Publications
Included here is a list of publications from OCG programs. All published data are available to the research community through the program-specific data matrices.
* denotes publications from the CTD2 initiative that are results of intra-Network collaborations
TransPRECISE, a cancer-specific integrated network estimation model, assesses pathway similarities between patients and cell lines at a sample-specific level. This framework bridges the gap and could be used to identify appropriate preclinical models for prioritizing specific drug targets.
Integrated analyses of multi-omics data indicate molecular and cellular differences in the clinical subtypes of high-grade serous ovarian cancer.
Protein dependency analytic module integrated in TCPA measures key cancer dependencies in a context dependent manner. This analysis suggests that protein expression data are a highly valuable information resource for understanding tumor vulnerabilities and identifying therapeutic opportunities.
In vitro and in vivo studies conducted by CTD2 scientists at Emory University demonstrate that combination of JNK inhibitor, AS602801 with androgen receptor inhibitor, enzalutamide synergistically inhibit proliferation, migration, invasion, and prevent tumor growth in prostate cancer.
DFCI scientists identified XL177A as potent irreversible inhibitor of USP7, a deubiquitinating enzyme. This study indicated that TP53 mutational status predicted inhibitory response across several cancer lineages; demonstrates TP53 mutational status as a biomarker for response to USP7.
Chemical biology approach reveals metabolic heterogeneity in cellular subtypes. This study suggests targeting both glucose reporter 1 and pyruvate dehydrogenase, components of glycolysis and mitochondrial metabolism, inhibit cancer cell invasion.
Study showed that combinatorial therapy with MLN4924, a drug that clears misfolded proteins, and anti-PD1, an immune-checkpoint blockade, enhances clinical responses in cancer patients with microsatellite instability.
Scientists at Stanford CTD2 Center showed that MethylMix, a tool to identify methylation driver genes in cancer, can predict DNA methylation profiles in whole slide cancer histopathology images. This analysis provides new insights into the link between histopathological and molecular data.
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