Aaron B. Beasley, Edith Cowan UniversityFollow
Timothy W. Isaacs
James Freeman, Edith Cowan UniversityFollow
Jean-Louis De Sousa
Anna Reid, Edith Cowan UniversityFollow
Fred K. Chen
R. Max Conway
Michelle R. Pereira, Edith Cowan UniversityFollow
Leslie Calapre, Edith Cowan UniversityFollow
Wendy N. Erber
Melanie R. Ziman, Edith Cowan UniversityFollow
Elin S. Gray, Edith Cowan UniversityFollow
School of Medical and Health Sciences / Centre for Precision Health
Edith Cowan University
Cancer Council of Western Australia
National Health and Medical Research Council
Raine Medical Research Foundation
Ophthalmic Research Institute of Australia
NHMRC Number : MRF1142962
The stratification of uveal melanoma (UM) patients into prognostic groups is critical for patient management and for directing patients towards clinical trials. Current classification is based on clinicopathological and molecular features of the tumour. Analysis of circulating tumour cells (CTCs) has been proposed as a tool to avoid invasive biopsy of the primary tumour. However, the clinical utility of such liquid biopsy depends on the detection rate of CTCs.
The expression of melanoma, melanocyte, and stem cell markers was tested in a primary tissue microarray (TMA) and UM cell lines. Markers found to be highly expressed in primary UM were used to either immunomagnetically isolate or immunostain UM CTCs prior to treatment of the primary lesion. (3)
TMA and cell lines had heterogeneous expression of common melanoma, melanocyte, and stem cell markers. A multi-marker panel of immunomagnetic beads enabled isolation of CTCs in 37/43 (86%) patients with UM. Detection of three or more CTCs using the multi-marker panel, but not MCSP alone, was a significant predictor of shorter progression free (p = 0.040) and overall (p = 0.022) survival.
The multi-marker immunomagnetic isolation protocol enabled the detection of CTCs in most primary UM patients. Overall, our results suggest that a multi-marker approach could be a powerful tool for CTC separation for non-invasive prognostication of UM.
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Multidisciplinary biological approaches to personalised disease diagnosis, prognosis and management