Background: Cachexia is a multifactorial metabolic syndrome with high morbidity and mortality in patients with advanced cancer. The diagnosis of cancer cachexia depends on objective measures of clinical symptoms and a history of weight loss, which lag behind disease progression and have limited utility for the early diagnosis of cancer cachexia. In this study, we performed a nuclear magnetic resonance‐based metabolomics analysis to reveal the metabolic profile of cancer cachexia and establish a diagnostic model.Methods: Eighty‐four cancer cachexia patients, 33 pre‐cachectic patients, 105 weight‐stable cancer patients, and 74 healthy controls were included in the training and validation sets. Comparative analysis was used to elucidate the distinct metabolites of cancer cachexia, while metabolic pathway analysis was employed to elucidate reprogramming pathways. Random forest, logistic regression, and receiver operating characteristic analyses were used to select and validate the biomarker metabolites and establish a diagnostic model.Results: Forty‐six cancer cachexia patients, 22 pre‐cachectic patients, 68 weight‐stable cancer patients, and 48 healthy controls were included in the training set, and 38 cancer cachexia patients, 11 pre‐cachectic patients, 37 weight‐stable cancer patients, and 26 healthy controls were included in the validation set. All four groups were age‐matched and sex‐matched in the training set. Metabolomics analysis showed a clear separation of the four groups. Overall, 45 metabolites and 18 metabolic pathways were associated with cancer cachexia. Using random forest analysis, 15 of these metabolites were identified as highly discriminating between disease states. Logistic regression and receiver operating characteristic analyses were used to create a distinct diagnostic model with an area under the curve of 0.991 based on three metabolites. The diagnostic equation was Logit(P) = −400.53 – 481.88 × log(Carnosine) −239.02 × log(Leucine) + 383.92 × log(Phenyl acetate), and the result showed 94.64% accuracy in the validation set.Conclusions: This metabolomics study revealed a distinct metabolic profile of cancer cachexia and established and validated a diagnostic model. This research provided a feasible diagnostic tool for identifying at‐risk populations through the detection of serum metabolites.

Cancer cachexia is a multifactorial syndrome affecting the skeletal muscle. Previous clinical trials showed that treatment with MEK inhibitor selumetinib resulted in skeletal muscle anabolism. However, it is conflicting that MAPK/ERK pathway controls the mass of the skeletal muscle. The current study investigated the therapeutic effect and mechanisms of selumetinib in amelioration of cancer cachexia. The classical cancer cachexia model was established via transplantation of CT26 colon adenocarcinoma cells into BALB/c mice. The effect of selumetinib on body weight, tumor growth, skeletal muscle, food intake, serum proinflammatory cytokines, E3 ligases, and MEK/ERK–related pathways was analyzed. Two independent experiments showed that 30 mg/kg/d selumetinib prevented the loss of body weight in murine cachexia mice. Muscle wasting was attenuated and the expression of E3 ligases, MuRF1 and Fbx32, was inhibited following selumetinib treatment of the gastrocnemius muscle. Furthermore, selumetinib efficiently reduced tumor burden without influencing the cancer cell proliferation, cumulative food intake, and serum cytokines. These results indicated that the role of selumetinib in attenuating muscle wasting was independent of cancer burden. Detailed analysis of the mechanism revealed AKT and mTOR were activated, while ERK, FoxO3a, and GSK3β were inhibited in the selumetinib -treated cachexia group. These indicated that selumetinib effectively prevented skeletal muscle wasting in cancer cachexia model through ERK inhibition and AKT activation in gastrocnemius muscle via cross-inhibition. The study not only elucidated the mechanism of MEK/ERK inhibition in skeletal muscle anabolism, but also validated selumetinib therapy as an effective intervention against cancer cachexia. Mol Cancer Ther; 16(2); 334–43. ©2016 AACR.

Accumulated oncometabolites in the tumor microenvironment (TME) suppresses the metabolism, expansion, and function of T cells. Immunosuppressive TME also impeded Chimeric Antigen Receptor (CAR)-T cells mediated cytotoxicity since CAR-T cells had to adapt the in vivo metabolic characteristics with high levels of oncometabolites. We screened oncometabolites for the inhibition of glucose uptake in CD8 + T cells and found Kynurenine (Kyn) showed the strongest inhibiting effect on glucose uptake. In vitro experiments showed that 120 μM Kyn treatment in CD8 + T cells resulted in inhibiting the expansion of CD8 + T cells, decreasing the production of granzyme B and interferon-γ. CAR-T cells mediated cytotoxicity was also impaired by the high Kyn treatment from killing assay. We then explored the anti-tumor effect of Kynureninase (KYNU) modified CAR-T cells through catabolism o oncometabolites Kyn. KYNU over-expression (OE) CAR-T cells showed a superior killing effect against cancer cells even in the immunosuppressive TME with high Kyn levels. In vivo experiments confirmed KYNU-OE CAR-T cells showed an excellent anti-tumor effect in a TME with high Kyn levels since it improved the survival of mice bearing NALM6 cancer cells and NALM6-IDO1 cancer cells. The KYNU-modified CAR-T cells displayed distinct phenotypes related to the expansion, function, and memory differentiation status of CAR-T cells. This study explores an immunotherapy strategy for patients with alterations in Kyn metabolism. KYNU-OE CAR-T cells take advantage of Kyn catabolism to improve anti-tumor activity in the metabolic immunosuppressive TME with high Kyn.

Cardioprotection of dexrazoxane (DZR) against doxorubicin (DOX)-induced cardiotoxicity is contentious and the indicator is controversial. A pairwise comparative metabolomics approach was used to delineate the potential metabolic processes in the present study. Ninety-six BALB/c mice were randomly divided into two supergroups: tumor and control groups. Each supergroup was divided into control, DOX, DZR, and DOX plus DZR treatment groups. DOX treatment resulted in a steady increase in 5-hydroxylysine, 2-hydroxybutyrate, 2-oxoglutarate, 3-hydroxybutyrate, and decrease in glucose, glutamate, cysteine, acetone, methionine, asparate, isoleucine, and glycylproline.DZR treatment led to increase in lactate, 3-hydroxybutyrate, glutamate, alanine, and decrease in glucose, trimethylamine N-oxide and carnosine levels. These metabolites represent potential biomarkers for early prediction of cardiotoxicity of DOX and the cardioprotective evaluation of DZR.