Abstract
Targeting a tumor’s metabolic dependencies is a clinically actionable therapeutic approach; however, identifying subtypes of tumors likely to respond remains difficult. The use of lipids as a nutrient source is of particular importance, especially in breast cancer. Imaging techniques offer the opportunity to quantify nutrient use in preclinical tumor models to guide development of new drugs that restrict uptake or utilization of these nutrients. We describe a fast and dynamic approach to image fatty acid uptake in vivo and demonstrate its relevance to study both tumor metabolic reprogramming directly, as well as the effectiveness of drugs targeting lipid metabolism. Specifically, we developed a quantitative optical approach to spatially and longitudinally map the kinetics of long-chain fatty acid uptake in in vivo murine models of breast cancer using a fluorescently labeled palmitate molecule, Bodipy FL c16. We chose intra-vital microscopy of mammary tumor windows to validate our approach in two orthotopic breast cancer models: a MYC-overexpressing, transgenic, triple-negative breast cancer (TNBC) model and a murine model of the 4T1 family. Following injection, Bodipy FL c16 fluorescence increased and reached its maximum after approximately 30 min, with the signal remaining stable during the 30–80 min post-injection period. We used the fluorescence at 60 min (Bodipy60), the mid-point in the plateau region, as a summary parameter to quantify Bodipy FL c16 fluorescence in subsequent experiments. Using our imaging platform, we observed a two- to four-fold decrease in fatty acid uptake in response to the downregulation of the MYC oncogene, consistent with findings from in vitro metabolic assays. In contrast, our imaging studies report an increase in fatty acid uptake with tumor aggressiveness (6NR, 4T07, and 4T1), and uptake was significantly decreased after treatment with a fatty acid transport inhibitor, perphenazine, in both normal mammary pads and in the most aggressive 4T1 tumor model. Our approach fills an important gap between in vitro assays providing rich metabolic information at static time points and imaging approaches visualizing metabolism in whole organs at a reduced resolution.
Document Type
Article
Publication Date
1-5-2021
Digital Object Identifier (DOI)
https://doi.org/10.3390/cancers13010148
Funding Information
This research was funded by the Mark Foundation Endeavor Award, the Duke Medical Imaging Training Program NIH Grant T32-EB001040, 1F31CA243194, R01EB028148-01, F32CA243548, 1F31CA243468, R01CA223817, and CDMRP W81XWH-18-1-0713 and W81XWH-16-1-0603.
Related Content
The RNA sequencing datasets accessed for this manuscript are available in the publicly accessible repository, National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO). Gene expression values for MYC-driven tumors are available from GEO at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE130922, GSE130922. Transcriptomic values of 4T1 and 67NR tumors are available from GEO at https://www.ncbi.nlm.nih. gov/geo/query/acc.cgi, GSE104765.
The following are available online at https://www.mdpi.com/2072-669 4/13/1/148/s1, Figure S1: In vitro Bodipy FL c16 uptake with doxycycline treatment in 4T1 cells, Figure S2: RNA sequencing and full Western blot confirm removal of dox is correlated with decreased SLC27a3/FATP3 expression, Figure S3: Bodipy60 is increased in metastatic-prone tumors in vitro, Figure S4: Bodipy60 decreases with the inhibition of fatty acid uptake in normal, mammary gland tissue, Figure S5: RNA sequencing data analysis of murine breast cancer models. The material is also available for download as the additional file listed at the end of this record.
Repository Citation
Madonna, Megan C.; Duer, Joy E.; Lee, Joyce V.; Williams, Jeremy; Avsaroglu, Baris; Zhu, Caigang; Deutsch, Riley; Wang, Roujia; Crouch, Brian T.; Hirschey, Matthew D.; Goga, Andrei; and Ramanujam, Nirmala, "In Vivo Optical Metabolic Imaging of Long-Chain Fatty Acid Uptake in Orthotopic Models of Triple-Negative Breast Cancer" (2021). Biomedical Engineering Faculty Publications. 44.
https://uknowledge.uky.edu/cbme_facpub/44
Supplementary file
Included in
Biomedical Engineering and Bioengineering Commons, Cancer Biology Commons, Cell Biology Commons
Notes/Citation Information
Published in Cancers, v. 13, issue 1, 148.
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).