bims-meluca Biomed news
on Metabolism of non-small cell lung carcinoma
Issue of 2018‒12‒02
three papers selected by
Cristina Muñoz Pinedo
L’Institut d’Investigació Biomèdica de Bellvitge

  1. Oncotarget. 2018 Oct 30. 9(85): 35528-35540
    Relli V, Trerotola M, Guerra E, Alberti S.
      The main non-small-cell lung cancer (NSCLC) histopathological subtypes are lung adenocarcinomas (LUAD) and lung squamous cell carcinomas (LUSC). To identify candidate progression determinants of NSCLC subtypes, we explored the transcriptomic signatures of LUAD versus LUSC. We then investigated the prognostic impact of the identified tumor-associated determinants. This was done utilizing DNA microarray data from 2,437 NSCLC patients. An independent analysis of a case series of 994 NSCLC was conducted by next-generation sequencing, together with gene expression profiling from GEO ( This work led us to identify 69 distinct tumor prognostic determinants, which impact on LUAD or LUSC clinical outcome. These included key drivers of tumor growth and cell cycle, transcription factors and metabolic determinants. Such disease determinants appeared vastly different in LUAD versus LUSC, and often had opposite impact on clinical outcome. These findings indicate that distinct tumor progression pathways are at work in the two NSCLC subtypes. Notably, most prognostic determinants would go inappropriately assessed or even undetected when globally investigating unselected NSCLC. Hence, differential consideration for NSCLC subtypes should be taken into account in current clinical evaluation procedures for lung cancer.
    Keywords:  lung adenocarcinomas; lung squamous cell carcinomas; non-small cell lung cancer; prognostic determinants; survival curves
  2. J Chromatogr A. 2018 Dec 14. pii: S0021-9673(18)31297-4. [Epub ahead of print]1580 80-89
    Yue X, He J, Zhang R, Xu J, Zhou Z, Zhang R, Bi N, Wang Z, Sun C, Wang L, Chen Y, Abliz Z.
      The discovery and identification of reliable disease biomarkers and relevant disrupted metabolic pathways is still a major challenge in metabolomics. Here, we proposed a biotransformation-based metabolomics profiling method to identify reliable disease biomarkers by simultaneous quantitation and qualification of cancer-related metabolites and their metabolic pathways via liquid chromatography-tandem mass spectrometry (LC-MS/MS). The approach was based on selecting a subset of known cancer-related metabolites from our previous metabolomics work, cancer research literature and biological significance. The metabolic profiling of pathway-related metabolites was developed by predicted multiple reaction monitoring (MRM) of ion pairs based on their chemical structures and biotransformation. Then, a high-throughput quantitative method was established. Overall, this approach enables the sensitive and accurate detection of cancer-related metabolites and the identification of other relevant metabolites, which facilitates better data quality and in-depth investigation of dysregulated metabolic pathways. As a proof of concept, the approach was applied to a small-cell lung cancer (SCLC) study. The results showed that 43 metabolites were significantly changed, and arginine metabolism was apparently disturbed, which proved the proposed approach could be a powerful tool for discovering reliable disease biomarkers and aberrant metabolic pathways.
    Keywords:  Arginine metabolism; Biomarkers; Biotransformation-based; Metabolomics; Small-cell lung cancer
  3. BMC Med Imaging. 2018 Nov 26. 18(1): 46
    Kumasaka S, Nakajima T, Arisaka Y, Tokue A, Achmad A, Fukushima Y, Shimizu K, Kaira K, Higuchi T, Tsushima Y.
      BACKGROUND: This study aimed to determine the prognostic value of positron emission tomography (PET) metabolic parameters-namely metabolic tumor volume (MTV), total lesion glycolysis (TLG), and total lesion retention (TLR)-on fluorine-18 (18F) fluorodeoxyglucose (FDG) and L- [3-18F]-α-methyltyrosine (18F-FAMT) PET/CT in patients with non-small-cell lung cancer (NSCLC).METHODS: The study group comprised 112 NSCLC patients who underwent 18F-FDG and 18F-FAMT PET/CT prior to any therapy. The MTV, TLG, TLR, and maximum standardized uptake value (SUVmax) of the primary tumors were determined. Automatic MTV measurement was performed using PET volume computer assisted reading software. (GE Healthcare). Cox proportional hazards models were built to assess the prognostic value of MTV, TLG (for 18F-FDG), TLR (for 18F-FAMT), SUVmax, T stage, N stage, M stage, clinical stage, age, sex, tumor histological subtype, and treatment method (surgery or other therapy) on overall survival (OS).
    RESULTS: Higher TNM, higher clinical stage, inoperable status, and higher values for all PET parameters (both 18F-FAMT and 18F-FDG PET) were significantly associated (P < 0.05) with shorter OS. Multivariate analysis revealed that a higher MTV of 18F-FAMT (hazard ratio [HR]: 2.88, CI: 1.63-5.09, P < 0.01) and advanced clinical stage (HR: 5.36, CI: 1.88-15.34, P < 0.01) were significant predictors of shorter OS.
    CONCLUSIONS: MTV of 18F-FAMT is of prognostic value for OS in NSCLC cases and can help guide decision-making during patient management.
    Keywords:  18F-FAMT PET/CT; Lung cancer; Metabolic tumor volume; PET; Prognosis