Molecular Subtype Conversion between Primary and Metastatic Breast Cancer Corresponding to the Dynamics of Apoptotic and Intact Circulating Tumor Cells.
Stefan StefanovicThomas M DeutschRalph WirtzAndreas HartkopfHans-Peter SinnFlorian SchuetzChristof SohnMichael K BohlmannMarc SütterlinAndreas SchneeweissMarkus WallwienerPublished in: Cancers (2019)
The presence of circulating tumor cells (CTCs), detected as a form of liquid biopsy is associated with poor survival in both early and metastatic breast cancer. Monitoring tumor biology based on intrinsic subtypes delivers treatment-relevant information on the heterogeneity or biomarker conversion between primary and metastatic tumors. This study aimed to correlate the change of the apoptotic and intact CTC counts with mRNA-assessed intrinsic subtype change. Thirty-four breast cancer patients with available triplets of primary tumors, distant metastasis biopsies and data on intact and apoptotic CTC dynamics were included in the analysis. The intrinsic subtype was determined per RT-qPCR quantification of the gene expression ESR1, PGR, ERBB2 and MKI67. Both luminal (p = 0.038) and triple negative (p = 0.035) patients showed a significant downregulation of apoptotic CTCs. Repeated biopsies of distant metastatic sites, as well as determining a potential shift of the intrinsic subtype, combined with data on intact and apoptotic CTC dynamics from liquid biopsies might help personalize systemic therapy and generate additional surrogate markers for successful systemic therapy.
Keyphrases
- circulating tumor cells
- metastatic breast cancer
- cell death
- gene expression
- anti inflammatory
- circulating tumor
- squamous cell carcinoma
- ultrasound guided
- small cell lung cancer
- end stage renal disease
- lymph node
- electronic health record
- ejection fraction
- chronic kidney disease
- big data
- dna methylation
- cell proliferation
- ionic liquid
- single cell
- prognostic factors
- mesenchymal stem cells
- binding protein
- young adults
- fine needle aspiration
- risk assessment
- cell therapy
- deep learning
- climate change