bims-meluca Biomed News
on Metabolism of non-small cell lung carcinoma
Issue of 2024–11–10
two papers selected by
the Muñoz-Pinedo/Nadal (PReTT) lab, L’Institut d’Investigació Biomèdica de Bellvitge
  1. Prognostic role of serum cytokines level in non-small cell lung cancer patients with anti-PD-1 and chemotherapy combined treatment.
  2. DeepGR: a deep-learning prognostic model based on glycolytic radiomics for non-small cell lung cancer.
  1. Front Immunol. 2024 ;15 1430301
    Prognostic role of serum cytokines level in non-small cell lung cancer patients with anti-PD-1 and chemotherapy combined treatment.
    Hongyu Liu, Chao Zhou, Haohua Jiang, Tianqing Chu, Runbo Zhong, Xueyan Zhang, Yinchen Shen, Baohui Han.
       Background: Chemotherapy combined with PD-1 inhibitor treatment has revolutionized the standard of care for patients with NSCLC. However, the benefit is not universal, highlighting the need for precise prediction factors. Given their relationship with the immune system and non-invasive nature, serum cytokines are potential candidates for predicting the clinical effects of chemoimmunotherapy. Our study aims to evaluate the association of serum cytokines with the prognosis of patients with NSCLC treated with chemoimmunotherapy.
    Methods: Levels of 10 serum cytokines were detected in 60 NSCLC patients receiving chemotherapy plus PD-1 inhibitor-based treatment. Of these, dynamic samples from 19 patients were collected at baseline and after two treatment cycles. Their association with patients' clinicopathological characteristics, PFS and OS was described and investigated using survival analysis, cox regression and time-dependent ROC analysis. Preliminary evaluation of changes in cytokine levels associated with treatment response was conducted.
    Results: Patients with lower baseline levels of serum IL-6, IL-5, IL-8, TNF-α and IL-10 had longer PFS, while patients with higher levels of IL-4 had longer PFS. Patients with lower levels of serum IL-6, IL-8, IL-22, TNF-α and IL-10 had longer OS, while patients with higher levels of IL-4 had longer OS. Multivariate analysis suggested that higher IL-6 and IL-5 levels were associated with poorer PFS, and higher IL-6 levels were associated with dismal OS. Additionally, changes in serum cytokine levels could be associated with treatment response.
    Conclusion: Our study suggests that serum cytokines, specifically IL-6, IL-5, IL-8, TNF-α, IL-10, and IL-4, are potential prognostic factors for patients with NSCLC receiving chemotherapy plus PD-1 inhibitor treatment.
    Keywords:  NSCLC; biomarker; checkpoint inhibitor; chemotherapy; cytokine
    DOI:  https://doi.org/10.3389/fimmu.2024.1430301
  2. Transl Lung Cancer Res. 2024 Oct 31. 13(10): 2746-2760
    DeepGR: a deep-learning prognostic model based on glycolytic radiomics for non-small cell lung cancer.
    Tingting Fu, Peipei Yan, Lina Zhou, Zhihua Lu, Ao Liu, Xiao Ding, Jacopo Vannucci, Paul Hofman, Andrzej Swierniak, Edyta Szurowska, Junjun Zhang, Shicheng Li.
       Background: Glycolysis proved to have a prognostic value in lung cancer; however, to identify glycolysis-related genomic markers is expensive and challenging. This study aimed at identifying glycolysis-related computed tomography (CT) radiomics features to develop a deep-learning prognostic model for non-small cell lung cancer (NSCLC).
    Methods: The study included 274 NSCLC patients from cohorts of The Second Affiliated Hospital of Soochow University (SZ; n=64), the Cancer Genome Atlas (TCGA)-NSCLC dataset (n=74), and the Gene Expression Omnibus dataset (n=136). Initially, the glycolysis enrichment scores were evaluated using a single-sample gene set enrichment analysis, and the cut-off values were optimized to investigate the prognostic potential of glycolysis genes. Radiomic features were then extracted using LIFEx software. The least absolute reduction and selection operator (LASSO) algorithm was employed to determine the glycolytic CT radiomics features. A deep-learning prognostic model was constructed by integrating CT radiomics and clinical features. The biological functions of the model were analyzed by incorporating RNA sequencing data.
    Results: Kaplan-Meier curves indicated that elevated glycolysis levels were associated with poorer survival outcomes. The LASSO algorithm identified 11 radiomic features that were then selected for inclusion in the deep-learning model. They have shown significant discrimination capability in assessing glycolysis status, achieving an area under the curve value of 0.8442. The glycolysis-based radiomics deep-learning model was named the DeepGR model. This model was able to effectively predict the clinical outcomes of NSCLC patients with AUCs of 0.8760 and 0.8259 in the SZ and TCGA cohorts, respectively. High-risk DeepGR scores were strongly associated with poor overall survival, resting memory CD4+ T cells, and a high response to programmed cell death protein 1 immunotherapy.
    Conclusions: The DeepGR model effectively predicted the prognosis of NSCLC patients.
    Keywords:  Non-small cell lung cancer (NSCLC); deep learning; glycolysis; prognostic model; radiomics
    DOI:  https://doi.org/10.21037/tlcr-24-716
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