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. 2023 Jan 17:12:1025046.
doi: 10.3389/fonc.2022.1025046. eCollection 2022.

Metabolomics profiling in prediction of chemo-immunotherapy efficiency in advanced non-small cell lung cancer

Lihong Mei  1 , Zhihua Zhang  2 , Xushuo Li  3 , Ying Yang  3 , Ruixue Qi  3
Affiliations

Metabolomics profiling in prediction of chemo-immunotherapy efficiency in advanced non-small cell lung cancer

Lihong Mei et al. Front Oncol. .

Abstract

Background: To explore potential metabolomics biomarker in predicting the efficiency of the chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC).

Methods: A total of 83 eligible patients were assigned to receive chemo-immunotherapy. Serum samples were prospectively collected before the treatment to perform metabolomics profiling analyses under the application of gas chromatography mass spectrometry (GC-MS). The key metabolites were identified using projection to latent structures discriminant analysis (PLS-DA). The key metabolites were used for predicting the chemo-immunotherapy efficiency in advanced NSCLC patients.

Results: Seven metabolites including pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate were identified as the key metabolites to the chemo-immunotherapy response. The receiver operating characteristic curves (AUC) were 0.79 (95% CI: 0.69-0.90), 0.60 (95% CI: 0.48-0.73), 0.69 (95% CI: 0.57-0.80), 0.63 (95% CI: 0.51-0.75), 0.60 (95% CI: 0.48-0.72), 0.56 (95% CI: 0.43-0.67), and 0.67 (95% CI: 0.55-0.80) for the key metabolites, respectively. A binary logistic regression was used to construct a combined biomarker model to improve the discriminating efficiency. The AUC was 0.86 (95% CI: 0.77-0.94) for the combined biomarker model. Pathway analyses showed that urea cycle, glucose-alanine cycle, glycine and serine metabolism, alanine metabolism, and glutamate metabolism were the key metabolic pathway to the chemo-immunotherapy response in patients with advanced NSCLC.

Conclusion: Metabolomics analyses of key metabolites and pathways revealed that GC-MS could be used to predict the efficiency of chemo-immunotherapy. Pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate played a central role in the metabolic of PD patients with advanced NSCLC.

Keywords: GC-MS; biomarker; chemo-immunotherapy; metabolomics; non-small cell lung cancer.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Work flow of this study. DC, disease control; ROC, receiver operating characteristic curve; PD, progressive disease.
Figure 2
Figure 2
Projection to latent structures discriminant analysis (PLS-DA) of the metabolites in DC and PD groups. The metabolites between DC and PD groups are well separated by PLS-DA.
Figure 3
Figure 3
The variable influence on projection (VIP) score of the metabolites by PLS-DA. Seven metabolites including pyruvate, threonine, alanine, urea, oxalate, elaidic acid and glutamate were identified as the key metabolites to the chemo-immunotherapy response.
Figure 4
Figure 4
The correlation heatmap shows the correlation of the key metabolites and the clinical features. Significantly negative correlation was found between the level of pyruvate and OS as well as PFS, and between the level of alanine and PFS. *, < 0.05; **< 0.01; ***, < 0.001.
Figure 5
Figure 5
The Kaplan-Meier analysis of prognostic prediction in DC group and PD group. Shown are the analysis of the progression-free survival (median time, 1252 d for DC and 326 d for PD) (A), and the overall survival (median time,1252 d for DC and 674 d for PD) (B). The 3-year survival rates were 38.7% (19/49) for DC group and 20.5% (7/34) for PD group. P values were calculated by log-rank test. DC, disease control; PD, progressive disease.
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References

    1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA: A Cancer J Clin (2022) 72:7–33. doi: 10.3322/caac.21708 - DOI - PubMed
    1. Xiang C, Han Y, Teng H, Zhu L, Lizaso A. Comprehensive investigation of mutational features of various lung adenocarcinoma histological subtypes among chinese patients. J Thorac Oncol (2021) 16:S460. doi: 10.1016/j.jtho.2021.01.788 - DOI
    1. Gharibvand L, Beeson WL, Shavlik D, Knutsen R, Knutsen SF. Lung adenocarcinoma and ambient particulate air pollution. Ann Epidemiol (2017) 27:506–7. doi: 10.1016/j.annepidem.2017.07.116 - DOI
    1. Larroquette M, Domblides C, Lefort F, Lasserre M, Quivy A, Sionneau B, et al. . Combining immune checkpoint inhibitors with chemotherapy in advanced solid tumours: A review. Eur J Cancer (2021) 158:47–62. doi: 10.1016/j.ejca.2021.09.013 - DOI - PubMed
    1. Paz-Ares L, Ciuleanu TE, Cobo M, Schenker M, Zurawski B, Menezes J, et al. . First-line nivolumab plus ipilimumab combined with two cycles of chemotherapy in patients with non-small-cell lung cancer (CheckMate 9LA): an international, randomised, open-label, phase 3 trial. Lancet Oncol (2021) 22:198–211. doi: 10.1016/S1470-2045(20)30641-0 - DOI - PubMed