High-throughput RNAi screen for essential genes and drug synergistic combinations in colorectal cancer

HTSplotter: An end-to-end data processing, analysis and visualisation tool for chemical and genetic in vitro perturbation screening

In biomedical research, high-throughput screening is often applied as it comes with automatization, higher-efficiency, and more and faster results. High-throughput screening experiments encompass drug, drug combination, genetic perturbagen or a combination of genetic and chemical perturbagen screens. These experiments are conducted in real-time assays over time or in an endpoint assay. The data analysis consists of data cleaning and structuring, as well as further data processing and visualisation, which, due to the amount of data, can easily become laborious, time-consuming and error-prone. Therefore, several tools have been developed to aid researchers in this process, but these typically focus on specific experimental set-ups and are unable to process data of several time points and genetic-chemical perturbagen screens. To meet these needs, we developed HTSplotter, a web tool and Python module that performs automatic data analysis and visualization of visualization of eitherendpoint or real-time assays from different high-throughput screening experiments: drug, drug combination, genetic perturbagen and genetic-chemical perturbagen screens. HTSplotter implements an algorithm based on conditional statements to identify experiment types and controls. After appropriate data normalization, including growth rate normalization, HTSplotter executes downstream analyses such as dose-response relationship and drug synergism assessment by the Bliss independence (BI), Zero Interaction Potency (ZIP) and Highest Single Agent (HSA) methods. All results are exported as a text file and plots are saved in a PDF file. The main advantage of HTSplotter over other available tools is the automatic analysis of genetic-chemical perturbagen screens and real-time assays where growth rate and perturbagen effect results are plotted over time. In conclusion, HTSplotter allows for the automatic end-to-end data processing, analysis and visualisation of various high-throughput in vitro cell culture screens, offering major improvements in terms of versatility, efficiency and time over existing tools.

BOston Neonatal Brain Injury Dataset for Hypoxic Ischemic Encephalopathy (BONBID-HIE): Part I. MRI and Manual Lesion Annotation

Hypoxic ischemic encephalopathy (HIE) is a brain injury that occurs in 1 ~ 5/1000 term neonates. Accurate identification and segmentation of HIE-related lesions in neonatal brain magnetic resonance images (MRIs) is the first step toward predicting prognosis, identifying high-risk patients, and evaluating treatment effects. It will lead to a more accurate estimation of prognosis, a better understanding of neurological symptoms, and a timely prediction of response to therapy. We release the first public dataset containing neonatal brain diffusion MRI and expert annotation of lesions from 133 patients diagnosed with HIE. HIE-related lesions in brain MRI are often diffuse (i.e., multi-focal), and small (over half the patients in our data having lesions occupying <1% of brain volume). Segmentation for HIE MRI data is remarkably different from, and arguably more challenging than, other segmentation tasks such as brain tumors with focal and relatively large lesions. We hope that this dataset can help fuel the development of MRI lesion segmentation methods for HIE and small diffuse lesions in general.

Kinome-Wide siRNA Screening Identifies DYRK1B as a Potential Therapeutic Target for Triple-Negative Breast Cancer Cells

Simple Summary

Therapeutic target is limited for patients with triple-negative breast cancer (TNBC). Through kinome-wide siRNA (709 genes) screening, DYRK1B was identified as a potential gene essential for cell proliferation and mobility of TNBC cells, particularly in DYRK1B highly expressed TNBC cells. TNBC patients with high expression of DYRK1B had poor overall survival and disease-free survival. CCDC97 and ZNF581 were positively correlated with DYRK1B expression and might be involved in DYRK1B-mediated tumor malignancy in TNBC patients, providing DYRK1B as a potential theranostic target for TNBC.

Abstract

Aims: The selective molecules for targeted therapy of triple-negative breast cancer (TNBC) are limited. Several kinases play pivotal roles in cancer development and malignancy. The study aims to determine if any kinases confer to malignancy of TNBC cells, which could serve as a theranostic target for TNBC. Methods: Kinome siRNA library was used to screen selective genes required for the proliferation of TNBC cells. The involvement of DYRK1B in cancer malignancy was evaluated with migration, invasion assays, and spheroid culture. The expression of DYRK1B was confirmed with quantitative PCR and immunoblotting. The clinical correlation of DYRK1B in TNBC patients was examined with tissue microarray and The Cancer Genome Atlas (TCGA) database. Results: Our results showed that silencing DYRK1B significantly suppressed cell viability in DYRK1B-high expressed TNBC cells, likely by arresting the cell cycle at the G₁ phase. Nevertheless, silencing DYRK1B had marginal effects on DYRK1B-low expressed TNBC cells. Similarly, the knockdown of DYRK1B decreased tumorsphere formation and increased cell death of the tumorsphere. Moreover, inactivation of DYRK1B by either specific inhibitor or ectopic expressing catalytic mutant of DYRK1B inhibited cell viability and metastatic characteristics, including migration and invasion. In addition, DYRK1B protein expression was elevated in tumor tissues compared to that in adjacent normal tissues of TNBC patients. Further, DYRK1B gene expression was highly correlated with CCDC97 or ZNF581 genes in TNBC cells and patients. High co-expression of DYRK1B with CCDC97 or ZNF581 was significantly associated with unfavorable overall survival and disease-free survival of TNBC patients. Conclusions: our results suggest DYRK1B might be essential for promoting tumor progression and could be a theranostic target for TNBC. Silencing or inactivation of DYRK1B might be a potential targeted therapy for TNBC.

Molecularly Targeted Clinical Trials

Glioblastoma remains incurable despite advances in surgery, radiation, and chemotherapy, underscoring the need for new therapies. The genetic heterogenicity, presence of redundant molecular pathways, and the blood-brain barrier have limited the applicability of molecularly targeted agents. The therapeutic benefit seen with a small subset of patients suggests, however, that patient selection is critical. Recent investigations show that molecularly targeted synthetic lethality is a promising complementary approach. The article provides an overview of the challenges of molecularly targeted therapy in adults with glioblastoma, including current trials and future therapeutic directions.

Targeting Cdc42 with the anticancer compound MBQ-167 inhibits cell polarity and growth in the budding yeast S. cerevisiae

The Rho GTPase Cdc42 is highly conserved in structure and function. Mechanical or chemical cues in the microenvironment stimulate the localized activation of Cdc42 to rearrange the actin cytoskeleton and establish cell polarity. A role for Cdc42 in cell polarization was first discovered in the budding yeast Saccharomyces cerevisiae, and subsequently shown to also regulate directional motility in animal cells. Accordingly, in cancer Cdc42 promotes migration, invasion, and spread of tumor cells. Therefore, we targeted Cdc42 as a therapeutic strategy to treat metastatic breast cancer and designed the small molecule MBQ-167 as a potent inhibitor against Cdc42 and the homolog Rac. MBQ-167 inhibited cancer cell proliferation and migration in-vitro, and tumor growth and spread in-vivo in a mouse xenograft model of metastatic breast cancer. Since haploid budding yeast express a single Cdc42 gene, and do not express Rac, we used this well characterized model of polarization to define the contribution of Cdc42 inhibition to the effects of MBQ-167 in eukaryotic cells. Growth, budding pattern, and Cdc42 activity was determined in wildtype yeast or cells expressing a conditional knockdown of Cdc42 in response to vehicle or MBQ-167 treatment. As expected, growth and budding polarity were reduced by knocking-down Cdc42, with a parallel effect observed with MBQ-167. Cdc42 activity assays confirmed that MBQ-167 inhibits Cdc42 activation in yeast, and thus, bud polarity. Hence, we have validated MBQ-167 as a Cdc42 inhibitor in another biological context and present a method to screen Cdc42 inhibitors with potential as anti-metastatic cancer drugs.

Identification of key genes involved in the development and progression of early-onset colorectal cancer by co-expression network analysis

A number of studies have revealed that there is an increasing incidence of early-onset colorectal cancer (CRC) in young adults (before the age of 50 years) and a progressive decline in CRC among older patients, after the age of 50 years (late-onset CRC). However, the etiology of early-onset CRC is not fully understood. The aim of the present study was to identify key genes associated with the development of early-onset CRC through weighted gene co-expression network analysis (WGCNA). The GSE39582 dataset was downloaded from the Gene Expression Omnibus database, and the data profiles of tissues from patients diagnosed before the age of 50 years were selected. The top 10,000 genes with the highest variability were used to construct the WGCNA. Hub genes were identified from the modules associated with clinical traits using gene significance >0.2 and module membership >0.8 as the cut-off criteria. Gene Ontology and pathway analyses were subsequently performed on the hub genes and a protein-protein interaction network (PPI) was constructed. The diagnostic value of module hub genes with a degree score >5 in the PPI network was verified in samples from patients with CRC diagnosed before the age of 50 years obtained from The Cancer Genome Atlas. Eight co-expressed gene modules were identified in the WGCNA and two modules (blue and turquoise) were associated with the tumor-node-metastasis stage. A total of 140 module hub genes were identified and found to be enriched in 'mitochondrial large ribosomal subunit', 'structural constituent of ribosome', 'poly (A) RNA binding', 'collagen binding', 'protein ubiquitination' and 'ribosome pathway'. Twenty-six module hub genes were found to have a degree score >5 in the PPI network, seven of which [secreted protein acidic and cysteine rich (SPARC), decorin (DCN), fibrillin 1 (FBN1), WW domain containing transcription regulator 1 (WWTR1), transgelin (TAGLN), DEAD-box helicase 28 (DDX28) and cold shock domain containing C2 (CSDC2)], had good prognostic values for patients with early-onset CRC, but not late-onset CRC. Therefore, SPARC, DCN, FBN1, WWTR1, TAGLN, DDX28 and CSDC2 may contribute to the development of early-onset CRC and may serve as potential diagnostic biomarkers.

Hacking the Cancer Genome: Profiling Therapeutically Actionable Long Non-coding RNAs Using CRISPR-Cas9 Screening

Long non-coding RNAs (lncRNAs) represent a huge reservoir of potential cancer targets. Such "onco-lncRNAs" have resisted traditional RNAi methods, but CRISPR-Cas9 genome editing now promises functional screens at high throughput and low cost. The unique biology of lncRNAs demands screening strategies distinct from protein-coding genes. The first such screens have identified hundreds of onco-lncRNAs promoting cell proliferation and drug resistance. Ongoing developments will further improve screen performance and translational relevance. This Review aims to highlight the potential of CRISPR screening technology for discovering new onco-lncRNAs, and to guide molecular oncologists wishing to apply it to their cancer of interest.

Polypharmacology by Design: A Medicinal Chemist's Perspective on Multitargeting Compounds

Multitargeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation, or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multitarget compound development with valuable tools. In this Perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite some characteristic challenges remaining unresolved, designed polypharmacology holds enormous potential to secure future therapeutic innovation.