* denotes a publication that resulted from CTD2 intra-Network collaborations
Scientists at JHU showed that there is an intra-tumor and inter-patient heterogeneity to drug responses in patient-derived primary liver organoids. These studies indicate the potential use of pre-clinical organoid models in screening small-molecules and identifying novel targets.
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.
UCSF (1) CTD2 researchers identified a synthetic lethal interaction between EGFR tyrosine kinase inhibitors and Aurora kinase inhibitors in acquired resistant cells. This study suggests combinatorial treatment to prevent treatment resistance with the monotherapies.
Bioinformatic approach, Similarity Weighted Nonnegative Embedding (SWNE), enables visualization of single-cell gene expression data into biologically relevant factors.
UCSD study suggests that the combination of APOBEC-related mutagenesis and tumor mutation burden may be used as a biomarker of response to immunotherapy.
Researchers at DFCI analyzed genome-scale loss-of-function datasets and identified adenosine deaminase acting on RNA as a gene dependency in subsets of lung cancer.
Analysis of next generation sequencing of patients with hematologic malignancies showed that patients had alterations that could be targeted with gene or immune-targeted therapies.