Nuclear stabilization of p53 requires a functional nucleolar surveillance pathway.

The nucleolar surveillance pathway monitors nucleolar integrity and responds to nucleolar stress by mediating binding of ribosomal proteins to MDM2, resulting in p53 accumulation. Inappropriate pathway activation is implicated in the pathogenesis of ribosomopathies, while drugs selectively activating the pathway are in trials for cancer. Despite this, the molecular mechanism(s) regulating this process are poorly understood.

The RNA polymerase I transcription inhibitor CX-5461 cooperates with topoisomerase 1 inhibition by enhancing the DNA damage response in homologous recombination-proficient high-grade serous ovarian cancer.

Background: Intrinsic and acquired drug resistance represent fundamental barriers to the cure of high-grade serous ovarian carcinoma (HGSC), the most common histological subtype accounting for the majority of ovarian cancer deaths. Defects in homologous recombination (HR) DNA repair are key determinants of sensitivity to chemotherapy and poly-ADP ribose polymerase inhibitors. Restoration of HR is a common mechanism of acquired resistance that results in patient mortality, highlighting the need to identify new therapies targeting HR-proficient disease.

Genome-wide RNAi screen for genes regulating glycolytic response to vemurafenib in BRAF melanoma cells.

Identification of mechanisms underlying sensitivity and response to targeted therapies, such as the BRAF inhibitor vemurafenib, is critical in order to improve efficacy of these therapies in the clinic and delay onset of resistance. Glycolysis has emerged as a key feature of the BRAF inhibitor response in melanoma cells, and importantly, the metabolic response to vemurafenib in melanoma patients can predict patient outcome. Here, we present a multiparameter genome-wide siRNA screening dataset of genes that when depleted improve the viability and glycolytic response to vemurafenib in BRAF mutated melanoma cells.

Lysosomal degradation products induce virulence.

is an intracellular pathogen that replicates in a lysosome-like vacuole through activation of a Dot/Icm-type IVB secretion system and subsequent translocation of effectors that remodel the host cell. Here a genome-wide small interfering RNA screen and reporter assay were used to identify host proteins required for Dot/Icm effector translocation. Significant, and independently validated, hits demonstrated the importance of multiple protein families required for endocytic trafficking of the -containing vacuole to the lysosome.

A functional genetic screen defines the AKT-induced senescence signaling network.

Exquisite regulation of PI3K/AKT/mTORC1 signaling is essential for homeostatic control of cell growth, proliferation, and survival. Aberrant activation of this signaling network is an early driver of many sporadic human cancers. Paradoxically, sustained hyperactivation of the PI3K/AKT/mTORC1 pathway in nontransformed cells results in cellular senescence, which is a tumor-suppressive mechanism that must be overcome to promote malignant transformation.

A Comprehensive Protocol Resource for Performing Pooled shRNA and CRISPR Screens.

This chapter details a compendium of protocols that collectively enable the reader to perform a pooled shRNA and/or CRISPR screen-with methods to identify and validate positive controls and subsequent hits; establish a viral titer in the cell line of choice; create and screen libraries, sequence strategies, and bioinformatics resources to analyze outcomes. Collectively, this provides an overarching resource from the start to finish of a screening project, making this technology possible in all laboratories.

An interactive web-based application for Comprehensive Analysis of RNAi-screen Data.

RNAi screens are widely used in functional genomics. Although the screen data can be susceptible to a number of experimental biases, many of these can be corrected by computational analysis. For this purpose, here we have developed a web-based platform for integrated analysis and visualization of RNAi screen data named CARD (for Comprehensive Analysis of RNAi Data; available at https://card.

Systematic Screening Identifies Dual PI3K and mTOR Inhibition as a Conserved Therapeutic Vulnerability in Osteosarcoma.

Purpose: Osteosarcoma is the most common cancer of bone occurring mostly in teenagers. Despite rapid advances in our knowledge of the genetics and cell biology of osteosarcoma, significant improvements in patient survival have not been observed. The identification of effective therapeutics has been largely empirically based.

Complex-based analysis of dysregulated cellular processes in cancer.

Background: Differential expression analysis of (individual) genes is often used to study their roles in diseases. However, diseases such as cancer are a result of the combined effect of multiple genes. Gene products such as proteins seldom act in isolation, but instead constitute stable multi-protein complexes performing dedicated functions.

INsPeCT: INtegrative Platform for Cancer Transcriptomics.

The emergence of transcriptomics, fuelled by high-throughput sequencing technologies, has changed the nature of cancer research and resulted in a massive accumulation of data. Computational analysis, integration, and data visualization are now major bottlenecks in cancer biology and translational research. Although many tools have been brought to bear on these problems, their use remains unnecessarily restricted to computational biologists, as many tools require scripting skills, data infrastructure, and powerful computational facilities.

RMaNI: Regulatory Module Network Inference framework.

Background: Cell survival and development are orchestrated by complex interlocking programs of gene activation and repression. Understanding how this gene regulatory network (GRN) functions in normal states, and is altered in cancers subtypes, offers fundamental insight into oncogenesis and disease progression, and holds great promise for guiding clinical decisions. Inferring a GRN from empirical microarray gene expression data is a challenging task in cancer systems biology.

Supervised, semi-supervised and unsupervised inference of gene regulatory networks.

Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensive evaluation of inference methods on simulated and experimental expression data.

Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets.

Background: Altered networks of gene regulation underlie many complex conditions, including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality.