Search

Additional file 4. Figure showing quantification of western blotting band intensities. Quantification of western blotting band intensities presented in Fig. 1c, 2a-c and 5a as well as Additional Files 3F, 5A-C and 9B. Combined data from at least three biological replicates. Bands were normalized to total lane protein and set relative to untreated parental cells, except for CDK6 in (J, L and M) that where set relative to fulvestrant-resistant cells. Statistical differences were determined with one-way ANOVA and Dunnett’s post-hoc test, * represents p-value ≤0.05, ** ≤0.01 and *** ≤0.001 compared to respective untreated parental control.

Additional file 5. Figure showing fulvestrant-withdrawal data for the ZR-75-1, T47D and EFM-19 models. Fulvestrant dose-response curves (5 nM to 10 μM) and western blotting for ERα expression in fulvestrant-resistant (-FR) ZR-75-1 (A), T47D (B) and EFM-19 (C) cells cultured either continuously with fulvestrant (+F, red solid lines) or after removal of fulvestrant (-F, red dotted lines) from the growth media for the indicated times (Week 1-Week 9). Parental (-P) cells cultured without fulvestrant were used as control (black solid lines). Graphs represent combined data (average ± SEM) from three biological replicates with at least three technical replicates each. Samples for western blotting were collected at each time-point and ERα protein expression was assessed. β-actin was used as loading control. Representative data from three biological replicates is presented under each graph. Quantification of band intensities is presented in Additional file 4E-G. Stars indicate differences between fulvestrant-resistant cells grown with (red, solid lines) or without (red, dotted lines) fulvestrant. Stars are indicated at every other data point due to restricted space.

Additional file 2. Table showing patient characteristics for patient samples pre and post fulvestrant treatment.

Additional file 10. Figure showing survival analyses of cyclin E1 and E2 in fulvestrant-treated metastatic breast cancer patients. Kaplan-Meier plot for overall survival (OS) in cyclin E2 low versus high expression in ER+ metastatic breast cancer patients treated with fulvestrant in the advanced setting (A) and for progression-free (PFS) (B) and overall (C) survival in cyclin E1 low versus high expression in the same patient material. P-value represents log-rank test for OS and PFS, respectively, between patients with high and low levels of cyclin E1 or E2 expression.

Additional file 7. Figure showing BAF and LogR files for copy number data. B allele frequency (BAF) and LogR genome-wide plots for SNP profiled cell line data presented in Fig. 4a. Chromosomes are ordered along the x-axis from 1 (left) to Y (right). Green lines represent segments derived from ASCAT 2 segmentation of the data.

Additional file 3. Figure showing that fulvestrant-resistant cells proliferate in the presence of fulvestrant and downregulate ER signaling. A) Time in weeks for each parental cell line to develop resistance to fulvestrant, from initial 100 pM dose until able to proliferate in presence of 100 nM fulvestrant. B) Proliferation curves for parental (-P, black lines) and fulvestrant-resistant (-FR, red lines) cells in the absence (ctrl, solid lines) or presence (+F, dotted lines) of 100 nM fulvestrant assessed using SRB assays (CAMA-1, MCF7, HCC1428, ZR-75-1) or xCELLigence system (T47D, EFM-19). Graphs represent combined data (average ± SEM) from two (xCELLigence) or three (SRB) biological experiments with at least three technical replicates each. Statistical differences were determined with two-way ANOVA and Tukey’s post-hoc test. * represents p-value ≤0.01, ** ≤0.001 and *** ≤0.0001 between fulvestrant-treated parental (black dotted lines) and fulvestrant-resistant (red dotted lines) cells. C) Fulvestrant IC50 values in parental and fulvestrant-resistant cells. Calculated from graphs presented in Fig. 1a. P-values were calculated using Extra sum-of-squares F test. D) Quantitative RT-PCR analysis of RNA expression for the ER downstream target genes insulin like growth factor binding protein 4 (IGFBP4), growth regulation by estrogen in breast cancer 1 (GREB1) and progesterone receptor (PGR) in parental and fulvestrant-resistant cells after 24-h treatment with 100 nM fulvestrant (+F) or no treatment (ctrl). Bar graphs represent average expression (± SEM) from two biological experiments with three technical replicates each, normalized against ACTB expression and set relative to untreated parental cells. Statistical differences were determined with one-way ANOVA and Dunnett’s post-hoc test, *** represents p-value ≤0.001 compared to respective untreated parental control. E) ERE reporter activity in parental and fulvestrant-resistant ZR-75-1, T47D and EFM-19 cells after treatment for 24 h with (+F) or without (-F) supplementation of 100 nM fulvestrant. Graphs represent combined data (average ± SEM) from two biological experiments with two technical replicates each. Statistical differences were determined with one-way ANOVA and Tukey’s post-hoc test, *** represents p-value ≤0.0001 between untreated parental and fulvestrant-resistant cells. F) Western blotting for ERα expression in parental and fulvestrant-resistant ZR-75-1, T47D and EFM-19 cells after treatment for 24 h with 100 nM fulvestrant (+F) or DMSO (-F). Representative data from three biological replicates. β-actin was used as loading control. Quantification of band intensities is presented in Additional file 4A.

Additional file 8. Table showing common processes enriched in fulvestrant-resistant cells identified by DAVID bioinformatics tool. DAVID functional categories and gene ontology (GO) functions as well as KEGG and Biocarta pathways for genes downregulated upon fulvestrant treatment in parental cells and then upregulated again upon development of resistance. The first table show data for CAMA-1, MCF7 and T47D and the second table show data HCC1428.

Additional file 6. Figure showing estradiol and tamoxifen response in the ZR-75-1, T47D and EFM-19 models. Relative proliferation of parental and fulvestrant-resistant cells in estrogen depleted (A) or normal (B) growth media with or without supplementation with 1 nM estradiol (E2) (A) or 100 nM 4-hydroxytamoxifen (4-OHT) (B) for 6 days. Each graph represents combined data (average ± SEM) from two biological experiments with three technical replicates each. Statistical differences were determined using one-way ANOVA with Tukey’s post-hoc test. *** represents p-value ≤0.0001, ** ≤0.001, ns represents no statistical differences. Stars and ‘ns’ in (A) indicate statistical differences compared to -E2 for each cell model unless indicated otherwise. Stars and ‘ns’ in (B) indicate statistical differences compared to -4-OHT for each cell model unless indicated otherwise.

Additional file 9. Figure showing cell cycle regulation and response to CDK inhibitors. A) Cell cycle profiles for parental (-P) and fulvestrant-resistant (-FR) CAMA-1 and MCF7 cells showing the proportion of cells in G0/G1, S, and G2/M phase, respectively, after treatment with 100 nM fulvestrant for 72 h (+F) or untreated (ctrl). Combined data from two biological replicates. Statistical differences between the proportion of cells in G0/G1-phase were calculated using student’s t-test. * represents p-value ≤0.05, ns represents no statistical differences. B) Western blotting for expression of cell cycle regulating proteins in parental and fulvestrant-resistant T47D and EFM-19 cells after treatment for 24 h with 100 nM fulvestrant (+F) or DMSO control (-F). Representative data from three biological replicates. β-actin was used as loading control. Quantification of band intensities is presented in Additional file 4 L-M. C) Palbociclib IC50 values in parental and fulvestrant-resistant cells. Calculated from graphs presented in (D) and in Fig. 5b. D) Dose-response curves for parental and fulvestrant-resistant T47D and EFM-19 cells in response to 6-days treatment with increasing concentrations of the CDK4/6 inhibitor palbociclib (1 nM to 10 μM). Graphs represent combined data (average ± SEM) from three biological experiments with three technical replicates each. IC50 values are shown in (C). E) CDKi3 IC50 values in parental and fulvestrant-resistant cells. Calculated from graphs presented in (F) and in Fig. 5c. F) Dose-response curves for parental and fulvestrant-resistant T47D and EFM-19 cells in response to 6-days treatment with increasing concentrations of the CDK1/2 inhibitor CDKi3 (100 pM to 50 μM). Graphs represent combined data (average ± SEM) from three biological experiments with three technical replicates each. IC50 values are shown in (E). G) Time in weeks needed for each parental and fulvestrant-resistant cell line to develop resistance to palbociclib, counted from initial exposure to 100 pM dose until the cells were able to proliferate in presence of 1 μM palbociclib. H) Dose-response curves for parental (-P, solid black lines), fulvestrant-resistant (-FR, solid red lines), palbociclib-resistant (-PalbRes, black dotted lines) and double fulvestrant- and palbociclib-resistant (-FR-PalbRes, red dotted lines) CAMA-1 and MCF7 cells in response to 6-days treatment with palbociclib (1 pM to 10 μM). Graphs represent combined data (average ± SEM) from two biological experiments with three technical replicates each. IC50 values are shown (I). I) Palbociclib IC50 values for graphs in (H). J) Fulvestrant IC50 values for graphs presented in Fig. 5d. P-values in (C, E, I and J) were calculated using Extra sum-of-squares F test.

Objective: Quantification of small molecule numbers often requires preamplification to generate enough copies for accurate downstream enumerations. Here, we studied experimental parameters in targeted preamplification and their effects on downstream quantitative real-time PCR (qPCR). Methods: To evaluate different strategies, we monitored the preamplification reaction in real-time using SYBR Green detection chemistry followed by melting curve analysis. Furthermore, individual targets were evaluated by qPCR. Result: The preamplification reaction performed best when a large number of primer pairs was included in the primer pool. In addition, preamplification efficiency, reproducibility and specificity were found to depend on the number of template molecules present, primer concentration, annealing time and annealing temperature. The amount of nonspecific PCR products could also be reduced about 1000-fold using bovine serum albumin, glycerol and formamide in the preamplification. Conclusion: On the basis of our findings, we provide recommendations how to perform robust and highly accurate targeted preamplification in combination with qPCR or next-generation sequencing.

Cell proliferation includes a series of events that is tightly regulated by several checkpoints and layers of control mechanisms. Most studies have been performed on large cell populations, but detailed understanding of cell dynamics and heterogeneity requires single-cell analysis. Here, we used quantitative real-time PCR, profiling the expression of 93 genes in single-cells from three different cell lines. Individual unsynchronized cells from three different cell lines were collected in different cell cycle phases (G0/G1 – S – G2/M) with variable cell sizes. We found that the total transcript level per cell and the expression of most individual genes correlated with progression through the cell cycle, but not with cell size. By applying the random forests algorithm, a supervised machine learning approach, we show how a multi-gene signature that classifies individual cells into their correct cell cycle phase and cell size can be generated. To identify the most predictive genes we used a variable selection strategy. Detailed analysis of cell cycle predictive genes allowed us to define subpopulations with distinct gene expression profiles and to calculate a cell cycle index that illustrates the transition of cells between cell cycle phases. In conclusion, we provide useful experimental approaches and bioinformatics to identify informative and predictive genes at the single-cell level, which opens up new means to describe and understand cell proliferation and subpopulation dynamics.

Breast cancer is characterized by a high degree of heterogeneity in terms of histological, molecular and clinical features, affecting disease progression and treatment response. The cancer stem cell (CSC) model suggests, that cancers are organized in a hierarchical fashion and driven by small subsets of CSCs, endowed with the capacity for self-renewal, differentiation, tumorigenicity, invasiveness and therapeutic resistance. The overall aim of this thesis was to characterize CSC phenotypes and the cellular organization in estrogen receptor α + (ERα+) and ERα- subtypes of breast cancer at the individual cell level. Furthermore, we aimed to identify novel functional CSC markers in a subtype-independent manner, allowing for better identification and targeting of breast-specific CSCs. At present, single-cell quantitative reverse transcription polymerase chain reaction represents the most commonly applied method to study transcript levels in individual cells. Inherent to most single-cell techniques is the difficulty to analyze minute amounts of starting material, which most often requires a preamplification step to multiply transcript copy numbers in a quantitative manner. In Paper I we have evaluated effects of variations of relevant parameters on targeted cDNA preamplification for single-cell applications, improving reaction sensitivity and specificity, pivotal prerequisites for accurate and reproducible transcript quantification. In Paper II we have applied single-cell gene expression profiling in combination with three functional strategies for CSC enrichment and identified distinct CSC/progenitor clusters in ERα+ breast cancer. ERα+ tumors display a hierarchical organization as well as different modes of cell transitions. In contrast, ERα- breast cancer show less prominent clustering but share a quiescent CSC pool with ERα+ cancer. This study underlines the importance of taking CSC heterogeneity into account for successful treatment design. In Paper III we have used a non-biased genome-wide screening approach to identify transcriptional networks specific to CSCs in ERα+ and ERα- subtypes. CSC-enriched models revealed a hyperactivation of the mevalonate metabolic pathway. When detailing the mevalonate pathway, we identified the mevalonate precursor enzyme 3-hydroxy-3-methylglutaryl-CoA synthase 1 (HMGCS1) as a specific marker of CSC-enrichment in ERα+ and ERα- subtypes, highlighting HMGCS1 as a potential gatekeeper for dysregulated mevalonate metabolism important for CSC-features. Pharmacological inhibition of HMGCS1 could therefore be a novel treatment approach for breast cancer patients targeting CSCs.

In the present work, the objective has been to analyse the compatibility of plant and human transcriptional machinery. The experiments revealed that nuclear import and export are conserved among plants and mammals. Further it has been shown that transactivation of a human promoter occurs by human transcription factor NF-\(\kappa\) B in plant cells, demonstrating that the transcriptional machinery is highly conserved in both kingdoms. Functionality was also seen for regulatory elements of NF-\(\kappa\) B such as its inhibitor I\(\kappa\)B isoform \(\alpha\) that negatively regulated the transactivation activity of the p50/RelA heterodimer by interaction with NF-\(\kappa\)B in plant cells. Nuclear export of RelA could be demonstrated by FRAP-measurements so that RelA shows nucleo-cytoplasmic shuttling as reported for RelA in mammalian cells. The data reveals the high level of compatibility of human transcriptional elements with the plant transcriptional machinery. Thus, Arabidopsis thaliana mesophyll protoplasts might provide a new heterologous expression system for the investigation of the human NF-\(\kappa\)B signaling pathways. The system successfully enabled the controlled manipulation of NF-\(\kappa\)B activity. We suggest the plant protoplast system as a tool for reconstitution and analyses of mammalian pathways and for direct observation of responses to e. g. pharmaceuticals. The major advantage of the system is the absence of interference with endogenous factors that affect and crosstalk with the pathway.

We present, for the red fluorescent protein mCherry acting as both fluorescence resonant energy transfer (FRET) donor and acceptor, Förster critical distance (r(0)) values with five important visible fluorescent protein (VFP) variants as well as with itself. The pair EYFP-mCherry exhibits an r(0) of 5.66nm, equaling or exceeding any combination of VFPs reported previously. Moreover, mCherry should be an excellent chromophore for homo-FRET with an r(0) of 5.10nm for energy transfer between two mCherry moieties. Finally, mCherry exhibits higher r(0) values than does DsRed. These characteristics, combined with mCherry's rapid folding and excellent spectral properties, suggest that mCherry constitutes a valuable long-wavelength hetero-FRET acceptor and probe for homo-FRET experiments.