Project description:In this study, we investigated the target landscape of the drug ibrutinib using thermal proteome profiling (TPP) and proteoform detection. Our findings demonstrated that ibrutinib interacts with multiple proteoforms, beyond its known targets. Specifically, we identified interactions related to immunomodulation, Golgi trafficking, endosomal trafficking, and glycosylation. These insights shed light on the clinical off-target effects and adverse events associated with ibrutinib. Moreover, our study emphasizes the significance of studyin drug interactions at the proteoform level and its potential implications for precision medicine.
Project description:Functional precision medicine (FPM) aims to match the right patients to the right drugs by using specific features of the individual’s cancer cells. Recently, FPM has been propelled by technologies that enable high throughput ex vivo drug profiling to tailor treatments for individual patients. Here, we present a proof of concept study for an integrated experimental system that incorporates ex vivo treatment response with a single-cell gene expression output that enables barcoding of several drug conditions in one single-cell sequencing experiment. We perform functional annotation of drug resistance using the glucocorticoid-resistant E/R+ REH cells as a cellular model and evaluate three different approaches for single-cell transcriptome sequencing (scRNA-seq). Using this integrated system, we show that all scRNA-seq methods accurately reflected gene expression changes in the system, with high cell recovery and accurate tagging of the different drug conditions. Furthermore, we identified a substantial single-cell transcriptional response to fludarabine, a drug of particular interest for treatment of high-risk ALL.
Project description:Currently, the identification of patient-specific therapies in cancer is mainly informed by personalized genomic analysis. In the setting of acute myeloid leukemia (AML), patient-drug treatment matching fails in a subset of patients harboring atypical internal tandem duplications (ITDs) in the tyrosine kinase domain of the FLT3 gene. To address this unmet medical need, here we develop a systems-based strategy that integrates multiparametric analysis of crucial signaling pathways, patient-specific genomic and transcriptomic data with a prior-knowledge signaling network using a Boolean-based formalism. By this approach, we derive personalized predictive models describing the signaling landscape of AML FLT3-ITD positive cell lines and patients. These models enable us to derive mechanistic insight into drug resistance mechanisms and suggest novel opportunities for combinatorial treatments. Interestingly, our analysis reveals that the JNK kinase pathway plays a crucial role in the tyrosine kinase inhibitor response of FLT3-ITD cells through cell cycle regulation. Finally, our work shows that patient-specific logic models have the potential to inform precision medicine approaches.
Project description:Development of resistance causes failure of drugs targeting receptor tyrosine kinase (RTK) networks, and represents a critical challenge for precision medicine. Here we show that PHLDA1 down-regulation is critical to acquisition and maintenance of drug resistance in RTK-driven cancer. Using FGFR inhibition in endometrial cancer cells, we identify an Akt-driven compensatory mechanism underpinned by down-regulation of PHLDA1. We demonstrate broad clinical relevance of our findings, showing that PHLDA1 down-regulation also occurs in response to RTK-targeted therapy in breast and renal cancer patients, as well as following trastuzumab treatment in HER2+ breast cancer cells. Crucially, knockdown of PHLDA1 alone was sufficient to confer de novo resistance to RTK inhibitors, and induction of PHLDA1 expression re-sensitised drug resistant cancer cells to targeted therapies, identifying PHLDA1 as a biomarker for drug response and highlighting the potential of PHLDA1 reactivation as a means of circumventing drug resistance.
Project description:Patient derived organoids (PDOs) closely resemble individual tumor biology. They are thus promising models for drug discovery and precision medicine. Here, we describe high-throughput imaging and automated image analysis of PDOs. We generated PDOs from colorectal cancer patients. Subsequently, we treated them with >500 substances to capture almost 6 million images by confocal microscopy. We developed a software framework to analyze how perturbations alter the organization of multicellular PDOs. Therewith, we observed a rich spectrum of reoccurring phenotypes. Targeting cellular processes, including signaling by MEK, GSK3 or CDKs, led to distinct architectural changes. Also, we detected compound-induced phenotypes only present in subsets of PDOs with specific molecular alterations. Finally, PDO response to anticancer drugs matched the clinical course of corresponding patients. The presented high-throughput imaging workflow and data allow compound profiling with complex multicellular organoid models for drug discovery and personalized medicine. We used microarrays to detail the global programme of gene expression underlying different lines of patient-derived colorectal cancer organoids.
Project description:A reliable animal model that can mimic the GBM intracranial infiltration and Blood-brain barrier (BBB) interaction is necessary for effective therapeutics development. Here, we report a zebrafish-based orthotopic GBM xenograft model, in which GBM cells from different species and even patients, can robustly propagate and faithfully reproduce their histological characteristics. Single-cell RNA-seq indicates a transcriptomic adaption of GBM xenografts to infiltrative phenotype within the zebrafish brains. We also provide evidence that the BBB in zebrafish larva is molecularly and functionally intact and can interact with GBM cells in similar ways as in mammals, which together enables this model to accurately identify BBB penetrating drugs. Using GBM patients’ samples, we further generate zebrafish patient-derived orthotopic xenografts (z-PDOX) and proof-of-concept experiments indicate the short-term temozolomide response in z-PDOX can predict the long-term prognosis of corresponding GBM patients. These together illustrate the value of zebrafish GBM model in drug discovery and precision medicine.