Abstract
Introduction: Philadelphia-negative myeloproliferative neoplasms (MPNs) are a group of diseases, including primary myelofibrosis (PMF), essential thrombocythemia (ET), and polycythemia vera (PV). Ruxolitinib, as a JAK1/2 inhibitor, can reduce splenomegaly and overproduction of blood cells. However, prolonged ruxolitinib treatment often leads to loss of therapeutic efficacy or intolerable side effects, ultimately resulting in treatment discontinuation in many patients. Genome-scale CRISPR-Cas9 screen has emerged as a robust, unbiased tool for discovering novel cancer vulnerabilities and therapeutic targets. In this study, we performed CRISPR-Cas9 knockout screening in vitro to identify genes whose loss was involved in resistance to ruxolitinib.
Methods: We employed the genome-scale CRISPR-Cas9 knockout (GeCKO) library and utilized the SET-2 cell line as a myeloproliferative neoplasm (MPN) model. The CRISPR/Cas9 library screening process involved several key steps. The first was pre-experiments, including GFP control virus infection and drug concentration tests. Next, stable Cas9-expressing SET-2 cells were generated using lentiviral vectors carrying Cas9 and selected with Blasticidin. For the main screening, the gRNA library was introduced into stable Cas9-expressing SET-2 cells, followed by puromycin selection to enrich successfully infected cells, named as Human GeCKO SET-2. Based on the results of the pre-experiment, Human GeCKO SET-2 cells were treated with 150 nM ruxolitinib for twenty-one days. Finally, genomic DNA was extracted from surviving cells, and the gRNA sequences were amplified and analyzed via next-generation sequencing (NGS). Bioinformatic tools were used to identify enriched or depleted gRNAs, linking gene knockouts to phenotypic changes.
Results: Raw sequencing data were subjected to stringent quality checks, including assessment of base quality (Q30 > 80%), error rates (<0.5%), and GC content uniformity. AGeCK software quantified gRNA and gene-level read counts, normalized for comparative analysis. Essential genes (e.g., KAT7, MRPL34) were identified via negative selection (depleted gRNAs), while resistance-associated genes (e.g., FAM71C, GPRIN2) emerged from positive selection (enriched gRNAs). We obtained 3069 negatively selected genes (FDR < 0.25) and 1913 positively selected candidates through Robust Rank Aggregation (RRA) analysis. Top hits included KAT7 (neg-score = 7.32e-06) and FAM71C (pos-score = 2.75e-06). Volcano plots highlighted genes with |log2FC| > 2 and FDR < 0.05, such as DHODH (negative) and CDC34 (positive). Gene Ontology (GO) and KEGG pathway analyses were conducted to elucidate the biological roles of the identified resistance-associated genes.
The top three enriched GO pathway were Cytoplasmic translation, Mitochondrial translation and Exonucleolytic nuclear-transcribed mrna catabolic process. The top three enriched KEGG pathway were Aminoacyl-trna biosynthesis, Proteasome and Rna polymerase. The top ten resistance-associated genes that we were interested in, were linked to cell cycle regulation and proliferation (2 genes), metabolic detoxification mechanisms (2 genes), transcriptional and translational control (2 genes), oxidative stress response (1 gene), epigenetic modulation and signal integration (1 gene), and unexpected involvement of neural-related functions (2 genes). They synergistically promote tumor cell survival under chemotherapeutic pressure by sustaining cell cycle progression, enhancing drug metabolism and clearance, stabilizing pro-survival gene expression, resisting oxidative damage, remodeling epigenetic states, and potentially co-opting neurodevelopmental mechanisms to reinforce adaptive resistance. This multifunctional interplay suggests that overcoming drug resistance requires a combinatorial therapeutic approach targeting metabolic, epigenetic, and stress-defense pathways. Next, we will conduct experimental studies to further validate these genes.
Conclusion: We performed the genome-scale CRISPR-Cas9 knockout screening to identify genetic drivers of ruxolitinib resistance, providing actionable targets for further validation.
Acknowledgement: This research was funded by Zhejiang Provincial Health High-level Innovative Talent Project (2022-2026).
*Correspondence to: Jian Huang, M.D., Ph.D., Department of Hematology, The First Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310003, Zhejiang, China. E-mail:househuang@zju.edu.cn.
