RESUMO
Mapping genetic interactions in mammalian cells is limited due to technical obstacles. Here we describe a method called TCGI (tRNA-CRISPR for genetic interactions) to generate a high-efficient, barcode-free and scalable pairwise CRISPR libraries in mammalian cells for identifying genetic interactions. We have generated a genome- wide library to identify genes genetically interacting with TAZ in cell viability regulation. Validation of candidate synergistic genes reveals the screening accuracy of 85% and TAZ-MCL1 is characterized as combinational drug targets for non-small cell lung cancer treatments. TCGI has dramatically improved the current methods for mapping genetic interactions and screening drug targets for combinational therapies.
Assuntos
Proteínas Adaptadoras de Transdução de Sinal/genética , Carcinoma Pulmonar de Células não Pequenas/genética , Proteína de Sequência 1 de Leucemia de Células Mieloides/genética , Transativadores/genética , Fatores de Transcrição/genética , Animais , Sistemas CRISPR-Cas/genética , Carcinoma Pulmonar de Células não Pequenas/patologia , Sobrevivência Celular/genética , Mapeamento Cromossômico , Epistasia Genética/genética , Genoma Humano/genética , Células HEK293 , Humanos , RNA de Transferência/genética , Proteínas com Motivo de Ligação a PDZ com Coativador Transcricional , Proteínas de Sinalização YAPRESUMO
High-grade serous ovarian carcinoma (HGSC) accounts for 70% of all epithelial ovarian cancers but clinical management is challenged by a lack of accurate prognostic and predictive biomarkers of chemotherapy response. This study evaluated the role of Signal Transducer and Activator of Transcription 1 (STAT1) as an independent prognostic and predictive biomarker and its correlation with intratumoural CD8+ T cells in a second independent biomarker validation study. Tumour STAT1 expression and intratumoural CD8+ T cell infiltration were assessed by immunohistochemistry as a multicentre validation study conducted on 734 chemotherapy-naïve HGSCs. NanoString-based profiling was performed to correlate expression of STAT1 target genes CXCL9, CXCL10 and CXCL11 with CD8A transcript expression in 143 primary tumours. Multiplexed cytokine analysis of pre-treatment plasma from resistant and sensitive patients was performed to assess systemic levels of STAT1-induced cytokines. STAT1 was validated as a prognostic and predictive biomarker in both univariate and multivariate models and its expression correlated significantly with intra-epithelial CD8+ T cell infiltration in HGSC. STAT1 levels increased the prognostic and predictive value of intratumoural CD8+ T cells, confirming their synergistic role as biomarkers in HGSC. In addition, expression of STAT1 target genes (CXCL9, CXCL10 and CXCL11) correlated significantly with levels of, and CD8A transcripts from intratumoural CD8+ T cells within the resistant and sensitive tumours. Our findings provide compelling evidence that high levels of STAT1, STAT1-induced chemokines and CD8+ T cells correlate with improved chemotherapy response in HGSC. These results identify STAT1 and its target genes as novel biomarkers of chemosensitivity in HGSC. These findings provide new translational opportunities for patient stratification for immunotherapies based on emerging biomarkers of inflammation in HGSC. An improved understanding of the role of interferon-inducible genes will be foundational for developing immunomodulatory therapies in ovarian cancer.