Natural abundance isotopomics reveals coordinated amino acid metabolic alterations in human lung cancer

@misc{alrahal2026natural,
  title = {{Natural abundance isotopomics reveals coordinated amino acid metabolic alterations in human lung cancer}},
  author = {Fatmeh Al-Rahal and Anthony Anchisi and Yohann Clement and Pierre Lanteri and Julie Lalande and Youssef Bakkour and José Hureaux and Guillaume Tcherkez and Illa Tea and Illa TEA},
  howpublished = {ChemRxiv},
  year = {2026},
  month = oct,
  doi = {10.26434/chemrxiv.15010495/v1},
  url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010495/v1},
  note = {CC BY},
}

TY  - UNPB
TI  - Natural abundance isotopomics reveals coordinated amino acid metabolic alterations in human lung cancer
AU  - Fatmeh Al-Rahal
AU  - Anthony Anchisi
AU  - Yohann Clement
AU  - Pierre Lanteri
AU  - Julie Lalande
AU  - Youssef Bakkour
AU  - José Hureaux
AU  - Guillaume Tcherkez
AU  - Illa Tea
AU  - Illa TEA
PY  - 2026
DA  - 2026/10/15
PB  - ChemRxiv
DO  - 10.26434/chemrxiv.15010495/v1
UR  - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010495/v1
AB  - Background Metabolic reprogramming in cancer is usually characterized through changes in metabolite levels, yet pool size alone does not reveal how elements are routed through tumour metabolism. We investigated whether natural abundance isotopomics could resolve this metabolic organization in human lung cancer from bulk tissue to individual protein-bound amino acids. Methods Paired tumour and adjacent non-tumour lung tissues from 35 patients with non-small cell lung cancer (25 adenocarcinomas and 10 squamous cell carcinomas) were analysed for elemental composition (C, N, S) and bulk δ13C, δ15N and δ34S by elemental analysis–isotope ratio mass spectrometry, together with amino acid specific δ15N and δ13C and protein-normalized amino acid concentrations by gas chromatography–combustion–isotope ratio mass spectrometry. Changes between tumour and adjacent non-tumour tissues were assessed using paired statistics, correlation analysis, ANOVA-simultaneous component analysis and multilevel partial least squares discriminant analysis. Results Lung tumours showed a reproducible multi-isotope phenotype characterized by lower bulk δ13C and δ15N, higher δ34S, reduced N content and modest increases in C and S content. At the molecular level, glycine showed the strongest δ15N depletion and a parallel decrease in protein-normalized abundance, whereas proline and glutamate were relatively 13C-enriched. Glycine, proline and hydroxyproline concentrations were reduced in tumour tissue. Despite pronounced inter-patient variability, the multivariate tumour effect remained highly significant (permutation p = 0.0008), and multilevel modelling separated tumour from adjacent tissue with a cross-validated area under the curve of 0.922. Compound specific isotope changes were not systematically coupled to pool size, indicating that isotopic composition captures metabolic information not contained in concentration alone. The coordinated glycine, glutamate, proline and hydroxyproline responses further pointed to glycine related N handling and a glutamate–proline–collagen axis linking tumour metabolism to extracellular matrix organization. Conclusions Natural abundance isotopomics reveals a coherent metabolic phenotype of human lung cancer across C, N and S metabolism. By resolving bulk isotope signals into molecule specific responses, this approach identifies pathway level metabolic organization. This information is not apparent from level measurements alone. It highlights glycine metabolism and glutamate–proline–collagen remodelling as biologically relevant features of the lung tumour microenvironment.
ER  -