* denotes a publication that resulted from CTD2 intra-Network collaborations
CTD2 scientists at Stanford University developed SCIMET, an analytical framework that provides quantitative measurement of dynamics of metastasis in a patient-specific manner; indicates early dissemination of colorectal cancer in the majority of the patients.
CTD2 scientists at Stanford University showed that radiomic analysis of computed tomography could be used to identify molecular subtypes of head and neck squamous cell carcinomas.
CTD2 scientists at UCSF showed that neuroepithelial stem cells derived from normal induced pluripotent stem cells could be a powerful experimental resource to evaluate genetic mutations in medulloblastoma.
CTD2 scientists at OHSU demonstrated that sequential therapy with PARP and either WEE1 or ATR inhibitors is effective and potentially less toxic in multiple relevant cancer models.
Scientists at the Broad Institute CTD2 Center identified 6-phosphogluconate dehydrogenase, a cytosolic enzyme as a link between carbohydrate metabolism and protein secretion.
Integrative genomic analyses of expression profiling, CRISPR-Cas9 and ORF/cDNA, identifies cell-essential genes suppressed by BET-bromodomain inhibition. The study suggests the use of cell-cycle inhibitors in combination with BET-bromodomain inhibitors to treat MYC-amplified medulloblastoma.
Columbia CTD2 researchers developed ADVOCATE, a computational model to analyze gene expression data of epithelial and stromal pancreatic ductal adenocarcinoma samples. These studies indicated a novel classification signature which could facilitate the development of precision oncology approaches.
Scientists at the UCSD CTD2 Center showed that epigenetic dysregulation and silencing are associated with chromatin repression and aberrant hypermethylation at the transcription start site in HPV-related oral cancers; independent of CpG island and is associated with MYC pathway activation.
Researchers profiled 225 metabolites in 928 cell lines from more than 20 cancer types in the cancer cell line encyclopedia using liquid chromatography – mass spectrometry.
Texomer, a statistical approach, improves molecular characterization of cancer samples by integrating cancer genome and transcriptome sequencing data obtained from patient tissue samples.