Size-based detection of sarcoma circulating tumor cells and cell clusters

• Published in: Oncotarget 2017-10, Vol.8 (45), p.78965-78977
• Main Authors: Hayashi, Masanori, Zhu, Peixuan, McCarty, Gregory, Meyer, Christian F, Pratilas, Christine A, Levin, Adam, Morris, Carol D, Albert, Catherine M, Jackson, Kyle W, Tang, Cha-Mei, Loeb, David M
• Format: Article
• Language: English
• Subjects: Research Paper
• ISSN: 1949-2553; 1949-2553
• DOI: 10.18632/oncotarget.20697

Metastatic disease is the most important factor in determining the survival of sarcoma patients. Since sarcoma metastasis is predominantly hematogenous, we hypothesized that detection and quantification of circulating tumor cells (CTCs) could reflect response to therapy and risk of metastatic relapse. We evaluated the presence of CTCs using a novel animal model and in the blood of patients with high grade sarcomas utilizing the CellSieve™ size-based low pressure microfiltration system. Sarcoma CTCs were identified based on antibody staining patterns and nuclear morphology. Additionally, RNA was extracted from the CTCs for molecular analysis including demonstration of an EWS-FLI1 translocation, identification of a previously unrecognized p53 mutation in a patient with Ewing sarcoma, and single cell RNA sequencing of CTC from a child with alveolar rhabdomyosarcoma. In mouse xenograft models, the presence of CTC correlates with disease burden and with clinically silent metastases. In human patients, CTCs were readily detected at diagnosis, decreased with successful treatment, and were detectable in the blood of patients with no radiographic evidence of disease prior to the development of overt metastasis. Although evaluation of CTC is established in the care of patients with carcinomas, this technology has yet to be effectively applied to the evaluation and treatment of sarcoma patients. Our work demonstrates that the CellSieve™ microfiltration system can be used to study the biology of CTC in both mouse models and human sarcoma patients, with the potential for application to the monitoring of disease response and prediction of metastatic relapse.