• info@icbcongress.com
• English
• Persian

• Login

Create Free Account

INTERNATIONAL CONGRESS OF

CancerGenetics

• Synthetic Lethality Approaches Using Gene Editing for Targeting Cancer Vulnerabilities

• Asal Naghipour-Kordlar,¹ Maryam Radmanfard,^2,*
  1. Faculty of Nursing, Tabriz University of Medical Sciences, Tabriz, Iran
  2. Department of Basic Sciences, Ta.C., Islamic Azad University, Tabriz, Iran
  
  
  
• Introduction: Synthetic lethality occurs when simultaneous inactivation of two genes results in cell death, whereas disruption of either gene alone is tolerated. The clinical success of PARP inhibitors in BRCA-mutant cancers validated SL as a therapeutic paradigm and motivated large-scale discovery efforts (Ngoi et al., 2025). Advances in CRISPR-based perturbation technologies now enable single-gene and combinatorial perturbation screens that reveal context-specific vulnerabilities across cancer lineages (Moffat et al., 2024). The central promise of SL is to convert tumor-specific alterations into druggable dependencies with improved selectivity compared to conventional cytotoxic therapies (Shi et al., 2024).
• Methods: A comprehensive literature search was conducted in PubMed, Web of Science, and Scopus covering the period 2020–2025. The search strategy used a combination of keywords including “synthetic lethality,” “CRISPR,” “gene editing,” “cancer vulnerabilities,” and “drug discovery.” Eligible studies included original research and reviews that reported CRISPR-based synthetic lethality mapping, dual-guide or CRISPRi libraries, and translational dependency maps. Studies without experimental data or not directly related to cancer gene-editing approaches were excluded. Reference lists of included articles were also manually screened to identify additional relevant studies. Preference was given to peer-reviewed publications in high-impact journals in oncology, genomics, and biotechnology.
• Results: CRISPR-enabled mapping of synthetic lethal interactions Large-scale CRISPR approaches have progressed from single-gene knockout screens to dual-guide and CRISPRi combinatorial libraries capable of probing pairwise interactions systematically. For example, the DDR-centric CRISPRi dual-guide SPIDR library screened 548 core DNA repair genes and mapped ~5,000 SL interactions, many previously uncharacterized (Fielden et al., 2025). Parallel multilineage dual-knockout screens identified broadly conserved vulnerabilities, facilitating prioritization of translatable targets (Fong et al., 2025). Together, these studies establish CRISPR functional genomics as a cornerstone of hypothesis-free SL discovery and target validation (Moffat et al., 2024). Paralog co-dependencies and target co-inhibition Genetic redundancy often masks vulnerabilities until both paralogs are co-inhibited. This strategy has revealed novel SL targets, such as MARK2/MARK3, critical in YAP/TAZ-driven tumors (Klingbeil et al., 2024), and CBP/p300, synthetically lethal in SMARCB1-deficient cancers (Sasaki et al., 2024). These findings underscore the therapeutic value of paralog-SL strategies, particularly in tumors driven by epigenetic dysregulation, thereby broadening the druggable landscape (Klingbeil et al., 2024; Sasaki et al., 2024). Mechanistic classes: DDR, RNA quality control, and beyond The DNA damage response remains a dominant source of SL interactions given its central role in genome integrity (Fielden et al., 2025). Beyond DDR, recent studies highlight dependencies in mRNA quality-control complexes as novel, non-DDR vulnerabilities (Prindle et al., 2025). Integrating mechanistic annotation with screen outputs prioritizes tractable target classes—including polymerases, ubiquitin-proteasome regulators, and RNA surveillance factors—and supports rational combination strategies tailored to tumor genotypes (Ngoi et al., 2025; Prindle et al., 2025). Translational frameworks and dependency maps Moving from in vitro findings to clinical application requires translational dependency maps that integrate CRISPR screens with patient tumor genomics and drug-response datasets. Machine learning-based approaches enhance prediction of clinically relevant dependencies (Shi et al., 2024). Resources such as multilineage screens (Fong et al., 2025) and the SCHEMATIC framework combine genetic and pharmacologic data, narrowing the drug development space to robust, tractable SL pairs. Table: Category Key Advances Examples Impact CRISPR-enabled SL mapping Dual-guide & CRISPRi combinatorial libraries systematically identify gene pairs DDR-centric SPIDR library; multilineage dual-knockout screens Expanded SL interaction networks, validated cross-lineage vulnerabilities Paralog co-dependencies Co-inhibition of redundant genes unmasks hidden vulnerabilities MARK2/MARK3 in YAP/TAZ tumors; CBP/p300 in SMARCB1-deficient cancers Broadens therapeutic space, especially in epigenetically driven tumors Mechanistic classes SL interactions beyond DDR, including RNA quality-control complexes mRNA surveillance factors, polymerases, proteasome regulators Novel druggable dependencies, new mechanistic insights Translational frameworks Integration of CRISPR data with patient genomics & drug-response datasets Machine learning–based dependency maps; SCHEMATIC framework Improves prioritization of clinically relevant, druggable SL targets Discussion Experimental design recommendations for SL discovery Key recommendations for combinatorial CRISPR studies include: Employ CRISPRi alongside nuclease-based libraries to minimize DNA damage artifacts. Use mismatched or hypomorphic sgRNAs to interrogate essential genes (Fielden et al., 2025). Validate hits across multiple cell lines and orthogonal assays, including xenografts (Klingbeil et al., 2024). Pair genetic perturbations with small-molecule profiling for early assessment of druggability (Fong et al., 2025). Challenges: heterogeneity, resistance, and delivery Major challenges in SL translation include: Tumor heterogeneity**, leading to partial penetrance of SL interactions (Shi et al., 2024). Adaptive resistance**, through pathway restoration, bypass signaling, or resistant clone selection (Ngoi et al., 2025). Efficient delivery**, particularly tumor-selective targeting of gene-editing tools (Moffat et al., 2024). Tolerability**, requiring mapping of essential genes in normal tissues to minimize toxicity (Shi et al., 2024). Future directions and clinical outlook Emerging trends include integrating SL discovery with single-cell sequencing to capture intra-tumor variability and AI-driven models to predict cross-context conservation (Shi et al., 2024). Development of inhibitors for previously undruggable targets identified by CRISPR will be crucial (Moffat et al., 2024). Combining SL-based therapeutics with immunotherapy or targeted agents may mitigate resistance (Ngoi et al., 2025). Co-clinical pipelines incorporating organoids and xenografts with CRISPR dependency profiling will accelerate biomarker discovery and clinical translation (Fong et al., 2025).
• Conclusion: Recent studies (2024–2025) demonstrate the power of CRISPR-enabled combinatorial screening and translational dependency mapping for uncovering clinically relevant SL targets. Paralog co-targeting, DDR vulnerabilities, and mRNA quality-control dependencies represent promising mechanistic classes. Careful experimental design, orthogonal validation, and integration with patient-derived data will be essential to prioritize safe, actionable targets. Gene-editing technologies are now firmly positioned as engines for the discovery of precision therapies that selectively exploit cancer vulnerabilities.
• Keywords: synthetic lethality, CRISPR, gene editing, cancer vulnerabilities, drug discovery