* denotes a publication that resulted from CTD2 intra-Network collaborations
Publications
Scientists show that loss of PIK3R1 in ovarian cancers activates AKT and JAK2/STAT3 signaling. These studies provide a rationale for mechanism-based combinatorial therapy with AKT and STAT3 inhibitors.
CTD2 researchers at the University of California, San Francisco showed that ROS1 fusion oncoproteins exhibit differential activation of MAPK signaling pathway in lung adenocarcinoma.
CTD2 scientists identified the developmental transcription factor T as an essential gene in chordoma through genome-scale CRISPR-Cas9 screening. Small-molecule sensitivity profiling showed that CDK7/12/13 and CDK9 inhibitors inhibit chordoma cell proliferation.
Computational analysis of laser capture microdissection and RNA sequencing data established a novel classification signature of molecular subtypes in pancreatic ductal adenocarcinoma.
OHSU CTD2 scientists identify distinct patterns of mutation dynamics during FLT3 inhibitor, crenolanib treatment in acute myeloid leukemia. This study indicates comprehensive sequencing should be carried before and during the treatment to identify combinatorial agents and prevent drug resistance.
Study identifies networks of DNA damage-up proteins that may predict tumorigenic functions of cancer-promoting proteins.
CTD2 researchers developed phospho-proteomic specific algorithm, pARACNe, which measures phospho-state dependencies between tyrosine kinases and their candidate substrates from large-scale LC-MS/MS phosphoproteomic profiles.
Review on using human pluripotent stem cells derived natural killer cells and macrophages as a novel cell-based approach for cancer immunotherapy.
Concurrent measurement of single cell expression in tumor cells and tumor-infiltrating lymphocytes revealed novel biological insights of the tumor microenvironment; provides basis for developing novel therapeutic targets in lymphoma.
Bioinformatic approach, Similarity Weighted Nonnegative Embedding (SWNE), enables visualization of single-cell gene expression data into biologically relevant factors.
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