Simple And Rapid Enrichment Of Circulating Tumor Cells (Ctcs) For Rnaseq In Metastatic Castrate Resistant Prostate Cancer (Mcrpc).

e16587 Background: CTCs have the potential to reflect not only genomic alterations but also cancer-relevant transcriptomic phenotypes. However, CTC gene expression has been hampered by signal-to-noise: rare CTC-derived transcripts are drowned out by abundant leukocyte-derived RNA. To date, a few specialized labs have achieved CTC RNAseq by capturing and analyzing single cells, a laborious and expensive approach not suitable for routine analysis of numerous samples. To address this need, we developed and validated a simple, rapid method for enrichment of live CTCs for RNAseq. Methods: Blood was drawn with informed consent under an IRB-approved protocol. Prostate cancer cell line spike-in samples were used to optimize live CTC enrichment by sequential leukocyte depletion (RosetteSep, Stem Cell Technologies) and size-based enrichment (Parsortix, Angle). Cancer-specific gene expression was first measured by multiplexed prostate specific qRT-PCR and subsequently by whole transcriptome amplification (WTA, SMARTer V2, Clontech) and RNAseq. Four patient samples were similarly analyzed by enrichment and RNAseq, along with spike-in positive controls and matched unenriched buffy coat negative controls. Results: Processing “from patient to RNA” took < 3 hrs. and achieved mean CTC recovery of 30% (range 28-33%) and mean leukocyte background of 100 (range 47-179), a 100,000-fold enrichment. Prostate specific genes (AR, PSA, PSMA) were consistently detected by qRT-PCR from enriched samples but not from unenriched samples. When analyzed by RNAseq, patient samples clustered with spike-in positive controls and away from matched buffy coat controls by principle component analysis and by unsupervised hierarchical clustering. Differential gene expression (enriched vs. matched buffy coat) identified prostate cancer-relevant transcripts. Conclusions: We developed a simple and efficient method for live CTC enrichment and expression profiling, applicable to large numbers of patient samples. This approach can be used serially over time to detect known cancer-specific transcripts and to discover new gene expression signatures that reflect tumor biology and inform disease management.