Severe metabolic alterations in liver cancer lead to ERK pathway activation and drug resistance

Background: The extracellular signal-regulated kinase (ERK) pathway regulates cell growth, and is hyper-activated and associated with drug resistance in hepatocellular carcinoma (HCC). Metabolic pathways are profoundly dysregulated in HCC. Whether an altered metabolic state is linked to activated ERK pathway and drug response in HCC is unaddressed. Methods: We deprived HCC cells of glutamine to induce metabolic alterations and performed various assays, including metabolomics (with 13C-glucose isotope tracing), microarray analysis, and cell proliferation assays. Glutamine-deprived cells were also treated with kinase inhibitors (e.g. Sorafenib, Erlotinib, U0126 amongst other MEK inhibitors). We performed bioinformatics analysis and stratification of HCC tumour microarrays to determine upregulated ERK gene signatures in patients. Findings: In a subset of HCC cells, the withdrawal of glutamine triggers a severe metabolic alteration and ERK phosphorylation (pERK). This is accompanied by resistance to the anti-proliferative effect of kinase inhibitors, despite pERK inhibition. High intracellular serine is a consistent feature of an altered metabolic state and contributes to pERK induction and the kinase inhibitor resistance. Blocking the ERK pathway facilitates cell proliferation by reprogramming metabolism, notably enhancing aerobic glycolysis. We have identified 24 highly expressed ERK gene signatures that their combined expression strongly indicates a dysregulated metabolic gene network in human HCC tissues. Interpretation: A severely compromised metabolism lead to ERK pathway induction, and primes some HCC cells to pro-survival phenotypes upon ERK pathway blockade. Our findings offer novel insights for understanding, predicting and overcoming drug resistance in liver cancer patients

TReCCA Analyser user interface and analysis steps.

<p>Consecutive tabs are clicked through for data analysis and plotting. These include the “Analysis options” tab (A), where the analyses to be performed on the data are selected and their corresponding parameters entered, and the “Graph output” tab (B), where the graphs are visualised and can be further customised. These tabs can be seen at a higher resolution in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131233#pone.0131233.s001" target="_blank">S1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131233#pone.0131233.s002" target="_blank">S2</a> Figs. All the analysis steps are summarised in the program flow chart (C). The black rectangle encloses a more precise representation of the available analysis options. The black arrows depict the succession of steps that are performed in our example of the analysis of the effect of Cisplatin on MCF-7 cells, the coloured arrows depict alternative analysis flows.</p

Smoothing and slope calculation.

<p>The oxygen levels in the media of MCF-7 cells exposed to different Cisplatin concentrations (as depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131233#pone.0131233.g002" target="_blank">Fig 2G</a>) between day 2.2 and 5 (A) are smoothed (C) by replacing each time point by the average of an 11 time point-neighbourhood. The smoothing can be seen more precisely on a bigger scale as it is the case for 75–80% a.s. and day 3–3.5 by comparing the raw data (B) to the smoothed data (D). The slope of each time point (E) is then obtained by performing a linear fit of each point and 15 points on either side of it. The residual error of the fit is displayed as a grey shadow around each curve (very small in this case). The legend in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131233#pone.0131233.g003" target="_blank">Fig 3E</a> is valid for all the subfigures, microM stands for micromolar.</p

Time-Resolved Cell Culture Assay Analyser (TReCCA Analyser) for the Analysis of On-Line Data: Data Integration—Sensor Correction—Time-Resolved IC₅₀ Determination

<div><p>Time-resolved cell culture assays circumvent the need to set arbitrary end-points and reveal the dynamics of quality controlled experiments. However, they lead to the generation of large data sets, which can represent a complexity barrier to their use. We therefore developed the Time-Resolved Cell Culture Assay (TReCCA) Analyser program to perform standard cell assay analyses efficiently and make sophisticated in-depth analyses easily available. The functions of the program include data normalising and averaging, as well as smoothing and slope calculation, pin-pointing exact change time points. A time-resolved IC₅₀/EC₅₀ calculation provides a better understanding of drug toxicity over time and a more accurate drug to drug comparison. Finally the logarithmic sensor recalibration function, for sensors with an exponential calibration curve, homogenises the sensor output and enables the detection of low-scale changes. To illustrate the capabilities of the TReCCA Analyser, we performed on-line monitoring of dissolved oxygen in the culture media of the breast cancer cell line MCF-7 treated with different concentrations of the anti-cancer drug Cisplatin. The TReCCA Analyser is freely available at <a href="http://www.uni-heidelberg.de/fakultaeten/biowissenschaften/ipmb/biologie/woelfl/Research.html" target="_blank">www.uni-heidelberg.de/fakultaeten/biowissenschaften/ipmb/biologie/woelfl/Research.html</a>. By introducing the program, we hope to encourage more systematic use of time-resolved assays and lead researchers to fully exploit their data.</p></div

Medium oxygen level of MCF-7 cells when exposed to different concentrations of Cisplatin over time.

<p>All the sensors are measured empty for sensor calibration between minutes 0 and 136 (A), then a sensor calibration is performed (B), followed by normalisation to the wells containing only medium (C). The whole time frame of the experiment is visible in the raw data (D): MCF-7 cell seeding at 136 min, medium change at day 1.09 and Cisplatin addition at different concentrations at day 2.1. The sensor correction and the normalisation to the conditions containing only medium are applied to all the data (E, F respectively). The time points used for sensor correction/normalisation (A, B, C) are depicted as black rectangles (D, E, F respectively). The triplicates of the sensor corrected and normalised data are averaged (G) and their standard deviation is displayed as a grey shadow around each curve. The legend at the bottom of the figure is valid for all the subfigures, microM stands for micromolar. These graphs are displayed as they are produced by the TReCCA Analyser.</p

Time-resolved IC₅₀ of MCF-7 cells exposed to Cisplatin.

<p>The oxygen level in the media of MCF-7 cells treated with different Cisplatin concentrations between days 2.2 and 5 (as depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0131233#pone.0131233.g003" target="_blank">Fig 3A</a>) are fitted for each time point, as exemplified for day 2.50 (green), 2.89 (turquoise), 3.28 (blue), 3.66 (pink), 4.05 (red), 4.43 (yellow) (A). MicroM stands for micromolar. The IC_<b>50</b> over time (B) is then determined from the values of all the fits between days 2.5 and 4.7. The residual error of the fit is displayed as a grey shadow around each curve.</p

Severe metabolic alterations in liver cancer lead to ERK pathway activation and drug resistance

Background: The extracellular signal-regulated kinase (ERK) pathway regulates cell growth, and is hyper-activated and associated with drug resistance in hepatocellular carcinoma (HCC). Metabolic pathways are profoundly dysregulated in HCC. Whether an altered metabolic state is linked to activated ERK pathway and drug response in HCC is unaddressed. Methods: We deprived HCC cells of glutamine to induce metabolic alterations and performed various assays, including metabolomics (with 13C-glucose isotope tracing), microarray analysis, and cell proliferation assays. Glutamine-deprived cells were also treated with kinase inhibitors (e.g. Sorafenib, Erlotinib, U0126 amongst other MEK inhibitors). We performed bioinformatics analysis and stratification of HCC tumour microarrays to determine upregulated ERK gene signatures in patients. Findings: In a subset of HCC cells, the withdrawal of glutamine triggers a severe metabolic alteration and ERK phosphorylation (pERK). This is accompanied by resistance to the anti-proliferative effect of kinase inhibitors, despite pERK inhibition. High intracellular serine is a consistent feature of an altered metabolic state and contributes to pERK induction and the kinase inhibitor resistance. Blocking the ERK pathway facilitates cell proliferation by reprogramming metabolism, notably enhancing aerobic glycolysis. We have identified 24 highly expressed ERK gene signatures that their combined expression strongly indicates a dysregulated metabolic gene network in human HCC tissues. Interpretation: A severely compromised metabolism lead to ERK pathway induction, and primes some HCC cells to pro-survival phenotypes upon ERK pathway blockade. Our findings offer novel insights for understanding, predicting and overcoming drug resistance in liver cancer patients