Targeting Energy Metabolism for Cancer Therapeutic Discovery using Agilent Seahorse XF Technology
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
Application Note
Cancer Research
Targeting Energy Metabolism for
Cancer Therapeutic Discovery using
Agilent Seahorse XF Technology
Authors Abstract
George W. Rogers
Altered energy metabolism is now recognized as a driver in many diseases. In
Heather Throckmorton
cancer research, significant advances have been made in defining druggable
Sarah E. Burroughs
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.Introduction Defining metabolic/bioenergetic
Background: Advancing opportunities in cancer drug phenotype
therapies
An initial step in understanding any type of cancer is to char-
Cancer cell proliferation is a dynamic process that demands acterize the metabolic and bioenergetic signature of the cell.
significant raw materials and energy. Conventional views The Oxygen Consumption Rate (OCR)/Extracellular Acidifica-
have commonly inferred that cancer cells rely on upregulated tion Rate (ECAR) ratio and Agilent Seahorse XF Cell Energy
glycolysis for their proliferation requirements, often character- Phenotype Test (described in detail in Rogers et al, 2019 11)
ized as the Warburg effect 1, 2. However, with advancements provide a high-level assessment of bioenergetic poise. Lan-
in metabolic analysis techniques, it is now clear that some ning et al. used this assay to show that triple-negative breast
cancer cells preferentially utilize mitochondrial respiration cancer (TNBC)-derived cell lines displayed significant hetero-
for unregulated proliferation and survival, often in concert geneity with respect to basal metabolic phenotype (Figure 1)
with downregulated glycolysis 3, 4, 5. Therefore, there has been 12
. In a similar study, Guha et al. profiled the basal metabolic
a recent paradigm shift towards understanding that cancer signatures of several breast cancer cell lines (Figure 2). Here,
metabolic dependencies are not only variable across cancer the authors showed that aggressive TNBC cells have a unique
subsets, but that they can also be measured to reveal cancer metabolic phenotype associated with mitochondrial genetic
cell liabilities 6, 7. Once identified and validated, these metabol- and functional defects 13. These oncogenic defects impaired
ic-pathway liabilities can be exploited for therapeutic targeting mitochondrial respiration and induced a metabolic switch
8
. Promising druggable targets for cancer metabolic therapies to glycolysis, which is associated with tumorigenicity. These
include oncogenes that rewire energy metabolism, interme- results suggest that metabolic phenotype could be used to
diates in oncogene pathways, and the genes, proteins, and identify TNBC patients at risk of metastasis and that altered
pathways associated with substrate and nutrient transport metabolism can be targeted to improve chemotherapeutic
and utilization/oxidation. response.
A paradigm shift: Heterogeneity in metabolic phenotype Based on OCR and ECAR XF measurements, a more function-
driving oncogenesis al and quantitative measure of the bioenergetic phenotype is
Cancer cells undergo various metabolic changes as they to quantify the rate and source (mitochondrial versus glyco-
acquire altered traits to adapt, survive, and metastasize in lytic) of ATP production. The Agilent Seahorse XF Real-Time
environments with varying substrate availability and oxygen ATP rate assay measures the total ATP production rate in
concentration 9. As a result, they display profound genetic, cells and distinguishes between ATP produced from mito-
bioenergetic, and functional differences from their nontrans- chondrial oxidative phosphorylation (OXPHOS) and glycolysis
formed parental cells. In the last decade, bioenergetic studies (Figure 3 and 14). When applied to a panel of cancer cell lines,
have highlighted the variability among cancer types, and even this assay shows that cancer cell lines utilize mitochondrial
within cancer types, in the mechanisms and the substrates OXPHOS and glycolytic activities differently to meet their
preferentially used for deriving this vital energy. While glycoly- energy demands (Figure 4 and 15). These findings underscore
sis (or Warburg metabolism) has long been considered the the fact that different cancer cells adopt unique metabolic
major metabolic process for energy production and anabolic signatures that are critical to setting the strategy for targeting
growth in cancer cells, it is now clear that mitochondria play a given disease subtype and determining whether a given cell
a key role in oncogenesis 10. In addition to central metabolic line is a good model for the disease of interest.
and bioenergetic function, mitochondria also provide build-
ing 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 opportuni-
ties for therapeutic intervention.
22.0 Oligomyin Rot/AA
pmol/min/cell number)
Increasing OXPHOS
1.5 MDA-MB-468
OCR (relative
HCC70
glycoATP
1.0 Production Rate
OCR or ECAR
MDA-MB-231
Hs578T
Report +
0.5 Generator mitoATP
HME1
Increasing Glycolysis Production Rate
0 II
0 0.5 1.0 1.5 2.0
ECAR (relative Total ATP
mpH/min/cell number) Production Rate
Time (min)
Figure 1. Agilent Seahorse XF energy map of breast cancer cell lines
Figure 3. Representative scheme of the Agilent Seahorse XF Real-Time
reveals different metabolic phenotypes. Normalized ECAR and OCR data
ATP rate assay. This assay provides quantitative measurements of ATP
were plotted to reveal overall relative basal metabolic profiles for each cell
production rate from both mitochondrial and glycolytic pathways. Both
model 12.
OCR and ECAR of live cells are simultaneously measured using the Agilent
Seahorse XF Analyzer. Using standardized control compound injections and
data analysis, cellular ATP production rates are reported 14.
500
Basal OCR (nmol oxygen/
400
min/0.1mg protein)
mitoATP Production Rate glycoATP Production Rate
Panc1
300 BT474
Hela
H1975
200 ZR75
HCT116
HMEC
** H460
100 ** ** ** MCF7
SK-OV-3
A431
0 A549
BT549
06
T
31
7
7D
15
PE
HT-29
CF
78
T47D
18
B2
13
52
T4
S5
M
SKBR3
CC
M
M
M
H
Hs578
SU
DA
SU
H
MCF10a
M
HL60
Non-TNBC Jurkat
TNBC
40 30 20 10 0 10 20 30 40
300
ATP Production Rate (pmol/min/1x10 cells) 3
250 Figure 4. Cancer cells have developed different strategies for cellular
T47D energy production, with significant implications for therapeutic strategy.
OCR (pmol/min)
Non-TNBC
200 ** Measuring ATP production rates across a panel of 20 cancer cell lines
reveals a wide range of energy phenotypes, from predominantly oxidative
150 SUM52PE **
(top) to predominantly glycolytic (bottom) 15.
SUM1315
100 **
TNBC MDA-MB231
50
**
HS578T
0
0 10 20 30 40 50 60
ECAR (mpH/min)
Figure 2. TNBC and non-TNBC cell lines display distinct metabolic
phenotypes. Top: Basal cellular oxygen consumption rate (OCR) of TNBC-
and non-TBNC cell lines. Bottom: XF energy map of TNBC and non-TNBC cell
lines 13.
3Modulating cancer cell energy metabolism
Measuring changes in cancer cell metabolism in response XF Cell Mito Stress Test, Wang et al. demonstrated dose-de-
to potential therapeutic compounds is paramount for under- pendent decreases in basal and maximal respiration for both
standing drug mechanism of action, efficacy, toxicity, and SGC-7901 and MGC-803 cells, both indicative of mitochon-
off-target effects. When investigating effects of γ-Tocotrienol drial dysfunction (Figure 6). Using the Agilent Seahorse XF
(γ-T3) on human gastric adenocarcinoma cells, Wang et al. Glycolytic Rate Assay to provide a quantifiable measurement
observed a significant shift away from mitochondrial and of glycolysis (Figure 7 17), the authors also showed a dose-de-
towards glycolytic ATP production. Further, γ-T3 reduced the pendent increase in basal glycolysis in both cell types (Figure
total ATP production rate in these cells (Figure 5) 16. 8). This data supports the observation of cellular ATP produc-
Once a change in bioenergetic phenotype is detected, further tion switching predominantly to glycolysis, but at a lower total
investigation often involves uncovering the mechanism re- production rate. These results, with orthogonal data, suggest
sponsible for the shift in metabolic poise. Thus, to gain insight that mitochondrial function may be an effective target for
into observations presented in Figure 5, further assays were anticancer drug development efforts 16.
performed to directly assess mitochondrial and glycolytic
function in the presence of γ-T3. Using the Agilent Seahorse 400
Proton Efflux Rate (pmol/min)
Rot/AA 2-DG
Total Proton Efflux
* * 300
Compensatory
800 glycolysis Glycolytic Proton Efflux
700
ATP Production Rate
200
600
(pmol/min/mg)
Mito
acidification
500
100 Basal
400 glycolysis
300 0
200 0 10 20 30 40 50 60 70
Time (minutes)
100
0 Figure 7. Representative scheme for the Agilent Seahorse XF Glycolytic
7901-0 7901-30 803-0 803-30
Rate Assay. This assay provides a quantitative measure of glycolysis using
mitoATP Production Rate glycoATP Production Rate both OCR and ECAR (simultaneously measured by the Agilent Seahorse XF
Analyzer) to measure and subtract mitochondrial acidification. The resulting
Figure 5. Quantification of ATP production rate reveals a drug-induced glycolytic Proton Efflux Rate (glycoPER) under both basal and compensatory
glycolytic switch in two adenocarcinoma cell lines. Gastric cancer cells conditions are reported 17.
were treated with 0 or 30 μM γ-T3 for 4 h, and the ATP production rate was
measured using the Agilent Seahorse XF Real Time ATP rate assay. Adapted
0 20 30 Basal Glycolysis Compensatory Glycolysis
from 16. Rot/AA 2-DG
glycoPER (pmol/min/mg protein)
1400 1500 1500
1200
(pmol/min/mg protein)
(pmol/min/mg protein)
***
1000 ***
SGC-7901 cells MGC-803 cells 1000 1000
GlycolPER
GlycolPER
800
Basal Maximal Basal Maximal 600
250 250 200 200 500 500
400
Pmol/min/mg protein
Pmol/min/mg protein
Pmol/min/mg protein
Pmol/min/mg protein
200 200 * 200
150 150
0 0 0
150 ** 150 0 20 40 60 80 100 0 20 30 0 20 30
100 100
Time (min) Concentrations Concentrations
100 100
** (µmol/L) (µmol/L)
50 ** 50
50 *** 50 *** *** ***
0 20 30 Basal Glycolysis Compensatory Glycolysis
Rot/AA 2-DG
glycoPER (pmol/min/mg protein)
0 0 0 0 1200 1000 1000
0 20 30 0 20 30 0 20 30 0 20 30
Concentrations (µmol/L) Concentrations (µmol/L) Concentrations (µmol/L) Concentrations (µmol/L4)
1000
(pmol/min/mg protein)
(pmol/min/mg protein)
800 *** 800
γ-T3 γ-T3 γ-T3 γ-T3
**
800
600 600
GlycolPER
GlycolPER
Figure 6. Dose-dependency of the effect of γ-T3 on mitochondrial function 600
is measured by the Agilent Seahorse XF Cell Mito Stress Test. SGC-7901 400
400 400
and MGC-803 cells were treated with indicated concentrations of γ-T3 for 4 200 200
200
h, then basal and maximal OCR were measured using the XF Cell Mito Stress
0 0 0
Test 16. 0 20 40 60 80 100 0 20 30 0 20 30
Figure 8. Dose-dependency
Time (min) of the effect of γ-T3 on the glycolytic
Concentrations rate is
Concentrations
(µmol/L) (µmol/L)
measured by the Agilent Seahorse XF Glycolytic Rate Assay. SGC-7901
(top) and MGC-803 (bottom) cells were treated with indicated concentrations
of γ-T3 for 4 h and the extracellular acidification rates were measured using
the XF Glycolytic Rate Assay. Bar graphs show quantitative data of basal and
compensatory glycolysis rates 16.
4Mitochondrial and glycolytic function as therapeutic targets: the genotype-metabolic
phenotype connection.
Beyond initial classification of cancer cell energetic phe- the SMARCA4 deficient H1299 lung cell line was reconstituted
notypes, ATP production rates and the proportion of ATP with SMARC4A (H1299 SMARCA4), both basal respiration
sourced from OXPHOS versus glycolysis can be further used and spare respiratory capacity decreased (Figure 10 C, D), fur-
to investigate the functional effects of cancer-associated mu- ther showing that these mutant lung cancer cells are sensitive
tations. For example, evaluation of KRAS-and EGFR-mutated to inhibition of OXPHOS 19.
NSCLC cells showed distinct metabolic phenotypes concern-
ing the source of ATP production (Figure 9 18). The cell lines % Glycolysis % OXPHOS
bearing EGFR mutations were, in general, more oxidative than
those bearing KRAS mutations, providing insight into meta- PC9
bolic vulnerabilities of cancer subsets, and EGFR as a poten- EGFR
tial therapeutic target. H1957
A study by Deribe et al. provides another example of how H460
measurements with the XF Cell Mito Stress Test revealed KRAS
mutations of well-known cancer-causing genes, which could A549
serve as a target for metabolic modulation 19. In this study,
the authors showed that mutations in the SWI/SNF chroma- 0 20 40 60 80 100
tin remodeling complex induce a targetable dependence on Figure 9. KRAS-and EGFR-mutated NSCLC cells exhibit different ATP
OXPHOS in lung cancer cell lines. Cells deficient in the most production patterns. Metabolic profiles of four cellular models of NSCLC
frequently inactivated complex subunit, SMARCA4 (KPS cells, in standard assay medium (XF RPMI containing 10 mM glucose, 1 mM
pyruvate, 2 mM glutamine, pH 7.4) These two KRAS mutant cell lines are
Figure 10A, B), had increased mitochondrial respiration. When
slightly more glycolytic than the EGFR mutant cell lines 18.
A Mitochondrial respiration B
700 FCCP Rotenone + 350
Antimycin A
600 300
per 10,000 cells)
per 10,000 cells)
OCR (pmol/min
KPS 250 KPS
OCR (pmol/min
500 Oligomycin
400 KP 200 KP
300 150 ***
200 100
100 50
***
0 0
0 50 100 Basal Spare
Time (min) respiration capacity
C D
Mitochondrial respiration
Rotenone +
FCCP
300 Antimycin A 140
250 120
Oligomycin
per 10,000 cells)
OCR (pmol/min
100
per 10,000 cells)
H1299 Parental H1299 Parental
OCR (pmol/min
200
H1299 SMARCA4 80 H1299 SMARCA4
150
60 *** **
100
40
50 20
0 0
0 50 100 Basal Spare
Time (min) respiration capacity
Figure 10. Measuring the functional effect of mitochondrial gene mutations using basal respiration and spare respiratory capacity. A) trace of OCR
values from a mitochondrial stress test showing Kras-p53 (KP) -derived cell line (red) and Kras-p53-SMARCA4 -derived cell line (KPS , i.e. SMARCA4
deficient, blue). B) basal respiration and spare respiratory capacity. C) kinetic trace of the mitochondrial stress test showing H1299 parental (i.e.
SMARCA4 deficient, blue) and H1299 cell line reconstituted with SMARCA4 (red). D) basal respiration and spare respiratory capacity 19.
5The previous examples have discussed how XF measure- and maximal (stressed) mitochondrial respiration of S47
ments and assays revealed metabolic vulnerabilities with cells compared to wild type cells (Figure 11A), suggesting a
upregulated OXPHOS. Similarly, metabolic vulnerabilities decreased ability to perform OXPHOS. In contrast, measure-
associated with upregulated glycolysis can be identified using ment of basal and compensatory glycolysis (Figure 11 B–D)
In another example of oncogenes driving changes in meta- showed that the S47 tumor cells were upregulating glycolysis
bolic phenotype, Barnoud et al. recently demonstrated that under both basal and stressed conditions. This indicates that
tumor cells expressing a p53 allele with a serine at amino acid metabolic rewiring resulted in a greater dependency on gly-
47 (S47 tumor cells) exhibited upregulated glycolysis with colysis compared to the wild type. Taken together, the results
reduced dependency on OXPHOS compared to cell with wild suggest that in these tumor cells, the glycolytic pathway is
type p53 20. This was accomplished by performing both XF a potential metabolic target for S47 variant cancers. This is
Glycolytic Rate and XF Mito Stress Test assays to investigate further supported by experiments showing increased sensitiv-
glycolytic and mitochondrial function, respectively. Measure- ity of S47 cells to the glycolytic inhibitor, 2-deoxyglucose 20.
ment of mitochondrial function revealed decreased basal
A Oxygen Consumption Rate (OCR)
C Basal Glycolysis
A B C
500 800
OCR (pmol/min/40,000 cells)
***
glycoPER (pmol/min)
400 WT E1A/RAS
600
S47 E1A/RAS
300
400
200
200
100
0 0
0 10 20 30 40 50 60 70 80 WT S47
Time (min) E1A/RAS E1A/RAS
B Glycolytic Rate Assay
D
A B
Compensatory Glycolysis
1500
ECAR (mpH/min/60,000 cells)
120 WT E1A/RAS
S47 E1A/RAS ***
glycoPER (pmol/min)
100
80 1000
60
40 500
20
0 0
0 20 40 60 80 WT S47
Time (min) E1A/RAS E1A/RAS
Figure 11. Metabolic changes in tumor cells induced by a p53 allele with a serine at amino acid 47 (S47 tumor cells). A) The Agilent Seahorse XF Cell Mito
Stress Test reveals a decrease in basal mitochondrial respiration and spare respiratory capacity in S47 tumor cells compared to wild type cells. B, C, D) The
Agilent Seahorse XF Glycolytic Rate Assay reveals a statistically significant difference in basal and compensatory glycolysis for S47 tumor cells compared to
WT cells (Figure adapted from 20).
6Summary
• To first understand changes in the bioenergetic poise be- • Used in combination the quantitative and functional
tween oxidative phosphorylation and glycolysis in cancer parameters provided by the XF Real-Time ATP rate assay
cells, it is recommended to measure the OCR/ECAR ratio. (total, glyco-, and mitoATP production rates), the XF Cell
This metric is an easy-to-use indicator of changes in cell Mito Stress Test (basal and maximal OCR), and the XF
phenotype or metabolic activity. Assays that can be used Glycolytic Rate Assay (basal and compensatory glycolysis)
to determine this ratio include the XF Cell Energy Pheno- can reveal the effects of anticancer agents in terms of
type Test, the XF Cell Mito Stress Test, and the XF Glyco- metabolic liabilities, efficacy, toxicity, target identification,
lytic Rate Assay. mechanism of action, etc.
• A more functional and quantitative measure of cancer cell • There is increasing evidence for a correlative pattern
metabolic phenotype is to quantify the rate and source among oncogenes (i.e. the expressed protein) and effects
(mitochondrial versus glycolytic) of ATP production. The on cancer cell metabolism regarding changes in mitochon-
XF Real-Time ATP rate assay measures the total ATP pro- drial and glycolytic function and the ATP production rate/
duction rate in cells and distinguishes between ATP pro- source. These metabolic changes or shifts can be poten-
duced from mitochondrial OXPHOS versus glycolysis. This tial targets for anticancer therapeutics.
assay therefore provides a detailed, functional overview of
cancer cell phenotype and metabolism.
7References
1. Counihan, J. L.; Grossman, E. A.; Nomura, D. K., Cancer Me- R., Metabolic profiling of triple-negative breast cancer cells
tabolism: Current Understanding and Therapies. Chemical reveals metabolic vulnerabilities. Cancer Metab 2017, 5, 6.
Reviews 2018, 118 (14), 6893-6923. 13. Guha, M.; Srinivasan, S.; Raman, P.; Jiang, Y.; Kaufman, B.
2. Zheng, J., Energy metabolism of cancer: Glycolysis versus A.; Taylor, D.; Dong, D.; Chakrabarti, R.; Picard, M.; Carstens,
oxidative phosphorylation (Review). Oncology letters 2012, R. P.; Kijima, Y.; Feldman, M.; Avadhani, N. G., Aggressive
4 (6), 1151-1157. triple negative breast cancers have unique molecular
3. Wallace, D. C., Mitochondria and cancer. Nat Rev Cancer signature on the basis of mitochondrial genetic and func-
2012, 12 (10), 685-98. tional defects. Biochimica et biophysica acta 2018, 1864 (4
Pt A), 1060-1071.
4. Vyas, S.; Zaganjor, E.; Haigis, M. C., Mitochondria and Can-
cer. Cell 2016, 166 (3), 555-566. 14. Romero, N.; Rogers, G. W.; Neilson, A.; Dranka, B. P., Quanti-
fying Cellular ATP Production Rate Using Agilent Seahorse
5. Danhier, P.; Bański, P.; Payen, V. L.; Grasso, D.; Ippolito, XF Technology. Agilent Technologies Application Note
L.; Sonveaux, P.; Porporato, P. E., Cancer metabolism in 2018, publication number 5991-9303EN.
space and time: Beyond the Warburg effect. Biochimica
et Biophysica Acta (BBA) - Bioenergetics 2017, 1858 (8), 15. Romero, N.; Swain, P.; Kam, Y.; Rogers, G. W.; Dranka., B. P.,
556-572. Bioenergetic profiling and fuel dependencies of cancer cell
lines: quantifying the impact of glycolytic and mitochon-
6. Ashton, T. M.; McKenna, W. G.; Kunz-Schughart, L. A.; Hig- drial ATP production on cell proliferation. Agilent Technolo-
gins, G. S., Oxidative Phosphorylation as an Emerging Tar- gies poster EACR, 2018 2018.
get in Cancer Therapy. Clinical cancer research : an official
journal of the American Association for Cancer Research 16. Wang, H.; Luo, J.; Tian, W.; Yan, W.; Ge, S.; Zhang, Y.; Sun,
2018, 24 (11), 2482-2490. W., gamma-Tocotrienol inhibits oxidative phosphorylation
and triggers apoptosis by inhibiting mitochondrial complex
7. Porporato, P. E.; Filigheddu, N.; Pedro, J. M. B.-S.; Kroemer, I subunit NDUFB8 and complex II subunit SDHB. Toxicol-
G.; Galluzzi, L., Mitochondrial metabolism and cancer. Cell ogy 2019, 417, 42-53.
Research 2017, 28, 265.
17. Romero, N.; Swain, P.; Dranka, B. P., Quantifying cellular gly-
8. Kalyanaraman, B.; Cheng, G.; Hardy, M.; Ouari, O.; Lopez, colytic rate using CO2-corrected extracellular acidification.
M.; Joseph, J.; Zielonka, J.; Dwinell, M. B., A review of the Agilent Technologies Application Note 2017, publication
basics of mitochondrial bioenergetics, metabolism, and number 5991-7894EN.
related signaling pathways in cancer cells: Therapeutic
targeting of tumor mitochondria with lipophilic cationic 18. Swain, P.; Romero, N.; Kam, Y.; Van de Bittner, G.; El Az-
compounds. Redox biology 2017, 14, 316-327. zouny, M.; Dranka., B. P., Differential use of lactate for
mitochondria by NSCLC cells. Agilent Technologies Poster,
9. Jose, C.; Bellance, N.; Rossignol, R., Choosing between gly- AACR 2019 2018.
colysis and oxidative phosphorylation: a tumor's dilemma?
Biochimica et biophysica acta 2011, 1807 (6), 552-61. 19. Lissanu Deribe, Y.; Sun, Y.; Terranova, C.; Khan, F.; Martinez-
Ledesma, J.; Gay, J.; Gao, G.; Mullinax, R. A.; Khor, T.; Feng,
10. Teoh, S. T.; Lunt, S. Y., Metabolism in cancer metastasis: N.; Lin, Y.-H.; Wu, C.-C.; Reyes, C.; Peng, Q.; Robinson, F.; In-
bioenergetics, biosynthesis, and beyond. Wiley Interdisci- oue, A.; Kochat, V.; Liu, C.-G.; Asara, J. M.; Moran, C.; Muller,
plinary Reviews: Systems Biology and Medicine 2018, 10 F.; Wang, J.; Fang, B.; Papadimitrakopoulou, V.; Wistuba, I.
(2), e1406. I.; Rai, K.; Marszalek, J.; Futreal, P. A., Mutations in the SWI/
11. Rogers, G. W.; Throckmorton, H.; Burroughs, S. E., Gain- SNF complex induce a targetable dependence on oxidative
ing Insights into Disease Biology for Target Identification phosphorylation in lung cancer. Nature Medicine 2018, 24
and Validation using Seahorse XF Technology. Agilent (7), 1047-1057.
Technologies Application Note 2019, publication number 20. Barnoud, T.; Parris, J. L. D.; Murphy, M. E., Tumor cells
5991-7894EN. containing the African-Centric S47 variant of TP53 show
12. Lanning, N. J.; Castle, J. P.; Singh, S. J.; Leon, A. N.; Tovar, increased Warburg metabolism. Oncotarget 2019, 10 (11),
E. A.; Sanghera, A.; MacKeigan, J. P.; Filipp, F. V.; Graveel, C. 1217-1223.
www.agilent.com/chem/drugdiscovery-cellmetabolism
For Research Use Only.
Not for use in diagnostic procedures.
This information is subject to change without notice.
© Agilent Technologies, Inc. 2019
Printed in the USA, May 24, 2019
5994-1017ENYou can also read
