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Acquired resistance to EGFR tyrosine kinase inhibitors alters the metabolism of human head and neck squamous carcinoma cells and xenograft tumours.

Beloueche-Babari M, Box C, Arunan V, Parkes HG, Valenti M, De Haven Brandon A, Jackson LE, Eccles SA, Leach MO - Br. J. Cancer (2015)

Bottom Line: Acquired resistance to molecularly targeted therapeutics is a key challenge in personalised cancer medicine, highlighting the need for identifying the underlying mechanisms and early biomarkers of relapse, in order to guide subsequent patient management.Our studies reveal metabolic signatures associated not only with acquired EGFR TKI resistance but also growth pattern, microenvironment and contributing mechanisms in HNSCC models.These findings warrant further investigation as metabolic biomarkers of disease relapse in the clinic.

View Article: PubMed Central - PubMed

Affiliation: Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London and The Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT, UK.

ABSTRACT

Background: Acquired resistance to molecularly targeted therapeutics is a key challenge in personalised cancer medicine, highlighting the need for identifying the underlying mechanisms and early biomarkers of relapse, in order to guide subsequent patient management.

Methods: Here we use human head and neck squamous cell carcinoma (HNSCC) models and nuclear magnetic resonance (NMR) spectroscopy to assess the metabolic changes that follow acquired resistance to EGFR tyrosine kinase inhibitors (TKIs), and which could serve as potential metabolic biomarkers of drug resistance.

Results: Comparison of NMR metabolite profiles obtained from control (CAL(S)) and EGFR TKI-resistant (CAL(R)) cells grown as 2D monolayers, 3D spheroids or xenograft tumours in athymic mice revealed a number of differences between the sensitive and drug-resistant models. In particular, we observed elevated levels of glycerophosphocholine (GPC) in CAL(R) relative to CAL(S) monolayers, spheroids and tumours, independent of the growth rate or environment. In addition, there was an increase in alanine, aspartate and creatine+phosphocreatine in resistant spheroids and xenografts, and increased levels of lactate, branched-chain amino acids and a fall in phosphoethanolamine only in xenografts. The xenograft lactate build-up was associated with an increased expression of the glucose transporter GLUT-1, whereas the rise in GPC was attributed to inhibition of GPC phosphodiesterase. Reduced glycerophosphocholine (GPC) and phosphocholine were observed in a second HNSCC model probably indicative of a different drug resistance mechanism.

Conclusions: Our studies reveal metabolic signatures associated not only with acquired EGFR TKI resistance but also growth pattern, microenvironment and contributing mechanisms in HNSCC models. These findings warrant further investigation as metabolic biomarkers of disease relapse in the clinic.

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Related in: MedlinePlus

Unbiased metabolomic profiling of CALS and CALR tumour models. (A) 2D PCA score scatter plots showing a separate clustering for 1H NMR data from cells grown as 2D monolayers, 3D spheroids and xenograft tumours within the CALS and CALR cell lines separately and when the data are merged. (B) 2D PCA score scatter plots showing separate clustering for CALS and CALR 1H NMR data points within the 2D cell model, 3D spheroids and tumours. PC1 and PC2 are the two most important principal components explaining the variation in the data (shown as percentages in the x and y axes).
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fig1: Unbiased metabolomic profiling of CALS and CALR tumour models. (A) 2D PCA score scatter plots showing a separate clustering for 1H NMR data from cells grown as 2D monolayers, 3D spheroids and xenograft tumours within the CALS and CALR cell lines separately and when the data are merged. (B) 2D PCA score scatter plots showing separate clustering for CALS and CALR 1H NMR data points within the 2D cell model, 3D spheroids and tumours. PC1 and PC2 are the two most important principal components explaining the variation in the data (shown as percentages in the x and y axes).

Mentions: As shown in Figure 1A, unbiased multivariate analysis with PCA of the 1H NMR spectral data indicated that the 2D cells, 3D spheroids and tumours exhibit separate clustering within each cell line, consistent with a distinct metabolic phenotype. The clustering was maintained even when data from CALS and CALR were merged, suggesting strong model-dependent patterns. The score scatter plots indicate that the variation along the PC1 axis is driven by differences between the 2D and tumour data vs the spheroid data while the variation along the PC2 axis is driven by differences between the 2D vs tumour data with spheroid data overlapping between the two. Thus, despite arising from the same cells of origin, the three experimental models used in this study have unique metabolic features which are likely to be a reflection of their growth phenotype and microenvironment.


Acquired resistance to EGFR tyrosine kinase inhibitors alters the metabolism of human head and neck squamous carcinoma cells and xenograft tumours.

Beloueche-Babari M, Box C, Arunan V, Parkes HG, Valenti M, De Haven Brandon A, Jackson LE, Eccles SA, Leach MO - Br. J. Cancer (2015)

Unbiased metabolomic profiling of CALS and CALR tumour models. (A) 2D PCA score scatter plots showing a separate clustering for 1H NMR data from cells grown as 2D monolayers, 3D spheroids and xenograft tumours within the CALS and CALR cell lines separately and when the data are merged. (B) 2D PCA score scatter plots showing separate clustering for CALS and CALR 1H NMR data points within the 2D cell model, 3D spheroids and tumours. PC1 and PC2 are the two most important principal components explaining the variation in the data (shown as percentages in the x and y axes).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4385966&req=5

fig1: Unbiased metabolomic profiling of CALS and CALR tumour models. (A) 2D PCA score scatter plots showing a separate clustering for 1H NMR data from cells grown as 2D monolayers, 3D spheroids and xenograft tumours within the CALS and CALR cell lines separately and when the data are merged. (B) 2D PCA score scatter plots showing separate clustering for CALS and CALR 1H NMR data points within the 2D cell model, 3D spheroids and tumours. PC1 and PC2 are the two most important principal components explaining the variation in the data (shown as percentages in the x and y axes).
Mentions: As shown in Figure 1A, unbiased multivariate analysis with PCA of the 1H NMR spectral data indicated that the 2D cells, 3D spheroids and tumours exhibit separate clustering within each cell line, consistent with a distinct metabolic phenotype. The clustering was maintained even when data from CALS and CALR were merged, suggesting strong model-dependent patterns. The score scatter plots indicate that the variation along the PC1 axis is driven by differences between the 2D and tumour data vs the spheroid data while the variation along the PC2 axis is driven by differences between the 2D vs tumour data with spheroid data overlapping between the two. Thus, despite arising from the same cells of origin, the three experimental models used in this study have unique metabolic features which are likely to be a reflection of their growth phenotype and microenvironment.

Bottom Line: Acquired resistance to molecularly targeted therapeutics is a key challenge in personalised cancer medicine, highlighting the need for identifying the underlying mechanisms and early biomarkers of relapse, in order to guide subsequent patient management.Our studies reveal metabolic signatures associated not only with acquired EGFR TKI resistance but also growth pattern, microenvironment and contributing mechanisms in HNSCC models.These findings warrant further investigation as metabolic biomarkers of disease relapse in the clinic.

View Article: PubMed Central - PubMed

Affiliation: Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, London and The Royal Marsden NHS Foundation Trust, Sutton, Surrey SM2 5PT, UK.

ABSTRACT

Background: Acquired resistance to molecularly targeted therapeutics is a key challenge in personalised cancer medicine, highlighting the need for identifying the underlying mechanisms and early biomarkers of relapse, in order to guide subsequent patient management.

Methods: Here we use human head and neck squamous cell carcinoma (HNSCC) models and nuclear magnetic resonance (NMR) spectroscopy to assess the metabolic changes that follow acquired resistance to EGFR tyrosine kinase inhibitors (TKIs), and which could serve as potential metabolic biomarkers of drug resistance.

Results: Comparison of NMR metabolite profiles obtained from control (CAL(S)) and EGFR TKI-resistant (CAL(R)) cells grown as 2D monolayers, 3D spheroids or xenograft tumours in athymic mice revealed a number of differences between the sensitive and drug-resistant models. In particular, we observed elevated levels of glycerophosphocholine (GPC) in CAL(R) relative to CAL(S) monolayers, spheroids and tumours, independent of the growth rate or environment. In addition, there was an increase in alanine, aspartate and creatine+phosphocreatine in resistant spheroids and xenografts, and increased levels of lactate, branched-chain amino acids and a fall in phosphoethanolamine only in xenografts. The xenograft lactate build-up was associated with an increased expression of the glucose transporter GLUT-1, whereas the rise in GPC was attributed to inhibition of GPC phosphodiesterase. Reduced glycerophosphocholine (GPC) and phosphocholine were observed in a second HNSCC model probably indicative of a different drug resistance mechanism.

Conclusions: Our studies reveal metabolic signatures associated not only with acquired EGFR TKI resistance but also growth pattern, microenvironment and contributing mechanisms in HNSCC models. These findings warrant further investigation as metabolic biomarkers of disease relapse in the clinic.

Show MeSH
Related in: MedlinePlus