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PURPOSE Metabolome analysis is an emerging method that provides insight into intracellular and physiological responses. Methotrexate (MTX) is an antifolate that suppresses DNA syntheses by inhibiting dihydrofolate reductase. High-dose MTX treatment with deferred radiotherapy is a standard protocol in primary central nervous system lymphoma (PCNSL) treatments. However, most cases come to relapse-acquired resistance, in which the role of metabolic pathways are largely unknown. METHODS Metabolome analysis in MTX-resistant PCNSL-derived cells (designated as TK-MTX and HKBML-MTX) was performed to detect alternative metabolites and pathways. RESULTS The metabolomic analyses using capillary electrophoresis-time-of-flight mass spectrometry detected 188 and 169 peaks in TK and HKBML-derived cells, respectively, including suppression of central carbon metabolism, lipid metabolism, nucleic acid metabolism, urea cycle, branched-chain and aromatic amino acids, and coenzyme metabolism. Particularly, whole suppressive metabolic pathways were demonstrated in TK-MTX, whereas HKBML-MTX indicated partially enhanced pathways of the urea cycle, amino acid metabolism, and coenzyme metabolism. Reciprocally detected metabolites for glycolysis, including induced glucose and reduced glycogen, and induced lactate and reduced pyruvate, in addition to increased lactate dehydrogenase activity, which is involved in Warburg effect. Thereby, ATP was increased in both MTX-resistant PCNSL-derived cells. Further, we specifically found that PI3K/AKT/mTOR and RAS/MAPK signaling pathways were activated in TK-MTX but not in HKBML-MTX by growth rate with inhibitors and gene expression analysis, suggestive of cell type-specific MTX-resistant metabolic pathways. CONCLUSIONS These results can help us understand targeted therapies with selective anticancer drugs in recurrent CNS lymphoma-acquired resistance against MTX. Copyright ©2020, American Association for Cancer Research.INTRODUCTION Both weight loss and low carbohydrate diets (LCD) without weight loss prolong survival in prostate cancer (PC) models. Few human trials tested weight loss or LCD on PC. METHODS We conducted a multi-site randomized 6-month trial of LCD vs control on PSA doubling time (PSADT) in PC patients with biochemical recurrence (BCR) after local treatment. Eligibility included BMI ≥24 kg/m2 and PSADT 3-36 months. LCD was instructed to eat 20 g/carbs/day; controls were instructed to avoid dietary changes. Primary outcome was PSADT. Secondary outcomes included weight, lipids, glucose metabolism, and diet. RESULTS Of 60 planned patients, the study stopped early after an interim analysis showed futility. 27 LCD and 18 controls completed the study. Telaprevir molecular weight At 6-month, while both arms consumed similar protein and fats, LCD reduced carbohydrates intake (-117 vs. 8g, p less then 0.001) and lost weight (-12.1 vs. -0.50Kg, p less then 0.001). LCD reduced HDL, triglycerides, and HbA1c with no difference in total cholesterol or glucose. Mean PSADT was similar between LCD (21 months) vs. control (15 months, p=0.316). In a post-hoc exploratory analysis accounting for pre-study PSADT, baseline PSA, primary treatment and hemoconcentration, PSADT was significantly longer in LCD vs. controls (28 vs 13 months, p=0.021). Adverse events were few, usually mild, and returned to baseline by 6-month. CONCLUSIONS Among BCR patients, LCD induced weight loss and metabolic benefits with acceptable safety without affecting PSADT suggesting LCD does not adversely affect PC growth and is safe. Given exploratory findings of longer PSADT, larger studies testing LCD on disease progression are warranted. Copyright ©2020, American Association for Cancer Research.Exosomes, extracellular vesicles (EVs) of endosomal origin, emerge as master regulators of cell-to-cell signaling in physiology and disease. Exosomes are highly enriched in tetraspanins (TSPNs) and syndecans (SDCs), the latter occurring mainly in proteolytically cleaved form, as membrane-spanning C-terminal fragments of the proteins. While both protein families are membrane scaffolds appreciated for their role in exosome formation, composition, and activity, we currently ignore whether these work together to control exosome biology. Here we show that TSPN6, a poorly characterized tetraspanin, acts as a negative regulator of exosome release, supporting the lysosomal degradation of SDC4 and syntenin. We demonstrate that TSPN6 tightly associates with SDC4, the SDC4-TSPN6 association dictating the association of TSPN6 with syntenin and the TSPN6-dependent lysosomal degradation of SDC4-syntenin. TSPN6 also inhibits the shedding of the SDC4 ectodomain, mimicking the effects of matrix metalloproteinase inhibitors. Taken together, our data identify TSPN6 as a regulator of the trafficking and processing of SDC4 and highlight an important physical and functional interconnection between these membrane scaffolds for the production of exosomes. These findings clarify our understanding of the molecular determinants governing EV formation and have potentially broad impact for EV-related biomedicine.BACKGROUND Lipid traits have been inconsistently linked to risk of non-Hodgkin lymphoma (NHL). We examined the association of genetically predicted lipid traits with risk of diffuse large B-cell lymphoma (DLBCL), chronic lymphocytic leukemia (CLL), follicular lymphoma (FL), and marginal zone lymphoma (MZL) using Mendelian randomization (MR) analysis. METHODS Genome-wide association study data from the InterLymph Consortium were available for 2,661 DLBCLs, 2,179 CLLs, 2,142 FLs, 824 MZLs, and 6,221 controls. SNPs associated (P less then 5 × 10-8) with high-density lipoprotein (HDL, n = 164), low-density lipoprotein (LDL, n = 137), total cholesterol (TC, n = 161), and triglycerides (TG, n = 123) were used as instrumental variables (IV), explaining 14.6%, 27.7%, 16.8%, and 12.8% of phenotypic variation, respectively. Associations between each lipid trait and NHL subtype were calculated using the MR inverse variance-weighted method, estimating odds ratios (OR) per standard deviation and 95% confidence intervals (CI).