Discovery of synthetic lethal and tumor suppressor paralog pairs in the human genome

Chromatin complex dependencies reveal targeting opportunities in leukemia

Chromatin regulators are frequently mutated in human cancer and are attractive drug targets. They include diverse proteins that share functional domains and assemble into related multi-subunit complexes. To investigate functional relationships among these regulators, here we apply combinatorial CRISPR knockouts (KOs) to test over 35,000 gene-gene pairings in leukemia cells, using a library of over 300,000 constructs. Top pairs that demonstrate either compensatory non-lethal interactions or synergistic lethality enrich for paralogs and targets that occupy the same protein complex. The screen highlights protein complex dependencies not apparent in single KO screens, for example MCM histone exchange, the nucleosome remodeling and deacetylase (NuRD) complex, and HBO1 (KAT7) complex. We explore two approaches to NuRD complex inactivation. Paralog and non-paralog combinations of the KAT7 complex emerge as synergistic lethal and specifically nominate the ING5 PHD domain as a potential therapeutic target when paired with other KAT7 complex member losses. These findings highlight the power of combinatorial screening to provide mechanistic insight and identify therapeutic targets within redundant networks.

Synthetic lethal interactions of DEAD/H-box helicases as targets for cancer therapy

DEAD/H-box helicases are implicated in virtually every aspect of RNA metabolism, including transcription, pre-mRNA splicing, ribosomes biogenesis, nuclear export, translation initiation, RNA degradation, and mRNA editing. Most of these helicases are upregulated in various cancers and mutations in some of them are associated with several malignancies. Lately, synthetic lethality (SL) and synthetic dosage lethality (SDL) approaches, where genetic interactions of cancer-related genes are exploited as therapeutic targets, are emerging as a leading area of cancer research. Several DEAD/H-box helicases, including DDX3, DDX9 (Dbp9), DDX10 (Dbp4), DDX11 (ChlR1), and DDX41 (Sacy-1), have been subjected to SL analyses in humans and different model organisms. It remains to be explored whether SDL can be utilized to identity druggable targets in DEAD/H-box helicase overexpressing cancers. In this review, we analyze gene expression data of a subset of DEAD/H-box helicases in multiple cancer types and discuss how their SL/SDL interactions can be used for therapeutic purposes. We also summarize the latest developments in clinical applications, apart from discussing some of the challenges in drug discovery in the context of targeting DEAD/H-box helicases.

Functional genomic analysis of adult and pediatric brain tumor isolates

Background

Adult and pediatric tumors display stark differences in their mutation spectra and chromosome alterations. Here, we attempted to identify common and unique gene dependencies and their associated biomarkers among adult and pediatric tumor isolates using functional genetic lethal screens and computational modeling.

Methods

We performed CRISRP-Cas9 lethality screens in two adult glioblastoma (GBM) tumor isolates and five pediatric brain tumor isolates representing atypical teratoid rhabdoid tumors (ATRT), diffuse intrinsic pontine glioma, GBM, and medulloblastoma. We then integrated the screen results with machine learning-based gene-dependency models generated from data from >900 cancer cell lines.

Results

We found that >50% of candidate dependencies of 280 identified were shared between adult GBM tumors and individual pediatric tumor isolates. 68% of screen hits were found as nodes in our network models, along with shared and tumor-specific predictors of gene dependencies. We investigated network predictors associated with ADAR, EFR3A, FGFR1 (pediatric-specific), and SMARCC2 (ATRT-specific) gene dependency among our tumor isolates.

Conclusions

The results suggest that, despite harboring disparate genomic signatures, adult and pediatric tumor isolates share a preponderance of genetic dependences. Further, combining data from primary brain tumor lethality screens with large cancer cell line datasets produced valuable insights into biomarkers of gene dependency, even for rare cancers.

Importance of the Study

Our results demonstrate that large cancer cell lines data sets can be computationally mined to identify known and novel gene dependency relationships in adult and pediatric human brain tumor isolates. Gene dependency networks and lethality screen results represent a key resource for neuro-oncology and cancer research communities. We also highlight some of the challenges and limitations of this approach.

annotate_my_genomes: an easy-to-use pipeline to improve genome annotation and uncover neglected genes by hybrid RNA sequencing

BACKGROUND

The advancement of hybrid sequencing technologies is increasingly expanding genome assemblies that are often annotated using hybrid sequencing transcriptomics, leading to improved genome characterization and the identification of novel genes and isoforms in a wide variety of organisms.

RESULTS

We developed an easy-to-use genome-guided transcriptome annotation pipeline that uses assembled transcripts from hybrid sequencing data as input and distinguishes between coding and long non-coding RNAs by integration of several bioinformatic approaches, including gene reconciliation with previous annotations in GTF format. We demonstrated the efficiency of this approach by correctly assembling and annotating all exons from the chicken SCO-spondin gene (containing more than 105 exons), including the identification of missing genes in the chicken reference annotations by homology assignments.

CONCLUSIONS

Our method helps to improve the current transcriptome annotation of the chicken brain. Our pipeline, implemented on Anaconda/Nextflow and Docker is an easy-to-use package that can be applied to a broad range of species, tissues, and research areas helping to improve and reconcile current annotations. The code and datasets are publicly available at https://github.com/cfarkas/annotate_my_genomes.

Synthetic lethal gene pairs: Experimental approaches and predictive models

Synthetic lethality (SL) refers to a genetic interaction in which the simultaneous perturbation of two genes leads to cell or organism death, whereas viability is maintained when only one of the pair is altered. The experimental exploration of these pairs and predictive modeling in computational biology contribute to our understanding of cancer biology and the development of cancer therapies. We extensively reviewed experimental technologies, public data sources, and predictive models in the study of synthetic lethal gene pairs and herein detail biological assumptions, experimental data, statistical models, and computational schemes of various predictive models, speculate regarding their influence on individual sample- and population-based synthetic lethal interactions, discuss the pros and cons of existing SL data and models, and highlight potential research directions in SL discovery.

Multiplexed engineering and precision gene editing in cellular immunotherapy

The advent of cellular immunotherapy in the clinic has entirely redrawn the treatment landscape for a growing number of human cancers. Genetically reprogrammed immune cells, including chimeric antigen receptor (CAR)-modified immune effector cells as well as T cell receptor (TCR) therapy, have demonstrated remarkable responses across different hard-to-treat patient populations. While these novel treatment options have had tremendous success in providing long-term remissions for a considerable fraction of treated patients, a number of challenges remain. Limited in vivo persistence and functional exhaustion of infused immune cells as well as tumor immune escape and on-target off-tumor toxicities are just some examples of the challenges which restrain the potency of today's genetically engineered cell products. Multiple engineering strategies are being explored to tackle these challenges.The advent of multiplexed precision genome editing has in recent years provided a flexible and highly modular toolkit to specifically address some of these challenges by targeted genetic interventions. This class of next-generation cellular therapeutics aims to endow engineered immune cells with enhanced functionality and shield them from immunosuppressive cues arising from intrinsic immune checkpoints as well as the hostile tumor microenvironment (TME). Previous efforts to introduce additional genetic modifications into immune cells have in large parts focused on nuclease-based tools like the CRISPR/Cas9 system or TALEN. However, nuclease-inactive platforms including base and prime editors have recently emerged and promise a potentially safer route to rewriting genetic sequences and introducing large segments of transgenic DNA without inducing double-strand breaks (DSBs). In this review, we discuss how these two exciting and emerging fields-cellular immunotherapy and precision genome editing-have co-evolved to enable a dramatic expansion in the possibilities to engineer personalized anti-cancer treatments. We will lay out how various engineering strategies in addition to nuclease-dependent and nuclease-inactive precision genome editing toolkits are increasingly being applied to overcome today's limitations to build more potent cellular therapeutics. We will reflect on how novel information-rich unbiased discovery approaches are continuously deepening our understanding of fundamental mechanisms governing tumor biology. We will conclude with a perspective of how multiplexed-engineered and gene edited cell products may upend today's treatment paradigms as they evolve into the next generation of more potent cellular immunotherapies.

Selective vulnerability of ARID1A deficient colon cancer cells to combined radiation and ATR-inhibitor therapy

ARID1A is frequently mutated in colorectal cancer (CRC) cells. Loss of ARID1A function compromises DNA damage repair and increases the reliance of tumor cells on ATR-dependent DNA repair pathways. Here, we investigated the effect of ionizing radiation (IR), in combination with ATR inhibitors (ATRi) in CRC cell lines with proficient and deficient ARID1A. The concept of selective vulnerability of ARID1A deficient CRC cells to ATRi was further tested in an ex vivo system by using the ATP-tumor chemosensitivity assay (ATP-TCA) in cells from untreated CRC patients, with and without ARID1A expression. We found selective sensitization upon ATRi treatment as well as after combined treatment with IR (P<0.001), especially in ARID1A deficient CRC cells (P <0.01). Knock-down of ARID1B further increased the selective radiosensitivity effect of ATRi in ARID1A negative cells (P<0.01). Mechanistically, ATRi abrogates the G2 checkpoint (P<0.01) and homologous recombination repair (P<0.01) in ARID1A deficient cells. Most importantly, ex-vivo experiments showed that ATRi had the highest radiosensitizing effect in ARID1A negative cells from CRC patients. Collectively, our results generate pre-clinical and clinical mechanistic rationale for assessing ARID1A defects as a biomarker for ATR inhibitor response as a single agent, or in a synthetic lethal approach in combination with IR.

Generation of dual-gRNA library for combinatorial CRISPR screening of synthetic lethal gene pairs

Combinatorial CRISPR screening is useful for investigating synthetic lethality (SL) gene pairs. Here, we detail the steps for dual-gRNA library construction, with the introduction of two backbones, LentiGuide_DKO and LentiCRISPR_DKO. We describe steps for in vitro screening with 22Rv1-Cas9 and SaOS2-Cas9 cells followed by sequencing and data analysis. By introducing two backbones, we optimized the library construction process, facilitated standard pair-end sequencing, and provided options of screening on cells with or without modification of Cas9 expression.

TP53-dependent toxicity of CRISPR/Cas9 cuts is differential across genomic loci and can confound genetic screening

CRISPR/Cas9 gene editing can inactivate genes in a precise manner. This process involves DNA double-strand breaks (DSB), which may incur a loss of cell fitness. We hypothesize that DSB toxicity may be variable depending on the chromatin environment in the targeted locus. Here, by analyzing isogenic cell line pair CRISPR experiments jointly with previous screening data from across ~900 cell lines, we show that TP53-associated break toxicity is higher in genomic regions that harbor active chromatin, such as gene regulatory elements or transcription elongation histone marks. DSB repair pathway choice and DNA sequence context also associate with toxicity. We also show that, due to noise introduced by differential toxicity of sgRNA-targeted sites, the power of genetic screens to detect conditional essentiality is reduced in TP53 wild-type cells. Understanding the determinants of Cas9 cut toxicity will help improve design of CRISPR reagents to avoid incidental selection of TP53-deficient and/or DNA repair deficient cells.

Reduced gene templates for supervised analysis of scale-limited CRISPR-Cas9 fitness screens

Pooled genome-wide CRISPR-Cas9 screens are furthering our mechanistic understanding of human biology and have allowed us to identify new oncology therapeutic targets. Scale-limited CRISPR-Cas9 screens-typically employing guide RNA libraries targeting subsets of functionally related genes, biological pathways, or portions of the druggable genome-constitute an optimal setting for investigating narrow hypotheses and are easier to execute on complex models, such as organoids and in vivo models. Different supervised methods are used for computational analysis of genome-wide CRISPR-Cas9 screens; most are not well suited for scale-limited screens, as they require large sets of positive/negative control genes (gene templates) to be included among the screened ones. Here, we develop a computational framework identifying optimal subsets of known essential and nonessential genes (at different subsampling percentages) that can be used as templates for supervised analyses of scale-limited CRISPR-Cas9 screens, while having a reduced impact on the size of the employed library.

Comparative optimization of combinatorial CRISPR screens

Combinatorial CRISPR technologies have emerged as a transformative approach to systematically probe genetic interactions and dependencies of redundant gene pairs. However, the performance of different functional genomic tools for multiplexing sgRNAs vary widely. Here, we generate and benchmark ten distinct pooled combinatorial CRISPR libraries targeting paralog pairs to optimize digenic knockout screens. Libraries composed of dual Streptococcus pyogenes Cas9 (spCas9), orthogonal spCas9 and Staphylococcus aureus (saCas9), and enhanced Cas12a from Acidaminococcus were evaluated. We demonstrate a combination of alternative tracrRNA sequences from spCas9 consistently show superior effect size and positional balance between the sgRNAs as a robust combinatorial approach to profile genetic interactions of multiple genes.

Interrogation of cancer gene dependencies reveals paralog interactions of autosome and sex chromosome-encoded genes

Genetic networks are characterized by extensive buffering. During tumor evolution, disruption of functional redundancies can create de novo vulnerabilities that are specific to cancer cells. Here, we systematically search for cancer-relevant paralog interactions using CRISPR screens and publicly available loss-of-function datasets. Our analysis reveals >2,000 candidate dependencies, several of which we validate experimentally, including CSTF2-CSTF2T, DNAJC15-DNAJC19, FAM50A-FAM50B, and RPP25-RPP25L. We provide evidence that RPP25L can physically and functionally compensate for the absence of RPP25 as a member of the RNase P/MRP complexes in tRNA processing. Our analysis also reveals unexpected redundancies between sex chromosome genes. We show that chrX- and chrY-encoded paralogs, such as ZFX-ZFY, DDX3X-DDX3Y, and EIF1AX-EIF1AY, are functionally linked. Tumor cell lines from male patients with loss of chromosome Y become dependent on the chrX-encoded gene. We propose targeting of chrX-encoded paralogs as a general therapeutic strategy for human tumors that have lost the Y chromosome.

CRISPR based therapeutics: a new paradigm in cancer precision medicine

Background

Clustered regularly interspaced short palindromic repeat (CRISPR)-CRISPR-associated protein (Cas) systems are the latest addition to the plethora of gene-editing tools. These systems have been repurposed from their natural counterparts by means of both guide RNA and Cas nuclease engineering. These RNA-guided systems offer greater programmability and multiplexing capacity than previous generation gene editing tools based on zinc finger nucleases and transcription activator like effector nucleases. CRISPR-Cas systems show great promise for individualization of cancer precision medicine.

Main body

The biology of Cas nucleases and dead Cas based systems relevant for in vivo gene therapy applications has been discussed. The CRISPR knockout, CRISPR activation and CRISPR interference based genetic screens which offer opportunity to assess functions of thousands of genes in massively parallel assays have been also highlighted. Single and combinatorial gene knockout screens lead to identification of drug targets and synthetic lethal genetic interactions across different cancer phenotypes. There are different viral and non-viral (nanoformulation based) modalities that can carry CRISPR-Cas components to different target organs in vivo.

Conclusion

The latest developments in the field in terms of optimization of performance of the CRISPR-Cas elements should fuel greater application of the latter in the realm of precision medicine. Lastly, how the already available knowledge can help in furtherance of use of CRISPR based tools in personalized medicine has been discussed.

Development of Metabolic Synthetic Lethality and Its Implications for Thyroid Cancer

Cancer therapies targeting genetic alterations are a topic of great interest in the field of thyroid cancer, which frequently harbors mutations in the RAS, RAF, and RET genes. Unfortunately, U.S. Food and Drug Administration-approved BRAF inhibitors have relatively low therapeutic efficacy against BRAF-mutant thyroid cancer; in addition, the cancer often acquires drug resistance, which prevents effective treatment. Recent advances in genomics and transcriptomics are leading to a more complete picture of the range of mutations, both driver and messenger, present in thyroid cancer. Furthermore, our understanding of cancer suggests that oncogenic mutations drive tumorigenesis and induce rewiring of cancer cell metabolism, which promotes survival of mutated cells. Synthetic lethality (SL) is a method of neutralizing mutated genes that were previously considered untargetable by traditional genotype-targeted treatments. Because these metabolic events are specific to cancer cells, we have the opportunity to develop new therapies that target tumor cells specifically without affecting healthy tissue. Here, we describe developments in metabolism-based cancer therapy, focusing on the concept of metabolic SL in thyroid cancer. Finally, we discuss the essential implications of metabolic reprogramming and its role in the future direction of SL for thyroid cancer.

Inhibition of T-antigen expression promoting glycogen synthase kinase 3 impairs merkel cell carcinoma cell growth

Merkel cell carcinoma is an aggressive skin cancer frequently caused by the Merkel cell polyomavirus (MCPyV). Since proliferation of MCPyV-positive MCC tumor cells strictly depends on expression of the virus-encoded T antigens (TA), these proteins theoretically represent ideal targets for different kinds of therapeutic approaches. Here we developed a cell-based assay to identify compounds which specifically inhibit growth of MCC cells by repressing TA expression. Applying this technique we screened a kinase inhibitor library and identified six compounds targeting glycogen synthase kinase 3 (GSK3) such as CHIR99021 as suppressors of TA transcription in MCC cells. Involvement of GSK3α and -β in the regulation of TA-expression was confirmed by combining GSK3A knockout with inducible GSK3B shRNA knockdown since double knockouts could not be generated. Finally, we demonstrate that CHIR99021 exhibits in vivo antitumor activity in an MCC xenograft mouse model suggesting GSK3 inhibitors as potential therapeutics for the treatment of MCC in the future.

Common computational tools for analyzing CRISPR screens

CRISPR–Cas technology offers a versatile toolbox for genome editing, with applications in various cancer-related fields such as functional genomics, immunotherapy, synthetic lethality and drug resistance, metastasis, genome regulation, chromatic accessibility and RNA-targeting. The variety of screening platforms and questions in which they are used have caused the development of a wide array of analytical methods for CRISPR analysis. In this review, we focus on the algorithms and frameworks used in the computational analysis of pooled CRISPR knockout (KO) screens and highlight some of the most significant target discoveries made using these methods. Lastly, we offer perspectives on the design and analysis of state-of-art multiplex screening for genetic interactions.

Tools for Decoding Ubiquitin Signaling in DNA Repair

The maintenance of genome stability requires dedicated DNA repair processes and pathways that are essential for the faithful duplication and propagation of chromosomes. These DNA repair mechanisms counteract the potentially deleterious impact of the frequent genotoxic challenges faced by cells from both exogenous and endogenous agents. Intrinsic to these mechanisms, cells have an arsenal of protein factors that can be utilised to promote repair processes in response to DNA lesions. Orchestration of the protein factors within the various cellular DNA repair pathways is performed, in part, by post-translational modifications, such as phosphorylation, ubiquitin, SUMO and other ubiquitin-like modifiers (UBLs). In this review, we firstly explore recent advances in the tools for identifying factors involved in both DNA repair and ubiquitin signaling pathways. We then expand on this by evaluating the growing repertoire of proteomic, biochemical and structural techniques available to further understand the mechanistic basis by which these complex modifications regulate DNA repair. Together, we provide a snapshot of the range of methods now available to investigate and decode how ubiquitin signaling can promote DNA repair and maintain genome stability in mammalian cells.