Targeted transcript quantification in single disseminated cancer cells after whole transcriptome amplification

Gene expression analysis of rare or heterogeneous cell populations such as disseminated cancer cells (DCCs) requires a sensitive method allowing reliable analysis of single cells. Therefore, we developed and explored the feasibility of a quantitative PCR (qPCR) assay to analyze single-cell cDNA pre-amplified using a previously established whole transcriptome amplification (WTA) protocol. We carefully selected and optimized multiple steps of the protocol, e.g. re-amplification of WTA products, quantification of amplified cDNA yields and final qPCR quantification, to identify the most reliable and accurate workflow for quantitation of gene expression of the ERBB2 gene in DCCs. We found that absolute quantification out-performs relative quantification. We then validated the performance of our method on single cells of established breast cancer cell lines displaying distinct levels of HER2 protein. The different protein levels were faithfully reflected by transcript expression across the tested cell lines thereby proving the accuracy of our approach. Finally, we applied our method to breast cancer DCCs of a patient undergoing anti-HER2-directed therapy. Here, we were able to measure ERBB2 expression levels in all HER2-protein-positive DCCs. In summary, we developed a reliable single-cell qPCR assay applicable to measure distinct levels of ERBB2 in DCCs

Reliable single cell array CGH for clinical samples.

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

PCR-based PCR-T2 labeling technique vs. RP labeling.

<p>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.</p

Quantitative assessment of copy number changes in tumor cells by single cell aCGH.

<p>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.</p

Molecular findings in individual DCCs over the course of systemic treatment.

<p>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.</p

Experimental approach.

<p>Overview of the single cell aCGH procedure: single cells were isolated by micromanipulation and subjected to <i>Ampli1™</i> 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.</p

In Vivo Detection of Circulating Tumor Cells in High-Risk Non-Metastatic Prostate Cancer Patients Undergoing Radiotherapy

High-risk non-metastatic prostate cancer (PCa) has the potential to progress into lethal disease. Treatment options are manifold but, given a lack of surrogate biomarkers, it remains unclear which treatment offers the best results. Several studies have reported circulating tumor cells (CTCs) to be a prognostic biomarker in metastatic PCa. However, few reports on CTCs in high-risk non-metastatic PCa are available. Herein, we evaluated CTC detection in high-risk non-metastatic PCa patients using the in vivo CellCollector CANCER01 (DC01) and CellSearch system. CTC counts were analyzed and compared before and after radiotherapy (two sampling time points) in 51 high-risk non-metastatic PCa patients and were further compared according to isolation technique; further, CTC counts were correlated to clinical features. Use of DC01 resulted in a significantly higher percentage of CTC-positive samples compared to CellSearch (33.7% vs. 18.6%; p = 0.024) and yielded significantly higher CTC numbers (range: 0–15 vs. 0–5; p = 0.006). Matched pair analysis of samples between two sampling time points showed no difference in CTC counts determined by both techniques. CTC counts were not correlated with clinicopathological features. In vivo enrichment using DC01 has the potential to detect CTC at a higher efficiency compared to CellSearch, suggesting that CTC is a suitable biomarker in high-risk non-metastatic PCa