Glucose transporters are the first and rate-limiting step for cellular glucose utilization, a process often exacerbated in tumor cells that enables their growth and proliferation (Ancey et al., 2018; Lunt and Vander Heiden, 2011). Although inhibiting glucose metabolism in lung tumors could become an efficient treatment strategy (Hensley et al., 2016; Patra et al., 2013; Xie et al., 2014), whether and which glucose transporter(s) should be targeted remains unclear because of their possible functional redundancy. Additionally, in solid cancers, tumor cell growth can be fueled by nutrients other than glucose, or by using transporter-independent metabolic processes including autophagy and macropinocytosis (Commisso et al., 2013; Karsli-Uzunbas et al., 2014; Romero et al., 2017; Son et al., 2013).

In this study, we exploited the Kras^LSL-G12D/WT; Trp53^Flox/Flox (KP) mouse model of lung adenocarcinoma to explore the importance of glucose transporters, expressed by tumor cells, in disease development. By glucose tracing and ultrastructural analyses, we identified the presence of lamellar body-like organelles in tumor cells, which are the primary site of glucose-derived biomass accumulation, occurring partly in a Glut1-dependent manner. We show that the deletion of Glut1 or Glut3 is not sufficient to decrease tumor progression, which is only affected significantly upon combined Glut1 and Glut3 loss.

To interrogate the importance of particular glucose transporters in the most frequent subtype of lung cancer, lung adenocarcinoma (LUAD), we used The Cancer Genome Atlas specific for LUAD (TCGA-LUAD) to compare gene expression within the facilitated glucose transporter (GLUT, gene name SLC2A) family. SLC2A1 (GLUT1) followed by SLC2A3 (GLUT3) were the most expressed members, (Figure 1a) and high SLC2A1 expression was correlated with poor overall survival (Figure 1—figure supplement 1a). From a human tissue microarray comprising 18 cases of stage IB-IIB LUAD and 18 cases of stage IA-IIB lung squamous cell carcinoma (LUSC), GLUT1 protein was detected in all tumors. Specifically, it showed intermediate and strong GLUT1 expression in 44% (8/18) and 56% (10/18) of the LUAD lesions, respectively (Figure 1b). In LUSCs, GLUT1 exhibited strong staining in all samples (Figure 1b), confirming previous results obtained from squamous cell carcinomas of lung and other tissues (Goodwin et al., 2017; Hsieh et al., 2019). To obtain more information about SLC2A1 expression in LUAD, we applied a non-negative matrix factorization of TCGA-LUAD samples, which generated four distinct clusters (NMF1-4, Figure 1—figure supplement 1b). SLC2A1 and SLC2A3 were the most expressed in NMF4, which contains the highest percentage of the TP53 mutation (Figure 1c). In the immunocompetent Kras^LSL-G12D/WT; Trp53^Flox/Flox (KP) mouse model where tumors consist of pure LUADs all harboring Trp53 gene deletion, RNA sequencing analyses of bulk tumor samples revealed a predominance of NMF4-like lesions (Figure 1—figure supplement 1c). Glut1 protein levels in tumor cells gradually increased along tumor progression, with the majority of advanced grade lesions being positive with intermediate or strong Glut1 staining (Figure 1—figure supplement 2a). In normal lung, Glut1 was only weakly expressed or undetectable in the alveolar compartment that comprises alveolar type-1 and 2 (AT1, AT2) cells, endothelial cells and alveolar macrophages (Figure 1—figure supplement 2b), AT2 cells being considered as the principal tumor cell-of-origin in this model (Desai et al., 2014; Sutherland et al., 2014; Xu et al., 2012). In contrast, Glut1 was strongly expressed in the bronchiolar epithelial compartment that contains Club cells, another cell type permissive to tumor initiation (Sutherland et al., 2014; Figure 1—figure supplement 2b). To determine the contribution of Glut1 for tumor development, we crossed KP mice to Slc2a1^Flox/Flox (G1) animals (Young et al., 2011), and initiated tumors in the resulting KPG1 mice and control KP mice by intratracheal lentiviral-Cre instillation (Lenti.PGK-Cre, Figure 1—figure supplement 2c–d). During tumor progression, we detected slightly but not significantly reduced changes in tumor growth rates monitored by X-rays micro-computed tomography (μCT) (Figure 1d). At sacrifice, although no significant difference was observed in the weight or in the number of lesions (Figure 1e–f), a significant reduction of high grade tumors (grades 4 and 5) was determined upon Glut1 loss in KP lesions (Figure 1g). Because there was no significant increase in KPG1 mouse survival (Figure 1h), these data together fail to reveal a clear impact of Glut1 deletion in the tumor epithelial cells on disease progression.

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Source files for tumor growth, grades and survival of KP and KPG1 mice.

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To detect if there are measurable changes in glucose utilization secondary to Glut1 deletion in vivo, we decided to monitor glucose-derived biomass accumulation in tumors with ultrastructural resolution. Specifically, we performed ¹³C-glucose injections followed by nanoscale secondary ion mass spectrometry (NanoSIMS) imaging (Hoppe et al., 2013) correlated with electron microscopy (EM) (Figure 2a). Intense ¹³C enrichments inside tumors revealed a compartmentalized intracellular accumulation of glucose-derived biomass, which can be attributed to a specific organelle of tumor cells visualized by the superposition of images obtained from scanning EM (SEM) and NanoSIMS (Figure 2b). From SEM and transmission EM (TEM), these organelles resemble lamellar bodies (LBs) based on their size, secretory behavior and morphology, which exhibit more or less packed, occasionally visible lamellae (Figure 2b–c). LBs are lipid-rich secretory organelles specifically produced in the lung by AT2 cells, responsible for surfactant production and release into the alveolar space. Within tumor cells, these LB-like organelles (LBOs) were typically from 200 nm to 2 μm in diameter, irregularly shaped and darkly stained. Often these contained more heavily stained regions and discernible tightly packed lamellae at higher magnifications (Figure 2c). Quantitative NanoSIMS images of these organelles revealed a lower ¹³C-enrichment signal intensity in KPG1 compared to KP tumor cells (Figure 2d), demonstrating the consequence of Glut1 loss in vivo.

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Despite the differences measured by NanoSIMS, Glut1 deficiency only minimally affected tumor progression (see Figure 1d–h). We therefore hypothesized the existence of an alternative mechanism that sustains lung tumor growth. First, we isolated KP and KPG1 tumors, prepared single cell suspensions and tested their glycolytic capacity. In this ex vivo experimental setting, KPG1 tumors failed to stimulate glycolysis in response to glucose (Figure 3a and Figure 3—figure supplement 1a). As exception, one KPG1 tumor displayed the strongest glycolytic response of all tested tumors, which coincided with elevated expression of another high-affinity glucose transporter, Glut3 (Figure 3a). To understand better the mechanisms of Glut1-deficient tumor progression, we also performed RNA sequencing analyses from the non-immune (CD45^-) fraction of KP and KPG1 tumors. To increase the relevance of this comparison, we analysed three cohorts of mice, where tumors were initiated with Lenti.PGK-Cre, Ad5.SPC-Cre or Ad5.CC10-Cre, the latter two providing SPC⁺- or CC10⁺-cell restricted Cre expression for tumor initiation in distinct cell types (Sutherland et al., 2014) (see Figure 1—figure supplement 2c). Gene Set Enrichment Analysis (GSEA) highlighted, from all the Hallmark and KEGG up- and downregulated pathways, a unique significantly shared pathway between the three cohorts, which was upregulated in KPG1: ‘Hallmark_bile acid metabolism’ (Figure 3—figure supplement 2a). Upon closer examination of the genes included in this pathway, we identified several target genes of peroxisome proliferator-activated receptor alpha (PPARα) (Figure 3b), a crucial transcription factor in fatty acid catabolism (Reddy and Hashimoto, 2001). Furthermore, when interrogating the expression of known PPARα targets (Rakhshandehroo et al., 2010), GSEA highlighted their enrichment in KPG1 compared to KP tumors (Figure 3c). Thus, KPG1 tumors have a stronger expression of known PPARα-target genes than KP lesions. Together, our metabolic and molecular analyses suggested two possible and distinct routes for Glut1-deficient tumor growth: the uptake of the same nutrient through another glucose transporter, Glut3, or via a PPARα-dependent metabolic shift (Figure 3d).

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To test the hypothesis of an involvement of PPARα in lung tumor progression, we cloned a mouse PPARα-dominant negative construct, PPARαΔ13 (Michalik et al., 2005), into a bi-promoter Lenti.Cre vector allowing doxycycline-inducible expression (Lenti.TRE-PPARαΔ13_PGK-Cre) in KP and KPG1 tumors (mice were crossed to CCSP-rtTA transgenic mice [Meylan et al., 2009; Figure 3—figure supplement 3a–b]). Doxycycline-mediated PPARαΔ13 induction in KP mice led only to a trend toward a reduction in tumor growth rates, comparable to the growth of KPG1 versus KP tumors. Within the KPG1 group, PPARαΔ13 induction did not further reduce tumor growth (Figure 3—figure supplement 3c). Global gene expression analyses from tumor samples confirmed the functionality of the PPARαΔ13 construct in both KP and KPG1 tumors, as ‘Hallmark_bile acid metabolism’, ‘Hallmark_fatty acid metabolism’, ‘KEGG_peroxisome’ and ‘KEGG_ fatty acid metabolism’ pathways were repressed in tumors harboring this construct compared to tumors from KP-rtTA or KPG1-rtTA mice not fed on a doxycycline diet (Figure 3—figure supplement 3d). Thus, although PPARαΔ13 downregulates PPARα-related pathways in tumors, it fails to affect their growth even in absence of Glut1.

To test our second hypothesis, we initially monitored Glut3 expression in tumors. By real-time PCR, Slc2a3 was expressed to varying levels but not significantly differently between KP and KPG1 tumors (Figure 4—figure supplement 1a). In contrast, Slc2a3 was almost undetectable in tumors from Kras^LSL-G12D; Stk11^Flox/Flox (KL) mice (Figure 4—figure supplement 1a), another lung cancer model where Lkb1 (Stk11)-deficient tumors progressing from an adeno- to a squamous histology become exquisitely Glut1-dependent (Hsieh et al., 2019). In absence of Glut1 (KLG1), Slc2a3 mRNA expression remained very low (Figure 4—figure supplement 1a). Accordingly, Glut3 protein was undetectable by immunohistochemistry in KL and KLG1 tumors, whereas it was expressed in KP and KPG1 lesions (Figure 4—figure supplement 1b). More specifically, Glut3 stained at the tumor cell membrane in a minority (37%) of small and a majority (76%) of big KP lesions (Figure 4—figure supplement 1c). Furthermore, within Glut3-expressing KP tumors, areas positive for Glut3 frequently correlated with a stronger expression of Glut1 (Figure 4a and Figure 4—figure supplement 1d), suggesting intra-tumor heterogeneity in glucose utilization in this mouse model, as demonstrated earlier in human non-small cell lung cancer (Hensley et al., 2016). We then crossed KP mice to Slc2a3^Flox/Flox mice (generating KPG3 mice, see Figure 1—figure supplement 2c–d). When comparing tumor development in KP and KPG3 animals, we failed to detect differences in overall tumor growth rates, ex vivo glycolysis, mouse survival, or tumor grade distribution (Figure 4—figure supplement 2a–d). Although this indicates that Glut3 deletion alone does not affect tumors in a detectable manner, we next wanted to evaluate its importance in a Glut1-deficient context. To do this, we generated KPG1G3 mice by interbreeding KP mice with Slc2a1^Flox/Flox and Slc2a3^Flox/Flox mice (see Figure 1—figure supplement 2c–d). Deletion of the two glucose transporters, Glut1 and Glut3, from the tumor epithelial cell compartment resulted in a decreased number of tumors (Figure 4b). Furthermore, contrasting with our data obtained from each separately-targeted Gluts, combined Glut1 and Glut3 deletion led to a significant reduction of tumor growth rates monitored by μCT scans, to a reduced tumor size with an absence of big (>50 mg) lesions at sacrifice, to a decrease of high grade tumors, and to an extended mouse survival (Figure 4c–g and Figure 4—figure supplement 3a–b). This synergistic action of the dual Glut1 and Glut3 deletion was also demonstrated in vitro with four different human NSCLC cell lines, on which the strongest reduction of tumor cell viability resulted from the combined knockdown of these glucose transporters (Figure 4—figure supplement 3c–f). Together, these data position Glut1 and Glut3 as a coordinated glucose transport unit required to fuel the growth of LUAD.

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To trace and compare directly glucose uptake in tumors from the different mouse genotypes, we used positron emission tomography with ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG-PET) in KP, KPG1, KPG3, and KPG1G3 tumors. The maximum standardized uptake value (SUV_max) of each tumor revealed that Glut1-deficient KP tumors incorporated significantly less ¹⁸F-FDG than control lesions (Figure 4h–i), which strengthens the NanoSIMS data (see Figure 2d). Glut3-deficient and control KP tumors presented a similar ¹⁸F-FDG absorption (Figure 4h–i). This result might be explained by the fact that Glut3 positive KP tumors mainly co-express Glut1 and Glut3 (see Figure 4—figure supplement 1d). Thus, in absence of Glut3, lesions might certainly incorporate ¹⁸F-FDG via Glut1. The ¹⁸F-FDG absorption of Glut1- and Glut3-deficient KP tumors was significantly reduced as compared to control KP lesions (Figure 4h–i). Because glucose uptake was not significantly lower in KPG3 compared to KP tumors, and in KPG1G3 compared to KPG1 tumors, a possible explanation is that most tumors analyzed had not yet gained Glut3 expression and become dependent on it, which agrees with an elevated Glut3 expression specifically in big lesions (see Figure 4—figure supplement 1c). Finally, to investigate if Glut1- and Glut3-deficient tumors might incorporate glucose via another glucose transporter, we performed a gene expression analysis of glucose transporter family members on KP and KPG1G3 lesions. This analysis showed that the expression of no other glucose transporter was changed upon dual Glut1 and Glut3 deletion in KP tumors (Figure 4—figure supplement 4a). Altogether, these data demonstrate that deleting Glut1 and Glut3 in KP tumors reduces glucose uptake, and that Glut1 highly contributes to glucose uptake in KP tumors. These results may also lead to the hypothesis that at least a part of the remaining ¹⁸F-FDG-PET signal detected in KPG1G3 tumors might come from glucose uptake by the cells of the tumor immune microenvironment.

Recently, pharmacological inhibition of a secondary active, Na⁺-dependent glucose transporter, Sglt2, showed its role in premalignant KP lung lesions (Scafoglio et al., 2018), while a glycolytic switch was identified in advanced compared to early tumors (Kerr et al., 2016). These studies, together with our gene targeting approaches, definitively demonstrate the importance of glucose utilization for LUAD development in vivo. In the KL model, Glut1 deletion impairs LUSC development (Hsieh et al., 2019), and we find that Lkb1-deficient lung tumors lack detectable Glut3, precluding any compensation occurring through this transporter. In contrast, in pure LUAD our results indicate that Trp53-deficient tumor cells depend on glucose uptake based on a redundant system, whereby the expression of either Glut1 or Glut3 is sufficient to sustain tumor growth (Figure 4j). For clinical perspectives, we anticipate that dual Glut1 and Glut3 blockade will be necessary to achieve strong anti-tumor efficacy.

The RNA sequencing data have been deposited to the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and assigned the identifier: GSE138757.

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Author details

1. Caroline Contat

   1. Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
   2. Swiss Cancer Center Léman, Lausanne, Switzerland

   Contribution

   Conceptualization, Formal analysis, Supervision, Validation, Investigation, Visualization, Methodology, Project administration

   Competing interests

   No competing interests declared

2. Pierre-Benoit Ancey

   1. Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
   2. Swiss Cancer Center Léman, Lausanne, Switzerland

   Contribution

   Formal analysis, Validation, Investigation, Visualization, Project administration

   Competing interests

   No competing interests declared

3. Nadine Zangger

   1. Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
   2. Swiss Cancer Center Léman, Lausanne, Switzerland
   3. Bioinformatics Core Facility, Swiss Institute of Bioinformatics, Lausanne, Switzerland

   Contribution

   Software, Formal analysis, Validation, Investigation, Visualization

   Competing interests

   No competing interests declared

4. Silvia Sabatino

   1. Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
   2. Swiss Cancer Center Léman, Lausanne, Switzerland

   Contribution

   Investigation

   Competing interests

   No competing interests declared

5. Justine Pascual

   1. Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
   2. Swiss Cancer Center Léman, Lausanne, Switzerland

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Stéphane Escrig

   Laboratory for Biological Geochemistry, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

   Contribution

   Formal analysis, Validation, Investigation, Visualization

   Competing interests

   No competing interests declared

7. Louise Jensen

   Laboratory for Biological Geochemistry, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

   Contribution

   Investigation

   Competing interests

   No competing interests declared

8. Christine Goepfert

   Institute of Animal Pathology (COMPATH), University of Bern, CH-3012 Bern, and Histology Core Facility, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

   Contribution

   Formal analysis, Investigation

   Competing interests

   No competing interests declared

9. Bernard Lanz

   Laboratory for Functional and Metabolic Imaging (LIFMET), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

10. Mario Lepore

    Center for Biomedical Imaging (CIBM), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Contribution

    Investigation, Methodology

    Competing interests

    No competing interests declared

11. Rolf Gruetter

    1. Laboratory for Functional and Metabolic Imaging (LIFMET), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    2. Center for Biomedical Imaging (CIBM), Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Contribution

    Resources

    Competing interests

    No competing interests declared

12. Anouk Rossier

    1. Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    2. Swiss Cancer Center Léman, Lausanne, Switzerland

    Contribution

    Investigation

    Competing interests

    No competing interests declared

13. Sabina Berezowska

    Institute of Pathology, University of Bern, Bern, Switzerland

    Contribution

    Resources, Supervision

    Competing interests

    No competing interests declared

14. Christina Neppl

    Institute of Pathology, University of Bern, Bern, Switzerland

    Contribution

    Formal analysis, Investigation, Visualization

    Competing interests

    No competing interests declared

15. Inti Zlobec

    Institute of Pathology, University of Bern, Bern, Switzerland

    Contribution

    Resources

    Competing interests

    No competing interests declared

16. Stéphanie Clerc-Rosset

    BioEM Facility, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Contribution

    Investigation

    Competing interests

    No competing interests declared

17. Graham William Knott

    BioEM Facility, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Contribution

    Formal analysis, Supervision

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-2956-9052

18. Jeffrey C Rathmell

    Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

19. E Dale Abel

    Fraternal Order of Eagles Diabetes Research Center and Division of Endocrinology and Metabolism, Carver College of Medicine, University of Iowa, Iowa City, United States

    Contribution

    Resources

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5290-0738

20. Anders Meibom

    1. Laboratory for Biological Geochemistry, School of Architecture, Civil and Environmental Engineering, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    2. Center for Advanced Surface Analysis, Faculty of Geosciences and Environment, University of Lausanne, Lausanne, Switzerland

    Contribution

    Resources, Supervision

    Competing interests

    No competing interests declared

21. Etienne Meylan

    1. Swiss Institute for Experimental Cancer Research (ISREC), School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
    2. Swiss Cancer Center Léman, Lausanne, Switzerland

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Validation, Visualization, Project administration

    For correspondence

    etienne.meylan@epfl.ch

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0899-2230

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