Thymic stromal lymphopoietin fosters human breast tumor growth by promoting type 2 inflammation

TSLP released from human breast cancer cells promotes OX40L expression on DCs, and these OX40L-expressing DCs drive development of inflammatory Th2 cells which promote breast tumor development

Targeted Toxins in Brain Tumor Therapy

Targeted toxins, also known as immunotoxins or cytotoxins, are recombinant molecules that specifically bind to cell surface receptors that are overexpressed in cancer and the toxin component kills the cell. These recombinant proteins consist of a specific antibody or ligand coupled to a protein toxin. The targeted toxins bind to a surface antigen or receptor overexpressed in tumors, such as the epidermal growth factor receptor or interleukin-13 receptor. The toxin part of the molecule in all clinically used toxins is modified from bacterial or plant toxins, fused to an antibody or carrier ligand. Targeted toxins are very effective against cancer cells resistant to radiation and chemotherapy. They are far more potent than any known chemotherapy drug. Targeted toxins have shown an acceptable profile of toxicity and safety in early clinical studies and have demonstrated evidence of a tumor response. Currently, clinical trials with some targeted toxins are complete and the final results are pending. This review summarizes the characteristics of targeted toxins and the key findings of the important clinical studies with targeted toxins in malignant brain tumor patients. Obstacles to successful treatment of malignant brain tumors include poor penetration into tumor masses, the immune response to the toxin component and cancer heterogeneity. Strategies to overcome these limitations are being pursued in the current generation of targeted toxins

Histone deacetylase inhibitors: potential targets responsible for their anti-cancer effect

The histone deacetylase inhibitors (HDACi) have demonstrated anticancer efficacy across a range of malignancies, most impressively in the hematological cancers. It is uncertain whether this clinical efficacy is attributable predominantly to their ability to induce apoptosis and differentiation in the cancer cell, or to their ability to prime the cell to other pro-death stimuli such as those from the immune system. HDACi-induced apoptosis occurs through altered expression of genes encoding proteins in both intrinsic and extrinsic apoptotic pathways; through effects on the proteasome/aggresome systems; through the production of reactive oxygen species, possibly by directly inducing DNA damage; and through alterations in the tumor microenvironment. In addition HDACi increase the immunogenicity of tumor cells and modulate cytokine signaling and potentially T-cell polarization in ways that may contribute the anti-cancer effect in vivo. Here, we provide an overview of current thinking on the mechanisms of HDACi activity, with attention given to the hematological malignancies as well as scientific observations arising from the clinical trials. We also focus on the immune effects of these agents

Enabling reproducible re-analysis of single-cell data

Other UBCNon UBCReviewedFacultyResearche

Genomic data integration systematically biases interactome mapping.

Elucidating the complete network of protein-protein interactions, or interactome, is a fundamental goal of the post-genomic era, yet existing interactome maps are far from complete. To increase the throughput and resolution of interactome mapping, methods for protein-protein interaction discovery by co-migration have been introduced. However, accurate identification of interacting protein pairs within the resulting large-scale proteomic datasets is challenging. Consequently, most computational pipelines for co-migration data analysis incorporate external genomic datasets to distinguish interacting from non-interacting protein pairs. The effect of this procedure on interactome mapping is poorly understood. Here, we conduct a rigorous analysis of genomic data integration for interactome recovery across a large number of co-migration datasets, spanning diverse experimental and computational methods. We find that genomic data integration leads to an increase in the functional coherence of the resulting interactome maps, but this comes at the expense of a decrease in power to discover novel interactions. Importantly, putative novel interactions predicted by genomic data integration are no more likely to later be experimentally discovered than those predicted from co-migration data alone. Our results reveal a widespread and unappreciated limitation in a methodology that has been widely used to map the interactome of humans and model organisms

Deep Generative Models Enable Navigation in Sparsely Populated Chemical Space

Deep generative models are powerful tools for the exploration of chemical space, enabling the on-demand gener- ation of molecules with desired physical, chemical, or biological properties. However, these models are typically thought to require training datasets comprising hundreds of thousands, or even millions, of molecules. This per- ception limits the application of deep generative models in regions of chemical space populated by only a small number of examples. Here, we systematically evaluate and optimize generative models of molecules for low-data settings. We carry out a series of systematic benchmarks, training more than 5,000 deep generative models and evaluating over 2.6 billion generated molecules. We find that robust models can be learned from far fewer examples than has been widely assumed. We further identify strategies that dramatically reduce the number of molecules required to learn a model of equivalent quality, and demonstrate the application of these principles by learning models of chemical structures found in bacterial, plant, and fungal metabolomes. The structure of our experiments also allows us to benchmark the metrics used to evaluate generative models themselves. We find that many of the most widely used metrics in the field fail to capture model quality, but identify a subset of well-behaved metrics that provide a sound basis for model development. Collectively, our work provides a foundation for directly learning generative models in sparsely populated regions of chemical space

A rapid and accurate approach for prediction of interactomes from co-elution data (PrInCE)

Background: An organism’s protein interactome, or complete network of protein-protein interactions, defines the protein complexes that drive cellular processes. Techniques for studying protein complexes have traditionally applied targeted strategies such as yeast two-hybrid or affinity purification-mass spectrometry to assess protein interactions. However, given the vast number of protein complexes, more scalable methods are necessary to accelerate interaction discovery and to construct whole interactomes. We recently developed a complementary technique based on the use of protein correlation profiling (PCP) and stable isotope labeling in amino acids in cell culture (SILAC) to assess chromatographic co-elution as evidence of interacting proteins. Importantly, PCP-SILAC is also capable of measuring protein interactions simultaneously under multiple biological conditions, allowing the detection of treatment-specific changes to an interactome. Given the uniqueness and high dimensionality of co-elution data, new tools are needed to compare protein elution profiles, control false discovery rates, and construct an accurate interactome. Results: Here we describe a freely available bioinformatics pipeline, PrInCE, for the analysis of co-elution data. PrInCE is a modular, open-source library that is computationally inexpensive, able to use label and label-free data, and capable of detecting tens of thousands of protein-protein interactions. Using a machine learning approach, PrInCE offers greatly reduced run time, more predicted interactions at the same stringency, prediction of protein complexes, and greater ease of use over previous bioinformatics tools for co-elution data. PrInCE is implemented in Matlab (version R2017a). Source code and standalone executable programs for Windows and Mac OSX are available at https://github.com/fosterlab/PrInCE , where usage instructions can be found. An example dataset and output are also provided for testing purposes. Conclusions: PrInCE is the first fast and easy-to-use data analysis pipeline that predicts interactomes and protein complexes from co-elution data. PrInCE allows researchers without bioinformatics expertise to analyze high-throughput co-elution datasets.Medicine, Faculty ofOther UBCNon UBCBiochemistry and Molecular Biology, Department ofReviewedFacult