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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

NMR profiling of CALS and CALR tumours. (A) Three-dimensional PCA score scatter plot showing separate clustering for 1H NMR data from CALS and CALR. (B) Score contribution plot showing changes in the 1H NMR peaks (and related metabolites) accounting for the differences between CALR and CALS tumours (plot obtained using the group-to-group comparison option in SIMCA). Positive scores represent increased levels, while negative scores indicate decreased levels in CALR relative to CALS. (C) Representative 31P NMR spectra showing the differences in 31P-containing metabolites between CALS and CALR tumours. Abbreviations: Asp=aspartate; BCAA=branched-chain amino acids; Cr=creatine; PCr=phosphocreatine; PC=phosphocholine; PE=phosphoethanolamine; GPC=glycerophosphocholine; GPE=glycerophosphoethanolamine; Pi=inorganic phosphate; Gln=glutamine; Glut=glutamate; Glx=glutathione; Myo-Ins=myo-inositol; ?=unidentified peak.
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fig2: NMR profiling of CALS and CALR tumours. (A) Three-dimensional PCA score scatter plot showing separate clustering for 1H NMR data from CALS and CALR. (B) Score contribution plot showing changes in the 1H NMR peaks (and related metabolites) accounting for the differences between CALR and CALS tumours (plot obtained using the group-to-group comparison option in SIMCA). Positive scores represent increased levels, while negative scores indicate decreased levels in CALR relative to CALS. (C) Representative 31P NMR spectra showing the differences in 31P-containing metabolites between CALS and CALR tumours. Abbreviations: Asp=aspartate; BCAA=branched-chain amino acids; Cr=creatine; PCr=phosphocreatine; PC=phosphocholine; PE=phosphoethanolamine; GPC=glycerophosphocholine; GPE=glycerophosphoethanolamine; Pi=inorganic phosphate; Gln=glutamine; Glut=glutamate; Glx=glutathione; Myo-Ins=myo-inositol; ?=unidentified peak.

Mentions: The clearest separation was obtained in the tumours which showed that variability in the data could be described according to three main principal components, PC1, PC2 and PC3 (Figure 1B and 2A), that between them explain ∼68% of the total variance (PC1: 34.8%, PC2: 18.4%, PC3: 15.1%). The resonances that appeared to be key in the separation between the CALS and CALR profiles include lactate, branched-chain amino acids (BCAAs), choline metabolites, acetate, myo-inositol, glutamine/glutamate and creatine (Cr)+phosphocreatine (PCr), as shown in Figure 2B.


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)

NMR profiling of CALS and CALR tumours. (A) Three-dimensional PCA score scatter plot showing separate clustering for 1H NMR data from CALS and CALR. (B) Score contribution plot showing changes in the 1H NMR peaks (and related metabolites) accounting for the differences between CALR and CALS tumours (plot obtained using the group-to-group comparison option in SIMCA). Positive scores represent increased levels, while negative scores indicate decreased levels in CALR relative to CALS. (C) Representative 31P NMR spectra showing the differences in 31P-containing metabolites between CALS and CALR tumours. Abbreviations: Asp=aspartate; BCAA=branched-chain amino acids; Cr=creatine; PCr=phosphocreatine; PC=phosphocholine; PE=phosphoethanolamine; GPC=glycerophosphocholine; GPE=glycerophosphoethanolamine; Pi=inorganic phosphate; Gln=glutamine; Glut=glutamate; Glx=glutathione; Myo-Ins=myo-inositol; ?=unidentified peak.
© Copyright Policy - open-access
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4385966&req=5

fig2: NMR profiling of CALS and CALR tumours. (A) Three-dimensional PCA score scatter plot showing separate clustering for 1H NMR data from CALS and CALR. (B) Score contribution plot showing changes in the 1H NMR peaks (and related metabolites) accounting for the differences between CALR and CALS tumours (plot obtained using the group-to-group comparison option in SIMCA). Positive scores represent increased levels, while negative scores indicate decreased levels in CALR relative to CALS. (C) Representative 31P NMR spectra showing the differences in 31P-containing metabolites between CALS and CALR tumours. Abbreviations: Asp=aspartate; BCAA=branched-chain amino acids; Cr=creatine; PCr=phosphocreatine; PC=phosphocholine; PE=phosphoethanolamine; GPC=glycerophosphocholine; GPE=glycerophosphoethanolamine; Pi=inorganic phosphate; Gln=glutamine; Glut=glutamate; Glx=glutathione; Myo-Ins=myo-inositol; ?=unidentified peak.
Mentions: The clearest separation was obtained in the tumours which showed that variability in the data could be described according to three main principal components, PC1, PC2 and PC3 (Figure 1B and 2A), that between them explain ∼68% of the total variance (PC1: 34.8%, PC2: 18.4%, PC3: 15.1%). The resonances that appeared to be key in the separation between the CALS and CALR profiles include lactate, branched-chain amino acids (BCAAs), choline metabolites, acetate, myo-inositol, glutamine/glutamate and creatine (Cr)+phosphocreatine (PCr), as shown in Figure 2B.

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