bims-meluca Biomed News
on Metabolism of non-small cell lung carcinoma
Issue of 2024‒01‒21
six papers selected by
the Muñoz-Pinedo/Nadal (PReTT) lab, L’Institut d’Investigació Biomèdica de Bellvitge



  1. Clin Nutr ESPEN. 2024 Feb;pii: S2405-4577(23)02243-X. [Epub ahead of print]59 412-421
      BACKGROUND: The association between the triglyceride glucose-body mass index (TyG-BMI) and various health outcomes has been postulated. Nonetheless, the application of TyG-BMI in predicting the prognosis of non-small cell lung cancer (NSCLC) remains poorly understood. Our objective was to explore the association between TyG-BMI and both progression-free survival (PFS) and overall survival (OS) in patients with advanced NSCLC.METHODS: A retrospective study was conducted on the data of 426 patients diagnosed with advanced NSCLC between 2018 and 2022. The TyG-BMI values were derived from the serum triglyceride, fasting plasma glucose, and BMI measurements obtained at the time of diagnosis. Cox proportional hazards regression models were applied to examine the impact of TyG-BMI on both progression-free survival (PFS) and overall survival (OS) in advanced NSCLC.
    RESULTS: The median duration of follow-up was 899 days, with an interquartile range of 256-1486 days. In comparison to the lowest tertile of TyG-BMI, the highest tertile model demonstrated a significant association with worse OS (hazard ratio [HR]: 1.85; 95% confidence interval [CI]: 1.21-2.80; P = 0.001) and PFS (HR: 2.10; 95% CI: 1.40-3.10; P < 0.001). Each standard deviation increase in TyG-BMI corresponded to a 10% reduction in OS (95% CI: 1%-24%) and a 14% reduction in PFS (95% CI: 2%-27%). Subgroup analyses indicated that cigarette smokers and individuals with elevated C-reactive protein (CRP) levels exhibited a notably unfavorable prognosis in relation to TyG-BMI-associated advanced NSCLC, as evident in both OS (P for interaction: 0.046 for smoking) and PFS (P for interaction: 0.033 for smoking and 0.049 for CRP).
    CONCLUSION: Our data revealed a causal relationship between TyG-BMI and an unfavorable prognosis in patients with advanced NSCLC. Furthermore, this independent association exhibits greater significance in individuals who are smokers and exhibit higher levels of CRP.
    Keywords:  Advanced non-small cell lung cancer; Prognosis; Triglyceride glucose-body mass index
    DOI:  https://doi.org/10.1016/j.clnesp.2023.12.018
  2. Lipids Health Dis. 2024 Jan 13. 23(1): 16
      BACKGROUND: Studies have shown that integrating anlotinib with programmed death 1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors enhances survival rates among progressive non-small-cell lung cancer (NSCLC) patients lacking driver mutations. However, not all individuals experience clinical benefits from this therapy. As a result, it is critical to investigate the factors that contribute to the inconsistent response of patients. Recent investigations have emphasized the importance of lipid metabolic reprogramming in the development and progression of NSCLC.METHODS: The objective of this investigation was to examine the correlation between lipid variations and observed treatment outcomes in advanced NSCLC patients who were administered PD-1/PD-L1 inhibitors alongside anlotinib. A cohort composed of 30 individuals diagnosed with advanced NSCLC without any driver mutations was divided into three distinct groups based on the clinical response to the combination treatment, namely, a group exhibiting partial responses, a group manifesting progressive disease, and a group demonstrating stable disease. The lipid composition of patients in these groups was assessed both before and after treatment.
    RESULTS: Significant differences in lipid composition among the three groups were observed. Further analysis revealed 19 differential lipids, including 2 phosphatidylglycerols and 17 phosphoinositides.
    CONCLUSION: This preliminary study aimed to explore the specific impact of anlotinib in combination with PD-1/PD-L1 inhibitors on lipid metabolism in patients with advanced NSCLC. By investigating the effects of using both anlotinib and PD-1/PD-L1 inhibitors, this study enhances our understanding of lipid metabolism in lung cancer treatment. The findings from this research provide valuable insights into potential therapeutic approaches and the identification of new therapeutic biomarkers.
    Keywords:  Advanced NSCLC; Anlotinib alongside PD-1/PD-L1 inhibitors; Lipid metabolism; Therapeutic effect
    DOI:  https://doi.org/10.1186/s12944-023-01960-7
  3. PLoS Genet. 2024 Jan 19. 20(1): e1011134
      It has been well established that cancer cells can evade immune surveillance by mutating themselves. Understanding genetic alterations in cancer cells that contribute to immune regulation could lead to better immunotherapy patient stratification and identification of novel immune-oncology (IO) targets. In this report, we describe our effort of genome-wide association analyses across 22 TCGA cancer types to explore the associations between genetic alterations in cancer cells and 76 immune traits. Results showed that the tumor microenvironment (TME) is shaped by different gene mutations in different cancer types. Out of the key genes that drive multiple immune traits, top hit KEAP1 in lung adenocarcinoma (LUAD) was selected for validation. It was found that KEAP1 mutations can explain more than 10% of the variance for multiple immune traits in LUAD. Using public scRNA-seq data, further analysis confirmed that KEAP1 mutations activate the NRF2 pathway and promote a suppressive TME. The activation of the NRF2 pathway is negatively correlated with lower T cell infiltration and higher T cell exhaustion. Meanwhile, several immune check point genes, such as CD274 (PD-L1), are highly expressed in NRF2-activated cancer cells. By integrating multiple RNA-seq data, a NRF2 gene signature was curated, which predicts anti-PD1 therapy response better than CD274 gene alone in a mixed cohort of different subtypes of non-small cell lung cancer (NSCLC) including LUAD, highlighting the important role of KEAP1-NRF2 axis in shaping the TME in NSCLC. Finally, a list of overexpressed ligands in NRF2 pathway activated cancer cells were identified and could potentially be targeted for TME remodeling in LUAD.
    DOI:  https://doi.org/10.1371/journal.pgen.1011134
  4. Cancer Immunol Immunother. 2024 Jan 17. 73(1): 12
      BACKGROUND: The introduction of the anti-PD-1 antibody has greatly improved the clinical outcomes of patients with non-small cell lung cancer (NSCLC). In this study, we retrospectively analyzed the efficacy of PD-1 antibody-based therapy in patients with locally advanced inoperable or metastatic NSCLC and reported an association between peripheral blood biomarkers and clinical response in these patients.METHODS: This single-center study included medical record data of patients with NSCLC treated with the PD-1 antibody as a first-line or subsequent line of treatment, either as monotherapy or in combination with chemotherapy. The patients were enrolled from 2020 to 2022. We dynamically evaluated multiple Th1 and Th2 cytokines in the blood serum and analyzed the phenotype of T cells from the peripheral blood to explore the correlation between cytokine levels, T cell phenotypes, and clinical response.
    RESULTS: A total of 88 patients with stage IIIA-IV NSCLC were enrolled, out of which 60 (68.18%) achieved a partial response (PR), 13 (14.77%) had stable disease (SD), and 15 (17.05%) experienced disease progression (PD). The disease control rate was 82.95%. Our results suggested a significant reduction (P = 0.002, P < 0.005) in lymphocyte absolute counts after treatment in patients with PD. Higher levels of IFN-γ (P = 0.023, P < 0.05), TNF-α (P = 0.00098, P < 0.005), IL-4 (P = 0.0031, P < 0.005), IL-5 (P = 0.0015, P < 0.005), and IL-10 (P = 0.036, P < 0.05) were detected in the peripheral blood before treatment in the PR group compared to the PD group. Moreover, patients with high levels of IL-5, IL-13, IL-4, IL-6, IFN-γ, and TNF-α (> 10 ng/mL) had superior progression-free survival compared to those with low levels (< 10 ng/mL). Furthermore, PD-1 expression on CD8+ T cells was higher in patients who showed a PR than in those who did not show a response (SD + PD; P = 0.042, P < 0.05).
    CONCLUSIONS: The findings of this study imply that the decrease in absolute blood lymphocyte counts after treatment is correlated with disease progression. Serum cytokine levels may predict the effectiveness and survival rates of anti-PD-1 blockade therapy in patients with NSCLC. In addition, PD-1 expression on CD8+ T cells was positively associated with better clinical response. Our findings highlight the potential of peripheral blood biomarkers to predict the effectiveness of PD-1-targeted treatments in patients with NSCLC. Larger prospective studies are warranted to further clarify the value of these biomarkers.
    Keywords:  Biomarker; NSCLC; PD-1
    DOI:  https://doi.org/10.1007/s00262-023-03620-2
  5. Nutrition. 2023 Dec 24. pii: S0899-9007(23)00364-7. [Epub ahead of print]120 112336
      OBJECTIVES: This study combined two novel approaches in oncology patient outcome predictions-body composition and radiomic features analysis. The aim of this study was to validate whether automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.METHODS: The study included 178 patients with non-small cell lung cancer receiving concurrent platinum-based chemoradiotherapy. Abdominal imaging was conducted as a part of whole-body positron emission tomography/computed tomography performed before therapy. Methods used included automated assessment of the volume of interest using densely connected convolutional network classification model - DenseNet121, automated muscle and adipose tissue segmentation using U-net architecture implemented in nnUnet framework, and radiomic features extraction. Acquired body composition radiomic features and clinical data were used for overall and 1-y survival prediction using machine learning classification algorithms.
    RESULTS: The volume of interest detection model achieved the following metric scores: 0.98 accuracy, 0.89 precision, 0.96 recall, and 0.92 F1 score. Automated segmentation achieved a median dice coefficient >0.99 in all segmented regions. We extracted 330 body composition radiomic features for every patient. For overall survival prediction using clinical and radiomic data, the best-performing feature selection and prediction method achieved areas under the curve-receiver operating characteristic (AUC-ROC) of 0.73 (P < 0.05); for 1-y survival prediction AUC-ROC was 0.74 (P < 0.05).
    CONCLUSION: Automatically extracted muscle and adipose tissue radiomic features could be used as a predictor of survival in patients with non-small cell lung cancer.
    Keywords:  Artificial intelligence; Body composition; Lung cancer; Radiomics; Survival
    DOI:  https://doi.org/10.1016/j.nut.2023.112336
  6. Cell Death Discov. 2024 Jan 18. 10(1): 36
      Chemoresistance poses a significant impediment to effective treatments for non-small-cell lung cancer (NSCLC). P21-activated kinase 4 (PAK4) has been implicated in NSCLC progression by invasion and migration. However, the involvement of PAK4 in cisplatin resistance is not clear. Here, we presented a comprehensive investigation into the involvement of PAK4 in cisplatin resistance within NSCLC. Our study revealed enhanced PAK4 expression in both cisplatin-resistant NSCLC tumors and cell lines. Notably, PAK4 silencing led to a remarkable enhancement in the chemosensitivity of cisplatin-resistant NSCLC cells. Cisplatin evoked endoplasmic reticulum stress in NSCLC. Furthermore, inhibition of PAK4 demonstrated the potential to sensitize resistant tumor cells through modulating endoplasmic reticulum stress. Mechanistically, we unveiled that the suppression of the MEK1-GRP78 signaling pathway results in the sensitization of NSCLC cells to cisplatin after PAK4 knockdown. Our findings establish PAK4 as a promising therapeutic target for addressing chemoresistance in NSCLC, potentially opening new avenues for enhancing treatment efficacy and patient outcomes.
    DOI:  https://doi.org/10.1038/s41420-024-01798-7