Targeting Energy Metabolism for Cancer Therapeutic Discovery using Agilent Seahorse XF Technology
This white paper discusses targeting energy metabolism for cancer therapeutic discovery using Agilent Seahorse XF technology.
Altered energy metabolism is now recognized as a driver in many diseases. In cancer research, significant advances have been made in defining druggable targets, with metabolic intermediates emerging as promising therapeutic targets. Agilent Seahorse XF technology provides critical functional measurements in live cells to identify potential druggable gene and protein targets, and to validate their role in cancer cell proliferation, adaptation, and survival. By measuring bioenergetic phenotype, cellular ATP production rate, and mitochondrial and glycolytic function, metabolic vulnerabilities in cancer cells can be revealed to advance cancer therapeutics.
Background: Advancing opportunities in cancer drug therapies
Cancer cell proliferation is a dynamic process that demands significant raw materials and energy. Conventional views have commonly inferred that cancer cells rely on upregulated glycolysis for their proliferation requirements, often characterized as the Warburg effect (1,2). However, with advancements in metabolic analysis techniques, it is now clear that some cancer cells preferentially utilize mitochondrial respiration for unregulated proliferation and survival, often in concert with downregulated glycolysis (3,4,5).
Therefore, there has been a recent paradigm shift towards understanding that cancer metabolic dependencies are not only variable across cancer subsets, but that they can also be measured to reveal cancer cell liabilities (6,7). Once identified and validated, these metabolic- pathway liabilities can be exploited for therapeutic targeting (8). Promising druggable targets for cancer metabolic therapies include oncogenes that rewire energy metabolism, intermediates in oncogene pathways, and the genes, proteins, and pathways associated with substrate and nutrient transport and utilization/oxidation.
A paradigm shift: Heterogeneity in metabolic phenotype driving oncogenesis
Cancer cells undergo various metabolic changes as they acquire altered traits to adapt, survive, and metastasize in environments with varying substrate availability and oxygen concentration (9). As a result, they display profound genetic, bioenergetic, and functional differences from their nontransformed parental cells.
In the last decade, bioenergetic studies have highlighted the variability among cancer types, and even within cancer types, in the mechanisms and the substrates preferentially used for deriving this vital energy. While glycolysis (or Warburg metabolism) has long been considered the major metabolic process for energy production and anabolic growth in cancer cells, it is now clear that mitochondria play a key role in oncogenesis (10).
In addition to central metabolic and bioenergetic function, mitochondria also provide building blocks for tumor anabolism, as well as controlling redox and calcium homeostasis (6). Although a main tenet of cancer is dysregulation of normal cell metabolism that contributes to abnormal cell growth, cancer is not one disease. Thus, understanding how the metabolic pathways of a given type of cancer are rewired is critical to identify and develop opportunities for therapeutic intervention.
Defining metabolic/bioenergetic phenotype
An initial step in understanding any type of cancer is to characterize the metabolic and bioenergetic signature of the cell. The Oxygen Consumption Rate (OCR)/Extracellular Acidification Rate (ECAR) ratio and Agilent Seahorse XF Cell Energy Phenotype Test (described in detail in Rogers et al, 2019 (11)) provide a high-level assessment of bioenergetic poise. Lanning et al. used this assay to show that triple-negative breast cancer (TNBC)-derived cell lines displayed significant heterogeneity with respect to basal metabolic phenotype (Figure 1) (12).
In a similar study, Guha et al. profiled the basal metabolic signatures of several breast cancer cell lines (Figure 2).
Here, the authors showed that aggressive TNBC cells have a unique metabolic phenotype associated with mitochondrial genetic and functional defects (13). These oncogenic defects impaired mitochondrial respiration and induced a metabolic switch to glycolysis, which is associated with tumorigenicity. These results suggest that metabolic phenotype could be used to identify TNBC patients at risk of metastasis and that altered metabolism can be targeted to improve chemotherapeutic response.
Based on OCR and ECAR XF measurements, a more functional and quantitative measure of the bioenergetic phenotype is to quantify the rate and source (mitochondrial versus glycolytic) of ATP production. The Agilent Seahorse XF Real-Time ATP rate assay measures the total ATP production rate in cells and distinguishes between ATP produced from mitochondrial oxidative phosphorylation (OXPHOS) and glycolysis (Figure 3 and (14)).
When applied to a panel of cancer cell lines, this assay shows that cancer cell lines utilize mitochondrial OXPHOS and glycolytic activities differently to meet their energy demands (Figure 4 and (15)).
These findings underscore the fact that different cancer cells adopt unique metabolic signatures that are critical to setting the strategy for targeting a given disease subtype and determining whether a given cell line is a good model for the disease of interest.
Modulating cancer cell energy metabolism
Measuring changes in cancer cell metabolism in response to potential therapeutic compounds is paramount for understanding drug mechanism of action, efficacy, toxicity, and off-target effects. When investigating effects of γ-Tocotrienol (γ-T3) on human gastric adenocarcinoma cells, Wang et al. observed a significant shift away from mitochondrial and towards glycolytic ATP production. Further, γ-T3 reduced the total ATP production rate in these cells (Figure 5) (16).
Once a change in bioenergetic phenotype is detected, further investigation often involves uncovering the mechanism responsible for the shift in metabolic poise. Thus, to gain insight into observations presented in Figure 5, further assays were performed to directly assess mitochondrial and glycolytic function in the presence of γ-T3. Using the Agilent Seahorse
XF Cell Mito Stress Test, Wang et al. demonstrated dose-dependent decreases in basal and maximal respiration for both SGC-7901 and MGC-803 cells, both indicative of mitochondrial dysfunction (Figure 6).
Using the Agilent Seahorse XF Glycolytic Rate Assay to provide a quantifiable measurement of glycolysis (Figure 7 (17)), the authors also showed a dose-dependent increase in basal glycolysis in both cell types (Figure 8).
This data supports the observation of cellular ATP production switching predominantly to glycolysis, but at a lower total production rate. These results, with orthogonal data, suggest that mitochondrial function may be an effective target for anticancer drug development efforts (16).
Mitochondrial and glycolytic function as therapeutic targets: the genotype-metabolic phenotype connection
Beyond initial classification of cancer cell energetic phenotypes, ATP production rates and the proportion of ATP sourced from OXPHOS versus glycolysis can be further used to investigate the functional effects of cancer-associated mutations. For example, evaluation of KRAS-and EGFR-mutated NSCLC cells showed distinct metabolic phenotypes concerning the source of ATP production (Figure 9 (18)).
The cell lines bearing EGFR mutations were, in general, more oxidative than those bearing KRAS mutations, providing insight into metabolic vulnerabilities of cancer subsets, and EGFR as a potential therapeutic target. A study by Deribe et al. provides another example of how measurements with the XF Cell Mito Stress Test revealed mutations of well-known cancer-causing genes, which could serve as a target for metabolic modulation (19). In this study, the authors showed that mutations in the SWI/SNF chromatin remodeling complex induce a targetable dependence on OXPHOS in lung cancer cell lines.
Cells deficient in the most frequently inactivated complex subunit, SMARCA4 (KPS cells, Figure 10A, B), had increased mitochondrial respiration. When the SMARCA4 deficient H1299 lung cell line was reconstituted with SMARC4A (H1299 SMARCA4), both basal respiration and spare respiratory capacity decreased (Figure 10 C, D), further showing that these mutant lung cancer cells are sensitive to inhibition of OXPHOS (19).
The previous examples have discussed how XF measurements and assays revealed metabolic vulnerabilities with upregulated OXPHOS. Similarly, metabolic vulnerabilities associated with upregulated glycolysis can be identified using In another example of oncogenes driving changes in metabolic phenotype, Barnoud et al. recently demonstrated that tumor cells expressing a p53 allele with a serine at amino acid 47 (S47 tumor cells) exhibited upregulated glycolysis with reduced dependency on OXPHOS compared to cell with wild type p53 (20). This was accomplished by performing both XF Glycolytic Rate and XF Mito Stress Test assays to investigate glycolytic and mitochondrial function, respectively.
Measurement of mitochondrial function revealed decreased basal and maximal (stressed) mitochondrial respiration of S47 cells compared to wild type cells (Figure 11A), suggesting a decreased ability to perform OXPHOS. In contrast, measurement of basal and compensatory glycolysis (Figure 11 B–D) showed that the S47 tumor cells were upregulating glycolysis under both basal and stressed conditions.
This indicates that metabolic rewiring resulted in a greater dependency on glycolysis compared to the wild type. Taken together, the results suggest that in these tumor cells, the glycolytic pathway is a potential metabolic target for S47 variant cancers. This is further supported by experiments showing increased sensitivity of S47 cells to the glycolytic inhibitor, 2-deoxyglucose (20).
To first understand changes in the bioenergetic poise between oxidative phosphorylation and glycolysis in cancer cells, it is recommended to measure the OCR/ECAR ratio. This metric is an easy-to-use indicator of changes in cell phenotype or metabolic activity. Assays that can be used to determine this ratio include the XF Cell Energy Phenotype Test, the XF Cell Mito Stress Test, and the XF Glycolytic Rate Assay.
A more functional and quantitative measure of cancer cell metabolic phenotype is to quantify the rate and source (mitochondrial versus glycolytic) of ATP production. The XF Real-Time ATP rate assay measures the total ATP production rate in cells and distinguishes between ATP produced from mitochondrial OXPHOS versus glycolysis. This assay therefore provides a detailed, functional overview of cancer cell phenotype and metabolism.
Used in combination the quantitative and functional parameters provided by the XF Real-Time ATP rate assay (total, glyco-, and mitoATP production rates), the XF Cell Mito Stress Test (basal and maximal OCR), and the XF Glycolytic Rate Assay (basal and compensatory glycolysis) can reveal the effects of anticancer agents in terms of metabolic liabilities, efficacy, toxicity, target identification, mechanism of action, etc.
There is increasing evidence for a correlative pattern among oncogenes (i.e. the expressed protein) and effects on cancer cell metabolism regarding changes in mitochondrial and glycolytic function and the ATP production rate/ source. These metabolic changes or shifts can be potential targets for anticancer therapeutics.
This White Paper was authored by George W. Rogers, Heather Throckmorton and Sarah E. Burroughs.
1. Counihan, J. L.; Grossman, E. A.; Nomura, D. K., Cancer Metabolism: Current Understanding and Therapies. Chemical Reviews 2018, 118 (14), 6893-6923.
2. Zheng, J., Energy metabolism of cancer: Glycolysis versus oxidative phosphorylation (Review). Oncology letters 2012, 4 (6), 1151-1157.
3. Wallace, D. C., Mitochondria and cancer. Nat Rev Cancer 2012, 12 (10), 685-98.
4. Vyas, S.; Zaganjor, E.; Haigis, M. C., Mitochondria and Cancer. Cell 2016, 166 (3), 555-566.
5. Danhier, P.; Bański, P.; Payen, V. L.; Grasso, D.; Ippolito, L.; Sonveaux, P.; Porporato, P. E., Cancer metabolism in space and time: Beyond the Warburg effect. Biochimica et Biophysica Acta (BBA) – Bioenergetics 2017, 1858 (8), 556-572.
6. Ashton, T. M.; McKenna, W. G.; Kunz-Schughart, L. A.; Higgins, G. S., Oxidative Phosphorylation as an Emerging Target in Cancer Therapy. Clinical cancer research : an official journal of the American Association for Cancer Research 2018, 24 (11), 2482-2490.
7. Porporato, P. E.; Filigheddu, N.; Pedro, J. M. B.-S.; Kroemer, G.; Galluzzi, L., Mitochondrial metabolism and cancer. Cell Research 2017, 28, 265.
8. Kalyanaraman, B.; Cheng, G.; Hardy, M.; Ouari, O.; Lopez, M.; Joseph, J.; Zielonka, J.; Dwinell, M. B., A review of the basics of mitochondrial bioenergetics, metabolism, and related signaling pathways in cancer cells: Therapeutic targeting of tumor mitochondria with lipophilic cationic compounds. Redox biology 2017, 14, 316-327.
9. Jose, C.; Bellance, N.; Rossignol, R., Choosing between glycolysis and oxidative phosphorylation: a tumor’s dilemma? Biochimica et biophysica acta 2011, 1807 (6), 552-61.
10. Teoh, S. T.; Lunt, S. Y., Metabolism in cancer metastasis: bioenergetics, biosynthesis, and beyond. Wiley Interdisciplinary Reviews: Systems Biology and Medicine 2018, 10 (2), e1406.
11. Rogers, G. W.; Throckmorton, H.; Burroughs, S. E., Gaining Insights into Disease Biology for Target Identification and Validation using Seahorse XF Technology. Agilent Technologies Application Note 2019, publication number 5991-7894EN.
12. Lanning, N. J.; Castle, J. P.; Singh, S. J.; Leon, A. N.; Tovar, E. A.; Sanghera, A.; MacKeigan, J. P.; Filipp, F. V.; Graveel, C. R., Metabolic profiling of triple-negative breast cancer cells reveals metabolic vulnerabilities. Cancer Metab 2017, 5, 6.
13. Guha, M.; Srinivasan, S.; Raman, P.; Jiang, Y.; Kaufman, B. A.; Taylor, D.; Dong, D.; Chakrabarti, R.; Picard, M.; Carstens, R. P.; Kijima, Y.; Feldman, M.; Avadhani, N. G., Aggressive triple negative breast cancers have unique molecular signature on the basis of mitochondrial genetic and functional defects. Biochimica et biophysica acta 2018, 1864 (4 Pt A), 1060-1071.
14. Romero, N.; Rogers, G. W.; Neilson, A.; Dranka, B. P., Quantifying Cellular ATP Production Rate Using Agilent Seahorse XF Technology. Agilent Technologies Application Note 2018, publication number 5991-9303EN.
15. Romero, N.; Swain, P.; Kam, Y.; Rogers, G. W.; Dranka., B. P., Bioenergetic profiling and fuel dependencies of cancer cell lines: quantifying the impact of glycolytic and mitochondrial ATP production on cell proliferation. Agilent Technologies poster EACR, 2018 2018.
16. Wang, H.; Luo, J.; Tian, W.; Yan, W.; Ge, S.; Zhang, Y.; Sun, W., gamma-Tocotrienol inhibits oxidative phosphorylation and triggers apoptosis by inhibiting mitochondrial complex I subunit NDUFB8 and complex II subunit SDHB. Toxicology 2019, 417, 42-53.
17. Romero, N.; Swain, P.; Dranka, B. P., Quantifying cellular glycolytic rate using CO2-corrected extracellular acidification. Agilent Technologies Application Note 2017, publication number 5991-7894EN.
18. Swain, P.; Romero, N.; Kam, Y.; Van de Bittner, G.; El Azzouny, M.; Dranka., B. P., Differential use of lactate for mitochondria by NSCLC cells. Agilent Technologies Poster, AACR 2019 2018.
19. Lissanu Deribe, Y.; Sun, Y.; Terranova, C.; Khan, F.; Martinez- Ledesma, J.; Gay, J.; Gao, G.; Mullinax, R. A.; Khor, T.; Feng, N.; Lin, Y.-H.; Wu, C.-C.; Reyes, C.; Peng, Q.; Robinson, F.; Inoue, A.; Kochat, V.; Liu, C.-G.; Asara, J. M.; Moran, C.; Muller, F.; Wang, J.; Fang, B.; Papadimitrakopoulou, V.; Wistuba, I. I.; Rai, K.; Marszalek, J.; Futreal, P. A., Mutations in the SWI/ SNF complex induce a targetable dependence on oxidative phosphorylation in lung cancer. Nature Medicine 2018, 24 (7), 1047-1057.
20. Barnoud, T.; Parris, J. L. D.; Murphy, M. E., Tumor cells containing the African-Centric S47 variant of TP53 show increased Warburg metabolism. Oncotarget 2019, 10 (11), 1217-1223.