Immune regulatory metabolites are key features of the tumor microenvironment (TME), yet with a few exceptions, their identities remain largely unknown. Here, we profiled tumor and T cells from tumor and ascites of patients with high-grade serous carcinoma (HGSC) to uncover the metabolomes of these distinct TME compartments. Cells within the ascites and tumor had pervasive metabolite differences, with a notable enrichment in 1-methylnicotinamide (MNA) in T cells infiltrating the tumor compared with ascites. Despite the elevated levels of MNA in T cells, the expression of nicotinamide N-methyltransferase, the enzyme that catalyzes the transfer of a methyl group from S-adenosylmethionine to nicotinamide, was restricted to fibroblasts and tumor cells. Functionally, MNA induces T cells to secrete the tumor-promoting cytokine tumor necrosis factor alpha. Thus, TME-derived MNA contributes to the immune modulation of T cells and represents a potential immunotherapy target to treat human cancer.
This study investigates whether Genomic Organization at Large Scales (which we propose to call GOALS) as quantified via nuclear phenotype characteristics and cell sociology features (describing cell organization within tissue) collected from prostate tissue microarrays (TMAs) can separate biochemical failure from biochemical nonevidence of disease (BNED) after radical prostatectomy (RP). Of the 78 prostate cancer tissue cores collected from patients treated with RP, 16 who developed biochemical relapse (failure group) and 16 who were BNED patients (nonfailure group) were included in the analyses (36 cores from 32 patients). A section from this TMA was stained stoichiometrically for DNA using the Feulgen-Thionin methodology, and scanned with a Pannoramic MIDI scanner. Approximately 110 nuclear phenotypic features, predominately quantifying large scale DNA organization (GOALS), were extracted from each segmented nuclei. In addition, the centers of these segmented nuclei defined a Voronoi tessellation and subsequent architectural analysis. Prostate TMA core classification as biochemical failure or BNED after RP using GOALS features was conducted (a) based on cell type and cell position within the epithelium (all cells, all epithelial cells, epithelial >2 cell layers away from basement membrane) from all cores, and (b) based on epithelial cells more than two cell layers from the basement membrane using a Classifier trained on Gleason 6, 8, 9 (16 cores) only and applied to a Test set consisting of the Gleason 7 cores (20 cores). Successful core classification as biochemical failure or BNED after RP by a linear classifier was 75% using all cells, 83% using all epithelial cells, and 86% using epithelial >2 layers. Overall success of predicted classification by the linear Classifier of (b) was 87.5% using the Training Set and 80% using the Test Set. Overall success of predicted progression using Gleason score alone was 75% for Gleason >7 as failures and 69% for Gleason >6 as failures. © 2017 International Society for Advancement of Cytometry.
15Immune regulatory metabolites are key features of the tumor microenvironment (TME), yet with 16 a few notable exceptions, their identities remain largely unknown. We uncovered the immune 17 regulatory metabolic states and metabolomes of sorted tumor and stromal, CD4+, and CD8+ cells 18 from the tumor and ascites of patients with high-grade serous ovarian cancer (HGSC) using high- 19 dimensional flow cytometry and metabolomics supplemented with single cell RNA sequencing. 20 Flow cytometry revealed that tumor cells show a consistently greater uptake of glucose than T 21 cells, but similar mitochondrial activity. Cells within the ascites and tumor had pervasive 22 metabolite differences, with a striking enrichment in 1-methylnicotinamide (MNA) in T cells 23 infiltrating the tumor compared to ascites. Despite the elevated levels of MNA in T cells, the 24 expression of nicotinamide N-methyltransferase, the gene encoding the enzyme that catalyses the 25 transfer of a methyl group from S-adenosylmethionine to nicotinamide, was restricted to 26 fibroblasts and tumor cells. Treatment of T cells with MNA resulted in an increase in T cell-27 mediated secretion of the tumor promoting cytokine tumor necrosis factor alpha. Thus, the TME-28 derived metabolite MNA contributes to an alternative and non-cell autonomous mechanism of 29 immune modulation of T cells in HGSC. Collectively, uncovering the tumor-T cell metabolome 30 may reveal metabolic vulnerabilities that can be exploited using T cell-based immunotherapies to 31 treat human cancer. 32 Tumor-derived metabolites can have profound suppressive effects on anti-tumor immunity, with 33 increasing evidence that they can also function as key drivers of disease progression 1,2 . Beyond the 34 Warburg effect, recent work has begun to characterize the metabolic states of tumor cells and their 35 relationship to the immunological state of the TME. Studies in murine models have helped uncover the 36 role of metabolites such as (R)-2-hydroxyglutarate 3 , BH4 4 and methylglyoxal 5 as well as pathways 37 including glutamine metabolism 6 , oxidative metabolism 7 , and glucose metabolism 8 that impact T cell 38 function and antitumor immunity. Furthermore, studies in humans have elucidated key metabolic 39 pathways in tumors, for example demonstrating that tumors can use lactate as fuel 9 . Despite this, the 40 diversity and impact of specific metabolites on tumor-infiltrating lymphocytes (TILs) are largely 41 unknown. To characterize this diversity and better understand how metabolites in the TME influence T 42 cell function, a combined flow cytometry and mass-spectrometry approach was used to profile tumor 43 and TIL from patients with HGSC. Using this approach two spatially distinct microenvironments were 44 interrogated, the ascites 10 and tumor, within the same patients to reveal potential reciprocal metabolic 45 interactions between tumor cells and TIL. 47The phenotypic and metabolic states of cells in the matched ascites and tumor environments from six 48 patients ...
Ex vivo expansion conditions used to generate T cells for immunotherapy are thought to adopt metabolic phenotypes that impede therapeutic efficacy in vivo. The comparison of five different culture media used for clinical T cell expansion revealed unique optima based on different output variables including proliferation, differentiation, function, activation and mitochondrial phenotypes. T cells adapted their metabolism to match their media expansion condition as shown by glucose and glutamine uptake, and patterns of glucose isotope labeling. However, adoption of these metabolic phenotypes was uncoupled to T cell function. Furthermore, T cell products cultured in ascites from ovarian cancer patients displayed suppressed mitochondrial activity and function irrespective of the ex vivo expansion media. In one case, culturing in ascites resulted in increased glucose uptake which was insufficient to rescue T cell function. Thus, ex vivo T cell expansion conditions have profound impacts on metabolism and function.
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