Cancer genomes often harbor hundreds of molecular aberrations. Such genetic variants can be drivers or passengers of tumorigenesis and create vulnerabilities for potential therapeutic exploitation. To identify genotype‐dependent vulnerabilities, forward genetic screens in different genetic backgrounds have been conducted. We devised MINGLE, a computational framework to integrate CRISPR/Cas9 screens originating from different libraries building on approaches pioneered for genetic network discovery in model organisms. We applied this method to integrate and analyze data from 85 CRISPR/Cas9 screens in human cancer cells combining functional data with information on genetic variants to explore more than 2.1 million gene‐background relationships. In addition to known dependencies, we identified new genotype‐specific vulnerabilities of cancer cells. Experimental validation of predicted vulnerabilities identified GANAB and PRKCSH as new positive regulators of Wnt/β‐catenin signaling. By clustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data are included.

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Background In recent years, large-scale genetic screens using the CRISPR/Cas9 system have emerged as scalable approaches able to interrogate gene function with unprecedented efficiency and specificity in various biological contexts. By this means, functional dependencies on both the protein-coding and noncoding genome of numerous cell types in different organisms have been interrogated. However, screening designs vary greatly and criteria for optimal experimental implementation and library composition are still emerging. Given their broad utility in functionally annotating genomes, the application and interpretation of genome-scale CRISPR screens would greatly benefit from consistent and optimal design criteria. Results We report advantages of conducting viability screens in selected Cas9 single-cell clones in contrast to Cas9 bulk populations. We further systematically analyzed published CRISPR screens in human cells to identify single-guide (sg) RNAs with consistent high on-target and low off-target activity. Selected guides were collected in a novel genome-scale sgRNA library, which efficiently identifies core and context-dependent essential genes. Conclusion We show how empirically designed libraries in combination with an optimized experimental design increase the dynamic range in gene essentiality screens at reduced library coverage.

Measles viruses derived from the live-attenuated Edmonton-B vaccine lineage are currently investigated as novel anti-cancer therapeutics. In this context, tumor specificity and oncolytic potency are key determinants of the therapeutic index. Here, we describe a systematic and comprehensive analysis of a recently developed post-entry targeting strategy based on the incorporation of microRNA target sites (miRTS) into the measles virus genome. We have established viruses with target sites for different microRNA species in the 3′ untranslated regions of either the N, F, H, or L genes and generated viruses harboring microRNA target sites in multiple genes. We report critical importance of target-site positioning with proximal genomic positions effecting maximum vector control. No relevant additional effect of six versus three miRTS copies for the same microRNA species in terms of regulatory efficiency was observed. Moreover, we demonstrate that, depending on the microRNA species, viral mRNAs containing microRNA target sites are directly cleaved and/or translationally repressed in presence of cognate microRNAs. In conclusion, we report highly efficient control of measles virus replication with various miRTS positions for development of safe and efficient cancer virotherapy and provide insights into the mechanisms underlying microRNA-mediated vector control.

Cancer cells rely on dysregulated gene expression programs to maintain their malignant phenotype. A cell's transcriptional state is controlled by a small set of interconnected transcription factors that form its core-regulatory circuit (CRC).Previous work in pediatric cancers has shown, that disruption of the CRC by genetic alterations causes tumor cells to become highly dependent on its components creating new opportunities for therapeutic intervention. However, the role of CRCs and the mechanisms by which they are controlled remain largely unknown for most tumor types. Here, we developed a method that infers lineage dependency scores to systematically predict functional CRCs and associated biological processes from context-dependent essentiality data sets. Analysis of genome-scale CRISPR-Cas9 screens in 558 cancer cell lines showed that most tumor types specifically depend on a small number of transcription factors for proliferation. We found that these transcription factors compose the CRCs in these tumor types. Moreover, they are frequently altered in patient tumor samples indicating their oncogenic potential.Finally, we show that biological processes associated with each CRC are revealed by analyzing codependency between lineage-specific essential genes. Our results demonstrate that genetic addiction to lineage-specific core transcriptional mechanisms occurs across a broad range of tumor types. We exploit this phenomenon to systematically infer CRCs from lineage specific gene essentiality. Furthermore, our findings shed light on the selective genetic vulnerabilities that arise as the consequence of transcriptional dysregulation in different tumor types and show how the plasticity of regulatory circuits might influence drug resistance and metastatic potential.

ABSTRACTclustering genes with similar genetic interaction profiles, we drew the largest genetic network in cancer cells to date. Our scalable approach highlights how diverse genetic screens can be integrated to systematically build informative maps of genetic interactions in cancer, which can grow dynamically as more data is included.

Wnt pathways are important for the modulation of tissue homeostasis, and their deregulation is linked to cancer development. Canonical Wnt signaling is hyperactivated in many human colorectal cancers due to genetic alterations of the negative Wnt regulator APC . However, the expression levels of Wnt‐dependent targets vary between tumors, and the mechanisms of carcinogenesis concomitant with this Wnt signaling dosage have not been understood. In this study, we integrate whole‐genome CRISPR/Cas9 screens with large‐scale multi‐omic data to delineate functional subtypes of cancer. We engineer APC loss‐of‐function mutations and thereby hyperactivate Wnt signaling in cells with low endogenous Wnt activity and find that the resulting engineered cells have an unfavorable metabolic equilibrium compared with cells which naturally acquired Wnt hyperactivation. We show that the dosage level of oncogenic Wnt hyperactivation impacts the metabolic equilibrium and the mitochondrial phenotype of a given cell type in a context‐dependent manner. These findings illustrate the impact of context‐dependent genetic interactions on cellular phenotypes of a central cancer driver mutation and expand our understanding of quantitative modulation of oncogenic signaling in tumorigenesis.

Given their broad utility in functionally annotating genomes, the experimental design of genome-scale CRISPR screens can vary greatly and criteria for optimal experimental implementation and library composition are still emerging. In this study, we report advantages of conducting viability screens in selected Cas9 single cell clones in contrast to Cas9 bulk populations. We further systematically analyzed published CRISPR screens in human cells to identify single-guide (sg)RNAs with consistent high on-target and low off-target activity. Selected guides were collected in a new genome-scale sgRNA library, which efficiently identifies core and context-dependent essential genes. In summary, we show how empirically designed libraries in combination with an optimised experimental design increase the dynamic range in gene essentiality screens at reduced library coverage.