Notes

Comparison immunogenicity examination associated with intradermal as opposed to intramuscular administration regarding SARS-CoV-2 RBD epitope peptide-based immunogen Within vivo.
A central statistical assessment of the quality of data collected in clinical trials can improve the quality and efficiency of sponsor oversight of clinical investigations.

The database of a large randomized clinical trial with known fraud was reanalyzed with a view to identifying, using only statistical monitoring techniques, the center where fraud had been confirmed. The analysis was conducted with an unsupervised statistical monitoring software using mixed-effects statistical models. The statistical analyst was unaware of the location, nature, and extent of the fraud.

Five centers were detected as atypical, including the center with known fraud (which was ranked 2). An incremental analysis showed that the center with known fraud could have been detected after only 25% of its data had been reported.

An unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.
An unsupervised approach to central monitoring, using mixed-effects statistical models, is effective at detecting centers with fraud or other data anomalies in clinical trials.Cancer is one of the leading causes of death and chromosomal instability (CIN) is a hallmark feature of cancer. CIN, a source of genetic variation in either altered chromosome number or structure contributes to tumor heterogeneity and has become a hot topic in recent years prominently for its role in therapeutic responses. Synthetic lethality and synthetic rescue based approaches, for example, advancing CRISPR-Cas9 platform, are emerging as a powerful strategy to identify new potential targets to selectively eradicate cancer cells. Unfortunately, only few of them are further explored therapeutically due to the difficulty in linking these targets to small molecules for pharmacological intervention. Vorinostat This, however, can be alleviated by the efforts to bring chemical, bioactivity, and genomic data together, as well as established computational approaches. In this chapter, we will discuss some of these advances, including established databases and in silico target-ligand prediction, with the aim to navigate through the synthetically available chemical space to the biologically targetable landscape, and eventually, to the chemical modeling of synthetic lethality and synthetic rescue interactions, that are of great clinical and pharmaceutical relevance and significance.Functional genomic screens can identify several proteins as potential targets for drug development in cancer. Typically, these drug targets are validated with pharmacological inhibition using small molecules. Given that chemical inhibitors do not exist for a many of these proteins, several promising candidates often remain unexplored. In this chapter, we describe methods for generating protein-based inhibitors of intracellular targets using phage display. This is a scalable and inexpensive approach that can be applied to several protein targets identified in genetic screens. We describe methods for expression of target proteins, construction of phage-display libraries and selection of binding proteins. These synthetic binding proteins can block natural protein interactions within the cancer cell and act as inhibitors. Protein inhibitors have utility in validation of drug targets and can also guide small-molecule drug development.Cancer can develop from an accumulation of alterations, some of which cause a nonmalignant cell to transform to a malignant state exhibiting increased rate of cell growth and evasion of growth suppressive mechanisms, eventually leading to tissue invasion and metastatic disease. Triple-negative breast cancers (TNBC) are heterogeneous and are clinically characterized by the lack of expression of hormone receptors and human epidermal growth factor receptor 2 (HER2), which limits its treatment options. Since tumor evolution is driven by diverse cancer cell populations and their microenvironment, it is imperative to map TNBC at single-cell resolution. Here, we describe an experimental procedure for isolating a single-cell suspension from a TNBC patient-derived xenograft, subjecting it to single-cell RNA sequencing using droplet-based technology from 10× Genomics and analyzing the transcriptomic data at single-cell resolution to obtain inferred copy number aberration profiles, using scCNA. Data obtained using this single-cell RNA sequencing experimental and analytical methodology should enhance our understanding of intratumor heterogeneity which is key for identifying genetic vulnerabilities and developing effective therapies.Genetic mutations, whether they occur within protein-coding or noncoding regions of the genome, can affect various aspects of gene expression by influencing the complex network of intra- and intermolecular interactions that occur between cellular nucleic acids and proteins. One aspect of gene expression control that can be impacted is the intracellular trafficking and translation of mRNA molecules. To study the occurrence and dynamics of translational regulation, researchers have developed approaches such as genome-wide ribosome profiling and artificial reporters that enable single molecule imaging. In this paper, we describe a complementary and optimized approach that combines puromycin labeling with a proximity ligation assay (Puro-PLA) to define sites of translation of specific mRNAs in tissues or cells. This method can be used to study the mechanisms driving the translation of select mRNAs and to access the impact of genetic mutations on local protein synthesis. This approach involves the treatment of cell or tissue specimens with puromycin to label nascently translated peptides, rapid fixation, followed by immunolabeling with appropriate primary and secondary antibodies coupled to PLA oligonucleotide probes, ligation, amplification, and signal detection via fluorescence microscopy. Puro-PLA can be performed at small scale in individual tubes or in chambered slides, or in a high-throughput setup with 96-well plate, for both in situ and in vitro experimentation.The combination of model organisms and comprehensive genome-wide screens has provided a wealth of data into the structure and regulation of the genome, gene-environment interactions, and more recently, into the mechanism of action of human therapeutics. The success of these studies relies, in part, on the ability to quantify the combined effects of multifactorial biological interactions. In this review, we explore the history and rationale behind genetic and chemical-genetic interactions with an emphasis on the phenomena of drug synergy and then briefly describe the theoretical models that we can leverage to investigate the synergy between compounds. In addition to reviewing the literature, we also provide a reference list including many of the most important studies in this field. The concept of chemical genetics interactions derives from classical studies of synthetic lethality and functional genomics. These techniques have recently graduated from the research lab to the clinic, and a better understanding of the basic principles can help accelerate this translation.In addition to advancing the development of gene-editing therapeutics, CRISPR/Cas9 is transforming how functional genetic studies are carried out in the lab. By increasing the ease with which genetic information can be inserted, deleted, or edited in cell and organism models, it facilitates genotype-phenotype analysis. Moreover, CRISPR/Cas9 has revolutionized the speed at which new genes underlying a particular phenotype can be identified through its application in genomic screens. Arrayed high-throughput and pooled lentiviral-based CRISPR/Cas9 screens have now been used in a wide variety of contexts, including the identification of essential genes, genes involved in cancer metastasis and tumor growth, and even genes involved in viral response. This technology has also been successfully used to identify drug targets and drug resistance mechanisms. Here, we provide a detailed protocol for performing a genome-wide pooled lentiviral CRISPR/Cas9 knockout screen to identify genetic modulators of a small-molecule drug. While we exemplify how to identify genes involved in resistance to a cytotoxic histone deacetylase inhibitor, Trichostatin A (TSA), the workflow we present can easily be adapted to different types of selections and other types of exogenous ligands or drugs.Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes in thousands of crossed strains followed by statistical analysis to compare single and double mutants. The preferred computational approach is to use a multiplicative model that factors phenotype scores of single gene mutants to identify gene interactions in double mutants. Here we present how machine learning models that consider the characteristics of the phenotypic data improve on the classical multiplicative model. Importantly, machine learning improves the selection of cutoff values to identify gene interactions from phenotypic scores.Despite the success of targeted therapies including immunotherapies in cancer treatments, tumor resistance to targeted therapies remains a fundamental challenge. Tumors can evolve resistance to a therapy that targets one gene by acquiring compensatory alterations in another gene, such compensatory interaction between two genes is referred to as synthetic rescue (SR) interactions. To identify SRs, here we describe an algorithm, INCISOR, that leverages tumor transcriptomics and clinical information from 10,000 patients as well as data from experimental screens. INCISOR can identify SRs that are common across several cancer-types in genome-wide fashion by sifting through half a billion possible gene-gene combinations and provide a framework to design therapies to tackle resistance.Large-scale RNAi screens (i.e., genome-wide arrays and pools) can reveal the essential biological functions of previously uncharacterized genes. Due to the nature of the selection process involved in screens, RNAi screens are also very useful for identifying genes involved in drug responses. The information gained from these screens could be used to predict a cancer patient's response to a specific drug (i.e., precision medicine) or identify anti-cancer drug resistance genes, which could be targeted to improve treatment outcomes. In this capacity, screens have been most often performed in vitro. However, there is limitation to performing these screens in vitro genes which are required in only an in vivo setting (e.g., rely on the tumor microenvironment for function) will not be identified. As such, it can be desirable to perform RNAi screens in vivo. Here we outline the additional technical details that should be considered for performing genome-wide RNAi drug screens of cancer cells under in vivo conditions (i.e., tumor xenografts).While well studied in yeast, mapping genetic interactions in mammalian cells has been limited due to many technical obstacles. We have recently developed a new one-step tRNA-CRISPR method called TCGI (tRNA-CRISPR for genetic interactions) which generates high-efficiency, barcode-free, and scalable pairwise CRISPR libraries to identify genetic interactions in mammalian cells. Here we describe this method in detail regarding the construction of the pairwise CRISPR libraries and performing high throughput genetic interacting screening and data analysis. This novel TCGI dramatically improves upon the current methods for mapping genetic interactions and screening drug targets for combinational therapies.
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