Enhancer-Gene Networks as Therapeutic Targets in Multiple Myeloma

This PhD project, undertaken by the student, will aims to map enhancer-gene (E-G) networks in multiple myeloma (MM) and its precursor, monoclonal gammopathy of undetermined significance (MGUS), to develop novel therapeutic and preventive strategies. The student will employ single-cell multi-omics, CRISPR screens, and computational modelling to identify and validate therapeutic targets, contributing to precision medicine for MM and prevention of MGUS progression.

Work Package 1: Mapping Enhancer-Gene Interaction. The student will generate an E-G atlas using bone marrow patient samples from MM patients (hyperdiploid, t(11;14), t(4;14) subtypes), and cell lines (e.g., MM.1S, U266, RPMI-8226). The student will perform assays to profile chromatin accessibility and gene expression. Hi-C will be used to detect enhancer-promoter loops, with 4C-seq validating key targets (e.g., MYC, IRF4 enhancers). Structural variants (SVs) will be annotated using in house whole-genome sequencing data. The student will process data with Cell Ranger pipelines, integrate it using Seurat/Signac, and predict E-G linkages using Cicero. Non-MM cell lines (e.g., Jurkat) will serve as controls. Expected Outcome: A subtype-specific E-G atlas annotated with SVs, providing a foundation for identifying regulatory elements.

Work Package 2: Identifying Key Regulatory Elements. The student will identify transcription factors (TFs), such as IRF4 and MYC, and super-enhancers driving oncogenic expression. Using CAP-SELEX, the student will map TF–TF interactions for selected candidate TFs, while BioID will be employed to map protein–protein interactions in MM cell lines via mass spectrometry. ChIP-seq (H3K27ac, H3K4me1) will profile super-enhancers, with ROSE identifying high-intensity regions. The student will validate findings using siRNA knockdown and CRISPR interference (CRISPRi) in patient samples and cell lines, assessing proliferation (MTT assays) and apoptosis (Annexin V). AlphaFold2 will be used to predict TF interaction interfaces to inform PROTAC design in collaboration with the ICR. Controls will include scrambled siRNAs and non-targeting CRISPRi guides. Expected Outcome: A prioritised list of TFs and super-enhancers validated through direct mapping and functional assays.

Work Package 3: Functional Validation. The student will design and apply CRISPR interference (CRISPRi) and activation (CRISPRa) libraries targeting enhancers and TFs, delivered to MM cell lines and patient samples. Pooled CRISPR screens will assess proliferation (CellTiter-Glo) and apoptosis (Caspase-3/7). The student will validate significant hits with individual CRISPRi/a guides, analysed using MAGeCK. Single-cell RNA-seq will capture transcriptional changes, with analysis performed using Seurat. Controls will include non-targeting guides and non-MM cell lines. Expected Outcome: Validated E-G dependencies identifying high-priority therapeutic targets.

Work Package 4: Computational Modelling and Therapeutic Prediction. The student will integrate multi-omics data (ATAC-seq, RNA-seq, ChIP-seq, Hi-C, CAP-SELEX, BioID) using Graph Convolutional Networks, representing genes/enhancers as nodes and SVs as edges. A random forest classifier will predict therapeutic vulnerabilities. The student will conduct in vivo drug screens to test BET inhibitors (e.g., JQ1) and other compounds. Expected Outcome: A validated computational framework predicting therapeutic targets for clinical translation.

BSc/BA or equivalent in molecular biology, bioinformatics, or related field.

Interest in cancer genomics and translational research.