PICKLES: the database of pooled in-vitro CRISPR knockout library essentiality screens

Targeting axonal guidance dependencies in glioblastoma with ROBO1 CAR T cells

Resistance to genotoxic therapies and tumor recurrence are hallmarks of glioblastoma (GBM), an aggressive brain tumor. In this study, we investigated functional drivers of post-treatment recurrent GBM through integrative genomic analyses, genome-wide genetic perturbation screens in patient-derived GBM models and independent lines of validation. Specific genetic dependencies were found consistent across recurrent tumor models, accompanied by increased mutational burden and differential transcript and protein expression compared to its primary GBM predecessor. Our observations suggest a multi-layered genetic response to drive tumor recurrence and implicate PTP4A2 (protein tyrosine phosphatase 4A2) as a modulator of self-renewal, proliferation and tumorigenicity in recurrent GBM. Genetic perturbation or small-molecule inhibition of PTP4A2 acts through a dephosphorylation axis with roundabout guidance receptor 1 (ROBO1) and its downstream molecular players, exploiting a functional dependency on ROBO signaling. Because a pan-PTP4A inhibitor was limited by poor penetrance across the blood-brain barrier in vivo, we engineered a second-generation chimeric antigen receptor (CAR) T cell therapy against ROBO1, a cell surface receptor enriched across recurrent GBM specimens. A single dose of ROBO1-targeted CAR T cells doubled median survival in cell-line-derived xenograft (CDX) models of recurrent GBM. Moreover, in CDX models of adult lung-to-brain metastases and pediatric relapsed medulloblastoma, ROBO1 CAR T cells eradicated tumors in 50-100% of mice. Our study identifies a promising multi-targetable PTP4A-ROBO1 signaling axis that drives tumorigenicity in recurrent GBM, with potential in other malignant brain tumors.

3Bs of CRISPR-Cas mediated genome editing in plants: exploring the basics, bioinformatics and biosafety landscape

The recent thrust in research has projected the type II clustered regularly interspaced short palindromic repeats and associated protein 9 (CRISPR-Cas9) system as an avant-garde plant genome editing tool. It facilitates the induction of site-specific double-stranded DNA cleavage by the RNA-guided DNA endonuclease (RGEN), Cas9. Elimination, addition, or alteration of sections in DNA sequence besides the creation of a knockout genotype (CRISPRko) is aided by the CRISPR-Cas9 system in its wild form (wtCas9). The inactivation of the nuclease domain generates a dead Cas9 (dCas9), which is capable of targeting genomic DNA without scissoring it. The dCas9 system can be engineered by fusing it with different effectors to facilitate transcriptional activation (CRISPRa) and transcriptional interference (CRISPRi). CRISPR-Cas thus holds tremendous prospects as a genome-manipulating stratagem for a wide gamut of crops. In this article, we present a brief on the fundamentals and the general workflow of the CRISPR-Cas system followed by an overview of the prospects of bioinformatics in propelling CRISPR-Cas research with a special thrust on the available databases and algorithms/web-accessible applications that have aided in increasing the usage and efficiency of editing. The article also provides an update on the current regulatory landscape in different countries on the CRISPR-Cas edited plants to emphasize the far-reaching impact of the genomic editing technology.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01397-3.

Systems Medicine for Precise Targeting of Glioblastoma

Glioblastoma (GBM) is a malignant cancer that is fatal even after standard therapy and the effects of current available therapeutics are not promising due its complex and evolving epigenetic and genetic profile. The mysteries that lead to GBM intratumoral heterogeneity and subtype transitions are not entirely clear. Systems medicine is an approach to view the patient in a whole picture integrating systems biology and synthetic biology along with computational techniques. Since the GBM oncogenesis involves genetic mutations, various therapies including gene therapeutics based on CRISPR-Cas technique, MicroRNAs, and implanted synthetic cells endowed with synthetic circuits against GBM with neural stem cells and mesenchymal stem cells acting as potential vehicles carrying therapeutics via the intranasal route, avoiding the risks of invasive methods in order to reach the GBM cells in the brain are discussed and proposed in this review. Systems medicine approach is a rather novel strategy, and since the GBM of a patient is complex and unique, thus to devise an individualized treatment strategy to tailor personalized multimodal treatments for the individual patient taking into account the phenotype of the GBM, the unique body health profile of the patient and individual responses according to the systems medicine concept might show potential to achieve optimum effects.

Highlights from the 1st European cancer dependency map symposium and workshop

The systematic identification of tumour vulnerabilities through perturbational experiments on cancer models, including genome editing and drug screens, is playing a crucial role in combating cancer. This collective effort is known as the Cancer Dependency Map (DepMap). The 1st European Cancer Dependency Map Symposium (EuroDepMap), held in Milan last May, featured talks, a roundtable discussion, and a poster session, showcasing the latest discoveries and future challenges related to the DepMap. The symposium aimed to facilitate interactions among participants across Europe, encourage idea exchange with leading experts, and present their work and future projects. Importantly, it sparked discussions on future endeavours, such as screening more complex cancer models and accounting for tumour evolution.

Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens

Most variants associated with complex traits and diseases identified by genome-wide association studies (GWAS) map to noncoding regions of the genome with unknown effects. Using ancestrally diverse, biobank-scale GWAS data, massively parallel CRISPR screens, and single-cell transcriptomic and proteomic sequencing, we discovered 124 cis-target genes of 91 noncoding blood trait GWAS loci. Using precise variant insertion through base editing, we connected specific variants with gene expression changes. We also identified trans-effect networks of noncoding loci when cis target genes encoded transcription factors or microRNAs. Networks were themselves enriched for GWAS variants and demonstrated polygenic contributions to complex traits. This platform enables massively parallel characterization of the target genes and mechanisms of human noncoding variants in both cis and trans.

NRF2 activation induces NADH-reductive stress, providing a metabolic vulnerability in lung cancer

Multiple cancers regulate oxidative stress by activating the transcription factor NRF2 through mutation of its negative regulator, KEAP1. NRF2 has been studied extensively in KEAP1-mutant cancers; however, the role of this pathway in cancers with wild-type KEAP1 remains poorly understood. To answer this question, we induced NRF2 via pharmacological inactivation of KEAP1 in a panel of 50+ non-small cell lung cancer cell lines. Unexpectedly, marked decreases in viability were observed in >13% of the cell lines-an effect that was rescued by NRF2 ablation. Genome-wide and targeted CRISPR screens revealed that NRF2 induces NADH-reductive stress, through the upregulation of the NAD+-consuming enzyme ALDH3A1. Leveraging these findings, we show that cells treated with KEAP1 inhibitors or those with endogenous KEAP1 mutations are selectively vulnerable to Complex I inhibition, which impairs NADH oxidation capacity and potentiates reductive stress. Thus, we identify reductive stress as a metabolic vulnerability in NRF2-activated lung cancers.

Subtype-specific kinase dependency regulates growth and metastasis of poor-prognosis mesenchymal colorectal cancer

BACKGROUND

Colorectal cancer (CRC) can be divided into four consensus molecular subtypes (CMS), each with distinct biological features. CMS4 is associated with epithelial-mesenchymal transition and stromal infiltration (Guinney et al., Nat Med 21:1350-6, 2015; Linnekamp et al., Cell Death Differ 25:616-33, 2018), whereas clinically it is characterized by lower responses to adjuvant therapy, higher incidence of metastatic spreading and hence dismal prognosis (Buikhuisen et al., Oncogenesis 9:66, 2020).

METHODS

To understand the biology of the mesenchymal subtype and unveil specific vulnerabilities, a large CRISPR-Cas9 drop-out screen was performed on 14 subtyped CRC cell lines to uncover essential kinases in all CMSs. Dependency of CMS4 cells on p21-activated kinase 2 (PAK2) was validated in independent 2D and 3D in vitro cultures and in vivo models assessing primary and metastatic outgrowth in liver and peritoneum. TIRF microscopy was used to uncover actin cytoskeleton dynamics and focal adhesion localization upon PAK2 loss. Subsequent functional assays were performed to determine altered growth and invasion patterns.

RESULTS

PAK2 was identified as a key kinase uniquely required for growth of the mesenchymal subtype CMS4, both in vitro and in vivo. PAK2 plays an important role in cellular attachment and cytoskeletal rearrangements (Coniglio et al., Mol Cell Biol 28:4162-72, 2008; Grebenova et al., Sci Rep 9:17171, 2019). In agreement, deletion or inhibition of PAK2 impaired actin cytoskeleton dynamics in CMS4 cells and, as a consequence, significantly reduced invasive capacity, while it was dispensable for CMS2 cells. Clinical relevance of these findings was supported by the observation that deletion of PAK2 from CMS4 cells prevented metastatic spreading in vivo. Moreover, growth in a model for peritoneal metastasis was hampered when CMS4 tumor cells were deficient for PAK2.

CONCLUSION

Our data reveal a unique dependency of mesenchymal CRC and provide a rationale for PAK2 inhibition to target this aggressive subgroup of colorectal cancer.

Membrane compartmentalisation of the ubiquitin system

We now have a comprehensive inventory of ubiquitin system components. Understanding of any system also needs an appreciation of how components are organised together. Quantitative proteomics has provided us with a census of their relative populations in several model cell types. Here, by examining large scale unbiased data sets, we seek to identify and map those components, which principally reside on the major organelles of the endomembrane system. We present the consensus distribution of > 50 ubiquitin modifying enzymes, E2s, E3s and DUBs, that possess transmembrane domains. This analysis reveals that the ER and endosomal compartments have a diverse cast of resident E3s, whilst the Golgi and mitochondria operate with a more restricted palette. We describe key functions of ubiquitylation that are specific to each compartment and relate this to their signature complement of ubiquitin modifying components.

High-Throughput CRISPR Screening in Hematological Neoplasms

CRISPR is becoming an indispensable tool in biological research, revolutionizing diverse fields of medical research and biotechnology. In the last few years, several CRISPR-based genome-targeting tools have been translated for the study of hematological neoplasms. However, there is a lack of reviews focused on the wide uses of this technology in hematology. Therefore, in this review, we summarize the main CRISPR-based approaches of high throughput screenings applied to this field. Here we explain several libraries and algorithms for analysis of CRISPR screens used in hematology, accompanied by the most relevant databases. Moreover, we focus on (1) the identification of novel modulator genes of drug resistance and efficacy, which could anticipate relapses in patients and (2) new therapeutic targets and synthetic lethal interactions. We also discuss the approaches to uncover novel biomarkers of malignant transformations and immune evasion mechanisms. We explain the current literature in the most common lymphoid and myeloid neoplasms using this tool. Then, we conclude with future directions, highlighting the importance of further gene candidate validation and the integration and harmonization of the data from CRISPR screening approaches.

Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions

BACKGROUND

Coessentiality networks derived from CRISPR screens in cell lines provide a powerful framework for identifying functional modules in the cell and for inferring the roles of uncharacterized genes. However, these networks integrate signal across all underlying data and can mask strong interactions that occur in only a subset of the cell lines analyzed.

RESULTS

Here, we decipher dynamic functional interactions by identifying significant cellular contexts, primarily by oncogenic mutation, lineage, and tumor type, and discovering coessentiality relationships that depend on these contexts. We recapitulate well-known gene-context interactions such as oncogene-mutation, paralog buffering, and tissue-specific essential genes, show how mutation rewires known signal transduction pathways, including RAS/RAF and IGF1R-PIK3CA, and illustrate the implications for drug targeting. We further demonstrate how context-dependent functional interactions can elucidate lineage-specific gene function, as illustrated by the maturation of proreceptors IGF1R and MET by proteases FURIN and CPD.

CONCLUSIONS

This approach advances our understanding of context-dependent interactions and how they can be gleaned from these data. We provide an online resource to explore these context-dependent interactions at diffnet.hart-lab.org.

Synthetic Vulnerabilities in the KRAS Pathway

Mutations in Kristen Rat Sarcoma viral oncogene (KRAS) are among the most frequent gain-of-function genetic alterations in human cancer. Most KRAS-driven cancers depend on its sustained expression and signaling. Despite spectacular recent success in the development of inhibitors targeting specific KRAS alleles, the discovery and utilization of effective directed therapies for KRAS-mutant cancers remains a major unmet need. One potential approach is the identification of KRAS-specific synthetic lethal vulnerabilities. For example, while KRAS-driven oncogenesis requires the activation of a number of signaling pathways, it also triggers stress response pathways in cancer cells that could potentially be targeted for therapeutic benefit. This review will discuss how the latest advances in functional genomics and the development of more refined models have demonstrated the existence of molecular pathways that can be exploited to uncover synthetic lethal interactions with a promising future as potential clinical treatments in KRAS-mutant cancers.

Deubiquitinase Vulnerabilities Identified through Activity-Based Protein Profiling in Non-Small Cell Lung Cancer

To aid in the prioritization of deubiquitinases (DUBs) as anticancer targets, we developed an approach combining activity-based protein profiling (ABPP) with mass spectrometry in both non-small cell lung cancer (NSCLC) tumor tissues and cell lines along with analysis of available RNA interference and CRISPR screens. We identified 67 DUBs in NSCLC tissues, 17 of which were overexpressed in adenocarcinoma or squamous cell histologies and 12 of which scored as affecting lung cancer cell viability in RNAi or CRISPR screens. We used the CSN5 inhibitor, which targets COPS5/CSN5, as a tool to understand the biological significance of one of these 12 DUBs, COPS6, in lung cancer. Our study provides a powerful resource to interrogate the role of DUB signaling biology and nominates druggable targets for the treatment of lung cancer subtypes.

Protein degradation on the global scale

Protein degradation occurs through proteasomal, endosomal, and lysosomal pathways. Technological advancements have allowed for the determination of protein copy numbers and turnover rates on a global scale, which has provided an overview of trends and rules governing protein degradation. Sharper chemical and gene-editing tools have enabled the specific perturbation of each degradation pathway, whose effects on protein dynamics can now be comprehensively analyzed. We review major studies and innovation in this field and discuss the interdependence between the major pathways of protein degradation.

Comparative assessment of genes driving cancer and somatic evolution in non-cancer tissues: an update of the Network of Cancer Genes (NCG) resource

Background

Genetic alterations of somatic cells can drive non-malignant clone formation and promote cancer initiation. However, the link between these processes remains unclear and hampers our understanding of tissue homeostasis and cancer development.

Results

Here, we collect a literature-based repertoire of 3355 well-known or predicted drivers of cancer and non-cancer somatic evolution in 122 cancer types and 12 non-cancer tissues. Mapping the alterations of these genes in 7953 pan-cancer samples reveals that, despite the large size, the known compendium of drivers is still incomplete and biased towards frequently occurring coding mutations. High overlap exists between drivers of cancer and non-cancer somatic evolution, although significant differences emerge in their recurrence. We confirm and expand the unique properties of drivers and identify a core of evolutionarily conserved and essential genes whose germline variation is strongly counter-selected. Somatic alteration in even one of these genes is sufficient to drive clonal expansion but not malignant transformation.

Conclusions

Our study offers a comprehensive overview of our current understanding of the genetic events initiating clone expansion and cancer revealing significant gaps and biases that still need to be addressed. The compendium of cancer and non-cancer somatic drivers, their literature support, and properties are accessible in the Network of Cancer Genes and Healthy Drivers resource at http://www.network-cancer-genes.org/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13059-022-02607-z.

Identification of Essential Genes Using Sequential CRISPR and siRNA Screens

Genome-wide CRISPR and siRNA screening methodologies are powerful tools that are aptly suited to the discovery of essential genes. In this chapter, we outline our methods to conduct sequential CRISPR and siRNA screens to quickly and efficiently identify essential genes within a collection of cell lines. The utilization of both screening methodologies provides a pipeline that minimizes costs and time while enabling the robust detection of candidate genes.

Futuristic CRISPR-based biosensing in the cloud and internet of things era: an overview

Biosensors-based devices are transforming medical diagnosis of diseases and monitoring of patient signals. The development of smart and automated molecular diagnostic tools equipped with biomedical big data analysis, cloud computing and medical artificial intelligence can be an ideal approach for the detection and monitoring of diseases, precise therapy, and storage of data over the cloud for supportive decisions. This review focused on the use of machine learning approaches for the development of futuristic CRISPR-biosensors based on microchips and the use of Internet of Things for wireless transmission of signals over the cloud for support decision making. The present review also discussed the discovery of CRISPR, its usage as a gene editing tool, and the CRISPR-based biosensors with high sensitivity of Attomolar (10^-18 M), Femtomolar (10^-15 M) and Picomolar (10^-12 M) in comparison to conventional biosensors with sensitivity of nanomolar 10^-9 M and micromolar 10^-3 M. Additionally, the review also outlines limitations and open research issues in the current state of CRISPR-based biosensing applications.

CoRe: a robustly benchmarked R package for identifying core-fitness genes in genome-wide pooled CRISPR-Cas9 screens

BACKGROUND

CRISPR-Cas9 genome-wide screens are being increasingly performed, allowing systematic explorations of cancer dependencies at unprecedented accuracy and scale. One of the major computational challenges when analysing data derived from such screens is to identify genes that are essential for cell survival invariantly across tissues, conditions, and genomic-contexts (core-fitness genes), and to distinguish them from context-specific essential genes. This is of paramount importance to assess the safety profile of candidate therapeutic targets and for elucidating mechanisms involved in tissue-specific genetic diseases.

RESULTS

We have developed CoRe: an R package implementing existing and novel methods for the identification of core-fitness genes (at two different level of stringency) from joint analyses of multiple CRISPR-Cas9 screens. We demonstrate, through a fully reproducible benchmarking pipeline, that CoRe outperforms state-of-the-art tools, yielding more reliable and biologically relevant sets of core-fitness genes.

CONCLUSIONS

CoRe offers a flexible pipeline, compatible with many pre-processing methods for the analysis of CRISPR data, which can be tailored onto different use-cases. The CoRe package can be used for the identification of high-confidence novel core-fitness genes, as well as a means to filter out potentially cytotoxic hits while analysing cancer dependency datasets for identifying and prioritising novel selective therapeutic targets.

To Discover the Efficient and Novel Drug Targets in Human Cancers Using CRISPR/Cas Screening and Databases

CRISPR/Cas has emerged as an excelle nt gene-editing technology and is used worldwide for research. The CRISPR library is an ideal tool for identifying essential genes and synthetic lethality targeted for cancer therapies in human cancers. Synthetic lethality is defined as multiple genetic abnormalities that, when present individually, do not affect function or survival, but when present together, are lethal. Recently, many CRISPR libraries are available, and the latest libraries are more accurate and can be applied to few cells. However, it is easier to efficiently search for cancer targets with their own screenings by effectively using databases of CRISPR screenings, such as Depmap portal, PICKLES (Pooled In-Vitro CRISPR Knockout Library Essentiality Screens), iCSDB, Project Score database, and CRISP-view. This review will suggest recent optimal CRISPR libraries and effective databases for Novel Approaches in the Discovery and Design of Targeted Therapies.

Discovery of putative tumor suppressors from CRISPR screens reveals rewired lipid metabolism in acute myeloid leukemia cells

CRISPR knockout fitness screens in cancer cell lines reveal many genes whose loss of function causes cell death or loss of fitness or, more rarely, the opposite phenotype of faster proliferation. Here we demonstrate a systematic approach to identify these proliferation suppressors, which are highly enriched for tumor suppressor genes, and define a network of 145 such genes in 22 modules. One module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of acute myeloid leukemia cell lines. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe loss of fitness phenotype for genes in the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirm biochemically. Overexpression of this module is associated with a survival advantage in juvenile leukemias, suggesting a clinically relevant subtype.

CRISPR-based knockout screens in cancer cells have suggested the existence of proliferation suppressor genes (PSG). Here, the authors develop an approach to systematically identify them, and reveal a PSG module involved in fatty acid synthesis and tumour suppression in acute myeloid leukemia cell lines.

CSC software corrects off-target mediated gRNA depletion in CRISPR-Cas9 essentiality screens

Off-target effects are well established confounders of CRISPR negative selection screens that impair the identification of essential genomic loci. In particular, non-coding regulatory elements and repetitive regions are often difficult to target with specific gRNAs, effectively precluding the unbiased screening of a large portion of the genome. To address this, we developed CRISPR Specificity Correction (CSC), a computational method that corrects for the effect of off-targeting on gRNA depletion. We benchmark CSC with data from the Cancer Dependency Map and show that it significantly improves the overall sensitivity and specificity of viability screens while preserving known essentialities, particularly for genes targeted by highly promiscuous gRNAs. We believe this tool will further enable the functional annotation of the genome as it represents a robust alternative to the traditional filtering strategy of discarding unspecific guides from the analysis. CSC is an open-source software that can be seamlessly integrated into current CRISPR analysis pipelines.

A global cancer data integrator reveals principles of synthetic lethality, sex disparity and immunotherapy

BACKGROUND

Advances in cancer biology are increasingly dependent on integration of heterogeneous datasets. Large-scale efforts have systematically mapped many aspects of cancer cell biology; however, it remains challenging for individual scientists to effectively integrate and understand this data.

RESULTS

We have developed a new data retrieval and indexing framework that allows us to integrate publicly available data from different sources and to combine publicly available data with new or bespoke datasets. Our approach, which we have named the cancer data integrator (CanDI), is straightforward to implement, is well documented, and is continuously updated which should enable individual users to take full advantage of efforts to map cancer cell biology. We show that CanDI empowered testable hypotheses of new synthetic lethal gene pairs, genes associated with sex disparity, and immunotherapy targets in cancer.

CONCLUSIONS

CanDI provides a flexible approach for large-scale data integration in cancer research enabling rapid generation of hypotheses. The CanDI data integrator is available at https://github.com/GilbertLabUCSF/CanDI .

An integrated functional and clinical genomics approach reveals genes driving aggressive metastatic prostate cancer

Genomic sequencing of thousands of tumors has revealed many genes associated with specific types of cancer. Similarly, large scale CRISPR functional genomics efforts have mapped genes required for cancer cell proliferation or survival in hundreds of cell lines. Despite this, for specific disease subtypes, such as metastatic prostate cancer, there are likely a number of undiscovered tumor specific driver genes that may represent potential drug targets. To identify such genetic dependencies, we performed genome-scale CRISPRi screens in metastatic prostate cancer models. We then created a pipeline in which we integrated pan-cancer functional genomics data with our metastatic prostate cancer functional and clinical genomics data to identify genes that can drive aggressive prostate cancer phenotypes. Our integrative analysis of these data reveals known prostate cancer specific driver genes, such as AR and HOXB13, as well as a number of top hits that are poorly characterized. In this study we highlight the strength of an integrated clinical and functional genomics pipeline and focus on two top hit genes, KIF4A and WDR62. We demonstrate that both KIF4A and WDR62 drive aggressive prostate cancer phenotypes in vitro and in vivo in multiple models, irrespective of AR-status, and are also associated with poor patient outcome.

SynLeGG: analysis and visualization of multiomics data for discovery of cancer 'Achilles Heels' and gene function relationships

Achilles’ heel relationships arise when the status of one gene exposes a cell's vulnerability to perturbation of a second gene, such as chemical inhibition, providing therapeutic opportunities for precision oncology. SynLeGG (www.overton-lab.uk/synlegg) identifies and visualizes mutually exclusive loss signatures in ‘omics data to enable discovery of genetic dependency relationships (GDRs) across 783 cancer cell lines and 30 tissues. While there is significant focus on genetic approaches, transcriptome data has advantages for investigation of GDRs and remains relatively underexplored. SynLeGG depends upon the MultiSEp algorithm for unsupervised assignment of cell lines into gene expression clusters, which provide the basis for analysis of CRISPR scores and mutational status in order to propose candidate GDRs. Benchmarking against SynLethDB demonstrates favourable performance for MultiSEp against competing approaches, finding significantly higher area under the Receiver Operator Characteristic curve and between 2.8-fold to 8.5-fold greater coverage. In addition to pan-cancer analysis, SynLeGG offers investigation of tissue-specific GDRs and recovers established relationships, including synthetic lethality for SMARCA2 with SMARCA4. Proteomics, Gene Ontology, protein-protein interactions and paralogue information are provided to assist interpretation and candidate drug target prioritization. SynLeGG predictions are significantly enriched in dependencies validated by a recently published CRISPR screen.

Chemoproteomic Profiling of Covalent XPO1 Inhibitors to Assess Target Engagement and Selectivity

Selinexor, a covalent XPO1 inhibitor, is approved in the USA in combination with dexamethasone for penta-refractory multiple myeloma. Additional XPO1 covalent inhibitors are currently in clinical trials for multiple diseases including hematologic malignancies, solid tumor malignancies, glioblastoma multiforme (GBM), and amyotrophic lateral sclerosis (ALS). It is important to measure the target engagement and selectivity of covalent inhibitors to understand the degree of engagement needed for efficacy, while avoiding both mechanism-based and off-target toxicity. Herein, we report clickable probes based on the XPO1 inhibitors selinexor and eltanexor for the labeling of XPO1 in live cells to assess target engagement and selectivity. We used mass spectrometry-based chemoproteomic workflows to profile the proteome-wide selectivity of selinexor and eltanexor and show that they are highly selective for XPO1. Thermal profiling analysis of selinexor further offers an orthogonal approach to measure XPO1 engagement in live cells. We believe these probes and assays will serve as useful tools to further interrogate the biology of XPO1 and its inhibition in cellular and in vivo systems.

Targeted Treatment of Head and Neck (Pre)Cancer: Preclinical Target Identification and Development of Novel Therapeutic Applications

Head and neck squamous cell carcinomas (HNSCC) develop in the mucosal lining of the upper-aerodigestive tract. In carcinogen-induced HNSCC, tumors emerge from premalignant mucosal changes characterized by tumor-associated genetic alterations, also coined as 'fields' that are occasionally visible as leukoplakia or erythroplakia lesions but are mostly invisible. Consequently, HNSCC is generally diagnosed de novo at more advanced stages in about 70% of new diagnosis. Despite intense multimodality treatment protocols, the overall 5-years survival rate is 50-60% for patients with advanced stage of disease and seems to have reached a plateau. Of notable concern is the lack of further improvement in prognosis despite advances in treatment. This can be attributed to the late clinical presentation, failure of advanced HNSCC to respond to treatment, the deficit of effective targeted therapies to eradicate tumors and precancerous changes, and the lack of suitable markers for screening and personalized therapy. The molecular landscape of head and neck cancer has been elucidated in great detail, but the absence of oncogenic mutations hampers the identification of druggable targets for therapy to improve outcome of HNSCC. Currently, functional genomic approaches are being explored to identify potential therapeutic targets. Identification and validation of essential genes for both HNSCC and oral premalignancies, accompanied with biomarkers for therapy response, are being investigated. Attentive diagnosis and targeted therapy of the preceding oral premalignant (preHNSCC) changes may prevent the development of tumors. As classic oncogene addiction through activating mutations is not a realistic concept for treatment of HNSCC, synthetic lethality and collateral lethality need to be exploited, next to immune therapies. In recent studies it was shown that cell cycle regulation and DNA damage response pathways become significantly altered in HNSCC causing replication stress, which is an avenue that deserves further exploitation as an HNSCC vulnerability for treatment. The focus of this review is to summarize the current literature on the preclinical identification of potential druggable targets for therapy of (pre)HNSCC, emerging from the variety of gene knockdown and knockout strategies, and the testing of targeted inhibitors. We will conclude with a future perspective on targeted therapy of HNSCC and premalignant changes.

Targeting p130Cas- and microtubule-dependent MYC regulation sensitizes pancreatic cancer to ERK MAPK inhibition

To identify therapeutic targets for KRAS mutant pancreatic cancer, we conduct a druggable genome small interfering RNA (siRNA) screen and determine that suppression of BCAR1 sensitizes pancreatic cancer cells to ERK inhibition. Integrative analysis of genome-scale CRISPR-Cas9 screens also identify BCAR1 as a top synthetic lethal interactor with mutant KRAS. BCAR1 encodes the SRC substrate p130Cas. We determine that SRC-inhibitor-mediated suppression of p130Cas phosphorylation impairs MYC transcription through a DOCK1-RAC1-β-catenin-dependent mechanism. Additionally, genetic suppression of TUBB3, encoding the βIII-tubulin subunit of microtubules, or pharmacological inhibition of microtubule function decreases levels of MYC protein in a calpain-dependent manner and potently sensitizes pancreatic cancer cells to ERK inhibition. Accordingly, the combination of a dual SRC/tubulin inhibitor with an ERK inhibitor cooperates to reduce MYC protein and synergistically suppress the growth of KRAS mutant pancreatic cancer. Thus, we demonstrate that mechanistically diverse combinations with ERK inhibition suppress MYC to impair pancreatic cancer proliferation.

Data mining for traffic information

Modern cell biology is now rich with data acquired at the whole genome and proteome level. We can add value to this data through integration and application of specialist knowledge. To illustrate, we will focus on the SNARE and RAB proteins; key regulators of intracellular fusion specificity and organelle identity. We examine published mass spectrometry data to gain an estimate of protein copy number and organelle distribution in HeLa cells for each family member. We also survey recent global CRISPR/Cas9 screens for essential genes from these families. We highlight instances of co-essentiality with other genes across a large panel of cell lines that allows for the identification of functionally coherent clusters. Examples of such correlations include RAB10 with the SNARE protein Syntaxin4 (STX4) and RAB7/RAB21 with the WASH and the CCC (COMMD/CCDC22/CCDC93) complexes, both of which are linked to endosomal recycling pathways.

CRISPR/Cas9 based genome editing for targeted transcriptional control in triple-negative breast cancer

Breast cancer (BC) is the most common type of cancer in women at the global level and the highest mortality rate has been observed with triple-negative breast cancer (TNBC). Accumulation of genetic lesions an aberrant gene expression and protein degradation are considered to underlie the onset of tumorigenesis and metastasis. Therefore, the challenge to identify the genes and molecules that could be potentially used as potent biomarkers for personalized medicine against TNBC with minimal or no associated side effects. Discovery of the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated protein 9 (Cas9) arrangement and an increasing repertoire of its new variants has provided a much-needed fillip towards editing TNBC genomes. In this review, we discuss the CRISPR/Cas9 genome editing, CRISPR Technology for diagnosis of (Triple-negative breast cancer) TNBC, Drug Resistance, and potential applications of CRISPR/Cas9 and its variants in deciphering or engineering intricate molecular and epigenetic mechanisms associated with TNBC. Furthermore, we have also explored the TNBC and CRISPR/Cas9 genome editing potential for repairing, genetic modifications in TNBC.

Integrated cross-study datasets of genetic dependencies in cancer

CRISPR-Cas9 viability screens are increasingly performed at a genome-wide scale across large panels of cell lines to identify new therapeutic targets for precision cancer therapy. Integrating the datasets resulting from these studies is necessary to adequately represent the heterogeneity of human cancers and to assemble a comprehensive map of cancer genetic vulnerabilities. Here, we integrated the two largest public independent CRISPR-Cas9 screens performed to date (at the Broad and Sanger institutes) by assessing, comparing, and selecting methods for correcting biases due to heterogeneous single-guide RNA efficiency, gene-independent responses to CRISPR-Cas9 targeting originated from copy number alterations, and experimental batch effects. Our integrated datasets recapitulate findings from the individual datasets, provide greater statistical power to cancer- and subtype-specific analyses, unveil additional biomarkers of gene dependency, and improve the detection of common essential genes. We provide the largest integrated resources of CRISPR-Cas9 screens to date and the basis for harmonizing existing and future functional genetics datasets.

iCSDB: an integrated database of CRISPR screens

High-throughput screening based on CRISPR-Cas9 libraries has become an attractive and powerful technique to identify target genes for functional studies. However, accessibility of public data is limited due to the lack of user-friendly utilities and up-to-date resources covering experiments from third parties. Here, we describe iCSDB, an integrated database of CRISPR screening experiments using human cell lines. We compiled two major sources of CRISPR-Cas9 screening: the DepMap portal and BioGRID ORCS. DepMap portal itself is an integrated database that includes three large-scale projects of CRISPR screening. We additionally aggregated CRISPR screens from BioGRID ORCS that is a collection of screening results from PubMed articles. Currently, iCSDB contains 1375 genome-wide screens across 976 human cell lines, covering 28 tissues and 70 cancer types. Importantly, the batch effects from different CRISPR libraries were removed and the screening scores were converted into a single metric to estimate the knockout efficiency. Clinical and molecular information were also integrated to help users to select cell lines of interest readily. Furthermore, we have implemented various interactive tools and viewers to facilitate users to choose, examine and compare the screen results both at the gene and guide RNA levels. iCSDB is available at https://www.kobic.re.kr/icsdb/.

Pan-cancer detection of driver genes at the single-patient resolution

BACKGROUND

Identifying the complete repertoire of genes that drive cancer in individual patients is crucial for precision oncology. Most established methods identify driver genes that are recurrently altered across patient cohorts. However, mapping these genes back to patients leaves a sizeable fraction with few or no drivers, hindering our understanding of cancer mechanisms and limiting the choice of therapeutic interventions.

RESULTS

We present sysSVM2, a machine learning software that integrates cancer genetic alterations with gene systems-level properties to predict drivers in individual patients. Using simulated pan-cancer data, we optimise sysSVM2 for application to any cancer type. We benchmark its performance on real cancer data and validate its applicability to a rare cancer type with few known driver genes. We show that drivers predicted by sysSVM2 have a low false-positive rate, are stable and disrupt well-known cancer-related pathways.

CONCLUSIONS

sysSVM2 can be used to identify driver alterations in patients lacking sufficient canonical drivers or belonging to rare cancer types for which assembling a large enough cohort is challenging, furthering the goals of precision oncology. As resources for the community, we provide the code to implement sysSVM2 and the pre-trained models in all TCGA cancer types ( https://github.com/ciccalab/sysSVM2 ).

CRISP-view: a database of functional genetic screens spanning multiple phenotypes

High-throughput genetic screening based on CRISPR/Cas9 or RNA-interference (RNAi) enables the exploration of genes associated with the phenotype of interest on a large scale. The rapid accumulation of public available genetic screening data provides a wealth of knowledge about genotype-to-phenotype relationships and a valuable resource for the systematic analysis of gene functions. Here we present CRISP-view, a comprehensive database of CRISPR/Cas9 and RNAi screening datasets that span multiple phenotypes, including in vitro and in vivo cell proliferation and viability, response to cancer immunotherapy, virus response, protein expression, etc. By 22 September 2020, CRISP-view has collected 10 321 human samples and 825 mouse samples from 167 papers. All the datasets have been curated, annotated, and processed by a standard MAGeCK-VISPR analysis pipeline with quality control (QC) metrics. We also developed a user-friendly webserver to visualize, explore, and search these datasets. The webserver is freely available at http://crispview.weililab.org.

Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets

CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and to identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR-Cas9 dropout screening data in hundreds of highly annotated cancer cell models to identify genes required for cell fitness and prioritize novel oncology targets. The Project Score database currently allows users to investigate the fitness effect of 18 009 genes tested across 323 cancer cell models. Through interactive interfaces, users can investigate data by selecting a specific gene, cancer cell model or tissue type, as well as browsing all gene fitness scores. Additionally, users can identify and rank candidate drug targets based on an established oncology target prioritization pipeline, incorporating genetic biomarkers and clinical datasets for each target, and including suitability for drug development based on pharmaceutical tractability. Data are freely available and downloadable. To enhance analyses, links to other key resources including Open Targets, COSMIC, the Cell Model Passports, UniProt and the Genomics of Drug Sensitivity in Cancer are provided. The Project Score database is a valuable new tool for investigating genetic dependencies in cancer cells and the identification of candidate oncology targets.

Project Score database: a resource for investigating cancer cell dependencies and prioritizing therapeutic targets

CRISPR genetic screens in cancer cell models are a powerful tool to elucidate oncogenic mechanisms and to identify promising therapeutic targets. The Project Score database (https://score.depmap.sanger.ac.uk/) uses genome-wide CRISPR-Cas9 dropout screening data in hundreds of highly annotated cancer cell models to identify genes required for cell fitness and prioritize novel oncology targets. The Project Score database currently allows users to investigate the fitness effect of 18 009 genes tested across 323 cancer cell models. Through interactive interfaces, users can investigate data by selecting a specific gene, cancer cell model or tissue type, as well as browsing all gene fitness scores. Additionally, users can identify and rank candidate drug targets based on an established oncology target prioritization pipeline, incorporating genetic biomarkers and clinical datasets for each target, and including suitability for drug development based on pharmaceutical tractability. Data are freely available and downloadable. To enhance analyses, links to other key resources including Open Targets, COSMIC, the Cell Model Passports, UniProt and the Genomics of Drug Sensitivity in Cancer are provided. The Project Score database is a valuable new tool for investigating genetic dependencies in cancer cells and the identification of candidate oncology targets.

In Vivo Genome-Wide Pooled RNAi Screens in Cancer Cells to Identify Determinants of Chemotherapy/Drug Response

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).

DNA polymerase ι compensates for Fanconi anemia pathway deficiency by countering DNA replication stress

Fanconi anemia (FA) is caused by defects in cellular responses to DNA crosslinking damage and replication stress. Given the constant occurrence of endogenous DNA damage and replication fork stress, it is unclear why complete deletion of FA genes does not have a major impact on cell proliferation and germ-line FA patients are able to progress through development well into their adulthood. To identify potential cellular mechanisms that compensate for the FA deficiency, we performed dropout screens in FA mutant cells with a whole genome guide RNA library. This uncovered a comprehensive genome-wide profile of FA pathway synthetic lethality, including POLI and CDK4 As little is known of the cellular function of DNA polymerase iota (Pol ι), we focused on its role in the loss-of-function FA knockout mutants. Loss of both FA pathway function and Pol ι leads to synthetic defects in cell proliferation and cell survival, and an increase in DNA damage accumulation. Furthermore, FA-deficient cells depend on the function of Pol ι to resume replication upon replication fork stalling. Our results reveal a critical role for Pol ι in DNA repair and replication fork restart and suggest Pol ι as a target for therapeutic intervention in malignancies carrying an FA gene mutation.

Replication stress conferred by POT1 dysfunction promotes telomere relocalization to the nuclear pore

In this study, Pinzaru et al. set out to uncover the pathways that enable the proliferation of cells expressing cancer-associated POT1 mutations. Using complementary genetic and proteomic approaches, the authors identify a conserved function for the NPC in resolving replication defects at telomere loci.

Mutations in the telomere-binding protein POT1 are associated with solid tumors and leukemias. POT1 alterations cause rapid telomere elongation, ATR kinase activation, telomere fragility, and accelerated tumor development. Here, we define the impact of mutant POT1 alleles through complementary genetic and proteomic approaches based on CRISPR interference and biotin-based proximity labeling, respectively. These screens reveal that replication stress is a major vulnerability in cells expressing mutant POT1, which manifests as increased telomere mitotic DNA synthesis at telomeres. Our study also unveils a role for the nuclear pore complex in resolving replication defects at telomeres. Depletion of nuclear pore complex subunits in the context of POT1 dysfunction increases DNA damage signaling, telomere fragility and sister chromatid exchanges. Furthermore, we observed telomere repositioning to the nuclear periphery driven by nuclear F-actin polymerization in cells with POT1 mutations. In conclusion, our study establishes that relocalization of dysfunctional telomeres to the nuclear periphery is critical to preserve telomere repeat integrity.

Multiplex enCas12a screens detect functional buffering among paralogs otherwise masked in monogenic Cas9 knockout screens

BACKGROUND

Pooled library CRISPR/Cas9 knockout screening across hundreds of cell lines has identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens is low compared to the number of constitutively expressed genes in a cell.

RESULTS

Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observe that half of all constitutively expressed genes are never detected in any CRISPR screen and that these never-essentials are highly enriched for paralogs. We investigated functional buffering among approximately 400 candidate paralog pairs using CRISPR/enCas12a dual-gene knockout screening in three cell lines. We observe 24 synthetic lethal paralog pairs that have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions are present in at least two out of three cell lines and 14 of 24 (58%) are present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes.

CONCLUSIONS

Together, these observations strongly suggest that functionally redundant paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes in monogenic CRISPR-based loss of function screens.

In silico Method in CRISPR/Cas System: An Expedite and Powerful Booster

The CRISPR/Cas system has stood in the center of attention in the last few years as a revolutionary gene editing tool with a wide application to investigate gene functions. However, the labor-intensive workflow requires a sophisticated pre-experimental and post-experimental analysis, thus becoming one of the hindrances for the further popularization of practical applications. Recently, the increasing emergence and advancement of the in silico methods play a formidable role to support and boost experimental work. However, various tools based on distinctive design principles and frameworks harbor unique characteristics that are likely to confuse users about how to choose the most appropriate one for their purpose. In this review, we will present a comprehensive overview and comparisons on the in silico methods from the aspects of CRISPR/Cas system identification, guide RNA design, and post-experimental assistance. Furthermore, we establish the hypotheses in light of the new trends around the technical optimization and hope to provide significant clues for future tools development.

CEN-tools: an integrative platform to identify the contexts of essential genes

An emerging theme from large-scale genetic screens that identify genes essential for cell fitness is that essentiality of a given gene is highly context-specific. Identification of such contexts could be the key to defining gene function and also to develop novel therapeutic interventions. Here, we present Context-specific Essentiality Network-tools (CEN-tools), a website and python package, in which users can interrogate the essentiality of a gene from large-scale genome-scale CRISPR screens in a number of biological contexts including tissue of origin, mutation profiles, expression levels and drug responses. We show that CEN-tools is suitable for the systematic identification of genetic dependencies and for more targeted queries. The associations between genes and a given context are represented as dependency networks (CENs), and we demonstrate the utility of these networks in elucidating novel gene functions. In addition, we integrate the dependency networks with existing protein-protein interaction networks to reveal context-dependent essential cellular pathways in cancer cells. Together, we demonstrate the applicability of CEN-tools in aiding the current efforts to define the human cellular dependency map.

Repression of the Type I Interferon Pathway Underlies MYC- and KRAS-Dependent Evasion of NK and B Cells in Pancreatic Ductal Adenocarcinoma

MYC is implicated in the development and progression of pancreatic cancer, yet the precise level of MYC deregulation required to contribute to tumor development has been difficult to define. We used modestly elevated expression of human MYC, driven from the Rosa26 locus, to investigate the pancreatic phenotypes arising in mice from an approximation of MYC trisomy. We show that this level of MYC alone suffices to drive pancreatic neuroendocrine tumors, and to accelerate progression of KRAS-initiated precursor lesions to metastatic pancreatic ductal adenocarcinoma (PDAC). Our phenotype exposed suppression of the type I interferon (IFN) pathway by the combined actions of MYC and KRAS, and we present evidence of repressive MYC-MIZ1 complexes binding directly to the promoters of the genes encodiing the type I IFN regulators IRF5, IRF7, STAT1, and STAT2. Derepression of IFN regulator genes allows pancreatic tumor infiltration by B and natural killer (NK) cells, resulting in increased survival. SIGNIFICANCE: We define herein a novel mechanism of evasion of NK cell-mediated immunity through the combined actions of endogenously expressed mutant KRAS and modestly deregulated expression of MYC, via suppression of the type I IFN pathway. Restoration of IFN signaling may improve outcomes for patients with PDAC.This article is highlighted in the In This Issue feature, p. 747.

Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes

Multi-nucleotide variants (MNVs), defined as two or more nearby variants existing on the same haplotype in an individual, are a clinically and biologically important class of genetic variation. However, existing tools typically do not accurately classify MNVs, and understanding of their mutational origins remains limited. Here, we systematically survey MNVs in 125,748 whole exomes and 15,708 whole genomes from the Genome Aggregation Database (gnomAD). We identify 1,792,248 MNVs across the genome with constituent variants falling within 2 bp distance of one another, including 18,756 variants with a novel combined effect on protein sequence. Finally, we estimate the relative impact of known mutational mechanisms - CpG deamination, replication error by polymerase zeta, and polymerase slippage at repeat junctions - on the generation of MNVs. Our results demonstrate the value of haplotype-aware variant annotation, and refine our understanding of genome-wide mutational mechanisms of MNVs.

HumanNet v2: human gene networks for disease research

Human gene networks have proven useful in many aspects of disease research, with numerous network-based strategies developed for generating hypotheses about gene-disease-drug associations. The ability to predict and organize genes most relevant to a specific disease has proven especially important. We previously developed a human functional gene network, HumanNet, by integrating diverse types of omics data using Bayesian statistics framework and demonstrated its ability to retrieve disease genes. Here, we present HumanNet v2 (http://www.inetbio.org/humannet), a database of human gene networks, which was updated by incorporating new data types, extending data sources and improving network inference algorithms. HumanNet now comprises a hierarchy of human gene networks, allowing for more flexible incorporation of network information into studies. HumanNet performs well in ranking disease-linked gene sets with minimal literature-dependent biases. We observe that incorporating model organisms’ protein–protein interactions does not markedly improve disease gene predictions, suggesting that many of the disease gene associations are now captured directly in human-derived datasets. With an improved interactive user interface for disease network analysis, we expect HumanNet will be a useful resource for network medicine.

Ubiquitin-specific protease 8 (USP8/UBPy): a prototypic multidomain deubiquitinating enzyme with pleiotropic functions

Protein modification by ubiquitin is one of the most versatile posttranslational regulations and counteracted by almost 100 deubiquitinating enzymes (DUBs). USP8 was originally identified as a growth regulated ubiquitin-specific protease and is like many other DUBs characterized by its multidomain architecture. Besides the catalytic domain, specific protein-protein interaction modules were characterized which contribute to USP8 substrate recruitment, regulation and targeting to distinct protein complexes. Studies in mice and humans impressively showed the physiological relevance and non-redundant function of USP8 within the context of the whole organism. USP8 knockout (KO) mice exhibit early embryonic lethality while induced deletion in adult animals rapidly causes lethal liver failure. Furthermore, T-cell specific ablation disturbs T-cell development and function resulting in fatal autoimmune inflammatory bowel disease. In human patients, somatic mutations in USP8 were identified as the underlying cause of adrenocorticotropic hormone (ACTH) releasing pituitary adenomas causing Cushing's disease (CD). Here we provide an overview of the versatile molecular, cellular and pathology associated function and regulation of USP8 which appears to depend on specific protein binding partners, substrates and the cellular context.

IPO11 mediates βcatenin nuclear import in a subset of colorectal cancers

Activation of Wnt signaling entails βcatenin protein stabilization and translocation to the nucleus to regulate context-specific transcriptional programs. The majority of colorectal cancers (CRCs) initiate following APC mutations, resulting in Wnt ligand-independent stabilization and nuclear accumulation of βcatenin. The mechanisms underlying βcatenin nucleocytoplasmic shuttling remain incompletely defined. Using a novel, positive selection, functional genomic strategy, DEADPOOL, we performed a genome-wide CRISPR screen and identified IPO11 as a required factor for βcatenin-mediated transcription in APC mutant CRC cells. IPO11 (Importin-11) is a nuclear import protein that shuttles cargo from the cytoplasm to the nucleus. IPO11-/- cells exhibit reduced nuclear βcatenin protein levels and decreased βcatenin target gene activation, suggesting IPO11 facilitates βcatenin nuclear import. IPO11 knockout decreased colony formation of CRC cell lines and decreased proliferation of patient-derived CRC organoids. Our findings uncover a novel nuclear import mechanism for βcatenin in cells with high Wnt activity.

A genome-scale CRISPR/Cas9 knockout screening reveals SH3D21 as a sensitizer for gemcitabine

Gemcitabine, 2',2'-difluoro-2'-deoxycytidine, is used as a pro-drug in treatment of variety of solid tumour cancers including pancreatic cancer. After intake, gemcitabine is transferred to the cells by the membrane nucleoside transporter proteins. Once inside the cells, it is converted to gemcitabine triphosphate followed by incorporation into DNA chains where it causes inhibition of DNA replication and thereby cell cycle arrest and apoptosis. Currently gemcitabine is the standard drug for treatment of pancreatic cancer and despite its widespread use its effect is moderate. In this study, we performed a genome-scale CRISPR/Cas9 knockout screening on pancreatic cancer cell line Panc1 to explore the genes that are important for gemcitabine efficacy. We found SH3D21 as a novel gemcitabine sensitizer implying it may act as a therapeutic target for improvement of gemcitabine efficacy in treatment of pancreatic cancer.

Chemogenetic interactions in human cancer cells

Chemogenetic profiling enables the identification of genes that enhance or suppress the phenotypic effect of chemical compounds. Using this approach in cancer therapies could improve our ability to predict the response of specific tumor genotypes to chemotherapeutic agents, thus accelerating the development of personalized drug therapy. In the not so distant past, this strategy was only applied in model organisms because there was no feasible technology to thoroughly exploit desired genetic mutations and their impact on drug efficacy in human cells. Today, with the advent of CRISPR gene-editing technology and its application to pooled library screens in mammalian cells, chemogenetic screens are performed directly in human cell lines with high sensitivity and specificity. Chemogenetic profiling provides insights into drug mechanism-of-action, genetic vulnerabilities, and resistance mechanisms, all of which will help to accurately deliver the right drug to the right target in the right patient while minimizing side effects.

Proteogenomic landscape of squamous cell lung cancer

How genomic and transcriptomic alterations affect the functional proteome in lung cancer is not fully understood. Here, we integrate DNA copy number, somatic mutations, RNA-sequencing, and expression proteomics in a cohort of 108 squamous cell lung cancer (SCC) patients. We identify three proteomic subtypes, two of which (Inflamed, Redox) comprise 87% of tumors. The Inflamed subtype is enriched with neutrophils, B-cells, and monocytes and expresses more PD-1. Redox tumours are enriched for oxidation-reduction and glutathione pathways and harbor more NFE2L2/KEAP1 alterations and copy gain in the 3q2 locus. Proteomic subtypes are not associated with patient survival. However, B-cell-rich tertiary lymph node structures, more common in Inflamed, are associated with better survival. We identify metabolic vulnerabilities (TP63, PSAT1, and TFRC) in Redox. Our work provides a powerful resource for lung SCC biology and suggests therapeutic opportunities based on redox metabolism and immune cell infiltrates.

Squamous cell lung cancer has dismal prognosis due to the dearth of effective treatments. Here, the authors perform an integrated proteogenomic analysis of the disease, revealing three proteomics-based subtypes and suggesting potential therapeutic opportunities.

Guidelines for optimized gene knockout using CRISPR/Cas9

CRISPR/Cas9 technology has evolved as the most powerful approach to generate genetic models both for fundamental and preclinical research. Despite its apparent simplicity, the outcome of a genome-editing experiment can be substantially impacted by technical parameters and biological considerations. Here, we present guidelines and tools to optimize CRISPR/Cas9 genome-targeting efficiency and specificity. The nature of the target locus, the design of the single guide RNA and the choice of the delivery method should all be carefully considered prior to a genome-editing experiment. Different methods can also be used to detect off-target cleavages and decrease the risk of unwanted mutations. Together, these optimized tools and proper controls are essential to the assessment of CRISPR/Cas9 genome-editing experiments.