Cancer Clinical Trials Optimization and Pharmacogenomics

A critical overview of recent clinical trials in cancer is presented focused on signaling pathways blockers or inhibitors with a view to developing successful clinical trials employing personalized cancer therapies. Rational, pharmacogenomic strategies in cancer trials should be adopted that include specific molecular targeting based on adequate data for, and detailed modeling of, cancer cell genomes, modifications of cancer signaling pathways and epigenetic mechanisms. Novel translational oncogenomics research is rapidly expanding through the application of highly sensitive and specific advanced technology, research findings and computational tools and complex models to both pharmaceutical and clinical problems. Multiple sample analyses from several recent clinical studies have shown that gene expression data for cancer cells can be employed to distinguish between tumor types as well as to predict outcomes. Potentially important applications of such results are individualized human cancer therapies or, in general,'personalized medicine' that will have to be validated through optimally designed clinical trials in cancer. A Human Cancer Genomes and Epigenetics Project is proposed that can provide the essential data required for the optimal design of clinical trials with the goal of achieving significant improvements of the survival rates of cancer patients participating in clinical trials for advanced cancer stages. The results of such a six-year Human Cancer Genomes and Epigenetics Project should also greatly aid with the accelerated, rational development of effective anti-cancer medicines and the chemoprevention of cancers

Optimization of carbon and energy utilization through differential translational efficiency.

Control of translation is vital to all species. Here we employ a multi-omics approach to decipher condition-dependent translational regulation in the model acetogen Clostridium ljungdahlii. Integration of data from cells grown autotrophically or heterotrophically revealed that pathways critical to carbon and energy metabolism are under strong translational regulation. Major pathways involved in carbon and energy metabolism are not only differentially transcribed and translated, but their translational efficiencies are differentially elevated in response to resource availability under different growth conditions. We show that translational efficiency is not static and that it changes dynamically in response to mRNA expression levels. mRNAs harboring optimized 5'-untranslated region and coding region features, have higher translational efficiencies and are significantly enriched in genes encoding carbon and energy metabolism. In contrast, mRNAs enriched in housekeeping functions harbor sub-optimal features and have lower translational efficiencies. We propose that regulation of translational efficiency is crucial for effectively controlling resource allocation in energy-deprived microorganisms

Complete Genome Sequences of Mycobacterium smegmatis Phages Chewbacca, Reptar3000, and Riparian, Isolated in Las Vegas, Nevada

Here, we present the complete genome sequences of Mycobacterium smegmatis phages Chewbacca, Reptar3000, and Riparian, isolated from soil in Las Vegas, NV. The phages were isolated and annotated by undergraduate students enrolled in the Phage Discovery course offered by the School of Life Sciences at the University of Nevada, Las Vega

A mass spectrometry-guided genome mining approach for natural product peptidogenomics.

Peptide natural products show broad biological properties and are commonly produced by orthogonal ribosomal and nonribosomal pathways in prokaryotes and eukaryotes. To harvest this large and diverse resource of bioactive molecules, we introduce here natural product peptidogenomics (NPP), a new MS-guided genome-mining method that connects the chemotypes of peptide natural products to their biosynthetic gene clusters by iteratively matching de novo tandem MS (MS(n)) structures to genomics-based structures following biosynthetic logic. In this study, we show that NPP enabled the rapid characterization of over ten chemically diverse ribosomal and nonribosomal peptide natural products of previously unidentified composition from Streptomycete bacteria as a proof of concept to begin automating the genome-mining process. We show the identification of lantipeptides, lasso peptides, linardins, formylated peptides and lipopeptides, many of which are from well-characterized model Streptomycetes, highlighting the power of NPP in the discovery of new peptide natural products from even intensely studied organisms