PWD and PCP are employees of Cynvenio Biosystems Inc. FB is employed by Menarini Silicon Biosystems. SK, MT, PDC, MWM, and SG are employees of ResearchDx. JU and KD are employees of Liquid Genomics. SR is an employee of NantHealth. PD is affiliated with Liquid Genomics. These companies all developed platforms used in this work.

PURPOSE:In metastatic castrate sensitive prostate cancer (mCSPC), combined androgen axis inhibition is a standard of care. Noninvasive biomarkers that guide initial therapy decisions are needed. We hypothesized that CellSearch CTC count, an FDA-cleared assay in metastatic castrate resistant PC (mCRPC), is a relevant biomarker in mCSPC.METHODS: SWOG S1216 is a phase 3 prospective randomized trial of androgen deprivation therapy (ADT) combined with orteronel or bicalutamide for mCSPC. CellSearch CTC count was measured at registration (baseline). Prespecified CTC cutpoints of 0, 1-4 and ≥5 were correlated with baseline patient characteristics and, in a stratified subsample, were also correlated with two prespecified trial secondary endpoints: 7-month PSA ≤0.2 ng/ml vs. 0.2-4.0 vs. >4.0 (intermediate endpoint for overall survival); and progression-free survival (PFS) ≤ vs. >2 years.RESULTS: 523 patients submitted baseline samples, and CTCs were detected (median 3) in 33%. Adjusting for two trial stratification factors (disease burden and timing of ADT initiation), men with undetectable CTCs had nearly 9-times the odds of attaining 7-month PSA ≤ 0.2 vs. > 4.0 (odds ratio [OR] 8.8, 95%CI 2.7-28.6, p < 0.001, N=264) and 4-times the odds of achieving > 2 years PFS (OR 4.0, 95%CI 1.9-8.5, p < 0.001, N=336) compared to men with baseline CTCs ≥5.

Transcriptomic profiling of metastatic cancer can illuminate mechanisms of progression and lead to new therapies, but standard biopsy is invasive and reflects only a single metastatic site. In contrast, circulating tumor cell (CTC) profiling is noninvasive and repeatable, reflecting the dynamic and systemic nature of advanced disease. To date, transcriptomic profiling of CTCs has not delivered on its full potential, because white blood cells (WBCs) vastly outnumber CTCs. Current profiling strategies either lack cancer sensitivity and specificity or require specialized CTC capture protocols that are not readily scalable to large patient cohorts. Here, we describe a new strategy for rapid CTC enrichment and transcriptomic profiling using commercially available WBC depletion, microfluidic enrichment and RNA sequencing. When applied to blood samples from patients with advanced prostate cancer (PC), transcriptomes from enriched samples cluster with cancer positive controls and previously undetectable prostate‐specific transcripts become readily measurable. Gene set enrichment analysis reveals multiple significantly enriched signaling pathways associated with PC, as well as novel pathways that merit further study. This accessible and scalable approach yields cancer‐specific transcriptomic data and can be applied repeatedly and noninvasively in large cancer patient cohorts to discover new therapeutic targets in advanced disease.

Integrating liquid biopsies of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) with other minimally invasive measures may yield more comprehensive disease profiles. We evaluated the feasibility of concurrent cellular and molecular analysis of CTCs and cfDNA combined with radiomic analysis of CT scans from patients with metastatic castration-resistant PC (mCRPC). CTCs from 22 patients were enumerated, stained for PC-relevant markers, and clustered based on morphometric and immunofluorescent features using machine learning. DNA from single CTCs, matched cfDNA, and buffy coats was sequenced using a targeted amplicon cancer hotspot panel. Radiomic analysis was performed on bone metastases identified on CT scans from the same patients. CTCs were detected in 77% of patients and clustered reproducibly. cfDNA sequencing had high sensitivity (98.8%) for germline variants compared to WBC. Shared and unique somatic variants in PC-related genes were detected in cfDNA in 45% of patients (MAF > 0.1%) and in CTCs in 92% of patients (MAF > 10%). Radiomic analysis identified a signature that strongly correlated with CTC count and plasma cfDNA level. Integration of cellular, molecular, and radiomic data in a multi-parametric approach is feasible, yielding complementary profiles that may enable more comprehensive non-invasive disease modeling and prediction.