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In vivo genome-wide CRISPR screening in PDAC. a Schematic representation of the loss-of-function genome-wide screen using the human <t>lentiviral</t> <t>CRISPR/Cas9</t> library <t>(GeCKOv2)</t> library A in pancreatic ductal adenocarcinoma cell line (HPAF-II). b Average mass of extracted tumor from NSG mice subcutaneous transplanted with 30 million cells and grown for 28 days. Mean of three independent infection replicate experiments ( n = 5, 1 mouse per biological replicate was randomly selected for deep sequencing). Data are represented as mean ± SEM. c Normalized read count distribution from sequenced amplicons. d The unmapped percentage of sgRNAs in the library in cells before transplantation ( n = 3), and tumor samples ( n = 3) on day 28. e Statistical dispersion graphic (Gini index) of the sgRNA distribution within samples from cells and tumor replicates. f Cumulative distribution function (CDF) of library sgRNAs in the cell representation and tumor sample replicates. Shifts in tumor samples reflect altered read counts in subset of sgRNAs. g Pearson correlation of the sgRNA reads between all samples from in vitro and in vivo
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Genome-scale <t>CRISPR</t> knockout screen and candidate identification. (A) Schematic representation of the CRISPR knockout screen in human endometrial cancer HEC-1A cells. (B) KEGG analysis the top 250 palbociclib negatively selected hits in the CRISPR-Cas9 screen. (C) The top 10 candidates that were most significantly depleted after palbociclib selection are ranked by a modified robust ranking aggregation (RRA score). (D) NEK6 expression in endometrial cancer was analyzed using RNA-Seq datasets from the TCGA database. (E) NEK6 gene expression level from GEPIA ( http://gepia.cancer-pku.cn ). It includes 174 TCGA-UCSC samples and 91 GTEx normal endometrial samples. (F) NEK6 was detected in 47 EC tissues and 19 normal endometrial tissues by qRT-PCR. (G) Representative images of NEK6 in paracancerous and EC tissues detected on a TMA. (H) Quantification of NEK6 staining intensities in human EC. The Y-axis shows the percentage of tumors with negative/weak, moderate and strong IHC staining.
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Genome-scale <t>CRISPR</t> knockout screen and candidate identification. (A) Schematic representation of the CRISPR knockout screen in human endometrial cancer HEC-1A cells. (B) KEGG analysis the top 250 palbociclib negatively selected hits in the CRISPR-Cas9 screen. (C) The top 10 candidates that were most significantly depleted after palbociclib selection are ranked by a modified robust ranking aggregation (RRA score). (D) NEK6 expression in endometrial cancer was analyzed using RNA-Seq datasets from the TCGA database. (E) NEK6 gene expression level from GEPIA ( http://gepia.cancer-pku.cn ). It includes 174 TCGA-UCSC samples and 91 GTEx normal endometrial samples. (F) NEK6 was detected in 47 EC tissues and 19 normal endometrial tissues by qRT-PCR. (G) Representative images of NEK6 in paracancerous and EC tissues detected on a TMA. (H) Quantification of NEK6 staining intensities in human EC. The Y-axis shows the percentage of tumors with negative/weak, moderate and strong IHC staining.
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In vivo genome-wide CRISPR screening in PDAC. a Schematic representation of the loss-of-function genome-wide screen using the human lentiviral CRISPR/Cas9 library (GeCKOv2) library A in pancreatic ductal adenocarcinoma cell line (HPAF-II). b Average mass of extracted tumor from NSG mice subcutaneous transplanted with 30 million cells and grown for 28 days. Mean of three independent infection replicate experiments ( n = 5, 1 mouse per biological replicate was randomly selected for deep sequencing). Data are represented as mean ± SEM. c Normalized read count distribution from sequenced amplicons. d The unmapped percentage of sgRNAs in the library in cells before transplantation ( n = 3), and tumor samples ( n = 3) on day 28. e Statistical dispersion graphic (Gini index) of the sgRNA distribution within samples from cells and tumor replicates. f Cumulative distribution function (CDF) of library sgRNAs in the cell representation and tumor sample replicates. Shifts in tumor samples reflect altered read counts in subset of sgRNAs. g Pearson correlation of the sgRNA reads between all samples from in vitro and in vivo

Journal: Molecular Cancer

Article Title: Identification of HSPE1 as a new actionable cancer vulnerability leads to an innovative and effective combination therapy for pancreatic ductal adenocarcinoma

doi: 10.1186/s12943-026-02587-9

Figure Lengend Snippet: In vivo genome-wide CRISPR screening in PDAC. a Schematic representation of the loss-of-function genome-wide screen using the human lentiviral CRISPR/Cas9 library (GeCKOv2) library A in pancreatic ductal adenocarcinoma cell line (HPAF-II). b Average mass of extracted tumor from NSG mice subcutaneous transplanted with 30 million cells and grown for 28 days. Mean of three independent infection replicate experiments ( n = 5, 1 mouse per biological replicate was randomly selected for deep sequencing). Data are represented as mean ± SEM. c Normalized read count distribution from sequenced amplicons. d The unmapped percentage of sgRNAs in the library in cells before transplantation ( n = 3), and tumor samples ( n = 3) on day 28. e Statistical dispersion graphic (Gini index) of the sgRNA distribution within samples from cells and tumor replicates. f Cumulative distribution function (CDF) of library sgRNAs in the cell representation and tumor sample replicates. Shifts in tumor samples reflect altered read counts in subset of sgRNAs. g Pearson correlation of the sgRNA reads between all samples from in vitro and in vivo

Article Snippet: Human genome-wide CRISPR/cas9 knockout pooled library GeCKOv2 was a gift from Feng Zhang (Addgene#1,000,000,048).

Techniques: In Vivo, Genome Wide, CRISPR, Infection, Sequencing, Transplantation Assay, Dispersion, In Vitro

Genome-scale CRISPR knockout screen and candidate identification. (A) Schematic representation of the CRISPR knockout screen in human endometrial cancer HEC-1A cells. (B) KEGG analysis the top 250 palbociclib negatively selected hits in the CRISPR-Cas9 screen. (C) The top 10 candidates that were most significantly depleted after palbociclib selection are ranked by a modified robust ranking aggregation (RRA score). (D) NEK6 expression in endometrial cancer was analyzed using RNA-Seq datasets from the TCGA database. (E) NEK6 gene expression level from GEPIA ( http://gepia.cancer-pku.cn ). It includes 174 TCGA-UCSC samples and 91 GTEx normal endometrial samples. (F) NEK6 was detected in 47 EC tissues and 19 normal endometrial tissues by qRT-PCR. (G) Representative images of NEK6 in paracancerous and EC tissues detected on a TMA. (H) Quantification of NEK6 staining intensities in human EC. The Y-axis shows the percentage of tumors with negative/weak, moderate and strong IHC staining.

Journal: Frontiers in Pharmacology

Article Title: Genome-wide CRISPR screen identified NEK6 as a determinant of sensitivity to CDK4/6 inhibitor in endometrial cancer

doi: 10.3389/fphar.2025.1725886

Figure Lengend Snippet: Genome-scale CRISPR knockout screen and candidate identification. (A) Schematic representation of the CRISPR knockout screen in human endometrial cancer HEC-1A cells. (B) KEGG analysis the top 250 palbociclib negatively selected hits in the CRISPR-Cas9 screen. (C) The top 10 candidates that were most significantly depleted after palbociclib selection are ranked by a modified robust ranking aggregation (RRA score). (D) NEK6 expression in endometrial cancer was analyzed using RNA-Seq datasets from the TCGA database. (E) NEK6 gene expression level from GEPIA ( http://gepia.cancer-pku.cn ). It includes 174 TCGA-UCSC samples and 91 GTEx normal endometrial samples. (F) NEK6 was detected in 47 EC tissues and 19 normal endometrial tissues by qRT-PCR. (G) Representative images of NEK6 in paracancerous and EC tissues detected on a TMA. (H) Quantification of NEK6 staining intensities in human EC. The Y-axis shows the percentage of tumors with negative/weak, moderate and strong IHC staining.

Article Snippet: Human GeCKO v2 CRISPR knockout pooled library was a gift from Feng Zhang (Addgene # 1 000 000 048).

Techniques: CRISPR, Knock-Out, Selection, Modification, Expressing, RNA Sequencing, Gene Expression, Quantitative RT-PCR, Staining, Immunohistochemistry