Cobalt Chloride stabilises and transcriptionally activates HIF-1α in normoxia but does not induce resistance.

<p>B, 24 hours after plating osteosarcoma cells were treated with cobalt chloride (791T 50 µM; HOS 25 µM; U2OS 25 µM) for 24 hours before treatment with a range of concentrations of cisplatin (791T 0–50 µM; HOS 0–25 µM; U2OS 0–200 µM), doxorubicin (791T 0–16 µM; HOS 0–5 µM; U2OS 0–40 µM) or etoposide (791T 0–50 µM; HOS 0–50 µM; U2OS 0–1000 µM). Following a one hour drug exposure cells were incubated with or without cobalt chloride for a further 72 hours before fixing and performing a sulphorhodamine-B assay. Graphs show the mean absorbance relative to the untreated controls (UnT) against the log of the drug concentrations and are the average of 3 independent experiments ± SEM. A, Whole cell lysates of cells treated with the above doses of cobalt chloride for the length of the experiment (96 hours) were harvested for western blotting to determine HIF-1α stabilisation and expression of downstream target CA IX. The western blots are representative of 3 independent experiments with GAPDH as a loading control.</p

Osteosarcoma cells expressing dominant-negative HIF-1α remain resistant to cisplatin, doxorubicin and etoposide in hypoxia.

<p>U2OS cells were transiently transfected with the pEF-IRES-P-HIF-no-TAD-EGFP vector (Dominant-negative HIF) (DN) or the empty vector control (EV). Following a 24 hour pre-treatment incubation period in either normoxia (N) or hypoxia (H) cells were exposed to a range of concentrations of cisplatin (0–300 µM), doxorubicin (0–100 µM) or etoposide (0–4000 µM) for 1 hour. 72 hours after treatment cells were fixed and assessed by SRB assay (B). Simultaneously transfected and plated cells were maintained in normoxia or hypoxia and harvested at 24 hours hypoxia (at time of treatment) or 96 hours hypoxia (at the end of the experiment). RNA was extracted and qPCR performed for CA IX and Glut-1 expression (A). Graphs show 2^(−ΔΔCT) where CT is the Cross Threshold and represents the change in mRNA expression in hypoxia relative to normoxia, where 1 would be equivalent expression in normoxia and hypoxia and greater than 1 represents an increase in hypoxia relative to normoxia. Data are the mean ± SEM of 3 independent experiments. * indicates p<0.05 as determined by the 2-tailed student t-test.</p

Osteosarcoma cells treated with the small molecule inhibitor of HIF-1α NSC134754 remain resistant in hypoxia.

<p>A, U2OS cells were treated with 20 µM NSC-134754 for 24 hours in hypoxia prior to exposure to a range of concentrations of cisplatin (0–300 µM), doxorubicin (0–100 µM) or etoposide (0–4000 µM) for 1 hour. Untreated controls were exposed to the same concentration ranges of cisplatin, doxorubicin and etoposide in normoxia and hypoxia. 72 hours after treatment cells were fixed and a SRB assay performed Graphs show the mean absorbance relative to the untreated controls (no chemotherapy agent) and are the average of 3 independent experiments ± SEM. B, Simultaneously plated cells treated with NSC134754 and incubated in hypoxia for 24 hours (time of treatment) or 96 hours (end of experiment) were harvested for whole cell lysates and western blotting performed for HIF-1α and CA IX. Western blots are representative 3 independent experiments with GAPDH used as a loading control. The difference between the response to cytotoxics in normoxia and hypoxia remains highly significant despite treatment with NSC134754 (p<0.001 2-way ANOVA).</p

Hypoxia leads to cytotoxic drug resistance and reduces cytotoxic-induced apoptosis in osteosarcoma cells.

<p>A, Following a 24 hour pre-treatment incubation period in normoxia or hypoxia 791T, HOS and U2OS cells were treated with a range of concentrations of cisplatin (791T 0–150 µM; HOS 0–450 µM; U2OS 0–300 µM), etoposide (791T 0–180 µM; HOS 0–50 µM; U2OS 0–4000 µM) or doxorubicin (791T 0–48 µM; HOS 0–5 µM; U2OS 0–100 µM) for 1 hour. After a further 72 hours an SRB assay was performed. Graphs show the mean absorbance relative to the untreated controls (UnT) against log cisplatin, etoposide or doxorubicin concentration and are the mean ± SEM of 3 independent experiments. The difference between drug response in hypoxia and normoxia is highly significant p<0.001 in all cases (2-way ANOVA). B, 48 hours after exposure to a 1 hour pulse of doxorubicin (10 µM), etoposide (500 µM) or cisplatin (75 µM) in normoxia or hypoxia U2OS cells were stained with DAPI and morphologically apoptotic cells counted with a fluorescent microscope. Graphs represent the percentage of apoptotic cells in normoxia and hypoxia. Data are the mean ± SEM of 3 independent experiments. * indicates p<0.05 and *** indicates p<0.001 determined by the 2-tailed student t-test. C, 72 hours after exposure to a 1 hour pulse of doxorubicin (0.14 µM), etoposide (0.8 µM) or cisplatin (6 µM) in normoxia or hypoxia U2OS cells were stained with annexin V and 7-AAD and analysed by flow cytometry. Annexin V positive and/or 7-AAD positive cells were counted as apoptotic and graphs represent the percentage of apoptotic cells and are the mean ± SEM of 3 independent experiments.* indicates p<0.05 determined by the 2-tailed student t-test. D, Protein from this experiment was immunoblotted for PARP, cleaved PARP, caspase-3 and cleaved caspase-3 The amount of cleavage of PARP and caspase-3 was indicative of the amount of apoptosis occurring at that time point and was compared between normoxia and hypoxia.</p

Liquid Chromatography–Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion

Metabolic profiling is routinely performed on multiple analytical platforms to increase the coverage of detected metabolites, and it is often necessary to distribute biological and clinical samples from a study between instruments of the same type to share the workload between different laboratories. The ability to combine metabolomics data arising from different sources is therefore of great interest, particularly for large-scale or long-term studies, where samples must be analyzed in separate blocks. This is not a trivial task, however, due to differing data structures, temporal variability, and instrumental drift. In this study, we employed blood serum and plasma samples collected from 29 subjects diagnosed with small cell lung cancer and analyzed each sample on two liquid chromatography–mass spectrometry (LC-MS) platforms. We describe a method for mapping retention times and matching metabolite features between platforms and approaches for fusing data acquired from both instruments. Calibration transfer models were developed and shown to be successful at mapping the response of one LC-MS instrument to another (Procrustes dissimilarity = 0.04; Mantel correlation = 0.95), allowing us to merge the data from different samples analyzed on different instruments. Data fusion was assessed in a clinical context by comparing the correlation of each metabolite with subject survival time in both the original and fused data sets: a simple autoscaling procedure (Pearson’s <i>R</i> = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (<i>R</i> = 0.94)

Liquid Chromatography–Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion

Metabolic profiling is routinely performed on multiple analytical platforms to increase the coverage of detected metabolites, and it is often necessary to distribute biological and clinical samples from a study between instruments of the same type to share the workload between different laboratories. The ability to combine metabolomics data arising from different sources is therefore of great interest, particularly for large-scale or long-term studies, where samples must be analyzed in separate blocks. This is not a trivial task, however, due to differing data structures, temporal variability, and instrumental drift. In this study, we employed blood serum and plasma samples collected from 29 subjects diagnosed with small cell lung cancer and analyzed each sample on two liquid chromatography–mass spectrometry (LC-MS) platforms. We describe a method for mapping retention times and matching metabolite features between platforms and approaches for fusing data acquired from both instruments. Calibration transfer models were developed and shown to be successful at mapping the response of one LC-MS instrument to another (Procrustes dissimilarity = 0.04; Mantel correlation = 0.95), allowing us to merge the data from different samples analyzed on different instruments. Data fusion was assessed in a clinical context by comparing the correlation of each metabolite with subject survival time in both the original and fused data sets: a simple autoscaling procedure (Pearson’s <i>R</i> = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (<i>R</i> = 0.94)

Liquid Chromatography–Mass Spectrometry Calibration Transfer and Metabolomics Data Fusion

Metabolic profiling is routinely performed on multiple analytical platforms to increase the coverage of detected metabolites, and it is often necessary to distribute biological and clinical samples from a study between instruments of the same type to share the workload between different laboratories. The ability to combine metabolomics data arising from different sources is therefore of great interest, particularly for large-scale or long-term studies, where samples must be analyzed in separate blocks. This is not a trivial task, however, due to differing data structures, temporal variability, and instrumental drift. In this study, we employed blood serum and plasma samples collected from 29 subjects diagnosed with small cell lung cancer and analyzed each sample on two liquid chromatography–mass spectrometry (LC-MS) platforms. We describe a method for mapping retention times and matching metabolite features between platforms and approaches for fusing data acquired from both instruments. Calibration transfer models were developed and shown to be successful at mapping the response of one LC-MS instrument to another (Procrustes dissimilarity = 0.04; Mantel correlation = 0.95), allowing us to merge the data from different samples analyzed on different instruments. Data fusion was assessed in a clinical context by comparing the correlation of each metabolite with subject survival time in both the original and fused data sets: a simple autoscaling procedure (Pearson’s <i>R</i> = 0.99) was found to improve upon a calibration transfer method based on partial least-squares regression (<i>R</i> = 0.94)

Statistical Considerations of Optimal Study Design for Human Plasma Proteomics and Biomarker Discovery

A mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a <i>q</i>-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An <i>a priori</i> power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a <i>post hoc</i> power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes >2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This <i>q</i>-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation

Statistical Considerations of Optimal Study Design for Human Plasma Proteomics and Biomarker Discovery

A mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a <i>q</i>-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An <i>a priori</i> power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a <i>post hoc</i> power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes >2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This <i>q</i>-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation