bioRxivCancer BiologyPreprint
Multi-omics characterization of breast cancer metabolism identifies new metabolic targets
Hanneke LeegwateriD, Xiaobing ZhangiD, Luojiao HuangiD, Christine Hoegen, Xuesong WangiD, Agnieszka B. WegrzyniD, Thomas HankemeieriD, Bob van de WateriD, Erik H.J. DaneniD, Annelien J.M. ZweemeriD, Amy C. HarmsiD, Sylvia E. Le DévédeciD
Abstract
Breast cancer cells undergo metabolic reprogramming to support proliferation and metastasis, but these alterations are heterogeneous. To systematically assess this heterogeneity under controlled conditions, we profiled 31 intracellular polar metabolites and 50 amines, together with exchange rates of 57 amines, in a panel of 51 breast cancer cell lines spanning diverse phenotypes. These data were integrated with lipidomics and transcriptomics. Multi-omics factor analysis identified metabolic signatures linked to proliferation and to differences between luminal, HER2-positive, basal A, and basal B cell lines, overlapping with an epithelial-to-mesenchymal transition phenotype. Fast-proliferating, aggressive cell lines showed increased uptake of essential amino acids and altered nucleotide levels, while heterogeneity in glutamine metabolism was mainly driven the subtype. This was partially associated with heterogeneous expression of metabolite transporters. To test the functional relevance, 67 metabolic genes were silenced in Hs578T using siRNA, and effects on proliferation and migration were measured by a sulforhodamine B assay, live-cell imaging, and a random cell migration assay. This resulted in 34 knockdowns that reduced proliferation and 20 reduced migration, with strong effects for the glycosyltransferases EXT1 and EXT2, nucleotide metabolism genes GART and HPRT1, and metabolite transporters SLC7A1, SLC7A11, and SLC16A3. These findings highlight phenotype-specific metabolic dependencies and identify candidate drug targets in aggressive breast cancer.
- organism: human
- assay: metabolomics
- assay: proteomics
- assay: long-read sequencing
- assay: bulk RNA-seq
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 0
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