* denotes a publication that resulted from CTD2 intra-Network collaborations
Publications
Integration of genome-scale RNAi and CRISPR-Cas9 screens across cancer cell lines with large protein-protein interaction networks revealed novel protein complexes.
Scientists established a panel of patient-derived xenografts (PDXs) from subtypes of T-cell lymphomas (TCL) and cell lines for target validation and drug testing. Stapled peptide ALRN-6924 which blocks interactions between p53, MDM2 and MDMX showed pre-clinical activity against TCL lines and PDXs.
Researchers performed an integrated analysis of TCGA genomic data and CPTAC proteomic data and demonstrated that A-to-I RNA editing contributes to proteomic diversity in breast cancer.
CTD2 scientists at Fred Hutchinson showed that the triplet combination of WEE1 tyrosine kinase inhibitor AZD1775, cisplatin, and docetaxel is safe and tolerable in a phase I clinical trial of head and neck cancer.
CTD2 scientists at UTSW employed a chemistry-first approach to map the associations between chemicals and genetic lesions in lung cancer. These chemical vulnerabilities may reveal novel druggable targets for lung cancer.
Researchers at the UCSD CTD2 Center created a parsimonious composite network (PCNet), which has high efficiency and performance over any single network.
Broad Institute CTD2 scientists developed a bioinformatic approach, RWEN, that predicts the responses of human cancer cell lines to a panel of compounds using the gene-expression profiles.
CTD2 researchers at UCSF-1 present a quantitative map linking the influence of chemotherapeutic agents to tumor genetics. This chemical-genetic interaction map can aid in identifying new factors that dictate responses to chemotherapy and prioritize drug combinations.
Scientists developed metaVIPER to assess protein activity in orphan tissues and single cells by integrative analysis of multiple, non-tissue-matched regulatory models. This approach could help to identify critical dependencies within molecularly heterogeneous sub-populations of cancer tissues.
Researchers developed MethylMix 2.0, an algorithm implemented in R that facilitates automated downloading and preprocessing of DNA methylation and gene expression datasets from pan-cancer studies. The tool can be used to identify disease specific hyper and hypomethylated genes and cancer subtyping.
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