Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
Circulating tumor cells (CTCs) represent a unique population of cells that can be used to investigate the mechanistic underpinnings of metastasis. Unfortunately, current technologies designed for the isolation and capture of CTCs are inefficient. Existing literature for in vitro CTC cultures report low (6−20%) success rates. Here, we describe a new method for the isolation and culture of CTCs. Once optimized, we employed the method on 12 individual metastatic breast cancer patients and successfully established CTC cultures from all 12 samples. We demonstrate that cells propagated were of breast and epithelial origin. RNA-sequencing and pathway analysis demonstrated that CTC cultures were distinct from cells obtained from healthy donors. Finally, we observed that CTC cultures that were associated with CD45+ leukocytes demonstrated higher viability. The presence of CD45+ leukocytes significantly enhanced culture survival and suggests a re-evaluation of the methods for CTC isolation and propagation. Routine access to CTCs is a valuable resource for identifying genetic and molecular markers of metastasis, personalizing the treatment of metastatic cancer patients and developing new therapeutics to selectively target metastatic cells.
Purpose: Imaging mass cytometry (IMC) uses metalconjugated antibodies to provide multidimensional, objective measurement of protein targets. We used this highthroughput platform to perform an 18-plex assessment of HER2 ICD/ECD, cytotoxic T-cell infiltration and other structural and signaling proteins in a cohort of patients treated with trastuzumab to discover associations with trastuzumab benefit. Experimental Design: An antibody panel for detection of 18 targets (pan-cytokeratin, HER2 ICD, HER2 ECD, CD8, vimentin, cytokeratin 7, b-catenin, HER3, MET, EGFR, ERK 1-2, MEK 1-2, PTEN, PI3K p110 a, Akt, mTOR, Ki67, and Histone H3) was used with a selection of trastuzumab-treated patients from the Hellenic Cooperative Oncology Group 10/ 05 trial (n ¼ 180), and identified a case-control series. Results: Patients that recurred after adjuvant treatment with trastuzumab trended toward a decreased fraction of HER2 ECD pixels over threshold compared with cases without recurrence (P ¼ 0.057). After exclusion of the lowest HER2 expressers, 5-year recurrence events were associated with reduced total extracellular domain (ECD)/intracellular domain (ICD) ratio intensity in tumor (P ¼ 0.044). These observations are consistent with our previous work using quantitative immunofluorescence, but represent the proof on identical cell content. We also describe the association of the ECD of HER2 with CD8 T-cell infiltration on the same slide. Conclusions: The proximity of CD8 cells as a function of the expression of the ECD of HER2 provides further evidence for the role of the immune system in the mechanism of action of trastuzumab.
Mass cytometry or CyTOF is an emerging technology for high-dimensional multiparameter single cell analysis that overcomes many limitations of fluorescence-based flow cytometry. New methods for analyzing CyTOF data attempt to improve automation, scalability, performance, and interpretation of data generated in large studies. Assigning individual cells into discrete groups of cell types (gating) involves time-consuming sequential manual steps, untenable for larger studies. We introduce DeepCyTOF, a standardization approach for gating, based on deep learning techniques. DeepCyTOF requires labeled cells from only a single sample. It is based on domain adaptation principles and is a generalization of previous work that allows us to calibrate between a target distribution and a source distribution in an unsupervised manner. We show that Deep-CyTOF is highly concordant (98%) with cell classification obtained by individual manual gating of each sample when applied to a collection of 16 biological replicates of primary immune blood cells, even when measured accross several instruments. Further, DeepCyTOF achieves very high accuracy on the semi-automated gating challenge of the FlowCAP-I competition as well as two CyTOF datasets generated from primary immune blood cells: (i) 14 subjects with a history of infection with West Nile virus (WNV), (ii) 34 healthy subjects of different ages. We conclude that deep learning in general, and DeepCyTOF specifically, offers a powerful computational approach for semi-automated gating of CyTOF and flow cytometry data.
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