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Decreased glutathione biosynthesis contributes to EGFR T790M-driven erlotinib resistance in non-small cell lung cancer

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ABSTRACT

Epidermal growth factor receptor (EGFR) inhibitors such as erlotinib are novel effective agents in the treatment of EGFR-driven lung cancer, but their clinical impact is often impaired by acquired drug resistance through the secondary T790M EGFR mutation. To overcome this problem, we analysed the metabonomic differences between two independent pairs of erlotinib-sensitive/resistant cells and discovered that glutathione (GSH) levels were significantly reduced in T790M EGFR cells. We also found that increasing GSH levels in erlotinib-resistant cells re-sensitised them, whereas reducing GSH levels in erlotinib-sensitive cells made them resistant. Decreased transcription of the GSH-synthesising enzymes (GCLC and GSS) due to the inhibition of NRF2 was responsible for low GSH levels in resistant cells that was directly linked to the T790M mutation. T790M EGFR clinical samples also showed decreased expression of these key enzymes; increasing intra-tumoural GSH levels with a small-molecule GST inhibitor re-sensitised resistant tumours to erlotinib in mice. Thus, we identified a new resistance pathway controlled by EGFR T790M and a therapeutic strategy to tackle this problem in the clinic.

No MeSH data available.


Metabolic characteristics for the erlotinib-resistant and -sensitive cells. (a) Typical 600 MHz 1H-NMR spectra of aqueous extracts from PC9, PC9ER, H3255 and H1975 cells. The region (δ 5.0–9.5) is vertically expanded four times (4×). Data representative of n=10. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) score plots (left) and coefficient plots (right) for 1H-NMR spectra of aqueous cellular extracts from PC9ER and PC9 showing significantly differentiated metabolites (b), H1975 and H3255 (c). Models validated by CV-ANOVA, P=2.36×10−17 (b) and P=3.04×10−19 (c). The Q2 is 0.99 for both models. The colour scale for coefficient plots reflects the differences in the contribution of metabolite variations between groups. /r/ cutoff value is 0.602 (n=10, P<0.05). For identification of peak numbers, see Supplementary Table S1 and d. (d) Metabolites showed statistically significant differences between resistant and sensitive cells in both cell line pairs with statistically significant ‘decreases’ or ‘increases’ detected in the erlotinib-resistant (ER) cells as compared with erlotinib-sensitive (ES) ones. (e, f) GSH levels in PC9 and PC9ER (e) or H3255 and H1975 (f) cells determined by colorimetric assay. Data are average±s.e.m. of n=4. Statistics: Student’s t-test. ***P<0.001. See also Supplementary Figures S1 and S2.
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fig1: Metabolic characteristics for the erlotinib-resistant and -sensitive cells. (a) Typical 600 MHz 1H-NMR spectra of aqueous extracts from PC9, PC9ER, H3255 and H1975 cells. The region (δ 5.0–9.5) is vertically expanded four times (4×). Data representative of n=10. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) score plots (left) and coefficient plots (right) for 1H-NMR spectra of aqueous cellular extracts from PC9ER and PC9 showing significantly differentiated metabolites (b), H1975 and H3255 (c). Models validated by CV-ANOVA, P=2.36×10−17 (b) and P=3.04×10−19 (c). The Q2 is 0.99 for both models. The colour scale for coefficient plots reflects the differences in the contribution of metabolite variations between groups. /r/ cutoff value is 0.602 (n=10, P<0.05). For identification of peak numbers, see Supplementary Table S1 and d. (d) Metabolites showed statistically significant differences between resistant and sensitive cells in both cell line pairs with statistically significant ‘decreases’ or ‘increases’ detected in the erlotinib-resistant (ER) cells as compared with erlotinib-sensitive (ES) ones. (e, f) GSH levels in PC9 and PC9ER (e) or H3255 and H1975 (f) cells determined by colorimetric assay. Data are average±s.e.m. of n=4. Statistics: Student’s t-test. ***P<0.001. See also Supplementary Figures S1 and S2.

Mentions: To identify these, we comprehensively analysed the 1H-NMR metabonomic profiles of our erlotinib-sensitive and -resistant cells. 1H-NMR analysis of cell extracts from our cell lines identified 36 metabolites (Figure 1a) for which unambiguous assignments were obtained using various two-dimensional NMR methods (Supplementary Table S1). Statistical analysis of the spectral data by orthogonal projections to latent structures discriminant analysis (OPLS-DA) showed significant metabonomic differences between the erlotinib-resistant and -sensitive cells (Figure 1b and c). Changes in 14 metabolites mainly involved in GSH, amino acids, nucleotides and choline metabolism (Supplementary Figure S2A–C) correlated with resistance in both cell line pairs (Figure 1d; Supplementary Table S2). Noticeably, a significant drop in the intracellular levels of GSH accompanied erlotinib resistance (Figure 1d; Supplementary Table S2). Such GSH decrease observed by NMR was independently confirmed using a colorimetric assay (Figure 1e and f). This was intriguing, as drug resistance was traditionally associated with increased GSH levels [25, 26]. Nevertheless, GSH covalently binds some chemotherapeutic drugs leading to their glutathione-S-transferase-mediated extracellular export and resistance of cancer cells to these compounds [27, 28]. Hence, the increased export of this metabolite in complex with erlotinib could account for the lower GSH levels in these resistant cell lines. 1H-NMR analysis of the culture medium from our four cell lines disproved this possibility by showing no difference in secreted GSH between TKI-resistant and -sensitive cells (Supplementary Figure S2D). Hence, decreased intracellular GSH levels in erlotinib-resistant cells are likely due to the changes in GSH metabolism.


Decreased glutathione biosynthesis contributes to EGFR T790M-driven erlotinib resistance in non-small cell lung cancer
Metabolic characteristics for the erlotinib-resistant and -sensitive cells. (a) Typical 600 MHz 1H-NMR spectra of aqueous extracts from PC9, PC9ER, H3255 and H1975 cells. The region (δ 5.0–9.5) is vertically expanded four times (4×). Data representative of n=10. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) score plots (left) and coefficient plots (right) for 1H-NMR spectra of aqueous cellular extracts from PC9ER and PC9 showing significantly differentiated metabolites (b), H1975 and H3255 (c). Models validated by CV-ANOVA, P=2.36×10−17 (b) and P=3.04×10−19 (c). The Q2 is 0.99 for both models. The colour scale for coefficient plots reflects the differences in the contribution of metabolite variations between groups. /r/ cutoff value is 0.602 (n=10, P<0.05). For identification of peak numbers, see Supplementary Table S1 and d. (d) Metabolites showed statistically significant differences between resistant and sensitive cells in both cell line pairs with statistically significant ‘decreases’ or ‘increases’ detected in the erlotinib-resistant (ER) cells as compared with erlotinib-sensitive (ES) ones. (e, f) GSH levels in PC9 and PC9ER (e) or H3255 and H1975 (f) cells determined by colorimetric assay. Data are average±s.e.m. of n=4. Statistics: Student’s t-test. ***P<0.001. See also Supplementary Figures S1 and S2.
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fig1: Metabolic characteristics for the erlotinib-resistant and -sensitive cells. (a) Typical 600 MHz 1H-NMR spectra of aqueous extracts from PC9, PC9ER, H3255 and H1975 cells. The region (δ 5.0–9.5) is vertically expanded four times (4×). Data representative of n=10. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) score plots (left) and coefficient plots (right) for 1H-NMR spectra of aqueous cellular extracts from PC9ER and PC9 showing significantly differentiated metabolites (b), H1975 and H3255 (c). Models validated by CV-ANOVA, P=2.36×10−17 (b) and P=3.04×10−19 (c). The Q2 is 0.99 for both models. The colour scale for coefficient plots reflects the differences in the contribution of metabolite variations between groups. /r/ cutoff value is 0.602 (n=10, P<0.05). For identification of peak numbers, see Supplementary Table S1 and d. (d) Metabolites showed statistically significant differences between resistant and sensitive cells in both cell line pairs with statistically significant ‘decreases’ or ‘increases’ detected in the erlotinib-resistant (ER) cells as compared with erlotinib-sensitive (ES) ones. (e, f) GSH levels in PC9 and PC9ER (e) or H3255 and H1975 (f) cells determined by colorimetric assay. Data are average±s.e.m. of n=4. Statistics: Student’s t-test. ***P<0.001. See also Supplementary Figures S1 and S2.
Mentions: To identify these, we comprehensively analysed the 1H-NMR metabonomic profiles of our erlotinib-sensitive and -resistant cells. 1H-NMR analysis of cell extracts from our cell lines identified 36 metabolites (Figure 1a) for which unambiguous assignments were obtained using various two-dimensional NMR methods (Supplementary Table S1). Statistical analysis of the spectral data by orthogonal projections to latent structures discriminant analysis (OPLS-DA) showed significant metabonomic differences between the erlotinib-resistant and -sensitive cells (Figure 1b and c). Changes in 14 metabolites mainly involved in GSH, amino acids, nucleotides and choline metabolism (Supplementary Figure S2A–C) correlated with resistance in both cell line pairs (Figure 1d; Supplementary Table S2). Noticeably, a significant drop in the intracellular levels of GSH accompanied erlotinib resistance (Figure 1d; Supplementary Table S2). Such GSH decrease observed by NMR was independently confirmed using a colorimetric assay (Figure 1e and f). This was intriguing, as drug resistance was traditionally associated with increased GSH levels [25, 26]. Nevertheless, GSH covalently binds some chemotherapeutic drugs leading to their glutathione-S-transferase-mediated extracellular export and resistance of cancer cells to these compounds [27, 28]. Hence, the increased export of this metabolite in complex with erlotinib could account for the lower GSH levels in these resistant cell lines. 1H-NMR analysis of the culture medium from our four cell lines disproved this possibility by showing no difference in secreted GSH between TKI-resistant and -sensitive cells (Supplementary Figure S2D). Hence, decreased intracellular GSH levels in erlotinib-resistant cells are likely due to the changes in GSH metabolism.

View Article: PubMed Central - PubMed

ABSTRACT

Epidermal growth factor receptor (EGFR) inhibitors such as erlotinib are novel effective agents in the treatment of EGFR-driven lung cancer, but their clinical impact is often impaired by acquired drug resistance through the secondary T790M EGFR mutation. To overcome this problem, we analysed the metabonomic differences between two independent pairs of erlotinib-sensitive/resistant cells and discovered that glutathione (GSH) levels were significantly reduced in T790M EGFR cells. We also found that increasing GSH levels in erlotinib-resistant cells re-sensitised them, whereas reducing GSH levels in erlotinib-sensitive cells made them resistant. Decreased transcription of the GSH-synthesising enzymes (GCLC and GSS) due to the inhibition of NRF2 was responsible for low GSH levels in resistant cells that was directly linked to the T790M mutation. T790M EGFR clinical samples also showed decreased expression of these key enzymes; increasing intra-tumoural GSH levels with a small-molecule GST inhibitor re-sensitised resistant tumours to erlotinib in mice. Thus, we identified a new resistance pathway controlled by EGFR T790M and a therapeutic strategy to tackle this problem in the clinic.

No MeSH data available.