bims-meluca 2024-09-15 papers

on Metabolism of non-small cell lung carcinoma

Issue of 2024‒09‒15
eight papers selected by
the Muñoz-Pinedo/Nadal (PReTT) lab, L’Institut d’Investigació Biomèdica de Bellvitge

The prognosis and metabolite changes of NSCLC patients receiving first-line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification.
The peripheral CD4+ T cells predict efficacy in non-small cell lung cancer (NSCLC) patients with the anti-PD-1 treatment.
Prognostic role of dynamic changes in inflammatory indicators in patients with non-small cell lung cancer treated with immune checkpoint inhibitors-a retrospective cohort study.
Exploring clinical factors to predict the survival for resectable non-small cell lung cancer patients with neoadjuvant immunotherapy.
A scoring model for the expression of genes related to programmed cell death predicts immunotherapy response and prognosis in lung adenocarcinoma.
Glycemic Control in Diabetic Patients Improved Overall Lung Cancer Survival across Diverse Populations.
FHL2 expression by cancer-associated fibroblasts promotes metastasis and angiogenesis in lung adenocarcinoma.
The NRF2-CARM1 axis links glucose sensing to transcriptional and epigenetic regulation of the pentose phosphate pathway in gastric cancer.

Cancer Med. 2024 Sep;13(17): e70223

The prognosis and metabolite changes of NSCLC patients receiving first-line immunotherapy combined chemotherapy in different M1c categories according to 9th edition of TNM classification.

Liang Zheng, Fang Hu, Wei Nie, Jun Lu, Bo Zhang, Jianlin Xu, Shuyuan Wang, Ying Li, Xiaoxuan Zheng, Wei Zhang, Yinchen Shen, Runbo Zhong, Tianqing Chu, Baohui Han, Hua Zhong, Xueyan Zhang.

  BACKGROUND: The 9th edition of the TNM Classification for lung cancer delineates M1c into two subcategories: M1c1 (Multiple extrathoracic lesions within a single organ system) and M1c2 (Multiple extrathoracic lesions involving multiple organ systems). Existing research indicates that patients with lung cancer in stage M1c1 exhibit superior overall survival compared to those in stage M1c2. The primary frontline therapy for patients with advanced non-small cell lung cancer (NSCLC), lacking driver gene mutations, involves the use of immune checkpoint inhibitors (ICIs) combined with chemotherapy. Nevertheless, a dearth of evidence exists regarding potential survival disparities between NSCLC patients with M1c1 and M1c2 undergoing first-line immune-chemotherapy, and reliable biomarkers for predicting treatment outcomes are elusive. Serum metabolic profiles may elucidate distinct prognostic mechanisms, necessitating the identification of divergent metabolites in M1c1 and M1c2 undergoing combination therapy. This study seeks to scrutinize survival discrepancies between various metastatic patterns (M1c1 and M1c2) and pinpoint metabolites associated with treatment outcomes in NSCLC patients undergoing first-line ICIs combined with chemotherapy.METHOD: In this study, 33 NSCLC patients lacking driver gene mutations diagnosed with M1c1, and 22 similarly diagnosed with M1c2 according to the 9th edition of TNM Classification, were enrolled. These patients received first-line PD-1 inhibitor plus chemotherapy. The relationship between metastatic patterns and progression-free survival (PFS) in patients undergoing combination therapy was analyzed using univariate and multivariate Cox regression models. Serum samples were obtained from all patients before treatment initiation for untargeted metabolomics analysis, aiming to identify differential metabolites.
RESULTS: In the univariate analysis of PFS, NSCLC patients in M1c1 receiving first-line PD-1 inhibitor plus chemotherapy exhibited an extended PFS (HR = 0.49, 95% CI, 0.27-0.88, p = 0.017). In multivariate PFS analyses, these M1c1 patients receiving first-line PD-1 inhibitor plus chemotherapy also demonstrated prolonged PFS (HR = 0.45, 95% CI, 0.22-0.92, p = 0.028). The serum metabolic profiles of M1c1 and M1c2 undergoing first-line PD-1 inhibitors plus chemotherapy displayed notable distinctions. In comparison to M1c1 patients, M1c2 patients exhibited alterations in various pathways pretreatment, including platelet activation, linoleic acid metabolism, and the VEGF signaling pathway. Diminished levels of lipid-associated metabolites (diacylglycerol, sphingomyelin) were correlated with adverse outcomes.
CONCLUSION: NSCLC patients in M1c1, devoid of driver gene mutations, receiving first-line PD-1 inhibitors combined with chemotherapy, experienced superior outcomes compared to M1c2 patients. Moreover, metabolomic profiles strongly correlated with the prognosis of these patients, and M1c2 patients with unfavorable outcomes manifested distinct changes in metabolic pathways before treatment. These changes predominantly involved alterations in lipid metabolism, such as decreased diacylglycerol and sphingomyelin, which may impact tumor migration and invasion.

Keywords:  9th edition TNM classification; immune‐checkpoint inhibitors; non‐small cell lung cancer; prognosis; untargeted metabolomics

DOI:  https://doi.org/10.1002/cam4.70223
Transl Cancer Res. 2024 Aug 31. 13(8): 4052-4061

The peripheral CD4+ T cells predict efficacy in non-small cell lung cancer (NSCLC) patients with the anti-PD-1 treatment.

Weixia Zhuang, Moufeng Wang, Li Jiang, Zudong Su, Shenglu Lin.

  Background: Programmed cell death protein 1 (PD-1) inhibitor therapy has become a routine treatment for advanced non-small cell lung cancer (NSCLC). However, only some NSCLC patients would benefit from anti-PD-1 therapy. We urgently need to identify biomarkers associated with clinical response to change treatment strategies promptly for patients who fail to benefit from anti-PD-1 treatment. This study was aimed to explore whether circulating CD4+ T cells and CD8+ T cells could be biomarkers for predicting anti-PD-1 efficacy.Methods: In this study, 118 NSCLC patients who received anti-PD-1 therapy were enrolled. The percentages of circulating CD4+ T cells and CD8+ T cells before and after anti-PD-1 treatment were determined by flow cytometry. The programmed cell death ligand 1 (PD-L1) expression of tumor tissues was detected by immunocytochemistry. The anti-PD-1 treatment efficacy was assessed by immune response evaluation criteria in solid tumors (iRECIST).
Results: The percentage of CD4+ T cells and CD4+/CD8+ ratio in the peripheral blood (PB) was significantly elevated after anti-PD-1 treatment. In contrast, the percentage of CD8+ T cells in the PB was significantly decreased after anti-PD-1 treatment. Furthermore, we found that the percentages of CD4+ T cells and CD4+/CD8+ ratios considerably increased, and the percentages of CD8+ T cells significantly reduced in the effective group. On the contrary, the patients in the ineffective group showed no significant differences in the biomarkers. Multivariate logistic revealed that the percentage of CD4+ T cells at baseline was an independent predictor of anti-PD-1 treatment. The area under the curve (AUC) of the CD4+ T cells percentage was 0.7834 with a cut-off value of 28.53% (sensitivity =82.5%, specificity =66.23%).
Conclusions: The percentage of CD4+ T cells at baseline could predict anti-PD-1 efficacy in NSCLC patients.

Keywords:  CD4+ T cells; CD4+/CD8+ ratio; Programmed cell death protein 1 inhibitor (PD-1 inhibitor); non-small cell lung cancer (NSCLC)

DOI:  https://doi.org/10.21037/tcr-24-405
Transl Lung Cancer Res. 2024 Aug 31. 13(8): 1975-1987

Prognostic role of dynamic changes in inflammatory indicators in patients with non-small cell lung cancer treated with immune checkpoint inhibitors-a retrospective cohort study.

Liang Guo, Juanjuan Li, Jing Wang, Xinru Chen, Chenlei Cai, Fei Zhou, Anwen Xiong.

  Background: Immune checkpoint inhibitors (ICIs) have become one of the standard treatments for non-small cell lung cancer (NSCLC) patients without driver mutations. However, a considerable proportion of patients suffer from severe immune side effects and fail to respond to ICIs. As effective biomarkers, programmed cell death ligand 1 (PD-L1) expression, microsatellite instability (MSI), the tumor mutation burden (TMB) and tumor-infiltrating lymphocytes (TILs) require invasive procedures that place heavy physical and psychological burdens on patients. This study aims to identify simple and effective markers to optimize patient selection through therapeutic decisions and outcome prediction.Methods: This retrospective study comprised 95 patients with metastatic NSCLC who were treated with ICIs either as the standard of care or in a clinical trial. The following data were extracted from the medical records. The baseline and dynamic neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were calculated in the present study. Responses were assessed by computed tomography (CT) imaging and classified according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 every 6-12 weeks during treatment.
Results: In total, 95 patients were included in the present study. The median age of patients was 61 years, 83.2% (79/95) patients were male, 62.1% (59/95) were former or current smokers, 66.3% (63/95) had adenocarcinoma, 93.7% (89/95) had stage IV disease, and 87.4% were without molecular alterations. A higher overall response rate (ORR) and prolonged median progression-free survival (PFS) was observed in patients with a lower cycle 3 (C3) NLR [7.7 vs. 5.5 months, hazard ratio (HR): 1.70, 95% confidence interval (CI): 0.90-3.22; P=0.12] and derived NLR (dNLR) (8.2 vs. 5.6 months, HR: 1.67, 95% CI: 0.94-2.97; P=0.08). After two cycles of ICI treatment, patients who had an increased NLR, dNLR, and PLR had a lower ORR and an inferior median PFS than those with a decreased NLR (5.5 vs. 8.5 months, HR: 1.87, 95% CI: 1.09-3.21; P=0.02), dNLR (5.6 vs. 8.4 months, HR: 1.49, 95% CI: 0.87-2.57; P=0.15), and PLR (11.8 vs. 5.5 months, HR: 2.28, 95% CI: 1.32-3.94; P=0.003). Moreover, patients with both an increased NLR and PLR had a worse ORR and median PFS than those with either an increased NLR or PLR, or both an increased NLR and PLR (11.8 vs. 5.5 vs. 5.6 months, P=0.003). In addition, the dynamic changes in the PLR could serve as an independent predictive factor of PFS in NSCLC patients treated with ICIs.
Conclusions: Elevated dynamic changes in the NLR and PLR were associated with lower response rates and shorter PFS in the patients with NSCLC treated with ICIs. Our results also highlight the role of dynamic changes in the PLR in identifying patients with NSCLC who could benefit from ICIs.

Keywords:  Immune checkpoint inhibitors (ICIs); neutrophil-to-lymphocyte ratio (NLR); non-small cell lung cancer (NSCLC); platelet-to-lymphocyte ratio (PLR)

DOI:  https://doi.org/10.21037/tlcr-24-637
Eur J Cardiothorac Surg. 2024 Sep 13. pii: ezae335. [Epub ahead of print]

Exploring clinical factors to predict the survival for resectable non-small cell lung cancer patients with neoadjuvant immunotherapy.

Mengzhe Zhang, Meng Yan, Zengtuan Xiao, Yue Li, Zuo Liu, Pengpeng Zhang, Xiaofei Wang, Lianmin Zhang, Zhenfa Zhang.

  OBJECTIVES: To explore clinical factors and build a predictive model for the disease-free and overall survival in non-small cell lung cancer patients receiving neoadjuvant chemotherapy combined with immune checkpoint inhibitors.METHODS: Inclusion criteria for patients in this multicentre study were: (1) patients were diagnosed with stage I-III non-small cell lung cancer diagnosed by bronchoscopy biopsy or puncture; (2) computed tomography/positron emission tomography-computed tomography was applied before treatment and surgery; (3) neoadjuvant chemotherapy combined with immune checkpoint inhibitors were applied for 2-6 cycles preoperatively; (4) peripheral blood indicators and tumour markers were assessed before treatment and surgery; (5) patients underwent radical lung cancer surgery after neoadjuvant therapy. Cases were divided into high- and low-risk groups according to 78 clinical indicators based on 10-fold LASSO selection. We employed Cox proportional hazards models in predicting disease-free and overall survival. Then, we used time-dependent area under the curve and decision curve analyses to examine the accuracy of the results.
RESULTS: Data were collected continuously, and 212 and 85 cases were randomly assigned to training and testing sets, respectively. The area under curve for the prediction of disease-free survival (training-1-year, 0.83; 2-year, 0.81; 3-year, 0.83 vs testing-1-year, 0.65; 2-year, 0.66; 3-year, 0.70), overall survival (training-1-year, 0.86; 2-year, 0.85; 3-year, 0.86 vs testing-1-year, 0.66; 2-year, 0.57; 3-year, 0.70) were determined. The coefficient factors including pathological response, preoperative tumour maximum diameter, preoperative lymph shorter-diameter, preoperative tumour&lymph maximum standardized uptake value, change in tumour standardized uptake value preoperative, and blood related risk factors were favorably associated with prognosis (P < 0.001).
CONCLUSIONS: Our prediction model integrating data from preoperative positron emission tomography-CT, preoperative blood parameters, and pathological response was able to make high accuracy predictions for disease-free and overall survival in non-small cell lung cancer patients receiving neoadjuvant immunity with chemical therapy.

Keywords:  Clinical prognostic model; neoadjuvant immunity combined with chemotherapy; non-small cell lung cancer

DOI:  https://doi.org/10.1093/ejcts/ezae335
Discov Oncol. 2024 Sep 12. 15(1): 435

A scoring model for the expression of genes related to programmed cell death predicts immunotherapy response and prognosis in lung adenocarcinoma.

Runan Jia, Xiaolong Liang, Jianfei Tu, Hongyuan Yang.

  BACKGROUND: Lung adenocarcinoma (LUAD) continues to be the leading cause of cancer death worldwide, driven by environmental factors like smoking and genetic predispositions. LUAD has a high mortality rate, and new biomarkers are urgently needed to improve treatment strategies and patient management. Programmed cell death (PCD) is involved in tumor progression and response to treatment. Therefore, there is a need for an extensive study of the role and functions of PCD-related genes (PCDRGs) in lung adenocarcinoma so as to understand the pathophysiologic features of lung adenocarcinoma.METHODS: Based on TCGA and GEO databases, this research is aimed at screening differentially expressed PCD-related genes in lung adenocarcinoma. We conducted GO, and KEGG analysis to establish the link between these genes and biological processes. By applying various machine learning algorithms such as CoxBoost analysis, we developed PCD-related indices (PCDI) that were used to verify their ability to predict prognosis with the use of other datasets. This was done in addition to exploring the biological functions of PCD genes associated with lung adenocarcinoma by assessing the relationship between immune cell components of tumor microenvironment and PCD genes together with examining how they affect drug sensitivity.
RESULTS: The research presented in this article offers significant insights into LUAD. The authors identified 113 PCDRGs that were differentially expressed in LUAD. These genes are implicated in various biological functions, including High risk ing apoptosis, ferroptosis, and pathways specific to non-small cell lung cancer. Notably, the PCDI proved effective in distinguishing between High risk and Low risk LUAD patients, demonstrating a higher accuracy in prognosis prediction compared to traditional clinical indicators such as age and gender. This high prediction accuracy was validated in both test and validation cohorts. Additionally, these genes showed significant correlations with immune cell infiltration and drug sensitivity in LUAD patients.
CONCLUSION: We analysed the expression and function of PCDRGs in LUAD and revealed their correlation with patient survival, the immune microenvironment and drug sensitivity. The constructed PCDI model provides a scientific basis for the personalised treatment of lung adenocarcinoma, and future optimisation of treatment strategies based on these genes may improve patient clinical outcomes.

Keywords:   Neoplastic; Pharmacological; Biomarkers; Gene expression regulation; Lung adenocarcinoma; Machine learning; Programmed cell death

DOI:  https://doi.org/10.1007/s12672-024-01319-z
JNCI Cancer Spectr. 2024 Sep 12. pii: pkae081. [Epub ahead of print]

Glycemic Control in Diabetic Patients Improved Overall Lung Cancer Survival across Diverse Populations.

Yi-Hung Wu, Brian Luke, Xiao-Cheng Wu, J Jack Lee, Yong Yi, Samuel C Okpechi, Barry Gause, Paras Mehta, Steven I Sherman, Augusto Ochoa, Ethan Dmitrovsky, Xi Liu.

  BACKGROUND: The consequence of diabetes on lung cancer overall survival (OS) is debated. This retrospective study used two large lung cancer databases to assess comprehensively diabetes effects on lung cancer OS in diverse demographic populations, including health disparity.METHODS: The University of Texas MD Anderson Cancer Center database (32,643 lung cancer cases with 11,973 diabetics) was extracted from electronic health records (EHRs) using natural language processing (NLP). Associations were between diabetes and lung cancer prognostic features [age, sex, race, body mass index (BMI), insurance status, smoking, stage, and histopathology]. Hemoglobin A1C (HgbA1c) and glucose levels assessed glycemic control. Validation was with a Louisiana cohort (17,768 lung cancer cases with 4,746 diabetics) enriched for health disparity cases. Kaplan-Meier analysis, log-rank test, multivariable Cox proportional hazard models, and survival tree analyses were employed.
RESULTS: Lung cancer patients with diabetes exhibited marginally elevated OS or no statistically-significant difference versus non-diabetic patients. When examining OS for two glycemic levels (HgbA1c > 7.0 or glucose > 154 mg/dL versus HgbA1c > 9.0 or glucose > 215 mg/dL), a statistically significant improvement in OS occurred in lung cancers with controlled versus uncontrolled glycemia (P < 0.0001). This improvement spanned gender, age, smoking status, insurance status, stage, race, BMI, histopathology and therapy. Survival tree analysis revealed that obese and morbidly obese patients with controlled glycemia or no known diabetes had higher lung cancer OS than comparison groups.
CONCLUSION: These findings indicate a need for optimal glycemic control to improve lung cancer OS in diverse populations with diabetes.

Keywords:  and health disparity; body mass index; diabetes; diversity; lung cancer; overall survival

DOI:  https://doi.org/10.1093/jncics/pkae081
Int J Cancer. 2024 Sep 08.

FHL2 expression by cancer-associated fibroblasts promotes metastasis and angiogenesis in lung adenocarcinoma.

Ryu Kanzaki, Steven Reid, Paulina Bolivar, Jonas Sjölund, Johan Staaf, Sara Larsson, Yasushi Shintani, Kristian Pietras.

  Cancer-associated fibroblasts (CAFs) contribute to the progression of lung cancer. Four and a half LIM domain protein-2 (FHL2) is a component of focal adhesion structures. We analyzed the function of FHL2 expressed by CAFs in lung adenocarcinoma. Expression of FHL2 in fibroblast subtypes was investigated using database of single-cell RNA-sequencing of lung cancer tissue. The role of FHL2 in the proliferation and migration of CAFs was assessed. The effects of FHL2 knockout on the migration and invasion of human lung adenocarcinoma cells and tube formation of endothelial cells induced by CAF-conditioned medium (CM) were evaluated. The effect of FHL2 knockout in CAFs on metastasis was determined using a murine orthotopic lung cancer model. The prognostic significance of stromal FHL2 was assessed by immunohistochemistry in human adenocarcinoma specimens. FHL2 is highly expressed in myofibroblasts in cancer tissue. TGF-β1 upregulated FHL2 expression in CAFs and FHL2 knockdown attenuated CAF proliferation. FHL2 knockout reduced CAF induced migration of A110L and H23 human lung adenocarcinoma cell lines, and the induction of tube formation of endothelial cells. FHL2 knockout reduced CAF-induced metastasis of lung adenocarcinomas in an orthotopic model in vivo. The concentration of Osteopontin (OPN) in CM from CAF was downregulated by FHL2 knockout. siRNA silencing and antibody blocking of OPN reduced the pro-migratory effect of CM from CAF on lung cancer cells. In resected lung adenocarcinoma specimens, positive stromal FHL2 expression was significantly associated with higher microvascular density and worse prognosis. In conclusion, FHL2 expression by CAFs enhances the progression of lung adenocarcinoma by promoting angiogenesis and metastasis.

Keywords:  FHL2; cancer‐associated fibroblast; lung adenocarcinoma; tumor microenvironment

DOI:  https://doi.org/10.1002/ijc.35174
Cell Death Dis. 2024 Sep 12. 15(9): 670

The NRF2-CARM1 axis links glucose sensing to transcriptional and epigenetic regulation of the pentose phosphate pathway in gastric cancer.

Miaomiao Ping, Guangyao Li, Qijiao Li, Yang Fang, Taotao Fan, Jing Wu, Ruiyi Zhang, Lesha Zhang, Bing Shen, Jizheng Guo.

Cancer cells autonomously alter metabolic pathways in response to dynamic nutrient conditions in the microenvironment to maintain cell survival and proliferation. A better understanding of these adaptive alterations may reveal the vulnerabilities of cancer cells. Here, we demonstrate that coactivator-associated arginine methyltransferase 1 (CARM1) is frequently overexpressed in gastric cancer and predicts poor prognosis of patients with this cancer. Gastric cancer cells sense a reduced extracellular glucose content, leading to activation of nuclear factor erythroid 2-related factor 2 (NRF2). Subsequently, NRF2 mediates the classic antioxidant pathway to eliminate the accumulation of reactive oxygen species induced by low glucose. We found that NRF2 binds to the CARM1 promoter, upregulating its expression and triggering CARM1-mediated hypermethylation of histone H3 methylated at R arginine 17 (H3R17me2) in the glucose-6-phosphate dehydrogenase gene body. The upregulation of this dehydrogenase, driven by the H3R17me2 modification, redirects glucose carbon flux toward the pentose phosphate pathway. This redirection contributes to nucleotide synthesis (yielding nucleotide precursors, such as ribose-5-phosphate) and redox homeostasis and ultimately facilitates cancer cell survival and growth. NRF2 or CARM1 knockdown results in decreased H3R17me2a accompanied by the reduction of glucose-6-phosphate dehydrogenase under low glucose conditions. Collectively, this study reveals a significant role of CARM1 in regulating the tumor metabolic switch and identifies CARM1 as a potential therapeutic target for gastric cancer treatment.

DOI: https://doi.org/10.1038/s41419-024-07052-3