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: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: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:Colorectal cancer is a major cause of mortality worldwide. Most patients develop colorectal liver metastases (CLM), and for such patients hepatectomy combined with chemotherapy may be curative. Nevertheless, in the era of precision medicine there is a critical need of prognostic markers to cope with the heterogeneity of CLM patients. Tumor-associated macrophages (TAMs) pave the way to tissue invasion and intravasation providing a nurturing microenvironment formetastases. The quantification of immune landscape of tumors has provided novel prognostic indicators of cancer progression, and the quantification of TAMs might explain the heterogeneity of CLM patients. Here, we will investigate the development of a new diagnostic tool based on TAMs with the aim to define the causative role of TAMs in CLM patients. This will open new clinical scenarios both for the diagnosis, therapy and prognosis, leading to the refinement of the therapeutic output in a personalized medicine perspective.