In vivo CRISPR screens identify Mga as an immunotherapy target in triple-negative breast cancer.

Immune evasion is not only critical for tumor initiation and progression, but also determines the efficacy of immunotherapies. Through iterative in vivo CRISPR screens with seven syngeneic tumor models, we identified core and context-dependent immune evasion pathways across cancer types. This valuable high-confidence dataset is available for the further understanding of tumor intrinsic immunomodulators, which may lead to the discovery of effective anticancer therapeutic targets. With a focus on triple-negative breast cancer (TNBC), we found that Mga knock-out significantly enhances antitumor immunity and inhibits tumor growth. Transcriptomics and single-cell RNA sequencing analyses revealed that Mga influences various immune-related pathways in the tumor microenvironment. Our findings suggest that Mga may play a role in modulating the tumor immune landscape, though the precise mechanisms require further investigation. Interestingly, we observed that low MGA expression in breast cancer patients correlates with a favorable prognosis, particularly in those with active interferon-γ signaling. These observations provide insights into tumor immune escape mechanisms and suggest that further exploration of MGA's function could potentially lead to effective therapeutic strategies in TNBC.

Metabolic reprogramming by mutant GNAS creates an actionable dependency in intraductal papillary mucinous neoplasms of the pancreas.

BACKGROUND: Oncogenic 'hotspot' mutations of KRAS and GNAS are two major driver alterations in intraductal papillary mucinous neoplasms (IPMNs), which are bona fide precursors to pancreatic ductal adenocarcinoma. We previously reported that pancreas-specific Kras G12D and Gnas R201C co-expression in p48Cre; KrasLSL-G12D; Rosa26LSL-rtTA; Tg (TetO-GnasR201C) mice ('Kras;Gnas' mice) caused development of cystic lesions recapitulating IPMNs. OBJECTIVE: We aim to unveil the consequences of mutant Gnas R201C expression on phenotype, transcriptomic profile and genomic dependencies. DESIGN: We performed multimodal transcriptional profiling (bulk RNA sequencing, single-cell RNA sequencing and spatial transcriptomics) in the 'Kras;Gnas' autochthonous model and tumour-derived cell lines (Kras;Gnas cells), where Gnas R201C expression is inducible. A genome-wide CRISPR/Cas9 screen was conducted to identify potential vulnerabilities in KrasG12D;GnasR201C co-expressing cells. RESULTS: Induction of Gnas R201C-and resulting G(s)alpha signalling-leads to the emergence of a gene signature of gastric (pyloric type) metaplasia in pancreatic neoplastic epithelial cells. CRISPR screening identified the synthetic essentiality of glycolysis-related genes Gpi1 and Slc2a1 in Kras G12D;Gnas R201C co-expressing cells. Real-time metabolic analyses in Kras;Gnas cells and autochthonous Kras;Gnas model confirmed enhanced glycolysis on Gnas R201C induction. Induction of Gnas R201C made Kras G12D expressing cells more dependent on glycolysis for their survival. Protein kinase A-dependent phosphorylation of the glycolytic intermediate enzyme 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3) was a driver of increased glycolysis on Gnas R201C induction. CONCLUSION: Multiple orthogonal approaches demonstrate that Kras G12D and Gnas R201C co-expression results in a gene signature of gastric pyloric metaplasia and glycolytic dependency during IPMN pathogenesis. The observed metabolic reprogramming may provide a potential target for therapeutics and interception of IPMNs.

Optimal methods for analyzing targeted pairwise knockout screens.

Background: Synthetic lethality offers a promising strategy for cancer treatment by targeting genetic vulnerabilities unique to tumor cells, leading to selective tumor cell death. However, single-gene knockout screens often miss functional redundancy due to paralog genes. Multiplex CRISPR systems, including various Cas9 and Cas12a platforms, have been developed to assay genetic interactions, yet no systematic comparison of method to identify synthetic lethality from CRISPR screens has been conducted. Results: We evaluated data from four in4mer CRISPR/Cas12a screens in cancer cell lines, using three bioinformatic approaches to identify synthetic lethal interactions: delta log fold change (dLFC), Z-transformed dLFC (ZdLFC), and rescaled dLFC (RdLFC). Both ZdLFC and RdLFC provided more consistent identification of synthetic lethal pairs across cell lines compared to the unscaled dLFC method. Conclusions: The ZdLFC method offers a robust framework for scoring synthetic lethal interactions from paralog screens, providing consistent results across different cell lines without requiring a training set of known positive interactors.

Efficient gene knockout and genetic interaction screening using the in4mer CRISPR/Cas12a multiplex knockout platform.

Genetic interactions mediate the emergence of phenotype from genotype, but technologies for combinatorial genetic perturbation in mammalian cells are challenging to scale. Here, we identify background-independent paralog synthetic lethals from previous CRISPR genetic interaction screens, and find that the Cas12a platform provides superior sensitivity and assay replicability. We develop the in4mer Cas12a platform that uses arrays of four independent guide RNAs targeting the same or different genes. We construct a genome-scale library, Inzolia, that is ~30% smaller than a typical CRISPR/Cas9 library while also targeting ~4000 paralog pairs. Screens in cancer cells demonstrate discrimination of core and context-dependent essential genes similar to that of CRISPR/Cas9 libraries, as well as detection of synthetic lethal and masking/buffering genetic interactions between paralogs of various family sizes. Importantly, the in4mer platform offers a fivefold reduction in library size compared to other genetic interaction methods, substantially reducing the cost and effort required for these assays.

CRISPR screening identifies BET and mTOR inhibitor synergy in cholangiocarcinoma through serine glycine one carbon.

Patients with cholangiocarcinoma have poor clinical outcomes due to late diagnoses, poor prognoses, and limited treatment strategies. To identify drug combinations for this disease, we have conducted a genome-wide CRISPR screen anchored on the bromodomain and extraterminal domain (BET) PROTAC degrader ARV825, from which we identified anticancer synergy when combined with genetic ablation of members of the mTOR pathway. This combination effect was validated using multiple pharmacological BET and mTOR inhibitors, accompanied by increased levels of apoptosis and cell cycle arrest. In a xenograft model, combined BET degradation and mTOR inhibition induced tumor regression. Mechanistically, the 2 inhibitor classes converged on H3K27ac-marked epigenetic suppression of the serine glycine one carbon (SGOC) metabolism pathway, including the key enzymes PHGDH and PSAT1. Knockdown of PSAT1 was sufficient to replicate synergy with single-agent inhibition of either BET or mTOR. Our results tie together epigenetic regulation, metabolism, and apoptosis induction as key therapeutic targets for further exploration in this underserved disease.

FACS-based genome-wide CRISPR screens define key regulators of DNA damage signaling pathways.

DNA damage-activated signaling pathways are critical for coordinating multiple cellular processes, which must be tightly regulated to maintain genome stability. To provide a comprehensive and unbiased perspective of DNA damage response (DDR) signaling pathways, we performed 30 fluorescence-activated cell sorting (FACS)-based genome-wide CRISPR screens in human cell lines with antibodies recognizing distinct endogenous DNA damage signaling proteins to identify critical regulators involved in DDR. We discovered that proteasome-mediated processing is an early and prerequisite event for cells to trigger camptothecin- and etoposide-induced DDR signaling. Furthermore, we identified PRMT1 and PRMT5 as modulators that regulate ATM protein level. Moreover, we discovered that GNB1L is a key regulator of DDR signaling via its role as a co-chaperone specifically regulating PIKK proteins. Collectively, these screens offer a rich resource for further investigation of DDR, which may provide insight into strategies of targeting these DDR pathways to improve therapeutic outcomes.

Genome-wide CRISPR-Cas9 screen analyzed by SLIDER identifies network of repressor complexes that regulate TRIM24.

TRIM24 is an oncogenic chromatin reader that is frequently overexpressed in human tumors and associated with poor prognosis. However, TRIM24 is rarely mutated, duplicated, or rearranged in cancer. This raises questions about how TRIM24 is regulated and what changes in its regulation are responsible for its overexpression. Here, we perform a genome-wide CRISPR-Cas9 screen by fluorescence-activated cell sorting (FACS) that nominated 220 negative regulators and elucidated a regulatory network that includes the KAP1 corepressor, CNOT deadenylase, and GID/CTLH E3 ligase. Knocking out required components of these three complexes caused TRIM24 overexpression, confirming their negative regulation of TRIM24. Our findings identify regulators of TRIM24 that nominate previously unexplored contexts for this oncoprotein in biology and disease. These findings were enabled by SLIDER, a new scoring system designed and vetted in our study as a broadly applicable tool for analysis of CRISPR screens performed by FACS.

Genome-Wide CRISPR Screens Reveal ZATT as a Synthetic Lethal Target of TOP2-Poison Etoposide That Can Act in a TDP2-Independent Pathway.

Etoposide (ETO) is an anticancer drug that targets topoisomerase II (TOP2). It stabilizes a normally transient TOP2-DNA covalent complex (TOP2cc), thus leading to DNA double-strand breaks (DSBs). Tyrosyl-DNA phosphodiesterases two (TDP2) is directly involved in the repair of TOP2cc by removing phosphotyrosyl peptides from 5'-termini of DSBs. Recent studies suggest that additional factors are required for TOP2cc repair, which include the proteasome and the zinc finger protein associated with TDP2 and TOP2, named ZATT. ZATT may alter the conformation of TOP2cc in a way that renders the accessibility of TDP2 for TOP2cc removal. In this study, our genome-wide clustered regularly interspaced short palindromic repeats (CRISPR) screens revealed that ZATT also has a TDP2-independent role in promoting cell survival following ETO treatment. ZATT KO cells showed relatively higher ETO sensitivity than TDP2-KO cells, and ZATT/TDP2 DKO cells displayed additive hypersensitivity to ETO treatment. The study using a series of deletion mutants of ZATT determined that the N-terminal 1-168 residues of ZATT are required for interaction with TOP2 and this interaction is critical to ETO sensitivity. Moreover, depletion of ZATT resulted in accelerated TOP2 degradation after ETO or cycloheximide (CHX) treatment, suggesting that ZATT may increase TOP2 stability and likely participate in TOP2 turnover. Taken together, this study suggests that ZATT is a critical determinant that dictates responses to ETO treatment and targeting. ZATT is a promising strategy to increase ETO efficacy for cancer therapy.

Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis.

SLC7A11-mediated cystine uptake suppresses ferroptosis yet promotes cell death under glucose starvation; the nature of the latter cell death remains unknown. Here we show that aberrant accumulation of intracellular disulfides in SLC7A11high cells under glucose starvation induces a previously uncharacterized form of cell death distinct from apoptosis and ferroptosis. We term this cell death disulfidptosis. Chemical proteomics and cell biological analyses showed that glucose starvation in SLC7A11high cells induces aberrant disulfide bonds in actin cytoskeleton proteins and F-actin collapse in a SLC7A11-dependent manner. CRISPR screens and functional studies revealed that inactivation of the WAVE regulatory complex (which promotes actin polymerization and lamellipodia formation) suppresses disulfidptosis, whereas constitutive activation of Rac promotes disulfidptosis. We further show that glucose transporter inhibitors induce disulfidptosis in SLC7A11high cancer cells and suppress SLC7A11high tumour growth. Our results reveal that the susceptibility of the actin cytoskeleton to disulfide stress mediates disulfidptosis and suggest a therapeutic strategy to target disulfidptosis in cancer treatment.

Recovering false negatives in CRISPR fitness screens with JLOE.

It is widely accepted that pooled library CRISPR knockout screens offer greater sensitivity and specificity than prior technologies in detecting genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the assumption that CRISPR screens are saturating has been largely untested. Through integrated analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we show that a typical CRISPR screen has a ∼20% false negative rate, in addition to library-specific false negatives. Replicability falls sharply as gene expression decreases, while cancer subtype-specific genes within a tissue show distinct profiles compared to false negatives. Cumulative analyses across tissues improves our understanding of core essential genes and suggest only a small number of lineage-specific essential genes, enriched for transcription factors that define pathways of tissue differentiation. To recover false negatives, we introduce a method, Joint Log Odds of Essentiality (JLOE), which builds on our prior work with BAGEL to selectively rescue the false negatives without an increased false discovery rate.

Efficient gene knockout and genetic interactions: the IN4MER CRISPR/Cas12a multiplex knockout platform.

Genetic interactions mediate the emergence of phenotype from genotype, but initial technologies for combinatorial genetic perturbation in mammalian cells suffer from inefficiency and are challenging to scale. Recent focus on paralog synthetic lethality in cancer cells offers an opportunity to evaluate different approaches and improve on the state of the art. Here we report a meta-analysis of CRISPR genetic interactions screens, identifying a candidate set of background-independent paralog synthetic lethals, and find that the Cas12a platform provides superior sensitivity and assay replicability. We demonstrate that Cas12a can independently target up to four genes from a single guide array, and we build on this knowledge by constructing a genome-scale library that expresses arrays of four guides per clone, a platform we call 'in4mer'. Our genome-scale human library, with only 49k clones, is substantially smaller than a typical CRISPR/Cas9 monogenic library while also targeting more than four thousand paralog pairs, triples, and quads. Proof of concept screens in four cell lines demonstrate discrimination of core and context-dependent essential genes similar to that of state-of-the-art CRISPR/Cas9 libraries, as well as detection of synthetic lethal and masking/buffering genetic interactions between paralogs of various family sizes, a capability not offered by any extant library. Importantly, the in4mer platform offers a fivefold reduction in the number of clones required to assay genetic interactions, dramatically improving the cost and effort required for these studies.

PICKLES v3: the updated database of pooled in vitro CRISPR knockout library essentiality screens.

PICKLES (https://pickles.hart-lab.org) is an updated web interface to a freely available database of genome-scale CRISPR knockout fitness screens in human cell lines. Using a completely rewritten interface, researchers can explore gene knockout fitness phenotypes across cell lines and tissue types and compare fitness profiles with fitness, expression, or mutation profiles of other genes. The database has been updated to include data from three CRISPR libraries (Avana, Score, and TKOv3), and includes information from 1162 whole-genome screens probing the knockout fitness phenotype of 18 959 genes. Source code for the interface and the integrated database are available for download.

Optimal construction of a functional interaction network from pooled library CRISPR fitness screens.

BACKGROUND: Functional interaction networks, where edges connect genes likely to operate in the same biological process or pathway, can be inferred from CRISPR knockout screens in cancer cell lines. Genes with similar knockout fitness profiles across a sufficiently diverse set of cell line screens are likely to be co-functional, and these "coessentiality" networks are increasingly powerful predictors of gene function and biological modularity. While several such networks have been published, most use different algorithms for each step of the network construction process. RESULTS: In this study, we identify an optimal measure of functional interaction and test all combinations of options at each step-essentiality scoring, sample variance and covariance normalization, and similarity measurement-to identify best practices for generating a functional interaction network from CRISPR knockout data. We show that Bayes Factor and Ceres scores give the best results, that Ceres outperforms the newer Chronos scoring scheme, and that covariance normalization is a critical step in network construction. We further show that Pearson correlation, mathematically identical to ordinary least squares after covariance normalization, can be extended by using partial correlation to detect and amplify signals from "moonlighting" proteins which show context-dependent interaction with different partners. CONCLUSIONS: We describe a systematic survey of methods for generating coessentiality networks from the Cancer Dependency Map data and provide a partial correlation-based approach for exploring context-dependent interactions.

Integrated screens uncover a cell surface tumor suppressor gene KIRREL involved in Hippo pathway.

Cell surface proteins play essential roles in various biological processes and are highly related to cancer development. They also serve as important markers for cell identity and targets for pharmacological intervention. Despite their great potentials in biomedical research, comprehensive functional analysis of cell surface proteins remains scarce. Here, with a de novo designed library targeting cell surface proteins, we performed in vivo CRISPR screens to evaluate the effects of cell surface proteins on tumor survival and proliferation. We found that Kirrel1 loss markedly promoted tumor growth in vivo. Moreover, KIRREL was significantly enriched in a separate CRISPR screen based on a specific Hippo pathway reporter. Further studies revealed that KIRREL binds directly to SAV1 to activate the Hippo tumor suppressor pathway. Together, our integrated screens reveal a cell surface tumor suppressor involved in the Hippo pathway and highlight the potential of these approaches in biomedical research.

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.

Genome-wide CRISPR screens using isogenic cells reveal vulnerabilities conferred by loss of tumor suppressors.

Exploiting cancer vulnerabilities is critical for the discovery of anticancer drugs. However, tumor suppressors cannot be directly targeted because of their loss of function. To uncover specific vulnerabilities for cells with deficiency in any given tumor suppressor(s), we performed genome-scale CRISPR loss-of-function screens using a panel of isogenic knockout cells we generated for 12 common tumor suppressors. Here, we provide a comprehensive and comparative dataset for genetic interactions between the whole-genome protein-coding genes and a panel of tumor suppressor genes, which allows us to uncover known and new high-confidence synthetic lethal interactions. Mining this dataset, we uncover essential paralog gene pairs, which could be a common mechanism for interpreting synthetic lethality. Moreover, we propose that some tumor suppressors could be targeted to suppress proliferation of cells with deficiency in other tumor suppressors. This dataset provides valuable information that can be further exploited for targeted cancer therapy.

HumanNet v3: an improved database of human gene networks for disease research.

Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein-protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene-phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein-protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.

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.

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.

AR-negative prostate cancer is vulnerable to loss of JMJD1C demethylase.

Prostate cancer is a leading cause of cancer-related mortality in men. The widespread use of androgen receptor (AR) inhibitors has generated an increased incidence of AR-negative prostate cancer, triggering the need for effective therapies for such patients. Here, analysis of public genome-wide CRISPR screens in human prostate cancer cell lines identified histone demethylase JMJD1C (KDM3C) as an AR-negative context-specific vulnerability. Secondary validation studies in multiple cell lines and organoids, including isogenic models, confirmed that small hairpin RNA (shRNA)-mediated depletion of JMJD1C potently inhibited growth specifically in AR-negative prostate cancer cells. To explore the cooperative interactions of AR and JMJD1C, we performed comparative transcriptomics of 1) isogenic AR-positive versus AR-negative prostate cancer cells, 2) AR-positive versus AR-negative prostate cancer tumors, and 3) isogenic JMJD1C-expressing versus JMJD1C-depleted AR-negative prostate cancer cells. Loss of AR or JMJD1C generates a modest tumor necrosis factor alpha (TNFα) signature, whereas combined loss of AR and JMJD1C strongly up-regulates the TNFα signature in human prostate cancer, suggesting TNFα signaling as a point of convergence for the combined actions of AR and JMJD1C. Correspondingly, AR-negative prostate cancer cells showed exquisite sensitivity to TNFα treatment and, conversely, TNFα pathway inhibition via inhibition of its downstream effector MAP4K4 partially reversed the growth defect of JMJD1C-depleted AR-negative prostate cancer cells. Given the deleterious systemic side effects of TNFα therapy in humans and the viability of JMJD1C-knockout mice, the identification of JMJD1C inhibition as a specific vulnerability in AR-negative prostate cancer may provide an alternative drug target for prostate cancer patients progressing on AR inhibitor therapy.