Offered dissertation projects are displayed here.
Long-term survival rates in AML patients, especially in those of advanced age, are very low. Hence the need for new, effective, less toxic therapeutic options. The bone marrow niche (BM), specifically its interactions with AML blasts, has been identified as a major player in the development of chemoresistance and relapse. Thus, research into this area has great potential therapeutic value.
The goal of this dissertation is to contribute to the elucidation of BM-haematopoetic stem cell (HSC) interactions with respect to their relevance in leukaemia development, maintenance of leukaemia stem cells (LSCs), and chemoresistance. In particular, the aim is to investigate the role of specific candidate genes in BM-HSC/LSC interactions, and asses their theoretical potential as therapeutic targets.
Ex vivo culture and manipulation (genetic engineering) of primary Human HSCs; from different sources including cord blood, mobilised peripheral blood, bone marrow, AML patient cells, model leukaemias, cell lines. Virus production, knock-down and overexpression of genes in these cells, CRSIPR/Cas9 technology, Flow cytometry and immunofluorescence, Xenotransplantation.
The project aims to understand how a novel mutation that causes iron overload in a pediatric patient acts at the molecular level.
Reference: Muckenthaler MU, Rivella S, Hentze MW, Galy B: A Red Carpet for Iron Metabolism. Cell. 2017 Jan 26;168(3):344-361.
Therapy response in blood cancers can be highly variable. Possible reasons for therapy resistance comprise heterogeneous tumour genetics and variations in the role of the microenvironment. Our labs have established systems for large-scale ex-vivo testing of drug response across diverse tumours, large patient cohorts, and sophisticated co-culture systems that mimic the microenvironment in tumour niches.
You will take the responsibility of novel aspects in the bioinformatic and statist ical data analysis of these on-going studies, including high-throughput imaging of cells and single-cell RNA-seq, using machine learning and multivariate statistics methods. You will also get the opportunity to design and perform own follow-up experiments.