Metabolic and protein interaction sub-networks controlling the proliferation rate of cancer cells and their impact on patient survival

Induction of ferroptosis and apoptosis in endometrial cancer cells by dihydroisotanshinone I

Danshen, also known as Salvia miltiorrhiza, is a medicinal herb used in traditional Chinese medicine. Its potential impact on endometrial cancer has not been thoroughly investigated. This study aimed to examine the effect of dihydroisotanshinone I (DT), a compound found in Danshen, on the viability of ARK1 and ARK2 endometrial cancer cells and its mechanisms. The results showed that 10 μM DT inhibited cell viability of ARK1 and ARK2 cells by inducing apoptosis and ferroptosis, which was achieved by blocking the expression of GPX4. In vivo experiments using a xenograft nude mouse model indicated that DT treatment significantly reduced tumor volume without causing any adverse effects. These findings suggest that DT may be a potential therapeutic agent for inhibiting endometrial cancer cell viability, but further research is needed to confirm these results.

Towards the routine use of in silico screenings for drug discovery using metabolic modelling

Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.

Fatty Acid Synthesis and Degradation Interplay to Regulate the Oxidative Stress in Cancer Cells

Both cytosolic fatty acid synthesis (FAS) and mitochondrial fatty acid oxidation (FAO) have been shown to play a role in the survival and proliferation of cancer cells. This study aimed to confirm experimentally whether FAS and FAO coexist in breast cancer cells (BCC). By feeding cells with ¹³C-labeled glutamine and measuring labeling patterns of TCA intermediates, it was possible to show that part of the cytosolic acetyl-CoA used in lipid synthesis is also fed back into the mitochondrion via fatty acid degradation. This results in the transfer of reductive potential from the cytosol (in the form of NADPH) to the mitochondrion (in the form of NADH and FADH₂). The hypothesized mechanism was further confirmed by blocking FAS and FAO with siRNAs. Exposure to staurosporine (which induces ROS production) resulted in the disruption of simultaneous FAS and FAO, which could be explained by NADPH depletion.

Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows

In order to choose optimal personalized anticancer treatments, transcriptomic data should be analyzed within the frame of biological networks. The best known human biological network (in terms of the interactions between its different components) is metabolism. Cancer cells have been known to have specific metabolic features for a long time and currently there is a growing interest in characterizing new cancer specific metabolic hallmarks. In this article it is presented a method to find personalized therapeutic windows using RNA-seq data and Genome Scale Metabolic Models. This method is implemented in the python library, pyTARG. Our predictions showed that the most anticancer selective (affecting 27 out of 34 considered cancer cell lines and only 1 out of 6 healthy mesenchymal stem cell lines) single metabolic reactions are those involved in cholesterol biosynthesis. Excluding cholesterol biosynthesis, all the considered cell lines can be selectively affected by targeting different combinations (from 1 to 5 reactions) of only 18 metabolic reactions, which suggests that a small subset of drugs or siRNAs combined in patient specific manners could be at the core of metabolism based personalized treatments.

Genome scale metabolic models as tools for drug design and personalized medicine

In this work we aim to show how Genome Scale Metabolic Models (GSMMs) can be used as tools for drug design. By comparing the chemical structures of human metabolites (obtained using their KEGG indexes) and the compounds contained in the DrugBank database, we have observed that compounds showing Tanimoto scores higher than 0.9 with a metabolite, are 29.5 times more likely to bind the enzymes metabolizing the considered metabolite, than ligands chosen randomly. By using RNA-seq data to constrain a human GSMM it is possible to obtain an estimation of its distribution of metabolic fluxes and to quantify the effects of restraining the rate of chosen metabolic reactions (for example using a drug that inhibits the enzymes catalyzing the mentioned reactions). This method allowed us to predict the differential effects of lipoamide analogs on the proliferation of MCF7 (a breast cancer cell line) and ASM (airway smooth muscle) cells respectively. These differential effects were confirmed experimentally, which provides a proof of concept of how human GSMMs could be used to find therapeutic windows against cancer. By using RNA-seq data of 34 different cancer cell lines and 26 healthy tissues, we assessed the putative anticancer effects of the compounds in DrugBank which are structurally similar to human metabolites. Among other results it was predicted that the mevalonate pathway might constitute a good therapeutic window against cancer proliferation, due to the fact that most cancer cell lines do not express the cholesterol transporter NPC1L1 and the lipoprotein lipase LPL, which makes them rely on the mevalonate pathway to obtain cholesterol.

Transcriptional hallmarks of cancer cell lines reveal an emerging role of branched chain amino acid catabolism

A comparative analysis between cancer cell lines and healthy dividing cells was performed using data (289 microarrays and 50 RNA-seq samples) from 100 different cancer cell lines and 6 types of healthy stem cells. The analysis revealed two large-scale transcriptional events that characterize cancer cell lines. The first event was a large-scale up-regulation pattern associated to epithelial-mesenchymal transition, putatively driven by the interplay of the SP1 transcription factor and the canonical Wnt signaling pathway; the second event was the failure to overexpress a diverse set of genes coding membrane and extracellular proteins. This failure is putatively caused by a lack of activity of the AP-1 complex. It was also shown that the epithelial-mesenchymal transition was associated with the up-regulation of 5 enzymes involved in the degradation of branched chain amino acids. The suitability of silencing one of this enzymes (branched chain amino acid transaminase 2; BCAT2) with therapeutic effects was tested experimentally on the breast cancer cell line MCF-7 and primary cell culture of breast tumor (BCC), leading to lower cell proliferation. The silencing of BCAT2 did not have any significant effect on ASM and MCF10A cells, which were used as models of healthy dividing cells.

Danshen improves survival of patients with advanced lung cancer and targeting the relationship between macrophages and lung cancer cells

In traditional Chinese medicine, Salvia miltiorrhiza Bunge (danshen) is widely used in the treatment of numerous cancers. However, its clinical effort and mechanism in the treatment of advanced lung cancer are unclear. In our study, the in vivo protective effort of danshen in patients with advanced lung cancer were validated using data from the National Health Insurance Research Database in Taiwan. We observed in vitro that dihydroisotanshinone I (DT), a bioactive compound in danshen, exerts anticancer effects through many pathways. First, 10 μM DT substantially inhibited the migration ability of lung cancer cells in both macrophage and macrophage/lung cancer direct mixed coculture media. Second, 10 μM DT repressed the phosphorylation of signal transducer and activator of transcription 3 (STAT3), the protein expression of S-phase kinase associated protein-2 (Skp2), and the mRNA levels of STAT3-related genes, including chemokine (C–C motif) ligand 2 (CCL2). In addition, 10 μM DT suppressed the macrophage recruitment ability of lung cancer cells by reducing CCL2 secretion from both macrophages and lung cancer cells. Third, 20 μM DT induced apoptosis in lung cancer cells. Furthermore, DT treatment significantly inhibited the final tumor volume in a xenograft nude mouse model. In conclusion, danshen exerts protective efforts in patients with advanced lung cancer. These effects can be attributed to DT-mediated interruption of the cross talk between lung cancer cells and macrophages and blocking of lung cancer cell proliferation.

A link between lipid metabolism and epithelial-mesenchymal transition provides a target for colon cancer therapy

The alterations in carbohydrate metabolism that fuel tumor growth have been extensively studied. However, other metabolic pathways involved in malignant progression, demand further understanding. Here we describe a metabolic acyl-CoA synthetase/stearoyl-CoA desaturase ACSL/SCD network causing an epithelial-mesenchymal transition (EMT) program that promotes migration and invasion of colon cancer cells. The mesenchymal phenotype produced upon overexpression of these enzymes is reverted through reactivation of AMPK signaling. Furthermore, this network expression correlates with poorer clinical outcome of stage-II colon cancer patients. Finally, combined treatment with chemical inhibitors of ACSL/SCD selectively decreases cancer cell viability without reducing normal cells viability. Thus, ACSL/SCD network stimulates colon cancer progression through conferring increased energetic capacity and invasive and migratory properties to cancer cells, and might represent a new therapeutic opportunity for colon cancer treatment.

G-DOC Plus - an integrative bioinformatics platform for precision medicine

BACKGROUND

G-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information.

RESULTS

G-DOC Plus currently holds data from over 10,000 patients selected from private and public resources including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT), caArray studies of lung and colon cancer, ImmPort and the 1000 genomes data sets. The system allows researchers to explore clinical-omic data one sample at a time, as a cohort of samples; or at the level of population, providing the user with a comprehensive view of the data. G-DOC Plus tools have been leveraged in cancer and non-cancer studies for hypothesis generation and validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer MRI images; as well as for training and graduate education in bioinformatics, data and computational sciences. Several of these use cases are described in this paper to demonstrate its multifaceted usability.

CONCLUSION

G-DOC Plus can be used to support a variety of user groups in multiple domains to enable hypothesis generation for precision medicine research. The long-term vision of G-DOC Plus is to extend this translational bioinformatics platform to stay current with emerging omics technologies and analysis methods to continue supporting novel hypothesis generation, analysis and validation for integrative biomedical research. By integrating several aspects of the disease and exposing various data elements, such as outpatient lab workup, pathology, radiology, current treatments, molecular signatures and expected outcomes over a web interface, G-DOC Plus will continue to strengthen precision medicine research. G-DOC Plus is available at: https://gdoc.georgetown.edu .

Cancer Metabolism: A Modeling Perspective

Tumor cells alter their metabolism to maintain unregulated cellular proliferation and survival, but this transformation leaves them reliant on constant supply of nutrients and energy. In addition to the widely studied dysregulated glucose metabolism to fuel tumor cell growth, accumulating evidences suggest that utilization of amino acids and lipids contributes significantly to cancer cell metabolism. Also recent progresses in our understanding of carcinogenesis have revealed that cancer is a complex disease and cannot be understood through simple investigation of genetic mutations of cancerous cells. Cancer cells present in complex tumor tissues communicate with the surrounding microenvironment and develop traits which promote their growth, survival, and metastasis. Decoding the full scope and targeting dysregulated metabolic pathways that support neoplastic transformations and their preservation requires both the advancement of experimental technologies for more comprehensive measurement of omics as well as the advancement of robust computational methods for accurate analysis of the generated data. Here, we review cancer-associated reprogramming of metabolism and highlight the capability of genome-scale metabolic modeling approaches in perceiving a system-level perspective of cancer metabolism and in detecting novel selective drug targets.

FTIR spectroscopy reveals lipid droplets in drug resistant laryngeal carcinoma cells through detection of increased ester vibrational bands intensity

The major obstacle to successful chemotherapy of cancer patients is drug resistance. Previously we explored the molecular mechanisms of curcumin cross-resistance in carboplatin resistant human laryngeal carcinoma 7T cells. Following curcumin treatment we found a reduction in curcumin accumulation, and reduced induction of reactive oxygen species (ROS) and their downstream effects, compared to parental HEp-2 cells. In order to shed more light on mechanisms involved in drug resistance of 7T cells, in the present study we applied Fourier transform infrared (FTIR) spectroscopy, a technique that provides information about the nature and quantities of all molecules present in the cell. By comparing the spectra from parental HEp-2 cells and their 7T subline, we found an increase in the intensity of ester vibrational bands in 7T cells. This implied an increase in the amount of cholesteryl esters in resistant cells, which we confirmed by an enzymatic assay. Since cholesteryl esters are localized in lipid droplets, we confirmed their higher quantity and serum dependency in 7T cells compared to HEp-2 cells. Moreover, treatment with oleic acid induced more lipid droplets in 7T when compared to HEp-2 cells, as shown by flow cytometry. We can conclude that along with previously determined molecular mechanisms of curcumin resistance in 7T cells, these cells exhibit an increased content of cholesteryl esters and lipid droplets, suggesting an alteration in cellular lipid metabolism as a possible additional mechanism of drug resistance. Furthermore, our results suggest the use of FTIR spectroscopy as a promising technique in drug resistance research.

Modeling cancer metabolism on a genome scale

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field.