2458 Despite dramatic improvements in treatment over the past 40 years, acute lymphoblastic leukemia (ALL) remains one of the most common causes of death from disease in childhood. Glucocorticoids are among the most effective agents used in the treatment of lymphoid malignancies, and patient response to treatment is an important determinant of long-term outcome in childhood ALL. In spite of its clinical significance, the molecular basis of glucocorticoid resistance is still poorly understood. The aim of this study was to develop an experimental model system to define clinically relevant mechanisms of glucocorticoid resistance in childhood ALL. An in vivo model of childhood ALL has been developed in our laboratory, using patient biopsies established as xenografts in immune-deficient nonobese diabetic severe-combined immunodeficient (NOD/SCID) mice. This model is highly representative of the human disease (Lock et al., Blood, 99: 4100-4108, 2002). The in vivo responses of these xenografts to the glucocorticoid dexamethasone (DEX) correlated significantly with patient outcome (p 1 μM) in xenografts from six patients, five of whom died of their disease. In contrast, four DEX-sensitive xenografts (IC50 values 2-fold in sensitive xenografts within 8 hours of treatment. In contrast, Bim induction was dramatically attenuated in DEX-resistant xenografts. These results have identified a clinically significant and novel mechanism of glucocorticoid resistance in childhood ALL, which occurs downstream of receptor-ligand interactions, but upstream of the signalling pathway resulting in Bim induction and apoptosis.

Novel mechanisms of glucocorticoid resistance in childhood acute lymphoblastic leukemia.

Advances in copper complexes as anticancer agents.

Gene Body Methylation Can Alter Gene Expression and Is a Therapeutic Target in Cancer

https://www.cell.com/cell/pdf/S0092-8674(12)01546-2.pdf

A Decade of Riboswitches

Altered energy metabolism is a biochemical fingerprint of cancer cells that represents one of the “hallmarks of cancer”. This metabolic phenotype is characterized by preferential dependence on glycolysis (the process of conversion of glucose into pyruvate followed by lactate production) for energy production in an oxygen-independent manner. Although glycolysis is less efficient than oxidative phosphorylation in the net yield of adenosine triphosphate (ATP), cancer cells adapt to this mathematical disadvantage by increased glucose up-take, which in turn facilitates a higher rate of glycolysis. Apart from providing cellular energy, the metabolic intermediates of glycolysis also play a pivotal role in macromolecular biosynthesis, thus conferring selective advantage to cancer cells under diminished nutrient supply. Accumulating data also indicate that intracellular ATP is a critical determinant of chemoresistance. Under hypoxic conditions where glycolysis remains the predominant energy producing pathway sensitizing cancer cells would require intracellular depletion of ATP by inhibition of glycolysis. Together, the oncogenic regulation of glycolysis and multifaceted roles of glycolytic components underscore the biological significance of tumor glycolysis. Thus targeting glycolysis remains attractive for therapeutic intervention. Several preclinical investigations have indeed demonstrated the effectiveness of this therapeutic approach thereby supporting its scientific rationale. Recent reviews have provided a wealth of information on the biochemical targets of glycolysis and their inhibitors. The objective of this review is to present the most recent research on the cancer-specific role of glycolytic enzymes including their non-glycolytic functions in order to explore the potential for therapeutic opportunities. Further, we discuss the translational potential of emerging drug candidates in light of technical advances in treatment modalities such as image-guided targeted delivery of cancer therapeutics.

/pdf/tumor-glycolysis-as-a-target-for-cancer-therapy-progress-and-2b8jy98sx7.pdf

Tumor glycolysis as a target for cancer therapy: progress and prospects.

The metabolic properties of cancer cells diverge significantly from those of normal cells. Energy production in cancer cells is abnormally dependent on aerobic glycolysis. In addition to the dependency on glycolysis, cancer cells have other atypical metabolic characteristics such as increased fatty acid synthesis and increased rates of glutamine metabolism. Emerging evidence shows that many features characteristic to cancer cells, such as dysregulated Warburg-like glucose metabolism, fatty acid synthesis and glutaminolysis are linked to therapeutic resistance in cancer treatment. Therefore, targeting cellular metabolism may improve the response to cancer therapeutics and the combination of chemotherapeutic drugs with cellular metabolism inhibitors may represent a promising strategy to overcome drug resistance in cancer therapy. Recently, several review articles have summarized the anticancer targets in the metabolic pathways and metabolic inhibitor-induced cell death pathways, however, the dysregulated metabolism in therapeutic resistance, which is a highly clinical relevant area in cancer metabolism research, has not been specifically addressed. From this unique angle, this review article will discuss the relationship between dysregulated cellular metabolism and cancer drug resistance and how targeting of metabolic enzymes, such as glucose transporters, hexokinase, pyruvate kinase M2, lactate dehydrogenase A, pyruvate dehydrogenase kinase, fatty acid synthase and glutaminase can enhance the efficacy of common therapeutic agents or overcome resistance to chemotherapy or radiotherapy.

/pdf/targeting-cellular-metabolism-to-improve-cancer-therapeutics-3om3oro68p.pdf

Targeting cellular metabolism to improve cancer therapeutics

Cancer cells are markedly different from normal cells with regards to how their metabolic pathways are used to fuel cellular growth and survival. Two basic metabolites that exemplify these differences through increased uptake and altered metabolic usage are glucose and glutamine. These molecules can be catabolized to manufacture many of the building blocks required for active cell growth and proliferation. The alterations in the metabolic pathways necessary to sustain this growth have been linked to therapeutic resistance, a trait that is correlated with poor patient outcomes. By targeting the metabolic pathways that import, catabolize, and synthesize essential cellular components, drug-resistant cancer cells can often be resensitized to anticancer treatments. The specificity and efficacy of agents directed at the unique aspects of cancer metabolism are expected to be high; and may, when in used in combination with more traditional therapeutics, present a pathway to surmount resistance within tumors that no longer respond to current forms of treatment.

https://cancerres.aacrjournals.org/content/canres/73/9/2709.full.pdf

Stalling the Engine of Resistance: Targeting Cancer Metabolism to Overcome Therapeutic Resistance

https://www.cell.com/cell-chemical-biology/pdf/S1074-5521(11)00042-1.pdf

Structural Basis of Cooperative Ligand Binding by the Glycine Riboswitch

The ribosomal peptidyl transferase center is expected to be regiospecific with regard to its tRNA substrates, yet the ester linkages between the tRNA and the amino acid or peptide are susceptible to isomerization between the O2‘ and O3‘ hydroxyls of the terminal A76 ribose sugar. To establish which isomer of the P site tRNA ester is utilized by the ribosome, we prepared two nonisomerizable transition state inhibitors with either an A76 O2‘ or O3‘ linkage. Strong preferential binding to the O3‘ regioisomer indicates that the peptidyl transferase proceeds through a transition state with an O3‘-linked peptide in the P-site.

Regiospecificity of the peptidyl tRNA ester within the ribosomal P site.

Analysing and classifying sequences based on similarities and differences is a mathematical problem of escalating relevance and importance in many scientific disciplines. One of the primary challenges in applying machine learning algorithms to sequential data, such as biological sequences, is the extraction and representation of significant features from the data. To address this problem, we have recently developed a representation, entitled Multi-Layered Vector Spaces MLVS, which is a simple mathematical model that maps sequences into a set of MLVS. We demonstrate the usefulness of the model by applying it to the problem of identifying signal peptides. MLVS feature vectors are generated from a collection of protein sequences and the resulting vectors are used to create support vector machine classifiers. Experiments show that the MLVS-based classifiers are able to outperform or perform on par with several existing methods that are specifically designed for the purpose of identifying signal peptides.

Ethan B. Butler

Papers

Targeting cellular metabolism to improve cancer therapeutics

Stalling the Engine of Resistance: Targeting Cancer Metabolism to Overcome Therapeutic Resistance

Structural Basis of Cooperative Ligand Binding by the Glycine Riboswitch

Regiospecificity of the peptidyl tRNA ester within the ribosomal P site.

Exploiting multi-layered vector spaces for signal peptide detection