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
Researchers identify that mechanistic target of rapamycin (mTOR) kinase inhibitor, PP242 induces cell death in glioblastoma cells by off-target inhibition of both protein kinase C alpha and Janus kinase 2 (JAK2).
Scientists compared performance of RNA-Seq processing pipelines for the expression quantification of long non-coding RNAs (lncRNAs) in cancer samples. This study indicates integrating pseudoalignment methods with transcriptome annotation is a recommended strategy for RNA-Seq analysis of lncRNAs.
CTD2 scientists at DFCI performed genome-scale open reading frame screens to identify mechanisms of resistance to androgen deprivation therapy. This study shows that transcription factor, CREB5, is overexpressed and mediates resistance to androgen receptor antagonists in prostate cancer.
Glioblastoma cells develop resistance to blockade the transcription factor STAT3. UCSF studies show that autocrine feedback loop among STAT3, EGFR and NF-KB mediates primary resistance and suggest combinatorial therapy to treat EGFR-amplified glioblastomas.
CTD2 scientists at Stanford University performed integrative analysis of early-stage breast cancer patient and cell line data to study the role of chromatin regulatory genes (CRG). These studies indicate, CRGs that promote DNA accessibility are associated with anthracycline sensitivity.
Neuroblastoma cells treated with RXDX-105, a small-molecule inhibitor of multiple kinases decreased cell viability and proliferation in vitro and in vivo.
Emory University CTD2 scientists showed that NSD3S plays a critical role in the regulation of MYC function through inhibition of FBXW7-mediated degradation of MYC. This interaction drives cancer cell survival and could be a potential therapeutic target in MYC-driven tumors.
Epigenomic analysis of methylation data using Onco-GPS computational approach identify subtypes of myelodysplastic syndromes. The subtypes had distinct patterns of genetic lesions, regulatory region methylation, and prognostic response.
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