Individual cancers rely on distinct essential genes for their survival. The Cancer Dependency Map (DepMap) is an ongoing project to uncover these gene dependencies in hundreds of cancer cell lines. To make this drug discovery resource more accessible to the scientific community we built an easy-to-use browser, shinyDepMap (https://labsyspharm.shinyapps.io/depmap). shinyDepMap combines CRISPR and shRNA data to determine, for each gene, the growth reduction caused by knockout/knockdown and the selectivity of this effect across cell lines. The tool also clusters genes with similar dependencies, revealing functional relationships. shinyDepMap can be used to 1) predict the efficacy and selectivity of drugs targeting particular genes; 2) identify maximally sensitive cell lines for testing a drug; 3) target hop, i.e., navigate from an undruggable protein with the desired selectivity profile, such as an activated oncogene, to more druggable targets with a similar profile; and 4) identify novel pathways driving cancer cell growth and survival.
Data files have been provided for Figures 1, 3, 4, and 5 on FigShare: https://figshare.com/projects/shinyDepMap_Source_Data/97382 (DOIs: 10.6084/m9.figshare.13653251.v1, 10.6084/m9.figshare.13653257.v1, 10.6084/m9.figshare.13653260.v1, 10.6084/m9.figshare.13653266.v1, 10.6084/m9.figshare.13653272.v1, 10.6084/m9.figshare.13653278.v1, 10.6084/m9.figshare.13653281.v2)
shinyDepMap - Source Data 1FigShare, doi:10.6084/m9.figshare.13653251.
shinyDepMap - Source Data 2FigShare, doi:10.6084/m9.figshare.13653257.v1.
shinyDepMap - Source Data 3FigShare, doi:10.6084/m9.figshare.13653260.v1.
shinyDepMap - Source Data 4FigShare, doi:10.6084/m9.figshare.13653266.v1.
shinyDepMap - Source Data 5FigShare, doi:10.6084/m9.figshare.13653272.v1.
shinyDepMap - Source Data 6FigShare, doi:10.6084/m9.figshare.13653278.v1.
shinyDepMap - Source Data 7FigShare, doi:10.6084/m9.figshare.13653281.v2.
Cancer Dependency Map (DepMap)DepMap.
- Kenichi Shimada
- Timothy J Mitchison
- Jeremy L Muhlich
- John A Bachman
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Erica A Golemis, Fox Chase Cancer Center, United States
© 2021, Shimada et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Gain-of-function mutations in the protein-tyrosine phosphatase SHP2 are the most frequently occurring mutations in sporadic juvenile myelomonocytic leukemia (JMML) and JMML-like myeloproliferative neoplasm (MPN) associated with Noonan syndrome (NS). Hematopoietic stem and progenitor cells (HSPCs) are the disease propagating cells of JMML. Here, we explored transcriptomes of HSPCs with SHP2 mutations derived from JMML patients and a novel NS zebrafish model. In addition to major NS traits, CRISPR/Cas9 knock-in Shp2D61G mutant zebrafish recapitulated a JMML-like MPN phenotype, including myeloid lineage hyperproliferation, ex vivo growth of myeloid colonies, and in vivo transplantability of HSPCs. Single-cell mRNA sequencing of HSPCs from Shp2D61G zebrafish embryos and bulk sequencing of HSPCs from JMML patients revealed an overlapping inflammatory gene expression pattern. Strikingly, an anti-inflammatory agent rescued JMML-like MPN in Shp2D61G zebrafish embryos. Our results indicate that a common inflammatory response was triggered in the HSPCs from sporadic JMML patients and syndromic NS zebrafish, which potentiated MPN and may represent a future target for JMML therapies.
Aneuploidy, a state of chromosome imbalance, is a hallmark of human tumors, but its role in cancer still remains to be fully elucidated. To understand the consequences of whole-chromosome-level aneuploidies on the proteome, we integrated aneuploidy, transcriptomic and proteomic data from hundreds of TCGA/CPTAC tumor samples. We found a surprisingly large number of expression changes happened on other, non-aneuploid chromosomes. Moreover, we identified an association between those changes and co-complex members of proteins from aneuploid chromosomes. This co-abundance association is tightly regulated for aggregation-prone aneuploid proteins and those involved in a smaller number of complexes. On the other hand, we observe that complexes of the cellular core machinery are under functional selection to maintain their stoichiometric balance in aneuploid tumors. Ultimately, we provide evidence that those compensatory and functional maintenance mechanisms are established through post-translational control and that the degree of success of a tumor to deal with aneuploidy-induced stoichiometric imbalance impacts the activation of cellular protein degradation programs and patient survival.