine Urology, Chicago, IL, ¹²Veracyte Inc., San Diego, CA, ¹³Weill Cornell Medical College/New York Presbyterian Hospital, New York, NY, ¹⁴Case Western, Cleveland, OH, ¹⁵Department of Urology, Yale School of Medicine, New Haven, CT, ¹⁶Johns Hopkins University, School of Medicine, Baltimore, MD

Purpose/Objective(s): Prognostic gene expression testing of primary tumor tissue has become widely adopted for localized prostate cancer risk stratification. Recent retrospective analyses of clinical trials have examined such testing in metastatic hormone-sensitive disease but little has been reported outside of this context. Here we examine the Decipher prostate genomic classifier (GC) used in the context of routine clinical practice. Materials/

Methods: Clinical and transcriptomic data from clinical use of the GC between 2013-2022 were linked with real-world data (RWD) aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Patients were anonymously linked between datasets by deterministic methods through a de-identification engine using encrypted tokens. A hierarchical claims-based algorithm was used to identify de novo distant metastasis in the patient’s record. De novo metastasis was defined using administrative claims and diagnosis codes within 90 days from initial prostate cancer diagnosis and at least 90 days earlier than non-prostate cancer diagnosis (if present), excluding a set of codes identifying metastases without specification of sites or for pelvic lymph node metastases. The distribution of GC scores in these samples was compared to localized prostate cancer after matching on baseline clinical and pathological factors drawn from 116,971 patients who received GC testing.
Results: A total of 92,976 patients with Decipher prostate GC were successfully linked to RWD, including 53,871 from biopsy and 39,105 from radical prostatectomy (RP) tests. De novo metastases were identified in 194 patients (0.21%) and compared to a matched set of 13,192 patients with localized disease. Among patients with de novo metastasis, the median age at Decipher testing was 69 years (IQR 63, 75), median percentage of positive cores was 58% (IQR 33-92%), median PSA was 14 ng/mL (IQR 6.3- 47) and 70% had NCCN high or very high-risk disease at diagnosis. Compared to the matched set 25% of metastatic patients had PSA > 50 vs. 5% for localized patients. Median Decipher score for metastatic patients was 0.88 (IQR 0.56, 0.98) compared to 0.69 (IQR 0.44, 0.9) and 0.46 (0.29, 0.68) in the matched and unmatched localized patient cohorts, respectively.
Conclusion: Using the largest linkage of transcriptomic and clinical data to date, we developed algorithms to identify de-novo metastatic disease from a cohort of patients tested with a GC. These patients tended to have high PSA and NCCN risk groups at time of diagnosis and accordingly had substantially elevated GC scores. This unique resource could be leveraged to enhance understandings of de novo metastatic disease biology, patterns of care and treatment effectiveness.