mTORC1 regulates cell survival under glucose starvation through 4EBP1/2-mediated translational reprogramming of fatty acid metabolism.

Energetic stress compels cells to evolve adaptive mechanisms to adjust their metabolism. Inhibition of mTOR kinase complex 1 (mTORC1) is essential for cell survival during glucose starvation. How mTORC1 controls cell viability during glucose starvation is not well understood. Here we show that the mTORC1 effectors eukaryotic initiation factor 4E binding proteins 1/2 (4EBP1/2) confer protection to mammalian cells and budding yeast under glucose starvation. Mechanistically, 4EBP1/2 promote NADPH homeostasis by preventing NADPH-consuming fatty acid synthesis via translational repression of Acetyl-CoA Carboxylase 1 (ACC1), thereby mitigating oxidative stress. This has important relevance for cancer, as oncogene-transformed cells and glioma cells exploit the 4EBP1/2 regulation of ACC1 expression and redox balance to combat energetic stress, thereby supporting transformation and tumorigenicity in vitro and in vivo. Clinically, high EIF4EBP1 expression is associated with poor outcomes in several cancer types. Our data reveal that the mTORC1-4EBP1/2 axis provokes a metabolic switch essential for survival during glucose starvation which is exploited by transformed and tumor cells.

Nanoformulation of dasatinib cannot overcome therapy resistance of pancreatic cancer cells with low LYN kinase expression.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is one of the most difficult to treat tumors. The Src (sarcoma) inhibitor dasatinib (DASA) has shown promising efficacy in preclinical studies of PDAC. However, clinical confirmation could not be achieved. Overall, our aim was to deliver arguments for the possible reinitiating clinical testing of this compound in a biomarker-stratifying therapy trial for PDAC patients. We tested if the nanofunctionalization of DASA can increase the drug efficacy and whether certain Src members can function as clinical predictive biomarkers. METHODS: Methods include manufacturing of poly(vinyl alcohol) stabilized gold nanoparticles and their drug loading, dynamic light scattering, transmission electron microscopy, thermogravimetric analysis, Zeta potential measurement, sterile human cell culture, cell growth quantification, accessing and evaluating transcriptome and clinical data from molecular tumor dataset TCGA, as well as various statistical analyses. RESULTS: We generated homo-dispersed nanofunctionalized DASA as an AuNP@PVA-DASA conjugate. The composite did not enhance the anti-growth effect of DASA on PDAC cell lines. The cell model with high LYN expression showed the strongest response to the therapy. We confirm deregulated Src kinetome activity as a prevalent feature of PDAC by revealing mRNA levels associated with higher malignancy grade of tumors. BLK (B lymphocyte kinase) expression predicts shorter overall survival of diabetic PDAC patients. CONCLUSIONS: Nanofunctionalization of DASA needs further improvement to overcome the therapy resistance of PDAC. LYN mRNA is augmented in tumors with higher malignancy and can serve as a predictive biomarker for the therapy resistance of PDAC cells against DASA. Studying the biological roles of BLK might help to identify underlying molecular mechanisms associated with PDAC in diabetic patients.

Integrating a microRNA signature as a liquid biopsy-based tool for the early diagnosis and prediction of potential therapeutic targets in pancreatic cancer.

INTRODUCTION: Pancreatic cancer is a highly aggressive cancer, and early diagnosis significantly improves patient prognosis due to the early implementation of curative-intent surgery. Our study aimed to implement machine-learning algorithms to aid in early pancreatic cancer diagnosis based on minimally invasive liquid biopsies. MATERIALS AND METHODS: The analysis data were derived from nine public pancreatic cancer miRNA datasets and two sequencing datasets from 26 pancreatic cancer patients treated in our medical center, featuring small RNAseq data for patient-matched tumor and non-tumor samples and serum. Upon batch-effect removal, systematic analyses for differences between paired tissue and serum samples were performed. The robust rank aggregation (RRA) algorithm was used to reveal feature markers that were co-expressed by both sample types. The repeatability and real-world significance of the enriched markers were then determined by validating their expression in our patients' serum. The top candidate markers were used to assess the accuracy of predicting pancreatic cancer through four machine learning methods. Notably, these markers were also applied for the identification of pancreatic cancer and pancreatitis. Finally, we explored the clinical prognostic value, candidate targets and predict possible regulatory cell biology mechanisms involved. RESULTS: Our multicenter analysis identified hsa-miR-1246, hsa-miR-205-5p, and hsa-miR-191-5p as promising candidate serum biomarkers to identify pancreatic cancer. In the test dataset, the accuracy values of the prediction model applied via four methods were 94.4%, 84.9%, 82.3%, and 83.3%, respectively. In the real-world study, the accuracy values of this miRNA signatures were 82.3%, 83.5%, 79.0%, and 82.2. Moreover, elevated levels of these miRNAs were significant indicators of advanced disease stage and allowed the discrimination of pancreatitis from pancreatic cancer with an accuracy rate of 91.5%. Elevated expression of hsa-miR-205-5p, a previously undescribed blood marker for pancreatic cancer, is associated with negative clinical outcomes in patients. CONCLUSION: A panel of three miRNAs was developed with satisfactory statistical and computational performance in real-world data. Circulating hsa-miRNA 205-5p serum levels serve as a minimally invasive, early detection tool for pancreatic cancer diagnosis and disease staging and might help monitor therapy success.

Seven oxidative stress-related genes predict the prognosis of hepatocellular carcinoma.

Predicting the prognosis of hepatocellular carcinoma (HCC) is a major medical challenge and of guiding significance for treatment. This study explored the actual relevance of RNA expression in predicting HCC prognosis. Cox's multiple regression was used to establish a risk score staging classification and to predict the HCC patients' prognosis on the basis of data in the Cancer Genome Atlas (TCGA). We screened seven gene biomarkers related to the prognosis of HCC from the perspective of oxidative stress, including Alpha-Enolase 1(ENO1), N-myc downstream-regulated gene 1 (NDRG1), nucleophosmin (NPM1), metallothionein-3, H2A histone family member X, Thioredoxin reductase 1 (TXNRD1) and interleukin 33 (IL-33). Among them we measured the expression of ENO1, NGDP1, NPM1, TXNRD1 and IL-33 to investigate the reliability of the multi-index prediction. The first four markers' expressions increased successively in the paracellular tissues, the hepatocellular carcinoma samples (from patients with better prognosis) and the hepatocellular carcinoma samples (from patients with poor prognosis), while IL-33 showed the opposite trend. The seven genes increased the sensitivity and specificity of the predictive model, resulting in a significant increase in overall confidence. Compared with the patients with higher-risk scores, the survival rates with lower-risk scores are significantly increased. Risk score is more accurate in predicting the prognosis HCC patients than other clinical factors. In conclusion, we use the Cox regression model to identify seven oxidative stress-related genes, investigate the reliability of the multi-index prediction, and develop a risk staging model for predicting the prognosis of HCC patients and guiding precise treatment strategy.

Rapid, label-free classification of glioblastoma differentiation status combining confocal Raman spectroscopy and machine learning.

Label-free identification of tumor cells using spectroscopic assays has emerged as a technological innovation with a proven ability for rapid implementation in clinical care. Machine learning facilitates the optimization of processing and interpretation of extensive data, such as various spectroscopy data obtained from surgical samples. The here-described preclinical work investigates the potential of machine learning algorithms combining confocal Raman spectroscopy to distinguish non-differentiated glioblastoma cells and their respective isogenic differentiated phenotype by means of confocal ultra-rapid measurements. For this purpose, we measured and correlated modalities of 1146 intracellular single-point measurements and sustainingly clustered cell components to predict tumor stem cell existence. By further narrowing a few selected peaks, we found indicative evidence that using our computational imaging technology is a powerful approach to detect tumor stem cells in vitro with an accuracy of 91.7% in distinct cell compartments, mainly because of greater lipid content and putative different protein structures. We also demonstrate that the presented technology can overcome intra- and intertumoral cellular heterogeneity of our disease models, verifying the elevated physiological relevance of our applied disease modeling technology despite intracellular noise limitations for future translational evaluation.

Concept of a fully-implantable system to monitor tumor recurrence.

Current treatment for glioblastoma includes tumor resection followed by radiation, chemotherapy, and periodic post-operative examinations. Despite combination therapies, patients face a poor prognosis and eventual recurrence, which often occurs at the resection site. With standard MRI imaging surveillance, histologic changes may be overlooked or misinterpreted, leading to erroneous conclusions about the course of adjuvant therapy and subsequent interventions. To address these challenges, we propose an implantable system for accurate continuous recurrence monitoring that employs optical sensing of fluorescently labeled cancer cells and is implanted in the resection cavity during the final stage of tumor resection. We demonstrate the feasibility of the sensing principle using miniaturized system components, optical tissue phantoms, and porcine brain tissue in a series of experimental trials. Subsequently, the system electronics are extended to include circuitry for wireless energy transfer and power management and verified through electromagnetic field, circuit simulations and test of an evaluation board. Finally, a holistic conceptual system design is presented and visualized. This novel approach to monitor glioblastoma patients is intended to early detect recurrent cancerous tissue and enable personalization and optimization of therapy thus potentially improving overall prognosis.

Oxidative stress genes define two subtypes of triple-negative breast cancer with prognostic and therapeutic implications.

Introduction: Oxidative stress (OS)-related genes have been confirmed to be closely related to the prognosis of triple-negative breast cancer (TNBC) patients; despite this fact, there is still a lack of TNBC subtype strategies based on this gene guidance. Here, we aimed to explore OS-related subtypes and their prognostic value in TNBC. Methods: Data from The Cancer Genome Atlas (TCGA)-TNBC and Sequence Read Archive (SRA) (SRR8518252) databases were collected, removing batch effects using a combat method before analysis. Consensus clustering analysis identified two OS subtypes (clusters A and B), with cluster A showing a better prognosis. Immune infiltration characteristics were analyzed using ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) algorithms, revealing higher ImmuneScore and ESTIMATEscore in cluster A. Tumor-suppressive immune cells, human leukocyte antigen (HLA) genes, and three immune inhibitors were more prevalent in cluster A. Results: An eight-gene signature, derived from differentially expressed genes, was developed and validated as an independent risk factor for TNBC. A nomogram combining the risk score and clinical variables accurately predicted patient outcomes. Finally, we also validated the classification effect of subtypes using hub markers of each subtype in the test dataset. Conclusion: Our study reveals distinct molecular clusters based on OS-related genes to better clarify the reactive oxygen species (ROS)-mediated progression and the crosstalk between the ROS and tumor microenvironment (TME) in this heterogenetic disease, and construct a risk prognostic model which could provide more support for clinical treatment decisions.

Mendelian Randomization Analyses of Chronic Immune-Mediated Diseases, Circulating Inflammatory Biomarkers, and Cytokines in Relation to Liver Cancer.

Liver cancer is closely linked to chronic inflammation. While observational studies have reported positive associations between extrahepatic immune-mediated diseases and systemic inflammatory biomarkers and liver cancer, the genetic association between these inflammatory traits and liver cancer remains elusive and merits further investigation. We conducted a two-sample Mendelian randomization (MR) analysis, using inflammatory traits as exposures and liver cancer as the outcome. The genetic summary data of both exposures and outcome were retrieved from previous genome-wide association studies (GWAS). Four MR methods, including inverse-variance-weighted (IVW), MR-Egger regression, weighted-median, and weighted-mode methods, were employed to examine the genetic association between inflammatory traits and liver cancer. Nine extrahepatic immune-mediated diseases, seven circulating inflammatory biomarkers, and 187 inflammatory cytokines were analyzed in this study. The IVW method suggested that none of the nine immune-mediated diseases were associated with the risk of liver cancer, with odds ratios of 1.08 (95% CI 0.87-1.35) for asthma, 0.98 (95% CI 0.91-1.06) for rheumatoid arthritis, 1.01 (95% CI 0.96-1.07) for type 1 diabetes, 1.01 (95% CI 0.98-1.03) for psoriasis, 0.98 (95% CI 0.89-1.08) for Crohn's disease, 1.02 (95% CI 0.91-1.13) for ulcerative colitis, 0.91 (95% CI 0.74-1.11) for celiac disease, 0.93 (95% CI 0.84-1.05) for multiple sclerosis, and 1.05 (95% CI 0.97-1.13) for systemic lupus erythematosus. Similarly, no significant association was found between circulating inflammatory biomarkers and cytokines and liver cancer after correcting for multiple testing. The findings were consistent across all four MR methods used in this study. Our findings do not support a genetic association between extrahepatic inflammatory traits and liver cancer. However, larger-scale GWAS summary data and more genetic instruments are needed to confirm these findings.

Transient receptor potential channels as predictive marker and potential indicator of chemoresistance in colon cancer.

Transient receptor potential (TRP) channels are strongly associated with colon cancer development and progression. This study leveraged a multivariate Cox regression model on publicly available datasets to construct a TRP channels-associated gene signature, with further validation of signature in real world samples from our hospital treated patient samples. Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curves were employed to evaluate this gene signature's predictive accuracy and robustness in both training and testing cohorts, respectively. Additionally, the study utilized the CIBERSORT algorithm and single-sample gene set enrichment analysis to explore the signature's immune infiltration landscape and underlying functional implications. The support vector machine algorithm was applied to evaluate the signature's potential in predicting chemotherapy outcomes. The findings unveiled a novel three TRP channels-related gene signature (MCOLN1, TRPM5, and TRPV4) in colon adenocarcinoma (COAD). The ROC and K-M survival curves in the training dataset (AUC = 0.761; p = 1.58e-05) and testing dataset (AUC = 0.699; p = 0.004) showed the signature's robust predictive capability for the overall survival of COAD patients. Analysis of the immune infiltration landscape associated with the signature revealed higher immune infiltration, especially an increased presence of M2 macrophages, in high-risk group patients compared to their low-risk counterparts. High-risk score patients also exhibited potential responsiveness to immune checkpoint inhibitor therapy, evident through increased CD86 and PD-1 expression profiles. Moreover, the TRPM5 gene within the signature was highly expressed in the chemoresistance group (p = 0.00095) and associated with poor prognosis (p = 0.036) in COAD patients, highlighting its role as a hub gene of chemoresistance. Ultimately, this signature emerged as an independent prognosis factor for COAD patients (p = 6.48e-06) and expression of model gene are validated by public data and real-world patients. Overall, this bioinformatics study provides valuable insights into the prognostic implications and potential chemotherapy resistance mechanisms associated with TRPs-related genes in colon cancer.

MYCBP2 expression correlated with inflammatory cell infiltration and prognosis immunotherapy in thyroid cancer patients.

Introduction: Immune checkpoint inhibitors (ICIs) have shown promising results for the treatment of multiple cancers. ICIs and related therapies may also be useful for the treatment of thyroid cancer (TC). In TC, Myc binding protein 2 (MYCBP2) is correlated with inflammatory cell infiltration and cancer prognosis. However, the relationship between MYCBP2 expression and ICI efficacy in TC patients is unclear. Methods: We downloaded data from two TC cohorts, including transcriptomic data and clinical prognosis data. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to predict the efficacy of ICIs in TC patients. MCPcounter, xCell, and quanTIseq were used to calculate immune cell infiltration scores. Gene set enrichment analysis (GSEA) and single sample GSEA (ssGSEA) were used to evaluate signaling pathway scores. Immunohistochemical (IHC) analysis and clinical follow up was used to identify the MYCBP2 protein expression status in patients and associated with clinical outcome. Results: A higher proportion of MYCBP2-high TC patients were predicted ICI responders than MYCBP2-low patients. MYCBP2-high patients also had significantly increased infiltration of CD8+ T cells, cytotoxic lymphocytes (CTLs), B cells, natural killer (NK) cells and dendritic cells (DC)s. Compared with MYCBP2-low patients, MYCBP2-high patients had higher expression of genes associated with B cells, CD8+ T cells, macrophages, plasmacytoid dendritic cells (pDCs), antigen processing and presentation, inflammatory stimulation, and interferon (IFN) responses. GSEA and ssGSEA also showed that MYCBP2-high patients had significantly increased activity of inflammatory factors and signaling pathways associated with immune responses.In addiation, Patients in our local cohort with high MYCBP2 expression always had a better prognosis and greater sensitivity to therapy while compared to patients with low MYCBP2 expression after six months clinic follow up. Conclusions: In this study, we found that MYCBP2 may be a predictive biomarker for ICI efficacy in TC patients. High MYCBP2 expression was associated with significantly enriched immune cell infiltration. MYCBP2 may also be involved in the regulation of signaling pathways associated with anti-tumor immune responses or the production of inflammatory factors.

COL10A1 allows stratification of invasiveness of colon cancer and associates to extracellular matrix and immune cell enrichment in the tumor parenchyma.

Background: Treatment options for metastatic colorectal cancer (CRC) are mostly ineffective. We present new evidence that tumor tissue collagen type X alpha 1 (COL10A1) is a relevant candidate biomarker to improve this dilemma. Methods: Several public databases had been screened to observe COL10A1 expression in transcriptome levels with cell lines and tissues. Protein interactions and alignment to changes in clinical parameters and immune cell invasion were performed, too. We also used algorithms to build a novel COL10A1-related immunomodulator signature. Various wet-lab experiments were conducted to quantify COL10A1 protein and transcript expression levels in disease and control cell models. Results: COL10A1 mRNA levels in tumor material is clinical and molecular prognostic, featuring upregulation compared to non-cancer tissue, increase with histomorphological malignancy grading of the tumor, elevation in tumors that invade perineural areas, or lymph node invasion. Transcriptomic alignment noted a strong positive correlation of COL10A1 with transcriptomic signature of cancer-associated fibroblasts (CAFs) and populations of the immune compartment, namely, B cells and macrophages. We verified those findings in functional assays showing that COL10A1 are decreased in CRC cells compared to fibroblasts, with strongest signal in the cell supernatant of the cells. Conclusion: COL10A1 abundance in CRC tissue predicts metastatic and immunogenic properties of the disease. COL10A1 transcription may mediate tumor cell interaction with its stromal microenvironment.

A Novel Extracellular Matrix Gene-Based Prognostic Model to Predict Overall Survive in Patients With Glioblastoma.

Background: Glioblastoma (GBM), one of the most prevalent brain tumor types, is correlated with an extremely poor prognosis. The extracellular matrix (ECM) genes could activate many crucial pathways that facilitate tumor development. This study aims to provide online models to predict GBM survival by ECM genes. Methods: The associations of ECM genes with the prognosis of GBM were analyzed, and the significant prognosis-related genes were used to develop the ECM index in the CGGA dataset. Furthermore, the ECM index was then validated on three datasets, namely, GSE16011, TCGA-GBM, and GSE83300. The prognosis difference, differentially expressed genes, and potential drugs were obtained. Multiple machine learning methods were selected to construct the model to predict the survival status of GBM patients at 6, 12, 18, 24, 30, and 36 months after diagnosis. Results: Five ECM gene signatures (AEBP1, F3, FLNC, IGFBP2, and LDHA) were recognized to be associated with the prognosis. GBM patients were divided into high- and low-ECM index groups with significantly different overall survival rates in four datasets. High-ECM index patients exhibited a worse prognosis than low-ECM index patients. Four small molecules (podophyllotoxin, lasalocid, MG-262, and nystatin) that might reduce GBM development were predicted by the Cmap dataset. In the independent dataset (GSE83300), the maximum values of prediction accuracy at 6, 12, 18, 24, 30, and 36 months were 0.878, 0.769, 0.748, 0.720, 0.705, and 0.868, respectively. These machine learning models were provided on a publicly accessible, open-source website (https://ospg.shinyapps.io/OSPG/). Conclusion: In summary, our findings indicated that ECM genes were prognostic indicators for patient survival. This study provided an online server for the prediction of survival curves of GBM patients.

In Vitro Validation of the Therapeutic Potential of Dendrimer-Based Nanoformulations against Tumor Stem Cells.

Tumor cells with stem cell properties are considered to play major roles in promoting the development and malignant behavior of aggressive cancers. Therapeutic strategies that efficiently eradicate such tumor stem cells are of highest clinical need. Herein, we performed the validation of the polycationic phosphorus dendrimer-based approach for small interfering RNAs delivery in in vitro stem-like cells as models. As a therapeutic target, we chose Lyn, a member of the Src family kinases as an example of a prominent enzyme class widely discussed as a potent anti-cancer intervention point. Our selection is guided by our discovery that Lyn mRNA expression level in glioma, a class of brain tumors, possesses significant negative clinical predictive value, promoting its potential as a therapeutic target for future molecular-targeted treatments. We then showed that anti-Lyn siRNA, delivered into Lyn-expressing glioma cell model reduces the cell viability, a fact that was not observed in a cell model that lacks Lyn-expression. Furthermore, we have found that the dendrimer itself influences various parameters of the cells such as the expression of surface markers PD-L1, TIM-3 and CD47, targets for immune recognition and other biological processes suggested to be regulating glioblastoma cell invasion. Our findings prove the potential of dendrimer-based platforms for therapeutic applications, which might help to eradicate the population of cancer cells with augmented chemotherapy resistance. Moreover, the results further promote our functional stem cell technology as suitable component in early stage drug development.

Artificial Ovary for Young Female Breast Cancer Patients.

In recent decades, there has been increasing attention toward the quality of life of breast cancer (BC) survivors. Meeting the growing expectations of fertility preservation and the generation of biological offspring remains a great challenge for these patients. Conventional strategies for fertility preservation such as oocyte and embryo cryopreservation are not suitable for prepubertal cancer patients or in patients who need immediate cancer therapy. Ovarian tissue cryopreservation (OTC) before anticancer therapy and autotransplantation is an alternative option for these specific indications but has a risk of retransplantation malignant cells. An emerging strategy to resolve these issues is by constructing an artificial ovary combined with stem cells, which can support follicle proliferation and ensure sex hormone secretion. This promising technique can meet both demands of improving the quality of life and meanwhile fulfilling their expectation of biological offspring without the risk of cancer recurrence.

Sensory Ion Channel Candidates Inform on the Clinical Course of Pancreatic Cancer and Present Potential Targets for Repurposing of FDA-Approved Agents.

Background: Transient receptor potential channels (TRPs) have been demonstrated to take on functions in pancreatic adenocarcinoma (PAAD) biology. However, little data are available that validate the potential of TRP in a clinical translational setting. Methods: A TRPs-related gene signature was constructed based on the Cox regression using a TCGA-PAAD cohort and receiver operating characteristic (ROC) was used to evaluate the predictive ability of this model. Core genes of the signature were screened by a protein-to-protein interaction (PPI) network, and expression validated by two independent datasets. The mutation analysis and gene set enrichment analysis (GSEA) were conducted. Virtual interventions screening was performed to discover substance candidates for the identified target genes. Results: A four TRPs-related gene signature, which contained MCOLN1, PKD1, TRPC3, and TRPC7, was developed and the area under the curve (AUC) was 0.758. Kaplan−Meier analysis revealed that patients with elevated signature score classify as a high-risk group featuring significantly shorter recurrence free survival (RFS) time, compared to the low-risk patients (p < 0.001). The gene prediction model also had a good predictive capability for predicting shortened overall survival (OS) and disease-specific survival (DSS) (AUC = 0.680 and AUC = 0.739, respectively). GSEA enrichment revealed the core genes of the signature, TRPC3 and TRPC7, were involved in several cancer-related pathways. TRPC3 mRNA is elevated in cancer tissue compared to control tissue and augmented in tumors with lymph node invasion compared to tumors without signs of lymph node invasion. Virtual substance screening of FDA approved compounds indicates that four small molecular compounds might be potentially selective not only for TRPC3 protein but also as a potential binding partner to TRPC7 protein. Conclusions: Our computational pipeline constructed a four TRP-related gene signature that enables us to predict clinical prognostic value of hitherto unrecognized biomarkers for PAAD. Sensory ion channels TRPC3 and TRPC7 could be the potential therapeutic targets in pancreatic cancer and TRPC3 might be involved in dysregulating mitochondrial functions during PAAD genesis.

Progenitor cells derived from gene-engineered human induced pluripotent stem cells as synthetic cancer cell alternatives for in vitro pharmacology.

Limitations in genetic stability and recapitulating accurate physiological disease properties challenge the utility of patient-derived (PD) cancer models for reproducible and translational research. A portfolio of isogenic human induced pluripotent stem cells (hiPSCs) with different pan-cancer relevant oncoprotein signatures followed by differentiation into lineage-committed progenitor cells was genetically engineered. Characterization on molecular and biological level validated successful stable genetic alterations in pluripotency state as well as upon differentiation to prove the functionality of our approach. Meanwhile proposing core molecular networks possibly involved in early dysregulation of stem cell homeostasis, the application of our cell systems in comparative substance testing indicates the potential for cancer research such as identification of augmented therapy resistance of stem cells in response to activation of distinct oncogenic signatures.

Multifaceted Analyses of Isolated Mitochondria Establish the Anticancer Drug 2-Hydroxyoleic Acid as an Inhibitor of Substrate Oxidation and an Activator of Complex IV-Dependent State 3 Respiration.

The synthetic fatty acid 2-hydroxyoleic acid (2OHOA) has been extensively investigated as a cancer therapy mainly based on its regulation of membrane lipid composition and structure, activating various cell fate pathways. We discovered, additionally, that 2OHOA can uncouple oxidative phosphorylation, but this has never been demonstrated mechanistically. Here, we explored the effect of 2OHOA on mitochondria isolated by ultracentrifugation from U118MG glioblastoma cells. Mitochondria were analyzed by shotgun lipidomics, molecular dynamic simulations, spectrophotometric assays for determining respiratory complex activity, mass spectrometry for assessing beta oxidation and Seahorse technology for bioenergetic profiling. We showed that the main impact of 2OHOA on mitochondrial lipids is their hydroxylation, demonstrated by simulations to decrease co-enzyme Q diffusion in the liquid disordered membranes embedding respiratory complexes. This decreased co-enzyme Q diffusion can explain the inhibition of disjointly measured complexes I-III activity. However, it doesn't explain how 2OHOA increases complex IV and state 3 respiration in intact mitochondria. This increased respiration probably allows mitochondrial oxidative phosphorylation to maintain ATP production against the 2OHOA-mediated inhibition of glycolytic ATP production. This work correlates 2OHOA function with its modulation of mitochondrial lipid composition, reflecting both 2OHOA anticancer activity and adaptation to it by enhancement of state 3 respiration.

Canonical WNT pathway inhibition reduces ATP synthesis rates in glioblastoma stem cells.

BACKGROUND: The conserved stem cell signaling network canonical Wingless (WNT) plays important roles in development and disease. Aberrant activation of this pathway has been linked to tumor progression and resistance to therapy. Industry and academia have substantially invested in developing substances, which can efficiently and specifically block the WNT signaling pathway. However, a clear clinical proof of the efficacy of this approach is still missing. Studies on the metabolomics dysregulation of cancer cells have led to innovations in oncological diagnostics. In addition, modulation of cancer cell metabolome is at the base of promising clinical oncology trials currently underway. While onco-protein activation can have profound metabolic outcomes, the involvement of stem cell signals, such as the WNT pathway, in tumor cell metabolomics is yet insufficiently characterized. MATERIAL AND METHODS: We determined live cell metabolism and bioenergetics in pathophysiological relevant, WNT-dependent glioblastoma stem cell (GSC) models. We quantified those parameters in cells with canonical WNT activity and in isogenic cells where WNT activity had been inhibited by short hairpin RNA against ß-catenin. Furthermore, we applied computational analysis of RNA sequencing to verify our functional findings in independent GSCs cohorts. RESULTS: The investigated collection of disease models allows the separation in tumors with low, moderate and high base line metabolic activity. Suppression of canonical WNT signaling led to significant reduction of total, mitochondrial, and glycolytic ATP production rates. Elevated canonical WNT transcription signature in GSCs positively correlated with transcription levels of mitochondrial ATP synthesis, whereas non-canonical WNT gene expression signature did not. CONCLUSION: The applied disease modeling technology allows the recapitulation of inter-tumoral heterogeneous metabolic properties of glioblastoma. Our data show for the first time that inhibition of canonical WNT signaling in alive GSCs functionally correlates with energy inhibition and glucose homeostasis. As this correlation occurs in GSCs from different transcriptional or epigenetic transcriptional subtypes, our results suggest that developing therapies directed against glycolysis/ATP-synthesis may be a promising strategy to overcome therapy resistance due to inter-tumoral heterogeneity and offers starting point to impair downstream signal WNT.