scholarly journals Detection of circulating tumor cells in the cerebrospinal fluid of a patient with a solitary metastasis from breast cancer: A case report

2014 ◽  
Vol 7 (6) ◽  
pp. 2110-2112 ◽  
Author(s):  
AKSHAL S. PATEL ◽  
JOSHUA E. ALLEN ◽  
DAVID T. DICKER ◽  
JONAS M. SHEEHAN ◽  
MICHAEL J. GLANTZ ◽  
...  
2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii3-ii3
Author(s):  
Veena Singh ◽  
Deanna Fisher ◽  
Robbie Schultz ◽  
Julie Mayer ◽  
Smitha Boorgula ◽  
...  

Abstract BACKGROUND Liquid biopsy has emerged as a minimally invasive and cost-effective strategy to assess cancer biomarkers without the risk of complications associated with biopsies. Once a tumor has metastasized to the brain, circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) can be found in the cerebrospinal fluid (CSF). We analyzed CSF samples from patients(pts) with primary lung or breast cancer with either brain (BM) or leptomeningeal disease (LMD). Here we report the analytical and clinical validation of Target Selector™ CSF assays. METHODS CSF was collected prospectively from pts with a prior solid tumor diagnosis and confirmed or clinical/ radiological suspicion of BM or LMD. CTCs were captured in microfluidic channel and classified as either CK+ or CK-. Cell-free total nucleic acids (cfTNA) was extracted from CSF supernatant and underwent both Target Selector™ single gene and next-generation sequencing (NGS) NSCLC and breast multi-gene testing. For NGS, data analysis was performed using Torrent Suite with annotation and curation by Ion Reporter and Oncomine Knowledgebase Reporter software. RESULTS The Target Selector™ CTC platform assays performed on clinical samples (n = 89) resulted in clinical sensitivity = 80.0%, clinical specificity = 96.6%, positive predictive value (PPV) = 98%, negative predictive value (NPV) = 70.0% at a limit of detection of 2 CTCs. For molecular analyses, Target Selector™ platform assays resulted in clinical sensitivity = 85.2%, clinical specificity = 88.3%, PPV = 76.7%, and NPV = 93.0%. CONCLUSIONS Target Selector™ is a viable and sensitive platform for CTC detection and molecular analysis of CSF samples from patients with NSCLC or breast cancer with CNS metastases compared to the current standard of care (CSF cytology) and when imaging findings are equivocal. Identifying CTCs and actionable biomarkers can help to confirm CNS involvement when clinically suspected, guide targeted therapy selection and potentially monitor treatment response.


2014 ◽  
Vol 74 (S 01) ◽  
Author(s):  
M Wallwiener ◽  
AD Hartkopf ◽  
S Riethdorf ◽  
J Nees ◽  
FA Taran ◽  
...  

2015 ◽  
Vol 75 (08) ◽  
Author(s):  
H Schneck ◽  
B Gierke ◽  
M Pawlak ◽  
M Templin ◽  
T Fehm ◽  
...  

2001 ◽  
Vol 6 (2) ◽  
pp. 79-91 ◽  
Author(s):  
RAYMOND L. HOUGHTON ◽  
DAVIN C. DILLON ◽  
DAVID A. MOLESH ◽  
BARBARA K. ZEHENTNER ◽  
JIANGCHUN XU ◽  
...  

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1119
Author(s):  
Ivonne Nel ◽  
Erik W. Morawetz ◽  
Dimitrij Tschodu ◽  
Josef A. Käs ◽  
Bahriye Aktas

Circulating tumor cells (CTCs) are a potential predictive surrogate marker for disease monitoring. Due to the sparse knowledge about their phenotype and its changes during cancer progression and treatment response, CTC isolation remains challenging. Here we focused on the mechanical characterization of circulating non-hematopoietic cells from breast cancer patients to evaluate its utility for CTC detection. For proof of premise, we used healthy peripheral blood mononuclear cells (PBMCs), human MDA-MB 231 breast cancer cells and human HL-60 leukemia cells to create a CTC model system. For translational experiments CD45 negative cells—possible CTCs—were isolated from blood samples of patients with mamma carcinoma. Cells were mechanically characterized in the optical stretcher (OS). Active and passive cell mechanical data were related with physiological descriptors by a random forest (RF) classifier to identify cell type specific properties. Cancer cells were well distinguishable from PBMC in cell line tests. Analysis of clinical samples revealed that in PBMC the elliptic deformation was significantly increased compared to non-hematopoietic cells. Interestingly, non-hematopoietic cells showed significantly higher shape restoration. Based on Kelvin–Voigt modeling, the RF algorithm revealed that elliptic deformation and shape restoration were crucial parameters and that the OS discriminated non-hematopoietic cells from PBMC with an accuracy of 0.69, a sensitivity of 0.74, and specificity of 0.63. The CD45 negative cell population in the blood of breast cancer patients is mechanically distinguishable from healthy PBMC. Together with cell morphology, the mechanical fingerprint might be an appropriate tool for marker-free CTC detection.


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