Abstract 3777: RNA profiles of circulating tumor cells and extracellular vesicles for therapy stratification of metastatic breast cancer patients

Abstract

Abstract Background: Blood analytes, as liquid biopsies, are discussed to be surrogate markers for therapy stratification of metastatic breast cancer (MBC) patients. Repeated analysis is enabled by the minimal invasive nature of blood draw. Analysis of RNA enclosed in circulating tumor cells (CTCs) or extracellular vesicles (EVs) may be sensitive enough to detect disease progression earlier than contemporary visual staging methods. A prediction of the ideal therapy strategy via characterization of CTCs or EVs would be even more beneficial. Here we compare RNA profiles of CTCs and EVs in MBC patients to get insight into their feasibility for therapy stratification. Patients and methods: Blood was collected from 10 MBC patients at the time of disease progression (T0) and at two consecutive clinical staging time points (T1 and T2) during therapy. Two cohorts were separated according to RECIST criteria a) Overall Responder showed response at T1 and T2 and b) Late Non-Responder displayed stable disease or partial remission at T1, but showed progressive disease at T2. CTCs were isolated from 5 ml blood by positive immunomagnetic selection targeting EpCAM, EGFR and HER2 (AdnaTest EMT2/StemCell Select TM, QIAGEN, Germany). EVs were isolated from 4 ml pre-filtered plasma by affinity-based binding to a spin column (exoRNeasy, QIAGEN, Germany). mRNA bound to Oligo-dT beads was purified and reverse transcribed (AdnaTest EMT2/StemCell Detect TM, QIAGEN, Germany). Pre-amplified cDNA was analysed by a multimarker qPCR (AdnaPanel TNBC, QIAGEN, Germany). RNA profiles of 18 genes (including AKT2, ALK, AR, AURKA, BRCA1, cKIT, cMET, EGFR, ERCC1, HER2, HER3, KRT5, mTOR, NOTCH1, PARP1, PI3K, SRC1, GAPDH) were normalized by data of healthy donors (n=20) and CD45 served as leukocyte control in the CTC preparation. Results: In general, data analysis showed great differences in RNA profiles of EVs and CTCs. However, PI3K and SRC1 signals were found in similar frequencies in the EV and CTC fraction. HER2, HER3, cKIT or cMET signals significantly correlated with disease progression by analysis of either CTCs or EVs (p=0.001). In EVs, BRCA1 signals positively indicated response, while AR and KRT5 signals were related to a negative response. No marker was exclusively found in CTCs to correlate to the therapy-response course. In CTCs and EVs, however, similar signal courses for PI3K and cMET were found across all time points, revealing PI3K as potential positive response marker and cMET as potential negative response marker in both blood analytes. Conclusions: Expression profiling in CTCs as well as in EVs is enabled by the described workflows. Preliminary data indicated great differences in RNA profiles of EVs and CTCs. The amount of included patients is continuously increased to validate the preliminary results obtained until now. Citation Format: Corinna Keup, Siegfried Hauch, Linda Plappert, Markus Sprenger-Haussels, Pawel Mach, Mitra Tewes, Bahriye Aktas, Hans-Christian Kolberg, Rainer Kimmig, Sabine Kasimir-Bauer. RNA profiles of circulating tumor cells and extracellular vesicles for therapy stratification of metastatic breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3777. doi:10.1158/1538-7445.AM2017-3777