The N-glycosylation module is demonstrated as a recurrent hub of context-dependent genetic interactions and combinatorial CRISPR screening as a scalable approach for identifying therapeutic targets and resistance mechanisms in oncogene-driven cancers.
Abstract
Oncogenic contexts shape synthetic lethal vulnerabilities and suppressor interactions that underlie drug resistance mechanisms, yet this context-dependence remains poorly characterized. Combinatorial CRISPR screens offer a principled approach to surface these interactions at scale with direct implications for therapeutic targeting and drug combination strategies. Using the In4mer 4-plex Cas12a knockout platform, we screened pairwise genetic interactions across RTK signaling and glycosylation networks in oncogene-stratified cancer cells (Lin et al., BioRxiv 2025), identifying hits enriched in the glycosylation machinery. Building on these findings, focused validation screens across cancer cell lines with KRAS-, BRAF-, and EGFR-drivers confirm high-effect genetic interactions concentrated in the glycosylation module across oncogenic backgrounds, revealing both synthetic lethal dependencies and suppressor interactions. Synthetic lethal candidates linking ER stress regulation and glycosyltransferase activity were supported by dependency-expression correlations in cancer cell lines, and recurrent amplification of glycosylation machinery in patient tumors is associated with poor clinical outcomes. Preliminary cell-based validation assays further corroborate a suppressor interaction, implicating a potential resistance mechanism to EGFR inhibition. Together, these findings demonstrate the N-glycosylation module as a recurrent hub of context-dependent genetic interactions and combinatorial CRISPR screening as a scalable approach for identifying therapeutic targets and resistance mechanisms in oncogene-driven cancers.
Subin Kim, Chenchu Lin, Sabriyeh Alibai, Iulia Veronica Gheorghe, Jui Hsuan Chou, Traver Hart. Combinatorial CRISPR screening reveals synthetic lethal and suppressor interactions in RTK signaling and glycosylation networks [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr B093.
Background CRISPR-mediated viability assays in diverse cancer cell lines have informed cancer biology and precision medicine, but cell fitness is not the only cancer-relevant phenotype. Gene expression profiling provides insight into cellular stress, inflammation, and differential state, while still identifying activation of cell-death pathways. Perturb-seq allows scalable functional genomics screening of expression phenotypes at single-cell resolution, however existing datasets cover only a small number of work-horse cell lines. Results We produced a proof-of-concept Perturb-seq dataset targeting 100 genes in 16 diverse cancer cell lines. In the process, we established methods to address single-cell technical artifacts, identified Cas9-mediated chromosomal aberrations and assessed screen quality. Even with a limited library, we observed common signatures of deleting essential genes as well as context-specific responses based on intrinsic genomic properties of the models. For example, we inferred a previously undescribed relationship between dependence on the ER-golgi transport gene immediate early response 3 interacting protein 1 (IER3IP1) and oxidative stress, demonstrating the potential of integrated Perturb-seq for hypothesis generation. Conclusions We established a framework for building a comprehensive map of post-perturbational transcriptional phenotypes using parallel Perturb-seq experiments across multiple cell lines. We demonstrated that integrated Perturb-seq experiments spanning diverse contexts enable hypotheses about gene function specific to tissue types or cancer subtypes – suggesting large-scale, genome-wide datasets would offer invaluable insight into the highly context-dependent nature of cancer biology.
Samuel Maffa, Isabella Boyle, Lie Ward et al.· bioRxiv· 0 citations
Genome-wide CRISPR screens have systematically identified genes required for cancer cell survival, yet these studies are typically performed under standardized conditions that do not fully recapitulate the physiological stresses encountered within the tumor microenvironment. In a recent issue of Nature Genetics, Cheruiyot and colleagues perform genome-wide loss-of-function screens under inflammatory conditions induced by interferon-β (IFN-β), interferon-γ (IFN-γ), and tumor necrosis factor (TNF), revealing that distinct cytokines impose different genetic requirements for tumor cell survival. The study shows that inflammatory signaling reshapes genetic dependency landscape in a cytokine-specific manner. Mechanistic analyses identify the glycosylphosphatidylinositol (GPI) transamidase complex and FITM2 as representative examples of genes that become selectively required under inflammatory stress by maintaining membrane protein maturation, endoplasmic reticulum homeostasis, and resistance to oxidative stress. These findings broaden our understanding of how inflammatory cytokines influence tumor cell biology beyond transcriptional regulation and immune recognition. More broadly, the study highlights the value of incorporating physiologically relevant conditions into functional genetic screens, suggesting that conventional dependency maps capture only part of the genetic requirements for tumor survival. Applying similar approaches to other microenvironmental stresses-including hypoxia, metabolic competition, extracellular matrix remodeling, and stromal signaling-may uncover additional therapeutic opportunities for cancer immunotherapy.
Zihan Ning, Guangchuan Wang· Cancer Research· 0 citations
Background: Leukemia is a cancer of hematopoietic stem cells in the bone marrow. It is classified as lymphoid or myeloid, and as acute (rapid onset) or chronic (slow progression). Advances in CRISPR technology enable deeper study of leukemia biology and therapeutic targets. While AML, CML, ALL, and CLL are distinct, comparative studies highlight shared and unique features. Identifying key pathways across subtypes may yield improved treatments. Our lab has established a unique CRISPR Activation (CRISPRa) platform for gain-of-function screening applications. We hypothesize that genome-wide CRISPRa screening will uncover subtype-specific genetic dependencies and drug resistance mechanisms, offering novel therapeutic insights.
We engineered leukemia cell lines by nucleofecting a self-selecting CRISPRa PiggyBac plasmid carrying a blasticidin resistance gene, enabling selection of CRISPRa-competent cells. Functionality was validated using lentiviral delivery of sgRNAs targeting cell surface markers. Cells were transduced with our whole-genome CRISPRa library, Sonata, at MOI 0.4, followed by puromycin selection to enrich for sgRNA-expressing cells. Post-transduction, cells were harvested at various timepoints for sequencing to track sgRNA abundance and identify growth-modulating genes.
We engineered and validated four CRISPRa-competent leukemia lines: K562 (CML), Jurkat (T-ALL), THP-1 (AML), and HL60 (AML). We have completed whole-genome screening campaigns in the CML and T-ALL backgrounds, identifying hundreds of shared and context specific growth modifiers. Functional validation, pathway analysis, and potential clinical significance is ongoing, as is expansion of our screens with the AML contexts.
Conclusions: Our screens reveal genes that, upon activation, influence leukemia cell growth. These findings support discovery of new therapeutic targets and enhance understanding of leukemia subtype biology, guiding future personalized treatment strategies.
The Cole Foundation, FHMR
Tumor Immunology: Checkpoints, Prevention, and Treatment (TIPT)
Jeffrey Sullivan, Xiaozen Wen, Gabriela Flores-Vargas et al.· Journal of Immunology· 0 citations
Key methodological steps for achieving high-efficiency lentiviral transduction and selection are described, enabling the successful application of EPIKOL CRISPR screens in chemoresistant TNBC models.
O. Yedier-Bayram, Elif Ayca Guvener, T. Bagci-Onder· Journal of Visualized Experi...· 0 citations
The study suggests that the inhibition of the Wnt/LEF1 axis is associated with the potential activation of a compensatory survival mechanism, involving the NF-kappa B/AP-1 pathway.
I. Alhamadani, Mohammad Alzeyadi· Asian Pacific Journal of Can...· 0 citations
Identifying cancer gene dependencies is essential for nominating new therapeutic targets. However, because most experimental models do not sufficiently represent the full diversity of tumors—especially for rare cancers—it remains challenging to use functional screening on experimental models to infer the dependency landscape of individual tumors. We used machine learning to infer gene dependencies from tumor transcriptional profiles, applying our model to the TCGA (>11000 tumors across 28 lineages), rare cancers (>1,000 samples, including multiple rare kidney cancer subtypes), and >500 previously unscreened cancer cell lines. In addition to validating our approach via recovery of known dependencies previously identified in functional genetic screens, we were able to directly infer drug response and synthetic essential relationships from tumor data, highlighting associations with RB1 inactivation, KRAS mutations, and microsatellite instability. Via dependency prediction, we discovered and validated a shared reliance on oxidative phosphorylation in two previously unscreened rare cancers both driven by TFE3 gene fusions: translocation renal cell carcinoma (tRCC) and alveolar soft part sarcoma (ASPS). We also nominate potentially actionable vulnerabilities across other rare cancers, most of which lack in experimental models, but for which RNA-Seq data from tumors are available. These findings demonstrate that machine learning applied to transcriptomic data can uncover novel cancer vulnerabilities and actionable targets in individual tumors, even in the absence of functional screening. This may represent a scalable approach to advance precision oncology in rare and/or under-characterized cancer types.
Ananthan Sadagopan, Bingchen Li, Jiao Li, Yantong Cui, Riva Deodhar, Di Yang, Yuqianxun Wu, Prathyusha Konda, Christy Biji, Dharma Thapa, Meha Thakur, Cary Weiss, Toni Choueiri, Jaime Cheah, John Doench, Benjamin Drapkin, Srinivas Viswanathan. Target discovery in rare cancers enabled by transcriptome-based virtual CRISPR screening [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Breaking Barriers in the Fight against Rare Cancers; 2026 Jul 18-20; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(14_Suppl):Abstract nr PR006.
Ananthan Sadagopan, Bingchen Li, Jiao Li et al.· Cancer Research· 0 citations