Neutrophil diversity is associated with T-cell immunity and clinical relevance in patients with thyroid cancer.

Neutrophil heterogeneity is involved in autoimmune diseases, sepsis, and several cancers. However, the link between neutrophil heterogeneity and T-cell immunity in thyroid cancer is incompletely understood. We investigated the circulating neutrophil heterogeneity in 3 undifferentiated thyroid cancer (UTC), 14 differentiated thyroid cancer (DTC) (4 Stage IV, 10 Stage I-II), and healthy controls (n = 10) by transcriptomic data and cytometry. Participants with UTC had a significantly higher proportion of immature high-density neutrophils (HDN) and lower proportion of mature HDN in peripheral blood compared to DTC. The proportion of circulating PD-L1+ immature neutrophils were significantly increased in advanced cancer patients. Unsupervised analysis of transcriptomics data from circulating HDN revealed downregulation of innate immune response and T-cell receptor signaling pathway in cancer patients. Moreover, UTC patients revealed the upregulation of glycolytic process and glutamate receptor signaling pathway. Comparative analysis across tumor types and stages revealed the downregulation of various T-cell-related pathways, such as T-cell receptor signaling pathway and T-cell proliferation in advanced cancer patients. Moreover, the proportions of CD8+ and CD4+ T effector memory CD45RA+ (TEMRA) cells from peripheral blood were significantly decreased in UTC patients compared to DTC patients. Finally, we demonstrated that proportions of tumor-infiltrated neutrophils were increased and related with poor prognosis in advanced thyroid cancer using data from our RNA-seq and TCGA (The Cancer Genome Atlas) data. In conclusion, observed prevalence of circulating immature high-density neutrophils and their immunosuppressive features in undifferentiated thyroid cancers underscore the importance of understanding neutrophil dynamics in the context of tumor progression in thyroid cancer.

Extracellular domains of CARs reprogramme T cell metabolism without antigen stimulation.

Metabolism is an indispensable part of T cell proliferation, activation and exhaustion, yet the metabolism of chimeric antigen receptor (CAR)-T cells remains incompletely understood. CARs are composed of extracellular domains-often single-chain variable fragments (scFvs)-that determine ligand specificity and intracellular domains that trigger signalling following antigen binding. Here, we show that CARs differing only in the scFv variously reprogramme T cell metabolism. Even without exposure to antigens, some CARs increase proliferation and nutrient uptake in T cells. Using stable isotope tracers and mass spectrometry, we observed basal metabolic fluxes through glycolysis doubling and amino acid uptake overtaking anaplerosis in CAR-T cells harbouring a rituximab scFv, unlike other similar anti-CD20 scFvs. Disparate rituximab and 14G2a-based anti-GD2 CAR-T cells are similarly hypermetabolic and channel excess nutrients to nitrogen overflow metabolism. Modest overflow metabolism of CAR-T cells and metabolic compatibility between cancer cells and CAR-T cells are identified as features of efficacious CAR-T cell therapy.

Determination of Metabolic Fluxes by Deep Learning of Isotope Labeling Patterns.

Fluxomics offers a direct readout of metabolic state but relies on indirect measurement. Stable isotope tracers imprint flux-dependent isotope labeling patterns on metabolites we measure; however, the relationship between labeling patterns and fluxes remains elusive. Here we innovate a two-stage machine learning framework termed ML-Flux that streamlines metabolic flux quantitation from isotope tracing. We train machine learning models by simulating atom transitions across five universal metabolic models starting from 26 13C-glucose, 2H-glucose, and 13C-glutamine tracers within feasible flux space. ML-Flux employs deep-learning-based imputation to take variable measurements of labeling patterns as input and successive neural networks to convert the ensuing comprehensive labeling information into metabolic fluxes. Using ML-Flux with multi-isotope tracing, we obtain fluxes through central carbon metabolism that are comparable to those from a least-squares method but orders-of-magnitude faster. ML-Flux is deployed as a webtool to expand the accessibility of metabolic flux quantitation and afford actionable information on metabolism.

Toward Systems-Level Metabolic Analysis in Endocrine Disorders and Cancer.

Metabolism is a dynamic network of biochemical reactions that support systemic homeostasis amidst changing nutritional, environmental, and physical activity factors. The circulatory system facilitates metabolite exchange among organs, while the endocrine system finely tunes metabolism through hormone release. Endocrine disorders like obesity, diabetes, and Cushing's syndrome disrupt this balance, contributing to systemic inflammation and global health burdens. They accompany metabolic changes on multiple levels from molecular interactions to individual organs to the whole body. Understanding how metabolic fluxes relate to endocrine disorders illuminates the underlying dysregulation. Cancer is increasingly considered a systemic disorder because it not only affects cells in localized tumors but also the whole body, especially in metastasis. In tumorigenesis, cancer-specific mutations and nutrient availability in the tumor microenvironment reprogram cellular metabolism to meet increased energy and biosynthesis needs. Cancer cachexia results in metabolic changes to other organs like muscle, adipose tissue, and liver. This review explores the interplay between the endocrine system and systems-level metabolism in health and disease. We highlight metabolic fluxes in conditions like obesity, diabetes, Cushing's syndrome, and cancers. Recent advances in metabolomics, fluxomics, and systems biology promise new insights into dynamic metabolism, offering potential biomarkers, therapeutic targets, and personalized medicine.

Extracellular Domains of CAR Reprogram T-Cell Metabolism Without Antigen Stimulation.

Metabolism is an indispensable part of T-cell proliferation, activation, and exhaustion, yet the metabolism of chimeric antigen receptor (CAR)-T cells remains incompletely understood. CARs are comprised of extracellular domains that determine cancer specificity, often using single-chain variable fragments (scFvs), and intracellular domains that trigger signaling upon antigen binding. Here we show that CARs differing only in the scFv reprogram T-cell metabolism differently. Even in the absence of antigens, some CARs increase proliferation and nutrient uptake in T cells. Using stable isotope tracers and mass spectrometry, we observe basal metabolic fluxes through glycolysis doubling and amino acid uptake overtaking anaplerosis in CAR-T cells harboring rituximab scFv, unlike other similar anti-CD20 scFvs. Disparate rituximab and 14g2a-based anti-GD2 CAR-T cells are similarly hypermetabolic and channel excess nutrients to nitrogen overflow metabolism. Since CAR-dependent metabolic reprogramming alters cellular energetics, nutrient utilization, and proliferation, metabolic profiling should be an integral part of CAR-T cell development.

Integrative metabolic flux analysis reveals an indispensable dimension of phenotypes.

Complete understanding of a biological system requires quantitation of metabolic fluxes that reflect its dynamic state. Various analytical chemistry tools, enzyme-based probes, and microscopy enable flux measurement. However, any method alone falls short of comprehensive flux quantitation. Here we show that integrating these techniques results in a systems-level quantitative map of absolute metabolic fluxes that constitute an indispensable dimension of characterizing phenotypes. Stable isotopes, mass spectrometry, and NMR spectroscopy reveal relative pathway fluxes. Biochemical probes reveal the physical rate of environmental changes. FRET-based and SRS-based microscopy reveal targeted metabolite and chemical bond formation. These techniques are complementary and can be computationally integrated to reveal actionable information on metabolism. Integrative metabolic flux analysis using various quantitative techniques advances biotechnology and medicine.

MR-detectable metabolic biomarkers of response to mutant IDH inhibition in low-grade glioma.

Mutations in isocitrate dehydrogenase 1 (IDH1mut) are reported in 70-90% of low-grade gliomas and secondary glioblastomas. IDH1mut catalyzes the reduction of α-ketoglutarate (α-KG) to 2-hydroxyglutarate (2-HG), an oncometabolite which drives tumorigenesis. Inhibition of IDH1mut is therefore an emerging therapeutic approach, and inhibitors such as AG-120 and AG-881 have shown promising results in phase 1 and 2 clinical studies. However, detection of response to these therapies prior to changes in tumor growth can be challenging. The goal of this study was to identify non-invasive clinically translatable metabolic imaging biomarkers of IDH1mut inhibition that can serve to assess response. Methods: IDH1mut inhibition was confirmed using an enzyme assay and 1H- and 13C- magnetic resonance spectroscopy (MRS) were used to investigate the metabolic effects of AG-120 and AG-881 on two genetically engineered IDH1mut-expressing cell lines, NHAIDH1mut and U87IDH1mut. Results:1H-MRS indicated a significant decrease in steady-state 2-HG following treatment, as expected. This was accompanied by a significant 1H-MRS-detectable increase in glutamate. However, other metabolites previously linked to 2-HG were not altered. 13C-MRS also showed that the steady-state changes in glutamate were associated with a modulation in the flux of glutamine to both glutamate and 2-HG. Finally, hyperpolarized 13C-MRS was used to show that the flux of α-KG to both glutamate and 2-HG was modulated by treatment. Conclusion: In this study, we identified potential 1H- and 13C-MRS-detectable biomarkers of response to IDH1mut inhibition in gliomas. Although further studies are needed to evaluate the utility of these biomarkers in vivo, we expect that in addition to a 1H-MRS-detectable drop in 2-HG, a 1H-MRS-detectable increase in glutamate, as well as a hyperpolarized 13C-MRS-detectable change in [1-13C] α-KG flux, could serve as metabolic imaging biomarkers of response to treatment.

MET-GRB2 Signaling-Associated Complexes Correlate with Oncogenic MET Signaling and Sensitivity to MET Kinase Inhibitors.

Purpose: Targeting MET in cancer is hampered by lack of diagnostics that accurately reflect high MET signaling and dependence. We hypothesized that assays reflecting MET signaling associated protein complexes could redefine tumors dependent on MET and could add additional precision beyond genomic assessments.Experimental Design: We used biochemical approaches, cellular viability studies, and proximity ligation assays to assess MET dependence. We examined MET signaling complexes in lung cancer patient specimens (N = 406) and patient-derived xenograft (PDX) models of solid tumors (N = 308). We evaluated response to crizotinib in a MET-amplified cohort of PDX models of lung cancer (N = 6) and provide a case report of a lung cancer patient harboring a Δexon14 MET splice variant.Results: We found the interaction of MET with the adaptor protein GRB2 is necessary for oncogenic survival signaling by MET. MET-GRB2 complexes were identified only within MET-amplified PDX models and patient specimens but exhibit substantial variability. Lack of MET-GRB2 complexes was associated with lack of response to MET TKI in cell lines and PDX models. Presence of MET-GRB2 complexes can further subtype tumors with Δexon14 MET splice variants. Presence of these complexes correlated with response to crizotinib in one patient with Δexon14 MET lacking MET gene amplification.Conclusions: Proximity assays measuring MET-GRB2 signaling complexes provide novel insights into MET-mediated signaling and could complement current clinical genomics-based assay platforms. Clin Cancer Res; 23(22); 7084-96. ©2017 AACR.

Implantation of Autologous Adipose Tissue-Derived Mesenchymal Stem Cells in Foot Fat Pad in Rats.

BACKGROUND: The foot fat pad (FFP) bears body weight and may become a source of foot pain during aging. This study investigated the regenerative effects of autologous adipose tissue-derived mesenchymal stem cells (AT-MSCs) in the FFP of rats. METHODS: Fat tissue was harvested from a total of 30 male Sprague-Dawley rats for isolation of AT-MSCs. The cells were cultured, adipogenic differentiation was induced for 1 week, and the AT-MSCs were labeled with fluorescent dye before injection. AT-MSCs (5 × 10(4) in 50 µL of saline) were injected into the second infradigital pad in the right hindfoot of the rat of origin. Saline only (50 µL) was injected into the corresponding fat pad in the left hind paw of each rat. Rats (n = 10) were euthanized at 1, 2, and 3 weeks, and the second infradigital fat pads were dissected for histologic examination. RESULTS: The fluorescence-labeled AT-MSCs were present in the foot pads throughout the 3-week experimental period. On histologic testing, the area of fat pad units (FPUs) in the fat pads that received AT-MSC injections was greater than that in the control fat pads. Although the thickness of septae was not changed by AT-MSC injections, the density of elastic fibers in the septae was increased in the fat pads with implanted AT-MSCs. CONCLUSION: In this short-term study, the implanted AT-MSCs largely survived and might have stimulated the expansion of individual FPUs and increased the density of elastic fibers in the FFP in this rat model. CLINICAL RELEVANCE: These data support the development of stem cell therapies for age-associated degeneration in FFP in humans.