Limits...
Identification of oncogenic driver mutations by genome-wide CRISPR-Cas9 dropout screening

View Article: PubMed Central - PubMed

ABSTRACT

Background: Genome-wide CRISPR-Cas9 dropout screens can identify genes whose knockout affects cell viability. Recent CRISPR screens detected thousands of essential genes required for cellular survival and key cellular processes; however discovering novel lineage-specific genetic dependencies from the many hits still remains a challenge.

Results: To assess whether CRISPR-Cas9 dropout screens can help identify cancer dependencies, we screened two human cancer cell lines carrying known and distinct oncogenic mutations using a genome-wide sgRNA library. We found that the gRNA targeting the driver mutation EGFR was one of the highest-ranking candidates in the EGFR-mutant HCC-827 lung adenocarcinoma cell line. Likewise, sgRNAs for NRAS and MAP2K1 (MEK1), a downstream kinase of mutant NRAS, were identified among the top hits in the NRAS-mutant neuroblastoma cell line CHP-212. Depletion of these genes targeted by the sgRNAs strongly correlated with the sensitivity to specific kinase inhibitors of the EGFR or RAS pathway in cell viability assays. In addition, we describe other dependencies such as TBK1 in HCC-827 cells and TRIB2 in CHP-212 cells which merit further investigation.

Conclusions: We show that genome-wide CRISPR dropout screens are suitable for the identification of oncogenic drivers and other essential genes.

Electronic supplementary material: The online version of this article (doi:10.1186/s12864-016-3042-2) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus

Representation of whole genome sgRNA library at different time points. a Schematic representation of the negative loss-of-function screen using lung cancer cell line HCC-827 and neuroblastoma cell line CHP-212. b Cumulative frequency of sgRNAs by deep sequencing at control time point (day −10), day 14, day 21, and day 28 for HCC-827 cell line. Shift in the curves at days 14, 21, and 28 represents the depletion of essential sgRNAs. Each time point was measured in duplicates. c Same as in b) but for CHP-212 cell line. d Plots of normalized sgRNA reads for HCC-827 cell line at time points day 14, day 21, and day 28. Dark colored dots represent the 1 000 non-targeting control sgRNAs and light colored dots represent the 57 096 targeting sgRNAs. Each time point was measured in duplicates and log2 of median fold changes versus the control time point (day −10) are represented. e Same as in d) but for CHP-212 cell line. f Gene ontology terms describing sgRNAs and genes whose knockdown causes under-representation of HCC-827 cells at day 14. g Gene ontology terms describing sgRNAs and genes whose knockdown cause under-representation of CHP-212 cells at day 14
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC5016932&req=5

Fig1: Representation of whole genome sgRNA library at different time points. a Schematic representation of the negative loss-of-function screen using lung cancer cell line HCC-827 and neuroblastoma cell line CHP-212. b Cumulative frequency of sgRNAs by deep sequencing at control time point (day −10), day 14, day 21, and day 28 for HCC-827 cell line. Shift in the curves at days 14, 21, and 28 represents the depletion of essential sgRNAs. Each time point was measured in duplicates. c Same as in b) but for CHP-212 cell line. d Plots of normalized sgRNA reads for HCC-827 cell line at time points day 14, day 21, and day 28. Dark colored dots represent the 1 000 non-targeting control sgRNAs and light colored dots represent the 57 096 targeting sgRNAs. Each time point was measured in duplicates and log2 of median fold changes versus the control time point (day −10) are represented. e Same as in d) but for CHP-212 cell line. f Gene ontology terms describing sgRNAs and genes whose knockdown causes under-representation of HCC-827 cells at day 14. g Gene ontology terms describing sgRNAs and genes whose knockdown cause under-representation of CHP-212 cells at day 14

Mentions: To investigate whether pooled whole-genome CRISPR-Cas9 screening is an appropriate means to identify oncogenic drivers and novel dependencies we selected two human cancer cell lines with known mutations: (1) the neuroblastoma-derived cell line CHP-212, which carries a RAS (NRAS) Q61K mutation and is highly sensitive to MEK inhibitors [12, 13]; (2) the lung cancer cell line HCC-827, which carries a deletion in the epidermal growth factor receptor (EGFR) delE746 and is sensitive to EGFR inhibitors including Gefitinib and Erlotinib [14]. We introduced a human sgRNA library consisting of 57 096 unique sgRNAs (3 sgRNAs/gene) and 1 000 non-targeting control sgRNAs [5] into CHP-212 and HCC-827 cells by lentiviral transduction. Cells were then grown under puromycin selection for 10 days, and genomic DNA samples were collected at days 14, 21, and 28 thereafter without any selection pressure. Experiments were conducted in duplicates (Fig. 1a).Fig. 1


Identification of oncogenic driver mutations by genome-wide CRISPR-Cas9 dropout screening
Representation of whole genome sgRNA library at different time points. a Schematic representation of the negative loss-of-function screen using lung cancer cell line HCC-827 and neuroblastoma cell line CHP-212. b Cumulative frequency of sgRNAs by deep sequencing at control time point (day −10), day 14, day 21, and day 28 for HCC-827 cell line. Shift in the curves at days 14, 21, and 28 represents the depletion of essential sgRNAs. Each time point was measured in duplicates. c Same as in b) but for CHP-212 cell line. d Plots of normalized sgRNA reads for HCC-827 cell line at time points day 14, day 21, and day 28. Dark colored dots represent the 1 000 non-targeting control sgRNAs and light colored dots represent the 57 096 targeting sgRNAs. Each time point was measured in duplicates and log2 of median fold changes versus the control time point (day −10) are represented. e Same as in d) but for CHP-212 cell line. f Gene ontology terms describing sgRNAs and genes whose knockdown causes under-representation of HCC-827 cells at day 14. g Gene ontology terms describing sgRNAs and genes whose knockdown cause under-representation of CHP-212 cells at day 14
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC5016932&req=5

Fig1: Representation of whole genome sgRNA library at different time points. a Schematic representation of the negative loss-of-function screen using lung cancer cell line HCC-827 and neuroblastoma cell line CHP-212. b Cumulative frequency of sgRNAs by deep sequencing at control time point (day −10), day 14, day 21, and day 28 for HCC-827 cell line. Shift in the curves at days 14, 21, and 28 represents the depletion of essential sgRNAs. Each time point was measured in duplicates. c Same as in b) but for CHP-212 cell line. d Plots of normalized sgRNA reads for HCC-827 cell line at time points day 14, day 21, and day 28. Dark colored dots represent the 1 000 non-targeting control sgRNAs and light colored dots represent the 57 096 targeting sgRNAs. Each time point was measured in duplicates and log2 of median fold changes versus the control time point (day −10) are represented. e Same as in d) but for CHP-212 cell line. f Gene ontology terms describing sgRNAs and genes whose knockdown causes under-representation of HCC-827 cells at day 14. g Gene ontology terms describing sgRNAs and genes whose knockdown cause under-representation of CHP-212 cells at day 14
Mentions: To investigate whether pooled whole-genome CRISPR-Cas9 screening is an appropriate means to identify oncogenic drivers and novel dependencies we selected two human cancer cell lines with known mutations: (1) the neuroblastoma-derived cell line CHP-212, which carries a RAS (NRAS) Q61K mutation and is highly sensitive to MEK inhibitors [12, 13]; (2) the lung cancer cell line HCC-827, which carries a deletion in the epidermal growth factor receptor (EGFR) delE746 and is sensitive to EGFR inhibitors including Gefitinib and Erlotinib [14]. We introduced a human sgRNA library consisting of 57 096 unique sgRNAs (3 sgRNAs/gene) and 1 000 non-targeting control sgRNAs [5] into CHP-212 and HCC-827 cells by lentiviral transduction. Cells were then grown under puromycin selection for 10 days, and genomic DNA samples were collected at days 14, 21, and 28 thereafter without any selection pressure. Experiments were conducted in duplicates (Fig. 1a).Fig. 1

View Article: PubMed Central - PubMed

ABSTRACT

Background: Genome-wide CRISPR-Cas9 dropout screens can identify genes whose knockout affects cell viability. Recent CRISPR screens detected thousands of essential genes required for cellular survival and key cellular processes; however discovering novel lineage-specific genetic dependencies from the many hits still remains a challenge.

Results: To assess whether CRISPR-Cas9 dropout screens can help identify cancer dependencies, we screened two human cancer cell lines carrying known and distinct oncogenic mutations using a genome-wide sgRNA library. We found that the gRNA targeting the driver mutation EGFR was one of the highest-ranking candidates in the EGFR-mutant HCC-827 lung adenocarcinoma cell line. Likewise, sgRNAs for NRAS and MAP2K1 (MEK1), a downstream kinase of mutant NRAS, were identified among the top hits in the NRAS-mutant neuroblastoma cell line CHP-212. Depletion of these genes targeted by the sgRNAs strongly correlated with the sensitivity to specific kinase inhibitors of the EGFR or RAS pathway in cell viability assays. In addition, we describe other dependencies such as TBK1 in HCC-827 cells and TRIB2 in CHP-212 cells which merit further investigation.

Conclusions: We show that genome-wide CRISPR dropout screens are suitable for the identification of oncogenic drivers and other essential genes.

Electronic supplementary material: The online version of this article (doi:10.1186/s12864-016-3042-2) contains supplementary material, which is available to authorized users.

No MeSH data available.


Related in: MedlinePlus