The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Inhibition of fatty acid oxidation as a therapy for MYC-overexpressing triple-negative breast cancer
Open accessAbstract
Expression of the oncogenic transcription factor MYC is disproportionately elevated in triple-negative breast cancer (TNBC), as compared to estrogen receptor–, progesterone receptor– or human epidermal growth factor 2 receptor–positive (RP) breast cancer. We and others have shown that MYC alters metabolism during tumorigenesis. However, the role of MYC in TNBC metabolism remains mostly unexplored. We hypothesized that MYC-dependent metabolic dysregulation is essential for the growth of MYC-overexpressing TNBC cells and may identify new therapeutic targets for this clinically challenging subset of breast cancer. Using a targeted metabolomics approach, we identified fatty acid oxidation (FAO) intermediates as being dramatically upregulated in a MYC-driven model of TNBC. We also identified a lipid metabolism gene signature in patients with TNBC that were identified from The Cancer Genome Atlas database and from multiple other clinical data sets, implicating FAO as a dysregulated pathway that is critical for TNBC cell metabolism. We found that pharmacologic inhibition of FAO catastrophically decreased energy metabolism in MYC-overexpressing TNBC cells and blocked tumor growth in a MYC-driven transgenic TNBC model and in a MYC-overexpressing TNBC patient–derived xenograft. These findings demonstrate that MYC-overexpressing TNBC shows an increased bioenergetic reliance on FAO and identify the inhibition of FAO as a potential therapeutic strategy for this subset of breast cancer.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
Comprehensive molecular portraits of human breast tumours
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11412 · Cited by 12,129
Rb-mediated heterochromatin formation and silencing of E2F target genes during cellular senescence
Narita, M., Nuñez, S., Heard, E., Narita, M., Lin, A. W., Hearn, S. A., Spector, D. L., Hannon, G. J., & Lowe, S. W. (2003) · Cell · doi:10.1016/s0092-8674(03)00401-x · Cited by 2,278
AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress
Jeon, S.-M., Chandel, N. S., & Hay, N. (2012) · Nature · doi:10.1038/nature11066 · Cited by 1,132
Antioxidant and oncogene rescue of metabolic defects caused by loss of matrix attachment
Schafer, Z. T., Grassian, A. R., Song, L., Jiang, Z., Gerhart-Hines, Z., Irie, H. Y., Gao, S., Puigserver, P., & Brugge, J. S. (2009) · Nature · doi:10.1038/nature08268 · Cited by 1,031
Carnitine palmitoyltransferases 1 and 2: biochemical, molecular and medical aspects
BONNEFONT, J. (2004) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2004.06.004 · Cited by 637
Cited by
Targeting cancer metabolism in the era of precision oncology
Stine, Z. E., Schug, Z. T., Salvino, J. M., & Dang, C. V. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-021-00339-6 · Cited by 1,189
Greasing the Wheels of the Cancer Machine: The Role of Lipid Metabolism in Cancer
Snaebjornsson, M. T., Janaki-Raman, S., & Schulze, A. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2019.11.010 · Cited by 1,097
Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention
Butler, L. M., Perone, Y., Dehairs, J., Lupien, L. E., de Laat, V., Talebi, A., Loda, M., Kinlaw, W. B., & Swinnen, J. V. (2020) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2020.07.013 · Cited by 589
Svensson, R. U., Parker, S. J., Eichner, L. J., Kolar, M. J., Wallace, M., Brun, S. N., Lombardo, P. S., Van Nostrand, J. L., Hutchins, A., Vera, L., et al. (2016) · Nature Medicine · doi:10.1038/nm.4181 · Cited by 536
Regulation of cancer cell metabolism: Oncogenic MYC in the driver’s seat
Dong, Y., Tu, R., Liu, H., & Qing, G. (2020) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00235-2 · Cited by 405
Nutrient acquisition strategies of mammalian cells
Palm, W., & Thompson, C. B. (2017) · Nature · doi:10.1038/nature22379 · Cited by 388
Elucidating cancer metabolic plasticity by coupling gene regulation with metabolic pathways
Jia, D., Lu, M., Jung, K. H., Park, J. H., Yu, L., Onuchic, J. N., Kaipparettu, B. A., & Levine, H. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816391116 · Cited by 336
Control of intestinal stem cell function and proliferation by mitochondrial pyruvate metabolism
Schell, J. C., Wisidagama, D. R., Bensard, C., Zhao, H., Wei, P., Tanner, J., Flores, A., Mohlman, J., Sorensen, L. K., Earl, C. S., et al. (2017) · Nature Cell Biology · doi:10.1038/ncb3593 · Cited by 322
The role of lipid metabolic reprogramming in tumor microenvironment
Yang, K., Wang, X., Song, C., He, Z., Wang, R., Xu, Y., Jiang, G., Wan, Y., Mei, J., & Mao, W. (2023) · Theranostics · doi:10.7150/thno.82920 · Cited by 270
MYC and tumor metabolism: chicken and egg
Dejure, F. R., & Eilers, M. (2017) · The EMBO Journal · doi:10.15252/embj.201796438 · Cited by 240
Metabolic reprogramming in triple-negative breast cancer
Wang, Z., Jiang, Q., & Dong, C. (2020) · Cancer Biology and Medicine · doi:10.20892/j.issn.2095-3941.2019.0210 · Cited by 248
“Etomoxir (ETX), as a clinically tested, specific inhibitor of the enzyme CPT1 involved in FAO, causes ATP depletion and energetic stress, retarding tumor growth in a xenograft model 74 .”
BackgroundInfluentialMetabolic reprogramming in triple-negative breast cancer
Sun, X., Wang, M., Wang, M., Yu, X., Guo, J., Sun, T., Li, X., Yao, L., Dong, H., & Xu, Y. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00428 · Cited by 243
Applications of metabolomics to study cancer metabolism
Kaushik, A. K., & DeBerardinis, R. J. (2018) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2018.04.009 · Cited by 179
Metabolic shifts in residual breast cancer drive tumor recurrence
Havas, K. M., Milchevskaya, V., Radic, K., Alladin, A., Kafkia, E., Garcia, M., Stolte, J., Klaus, B., Rotmensz, N., Gibson, T. J., et al. (2017) · Journal of Clinical Investigation · doi:10.1172/jci89914 · Cited by 168
Mitochondrial CPT1A: Insights into structure, function, and basis for drug development
Liang, K. (2023) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1160440 · Cited by 156
“Myc-overexpressing triple-negative breast cancer (TNBC) has a greater bioenergetic dependence on FAO, and CPT1A can be pharmaceutically inhibited to reduce energy metabolism in Mycoverexpressing TNBC cells and stop tumor growth in a xenograft model of Myc-overexpressing TNBC (Camarda et al., 2016; Park et al., 2016).”
BackgroundAloia, A., Müllhaupt, D., Chabbert, C. D., Eberhart, T., Flückiger-Mangual, S., Vukolic, A., Eichhoff, O., Irmisch, A., Alexander, L. T., Scibona, E., et al. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-0253 · Cited by 130
Targeting MYC-enhanced glycolysis for the treatment of small cell lung cancer
Cargill, K. R., Stewart, C. A., Park, E. M., Ramkumar, K., Gay, C. M., Cardnell, R. J., Wang, Q., Diao, L., Shen, L., Fan, Y.-H., et al. (2021) · Cancer & Metabolism · doi:10.1186/s40170-021-00270-9 · Cited by 62
Jiang, C., Zhu, Y., Chen, H., Lin, J., Xie, R., Li, W., Xue, J., Chen, L., Chen, X., & Xu, S. (2023) · Cell Death & Disease · doi:10.1038/s41419-023-06181-5 · Cited by 36
Metadata
- Authors
- Camarda, R., Zhou, A. Y., Kohnz, R. A., Balakrishnan, S., Mahieu, C., Anderton, B., Eyob, H., Kajimura, S., Tward, A., Krings, G., Nomura, D. K., & Goga, A.
- Venue
- Nature Medicine
- Published
- 2016-03-07
- Volume
- 22
- Issue
- 4
- Pages
- 427-432
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W2295060570
- Cited by
- 528
- References
- 6
- FWCI
- 16.62
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
