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TITLES and AUTHORS
T-cell prolymphocytic leukemia (T-PLL) is a rare and poor-prognostic mature T-cell malignancy. Here we integrated large-scale profiling data of alterations in gene expression, allelic copy number (CN), and nucleotide sequences in 111 well-characterized patients. Besides prominent signatures of T-cell activation and prevalent clonal variants, we also identify novel hot-spots for CN variability, fusion molecules, alternative transcripts, and progression-associated dynamics. The overall lesional spectrum of T-PLL is mainly annotated to axes of DNA damage responses, T-cell receptor/cytokine signaling, and histone modulation. We formulate a multi-dimensional model of T-PLL pathogenesis centered around a unique combination of TCL1 over expression with damaging ATM aberrations as initiating core lesions. The effects imposed by TCL1 cooperate with compromised ATM toward a leukemogenic phenotype of impaired DNA damage processing. Dysfunctional ATM appears inefficient in alleviating elevated redox burdens and telomere attrition and in evoking a p53-dependent apoptotic response to genotoxic insults. As non-genotoxic strategies, synergistic combinations of p53 reactivators and deacetylase inhibitors reinstate such cell death execution.
Drug discovery is undergoing a transformation powered by advances that provide more knowledge from biological assays, allow for screening orders of magnitude more molecules and enable smarter selection of compounds. We are on the cusp of having previously unimaginable amounts of information about each target and many more targets to prosecute. For instance, last year AstraZeneca launched an integrated genomics initiative through which scientists will investigate data from as many as two million human genomes, including more than 500,000 from clinical trials run by the company.
Many compounds with potentially reactive chemical motifs and poor physicochemical properties are published as selective modulators of biomolecules without sufficient validation and then propagated in the scientific literature as useful chemical probes. Several histone acetyltransferase (HAT) inhibitors with these liabilities are now routinely used to probe epigenetic pathways. We profile the most commonly used HAT inhibitors and confirm that the majority of them are nonselective interference compounds. Most (15 out of 23, 65%) of the inhibitors are flagged by ALARM NMR, an industry-developed counter-screen for promiscuous compounds. Biochemical counter-screens confirm that most of these compounds are either thiol-reactive or aggregators. Selectivity panels show many of these compounds modulate unrelated targets in vitro, while several also demonstrate nonspecific effects in cell assays. These data demonstrate the usefulness of performing counter-screens for bioassay promiscuity and assay interference, and raise caution about the utility of many widely used, but insufficiently validated, compounds employed in chemical biology.
Many cancer drugs are effective in only a small percentage of patients. This raises a somewhat profound question: how do we know that a certain drug will work well for some patients? If a potential drug cures 3 percent of patients, the data may never be found in the population at large. Likewise, adverse effects on a different – but still small – percentage of the total population, may be lost.
Pharmacological and functional genomic screens play an essential role in the discovery and characterization of therapeutic targets and associated pharmacological inhibitors. Although these screens affect thousands of gene products, the typical readout is based on low complexity rather than genome-wide assays. To address this limitation, we introduce pooled library amplification for transcriptome expression (PLATE-Seq), a low-cost, genome-wide mRNA profiling methodology specifically designed to complement high-throughput screening assays. Introduction of sample-specific barcodes during reverse transcription supports pooled library construction and low-depth sequencing that is 10- to 20-fold less expensive than conventional RNA-Seq. The use of network-based algorithms to infer protein activity from PLATE-Seq data results in comparable reproducibility to 30 M read sequencing. Indeed, PLATE-Seq reproducibility compares favorably to other large-scale perturbational profiling studies such as the connectivity map and library of integrated network-based cellular signatures.
If this is the biology laboratory of the future, it doesn’t look so different from today’s. Scientists in white lab coats walk by with boxes of frozen tubes. The chemicals on the shelves—bottles of pure alcohol, bins of sugar, protein, and salts—are standard issue for growing microbes and manipulating their genes. You don’t even notice the robots until you hear them: They sound like crickets singing to each other amid the low roar of fans.
Liquid handling plays an integral part inthe Biotechnology industry, since a major part of daily lab operations for testing, research, or production relies on the efficient transfer of samples and reagents to designated containers. Traditional liquid handling approaches that use pipettes often cannot achieve required levels of miniaturization, precision, and accuracy, and introduce chances of contamination.
Christopher Voigt is a professor of biological engineering at the Massachusetts Institute of Technology, where his lab focuses on synthetic biology. Two major areas of interest for him are developing a genetic programming language for cells and applying synthetic biology to biotechnology challenges. Ultimately, Voigt aims to design whole genomes for applications from agriculture to medicine. DDW chatted with Voigt to learn more about the impact this field will have on pharmaceutical workflows and drug pipelines.
There are people who will die of cancer this week even though there are drugs that could help them.
At the same time, hundreds of patients will undergo cancer chemotherapy that, while debilitating and expensive, will not cure them of their disease. While cancer is a formidable foe, there is a way to improve patient care and prognosis immediately.
Researchers in Finland, Sweden and Spain have modified ex-vivo testing of cancer cells with significant results. Their approach is far more “personalized” than traditional precision medicine. Their results are striking. They provide a missing link between genomics-based mutation determination and clinical efficacy. Precision medicine (also referred to as “personalized medicine” or PM) appears to many patients, doctors and researchers to be a golden highway from disease identification to cure. The idea of interrogating the genome of a particular cancer to determine its Achilles heel is intuitively satisfying and understandable. There are, however, significant problems. First, PM is neither personalized nor precise. PM strives to identify the appropriate biomarker (usually a DNA mutation but proteins, peptides and metabolites can stand as biomarkers as well) to categorize the patient as a member of a specific group of patients. The patient is treated with a drug that has shown positive results on previous members of the group. In other words, personalized medicine is actually population-based medicine.
A handful of academic groups in Europe and the US are using high-throughput (HT) ex vivo screening to test the sensitivity of individual patient tumors to hundreds of combinations of cancer drugs - a strategy that in earlier iterations failed to predict response to therapy. HT screening is used routinely in drug discovery. Thanks to advances in nanoliter-scale sample handling and computational biology, evidence is slowly building the potential of HT approaches to identify novel combination therapies, first in leukemia and hopefully for treating other cancers.
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