* denotes a publication that resulted from CTD2 intra-Network collaborations
CTD2 researchers at Dana Farber identify that 2-oxoglutarate dehydrogenase, a tricarboxylic acid cycle enzyme, is crucial to maintain PIK3CA mutant tumor survival or proliferation.
Researchers developed a platform that integrates whole exome sequencing with high throughput drug screening in patient-derived tumor organoids. This platform has the potential to identify effective therapeutic strategies for patients where standard treatment options have been exhausted.
CTD2 scientists at Emory University have identified Aurora Kinase A as a novel H-Ras binding partner in enhancing MAPK signaling. This novel protein-protein interaction is a potential therapeutic target in cancer.
CTD2 researchers at University of Texas Southwestern identify marine bacteria-derived natural product N⁶,N⁶-dimethyladenosine as a potent inhibitor of Akt signaling in non-small cell lung cancer cell lines.
CTD2 researchers at Dana Farber have identified the transcription factor ATXN1-CIC-ETS as a mediator of resistance to MAPK inhibitors in KRAS mutant pancreatic cancer cell lines using genome-scale CRISPR-Cas9 loss-Of-function screens.
Scientists at Dana Farber have performed CRISPR-Cas9 drug resistance screens and identified that loss of KEAP1 expression negates inhibitors targeting RTK/MAPK pathways in Lung cancer. This finding may assist with treatment decisions in managing lung cancer.
CTD2 scientists at Dana Farber have identified the serine/threonine kinase family member NEK6 as a central mediator in restoring sensitivity to hormone ablation in prostate cancer xenograft models.
The OncoPPi network is a resource of experimentally determined physical protein-protein interactions that builds on cancer genomics for discovery and exploitation of cancer vulnerabilities.
CTD2 scientists initiated MD Anderson Cell Lines Project (MCLP) and characterized the expression of ~ 230 proteins in >650 cell lines using reverse-phase protein arrays (RPPA). The data is available through an interactive web platform MCLP Data Portal.
MEDICI is a new computational method to predict the protein-protein interaction (PPI) essentiality which helps to prioritize PPIs for drug discovery.