Dataset Information

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells.

ABSTRACT: Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes, including signal transduction and gene expression patterns, arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here, we quantified metabolic features, mostly from the ¹³C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression, and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.

SUBMITTER: Chen PH

PROVIDER: S-EPMC6898782 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells.

Chen Pei-Hsuan PH Cai Ling L Huffman Kenneth K Yang Chendong C Kim Jiyeon J Faubert Brandon B Boroughs Lindsey L Ko Bookyung B Sudderth Jessica J McMillan Elizabeth A EA Girard Luc L Chen Dong D Peyton Michael M Shields Misty D MD Yao Bo B Shames David S DS Kim Hyun Seok HS Timmons Brenda B Sekine Ikuo I Britt Rebecca R Weber Stephanie S Byers Lauren A LA Heymach John V JV Chen Jing J White Michael A MA Minna John D JD Xiao Guanghua G DeBerardinis Ralph J RJ

Molecular cell 20190926 5

Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes, including signal transduction and gene expression patterns, arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here, we qu ...[more]

PMID: 31564558

Similar Datasets

Project description:Blood-based biomarkers including systemic inflammation (SI) indicators or circulating factors (cytokines, chemokines, or growth factors) are associated with a poor prognosis for lung cancer patients. Collectively these biomarkers can predict the immune state of a patient. We wanted to define and compare the immune states of small cell and non-small cell lung cancer patients, in the hopes that the information gained could lead to overall improvements in patient care and outcomes. Specimens and data from 235 patients was utilized, 49 surgically resected non-small cell lung cancer (NSCLC) patients with no evidence of disease (DF), 135 advanced non-small cell lung cancer (NSCLC), 51 small cell lung cancer (SCLC). SI markers neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte (PLR), systemic inflammation index (SII), and systemic inflammation response index (SIRI) were determined from blood counts. Forty-seven plasma cytokines were measured using a multiplex bead-based assay. Progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan-Meier and Cox Proportional Hazards models. NSCLC patients had significantly high levels of SI markers than SCLC and DF patients, while NLR, PLR and SII were also higher in SCLC than DF patients. SI optimized marker values to differentiate SI value were; 6.04 (NLR), 320 (PLR), 1615 (SII), and 7.3 (SIRI). Elevated levels NLR (p<0.001), PLR (p<0.001), and SII (p = 0.018) were associated with a worse PFS and OS in NSCLC, while none of the markers were associated with PFS in SCLC patients. NSCLC patients with a poor outcome displayed heterogeneous immune states relative to systemic inflammation and circulating IL-6 markers. These groups could be distinguished based on the cytokines IL-8, TNFα, and IL-27. We identified heterogeneity of immune states in SCLC and NSCLC patients and in NSCLC patients with the poorest prognosis. This heterogeneity could be exploited to improve outcomes for these patients.

Project description:Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related death in the world. Immunological analysis of the tumor microenvironment (immunoscore) shows great promise for improved prognosis and prediction of response to immunotherapy. However, the exact immune cell composition in NSCLC remains unclear. Here, we used flow cytometry to characterize the immune infiltrate in NSCLC tumors, non-cancerous lung tissue, regional lymph node, and blood. The cellular identity of >95% of all CD45+ immune cells was determined. Thirteen distinct immune cell types were identified in NSCLC tumors. T cells dominated the lung cancer landscape (on average 47% of all CD45+ immune cells). CD4+ T cells were the most abundant T cell population (26%), closely followed by CD8+ T cells (22%). Double negative CD4-CD8- T cells represented a small fraction (1.4%). CD19+ B cells were the second most common immune cell type in NSCLC tumors (16%), and four different B cell sub-populations were identified. Macrophages and natural killer (NK) cells composed 4.7 and 4.5% of the immune cell infiltrate, respectively. Three types of dendritic cells (DCs) were identified (plasmacytoid DCs, CD1c+ DCs, and CD141+ DCs) which together represented 2.1% of all immune cells. Among granulocytes, neutrophils were frequent (8.6%) with a high patient-to-patient variability, while mast cells (1.4%), basophils (0.4%), and eosinophils (0.3%) were less common. Across the cohort of patients, only B cells showed a significantly higher representation in NSCLC tumors compared to the distal lung. In contrast, the percentages of macrophages and NK cells were lower in tumors than in non-cancerous lung tissue. Furthermore, the fraction of macrophages with high HLA-DR expression levels was higher in NSCLC tumors relative to distal lung tissue. To make the method readily accessible, antibody panels and flow cytometry gating strategy used to identify the various immune cells are described in detail. This work should represent a useful resource for the immunomonitoring of patients with NSCLC.

Project description:BackgroundAlthough T cell abundance in solid tumours is associated with better outcomes, it also correlates with a stroma-mediated source of immune suppression driven by TGFβ1 and poor overall survival. Whether this also is observed in non-small cell lung cancer (NSCLC) is unknown.MethodsWe utilized molecular analysis of The Cancer Genome Atlas (TCGA) NSLCC cohort to correlate immune activation (IA) gene expression and extracellular matrix/stromal (ECM/stromal) gene expression with patient survival. In an independent cohort of NSCLC samples, we used flow cytometry to identify mesenchymal subsets and ex vivo functional studies to characterize their immune regulatory function.FindingsWe observed a high enrichment in a core set of genes defining an IA gene expression signature in NSCLC across TCGA Pan-cancer cohort. High IA signature score correlates with enrichment of ECM/stromal gene signature across TCGA NSCLC datasets. Importantly, a higher ratio of ECM/stromal to IA gene signature score was associated with shorter overall survival. In tumours resected from a separate cohort of NSCLC patients, we identified CD90+CD73+ peritumoral cells that were enriched in the ECM/stromal gene signature, which was amplified by TGFβ1. IFNγ and TNFα-primed peritumoral CD90+CD73+ cells upregulate immune checkpoint molecules PD-L1 and IDO1 and secrete an array of cytokines/chemokines including TGFβ1. Finally, immune primed peritumoral CD90+CD73+ cells suppress T cell function, which was relieved following combined blockade of PD-L1 and TGFβ1 with IDO1 inhibition but not PD-L1 or anti-CD73 alone.InterpretationOur findings suggest that targeting PD-L1 together with independent biological features of the stroma may enhance host antitumor immunity in NSCLC.FundingLW and HY are supported by a 4-year China Scholarship Council award. This work was funded, in part, by a grant from the Cancer League of Bern, Switzerland to SRRH. Laser scanning microscopy imaging was funded by the R'Equip grant from the Swiss National Science Foundation Nr. 316030_145003.

Dataset Information

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells.

Publications

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets