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Rationale: Our objective was to develop a circulating tumor cell (CTC)-RNA assay for characterizing clinically relevant RNA signatures for the assessment of androgen receptor signaling inhibitor (ARSI) sensitivity in metastatic castration-resistant prostate cancer (mCRPC) patients. Methods: We developed the NanoVelcro CTC-RNA assay by combining the Thermoresponsive (TR)-NanoVelcro CTC purification system with the NanoString nCounter platform for cellular purification and RNA analysis. Based on the well-validated, tissue-based Prostate Cancer Classification System (PCS), we focus on the most aggressive and ARSI-resistant PCS subtype, i.e., PCS1, for CTC analysis. We applied a rigorous bioinformatic process to develop the CTC-PCS1 panel that consists of prostate cancer (PCa) CTC-specific RNA signature with minimal expression in background white blood cells (WBCs). We validated the NanoVelcro CTC-RNA assay and the CTC-PCS1 panel with well-characterized PCa cell lines to demonstrate the sensitivity and dynamic range of the assay, as well as the specificity of the PCS1 Z score (the likelihood estimate of the PCS1 subtype) for identifying PCS1 subtype and ARSI resistance. We then selected 31 blood samples from 23 PCa patients receiving ARSIs to test in our assay. The PCS1 Z scores of each sample were computed and compared with ARSI treatment sensitivity. Results: The validation studies using PCa cell line samples showed that the NanoVelcro CTC-RNA assay can detect the RNA transcripts in the CTC-PCS1 panel with high sensitivity and linearity in the dynamic range of 5-100 cells. We also showed that the genes in CTC-PCS1 panel are highly expressed in PCa cell lines and lowly expressed in background WBCs. Using the artificial CTC samples simulating the blood sample conditions, we further demonstrated that the CTC-PCS1 panel is highly specific in identifying PCS1-like samples, and the high PCS1 Z score is associated with ARSI resistance samples. In patient bloods, ARSI-resistant samples (ARSI-R, n=14) had significantly higher PCS1 Z scores as compared with ARSI-sensitive samples (ARSI-S, n=17) (Rank-sum test, P=0.003). In the analysis of 8 patients who were initially sensitive to ARSI (ARSI-S) and later developed resistance (ARSI-R), we found that the PCS1 Z score increased from the time of ARSI-S to the time of ARSI-R (Pairwise T-test, P=0.016). Conclusions: Using our new methodology, we developed a first-in-class CTC-RNA assay and demonstrated the feasibility of transforming clinically-relevant tissue-based RNA profiling such as PCS into CTC tests. This approach allows for detecting RNA expression relevant to clinical drug resistance in a non-invasive fashion, which can facilitate patient-specific treatment selection and early detection of drug resistance, a goal in precision oncology.

e17509 Background: Genomic profiling has strongly impacted the contemporary understanding of prostate cancer (PCa). Clinical trials are now testing the utility of genomic classifiers such as the PCS (You, Ca Res 2016) and PAM50 (Zhao, JAMA Onc 2017) systems to optimize therapy selection. As contemporary tissue is not always readily available, especially in metastatic, castration-resistant PCa (mCRPC), a blood-based test would be better suited for assessing patients and predicting treatment response. Methods: The CTC-RNA assay combines the Thermoresponsive (TR)-NanoVelcro system with the NanoString nCounter platform. This allows for CTC purification and RNA analysis. Using a novel bioinformatics approach that accounts for differences in background signals between tissue and blood, we reconstructed the PCS and PAM50 panels to recapitulate both classifiers in this blood-based assay. A weighted Z-score and nearest centroid classifier were used to calculate gene expression and to assign PCS and PAM50 subtypes. Performance of the revised signatures and CTC-RNA assay was benchmarked on simulated spiked-blood specimens. An initial clinical test was performed using clinically annotated, banked blood specimens within the Translational Oncology Program Blood and Biospecimen Bank. Results: CTC-RNA profiles of C4-2B AR signaling inhibitor (ARSI)-resistant sublines were compared to parental C4-2B. C4-2B ARSI-resistant cells had significantly higher PCS1 Z scores, PCS1 probability, and basal probability compared to the parental C4-2B cells. Blood samples from 34 mCRPC patients prior to initiation of therapy with ARSIs (abiraterone, enzalutamide, or apalutamide) were then analyzed. Samples were classified as PCS1 (n = 3), PCS2 (n = 20), and PCS3 (n = 11); luminal A (n = 12), luminal B (n = 11), and basal (n = 11). The biochemical progression-free survival (bPFS) on ARSI and overall survival (OS) for PCS1/Basal vs. other are shown in the table. Conclusions: The CTC-RNA assay is capable of generating luminal-basal classifications such as those in the PCS and PAM50 systems. Given early data of these classifiers and their potential to guide therapeutic decisions, this approach may be useful as an alternative to biopsy to facilitate such decisions. Larger prospective studies will be needed to confirm and validate its clinical utility. [Table: see text]

Background: Tissue-based gene signatures can predict clinical behavior in prostate cancer (PC). Our objective was to extend their application to circulating tumor cells (CTCs) and to show that changes in the signature were associated with changes in clinical behavior. Methods: Our approach combined the Thermoresponsive(TR)-NanoVelcro CTC purification system with the Nanostring nCounter system for cellular purification and transcriptomic analysis. The Prostate Cancer Classification System (PCS) panel was modified for use in CTCs. We selected 31 blood samples from 23 PC patients receiving androgen receptor signaling inhibitors (ARSI) and measured the PCS1 Z score (probability). These findings were compared with clinical outcome data (responsiveness/resistance). Results: A modified, 16-gene PCS1 signature was established and validated through a rigorous bioinformatics process. We performed analytical validation of our combined CTC-RNA system to ensure reproducibility and specificity. In patient bloods, ARSI-resistant samples (ARSI-R, n=14) had significantly higher PCS1 Z scores as compared with ARSI-sensitive samples (ARSI-S, n=17) (Rank-sum test, P=0.003). The analyzed bloods contained samples from 8 patients who developed resistance to an ARSI allowing for dynamic measurement of gene expression. Our analysis found that the PCS1 Z score increased at the time that ARSI-resistance emerged (Pairwise T-test, P=0.016). Conclusions:Using this new methodology, contemporary, clinically-relevant gene signatures such as PCS could be measured non-invasively in CTCs. These findings can be used to relate gene expression to clinical drug response. This approach also allowed for measurement of dynamic variations of gene expression in individual patients over time that correlated to ARSI sensitivity. Citation Format: Pai-Chi Teng, Yu Jen Jan, Junhee Yoon, Jie-Fu Chen, Pin-Jung Chen, Nu Yao, Shirley Cheng, Amber Lozano, Michael R. Freeman, Sungyong You, Hsian-Rong Tseng, Edwin M. Posadas. A circulating tumor cell assay for dynamic assessment of drug sensitivity in metastatic castration-resistant prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 453.

5059 Background: Our objective is to develop a circulating tumor cell (CTC)-RNA assay for characterizing clinically relevant RNA signatures for the assessment of androgen receptor signaling inhibitors (ARSIs) sensitivity in metastatic castration-resistant prostate cancer (mCRPC) patients. Methods: We developed NanoVelcro CTC-RNA Assay by combining Thermoresponsive(TR)-NanoVelcro CTC purification system with NanoString nCounter platform for CTC purification and RNA analysis. Based on the well-validated, tissue-based Prostate Cancer Classification System (PCS), we selected the most aggressive and ARSI-resistant subtype- the PCS1, for CTC analysis. We applied a rigorous bioinformatic process to develop a CTC-PCS1 panel that is specific to PC CTCs. We validated NanoVelcro CTC-RNA Assay and CTC-PCS1 panel with PC cell lines to demonstrate sensitivity and specificity of the PCS1 Z score (the likelihood estimate of the PCS1 subtype) for identifying PCS1 subtype and ARSI resistance. We then selected 31 blood samples from 23 PC patients receiving ARSIs to test in our assay. The PCS1 Z score of each sample was computed and compared with ARSI treatment sensitivity. Results: We established a 16-gene CTC-PCS1 panel that consists of CTC-specific RNA signatures. The validation studies using PC cell lines showed that the assay can detect the RNA transcripts with high sensitivity and scalability in the range of 1-100 cells. We also showed that the genes in CTC-PCS1 panel is highly expressed in PC cells. We further demonstrated that the CTC-PCS1 panel is highly specific in identifying PCS1-like samples, and the high PCS1 Z score is associated with ARSI resistance. In patient bloods, ARSI-resistant samples (ARSI-R, n=14) had significantly higher PCS1 Z scores as compared with ARSI-sensitive samples (ARSI-S, n=17) (Rank-sum test, P=0.003). In 8 patients who were initially sensitive to ARSI (ARSI-S) and later developed resistance (ARSI-R), we found that the PCS1 Z score increased from the time of ARSI-S to the time of ARSI-R (Pairwise T-test, P=0.016). Conclusions: Using our new methodology, we developed a first-in-class CTC-RNA assay and demonstrated the feasibility of transforming clinically-relevant tissue-based RNA profiling into CTC tests. This approach allows for detecting RNA expression relevant to clinical drug resistance in a non-invasive fashion, which can facilitate patient-specific treatment selection and early detection of drug resistance- a goal in precision oncology.

179 Background: Circulating tumor cells (CTCs) have arisen as a contemporary biomarker for prostate cancer (PC). A subgroup of PC CTCs, with particularly small nuclei ( < 8.54 μm), were found to be correlated with poor prognosis and the emergence of visceral metastases (VM). This subgroup was named very-small nuclear CTCs (vsnCTCs). The findings led us to explore vsnCTCs as an aggressive biomarker in metastatic castration-resistant PC (mCRPC). We also explored a biological pathway that potentially drives this morphologic phenomenon. Studies showed that the disruption of the linker of nucleoskeleton and cytoskeleton (LINC) complex proteins, such as emerin, results in nuclear envelope instability and drives cancer cells to an amoeboid phenotype with increasing capacity of migration and invasion. We hypothesized that emerin mislocalization is associated with vsnCTC formation and may be a critical step of metastasis. Methods: Using our NanoVelcro CTC assay, we are able to capture and enumerate CTCs from patients' blood and correlate this data with clinical outcomes. We collected samples from 35 mCRPC patients who failed first-line androgen deprivation therapy and started treatment with abiraterone, enzalutamide, or taxane-based chemotherapy. Survival analyses were performed to exam the correlation between vsnCTC counts and patients’ prognosis. Concurrently, emerin staining was performed and the distribution and expression levels were analyzed in selected vsnCTC samples. Results: The presence of one or more vsnCTCs correlated with worse overall survival (P = 0.00013), progression free survival (PSA progression: P = 0.012; radiographic progression: P = 0.0015), and faster time to VM (P = 0.024). We also observed lower emerin content in vsnCTCs compared to WBC, and more prominent emerin mislocalization in vsnCTCs compared to CTCs with larger nuclei. Conclusions: Our study demonstrated the importance of morphologic characterization of CTCs and suggested that vsnCTCs is a putative biomarker for prediction of worse outcome. Additionally, our findings of emerin mislocalization in vsnCTCs suggested a potential biological pathway behind this nuclear morphologic phenomenon.

157 Background: Tissue-based gene signatures can predict clinical behavior in prostate cancer (PC). Our objective was to extend their application to circulating tumor cells (CTCs) and to show that changes in the signature were associated with changes in clinical behavior. Methods: Our approach combined the Thermoresponsive(TR)-NanoVelcro CTC purification system with the Nanostring nCounter system for cellular purification and transcriptomic analysis. The Prostate Cancer Classification System (PCS) panel was modified for use in CTCs. We selected 31 blood samples from 23 PC patients receiving androgen receptor signaling inhibitors (ARSI) and measured the PCS1 Z score (probability). These findings were compared with clinical outcome data (responsiveness/resistance). Results: A modified, 16-gene PCS1 signature was established and validated through a rigorous bioinformatics process. We performed analytical validation of our combined CTC-RNA system to ensure reproducibility and specificity. In patient bloods, ARSI-resistant samples (ARSI-R, n = 14) had significantly higher PCS1 Z scores as compared with ARSI-sensitive samples (ARSI-S, n = 17) (Rank-sum test, P = 0.003). The analyzed bloods contained samples from 8 patients who developed resistance to an ARSI allowing for dynamic measurement of gene expression. Our analysis found that the PCS1 Z score increased at the time that ARSI-resistance emerged (Pairwise T-test, P = 0.016). Conclusions: Using this new methodology, contemporary, clinically-relevant gene signatures such as PCS could be measured non-invasively in CTCs. These findings can be used to relate gene expression to clinical drug response. This approach also allowed for measurement of dynamic variations of gene expression in individual patients over time that correlated to ARSI sensitivity.