* denotes a publication that resulted from CTD2 intra-Network collaborations
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.
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.
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.
Scientists report that two myeloid leukemia cases with FMS-like tyrosine kinase (FLT3) fusions demonstrate mixed features of myelodysplastic and myeloproliferative syndromes; showed sensitivity to FLT3 inhibitors in ex vivo drug screening assays.
CTD2 scientists at Stanford University demonstrated that air-liquid interface patient-derived tumor organoid models retain the original tumor immune cells, enabling testing for personalized immunotherapy in cancer.