Abstract
Simple SummaryOvarian Cancer (OC) is one of the leading causes of death among gynecological tumors and there is still an insufficient understanding of its evolution. Blood, as a minimal invasive tool, allows multiple sampling over the treatment course and genomic single circulating tumor cell (sCTC) data provide the opportunity to investigate the genetic tumor evolution. CTC detection in OC remains difficult, due to epithelial-mesenchymal transition (EMT). This proof of principle study presents a workflow to generate sCTC genomic data, with the need of further studies to improve the CTC detection rate and enable insights into tumor evolution on a sCTC resolution to identify new treatment targets and/or biomarkers for an early treatment intervention.In Ovarian Cancer (OC), the analysis of single circulating tumor cells (sCTCs) might help to investigate genetic tumor evolution during the course of treatment. Since common CTC identification features failed to reliably detect CTCs in OC, we here present a workflow for their detection and genomic analysis. Blood of 13 high-grade serous primary OC patients was analyzed, using negative immunomagnetic enrichment, followed by immunofluorescence staining and imaging for Hoechst, ERCC1, CD45, CD11b and cytokeratin (CK) and sCTC sorting with the DEPArrayTM NxT. The whole genome of single cells was amplified and profiled for copy number variation (CNV). We detected: Type A-cells, epithelial (Hoechstpos, ERCC1pos, CD45neg, CD11bpos, CKpos); Type B-cells, potentially epithelial (Hoechstpos, ERCC1pos, CD45neg, CD11bpos, CKneg) and Type C-cells, potentially mesenchymal (Hoechstpos, ERCC1pos, CD45neg, CD11bneg, CKneg). In total, we identified five (38.5%) patients harboring sCTCs with an altered CN profile, which were mainly Type A-cells (80%). In addition to inter-and intra-patient genomic heterogeneity, high numbers of Type B- and C-cells were identified in every patient with their aberrant character only confirmed in 6.25% and 4.76% of cases. Further identification markers and studies in the course of treatment are under way to expand sCTC analysis for the identification of tumor evolution in OC.
Highlights
Ovarian Cancer (OC) is one of the leading causes of death among gynecological tumors, because of late-stage diagnosis and frequently occurring disease relapses
High-grade serous ovarian cancer (HGSOC) is the most frequent histological subtype and late stage diagnosis is strongly correlated with worse prognosis [1]
Most cancers can be linked to driver mutations, causing disease progression, whereas OC is commonly characterized by a chromosomal instability (CIN), resulting in great amounts of copy number alterations (CNA) with TP53 mutations being only one of the genetic driving forces [2,3,4]
Summary
Ovarian Cancer (OC) is one of the leading causes of death among gynecological tumors, because of late-stage diagnosis and frequently occurring disease relapses. There is still an insufficient understanding of OC disease evolution, since tumor tissue is usually only available at primary diagnosis To circumvent this problem, blood, as a minimally invasive tool, has frequently been used because it allows multiple sampling over the treatment course for the detection and characterization of different analytes. We here present a newly established workflow for sCTC detection and characterization in blood samples of 13 HGSOC patients at primary diagnosis, applying MACS technology for negative immunomagnetic enrichment with CD45 and CD235a antibodies, followed by sCTC sorting using the DEPArrayTM NXT based on fluorescence imaging for Hoechst, Cancers 2021, 13, 3748. To optimize ERCC1and CD11b- antibody concentration and DEPArrayTM NxT settings for single cell detection (Appendix A, Figures A2 and A3), as described below, an approximate number of 1000 OVCAR-3 cells were added to 10 mL EDTA blood from healthy donors
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