The aim of this research project is to better predict development and kinetics of minimal residual disease (MRD) and relapse in Acute Leukemic & Myeloid Leukemia (ALL & AML) over time by using multiparameter flow cytometry data measured at diagnosis and different time points after treatments in combination with artificial intelligence and bioinformatics.

Mathematical prediction models will be built discerning the best anti-leukemia efficacy of novel drugs and will find efficient combination strategies (for synergistic actions) potentially reducing MRD to increase disease-specific survival. For this available data of drug testing experiments will be used that have been performed in ALL and AML. 

Additionally, bioinformatic- and data science tools to integrate the data from different clinical trials, and different MRD biomarkers, including novel ones that will be identified within the network (both leukemia specific and environmental (immune) factors) will be created.

New advanced analytical data portals to integrate data from different platforms of the same material and derived from different materials, generated within and outside of this network will be developed.

The ultimate goal of this project is to generate products to support shared medical decision making based on better estimates of treatment outcomes using more and better data, which will finally result in the improvement of treatment outcome and patient.

Host:

Amsterdam AMC, Amsterdam, The Netherlands

Supervisors:

Prof. Jacqueline Cloos (AMC) and Dr. John Jacobs (Ortec)

Required profile Doctoral Candidate:  

Master’s degree in life sciences, other (bio)medical sciences, or related and proficiency in bioinformatics and R and/or artificial intelligence.

Duration:

48 months, starting 1-5-2025

In addition: 

As part of the project, the doctoral candidate will be intern at ORTEC (6 months, The Netherlands, supervisor John Jacobs) to develop artificial intelligence to dynamically follow MRD and obtain knowledge on business development, and at Charles University (2 months), Czech Republic, supervisor Jan Stuchly to analyze single cell RNA (scRNA) sequencing data using novel bioinformatic tools.