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. 2014 Jan 21;9(1):e85907.
doi: 10.1371/journal.pone.0085907. eCollection 2014.

Reliable single cell array CGH for clinical samples

Affiliations

Reliable single cell array CGH for clinical samples

Zbigniew T Czyż et al. PLoS One. .

Abstract

Background: Disseminated cancer cells (DCCs) and circulating tumor cells (CTCs) are extremely rare, but comprise the precursors cells of distant metastases or therapy resistant cells. The detailed molecular analysis of these cells may help to identify key events of cancer cell dissemination, metastatic colony formation and systemic therapy escape.

Methodology/principal findings: Using the Ampli1™ whole genome amplification (WGA) technology and high-resolution oligonucleotide aCGH microarrays we optimized conditions for the analysis of structural copy number changes. The protocol presented here enables reliable detection of numerical genomic alterations as small as 0.1 Mb in a single cell. Analysis of single cells from well-characterized cell lines and single normal cells confirmed the stringent quantitative nature of the amplification and hybridization protocol. Importantly, fixation and staining procedures used to detect DCCs showed no significant impact on the outcome of the analysis, proving the clinical usability of our method. In a proof-of-principle study we tracked the chromosomal changes of single DCCs over a full course of high-dose chemotherapy treatment by isolating and analyzing DCCs of an individual breast cancer patient at four different time points.

Conclusions/significance: The protocol enables detailed genome analysis of DCCs and thereby assessment of the clonal evolution during the natural course of the disease and under selection pressures. The results from an exemplary patient provide evidence that DCCs surviving selective therapeutic conditions may be recruited from a pool of genomically less advanced cells, which display a stable subset of specific genomic alterations.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Experimental approach.
Overview of the single cell aCGH procedure: single cells were isolated by micromanipulation and subjected to Ampli1™ WGA protocol. Primary WGA product can be re-amplified for further downstream applications (optional step). DNA was labeled by RP labeling or PCR-based approaches (PCR-T1 and PCR-T2). Subsequent hybridization was carried out on Agilent SurePrint G3 Human 4×180k arrays resulting data was evaluated with Agilent Genomic Workbench Software.
Figure 2
Figure 2. PCR-based PCR-T2 labeling technique vs. RP labeling.
A) Chromosome specific aCGH profiles of chromosome 8. B) Chromosome specific aCGH profiles of 17. Each panel represents aCGH profiles generated with unamplified and single-cell gDNA (PCR-T2 labeling) – left and middle plot, respectively. The right plot of each panel represents magnified graphical overview of genes within loci recognized as aberrant. C) ROC-curves (corresponding to profiles presented in panel A) depicting the accuracy of single cell aCGH assay for PCR-T2 or RP labeling. The array CGH profile generated using unamplified gDNA of OE-19 cells was taken as reference for the comparison. ROC analysis was performed on a genome-wide basis. D) Genome wide aCGH profiles of OE-19 cells generated using unamplified gDNA (upper panel) and a single-cell WGA product labeled with PCR-T2 (middle panel) or RP labeling approach.
Figure 3
Figure 3. Assessment of the cellular heterogeneity.
A) Vertical aCGH profiles of chromosome 17 for unamplified gDNA and single-cell WGA products of PT1590 cells. Red arrow indicate ERBB2 locus. Note, one single cell (cell 3) shows a balanced profile at this site. Black arrows indicate private genetic alteration detected only in individual cells. B) Representative FISH images of PT1590 cells. Red signals indicate ERBB2 locus and green CEP17. White arrows label cells with balanced copy number of ERBB2 vs. CEP17. Yellow arrowhead shows cells with high-level amplification of the ERBB2 locus.
Figure 4
Figure 4. Quantitative assessment of copy number changes in tumor cells by single cell aCGH.
A) Vertical aCGH profiles of chromosome 17 of four breast cancer cell lines with increasing copy number of ERBB2 locus (MDA-MB-453, MDA-MB-361, SKBR3 and BT474) generated using unamplified DNA (upper row) or single-cell WGA products (lower row). Red brackets indicate the position of the ERBB2 locus. Corresponding FISH ratios (ERBB2 vs. CEP17) of all cell lines are indicated in blue brackets. B) Correlation of average log2 values of ERBB2 specific probes obtained in single-cell aCGH experiments (Y-axis) vs. corresponding values obtained with unamplified DNA (X-axis). DNA samples from four breast cancer cell lines (MDA-MB-453, MDA-MB-361, SKBR3, BT474) have been included in the analysis. Pearson’s correlation coefficient 0.94. C) Correlation of average log2 values specific for ERBB2 locus obtained in single-cell aCGH experiments (Y-axis) vs. FISH ratios (ERBB2/CEP17) calculated for four breast cancer cell lines: MDA-MB-453, MDA-MB-361, SKBR3, BT474. Pearson’s correlation coefficient 0.97.
Figure 5
Figure 5. Molecular findings in individual DCCs over the course of systemic treatment.
A) Chemotherapy regime: first patient was subjected to three cycles of 75 mg/m2 Taxotere® (T) and 50 mg/m2 Doxorubicin (D) in three week intervals. Subsequently followed two cycles of high dose chemotherapy treatment with an intermediate interval of 4-6 weeks. In the first cycle 500 mg/m2 of Vepesid (V), 4000 mg/m2 of Isofamid (I) and 500 mg/m2 of Carboplatin (C) was administered. The last cycle consisted of 1500 mg/m2 of Cyclophosphamid (Cy) and 200 mg/m2 of Thiotepa (T). Both cycles of high dose chemotherapeutic treatment were accompanied by addition of granulocyte colony-stimulating factor (G-CSF), and autologous transplant of peripheral blood stem cells (PBSCs). B) The course and outcome of bone marrow sampling. DCC count indicated the number of identified DCCs in 1.0×106 mononuclear cells. C) Venn diagram depicting distribution of MRAs (gains and losses) across three types of clinical samples. D) Hierarchical clustering (distance: Euclidian; linkage: average) of samples including DCCs, the primary tumor and a metastatic lesion. E) Table depicting core MRAs that were found in all DCCs, the primary tumor and the metastatic lesion or in the metastatic compartments (DCCs and the lymph metastasis) only.

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