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dwb2023
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pmc_35559673_table_s6

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GENE
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Occurences
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2
7
Present In
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TYMS
7
LKB1 KO, NF1 KO, NF2 KO, PBRM1 KO, PTEN KO, TP53 KO, TP53BP1 KO
UBE2T
6
CDH1 KO, LKB1 KO, NF1 KO, NF2 KO, PBRM1 KO, TP53BP1 KO
BRIP1
5
LKB1 KO, NF1 KO, NF2 KO, PBRM1 KO, VHL KO
DDX19A
5
ARID1A KD, CDH1 KO, NF2 KO, PBRM1 KO, RB1 KO
PPP2R3C
5
CDH1 KO, LKB1 KO, NF2 KO, RB1 KO, VHL KO
RPRD1B
5
LKB1 KO, NF1 KO, PTEN KO, TP53 KO, VHL KO
BPTF
4
ARID1A KD, KEAP1 KO, PBRM1 KO, TP53 KO
FANCA
4
LKB1 KO, NF2 KO, PBRM1 KO, VHL KO
FANCD2
4
CDH1 KO, NF2 KO, PBRM1 KO, TP53BP1 KO
FANCF
4
CDH1 KO, LKB1 KO, NF2 KO, PBRM1 KO
MCM9
4
LKB1 KO, NF2 KO, VHL KO, TP53BP1 KO
SDHB
4
BAP1 KO, LKB1 KO, PBRM1 KO, VHL KO
USPL1
4
BAP1 KO, NF2 KO, PTEN KO, TP53BP1 KO
ATG9A
3
LKB1 KO, NF1 KO, VHL KO
C16orf72
3
ARID1A KD, BAP1 KO, NF1 KO
COQ2
3
BAP1 KO, KEAP1 KO, RB1 KO
FAM106A
3
LKB1 KO, NF1 KO, PTEN KO
FBXW11
3
LKB1 KO, NF1 KO, VHL KO
NDUFC1
3
NF1 KO, PBRM1 KO, PTEN KO
PMVK
3
BAP1 KO, NF1 KO, NF2 KO
PRDX1
3
NF1 KO, NF2 KO, PBRM1 KO
PRKRA
3
LKB1 KO, NF1 KO, VHL KO
SLC25A33
3
LKB1 KO, VHL KO, TP53BP1 KO
UIMC1
3
CDH1 KO, KEAP1 KO, NF1 KO
UMPS
3
PBRM1 KO, RB1 KO, VHL KO
ARID2
2
LKB1 KO, NF2 KO
BIRC6
2
LKB1 KO, VHL KO
BUB1
2
ARID1A KD, CDH1 KO
BZW1
2
BAP1 KO, NF2 KO
C12orf44
2
LKB1 KO, VHL KO
C1orf228
2
CDH1 KO, VHL KO
CAB39
2
TP53 KO, VHL KO
CAPRIN1
2
CDH1 KO, VHL KO
CHMP6
2
ARID1A KD, TP53BP1 KO
CNKSR2
2
NF1 KO, TP53 KO
CNOT4
2
PTEN KO, VHL KO
CTBP2
2
KEAP1 KO, RB1 KO
DHODH
2
BAP1 KO, VHL KO
DHX35
2
LKB1 KO, VHL KO
EBNA1BP2
2
NF1 KO, RB1 KO
ELMO2
2
KEAP1 KO, RB1 KO
FBXO42
2
LKB1 KO, VHL KO
G6PC
2
KEAP1 KO, NF1 KO
GAK
2
RB1 KO, VHL KO
GART
2
PBRM1 KO, RB1 KO
GTPBP2
2
LKB1 KO, VHL KO
IGFALS
2
ARID1A KD, VHL KO
IKBKG
2
LKB1 KO, TP53BP1 KO
JTB
2
LKB1 KO, NF1 KO
KIAA1524
2
NF1 KO, VHL KO
KPNA4
2
LKB1 KO, VHL KO
MAPKAP1
2
PBRM1 KO, TP53BP1 KO
MED13
2
KEAP1 KO, RB1 KO
MPV17
2
LKB1 KO, VHL KO
MTFMT
2
LKB1 KO, VHL KO
NDUFB6
2
LKB1 KO, NF1 KO
NUP160
2
ARID1A KD, PBRM1 KO
PARD6B
2
PBRM1 KO, PTEN KO
PDIA3
2
PBRM1 KO, VHL KO
PDS5A
2
LKB1 KO, VHL KO
PIN1
2
KEAP1 KO, RB1 KO
PSMF1
2
LKB1 KO, VHL KO
RAD54L
2
PTEN KO, VHL KO
RMI2
2
CDH1 KO, TP53BP1 KO
RRAS2
2
BAP1 KO, KEAP1 KO
SDHD
2
BAP1 KO, VHL KO
SHBG
2
KEAP1 KO, NF1 KO
STAC2
2
KEAP1 KO, RB1 KO
STUB1
2
BAP1 KO, NF1 KO
TADA1
2
RB1 KO, TP53 KO
TMCO6
2
KEAP1 KO, PTEN KO
DMAC1
2
BAP1 KO, NF1 KO
TOR4A
2
NF1 KO, VHL KO
TUSC2
2
CDH1 KO, KEAP1 KO
UNC13D
2
RB1 KO, TP53 KO
USH1G
2
KEAP1 KO, NF1 KO
USP1
2
PTEN KO, TP53BP1 KO
USP48
2
NF1 KO, NF2 KO
VGLL3
2
KEAP1 KO, RB1 KO
WRAP53
2
NF1 KO, TP53 KO
YPEL5
2
LKB1 KO, PBRM1 KO

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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