Prediction of metastatic castrate-resistant prostate cancer response to abiraterone or enzalutamide by a baseline blood-based CTC gene expression signature

• Published in: Journal of clinical oncology Vol. 37; no. 15_suppl; p. e16529
• Main Authors: Lariviere, Michael Joseph, Haas, Naomi B., Cherkas, Yauheniya, Nielsen, Karl, Foulk, Brad, Patel, Jaymala, Smirnov, Denis, Vaughn, David J., Amaravadi, Ravi K., Savitch, Samantha L., Majmundar, Krishna, Buckingham, Thomas H., Yee, Stephanie S., Jones, Jeremy, Pal, Sumanta K., Carpenter, Erica L.
• Format: Journal Article
• Language: English
• Published: 20.05.2019
• ISSN: 0732-183X; 1527-7755
• DOI: 10.1200/JCO.2019.37.15_suppl.e16529

Abstract only e16529 Background: Prostate cancer is the most common cancer in men in the U.S., with 30% 5-year overall survival (OS) for patients (pts) with metastases. To take a precision medicine approach to the management of metastatic castrate-resistant prostate cancer (mCRPC), we developed a blood circulating tumor cell (CTC)-based test to identify mCRPC pts most likely to benefit from abiraterone (abi) or enzalutamide (enza). Methods: In this multi-institution prospective study, men with mCRPC were enrolled prior to starting abi (1,000 mg/d plus prednisone 10 mg/d) or enza (160 mg/d). At baseline (BL), 12 w, and progression, blood samples were collected for CellSearch-based CTC enumeration and qPCR-based gene expression analysis. Results: 69 pts (median age 68 y [50-82]) received abi (n = 25) or enza (n = 44) and had evaluable blood samples. Consistent with prior publications, among 43 pts with BL CTC > 0, clearance of detectable CTCs (BL CTCs > 0 and 12 w CTCs = 0), was achieved in 24 patients (55.8%), and was associated with greater median OS (31 mo vs. 18 mo, log-rank p = 0.03). The 43 pts with BL CTC > 0 were then randomly divided into training (n = 31) and validation (n = 12) sets. Baseline gene expression data for the training set was used to develop a model to predict CTC clearance, starting with a panel of 141 expressed genes/isoforms including those associated with prostate cancer. Of the models tested, random forest yielded the best performance, with respective training and validation set sensitivity of 0.7 and 1, specificity 0.75 and 0.71, AUC 0.88 and 0.91. Top genes identified include those previously associated with disease – HOXB13, ESRP2, KLK3, GRHL2, and KRT19, among others. Conclusions: A gene expression signature from a baseline blood sample with CellSearch-enriched CTCs can predict clearance of detectable CTCs in response to abi/enza with high AUC and may give insight into molecular mechanisms of response. A prospective study with a larger number of patients will be required to further validate our findings. Ultimately, this blood test has the potential to select the patients most likely to benefit from second-generation antiandrogen vs. non-hormonal systemic treatment.