bioRxivCancer BiologyPreprint
L-pentahomoserine correlates with therapy outcome in esophageal cancer and promotes metabolic adaptations that support cell survival under nutrient-deprived conditions
Mark E. KellyiD, Alejandro Huerta UribeiD, Micheale Cerna, Keira Hughes, Hasnain Ahmed, Marco FernandesiD, Kevin M. RattiganiD, Ruhi S. DeshmukhiD, David S. MouraiD, Swati AryaiD, Stephanie MayiD, Miryam MülleriD, Thomas M. DrakeiD, Αναστασία ΓεωργακοπούλουiD, Thomas Graham BirdiD, Michael HisberguesiD, Guillaume PiesseniD, Alan James StewartiD, Allan J. B. WatsoniD, David P. SumptoniD, Victor Hugo VillariD
Abstract
Esophageal adenocarcinoma (EAC) is the sixth-leading cause of cancer-related death. Although pyrimidine analogue-based neoadjuvant and adjuvant therapies are widely used, patient responses remain variable. Emerging evidence indicates that bacteria-derived metabolites influence tumor biology and therapy outcomes. To identify non-canonical plasma metabolites linked to cancer biology, we performed correlation analyses between untargeted metabolomics profiles and overall survival. This approach revealed a bacterial metabolite called L-pentahomoserine, or L-2-amino-5-hydroxypentanoic acid (L-2A5HPA), to be positively associated with overall survival. Notably, L-2A5HPA promoted cell survival under nutrient limitation by redirecting glucose metabolism towards aspartate and pyrimidine biosynthesis. In vitro, L-2A5HPA uptake varied among cell lines and was controlled by stereospecific transporters. Furthermore, metabolic profiling in mouse models of liver cancer showed different levels of L-2A5HPA and a strong correlation with pyrimidine intermediates, dihydroorotate and orotate. The link between L-2A5HPA, pyrimidine nucleotide metabolism, and cell survival provides mechanistic insight into its association with patient outcome. Our findings position L-2A5HPA as a metabolite with potential to become a prognostic biomarker for EAC and underscores its role in metabolic adaptation under nutrient-deprived conditions.
- organism: mouse
- assay: metabolomics
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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