Identification of Cancer-Type-Specific CRISPR Targets Using DepMap Dependency Data

Identification of Cancer-Type-Specific CRISPR Targets Using DepMap Dependency Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Cancer-Type-Specific CRISPR Targets Using DepMap Dependency Data Li Ranfei, Wang Sirui, Yang Qiying This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9463556/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract CRISPR-Cas9 screening enables systematic identification of gene dependencies across diverse cancer cell lines. However, distinguishing lineage-specific vulnerabilities from broadly essential genes remains a key challenge for precision oncology. In this study, we analyzed DepMap 22Q1 data to develop a selectivity-centered computational framework for prioritizing cancer-type-specific dependencies. A selectivity score was defined as the difference between the median dependency within a given cancer type and the global median across all cell lines. To enrich lineage-informative signals, the top 500 genes ranked by dependency variance were retained. Candidate targets were identified using thresholds of Chronos score < -1.0 and selectivity score < -0.3, followed by one-sided Wilcoxon rank-sum testing with Benjamini–Hochberg correction (q < 0.05). A total of 1,991 candidate gene–cancer pairs were identified across 43 cancer types, among which 87 were statistically significant. Hematological malignancies showed strong enrichment of selective dependencies. The top-ranked association, ABL1 in myeloproliferative neoplasms, is a clinically validated target, supporting the reliability of the framework. Functional enrichment analysis revealed a strong association with purine metabolism, nucleotide biosynthesis, and methylation-related processes. Notably, 9 of the top 10 targets were metabolic genes. Importantly, comparison between selectivity-based ranking and statistical significance demonstrated that integrating effect size with significance improves prioritization robustness relative to single-metric approaches. These findings suggest that selectivity-driven analysis provides a complementary perspective to conventional dependency ranking strategies. This study presents a reproducible workflow for identifying lineage-specific CRISPR dependencies and offers candidate targets for further experimental validation. Cancer Biology Bioinformatics CRISPR-Cas9 DepMap cancer dependency bioinformatics lineage-specific targets nucleotide metabolism Figures Figure 1 Figure 2 Figure 3 Highlights • Novel selectivity framework identifies cancer-specific CRISPR targets from DepMap data • 87 lineage-specific dependencies discovered across 43 cancer types • Hematological malignancies show strong enrichment of metabolic vulnerabilities • Nine of top ten targets are nucleotide metabolism genes • ABL1 rediscovery validates the pipeline for precision oncology 1. Introduction Genome-scale CRISPR-Cas9 screening has changed how researchers identify gene essentiality in cancer models. These high-throughput tools make it possible to measure how individual genes contribute to cancer cell survival and proliferation in a systematic way. Among public resources, the Cancer Dependency Map (DepMap) provides consistent CRISPR screening data across hundreds of cell lines, enabling cross-cancer comparison of gene essentiality. Despite the scale and quality of DepMap data, one major challenge remains. Many genes appear essential across many cancer types simply because they support core cellular functions, not because they reflect cancer-specific vulnerabilities. These broadly essential genes are often poor therapeutic targets due to potential toxicity in normal tissues. Methods that can highlight lineage-selective dependencies are therefore more useful for discovering precise and safer targets. Cancer cells often remodel metabolic pathways to support rapid growth and survival. Fast-proliferating cancers, especially hematological malignancies, commonly rely on elevated nucleotide synthesis to sustain DNA and RNA production. However, many existing analysis pipelines prioritize absolute dependency strength rather than lineage-specific selectivity. As a result, biologically important context-dependent signals can be overlooked. In this study, a selectivity-centered computational framework was developed using DepMap 22Q1 data. By comparing each cancer type’s dependency profile to the global background, the framework enriches for genes that are disproportionately essential in specific lineages. Strict filtering, nonparametric statistical testing, and multiple-test correction were combined to improve reliability and reduce false positives. The workflow addresses common limitations such as data heterogeneity and uneven sample sizes across cancer types. The final output consists of robust, biologically relevant candidate targets for further validation. 2. Results 2.1 Overview of Candidate and Significant Targets Genes were first ranked by the variance of dependency scores across all cell lines, and the top 500 most variable genes were retained to enrich lineage-informative signals while reducing the influence of broadly essential genes. This threshold was selected to balance signal detection and noise reduction, and sensitivity analyses using alternative cutoffs (top 300 and 1000 genes) produced consistent results (Methods 4.9), indicating that the findings are not dependent on a specific parameter choice. Applying thresholds of Chronos score < -1.0 and selectivity score < -0.3, a total of 1,991 candidate gene–cancer pairs were identified across 43 cancer types (Fig. 1 ). Statistical testing using one-sided Wilcoxon rank-sum tests, followed by Benjamini–Hochberg correction, identified 87 lineage-specific dependencies that reached statistical significance (q < 0.05). The relationship between dependency strength and selectivity is illustrated in Fig. 2 . Notably, genes with strong dependency effects do not always exhibit high lineage selectivity, indicating that these two metrics capture distinct aspects of gene essentiality. This distribution highlights that selectivity provides complementary information beyond absolute dependency magnitude and supports its use as an additional prioritization criterion. Hematological malignancies contributed 31.9% of all candidate pairs, representing a disproportionate enrichment relative to their overall representation in the dataset. This pattern suggests that certain cancer lineages, particularly those characterized by rapid proliferation, may exhibit stronger lineage-specific dependency signals. 2.2 Top Statistically Significant Targets The strongest selective signal was observed for ABL1 in myeloproliferative neoplasms (selectivity = -1.68, q = 3.30 × 10⁻⁶), a well-established therapeutic target, supporting the validity of the analytical framework. The top 10 statistically significant targets are shown in Table 1 . Notably, 9 out of 10 genes are involved in metabolic processes, particularly nucleotide and purine metabolism. ADSL was repeatedly identified across five distinct hematological cancer types (Table 1 ), suggesting a shared lineage-specific dependency in purine biosynthesis. Table 1 Top 10 cancer-selective targets identified in this study Rank Gene Cancer Type Selectivity Score p-value q-value (FDR) 1 ABL1 Myeloproliferative Neoplasms -1.68 6.06×10⁻⁷ 3.30×10⁻⁶ 2 ADSL T-Lymphoblastic Leukemia/Lymphoma -0.97 3.59×10⁻⁵ 1.08×10⁻⁴ 3 AHCY Intracholecystic Papillary Neoplasms -0.83 9.93×10⁻³ 1.23×10⁻² 4 ADSL B-Cell Acute Lymphoblastic Leukemia -0.74 1.18×10⁻⁴ 2.78×10⁻⁴ 5 ADSS2 B-Cell Acute Lymphoblastic Leukemia -0.72 3.86×10⁻⁷ 2.24×10⁻⁶ 6 ADSL Myeloproliferative Neoplasms -0.71 4.29×10⁻³ 6.12×10⁻³ 7 ADSL Non-Hodgkin Lymphoma -0.7 3.78×10⁻³ 5.57×10⁻³ 8 ADSL Acute Myeloid Leukemia -0.69 5.05×10⁻⁹ 4.88×10⁻⁸ 9 AK2 B-Cell Acute Lymphoblastic Leukemia -0.6 3.13×10⁻⁶ 1.30×10⁻⁵ 10 AHCY T-Lymphoblastic Leukemia/Lymphoma -0.58 1.34×10⁻² 1.60×10⁻² Selectivity scores among the top targets ranged from − 1.68 to -0.58, with most associations showing strong statistical support (q < 0.01), indicating that combining selectivity and statistical testing effectively prioritizes biologically meaningful candidates. 2.3 Comparison Between Ranking Strategies Ranking by statistical significance was compared with ranking by selectivity score (Fig. 3 ). While the two approaches showed partial overlap, notable differences were observed. For example, ADSL in acute myeloid leukemia exhibited the most significant q-value but ranked lower by selectivity, whereas ABL1 showed the strongest selectivity but relatively higher q-value, likely due to limited sample size. These discrepancies indicate that reliance on a single metric may bias target prioritization. Compared to approaches that prioritize genes solely based on dependency magnitude (e.g., Chronos score), the selectivity-based framework emphasizes lineage specificity and reduces the influence of broadly essential genes. Integrating effect size (selectivity) with statistical significance therefore provides a more balanced prioritization strategy. 2.4 Functional Enrichment Analysis Functional enrichment analysis revealed that significant genes were strongly associated with purine metabolism, nucleotide biosynthesis, and methylation-related pathways. Key genes such as ADSL and ADSS2 are involved in de novo purine synthesis, while AHCY regulates methylation balance and AK2 contributes to nucleotide homeostasis. These processes are closely linked to rapid proliferation, particularly in hematological malignancies. Together, these results suggest that metabolic pathways, especially those related to nucleotide synthesis, may represent an important component of lineage-specific cancer vulnerabilities. 2.5 Key Observations Several consistent patterns emerged from the analysis: Known clinical targets such as ABL1 were successfully rediscovered, supporting pipeline reliability. Metabolic genes account for 90% of the top 10 targets, highlighting nucleotide metabolism as a major source of lineage-specific vulnerabilities. Hematological malignancies are strongly enriched in selective targets, even though they represent a moderate fraction of DepMap cell lines. Novel and understudied genes including AHCY and AK2 were identified as promising candidates for experimental follow-up. 3. Discussion 3.1 Interpretation of Findings The results indicate that a selectivity-centered framework can effectively identify lineage-specific dependencies from large-scale CRISPR screening data. The recovery of ABL1 as a top hit supports the biological relevance of the approach. The enrichment of nucleotide metabolism-related genes suggests that metabolic adaptations associated with rapid proliferation may contribute to lineage-specific vulnerabilities. In particular, the repeated identification of ADSL across multiple hematological malignancies highlights a potentially shared dependency in purine biosynthesis. However, not all highly selective genes reached statistical significance, often due to limited sample sizes. This observation underscores the importance of integrating both effect size and statistical confidence when interpreting dependency data. 3.2 Comparison with Existing Methods Compared with existing approaches that emphasize absolute dependency scores or pan-cancer essentiality, this study focuses on lineage-specific selectivity as a primary criterion. This design allows the identification of context-dependent vulnerabilities that may be overlooked by global ranking strategies. By combining selectivity scoring, nonparametric statistical testing, and multiple-testing correction, the framework provides a practical and reproducible strategy for prioritizing candidate targets. Rather than replacing existing methods, this approach complements conventional dependency analyses by introducing an additional dimension of specificity. 3.3 Limitations Uneven sample sizes across cancer types may influence statistical power and significance estimates. To mitigate this effect, nonparametric testing was used and robustness was evaluated across multiple parameter settings. Nevertheless, future work may incorporate more advanced normalization strategies to further reduce potential bias. 3.4 Translational Implications The identification of metabolic dependencies has potential therapeutic implications. While existing drugs target nucleotide metabolism in leukemia, the present findings suggest that more selective inhibition of enzymes such as ADSL, ADSS2, AHCY, and AK2 may provide improved specificity. Further integration with drug-response datasets and structural studies may help translate these findings into clinically actionable strategies. 3.5 Conclusion A robust and reproducible computational framework was developed to identify cancer-type-specific CRISPR targets using DepMap data. By integrating selectivity scoring, stringent filtering, and rigorous statistical testing, 87 high-confidence lineage-specific dependencies were identified. Hematological malignancies showed strong metabolic vulnerabilities, and known clinical targets such as ABL1 were successfully recovered. This workflow can be updated with newer DepMap releases and expanded to include multi-omics and drug-response data. Overall, this study provides a systematic and practical strategy for discovering cancer-selective therapeutic targets and supports the development of more precise cancer treatments. 4. Methods 4.1 Data Source DepMap 22Q1 public data were used, including Chronos-processed CRISPR gene effect scores and sample annotation files. 4.2 Data Processing All data processing and statistical analyses were performed in Python. Cell lines were mapped to their primary cancer types, and gene-level dependency matrices were constructed. 4.3 Gene Selection Genes were ranked by variance of dependency scores across all cell lines. The top 500 genes with the highest variance were selected to enrich lineage-specific signals. 4.4 Target Selection To identify cancer-selective targets, we applied two predefined thresholds: a Chronos dependency score < -1.0 (indicating strong essentiality in cancer cells) and a selectivity score < -0.3 (indicating greater dependency than the global average). The selection criteria and their rationales are summarized in Table 2 . These thresholds were chosen to prioritize genes with both robust cancer dependency and meaningful selectivity. Table 2 Selection criteria for cancer-selective targets Criterion Threshold Rationale Cancer dependency (Chronos) < -1.0 Strong essentiality in cancer cells Selectivity score < -0.3 More essential than global average These thresholds were chosen to identify genes with both strong dependency and meaningful selectivity. 4.5 Candidate Filtering Gene–cancer pairs were retained if they met both thresholds: Chronos score < -1.0 Selectivity score < -0.3 4.6 Statistical Analysis One-sided Wilcoxon rank-sum tests were used to compare dependency scores in the target cancer type versus all other cell lines. Benjamini–Hochberg correction was applied across all tested pairs. Pairs with q < 0.05 were considered statistically significant. 4.7 Inclusion Criteria Cancer types with fewer than 3 cell lines were generally excluded, except for clinically relevant subtypes with sufficient data support for target validation. 4.8 Functional Enrichment Analysis Enrichment analysis was performed using GO biological process terms and KEGG pathways. Fisher’s exact test was used with FDR correction (FDR < 0.05). The background gene set included all 18,531 genes in DepMap 22Q1. 4.9 Robustness Assessment Sensitivity analyses were conducted using alternative Chronos thresholds (-0.8, -1.0, -1.2) and different gene sets (top 300, 500, 1000 variable genes) to evaluate result stability. Declarations Acknowledgements We thank all members of the research group for their support during this project. Data Availability All data are publicly available from the DepMap portal (https://depmap.org). Code Availability Code and analysis scripts are available at https://github.com/liranfei/DepMap-analysis Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. CRediT Author Contribution Statement Li Ranfei: Conceptualization, Methodology, Software, Formal analysis, Data curation, Visualization, Writing – original draft, Writing – review & editing. Wang Sirui: Methodology, Writing – review & editing. Yang qiying: Conceptualization, Methodology, Supervision, Writing – review & editing. All authors have read and agreed to the published version of the manuscript. References Meyers RM et al (2017) Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat Genet 49:1779–1784 Tsherniak A et al (2017) Defining a cancer dependency map. Cell 170:564–576 Dempster JM et al (2019) Extracting biological insights from the Project Achilles genome-scale CRISPR screens in cancer cell lines. Nat Genet 51:38–45 Behan FM et al (2019) Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature 568:511–516 Pacini C et al (2021) Integrated cross-study datasets of genetic dependencies in cancer. Nat Commun 12:1661 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9463556","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625770016,"identity":"a4db833c-3201-4828-801c-1498b209ea88","order_by":0,"name":"Li Ranfei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYBACPmYQacPAw8/eAGQYWBDWwgbWksbAI9lzAKRFgggtDBAtDAYzEkAsYrSw8z78XJBgI2Mg+fzqhh8FEgz87d0JBBzGbiw9IyGNx1w6p+xmD9BhEmfObiCghY1BmvfHYR7L2TlpN3iAWgwkcglqYf7Nk/Cfx+DmmbSbf4jUwibNk3CAx+AG+7HbxNrCZs2TkAwM5By22zIGEjwE/cLPf4z5Nk+CnT0/+/FnN9/8sZHjb+/FrwUJ8BiASWKVgwD7A1JUj4JRMApGwQgCAMjrOcK7wq2nAAAAAElFTkSuQmCC","orcid":"","institution":"Xuecheng Shungong Experimental School, Zaozhuang, Shandong, China","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Ranfei","suffix":""},{"id":625770017,"identity":"af42841b-81c2-4ce8-ac29-6ddb71b4ed70","order_by":1,"name":"Wang Sirui","email":"","orcid":"","institution":"Xuecheng Shungong Experimental School, Zaozhuang, Shandong, China","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Sirui","suffix":""},{"id":625770018,"identity":"d60a08eb-f473-4134-b79e-884b84a2020f","order_by":2,"name":"Yang Qiying","email":"","orcid":"","institution":"Xuecheng Shungong Experimental School, Zaozhuang, Shandong, China","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Qiying","suffix":""}],"badges":[],"createdAt":"2026-04-19 16:22:44","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9463556/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9463556/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107488228,"identity":"81464ae6-e119-4652-9af9-e1f56cc64c0b","added_by":"auto","created_at":"2026-04-22 02:43:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":167833,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of candidate gene–cancer pairs across 43 cancer types.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9463556/v1/10b2ddc5c540d34605d50a29.png"},{"id":107487432,"identity":"b14878ee-5f97-424a-b72e-440b44213ee3","added_by":"auto","created_at":"2026-04-22 02:41:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":286489,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between Chronos dependency score and selectivity score.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9463556/v1/f4cb6cca3c9ff76eb2327fe7.png"},{"id":107387914,"identity":"31ffdc7a-ecd4-4baa-94df-ca4428715c8c","added_by":"auto","created_at":"2026-04-21 04:19:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":191160,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of target ranking by selectivity vs. statistical significance.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9463556/v1/c8e9067ef8d9dfd1fc019d9c.png"},{"id":107489550,"identity":"77a66d4d-4c37-4d6b-9bbf-de40e0adef23","added_by":"auto","created_at":"2026-04-22 02:48:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":862278,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9463556/v1/15144dda-0198-4db4-b069-f9b7f7710ef0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIdentification of Cancer-Type-Specific CRISPR Targets Using DepMap Dependency Data\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Novel selectivity framework identifies cancer-specific CRISPR targets from DepMap data\u003c/p\u003e\n\u003cp\u003e\u0026bull; 87 lineage-specific dependencies discovered across 43 cancer types\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hematological malignancies show strong enrichment of metabolic vulnerabilities\u003c/p\u003e\n\u003cp\u003e\u0026bull; Nine of top ten targets are nucleotide metabolism genes\u003c/p\u003e\n\u003cp\u003e\u0026bull; ABL1 rediscovery validates the pipeline for precision oncology\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eGenome-scale CRISPR-Cas9 screening has changed how researchers identify gene essentiality in cancer models. These high-throughput tools make it possible to measure how individual genes contribute to cancer cell survival and proliferation in a systematic way. Among public resources, the Cancer Dependency Map (DepMap) provides consistent CRISPR screening data across hundreds of cell lines, enabling cross-cancer comparison of gene essentiality.\u003c/p\u003e \u003cp\u003eDespite the scale and quality of DepMap data, one major challenge remains. Many genes appear essential across many cancer types simply because they support core cellular functions, not because they reflect cancer-specific vulnerabilities. These broadly essential genes are often poor therapeutic targets due to potential toxicity in normal tissues. Methods that can highlight lineage-selective dependencies are therefore more useful for discovering precise and safer targets.\u003c/p\u003e \u003cp\u003eCancer cells often remodel metabolic pathways to support rapid growth and survival. Fast-proliferating cancers, especially hematological malignancies, commonly rely on elevated nucleotide synthesis to sustain DNA and RNA production. However, many existing analysis pipelines prioritize absolute dependency strength rather than lineage-specific selectivity. As a result, biologically important context-dependent signals can be overlooked.\u003c/p\u003e \u003cp\u003eIn this study, a selectivity-centered computational framework was developed using DepMap 22Q1 data. By comparing each cancer type\u0026rsquo;s dependency profile to the global background, the framework enriches for genes that are disproportionately essential in specific lineages. Strict filtering, nonparametric statistical testing, and multiple-test correction were combined to improve reliability and reduce false positives. The workflow addresses common limitations such as data heterogeneity and uneven sample sizes across cancer types. The final output consists of robust, biologically relevant candidate targets for further validation.\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Overview of Candidate and Significant Targets\u003c/h2\u003e \u003cp\u003eGenes were first ranked by the variance of dependency scores across all cell lines, and the top 500 most variable genes were retained to enrich lineage-informative signals while reducing the influence of broadly essential genes. This threshold was selected to balance signal detection and noise reduction, and sensitivity analyses using alternative cutoffs (top 300 and 1000 genes) produced consistent results (Methods 4.9), indicating that the findings are not dependent on a specific parameter choice.\u003c/p\u003e \u003cp\u003eApplying thresholds of Chronos score \u0026lt; -1.0 and selectivity score \u0026lt; -0.3, a total of 1,991 candidate gene\u0026ndash;cancer pairs were identified across 43 cancer types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Statistical testing using one-sided Wilcoxon rank-sum tests, followed by Benjamini\u0026ndash;Hochberg correction, identified 87 lineage-specific dependencies that reached statistical significance (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe relationship between dependency strength and selectivity is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Notably, genes with strong dependency effects do not always exhibit high lineage selectivity, indicating that these two metrics capture distinct aspects of gene essentiality. This distribution highlights that selectivity provides complementary information beyond absolute dependency magnitude and supports its use as an additional prioritization criterion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHematological malignancies contributed 31.9% of all candidate pairs, representing a disproportionate enrichment relative to their overall representation in the dataset. This pattern suggests that certain cancer lineages, particularly those characterized by rapid proliferation, may exhibit stronger lineage-specific dependency signals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Top Statistically Significant Targets\u003c/h2\u003e \u003cp\u003eThe strongest selective signal was observed for ABL1 in myeloproliferative neoplasms (selectivity = -1.68, q\u0026thinsp;=\u0026thinsp;3.30 \u0026times; 10⁻⁶), a well-established therapeutic target, supporting the validity of the analytical framework.\u003c/p\u003e \u003cp\u003eThe top 10 statistically significant targets are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Notably, 9 out of 10 genes are involved in metabolic processes, particularly nucleotide and purine metabolism. ADSL was repeatedly identified across five distinct hematological cancer types (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting a shared lineage-specific dependency in purine biosynthesis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop 10 cancer-selective targets identified in this study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026times;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCancer Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSelectivity Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eq-value (FDR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eABL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMyeloproliferative Neoplasms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e6.06\u0026times;10⁻⁷\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e3.30\u0026times;10⁻⁶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT-Lymphoblastic Leukemia/Lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e3.59\u0026times;10⁻⁵\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e1.08\u0026times;10⁻⁴\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAHCY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntracholecystic Papillary Neoplasms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e9.93\u0026times;10⁻\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e1.23\u0026times;10⁻\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB-Cell Acute Lymphoblastic Leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.18\u0026times;10⁻⁴\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e2.78\u0026times;10⁻⁴\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADSS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB-Cell Acute Lymphoblastic Leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e3.86\u0026times;10⁻⁷\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e2.24\u0026times;10⁻⁶\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMyeloproliferative Neoplasms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e4.29\u0026times;10⁻\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e6.12\u0026times;10⁻\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-Hodgkin Lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e3.78\u0026times;10⁻\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e5.57\u0026times;10⁻\u0026sup3;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcute Myeloid Leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e5.05\u0026times;10⁻⁹\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e4.88\u0026times;10⁻⁸\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB-Cell Acute Lymphoblastic Leukemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e3.13\u0026times;10⁻⁶\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e1.30\u0026times;10⁻⁵\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAHCY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT-Lymphoblastic Leukemia/Lymphoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c5\"\u003e \u003cp\u003e1.34\u0026times;10⁻\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026times;\" colname=\"c6\"\u003e \u003cp\u003e1.60\u0026times;10⁻\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSelectivity scores among the top targets ranged from \u0026minus;\u0026thinsp;1.68 to -0.58, with most associations showing strong statistical support (q\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that combining selectivity and statistical testing effectively prioritizes biologically meaningful candidates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Comparison Between Ranking Strategies\u003c/h2\u003e \u003cp\u003eRanking by statistical significance was compared with ranking by selectivity score (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). While the two approaches showed partial overlap, notable differences were observed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor example, ADSL in acute myeloid leukemia exhibited the most significant q-value but ranked lower by selectivity, whereas ABL1 showed the strongest selectivity but relatively higher q-value, likely due to limited sample size. These discrepancies indicate that reliance on a single metric may bias target prioritization.\u003c/p\u003e \u003cp\u003eCompared to approaches that prioritize genes solely based on dependency magnitude (e.g., Chronos score), the selectivity-based framework emphasizes lineage specificity and reduces the influence of broadly essential genes. Integrating effect size (selectivity) with statistical significance therefore provides a more balanced prioritization strategy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis revealed that significant genes were strongly associated with purine metabolism, nucleotide biosynthesis, and methylation-related pathways.\u003c/p\u003e \u003cp\u003eKey genes such as ADSL and ADSS2 are involved in de novo purine synthesis, while AHCY regulates methylation balance and AK2 contributes to nucleotide homeostasis. These processes are closely linked to rapid proliferation, particularly in hematological malignancies.\u003c/p\u003e \u003cp\u003eTogether, these results suggest that metabolic pathways, especially those related to nucleotide synthesis, may represent an important component of lineage-specific cancer vulnerabilities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Key Observations\u003c/h2\u003e \u003cp\u003eSeveral consistent patterns emerged from the analysis:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eKnown clinical targets such as ABL1 were successfully rediscovered, supporting pipeline reliability.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMetabolic genes account for 90% of the top 10 targets, highlighting nucleotide metabolism as a major source of lineage-specific vulnerabilities.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHematological malignancies are strongly enriched in selective targets, even though they represent a moderate fraction of DepMap cell lines.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eNovel and understudied genes including AHCY and AK2 were identified as promising candidates for experimental follow-up.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Interpretation of Findings\u003c/h2\u003e \u003cp\u003eThe results indicate that a selectivity-centered framework can effectively identify lineage-specific dependencies from large-scale CRISPR screening data. The recovery of ABL1 as a top hit supports the biological relevance of the approach.\u003c/p\u003e \u003cp\u003eThe enrichment of nucleotide metabolism-related genes suggests that metabolic adaptations associated with rapid proliferation may contribute to lineage-specific vulnerabilities. In particular, the repeated identification of ADSL across multiple hematological malignancies highlights a potentially shared dependency in purine biosynthesis.\u003c/p\u003e \u003cp\u003eHowever, not all highly selective genes reached statistical significance, often due to limited sample sizes. This observation underscores the importance of integrating both effect size and statistical confidence when interpreting dependency data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison with Existing Methods\u003c/h2\u003e \u003cp\u003eCompared with existing approaches that emphasize absolute dependency scores or pan-cancer essentiality, this study focuses on lineage-specific selectivity as a primary criterion. This design allows the identification of context-dependent vulnerabilities that may be overlooked by global ranking strategies.\u003c/p\u003e \u003cp\u003eBy combining selectivity scoring, nonparametric statistical testing, and multiple-testing correction, the framework provides a practical and reproducible strategy for prioritizing candidate targets. Rather than replacing existing methods, this approach complements conventional dependency analyses by introducing an additional dimension of specificity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Limitations\u003c/h2\u003e \u003cp\u003eUneven sample sizes across cancer types may influence statistical power and significance estimates. To mitigate this effect, nonparametric testing was used and robustness was evaluated across multiple parameter settings. Nevertheless, future work may incorporate more advanced normalization strategies to further reduce potential bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Translational Implications\u003c/h2\u003e \u003cp\u003eThe identification of metabolic dependencies has potential therapeutic implications. While existing drugs target nucleotide metabolism in leukemia, the present findings suggest that more selective inhibition of enzymes such as ADSL, ADSS2, AHCY, and AK2 may provide improved specificity.\u003c/p\u003e \u003cp\u003eFurther integration with drug-response datasets and structural studies may help translate these findings into clinically actionable strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Conclusion\u003c/h2\u003e \u003cp\u003eA robust and reproducible computational framework was developed to identify cancer-type-specific CRISPR targets using DepMap data. By integrating selectivity scoring, stringent filtering, and rigorous statistical testing, 87 high-confidence lineage-specific dependencies were identified. Hematological malignancies showed strong metabolic vulnerabilities, and known clinical targets such as ABL1 were successfully recovered.\u003c/p\u003e \u003cp\u003eThis workflow can be updated with newer DepMap releases and expanded to include multi-omics and drug-response data. Overall, this study provides a systematic and practical strategy for discovering cancer-selective therapeutic targets and supports the development of more precise cancer treatments.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Methods","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data Source\u003c/h2\u003e \u003cp\u003eDepMap 22Q1 public data were used, including Chronos-processed CRISPR gene effect scores and sample annotation files.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Data Processing\u003c/h2\u003e \u003cp\u003eAll data processing and statistical analyses were performed in Python. Cell lines were mapped to their primary cancer types, and gene-level dependency matrices were constructed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Gene Selection\u003c/h2\u003e \u003cp\u003eGenes were ranked by variance of dependency scores across all cell lines. The top 500 genes with the highest variance were selected to enrich lineage-specific signals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Target Selection\u003c/h2\u003e \u003cp\u003eTo identify cancer-selective targets, we applied two predefined thresholds: a Chronos dependency score \u0026lt; -1.0 (indicating strong essentiality in cancer cells) and a selectivity score \u0026lt; -0.3 (indicating greater dependency than the global average). The selection criteria and their rationales are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These thresholds were chosen to prioritize genes with both robust cancer dependency and meaningful selectivity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelection criteria for cancer-selective targets\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriterion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThreshold\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRationale\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer dependency (Chronos)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; -1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStrong essentiality in cancer cells\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelectivity score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; -0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMore essential than global average\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eThese thresholds were chosen to identify genes with both strong dependency and meaningful selectivity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Candidate Filtering\u003c/h2\u003e \u003cp\u003eGene\u0026ndash;cancer pairs were retained if they met both thresholds:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eChronos score \u0026lt; -1.0\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSelectivity score \u0026lt; -0.3\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eOne-sided Wilcoxon rank-sum tests were used to compare dependency scores in the target cancer type versus all other cell lines. Benjamini\u0026ndash;Hochberg correction was applied across all tested pairs. Pairs with q\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Inclusion Criteria\u003c/h2\u003e \u003cp\u003eCancer types with fewer than 3 cell lines were generally excluded, except for clinically relevant subtypes with sufficient data support for target validation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Functional Enrichment Analysis\u003c/h2\u003e \u003cp\u003eEnrichment analysis was performed using GO biological process terms and KEGG pathways. Fisher\u0026rsquo;s exact test was used with FDR correction (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The background gene set included all 18,531 genes in DepMap 22Q1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.9 Robustness Assessment\u003c/h2\u003e \u003cp\u003eSensitivity analyses were conducted using alternative Chronos thresholds (-0.8, -1.0, -1.2) and different gene sets (top 300, 500, 1000 variable genes) to evaluate result stability.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all members of the research group for their support during this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are publicly available from the DepMap portal (https://depmap.org).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCode and analysis scripts are available at https://github.com/liranfei/DepMap-analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT Author Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLi Ranfei: Conceptualization, Methodology, Software, Formal analysis, Data curation, Visualization, Writing \u0026ndash; original draft, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eWang Sirui: Methodology, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eYang qiying: Conceptualization, Methodology, Supervision, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMeyers RM et al (2017) Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat Genet 49:1779\u0026ndash;1784\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsherniak A et al (2017) Defining a cancer dependency map. Cell 170:564\u0026ndash;576\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDempster JM et al (2019) Extracting biological insights from the Project Achilles genome-scale CRISPR screens in cancer cell lines. Nat Genet 51:38\u0026ndash;45\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBehan FM et al (2019) Prioritization of cancer therapeutic targets using CRISPR-Cas9 screens. Nature 568:511\u0026ndash;516\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePacini C et al (2021) Integrated cross-study datasets of genetic dependencies in cancer. Nat Commun 12:1661\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"CRISPR-Cas9, DepMap, cancer dependency, bioinformatics, lineage-specific targets, nucleotide metabolism","lastPublishedDoi":"10.21203/rs.3.rs-9463556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9463556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCRISPR-Cas9 screening enables systematic identification of gene dependencies across diverse cancer cell lines. However, distinguishing lineage-specific vulnerabilities from broadly essential genes remains a key challenge for precision oncology. In this study, we analyzed DepMap 22Q1 data to develop a selectivity-centered computational framework for prioritizing cancer-type-specific dependencies.\u003c/p\u003e \u003cp\u003eA selectivity score was defined as the difference between the median dependency within a given cancer type and the global median across all cell lines. To enrich lineage-informative signals, the top 500 genes ranked by dependency variance were retained. Candidate targets were identified using thresholds of Chronos score \u0026lt; -1.0 and selectivity score \u0026lt; -0.3, followed by one-sided Wilcoxon rank-sum testing with Benjamini\u0026ndash;Hochberg correction (q\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eA total of 1,991 candidate gene\u0026ndash;cancer pairs were identified across 43 cancer types, among which 87 were statistically significant. Hematological malignancies showed strong enrichment of selective dependencies. The top-ranked association, ABL1 in myeloproliferative neoplasms, is a clinically validated target, supporting the reliability of the framework. Functional enrichment analysis revealed a strong association with purine metabolism, nucleotide biosynthesis, and methylation-related processes. Notably, 9 of the top 10 targets were metabolic genes.\u003c/p\u003e \u003cp\u003eImportantly, comparison between selectivity-based ranking and statistical significance demonstrated that integrating effect size with significance improves prioritization robustness relative to single-metric approaches. These findings suggest that selectivity-driven analysis provides a complementary perspective to conventional dependency ranking strategies.\u003c/p\u003e \u003cp\u003eThis study presents a reproducible workflow for identifying lineage-specific CRISPR dependencies and offers candidate targets for further experimental validation.\u003c/p\u003e","manuscriptTitle":"Identification of Cancer-Type-Specific CRISPR Targets Using DepMap Dependency Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 04:19:39","doi":"10.21203/rs.3.rs-9463556/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"53c141c3-3261-44b0-a660-04c1c2054bb1","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66602241,"name":"Cancer Biology"},{"id":66602242,"name":"Bioinformatics"}],"tags":[],"updatedAt":"2026-04-21T04:19:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 04:19:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9463556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9463556","identity":"rs-9463556","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}