Decoding Drug Resistance Mechanisms in Acute Myeloid Leukaemia

Acute myeloid leukaemia (AML) is an aggressive blood cancer with survival rates below 25% at five years. Despite initial treatment success, many patients relapse due to the persistence of drug-resistant cancer cell populations. Understanding how drug resistance evolves within individual cancer cells is critical for improving outcomes and developing more effective treatments. Advances in single-cell sequencing and artificial intelligence (AI) have opened new doors for uncovering the genetic, epigenetic, and molecular mechanisms driving drug resistance. This project leverages these cutting-edge technologies to study AML in detail, aiming to unravel how resistance develops and persists over time.

Research Methodology

Students will work on an exciting, interdisciplinary project combining wet-lab experiments with advanced computational analysis, including

Single-Cell Multi-Omic Profiling: Students will analyse AML patient samples from diagnosis and after treatment using state-of-the-art sequencing techniques. This will involve single-cell RNA sequencing (short and long reads), ATAC-seq (chromatin accessibility), and variant calling to map gene expression, mutations, splice variants, fusion genes, and epigenetic changes.

AI-Based Data Integration and Analysis: Students will develop and apply deep-learning pipelines to integrate multi-omic data and identify patterns of drug resistance. They will reconstruct sub-clonal populations, trace their evolutionary trajectories, and infer the molecular and epigenetic drivers of resistance.

Experimental Validation: Insights gained from AI predictions will be tested in the lab using CRISPR-Cas9 gene editing, epigenetic profiling and assays to validate therapeutic targets.

Training Opportunities

Bioinformatics and AI: Training in single-cell sequencing data analysis, multi-omic data integration, and development of deep-learning models tailored to cancer biology.

Molecular Biology Techniques: Hands-on experience with CRISPR-Cas9, and ex vivo drug treatment assays.

Interdisciplinary Research: Work at the interface of computational biology, molecular genetics, and cancer biology.

Career Development: Opportunities to present findings at national and international conferences, collaborate with leading researchers, and contribute to publications in high-impact journals.

Click here to access the application form