bims-meluca Biomed News
on Metabolism of non-small cell lung carcinoma
Issue of 2024‒11‒03
seven papers selected by
the Muñoz-Pinedo/Nadal (PReTT) lab, L’Institut d’Investigació Biomèdica de Bellvitge
  1. Metabolomic Profiling of Large Extracellular Vesicles in Patients Suffering from Small Cell Lung Cancer.
  2. Predictive value of metabolic parameters and apparent diffusion coefficient derived from 18F-FDG PET/MR in patients with non-small cell lung cancer.
  3. Automatic lung cancer subtyping using rapid on-site evaluation slides and serum biological markers.
  4. Prognostic value of a lactate metabolism gene signature in lung adenocarcinoma and its associations with immune checkpoint blockade therapy response.
  5. Unraveling the Impact of Sarcopenia-Induced Lymphopenia on Treatment Response and Prognosis in Patients with Stage III Non-Small Cell Lung Cancer: Insights for Optimizing Chemoradiation and Immune Checkpoint Inhibitor.
  6. The effects of immune checkpoint inhibitors vs. chemotherapy combined with brain radiotherapy in non-small cell lung cancer patients with brain metastases.
  7. Comprehensive profiling of serum glycosphingolipids to discover the diagnostic biomarkers of lung cancer and uncover the variation of glycosphingolipid networks in different lung cancer subtypes.
  1. Anticancer Res. 2024 Nov;44(11): 4729-4735
    Metabolomic Profiling of Large Extracellular Vesicles in Patients Suffering from Small Cell Lung Cancer.
    Said Naser, Matthias Schulz, Sven Schuchart, Alexander VON Hammerstein-Equord, Judith Büntzel.
      BACKGROUND/AIM: Large extracellular vesicles (lEV) offer a unique window into the metabolism of their cells of orign and dysregulation of lipid metabolism has been described in patients with small cell lung cancer (SCLC). Therefore, metabolomic profiling of patients' lEVs may offer insight into cancer metabolism as well as new potential biomarkers for monitoring disease progression.MATERIALS AND METHODS: lEVs were isolated by differential centrifugation from the peripheral blood of SCLC patients and healthy controls. Targeted mass spectrometry was used to analyze the lipid composition of lEVs. After identifying relevant metabolites, biomarker and pathway analysis were conducted.
    RESULTS: SCLC patients exhibited a distinct metabolic profile compared to healthy controls. The metabolites TG(16:0:_38:3), TG(18:3_35:2), TG(16:0_40:7), Cer(d18:1/26:0), and CE(16:0) are not only able to discriminate between patients and control samples, but are also served as prognostic markers for survival. Patients with high concentrations of these metabolites showed significantly shorter survival times. Pathway analysis revealed alterations in 'sphingolipid metabolism', 'sphingolipid signaling pathway' and 'necroptosis'.
    CONCLUSION: Metabolic profiling of lEVs in SCLC patients is feasible and reveals a distinct metabolic profile. High concentrations of identified lipids are associated with poor prognosis.
    Keywords:  Large extracellular vesicles; SCLC; metabolic profiling; targeted mass spectrometry
    DOI:  https://doi.org/10.21873/anticanres.17299
  2. BMC Med Imaging. 2024 Oct 29. 24(1): 290
    Predictive value of metabolic parameters and apparent diffusion coefficient derived from 18F-FDG PET/MR in patients with non-small cell lung cancer.
    Han Jiang, Ziqiang Li, Nan Meng, Yu Luo, Pengyang Feng, Fangfang Fu, Yang Yang, Jianmin Yuan, Zhe Wang, Meiyun Wang.
      BACKGROUND: Multiple models intravoxel incoherent motion (IVIM) based 18F-fluorodeoxyglucose positron emission tomography-magnetic resonance(18F-FDG PET/MR) could reflect the microscopic information of the tumor from multiple perspectives. However, its value in the prognostic assessment of non-small cell lung cancer (NSCLC) still needs to be further explored.OBJECTIVE: To compare the value of 18F-FDG PET/MR metabolic parameters and diffusion parameters in the prognostic assessment of patients with NSCLC.
    METERIAL AND METHODS: Chest PET and IVIM scans were performed on 61 NSCLC patients using PET/MR. The maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), total lesion glycolysis (TLG), diffusion coefficient (D), perfusion fraction (f), pseudo diffusion coefficient (D*) and apparent diffusion coefficient (ADC) were calculated. The impact of SUVmax, MTV, TLG, D, f, D*and ADC on survival was measured in terms of the hazard ratio (HR) effect size. Overall survival time (OS) and progression-free survival time (PFS) were evaluated with the Kaplan-Meier and Cox proportional hazard models. Log-rank test was used to analyze the differences in parameters between groups.
    RESULTS: 61 NSCLC patients had an overall median OS of 18 months (14.75, 22.85) and a median PFS of 17 months (12.00, 21.75). Univariate analysis showed that pathological subtype, TNM stage, surgery, SUVmax, MTV, TLG, D, D* and ADC were both influential factors for OS and PFS in NSCLC patients. Multifactorial analysis showed that MTV, D* and ADC were independent predicting factors for OS and PFS in NSCLC patients.
    CONCLUSION: MTV, D* and ADC are independent predicting factors affecting OS and PFS in NSCLC patients. 18F-FDG PET/MR-derived metabolic parameters and diffusion parameters have clinical value for prognostic assessment of NSCLC patients.
    Keywords:  Diffusion parameters; Magnetic resonance imaging; Metabolic parameters; Non-small cell lung cancer; Positron emission computed tomography
    DOI:  https://doi.org/10.1186/s12880-024-01445-8
  3. Respir Res. 2024 Oct 29. 25(1): 391
    Automatic lung cancer subtyping using rapid on-site evaluation slides and serum biological markers.
    Junxiang Chen, Chunxi Zhang, Jun Xie, Xuebin Zheng, Pengchen Gu, Shuaiyang Liu, Yongzheng Zhou, Jie Wu, Ying Chen, Yanli Wang, Chuan He, Jiayuan Sun.
      BACKGROUND: Rapid on-site evaluation (ROSE) plays an important role during transbronchial sampling, providing an intraoperative cytopathologic evaluation. However, the shortage of cytopathologists limits its wide application. This study aims to develop a deep learning model to automatically analyze ROSE cytological images.METHODS: The hierarchical multi-label lung cancer subtyping (HMLCS) model that combines whole slide images of ROSE slides and serum biological markers was proposed to discriminate between benign and malignant lesions and recognize different subtypes of lung cancer. A dataset of 811 ROSE slides and paired serum biological markers was retrospectively collected between July 2019 and November 2020, and randomly divided to train, validate, and test the HMLCS model. The area under the curve (AUC) and accuracy were calculated to assess the performance of the model, and Cohen's kappa (κ) was calculated to measure the agreement between the model and the annotation. The HMLCS model was also compared with professional staff.
    RESULTS: The HMLCS model achieved AUC values of 0.9540 (95% confidence interval [CI]: 0.9257-0.9823) in malignant/benign classification, 0.9126 (95% CI: 0.8756-0.9365) in malignancy subtyping (non-small cell lung cancer [NSCLC], small cell lung cancer [SCLC], or other malignancies), and 0.9297 (95% CI: 0.9026-0.9603) in NSCLC subtyping (lung adenocarcinoma [LUAD], lung squamous cell carcinoma [LUSC], or NSCLC not otherwise specified [NSCLC-NOS]), respectively. In total, the model achieved an AUC of 0.8721 (95% CI: 0.7714-0.9258) and an accuracy of 0.7184 in the six-class classification task (benign, LUAD, LUSC, NSCLC-NOS, SCLC, or other malignancies). In addition, the model demonstrated a κ value of 0.6183 with the annotation, which was comparable to cytopathologists and superior to trained bronchoscopists and technicians.
    CONCLUSION: The HMLCS model showed promising performance in the multiclassification of lung lesions or intrathoracic lymphadenopathy, with potential application to provide real-time feedback regarding preliminary diagnoses of specimens during transbronchial sampling procedures.
    CLINICAL TRIAL NUMBER: Not applicable.
    Keywords:  Deep learning; Lung cancer; Rapid on-site evaluation; Serum biological markers; Subtyping
    DOI:  https://doi.org/10.1186/s12931-024-03021-8
  4. Medicine (Baltimore). 2024 Oct 04. 103(40): e39371
    Prognostic value of a lactate metabolism gene signature in lung adenocarcinoma and its associations with immune checkpoint blockade therapy response.
    Tengfei Huang, DuoHuang Lian, MengMeng Chen, YaMing Liu, MeiQing Zhang, DeHua Zeng, Shun-Kai Zhou, WenMin Ying.
      Lung adenocarcinoma (LUAD) is a study that examines the prognostic value of lactate metabolism genes in tumor cells, which are associated with clinical prognosis. We analyzed the expression and clinical data for LUAD from The Cancer Genome Atlas database, using the GSE68465 dataset from the Gene Expression Omnibus and the MSigDB database. LASSO Cox regression and stepwise Cox regression were used to identify the optimal lactate metabolism gene signature. Differences in immune infiltration, tumor mutation burden (TMB), and response to immune checkpoint blockade (ICB) therapy were evaluated between groups. LASSO and Cox regression analyses showed an eight-lactate metabolism gene signature for model construction in both TCGA cohort and GSE68465 data, with higher survival outcomes in high-risk groups. The lactate metabolism risk score had an independent prognostic value (hazard ratio: 2.279 [1.652-3.146], P < .001). Immune cell infiltration differed between the risk groups, such as CD8+ T cells, macrophages, dendritic cells, mast cells, and neutrophils. The high-risk group had higher tumor purity, lower immune and stromal scores, and higher TMB. High-risk samples had high tumor immune dysfunction and exclusion (TIDE) scores and low cytolytic activity (CYT) scores, indicating a poor response to ICB therapy. Similarly, most immune checkpoint molecules, immune inhibitors/stimulators, and major histocompatibility complex (MHC) molecules were highly expressed in the high-risk group. The 8-lactate metabolism gene-based prognostic model predicts patient survival, immune infiltration, and ICB response in patients with LUAD, driving the development of therapeutic strategies to target lactate metabolism.
    DOI:  https://doi.org/10.1097/MD.0000000000039371
  5. Cancer Res Treat. 2024 Oct 30.
    Unraveling the Impact of Sarcopenia-Induced Lymphopenia on Treatment Response and Prognosis in Patients with Stage III Non-Small Cell Lung Cancer: Insights for Optimizing Chemoradiation and Immune Checkpoint Inhibitor.
    Joongyo Lee, Kyung Hwan Kim, Jina Kim, Chang Geol Lee, Jaeho Cho, Hong In Yoon, Yeona Cho.
      PURPOSE: Sarcopenia is a poor prognostic factor in non-small cell lung cancer (NSCLC). However, its prognostic significance in patients with NSCLC receiving immune checkpoint inhibitors (ICIs) and its relationship with lymphopenia remain unclear. We aimed to investigate the prognostic role of sarcopenia and its effect on lymphocyte recovery in patients with stage III NSCLC treated with concurrent chemoradiotherapy (CCRT) followed by ICI.MATERIALS AND METHODS: We retrospectively evaluated 151 patients with stage III NSCLC who received definitive CCRT followed by maintenance ICI between January 2016 and June 2022. Sarcopenia was evaluated by measuring the skeletal muscle area at the L3 vertebra level using computed tomography scans. Lymphocyte level changes were assessed based on measurements taken before and during CCRT and at 1, 2, 3, 6, and 12 months post-CCRT completion.
    RESULTS: Even after adjusting for baseline absolute lymphocyte count through propensity score-matching, patients with pre-radiotherapy (RT) sarcopenia (n=86) exhibited poor lymphocyte recovery and a significantly high incidence of grade ≥3 lymphopenia during CCRT. Pre-RT sarcopenia and grade ≥3 lymphopenia during CCRT emerged as prognostic factors for overall survival and progression-free survival, respectively. Concurrent chemotherapy dose adjustments, objective response after CCRT, and discontinuation of maintenance ICI were also analyzed as independent prognostic factors.
    CONCLUSION: Our results demonstrated an association between pre-RT sarcopenia and poor survival, concurrent chemotherapy dose adjustments, and impaired lymphocyte recovery after definitive CCRT. Moreover, CCRT-induced lymphopenia not only contributed to poor prognosis but may have also impaired the therapeutic efficacy of subsequent maintenance ICI, ultimately worsening treatment outcomes.
    Keywords:  Immune checkpoint inhibitors; Lymphopenia; Non-small cell lung cancer; Radiotherapy; Sarcopenia
    DOI:  https://doi.org/10.4143/crt.2024.493
  6. BMC Cancer. 2024 Oct 31. 24(1): 1343
    The effects of immune checkpoint inhibitors vs. chemotherapy combined with brain radiotherapy in non-small cell lung cancer patients with brain metastases.
    Tengfei Wang, Rumeng Li, Shuyan Liu, Qiuji Wu, Wen Ouyang, Conghua Xie.
      BACKGROUND: Non-small cell lung cancer (NSCLC) is a prevalent form of cancer, often leading to brain metastases (BM) and a significant decline in patient prognosis. Whether immune checkpoint inhibitors (ICIs) combined with brain radiotherapy is superior to conventional chemotherapy combined with brain radiotherapy in those patients remains to be explored.MATERIALS AND METHODS: Our study enrolled 161 NSCLC patients with BM who underwent either ICIs combined with brain radiotherapy or chemotherapy combined with brain radiotherapy. End points included overall survival (OS), progression-free survival (PFS), intracranial PFS (IPFS), and extracranial PFS (EPFS). Univariate and multivariate Cox regressions were employed to identify prognostic risk variables.
    RESULTS: Patients receiving ICIs combined with brain radiotherapy exhibited significantly longer OS compared to those receiving chemotherapy combined with brain radiotherapy (34.80 months vs. 17.17 months, P = 0.005). In the Cox regression analysis, chemotherapy combined with brain radiotherapy (HR, 1.82; 95% CI, 1.09-3.05; P = 0.023), smoking (HR, 1.75; 95% CI, 1.02-2.99; P = 0.043) and squamous cell carcinoma (HR, 2.59; 95% CI, 1.31-5.13; P = 0.006) were associated with a worse prognosis. After propensity score matching (PSM), this finding remained consistent with before PSM (43.73 months vs. 17.17 months, P = 0.018). Squamous cell carcinoma (HR, 2.46; 95% CI, 1.15-5.26; P = 0.021) and CT + RT (HR, 2.11; 95% CI, 1.15-3.88; P = 0.016) were associated with a less favorable prognosis.
    CONCLUSION: The study suggests that the combination of ICIs and brain radiotherapy provides superior OS for NSCLC patients with BM, compared to the chemotherapy combined with brain radiotherapy.
    Keywords:  Brain metastasis; Brain radiotherapy; Chemotherapy; Immune checkpoint inhibitors; Non-small cell lung cancer
    DOI:  https://doi.org/10.1186/s12885-024-13110-y
  7. Anal Methods. 2024 Oct 30.
    Comprehensive profiling of serum glycosphingolipids to discover the diagnostic biomarkers of lung cancer and uncover the variation of glycosphingolipid networks in different lung cancer subtypes.
    Ting Hu, Feifei Han, Zhuoling An.
      Glycosphingolipids are glycolipid complexes formed by an oligosaccharide chain covalently linked to a ceramide backbone and play important roles in the occurrence and metastasis of lung cancer. In this study, an UHPLC-HRMS method was developed for the comprehensive profiling of glycosphingolipids, with an in-house library constructed for data interpretation. Serum glycosphingolipids were profiled in 31 healthy controls (HCs) and 92 lung cancer patients with different pathologic subtypes. Over 1700 glycosphingolipids were detected in human serum based on the novel method. A total of 567 differential glycosphingolipids (adjusted P < 0.05, and fold change > 2) were found between lung cancer patients and HCs. Glycosphingolipids can be used as potential biomarkers for lung cancer diagnosis, with sensitivity much higher than that of traditional serum tumor markers. The levels of most glycosphingolipids in squamous cell carcinoma (Squa) were significantly lower than those in small cell lung cancer (SCLC) and adenocarcinoma (Aden). The highest Cer1P abundance in SCLC patients among the three different subtypes of lung cancer was thought to be related to the high malignancy and metastasis of SCLC. An artificial neural network (ANN) model was constructed for the discrimination of the three different subtypes of lung cancer, with accuracy higher than 93%. Beyond providing biomarkers and statistical models for the diagnosis of lung cancer and discrimination of lung cancer subtypes, this study uncovered the variation of glycosphingolipid networks in different subtypes of lung cancer and thereby provided a novel insight to study the pathogenesis of lung cancer and explore therapeutic targets.
    DOI:  https://doi.org/10.1039/d4ay01685h
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