ARID1A KD
stringlengths
2
8
BAP1 KO
stringlengths
2
7
CDH1 KO
stringlengths
3
9
KEAP1 KO
stringlengths
3
8
STK11 KO
stringlengths
3
7
NF1 KO
stringlengths
3
9
NF2 KO
stringlengths
3
7
PBRM1 KO
stringlengths
3
9
PTEN KO
stringlengths
3
8
RB1 KO
stringlengths
3
7
TP53 KO
stringclasses
9 values
VHL KO
stringlengths
3
8
TP53BP1 KO
stringlengths
3
8
PHB
TMEM209
USP14
SMCHD1
TSC2
LLGL1
NARG2
TULP2
ARID1A
HNRNPDL
KLHL15
SLC20A1
SPPL3
MRTO4
FH
MAX
SGTA
GTPBP1
EIF2AK3
HELQ
AHR
SMARCB1
ENO3
LIN7C
DGUOK
MID1IP1
RNF7
UGP2
RAB25
CCDC71L
HOXC9
C20orf173
HNRNPH2
NDUFS3
CSK
RBBP7
CNBP
MYL6
APEX1
MSL1
PAPOLA
CCDC65
PIK3CA
LZTS1
ECHDC2
DYRK2
ENTPD8
CA11
FLT3LG
B4GALT7
FBXL4
NGLY1
IK
KDM5C
NR0B2
GRIN3B
BCLAF1
CTSK
ACOT9
SYVN1
COX6B2
ATP5J
NSMF
C1orf122
KBTBD6
GEMIN6
MARCH2
C16orf96
MAPK14
SLC30A9
SYT12
RUSC1
CHTF18
SMARCA4
P4HA1
IGF2BP1
SLC39A13
CHP1
CCDC174
GYS1
VPS37A
DYRK1A
FBXO41
PTRF
VPS18
C10orf105
ELL2
ACD
KIAA0907
RB1CC1
ATP1A1
TOPBP1
SUCO
HSPB7
PXN
BTK
SUZ12
C1orf27
FANCL
ZC3H7A
COMMD5
OPRD1
ADRA1A
SLC35D1
C19orf40
LIPT2
TNFRSF12A
MED9
PDSS1
MARK2
TGOLN2
SNX17
MVB12A
RIN1
STX18
WIPF1
MGA
USP8
NDUFAF4
CHD4
FAM69A
DNTTIP1
CA4
UBE2K
SFR1
SON
SIRT7
null
DIAPH2
SLC7A5
WDR73
LIPT1
TMEM79
FKRP
C8orf76
BFAR
KCNA3
null
PIGH
FRS2
null
MMP23B
MAGEB18
ZMAT5
EXT1
MAGOH
CANT1
BAG6
PSMC3IP
PSPC1
null
SERPINB6
SAP130
null
SLC4A1AP
FHOD1
ATP6V1C1
BCOR
ITGB7
GPRC5A
BRD9
SYNGR3
AGO4
null
KDELR1
RPS6KB1
null
ITLN2
FMO1
PTGDS
FDX1
HNRNPA3
EZH1
OTP
CDK10
AK2
null
STAMBP
CCAR1
null
FAM110C
MED4
NUBP2
ATIC
TMEM204
HSD11B1L
BRCC3
CCDC144A
ALS2CL
null
NFU1
STAT6
null
FASTKD5
IGSF3
PHF5A
DMPK
SLC18A3
ATP6V0A2
CSNK1A1
HADHB
SUPT20H
null
null
TAF5L
null
BMPR1A
NPEPPS
OR2C1
GATAD2A
DDI2
MEF2BNB
SOX18
COPG1
ASB6
null
null
ANKRD52
null
ZNF638
SAPCD2
MAPK8IP3
CREBBP
C2CD4D
TBKBP1
DNASE1
SRSF6
PTTG1
null
null
null
null
AAR2
MORF4L1
ATP6V1E1
TFAP4
GPR61
BRPF1
CAMSAP1
KAT5
NCSTN
null
null
null
null
PROM2
ACTR5
ESCO2
PIGM
FANCC
PALM
null
DCTN1
PCNP
null
null
null
null
C6orf136
BIRC5
null
SLC35B2
null
SLC38A10
null
MOGS
MPRIP
null
null
null
null
EIF4E2
TYRO3
null
MEIOB
null
MRRF
null
null
JMJD8
null
null
null
null
RASL10A
POLQ
null
null
null
NUMA1
null
null
HECTD1
null
null
null
null
SHARPIN
LRCH2
null
null
null
COA3
null
null
null
null
null
null
null
WDR7
RNF20
null
null
null
null
null
null
null
null
null
null
null
OTUB1
KIAA2013
null
null
null
null
null
null
null
null
null
null
null
SPOCD1
NOL8
null
null
null
null
null
null
null
null
null
null
null
GAL
TSEN34
null
null
null
null
null
null
null
null
null
null
null
UBE2H
ALX3
null
null
null
null
null
null
null
null
null
null
null
PHPT1
null
null
null
null
null
null
null
null
null
null
null
null
TRA2B
null
null
null
null
null
null
null
null
null
null
null
null
MXRA8
null
null
null
null
null
null
null
null
null
null
null
null
IL3
null
null
null
null
null
null
null
null
null
null
null
null
GTSE1
null
null
null
null
null
null
null
null
null
null
null
null
FANCG
null
null
null
null
null
null
null
null
null
null
null
null
C17orf53
null
null
null
null
null
null
null
null
null
null
null
null
MSMB
null
null
null
null
null
null
null
null
null
null
null
null
TPSG1
null
null
null
null
null
null
null
null
null
null
null
null
RHBDF1
null

Dataset Card for PMC_35559673_table_s datasets

Dataset Details

Dataset Description

This dataset contains the results of genome-wide CRISPR screens using isogenic knockout cells to uncover vulnerabilities in tumor suppressor-deficient cancer cells. The data were originally published by Feng et al., Sci. Adv. 8, eabm6638 (2022), and are available via PubMed Central (PMC). The supplementary tables included in this dataset provide detailed data on raw counts, essentiality calls, Bayes factors, and synthetic lethality (SL) hits. The dataset supports research into genetic dependencies and potential therapeutic targets.

  • Curated by: Feng et al., Sci. Adv. 8, eabm6638 (2022)
  • Funded by: Likely supported by institutions affiliated with the authors.
  • Shared by: Feng et al.
  • Language(s): Not applicable (biomedical dataset).
  • License: CC BY 4.0

Dataset Sources

Dataset Structure

This dataset consists of seven tables (S1-S7), each representing a different aspect of the CRISPR screen results:

  1. Table S1: Raw counts for all CRISPR screens in this study.

    • File Mapping: sciadv.abm6638_table_s1.xlsx
  2. Table S2: Binary essentiality calls matrix.

    • File Mapping: sciadv.abm6638_table_s2.xlsx
  3. Table S3: Quantile-normalized Bayes factor (QBF) matrix.

    • File Mapping: sciadv.abm6638_table_s3.xlsx
  4. Table S5: Total SL hits identified for each TSG KO screen.

    • File Mapping: sciadv.abm6638_table_s5.xlsx
  5. Table S6: Shared SL hits across each TSG KO screen.

    • File Mapping: sciadv.abm6638_table_s6.xlsx
  6. Table S7: Unique SL hits for each TSG KO screen.

    • File Mapping: sciadv.abm6638_table_s7.xlsx

Dataset Creation

Curation Rationale

This dataset was curated to facilitate research into the vulnerabilities of cancer cells deficient in tumor suppressor genes. The binary essentiality calls, synthetic lethality (SL) hits, and other data allow researchers to explore genetic interactions that could serve as potential therapeutic targets. The methodology behind the CRISPR screens and SL hit identification was detailed by Feng et al. in their 2022 study.

Data Collection and Processing

Data were collected from genome-wide CRISPR screens performed on isogenic knockout cells. The data were processed to produce raw counts, binary essentiality calls, and genetic interaction matrices, including shared and unique synthetic lethal hits.

Relevant references describing the data processing and methods can be found in the following sources:

  • Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens (PMID: 28655737)
  • High-resolution CRISPR screens reveal fitness genes and genotype-specific cancer liabilities (PMID: 26627737)
  • Identifying chemogenetic interactions from CRISPR screens with drugZ (PMID: 31439014)

Who are the source data producers?

The data were produced by Feng et al., as part of their research published in Science Advances. The researchers were affiliated with academic institutions engaged in cancer genomics and CRISPR screening methodologies.

Annotations

Annotation Process

Annotations were primarily focused on identifying shared and unique synthetic lethality hits across tumor suppressor knockout screens. Automated processing tools like CRISPR analysis pipelines were employed for initial hit identification, followed by manual validation based on genetic interactions.

Who are the annotators?

The original authors, including experts in CRISPR screening and cancer genomics, performed the annotations. No third-party annotations were added.

Bias, Risks, and Limitations

The dataset is limited to specific cancer cell lines and tumor suppressor gene knockouts. As a result, the findings may not be generalizable across all cancer types. Users should exercise caution when interpreting results outside the experimental context.

Recommendations

Users should consult the references provided to better understand the experimental design and limitations. The dataset is best suited for research applications in cancer genomics, genetic interactions, and therapeutic target discovery.

Citation

BibTeX:

@article{
  doi:10.1126/sciadv.abm6638,
  author = {Xu Feng  and Mengfan Tang  and Merve Dede  and Dan Su  and Guangsheng Pei  and Dadi Jiang  and Chao Wang  and Zhen Chen  and Mi Li  and Litong Nie  and Yun Xiong  and Siting Li  and Jeong-Min Park  and Huimin Zhang  and Min Huang  and Klaudia Szymonowicz  and Zhongming Zhao  and Traver Hart  and Junjie Chen },
  title = {Genome-wide CRISPR screens using isogenic cells reveal vulnerabilities conferred by loss of tumor suppressors},
  journal = {Science Advances},
  volume = {8},
  number = {19},
  pages = {eabm6638},
  year = {2022},
  doi = {10.1126/sciadv.abm6638},
  URL = {https://www.science.org/doi/abs/10.1126/sciadv.abm6638},
  eprint = {https://www.science.org/doi/pdf/10.1126/sciadv.abm6638},
  abstract = {Exploiting cancer vulnerabilities is critical for the discovery of anticancer drugs. However, tumor suppressors cannot be directly targeted because of their loss of function. To uncover specific vulnerabilities for cells with deficiency in any given tumor suppressor(s), we performed genome-scale CRISPR loss-of-function screens using a panel of isogenic knockout cells we generated for 12 common tumor suppressors. Here, we provide a comprehensive and comparative dataset for genetic interactions between the whole-genome protein-coding genes and a panel of tumor suppressor genes, which allows us to uncover known and new high-confidence synthetic lethal interactions. Mining this dataset, we uncover essential paralog gene pairs, which could be a common mechanism for interpreting synthetic lethality. Moreover, we propose that some tumor suppressors could be targeted to suppress proliferation of cells with deficiency in other tumor suppressors. This dataset provides valuable information that can be further exploited for targeted cancer therapy. Whole-genome CRISPR screens uncover synthetic lethal interactions for tumor suppressors.}
}

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