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

Evaluating sequence and structural similarity metrics for predicting shared paralog functions

Gene duplication is the primary source of new genes, resulting in most genes having identifiable paralogs. Over time, paralog pairs may diverge in some respects but many retain the ability to perform the same functional role. Protein sequence identity is often used as a proxy for functional similarity and can predict shared functions between paralogs as revealed by synthetic lethal experiments. However, the advent of alternative protein representations, including embeddings from protein language models (PLMs) and predicted structures from AlphaFold, raises the possibility that alternative similarity metrics could better capture functional similarity between paralogs. Here, using two species (budding yeast and human) and two different definitions of shared functionality (shared protein-protein interactions and synthetic lethality), we evaluated a variety of alternative similarity metrics. For some tasks, predicted structural similarity or PLM similarity outperform sequence identity, but more importantly these similarity metrics are not redundant with sequence identity, i.e. combining them with sequence identity leads to improved predictions of shared functionality. By adding contextual features, representing similarity to homologous proteins within and across species, we can significantly enhance our predictions of shared paralog functionality. Overall, our results suggest that alternative similarity metrics capture complementary aspects of functional similarity beyond sequence identity alone.

Incomplete paralog compensation generates selective dependency on TRA2A in cancer

Paralogs often exhibit functional redundancy, allowing them to effectively compensate for each other's loss. However, this buffering mechanism is frequently disrupted in cancer, exposing unique paralog-specific vulnerabilities. Here, we identify a selective dependency on the splicing factor TRA2A. We find that TRA2A and its paralog TRA2B are synthetic lethal partners that function as widespread and largely redundant activators of both alternative and constitutive splicing. While loss of TRA2A alone is typically neutral due to compensation by TRA2B, we discover that a subset of cancer cell lines are highly TRA2A-dependent. Upon TRA2A depletion, these cell lines exhibit a lack of paralog buffering specifically on shared splicing targets, leading to defects in mitosis and cell death. Notably, TRA2B overexpression rescues both the aberrant splicing and lethality associated with TRA2A loss, indicating that paralog compensation is dosage-sensitive. Together, these findings reveal a complex dosage-dependent relationship between paralogous splicing factors, and highlight how dysfunctional paralog buffering can create a selective dependency in cancer.

BaCoN (Balanced Correlation Network) improves prediction of gene buffering

Buffering between genes, where one gene can compensate for the loss of another gene, is fundamental for robust cellular functions. While experimentally testing all possible gene pairs is infeasible, gene buffering can be predicted genome-wide under the assumption that a gene's buffering capacity depends on its expression level and its absence primes a severe fitness phenotype of the buffered gene. We developed BaCoN (Balanced Correlation Network), a post hoc unsupervised correction method that amplifies specific signals in expression-vs-fitness correlation networks. We quantified 147 million potential buffering relationships by associating CRISPR-Cas9-screening fitness effects with transcriptomic data across 1019 Cancer Dependency Map (DepMap) cell lines. BaCoN outperformed state-of-the-art methods, including multiple linear regression based on our compiled gene buffering prediction metrics. Combining BaCoN with batch correction or Cholesky data whitening further boosts predictive performance. We characterized 808 high-confidence buffering predictions and found that in contrast to buffering gene pairs overall, buffering paralogs were on different chromosomes. BaCoN performance increases with more screens and genes considered, making it a valuable tool for gene buffering predictions from the growing DepMap.

Genome-wide profiling identifies the genetic dependencies of cell death following EGFR inhibition

EGFR is a proto-oncogene that is mutationally activated in a variety of cancers. Small molecule inhibitors targeting EGFR can be effective in slowing the progression of disease, and in some settings these drugs even cause dramatic tumor regression. However, responses to EGFR inhibitors are rarely durable, and the mechanisms contributing to response variation remain unclear. In particular, several distinct mechanisms have been proposed for how EGFR inhibition activates cell death, and a consensus has yet to emerge. In this study, we use functional genomics with specialized analyses to infer how genetic perturbations effect the drug-induced death rate. Our data clarify that inhibition of PI3K signaling drives the lethality of EGFR inhibition. Inhibition of other pathways downstream of EGFR, including the RAS-MAPK pathway, promote growth suppression, but not the lethal effects of EGFR inhibitors. Taken together, our study reveals the first "reference map" for the genome-wide genetic dependencies of lethality for EGFR inhibitors.

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs

Immunotherapies, including checkpoint blockade and chimeric antigen receptor T cell (CAR-T) therapy, have revolutionized cancer treatment; however, many patients remain unresponsive to these treatments or relapse following treatment. CRISPR screenings have been used to identify novel single gene targets that can enhance immunotherapy effectiveness, but the identification of combinational targets remains a challenge. Here, we introduce a computational approach that uses sgRNA set enrichment analysis to identify cancer-intrinsic paralog pairs for enhancing immunotherapy using genome-wide screens. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features to predict paralog gene pairs that influence immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using CRISPR double knockout (DKO). These data and analyses collectively provide a sensitive approach to identifying previously undetected paralog gene pairs that can significantly affect cancer immunotherapy response, even when individual genes within the pair have limited effect.

An allosteric cyclin E-CDK2 site mapped by paralog hopping with covalent probes

More than half of the ~20,000 protein-encoding human genes have paralogs. Chemical proteomics has uncovered many electrophile-sensitive cysteines that are exclusive to subsets of paralogous proteins. Here we explore whether such covalent compound-cysteine interactions can be used to discover ligandable pockets in paralogs lacking the cysteine. Leveraging the covalent ligandability of C109 in the cyclin CCNE2, we substituted the corresponding residue in paralog CCNE1 to cysteine (N112C) and found through activity-based protein profiling that this mutant reacts stereoselectively and site-specifically with tryptoline acrylamides. We then converted the tryptoline acrylamide-CCNE1-N112C interaction into in vitro NanoBRET (bioluminescence resonance energy transfer) and in cellulo activity-based protein profiling assays capable of identifying compounds that reversibly inhibit both the N112C mutant and wild-type CCNE1:CDK2 (cyclin-dependent kinase 2) complexes. X-ray crystallography revealed a cryptic allosteric pocket at the CCNE1:CDK2 interface adjacent to N112 that binds the reversible inhibitors. Our findings, thus, show how electrophile-cysteine interactions mapped by chemical proteomics can extend the understanding of protein ligandability beyond covalent chemistry.

PRODE recovers essential and context-essential genes through neighborhood-informed scores

Gene context-essentiality assessment supports precision oncology opportunities. The variability of gene effects inference from loss-of-function screenings across models and technologies limits identifying robust hits. We propose a computational framework named PRODE that integrates gene effects with protein-protein interactions to generate neighborhood-informed essential (NIE) and neighborhood-informed context essential (NICE) scores. It outperforms the canonical gene effect approach in recovering missed essential genes in shRNA screens and prioritizing context-essential hits from CRISPR-KO screens, as supported by in vitro validations. Applied to Her2 + breast cancer tumor samples, PRODE identifies oxidative phosphorylation genes as vulnerabilities with prognostic value, highlighting new therapeutic opportunities.

Computational modeling of human genetic variants in mice

Mouse models represent a powerful platform to study genes and variants associated with human diseases. While genome editing technologies have increased the rate and precision of model development, predicting and installing specific types of mutations in mice that mimic the native human genetic context is complicated. Computational tools can identify and align orthologous wild-type genetic sequences from different species; however, predictive modeling and engineering of equivalent mouse variants that mirror the nucleotide and/or polypeptide change effects of human variants remains challenging. Here, we present H2M (human-to-mouse), a computational pipeline to analyze human genetic variation data to systematically model and predict the functional consequences of equivalent mouse variants. We show that H2M can integrate mouse-to-human and paralog-to-paralog variant mapping analyses with precision genome editing pipelines to devise strategies tailored to model specific variants in mice. We leveraged these analyses to establish a database containing > 3 million human-mouse equivalent mutation pairs, as well as in silico-designed base and prime editing libraries to engineer 4,944 recurrent variant pairs. Using H2M, we also found that predicted pathogenicity and immunogenicity scores were highly correlated between human-mouse variant pairs, suggesting that variants with similar sequence change effects may also exhibit broad interspecies functional conservation. Overall, H2M fills a gap in the field by establishing a robust and versatile computational framework to identify and model homologous variants across species while providing key experimental resources to augment functional genetics and precision medicine applications. The H2M database (including software package and documentation) can be accessed at https://human2mouse.com.

Combinatorial In Vivo Genome Editing Identifies Widespread Epistasis and an Accessible Fitness Landscape During Lung Tumorigenesis

Lung adenocarcinoma, the most common subtype of lung cancer, is genomically complex, with tumors containing tens to hundreds of non-synonymous mutations. However, little is understood about how genes interact with each other to enable the evolution of cancer in vivo, largely due to a lack of methods for investigating genetic interactions in a high-throughput and quantitative manner. Here, we employed a novel platform to generate tumors with inactivation of pairs of ten diverse tumor suppressor genes within an autochthonous mouse model of oncogenic KRAS-driven lung cancer. By quantifying the fitness of tumors with every single and double mutant genotype, we show that most tumor suppressor genetic interactions exhibited negative epistasis, with diminishing returns on tumor fitness. In contrast, Apc inactivation showed positive epistasis with the inactivation of several other genes, including synergistic effects on tumor fitness in combination with Lkb1 or Nf1 inactivation. Sign epistasis was extremely rare, suggesting a surprisingly accessible fitness landscape during lung tumorigenesis. These findings expand our understanding of the interactions that drive tumorigenesis in vivo.

Efficacy of CBP/p300 Dual Inhibitors against Derepression of KREMEN2 in cBAF-Deficient Cancers

SIGNIFICANCE

In this study, we clarified that the cBAF subcomplex is deficient in the SWI/SNF complex, resulting in dependency on the CBP/p300 paralog pair. Simultaneous inhibitors of the CBP/p300 paralog pair show promise for cBAF-deficient lung cancer, as well as rare cancers such as malignant rhabdoid tumors, epithelioid sarcomas, and synovial sarcomas.

Synthetic lethal strategies for the development of cancer therapeutics

Synthetic lethality is a genetic phenomenon whereby the simultaneous presence of two different genetic alterations impairs cellular viability. Importantly, targeting synthetic lethal interactions offers potential therapeutic strategies for cancers with alterations in pathways that might otherwise be considered undruggable. High-throughput screening methods based on modern CRISPR-Cas9 technologies have emerged and become crucial for identifying novel synthetic lethal interactions with the potential for translation into biologically rational cancer therapeutic strategies as well as associated predictive biomarkers of response capable of guiding patient selection. Spurred by the clinical success of PARP inhibitors in patients with BRCA-mutant cancers, novel agents targeting multiple synthetic lethal interactions within DNA damage response pathways are in clinical development, and rational strategies targeting synthetic lethal interactions spanning alterations in epigenetic, metabolic and proliferative pathways have also emerged and are in late preclinical and/or early clinical testing. In this Review, we provide a comprehensive overview of established and emerging technologies for synthetic lethal drug discovery and development and discuss promising therapeutic strategies targeting such interactions.

The present and future of the Cancer Dependency Map

Despite tremendous progress in the past decade, the complex and heterogeneous nature of cancer complicates efforts to identify new therapies and therapeutic combinations that achieve durable responses in most patients. Further advances in cancer therapy will rely, in part, on the development of targeted therapeutics matched with the genetic and molecular characteristics of cancer. The Cancer Dependency Map (DepMap) is a large-scale data repository and research platform, aiming to systematically reveal the landscape of cancer vulnerabilities in thousands of genetically and molecularly annotated cancer models. DepMap is used routinely by cancer researchers and translational scientists and has facilitated the identification of several novel and selective therapeutic strategies for multiple cancer types that are being tested in the clinic. However, it is also clear that the current version of DepMap is not yet comprehensive. In this Perspective, we review (1) the impact and current uses of DepMap, (2) the opportunities to enhance DepMap to overcome its current limitations, and (3) the ongoing efforts to further improve and expand DepMap.

Collateral lethality: A unique type of synthetic lethality in cancers

Genetic interactions play crucial roles in cell-essential functions. Intrinsic genetic defects in tumors typically involve gain-of- and loss-of-function mutations in tumor suppressor genes (TSGs) and oncogenes, respectively, providing potential antitumor vulnerabilities. Moreover, tumor cells with TSG deficiencies exhibit heightened sensitivity to the inhibition of compensatory pathways. Synthetic and collateral lethality are two strategies used for exploiting novel drug targets in multiple types of cancer. Collateral lethality is a unique type of synthetic lethality that occurs when passenger genes are co-deleted in neighboring TSGs. Although synthetic lethality has already been successfully demonstrated in clinical practice, antitumor therapeutics based on collateral lethality are predominantly still in the preclinical phase. Therefore, screening for potential genetic interactions within the cancer genome has emerged as a promising approach for drug development. Here, the two conceptual therapeutic strategies that involve the deletion or inactivation of cancer-specific TSGs are discussed. Moreover, existing approaches for screening and identifying potential gene partners are also discussed. Particularly, this review highlights the current advances of "collateral lethality" in the preclinical phase and addresses the challenges involved in translating them into therapeutic applications. This review provides insights into these strategies as new opportunities for the development of personalized antitumor therapies.

State of the art CRISPR-based strategies for cancer diagnostics and treatment

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology is a groundbreaking and dynamic molecular tool for DNA and RNA "surgery". CRISPR/Cas9 is the most widely applied system in oncology research. It is a major advancement in genome manipulation due to its precision, efficiency, scalability and versatility compared to previous gene editing methods. It has shown great potential not only in the targeting of oncogenes or genes coding for immune checkpoint molecules, and in engineering T cells, but also in targeting epigenomic disturbances, which contribute to cancer development and progression. It has proven useful for detecting genetic mutations, enabling the large-scale screening of genes involved in tumor onset, progression and drug resistance, and in speeding up the development of highly targeted therapies tailored to the genetic and immunological profiles of the patient's tumor. Furthermore, the recently discovered Cas12 and Cas13 systems have expanded Cas9-based editing applications, providing new opportunities in the diagnosis and treatment of cancer. In addition to traditional cis-cleavage, they exhibit trans-cleavage activity, which enables their use as sensitive and specific diagnostic tools. Diagnostic platforms like DETECTR, which employs the Cas12 enzyme, that cuts single-stranded DNA reporters, and SHERLOCK, which uses Cas12, or Cas13, that specifically target and cleave single-stranded RNA, can be exploited to speed up and advance oncological diagnostics. Overall, CRISPR platform has the great potential to improve molecular diagnostics and the functionality and safety of engineered cellular medicines. Here, we will emphasize the potentially transformative impact of CRISPR technology in the field of oncology compared to traditional treatments, diagnostic and prognostic approaches, and highlight the opportunities and challenges raised by using the newly introduced CRISPR-based systems for cancer diagnosis and therapy.

Genome-aware annotation of CRISPR guides validates targets in variant cell lines and enhances discovery in screens

BACKGROUND

CRISPR-Cas9 technology has revolutionised genetic screens and can inform on gene essentiality and chemo-genetic interactions. It is easily deployed and widely supported with many pooled CRISPR libraries available commercially. However, discrepancies between the reference genomes used in the design of those CRISPR libraries and the cell line under investigation can lead to loss of signal or introduction of bias. The problem is particularly acute when dealing with variant cell lines such as cancer cell lines.

RESULTS

Here, we present an algorithm, EXOme-guided Re-annotation of nuCleotIde SEquences (Exorcise), which uses sequence search to detect and correct mis-annotations in CRISPR libraries. Exorcise verifies the presence of CRISPR targets in the target genome and applies corrections to CRISPR libraries using existing exome annotations. We applied Exorcise to re-annotate guides in pooled CRISPR libraries available on Addgene and found that libraries designed on a more permissive reference sequence had more mis-annotations. In simulated CRISPR screens, we modelled common mis-annotations and found that they adversely affect discovery of hits in the intermediate range. We then confirmed this by applying Exorcise on datasets from Dependency Map (DepMap) and the DNA Damage Response CRISPR Screen Viewer (DDRcs), where we found improved discovery power upon Exorcise while retaining the strongest hits.

CONCLUSIONS

Pooled CRISPR libraries map guide sequences to genes and these mappings might not be ready to use due to permissive library design or investigating a variant cell line. By re-annotating CRISPR guides, Exorcise focuses CRISPR experiments towards the genome of the cell line under investigation. Exorcise can be applied at the library design stage or the analysis stage and allows post hoc re-analysis of completed screens. It is available under a Creative Commons Zero v1.0 Universal licence at https://github.com/SimonLammmm/exorcise .

A framework for target discovery in rare cancers

While large-scale functional genetic screens have uncovered numerous cancer dependencies, rare cancers are poorly represented in such efforts and the landscape of dependencies in many rare cancers remains obscure. We performed genome-scale CRISPR knockout screens in an exemplar rare cancer, TFE3-translocation renal cell carcinoma (tRCC), revealing previously unknown tRCC-selective dependencies in pathways related to mitochondrial biogenesis, oxidative metabolism, and kidney lineage specification. To generalize to other rare cancers in which experimental models may not be readily available, we employed machine learning to infer gene dependencies in a tumor or cell line based on its transcriptional profile. By applying dependency prediction to alveolar soft part sarcoma (ASPS), a distinct rare cancer also driven by TFE3 translocations, we discovered and validated that MCL1 represents a dependency in ASPS but not tRCC. Finally, we applied our model to predict gene dependencies in tumors from the TCGA (11,373 tumors; 28 lineages) and multiple additional rare cancers (958 tumors across 16 types, including 13 distinct subtypes of kidney cancer), nominating potentially actionable vulnerabilities in several poorly-characterized cancer types. Our results couple unbiased functional genetic screening with a predictive model to establish a landscape of candidate vulnerabilities across cancers, including several rare cancers currently lacking in potential targets.

Estimating the Number of Polygenic Diseases Among Six Mutually Exclusive Entities of Non-Tumors and Cancer

In the era of precision medicine with increasing amounts of sequenced cancer and non-cancer genomes of different ancestries, we here enumerate the resulting polygenic disease entities. Based on the cell number status, we first identified six fundamental types of polygenic illnesses, five of which are non-cancerous. Like complex, non-tumor disorders, neoplasms normally carry alterations in multiple genes, including in 'Drivers' and 'Passengers'. However, tumors also lack certain genetic alterations/epigenetic changes, recently named 'Goners', which are toxic for the neoplasm and potentially constitute therapeutic targets. Drivers are considered essential for malignant transformation, whereas environmental influences vary considerably among both types of polygenic diseases. For each form, hyper-rare disorders, defined as affecting <1/108 individuals, likely represent the largest number of disease entities. Loss of redundant tumor-suppressor genes exemplifies such a profoundly rare mutational event. For non-tumor, polygenic diseases, pathway-centered taxonomies seem preferable. This classification is not readily feasible in cancer, but the inclusion of Drivers and possibly also of epigenetic changes to the existing nomenclature might serve as initial steps in this direction. Based on the detailed genetic alterations, the number of polygenic diseases is essentially countless, but different forms of nosologies may be used to restrict the number.

An interpretable artificial intelligence framework for designing synthetic lethality-based anti-cancer combination therapies

INTRODUCTION

Synthetic lethality (SL) provides an opportunity to leverage different genetic interactions when designing synergistic combination therapies. To further explore SL-based combination therapies for cancer treatment, it is important to identify and mechanistically characterize more SL interactions. Artificial intelligence (AI) methods have recently been proposed for SL prediction, but the results of these models are often not interpretable such that deriving the underlying mechanism can be challenging.

OBJECTIVES

This study aims to develop an interpretable AI framework for SL prediction and subsequently utilize it to design SL-based synergistic combination therapies.

METHODS

We propose a knowledge and data dual-driven AI framework for SL prediction (KDDSL). Specifically, we use gene knowledge related to the SL mechanism to guide the construction of the model and develop a method to identify the most relevant gene knowledge for the predicted results.

RESULTS

Experimental and literature-based validation confirmed a good balance between predictive and interpretable ability when using KDDSL. Moreover, we demonstrated that KDDSL could help to discover promising drug combinations and clarify associated biological processes, such as the combination of MDM2 and CDK9 inhibitors, which exhibited significant anti-cancer effects in vitro and in vivo.

CONCLUSION

These data underscore the potential of KDDSL to guide SL-based combination therapy design. There is a need for biomedicine-focused AI strategies to combine rational biological knowledge with developed models.

Impact of CRISPR in cancer drug discovery

Precision gene editing enables massively parallel identification of cancer-promoting genes.

Benchmarking machine learning methods for synthetic lethality prediction in cancer

Synthetic lethality (SL) is a gold mine of anticancer drug targets, exposing cancer-specific dependencies of cellular survival. To complement resource-intensive experimental screening, many machine learning methods for SL prediction have emerged recently. However, a comprehensive benchmarking is lacking. This study systematically benchmarks 12 recent machine learning methods for SL prediction, assessing their performance across diverse data splitting scenarios, negative sample ratios, and negative sampling techniques, on both classification and ranking tasks. We observe that all the methods can perform significantly better by improving data quality, e.g., excluding computationally derived SLs from training and sampling negative labels based on gene expression. Among the methods, SLMGAE performs the best. Furthermore, the methods have limitations in realistic scenarios such as cold-start independent tests and context-specific SLs. These results, together with source code and datasets made freely available, provide guidance for selecting suitable methods and developing more powerful techniques for SL virtual screening.

CDK11, a splicing-associated kinase regulating gene expression

The ability of a cell to properly express its genes depends on optimal transcription and splicing. RNA polymerase II (RNAPII) transcribes protein-coding genes and produces pre-mRNAs, which undergo, largely co-transcriptionally, intron excision by the spliceosome complex. Spliceosome activation is a major control step, leading to a catalytically active complex. Recent work has showed that cyclin-dependent kinase (CDK)11 regulates spliceosome activation via the phosphorylation of SF3B1, a core spliceosome component. Thus, CDK11 arises as a major coordinator of gene expression in metazoans due to its role in the rate-limiting step of pre-mRNA splicing. This review outlines the evolution of CDK11 and SF3B1 and their emerging roles in splicing regulation. It also discusses how CDK11 and its inhibition affect transcription and cell cycle progression.

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.

Building a translational cancer dependency map for The Cancer Genome Atlas

Cancer dependency maps have accelerated the discovery of tumor vulnerabilities that can be exploited as drug targets when translatable to patients. The Cancer Genome Atlas (TCGA) is a compendium of 'maps' detailing the genetic, epigenetic and molecular changes that occur during the pathogenesis of cancer, yet it lacks a dependency map to translate gene essentiality in patient tumors. Here, we used machine learning to build translational dependency maps for patient tumors, which identified tumor vulnerabilities that predict drug responses and disease outcomes. A similar approach was used to map gene tolerability in healthy tissues to prioritize tumor vulnerabilities with the best therapeutic windows. A subset of patient-translatable synthetic lethalities were experimentally tested, including PAPSS1/PAPSS12 and CNOT7/CNOT78, which were validated in vitro and in vivo. Notably, PAPSS1 synthetic lethality was driven by collateral deletion of PAPSS2 with PTEN and was correlated with patient survival. Finally, the translational dependency map is provided as a web-based application for exploring tumor vulnerabilities.

New Horizons of Synthetic Lethality in Cancer: Current Development and Future Perspectives

In recent years, synthetic lethality has been recognized as a solid paradigm for anticancer therapies. The discovery of a growing number of synthetic lethal targets has led to a significant expansion in the use of synthetic lethality, far beyond poly(ADP-ribose) polymerase inhibitors used to treat BRCA1/2-defective tumors. In particular, molecular targets within DNA damage response have provided a source of inhibitors that have rapidly reached clinical trials. This Perspective focuses on the most recent progress in synthetic lethal targets and their inhibitors, within and beyond the DNA damage response, describing their design and associated therapeutic strategies. We will conclude by discussing the current challenges and new opportunities for this promising field of research, to stimulate discussion in the medicinal chemistry community, allowing the investigation of synthetic lethality to reach its full potential.

Sensitive detection of synthetic response to cancer immunotherapy driven by gene paralog pairs

Emerging immunotherapies such as immune checkpoint blockade (ICB) and chimeric antigen receptor T-cell (CAR-T) therapy have revolutionized cancer treatment and have improved the survival of patients with multiple cancer types. Despite this success many patients are unresponsive to these treatments or relapse following treatment. CRISPR activation and knockout (KO) screens have been used to identify novel single gene targets that can enhance effector T cell function and promote immune cell targeting and eradication of tumors. However, cancer cells often employ multiple genes to promote an immunosuppressive pathway and thus modulating individual genes often has a limited effect. Paralogs are genes that originate from common ancestors and retain similar functions. They often have complex effects on a particular phenotype depending on factors like gene family similarity, each individual gene's expression and the physiological or pathological context. Some paralogs exhibit synthetic lethal interactions in cancer cell survival; however, a thorough investigation of paralog pairs that could enhance the efficacy of cancer immunotherapy is lacking. Here we introduce a sensitive computational approach that uses sgRNA sets enrichment analysis to identify cancer-intrinsic paralog pairs which have the potential to synergistically enhance T cell-mediated tumor destruction. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features such as gene characteristics, sequence and structural similarities, protein-protein interaction (PPI) networks, and gene coevolution data to predict paralog pairs that are likely to enhance immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using double knockout (DKO) of identified paralog gene pairs as compared to single gene knockouts (SKOs). These data and analyses collectively provide a sensitive approach to identify previously undetected paralog pairs that can enhance cancer immunotherapy even when individual genes within the pair has a limited effect.

Frequent loss of FAM126A expression in colorectal cancer results in selective FAM126B dependency

Most advanced colorectal cancer (CRC) patients cannot benefit from targeted therapy due to lack of actionable targets. By mining data from the DepMap, we identified FAM126B as a specific vulnerability in CRC cell lines exhibiting low FAM126A expression. Employing a combination of genetic perturbation and inducible protein degradation techniques, we demonstrate that FAM126A and FAM126B function in a redundant manner to facilitate the recruitment of PI4KIIIα to the plasma membrane for PI4P synthesis. Examination of data from TCGA and GTEx revealed that over 7% of CRC tumor samples exhibited loss of FAM126A expression, contrasting with uniform FAM126A expression in normal tissues. In both CRC cell lines and tumor samples, promoter hypermethylation correlated with the loss of FAM126A expression, which could be reversed by DNA methylation inhibitors. In conclusion, our study reveals that loss of FAM126A expression results in FAM126B dependency, thus proposing FAM126B as a therapeutic target for CRC treatment.

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.

CROPseq-multi: a versatile solution for multiplexed perturbation and decoding in pooled CRISPR screens

Forward genetic screens seek to dissect complex biological systems by systematically perturbing genetic elements and observing the resulting phenotypes. While standard screening methodologies introduce individual perturbations, multiplexing perturbations improves the performance of single-target screens and enables combinatorial screens for the study of genetic interactions. Current tools for multiplexing perturbations are incompatible with pooled screening methodologies that require mRNA-embedded barcodes, including some microscopy and single cell sequencing approaches. Here, we report the development of CROPseq-multi, a CROPseq1-inspired lentiviral system to multiplex Streptococcus pyogenes (Sp) Cas9-based perturbations with mRNA-embedded barcodes. CROPseq-multi has equivalent per-guide activity to CROPseq and low lentiviral recombination frequencies. CROPseq-multi is compatible with enrichment screening methodologies and optical pooled screens, and is extensible to screens with single-cell sequencing readouts. For optical pooled screens, an optimized and multiplexed in situ detection protocol improves barcode detection efficiency 10-fold, enables detection of recombination events, and increases decoding efficiency 3-fold relative to CROPseq. CROPseq-multi is a widely applicable multiplexing solution for diverse SpCas9-based genetic screening approaches.

Combinatorial in vivo genome editing identifies widespread epistasis during lung tumorigenesis

Lung adenocarcinoma, the most common subtype of lung cancer, is genomically complex, with tumors containing tens to hundreds of non-synonymous mutations. However, little is understood about how genes interact with each other to enable tumorigenesis in vivo , largely due to a lack of methods for investigating genetic interactions in a high-throughput and multiplexed manner. Here, we employed a novel platform to generate tumors with all pairwise inactivation of ten tumor suppressor genes within an autochthonous mouse model of oncogenic KRAS-driven lung cancer. By quantifying the fitness of tumors with every single and double mutant genotype, we show that most tumor suppressor genetic interactions exhibited negative epistasis, with diminishing returns on tumor fitness. In contrast, Apc inactivation showed positive epistasis with the inactivation of several other genes, including dramatically synergistic effects on tumor fitness in combination with Lkb1 or Nf1 inactivation. This approach has the potential to expand the scope of genetic interactions that may be functionally characterized in vivo , which could lead to a better understanding of how complex tumor genotypes impact each step of carcinogenesis.

CSSLdb: Discovery of cancer-specific synthetic lethal interactions based on machine learning and statistic inference

Synthetic lethality (SL) occurs when the inactivation of two genes results in cell death while the inactivation of either gene alone is non-lethal. SL-based therapy has become a promising anti-cancer treatment option with the increasing researches and applications in clinical practice, while the specific therapeutic opportunities for various cancers have not yet been comprehensively investigated. Herein, we described a computational approach based on machine learning and statistical inference to discover the cancer-specific synthetic lethal interactions. First, Random Forest and One-Class SVM were used to perform cancer unbiased prediction of synthetic lethality. Then, two strategies, including mutual exclusivity and differential expression, were used to screen cancer-specific synthetic lethal interactions, resulting in 14,582 SL gene pairs in 33 cancer types. Finally, we developed a freely available database of CSSLdb (Cancer Specific Synthetic Lethality Database, http://www.tmliang.cn/CSSL/) to present cancer-specific synthetic lethal genetic interactions, which would enrich the relevant research and contribute to underlying therapy strategies based on synthetic lethality.

A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization

Genetic screens in cancer cell lines inform gene function and drug discovery. More comprehensive screen datasets with multi-omics data are needed to enhance opportunities to functionally map genetic vulnerabilities. Here, we construct a second-generation map of cancer dependencies by annotating 930 cancer cell lines with multi-omic data and analyze relationships between molecular markers and cancer dependencies derived from CRISPR-Cas9 screens. We identify dependency-associated gene expression markers beyond driver genes, and observe many gene addiction relationships driven by gain of function rather than synthetic lethal effects. By combining clinically informed dependency-marker associations with protein-protein interaction networks, we identify 370 anti-cancer priority targets for 27 cancer types, many of which have network-based evidence of a functional link with a marker in a cancer type. Mapping these targets to sequenced tumor cohorts identifies tractable targets in different cancer types. This target prioritization map enhances understanding of gene dependencies and identifies candidate anti-cancer targets for drug development.

Higher-order combinatorial chromatin perturbations by engineered CRISPR-Cas12a for functional genomics

Multiplexed genetic perturbations are critical for testing functional interactions among coding or non-coding genetic elements. Compared to double-stranded DNA cutting, repressive chromatin formation using CRISPR interference (CRISPRi) avoids genotoxicity and is more effective for perturbing non-coding regulatory elements in pooled assays. However, current CRISPRi pooled screening approaches are limited to targeting 1-3 genomic sites per cell. To develop a tool for higher-order ( > 3) combinatorial targeting of genomic sites with CRISPRi in functional genomics screens, we engineered an Acidaminococcus Cas12a variant -- referred to as mul tiplexed transcriptional interference AsCas12a (multiAsCas12a). multiAsCas12a incorporates a key mutation, R1226A, motivated by the hypothesis of nicking-induced stabilization of the ribonucleoprotein:DNA complex for improving CRISPRi activity. multiAsCas12a significantly outperforms prior state-of-the-art Cas12a variants in combinatorial CRISPRi targeting using high-order multiplexed arrays of lentivirally transduced CRISPR RNAs (crRNA), including in high-throughput pooled screens using 6-plex crRNA array libraries. Using multiAsCas12a CRISPRi, we discover new enhancer elements and dissect the combinatorial function of cis-regulatory elements. These results instantiate a group testing framework for efficiently surveying potentially numerous combinations of chromatin perturbations for biological discovery and engineering.

SL-Miner: a web server for mining evidence and prioritization of cancer-specific synthetic lethality

SUMMARY

Synthetic lethality (SL) refers to a type of genetic interaction in which the simultaneous inactivation of two genes leads to cell death, while the inactivation of a single gene does not affect cell viability. It significantly expands the range of potential therapeutic targets for anti-cancer treatments. SL interactions are primarily identified through experimental screening and computational prediction. Although various computational methods have been proposed, they tend to ignore providing evidence to support their predictions of SL. Besides, they are rarely user-friendly for biologists who likely have limited programming skills. Moreover, the genetic context specificity of SL interactions is often not taken into consideration. Here, we introduce a web server called SL-Miner, which is designed to mine the evidence of SL relationships between a primary gene and a few candidate SL partner genes in a specific type of cancer, and to prioritize these candidate genes by integrating various types of evidence. For intuitive data visualization, SL-Miner provides a range of charts (e.g. volcano plot and box plot) to help users get insights from the data.

AVAILABILITY AND IMPLEMENTATION

SL-Miner is available at https://slminer.sist.shanghaitech.edu.cn.

Multiplexed screening reveals how cancer-specific alternative polyadenylation shapes tumor growth in vivo

Alternative polyadenylation (APA) is strikingly dysregulated in many cancers. Although global APA dysregulation is frequently associated with poor prognosis, the importance of most individual APA events is controversial simply because few have been functionally studied. Here, we address this gap by developing a CRISPR-Cas9-based screen to manipulate endogenous polyadenylation and systematically quantify how APA events contribute to tumor growth in vivo. Our screen reveals individual APA events that control mouse melanoma growth in an immunocompetent host, with concordant associations in clinical human cancer. For example, forced Atg7 3' UTR lengthening in mouse melanoma suppresses ATG7 protein levels, slows tumor growth, and improves host survival; similarly, in clinical human melanoma, a long ATG7 3' UTR is associated with significantly prolonged patient survival. Overall, our study provides an easily adaptable means to functionally dissect APA in physiological systems and directly quantifies the contributions of recurrent APA events to tumorigenic phenotypes.

SLKB: synthetic lethality knowledge base

Emerging CRISPR-Cas9 technology permits synthetic lethality (SL) screening of large number of gene pairs from gene combination double knockout (CDKO) experiments. However, the poor integration and annotation of CDKO SL data in current SL databases limit their utility, and diverse methods of calculating SL scores prohibit their comparison. To overcome these shortcomings, we have developed SL knowledge base (SLKB) that incorporates data of 11 CDKO experiments in 22 cell lines, 16,059 SL gene pairs and 264,424 non-SL gene pairs. Additionally, within SLKB, we have implemented five SL calculation methods: median score with and without background control normalization (Median-B/NB), sgRNA-derived score (sgRNA-B/NB), Horlbeck score, GEMINI score and MAGeCK score. The five scores have demonstrated a mere 1.21% overlap among their top 10% SL gene pairs, reflecting high diversity. Users can browse SL networks and assess the impact of scoring methods using Venn diagrams. The SL network generated from all data in SLKB shows a greater likelihood of SL gene pair connectivity with other SL gene pairs than non-SL pairs. Comparison of SL networks between two cell lines demonstrated greater likelihood to share SL hub genes than SL gene pairs. SLKB website and pipeline can be freely accessed at https://slkb.osubmi.org and https://slkb.docs.osubmi.org/, respectively.

Paralog dispensability shapes homozygous deletion patterns in tumor genomes

Genomic instability is a hallmark of cancer, resulting in tumor genomes having large numbers of genetic aberrations, including homozygous deletions of protein coding genes. That tumor cells remain viable in the presence of such gene loss suggests high robustness to genetic perturbation. In model organisms and cancer cell lines, paralogs have been shown to contribute substantially to genetic robustness-they are generally more dispensable for growth than singletons. Here, by analyzing copy number profiles of > 10,000 tumors, we test the hypothesis that the increased dispensability of paralogs shapes tumor genome evolution. We find that genes with paralogs are more likely to be homozygously deleted and that this cannot be explained by other factors known to influence copy number variation. Furthermore, features that influence paralog dispensability in cancer cell lines correlate with paralog deletion frequency in tumors. Finally, paralogs that are broadly essential in cancer cell lines are less frequently deleted in tumors than non-essential paralogs. Overall, our results suggest that homozygous deletions of paralogs are more frequently observed in tumor genomes because paralogs are more dispensable.

Synthetic Lethality Screening with Recursive Feature Machines

Synthetic lethality refers to a genetic interaction where the simultaneous perturbation of gene pairs leads to cell death. Synthetically lethal gene pairs (SL pairs) provide a potential avenue for selectively targeting cancer cells based on genetic vulnerabilities. The rise of large-scale gene perturbation screens such as the Cancer Dependency Map (DepMap) offers the opportunity to identify SL pairs automatically using machine learning. We build on a recently developed class of feature learning kernel machines known as Recursive Feature Machines (RFMs) to develop a pipeline for identifying SL pairs based on CRISPR viability data from DepMap. In particular, we first train RFMs to predict viability scores for a given CRISPR gene knockout from cell line embeddings consisting of gene expression and mutation features. After training, RFMs use a statistical operator known as average gradient outer product to provide weights for each feature indicating the importance of each feature in predicting cellular viability. We subsequently apply correlation-based filters to re-weight RFM feature importances and identify those features that are most indicative of low cellular viability. Our resulting pipeline is computationally efficient, taking under 3 minutes for analyzing all 17, 453 knockouts from DepMap for candidate SL pairs. We show that our pipeline more accurately recovers experimentally verified SL pairs than prior approaches. Moreover, our pipeline finds new candidate SL pairs, thereby opening novel avenues for identifying genetic vulnerabilities in cancer.

Synthetic viability induces resistance to immune checkpoint inhibitors in cancer cells

BACKGROUND

Immune checkpoint inhibitors (ICI) have revolutionized the treatment for multiple cancers. However, most of patients encounter resistance. Synthetic viability (SV) between genes could induce resistance. In this study, we established SV signature to predict the efficacy of ICI treatment for melanoma.

METHODS

We collected features and predicted SV gene pairs by random forest classifier. This work prioritized SV gene pairs based on CRISPR/Cas9 screens. SV gene pairs signature were constructed to predict the response to ICI for melanoma patients.

RESULTS

This study predicted robust SV gene pairs based on 14 features. Filtered by CRISPR/Cas9 screens, we identified 1,861 SV gene pairs, which were also related with prognosis across multiple cancer types. Next, we constructed the six SV pairs signature to predict resistance to ICI for melanoma patients. This study applied the six SV pairs signature to divide melanoma patients into high-risk and low-risk. High-risk melanoma patients were associated with worse response after ICI treatment. Immune landscape analysis revealed that high-risk melanoma patients had lower natural killer cells and CD8+ T cells infiltration.

CONCLUSIONS

In summary, the 14 features classifier accurately predicted robust SV gene pairs for cancer. The six SV pairs signature could predict resistance to ICI.

Paralog-specific signaling by IRAK1/4 maintains MyD88-independent functions in MDS/AML

Dysregulation of innate immune signaling is a hallmark of hematologic malignancies. Recent therapeutic efforts to subvert aberrant innate immune signaling in myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML) have focused on the kinase IRAK4. IRAK4 inhibitors have achieved promising, though moderate, responses in preclinical studies and clinical trials for MDS and AML. The reasons underlying the limited responses to IRAK4 inhibitors remain unknown. In this study, we reveal that inhibiting IRAK4 in leukemic cells elicits functional complementation and compensation by its paralog, IRAK1. Using genetic approaches, we demonstrate that cotargeting IRAK1 and IRAK4 is required to suppress leukemic stem/progenitor cell (LSPC) function and induce differentiation in cell lines and patient-derived cells. Although IRAK1 and IRAK4 are presumed to function primarily downstream of the proximal adapter MyD88, we found that complementary and compensatory IRAK1 and IRAK4 dependencies in MDS/AML occur via noncanonical MyD88-independent pathways. Genomic and proteomic analyses revealed that IRAK1 and IRAK4 preserve the undifferentiated state of MDS/AML LSPCs by coordinating a network of pathways, including ones that converge on the polycomb repressive complex 2 complex and JAK-STAT signaling. To translate these findings, we implemented a structure-based design of a potent and selective dual IRAK1 and IRAK4 inhibitor KME-2780. MDS/AML cell lines and patient-derived samples showed significant suppression of LSPCs in xenograft and in vitro studies when treated with KME-2780 as compared with selective IRAK4 inhibitors. Our results provide a mechanistic basis and rationale for cotargeting IRAK1 and IRAK4 for the treatment of cancers, including MDS/AML.

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.

A CRISPR Screen of HIV Dependency Factors Reveals That CCNT1 Is Non-Essential in T Cells but Required for HIV-1 Reactivation from Latency

We sought to explore the hypothesis that host factors required for HIV-1 replication also play a role in latency reversal. Using a CRISPR gene library of putative HIV dependency factors, we performed a screen to identify genes required for latency reactivation. We identified several HIV-1 dependency factors that play a key role in HIV-1 latency reactivation including ELL, UBE2M, TBL1XR1, HDAC3, AMBRA1, and ALYREF. The knockout of Cyclin T1 (CCNT1), a component of the P-TEFb complex that is important for transcription elongation, was the top hit in the screen and had the largest effect on HIV latency reversal with a wide variety of latency reversal agents. Moreover, CCNT1 knockout prevents latency reactivation in a primary CD4+ T cell model of HIV latency without affecting the activation of these cells. RNA sequencing data showed that CCNT1 regulates HIV-1 proviral genes to a larger extent than any other host gene and had no significant effects on RNA transcripts in primary T cells after activation. We conclude that CCNT1 function is non-essential in T cells but is absolutely required for HIV latency reversal.

Collateral lethality between HDAC1 and HDAC2 exploits cancer-specific NuRD complex vulnerabilities

Transcriptional co-regulators have been widely pursued as targets for disrupting oncogenic gene regulatory programs. However, many proteins in this target class are universally essential for cell survival, which limits their therapeutic window. Here we unveil a genetic interaction between histone deacetylase 1 (HDAC1) and HDAC2, wherein each paralog is synthetically lethal with hemizygous deletion of the other. This collateral synthetic lethality is caused by recurrent chromosomal deletions that occur in diverse solid and hematological malignancies, including neuroblastoma and multiple myeloma. Using genetic disruption or dTAG-mediated degradation, we show that targeting HDAC2 suppresses the growth of HDAC1-deficient neuroblastoma in vitro and in vivo. Mechanistically, we find that targeted degradation of HDAC2 in these cells prompts the degradation of several members of the nucleosome remodeling and deacetylase (NuRD) complex, leading to diminished chromatin accessibility at HDAC2-NuRD-bound sites of the genome and impaired control of enhancer-associated transcription. Furthermore, we reveal that several of the degraded NuRD complex subunits are dependencies in neuroblastoma and multiple myeloma, providing motivation to develop paralog-selective HDAC1 or HDAC2 degraders that could leverage HDAC1/2 synthetic lethality to target NuRD vulnerabilities. Altogether, we identify HDAC1/2 collateral synthetic lethality as a potential therapeutic target and reveal an unexplored mechanism for targeting NuRD-associated cancer dependencies.

A CRISPR screen of HIV dependency factors reveals CCNT1 is non-essential in T cells but required for HIV-1 reactivation from latency

We sought to explore the hypothesis that host factors required for HIV-1 replication also play a role in latency reversal. Using a CRISPR gene library of putative HIV dependency factors, we performed a screen to identify genes required for latency reactivation. We identified several HIV-1 dependency factors that play a key role in HIV-1 latency reactivation including ELL , UBE2M , TBL1XR1 , HDAC3 , AMBRA1 , and ALYREF . Knockout of Cyclin T1 ( CCNT1 ), a component of the P-TEFb complex important for transcription elongation, was the top hit in the screen and had the largest effect on HIV latency reversal with a wide variety of latency reversal agents. Moreover, CCNT1 knockout prevents latency reactivation in a primary CD4+ T cell model of HIV latency without affecting activation of these cells. RNA sequencing data showed that CCNT1 regulates HIV-1 proviral genes to a larger extent than any other host gene and had no significant effects on RNA transcripts in primary T cells after activation. We conclude that CCNT1 function is redundant in T cells but is absolutely required for HIV latency reversal.

Gene knock-outs in human CD34+ hematopoietic stem and progenitor cells and in the human immune system of mice

Human CD34+ hematopoietic stem and progenitor cells (HSPCs) are a standard source of cells for clinical HSC transplantations as well as experimental xenotransplantation to generate "humanized mice". To further extend the range of applications of these humanized mice, we developed a protocol to efficiently edit the genomes of human CD34+ HSPCs before transplantation. In the past, manipulating HSPCs has been complicated by the fact that they are inherently difficult to transduce with lentivectors, and rapidly lose their stemness and engraftment potential during in vitro culture. However, with optimized nucleofection of sgRNA:Cas9 ribonucleoprotein complexes, we are now able to edit a candidate gene in CD34+ HSPCs with almost 100% efficiency, and transplant these modified cells in immunodeficient mice with high engraftment levels and multilineage hematopoietic differentiation. The result is a humanized mouse from which we knocked out a gene of interest from their human immune system.

Paralog-based synthetic lethality: rationales and applications

Tumor cells can result from gene mutations and over-expression. Synthetic lethality (SL) offers a desirable setting where cancer cells bearing one mutated gene of an SL gene pair can be specifically targeted by disrupting the function of the other genes, while leaving wide-type normal cells unharmed. Paralogs, a set of homologous genes that have diverged from each other as a consequence of gene duplication, make the concept of SL feasible as the loss of one gene does not affect the cell's survival. Furthermore, homozygous loss of paralogs in tumor cells is more frequent than singletons, making them ideal SL targets. Although high-throughput CRISPR-Cas9 screenings have uncovered numerous paralog-based SL pairs, the unclear mechanisms of targeting these gene pairs and the difficulty in finding specific inhibitors that exclusively target a single but not both paralogs hinder further clinical development. Here, we review the potential mechanisms of paralog-based SL given their function and genetic combination, and discuss the challenge and application prospects of paralog-based SL in cancer therapeutic discovery.

Optimized metrics for orthogonal combinatorial CRISPR screens

CRISPR-based gene perturbation enables unbiased investigations of single and combinatorial genotype-to-phenotype associations. In light of efforts to map combinatorial gene dependencies at scale, choosing an efficient and robust CRISPR-associated (Cas) nuclease is of utmost importance. Even though SpCas9 and AsCas12a are widely used for single, combinatorial, and orthogonal screenings, side-by-side comparisons remain sparse. Here, we systematically compared combinatorial SpCas9, AsCas12a, and CHyMErA in hTERT-immortalized retinal pigment epithelial cells and extracted performance-critical parameters for combinatorial and orthogonal CRISPR screens. Our analyses identified SpCas9 to be superior to enhanced and optimized AsCas12a, with CHyMErA being largely inactive in the tested conditions. Since AsCas12a contains RNA processing activity, we used arrayed dual-gRNAs to improve AsCas12a and CHyMErA applications. While this negatively influenced the effect size range of combinatorial AsCas12a applications, it enhanced the performance of CHyMErA. This improved performance, however, was limited to AsCas12a dual-gRNAs, as SpCas9 gRNAs remained largely inactive. To avoid the use of hybrid gRNAs for orthogonal applications, we engineered the multiplex SpCas9-enAsCas12a approach (multiSPAS) that avoids RNA processing for efficient orthogonal gene editing.

Targeting synthetic lethal paralogs in cancer

Synthetic lethal interactions, where mutation of one gene renders cells sensitive to inhibition of another gene, can be exploited for the development of targeted therapeutics in cancer. Pairs of duplicate genes (paralogs) often share common functionality and hence are a potentially rich source of synthetic lethal interactions. Because the majority of human genes have paralogs, exploiting such interactions could be a widely applicable approach for targeting gene loss in cancer. Moreover, existing small-molecule drugs may exploit synthetic lethal interactions by inhibiting multiple paralogs simultaneously. Consequently, the identification of synthetic lethal interactions between paralogs could be extremely informative for drug development. Here we review approaches to identify such interactions and discuss some of the challenges of exploiting them.

E3 ubiquitin ligase ASB8 promotes selinexor-induced proteasomal degradation of XPO1

Selinexor (KPT-330), a small-molecule inhibitor of exportin-1 (XPO1, CRM1) with potent anticancer activity, has recently been granted FDA approval for treatment of relapsed/refractory multiple myeloma and diffuse large B-cell lymphoma (DLBCL), with a number of additional indications currently under clinical investigation. Since selinexor has often demonstrated synergy when used in combination with other drugs, notably bortezomib and dexamethasone, a more comprehensive approach to uncover new beneficial interactions would be of great value. Moreover, stratifying patients, personalizing therapeutics and improving clinical outcomes requires a better understanding of the genetic vulnerabilities and resistance mechanisms underlying drug response. Here, we used CRISPR-Cas9 loss-of-function chemogenetic screening to identify drug-gene interactions with selinexor in chronic myeloid leukemia, multiple myeloma and DLBCL cell lines. We identified the TGFβ-SMAD4 pathway as an important mediator of resistance to selinexor in multiple myeloma cells. Moreover, higher activity of this pathway correlated with prolonged progression-free survival in multiple myeloma patients treated with selinexor, indicating that the TGFβ-SMAD4 pathway is a potential biomarker predictive of therapeutic outcome. In addition, we identified ASB8 (ankyrin repeat and SOCS box containing 8) as a shared modulator of selinexor sensitivity across all tested cancer types, with both ASB8 knockout and overexpression resulting in selinexor hypersensitivity. Mechanistically, we showed that ASB8 promotes selinexor-induced proteasomal degradation of XPO1. This study provides insight into the genetic factors that influence response to selinexor treatment and could support both the development of predictive biomarkers as well as new drug combinations.

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.