Metabolic Imaging Using Hyperpolarized Pyruvate–Lactate Exchange Assesses Response or Resistance to the EGFR Inhibitor Cetuximab in Patient-Derived HNSCC Xenografts

Overexpression of the epidermal growth factor receptor (EGFR) in squamous cell carcinoma of the head and neck (HNSCC) is associated with poor prognosis, radioresistance, and chemoresistance (1, 2). Cetuximab is a chimeric IgG1 monoclonal antibody (mAb) that specifically binds to the EGFR with high affinity and improves overall survival when associated with radiotherapy in locally advanced HNSCC or with platinum-based chemotherapy in incurable disease (3, 4). However, only a minority of patients derive long-term benefit from anti-EGFR mAbs (5, 6). Resistance mechanisms to EGFR inhibition for HNSCC remain unclear (7), although multiple mechanisms of acquired and de novo resistance have been suggested (8, 9), paving the way for trials of combination strategies (7, 10, 11). Optimal HNSCC patient selection for anti–EGFR-based therapy remains a challenge (7). Even though initial approval for the clinical use of cetuximab was based on EGFR overexpression in primary or metastatic tumors (12), EGFR mutations, protein expression, or copy number are not considered relevant in making decisions on the use of EGFR inhibitors in several cancer types (13–16). In this context, early prediction of treatment sensitivity or resistance to EGFR inhibitors could spare patients from futile therapy cycles and unnecessary side effects.

Conventional anatomically based endpoints may be inadequate to monitor tumor response to targeted agents that usually do not result in tumor shrinkage but rather in tumor size stabilization. Imaging methods exploit the altered metabolism in treatment-responsive tumors as a method for the evaluation of treatment response (17). Within this scope, an early ¹⁸FDG-PET response has been associated with anti-EGFR therapy outcome in lung and colorectal cancers (18–20). A recent PET preclinical study has revealed higher relative uptake of ¹⁸F-FDG in multiple tyrosine kinase–resistant HNSCC patient-derived tumor xenografts (PDTXs) in mice (21). Our group, however, showed that SUVmax, if modified in response to cetuximab, was not always able to discriminate between cetuximab-sensitive and -resistant PDTXs in multiple models (22). In addition, EGFR phosphorylation was decreased in both sensitive and resistant PDTXs, showing that pEGFR quantification cannot be used as a marker of resistance to cetuximab (22).

A limited number of studies have focused on the effect of EGFR inhibitors on tumor metabolism in order to identify potential resistance mechanism pathways. Based on the fact that tumor cell proliferation was not systematically decreased in response to EGFR inhibition in multiple HNSCC cell line studies, Liu and colleagues observed that inhibition of glycolysis via LDH-A downregulation was required for cetuximab to induce antiproliferative effects, whereas resistant cells were highly glycolytic (23). Activated protein kinase AMPK was further shown to be transiently increased in response to glycolysis inhibition, helping cancer cells to restore energy homeostasis and survive cetuximab-induced inhibition of glycolysis (24). Finally, phosphorylation (inhibition) of acetyl-CoA carboxylase (ACC) following AMPK activation was shown to be followed by a compensatory increase in total ACC, rewiring the HNSCC tumor metabolism from glycolysis-dependent to lipogenesis-dependent (25).

The goal of the current study was to assess the ability of state-of-the-art noninvasive in vivo metabolic imaging to probe metabolic shifts in cetuximab-sensitive and -resistant HNSCC xenografts derived from three patients with HNSCC. One was sensitive to cetuximab treatment, with its acquired resistant counterpart being generated in mice, one showed resistance to the EGFR inhibitor, and one was also sensitive to cetuximab, thereby constituting good models for assessing the relevance of hyperpolarized ¹³C-pyruvate–lactate exchange as a potential marker for response and resistance to cetuximab treatment. Hyperpolarization of ¹³C-enriched metabolites increases ¹³C-magnetic resonance spectroscopy (MR) sensitivity by a factor of 10,000, allowing in vivo real-time assessment of metabolic fluxes (26). This state-of-the-art metabolic imaging method is currently used to assess tumor metabolism and treatment sensitivity in preclinical studies (27, 28), and was also recently translated to the clinical setting (29, 30). Pyruvate is the product of glycolysis and can be assessed using hyperpolarized (HP) MRI. In particular, [1-¹³C]pyruvate is reduced to [1-¹³C]lactate via the enzyme lactate dehydrogenase (LDH). This process results in an altered chemical shift that HP MRI can image at uniquely high temporal resolution (31). In addition, we used diffusion imaging to monitor cellularity (tumor cell density) in vivo, before and after treatment with cetuximab. DW-MRI has been used to detect early changes after standard or targeted therapies (32–34). Cell death in response to therapies can precede size change and increases the mobility of water molecules in the tissue environment. DW-MRI may therefore be an early biomarker for response since its measurable parameter apparent diffusion coefficient of water (ADCw) assesses tumor cellularity. In this way, both metabolic (¹³-C pyruvate to ¹³C-lactate conversion) and cell viability (ADCw) in vivo markers provide complementary information with respect to sensitivity or resistance to the EGFR inhibitor. In vivo imaging data were compared with ex vivo mRNA expression, protein levels, or IHC staining of enzymes and transporters involved in the glycolytic pathway. Finally, correlation with IHC staining for pEGFR was performed in control and treated cetuximab-sensitive and cetuximab-resistant tumors.

HNC007 and HNC010 PDTX models were established in collaboration with Trace, the PDTX platform of KU Leuven (www.uzleuven-kuleuven.be/lki/trace), as previously described (22). A UCLHN4–cetuximab-sensitive PDTX model was established in the medical oncology group at UCLouvain. All PDTX models were derived from patients with HNSCC. The study was approved by the local ethical committees (UCL/MD/2012/09July/314) in accordance with the principles of the Declaration of Helsinki, and all patients gave their written informed consent. Patient tumor materials were collected in RPMI medium (Gibco) supplemented with 0.4% fungizone (Bristol Myers Squibb), penicillin/streptomycin 2.5% (Sigma), and gentamycin (Braun Medical), and kept at 4°C for engraftment within 6 hours of resection. Necrotic and supporting tissues were carefully removed using a surgical blade. Some tumor fragments were flash frozen and stored at −80°C for genomic profiling, and other fragments were fixed in 4% neutral-buffered formalin and paraffin embedded for histopathologic analysis. The remaining tumor fragments were implanted subcutaneously into the backs of 4 to 6 weeks (22–25g) athymic nude female mice (NMRI-Foxn1nu, Taconic). Successfully engrafted tumor models were then passaged through several generations. Experiments were conducted on the fifth and sixth generations. All PDTX models were validated by comparing the clinical behavior (sensitivity to cetuximab) and immunochemistry [p16 (clone G175–405, BD Pharmingen); Ki67 (polyclonal rabbit, Thermo Fisher Scientific); p53 (clone SP5, Thermo Fisher Scientific); pEGFR (clone 7A5, Cell Signaling Technology); vimentin (clone SP20, Thermo Fisher Scientific); and E-Cadherin (clone 24E10, Cell Signaling Technology)] of the primary tumor with the tumors harvested from fourth- and sixth-generation mice. EGFR was not mutated in the PDTX models under study, as assessed by whole-exome sequencing (WES) analysis. Some mice from the initial HNC007 model were treated with cetuximab (30 mg/kg, once a week intraperitoneally) until some became resistant to cetuximab. Resistance was defined as continuous tumor growth under cetuximab and an increase in tumor volume of more than 200% compared with baseline. Mice were maintained and handled in accordance with the UCLouvain policy for animal care. Animal work was undertaken in compliance with Belgian law, and all the experiments were conducted in accordance with our local ethical committee. Animal welfare is regularly controlled by inspections in compliance with Belgian law, and all investigators performing animal work successfully completed FELASA C training. Tumor-bearing mice were randomly assigned to the cetuximab-treated and vehicle groups.

Cetuximab-sensitive and -resistant HNSCC-bearing mice were treated intraperitoneally with cetuximab (Merck Serono) at a dose of 30 mg/kg, as described previously (22). Two doses of cetuximab were given; the first was administered just after the initial MR assessments at baseline (day 0), and the second followed 1 week later on day 7. Posttreatment MR experiments were performed on day 8. The vehicle (saline solution) was administered to the control groups under the same conditions. Mice were distributed randomly in the control or cetuximab group. To evaluate treatment effects, tumor size was first measured by caliper once a week, and tumor volume was calculated according to the equation: V(mm³) = (the greatest length) × (the shortest length)²/2 (Fig. 1A and D). During MR experiments, tumor size was measured on the basis of anatomic T₂ images.

MR experiments were performed in an 11.7-Tesla, 16-cm inner diameter system (Bruker, Biospec) equipped with a ¹H-quadrature volume coil (40-mm inner diameter) and a double tuned ¹H-¹³C-surface coil (RAPID Biomedical), as described previously (22). Mice were anesthetized by isoflurane inhalation (2.5% in air for induction and 1%–2% in air for maintenance). Body temperature was maintained using a warm circulating water blanket and monitored using a rectal temperature probe. A pressure cushion was used to monitor breathing. Anatomic T₂-weighted images were used to assess tumor volume. The turbo RARE sequence had the following parameters: repetition time (TR) = 2.5 s, echo time (TE) = 30 ms, averages = 2, field of view = 3 × 3 cm, 15 slices with a 1-mm thickness. For diffusion MRI, a transverse echo planar imaging sequence was used with the following parameters: TR/TE = 3000/27 ms, duration of diffusion gradients δ = 7 ms, separation of diffusion gradients (Δ) = 14 ms, slice number = 7, slice distance = 1 mm, b = 100–200–400–600–800–1000 s/mm², acquisition time = 4 min 12 sec. Mean apparent diffusion coefficients (ADC) were extracted from DW images and averaged for every slice of tumor using Matlab software (The MathWorks Inc., Natick). A second anatomic T₂-weighted image was acquired with geometrical parameters similar to those of the diffusion-weighted MRI scan to allow coregistration of the diffusion map with anatomic images. The exponential decay of the signal as a function of the b-value was measured according to the Stejskal–Tanner equation. ADC maps were generated by nonlinear least-squares regression of a mono-exponential to the experimental signal intensity for all b values.

Hyperpolarized 13C-NMR data were acquired as previously described (35). [1–¹³C] pyruvic acid (Sigma-Aldrich) was mixed with 15 mmol/L trityl radical OX63 and doped with 2 mmol/L gadoteric acid (Guerbet). This solution of 40 μL was hyperpolarized by an Oxford DNP Polarizer (HyperSense) for approximately 45 minutes at 1.4 K and 3.35 T. The polarized substrate was quickly dissolved in 3 mL of heated buffer containing 100 mg/L EDTA, 40 mmol/L HEPES, 30 mmol/L NaCl, and 80 mmol/L NaOH. The final solution was adjusted to have a neutral pH and contained hyperpolarized [1-¹³C] pyruvate and nonhyperpolarized, unlabeled lactate (30 mmol/L) to increase ¹³C label exchange (35). This solution was quickly injected using a catheter into the tail vein of the mice in the MRI scanner (11.7-Tesla, Bruker, Biospec). Mice were scanned using a double tuned ¹H-¹³C-surface coil (RAPID Biomedical), which was designed for spectroscopy of subcutaneous tumors (i.e., tumor-shaped cavity of 12 mm in diameter). After administration of 0.2 mL of hyperpolarized pyruvate, ¹³C spectra were acquired using a single pulse sequence every 3 seconds for 210 seconds (70 repetitions), a flip angle of 10° and an acquisition bandwidth of 50 kHz (10,000 points). Peak areas under the curve were measured for each repetition time and each time point using homemade routines in MATLAB (Mathworks). The integrated peak intensities of hyperpolarized ¹³C-pyruvate, ¹³C-lactate, and ¹³C global signal were measured. The rate of exchange between pyruvate and lactate was calculated.

pEGFR IHC (clone 7A5, Cell Signaling Technology) was performed on 4-μm paraffin-embedded tumor sections. Slides were scanned (Leica SCN400 Slide Scanner, Leica Biosystems) and digitalized at ×20 magnification. The slides were analyzed using TissueIA software (Leica Biosystems). The quantification algorithm was run in the preselected viable part of the tissue samples to detect stained area and tissue area. The histoscore is calculated as follows: histoscore = (positive area × average staining intensity)/(total tissue area). The presented results are normalized to the mean of the control group.

Frozen tumor tissues were ground with a pestle in liquid nitrogen to obtain homogeneous tissue powder. The total RNA was isolated from the tumor powder using the TriPure Isolation Reagent (Roche Diagnostics). cDNA was prepared by reverse transcription using the GoScript Reverse Transcriptase System (Promega). Real-time PCR was performed using a QuantStudio 3 Real-Time PCR system (Thermo Fisher Scientific), using Mastermix Plus for SYBR Assay (Eurogentec). Data were analyzed using the 2-^ΔΔCT method. The glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene was chosen as a reference gene. Primer sequences are listed in Supplementary Data.

Homogeneous tumor powder was lysed in RIPA buffer (Sigma) supplemented with 1% of Halt protease inhibitors and Halt phosphatase inhibitors (Thermo Fisher). Equal amounts of proteins were separated by SDS-PAGE and transferred to PVDF membranes. Membranes were incubated overnight at 4°C with antibodies diluted in Tris-buffered saline tween-20 containing 1% bovine serum albumin. The revelation was performed using a chemiluminescent substrate (SuperSignal West Pico Thermo Scientific) and LAS 500 (GE Healthcare). Densitometry analysis was performed with ImageQuanTL software.

The two primary endpoints of this study aimed to determine (i) whether the imaging parameters experienced significant changes between baseline and day 8 and (ii) whether these changes differed between the cetuximab-treated groups versus the untreated group in both sensitive and resistant models at day 8.

All analyses were performed using GraphPad Prism 7 software, as described previously (22). In the HNC007 model, data at baseline and day 8 were compared using a paired samples t test. Comparisons of changes in tumor size and in imaging parameters (normalized to their baseline values) between control, sensitive, and resistant groups in the HNC007 model were carried out using a two-way ANOVA test followed by a Tukey post hoc test for group comparisons. For the UCLHN4 and HNC010 models including only two groups, one-way ANOVA with multiple comparison posttests were performed (Sidak for the comparison between control and treated group, and the two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli for the D0–D8 comparison). The pEGFR IHC data were analyzed using one-way ANOVA (Kruskal–Wallis test). Finally, the mRNA expression was compared between groups using one-way ANOVA with Tukey multiple comparison post hoc tests or Welch test where appropriate. For all tests cited above, a P < 0.05 was considered statistically significant.

Tumor growth was stabilized for 5 weeks under weekly treatment with 30 mg/kg i.p. injections of cetuximab in the HNC007 cetuximab-sensitive PDTXs, in contrast to the vehicle-treated xenografts (Fig. 1A, P = 0.0002, ANOVA two-way, n = 6–9/group). During the 8 days of our metabolic imaging study, tumor growth was not significantly modified with respect to pretreatment tumor volume in the control (Fig. 1B) and cetuximab-sensitive (Fig. 1C) treated groups, although a trend toward an increase was already present in the control group (Fig. 1B and C, n = 3/4 group, paired t test, P = 0.07 and 0.16, respectively). Figure 1C confirms the tumor size stabilization induced by cetuximab while comparing tumor size at D0 and D8. However, the limited number of animals in Fig. 1C does not allow a definitive conclusion to be drawn about the potential ability of cetuximab to decrease tumor size between D0 and D8. Nevertheless, statistics performed on the larger cohort used in Fig. 1A for tumor growth assay show a lack of reduction in tumor size in the cetuximab-treated group between D0 and D7, and ANOVA comparison between control and treated groups does not show any significant difference between control and cetuximab groups on D7, but only on D14. Resistance to cetuximab was induced in vivo in HNC007 mice–bearing xenografts. Xenografts were considered resistant when tumors reached 200% ± 10% of their initial size (Fig. 1D, n = 4). In this group, no significant change in tumor volume was observed within the 8 days of the metabolic imaging study (Fig. 1E, n = 4, paired t test, P = 0.28). Accordingly, the evolution of tumor volume between days 0 and 8 (normalized to pretreatment volume on day 0) was significantly different for control and cetuximab-sensitive treated groups (Fig. 1F, P = 0.05, n = 3/4 group, two-way ANOVA, Sidak multiple comparison test), but not for control xenografts and cetuximab resistant–treated xenografts (Fig. 1F, P = 0.51, n = 3/4 group, two-way ANOVA, Sidak multiple comparison test).

EGFR phosphorylation quantified by IHC was significantly decreased in cetuximab-sensitive HNC007 xenografts on day 8 after weekly treatment with cetuximab, but not in cetuximab-resistant xenografts, in comparison with vehicle-treated xenografts (Fig. 2, n = 3-4/group, Kruskal–Wallis test, P = 0.04).

Real-time pyruvate–lactate exchange after i.v. administration of hyperpolarized ¹³C-pyruvate (Fig. 3A) was assessed by calculating the area under the curve (AUC) of the dynamic acquisitions of lactate and pyruvate peaks over time (Fig. 3B). The calculated ¹³C-lactate to ¹³C-pyruvate ratio was not modified in control xenografts between pretreatment (day 0) and posttreatment (day 8; P = 0.37, n = 3; paired t test, Fig. 3C), or in cetuximab-resistant HNC007 xenografts (P = 0.38, n = 4, paired t test, Fig. 3D), whereas it significantly decreased in cetuximab-sensitive HNC007 xenografts (P = 0.004, n = 3; paired t test, Fig. 3E). There was therefore a significant difference in the evolution of the ratios between days 0 and 8 (Fig. 3F) for control and sensitive treated xenografts (P = 0.0003, n = 3/4 group; two-way ANOVA, Sidak multiple comparison test) and for sensitive and resistant xenografts (P = 0.03, n = 3/4 group, two-way ANOVA, Sidak multiple comparison test). In conclusion, cetuximab-sensitive xenografts showed a decreased pyruvate to lactate exchange within 1 week of treatment, unlike cetuximab-resistant xenografts. ¹³C-pyruvate–lactate exchange allowed sensitive and resistant xenografts to be distinguished. Of note, the times to maximum peak of the signals after tracer injection were not significantly different before and after treatment with cetuximab in HNC007-sensitive and -resistant xenografts (P = 1.00 and P = 0.39, respectively). These observations indicate that the delivery of hyperpolarized substrates was not likely to be modified by treatment with cetuximab.

Changes in tumor cellularity were measured using the ADCw via diffusion MRI (Fig. 4). Typical tumor ADCw maps overlaid on anatomic T₂ images of xenografts at days 0 and 8 are shown in Fig. 4A and B, respectively. Tumor ADCw had a tendency to increase in control xenografts (Fig. 4C, P = 0.051, n = 4) and significantly increased in response to cetuximab in the HNC007 cetuximab–sensitive group (Fig. 4D, P = 0.049, n = 3, paired t test). The resistant group, however, showed a significant decrease (Fig. 4E, P = 0.033, n = 4) in tumor ADCw. In conclusion, the ADCw coefficient allowed sensitive and resistant xenografts (Fig. 4F, n = 3/4 group, P = 0.005, two-way ANOVA, Sidak multiple comparison test) to be distinguished, but not control and cetuximab sensitive–treated xenografts (Fig. 4F, n = 3/4 group, P = 0.35, two-way ANOVA, Sidak multiple comparison test).

As detailed in the Materials and Methods section, xenografts were also generated from a second cetuximab-sensitive HNSCC tumor patient (UCLHN4) and from a cetuximab-resistant tumor patient (HNC010; Fig. 5). In UCLHN4 cetuximab–sensitive xenografts (Fig. 5A), the evolution of the tumor size between day 0 (pretreatment) and day 8 (posttreatment), although not significantly different within each individual group when compared with pretreatment values on day 0, was slightly opposite in control (vehicle-treated) and cetuximab-treated xenografts (Fig. 5A, P = 0.04, n = 5/7 group; two-way ANOVA, Sidak multiple comparison test). In HNC010 cetuximab–resistant xenografts (Fig. 5B), the evolution of the tumor size between days 0 and 8 was not significantly different for control and treated tumors (Fig. 5B, P = 0.57, n = 5/6 group; two-way ANOVA, Sidak multiple comparison test). In UCLHN4 cetuximab–sensitive xenografts (Fig. 5C), ¹³C-lactate to ¹³C-pyruvate exchange was not modified in control xenografts (Fig. 5C, P = 0.33, n = 4, two-way ANOVA, two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli), but was significantly decreased between pre- and posttreatment with cetuximab (Fig. 5C, P = 0.04, n = 6, 2-way ANOVA, two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli). In HNC010 cetuximab–resistant xenografts (Fig. 5D, n = 5/6 group), however, no change in the ¹³C-lactate to ¹³C-pyruvate ratio was observed in both control- and cetuximab-treated groups. In UCLHN4 cetuximab–sensitive xenografts, the ADCw coefficient allowed control and treated xenografts to be distinguished (Fig. 5E, P = 0.04, n = 5/7 group; two-way ANOVA, Sidak multiple comparison test) due to the significant decrease in the ADCw in the control group (Fig. 5E, P = 0.02, n = 5, two-way ANOVA, two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli). In HNC010 cetuximab–resistant xenografts (Fig. 5F, n = 5/6 group), no change in ADCw was observed in either group. EGFR phosphorylation was significantly decreased in cetuximab-sensitive UCLHN4 xenografts at day 8 after cetuximab treatment (Fig. 5G, P = 0.02 n = 6/7 group, unpaired t test), but not in HNC010 cetuximab–resistant xenografts, in comparison with vehicle-treated xenografts (Fig. 5H, P = 0.13 n = 5 group, unpaired t test).

Finally, in order to further assess the metabolic shift induced by treatment with cetuximab in our three models of PDTXs, mRNA expression of key enzymes and transporters of the glycolytic metabolism were analyzed by qPCR on ex vivo tumor samples resected from tumor-bearing mice (Fig. 6). The mRNA expression of lactate dehydrogenase A (LDH-A) was significantly decreased in HNC007 cetuximab–sensitive xenografts (Fig. 6A, P = 0.02; n = 3, one-way ANOVA, Tukey multiple comparison post hoc test) and UCLHN4 cetuximab–sensitive xenografts (P = 0.02, n = 6, Welch test), whereas the decrease was not significant in resistant xenografts (resistant HNC007, P = 0.10, n = 5; and resistant HNC010, P = 0.10, n = 5; Welch test). The mRNA expression of the lactate transporter MCT-1 (Fig. 6D–F) significantly decreased in cetuximab-sensitive xenografts (sensitive HNC007, P = 0.017, n = 3, one-way ANOVA, Tukey multiple comparison post hoc test; and sensitive UCLHN4, P = 0.0031, n = 4, Welch test), whereas the decrease was not significant in resistant xenografts (resistant HNC007, P = 0.126, n = 5, one-way ANOVA, Tukey multiple comparison post hoc test; and resistant HNC010, P = 0.0789, n = 5, Welch test). However, the protein levels of LDH-A were not mirroring the changes in mRNA levels, which were assessed ex vivo by WB analysis. With respect to MCT1, mRNA levels and IHC staining showed a significant decrease in the UCLHN4-sensitive model, contrarily to the HNC010-resistant model (P = 0.015 and P = 0.51, respectively, Welch test). The mRNA expression of the glucose transporter GLUT-1 (Fig. 6G–I), converting glucose into 2-deoxy-glucose inside the cell, showed a nonsignificant decrease in cetuximab-sensitive xenografts in comparison with control tumors, but WB analysis of GLUT-1 activity (Supplementary Fig. S1) showed a significant decrease in the cetuximab-sensitive tumor model UCLHN4, and was not modified in the cetuximab-resistant HNC010 model. However, this pattern was not confirmed in the HNC007 model. Hexokinase 2 (HK2) mRNA expression (Fig. 6J–L) was not modified under any of the conditions, whereas the HK2 protein levels was decreased in the cetuximab-sensitive UCLHN4 model only.

The three HNSCC PDTX models that we implemented for the current study involving cetuximab-sensitive and cetuximab-resistant xenografts were used for assessing metabolic imaging as a potential marker of response and resistance to the EGFR inhibitor cetuximab in head and neck cancer. Using state-of-the-art metabolic imaging, we performed the first in vivo assessment of cetuximab-induced metabolic changes in cetuximab-sensitive HNSCC xenografts using HP MRI. In the literature, most preclinical cancer studies using HP pyruvate as a biomarker have shown increased conversion to HP lactate, consistent with the elevated lactate production (Warburg effect) that is an established characteristic of most cancerous cells (27, 28). These studies also demonstrated a mechanistic link between an increased HP lactate signal and other cancer-associated cellular alterations such as the elevated expression of LDH-A and monocarboxylate transporters (27, 35). Importantly, in the current study, real-time monitoring of pyruvate–lactate exchange in vivo, estimated via the ¹³C-lactate to ¹³C-pyruvate ratio (36), was also able to distinguish between sensitive and resistant xenografts, and suggested a recovery of the glycolytic metabolism in resistant tumors, indicated by a higher rate of conversion from pyruvate to lactate, as observed in control untreated tumors. Hyperpolarized ¹³C-pyruvate to ¹³C-lactate conversion was indeed significantly decreased in vivo in cetuximab-sensitive xenografts 8 days after weekly treatment with cetuximab, whereas it was not modified in cetuximab-resistant xenografts using a similar protocol.

Ex vivo analysis of the mRNA expression and protein levels of the glycolytic pathway of tumors resected at day 8 after treatment corroborated the in vivo metabolic data in the sensitive UCLHN4 and resistant HNC010 models. Compared with control tumors, we observed a lack of change in the LDH-A enzyme expression involved in the conversion of pyruvate into lactate, but a significant decrease in the expression of the lactate transporter MCT-1 (as observed by MCT-1 IHC staining), likely to participate in the decrease in the observed lactate to pyruvate ratio in vivo. The significant decrease observed in UCLHN4 cetuximab–sensitive tumors was not present in the cetuximab-resistant HNC010 tumors. With respect to glycolysis, ex vivo analysis of the UCLHN4 cetuximab–sensitive model suggested a decrease of the glycolytic phenotype in cetuximab-treated tumors (corroborated by significant decreases in GLUT-1 and HK2 protein levels) that was not observed in the cetuximab-resistant HNC010 tumors. However, this pattern could not be confirmed in the HNC007 model. It is noteworthy that the HNC007- and UCLHN4-sensitive models originate from two different patients: the first had been treated with cetuximab in the clinic, whereas the second was naïve to cetuximab. Also, resistance in the HNC007 model was generated in mice, whereas the HNC010 model was intrinsically resistant in patients. Therefore, a straightforward comparison between the models is limited by the different backgrounds of the models. Further studies on larger PDTX cohorts would be required to fully characterize the metabolic changes induced by cetuximab in sensitive and resistant tumors accompanying the changes observed with metabolic imaging. Finally, the potential role of GLUT-3 and GLUT-4 should be considered in the context of HNSCC, as recently suggested by Feitosa and colleagues (37).

Tumor cellularity assessed by diffusion MRI was significantly increased at day 8 after treatment in cetuximab-sensitive HNC007 xenografts when compared with pretreatment ADCw values, but was not different for control and treated tumors at day 8. In both the HNC007 and HNC010 models, the ADCw parameter was able to distinguish cetuximab-sensitive and -resistant xenografts. However, longitudinal monitoring of ADCw between days 0 and 8 did not show any significant change in the UCLHN4 model. This is in accordance with previous data from our group (22), in which we showed that ADCw was able to discriminate between sensitive and resistant tumors in one model but not in all models (Fig. 2; ref 23). Nevertheless, it is important to keep in mind that ADCw values may be tumor-type specific and further studies should be conducted to establish the relevance of this marker in response to cetuximab. In this study, ¹³C-pyruvate–lactate exchange was able to indicate tumor response in this model, in accordance with tumor size stabilization and decrease in pEGFR. Accordingly, both ¹³C-pyruvate–lactate exchange and ADCw were able to assess response or resistance to cetuximab in all models; however, only ¹³C-pyruvate–lactate exchange was able to assess response/resistance longitudinally (i.e., when comparing D0 and D8 posttreatment) in all models, suggesting that the assessment of metabolic changes could be more suitable for assessing clinically relevant longitudinal response to the EGFR inhibitor in HNSCC.

Metabolic imaging using hyperpolarized ¹³C-pyruvate has recently been introduced into the clinical setting for patients with prostate cancer (29). This study shows the potential added value of metabolic imaging for translational purposes. The study of pyruvate–lactate exchange provides the unique ability to assess metabolic changes in real time and to assess the relevance of combining EGFR inhibition with potential newly targeted therapies, paving the way for the future testing of combination therapies involving metabolically targeted therapies with EGFR inhibitors in HNSCC in order to counteract cetuximab-acquired or native resistance.

This proof-of-concept study constitutes the first in vivo assessment of cetuximab-induced inhibition of pyruvate to lactate conversion in cetuximab-sensitive HNSCC PDTXs using state-of-the-art metabolic imaging. Importantly, the real-time monitoring of pyruvate–lactate exchange suggested a recovery of the glycolytic phenotype in cetuximab-resistant xenografts, thereby identifying hyperpolarized ¹³C-pyruvate as a potential marker for response and resistance to the EGFR inhibitor in HNSCC. Further studies are, however, required to definitely establish the link between the changes in pyruvate to lactate conversion, metabolic changes, and sensitivity or resistance to EGFR inhibitors in HNSCC.

No potential conflicts of interest were disclosed.

Conception and design: L. Mignion, J.-P. Machiels, S. Schmitz, B.F. Jordan

Development of methodology: L. Mignion, N. Joudiou, C. Bouzin, J.-P. Machiels, S. Schmitz

Acquisition of data (provided animals, acquired and managed patients, provided facilities, etc.): L. Mignion, S. Acciardo, F. Gourgue, X. Caignet, C. Corbet, O. Feron, C. Bouzin, P.D. Cani, S. Schmitz

Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): L. Mignion, F. Gourgue, N. Joudiou, J.-P. Machiels, S. Schmitz, B.F. Jordan

Writing, review, and/or revision of the manuscript: L. Mignion, F. Gourgue, P.D. Cani, J.-P. Machiels, S. Schmitz, B.F. Jordan

Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): L. Mignion, R.-M. Goebbels

Study supervision: J.-P. Machiels, B.F. Jordan

This study was supported by grants from the Belgian National Fund for Scientific Research (FNRS), the “Actions de Recherches Concertées-Communauté Française de Belgique-ARC 09/14-020.” B.F. Jordan is Research Director, P.D. Cani is Senior Research Associate, and C. Corbet is Research Associate of the Belgian National Fund for Scientific Research (FNRS). S. Acciardo is a Televie researcher and F. Gourgue is a FRIA grant holder. We thank Michele de Beukelaer from the 2IP imaging platform of UCLouvain for technical assistance.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.