Helena Jernberg Wiklund's research projects
Novel combinatorial targeted therapies for multiple myeloma
We have previously approached possible targets for therapeutic intervention in multiple myeloma (MM) by studying resistance mechanisms and their combating by evaluating novel rational drug combinations. By studying resistance mechanisms as concomitant presence of multiple genetic lesions and by high throughput screening, we have successfully identified drugs to be selected for combinatorial studies in vitro and in vivo.
Cell death and apoptosis by combinatorial approaches
When it comes to the role of Polycomb repressive complex 2 (PRC2) in MM, we have identified that in a proportion of MM cell lines and patient cells viability is not affected by inhibition of the epigenetic silencer EZH2. We are currently working on the hypothesis that gene silencing by collaborating partners would continue halting reactivation of crucial target genes for e.g., apoptosis. As a result, we have identified that targeting both DNA methylation and the lysine 9 histone H3 methyltransferase G9a in different combinatorial approaches potentiates induction of cell death and apoptosis both in vitro and in vivo.
Identifying treatment targets
To identify novel targets for combinatorial treatments we integrate data from different in-depth methods to study the MM epigenome (ChIP-seq/CUT&Tag, RIP-seq and RADICL-seq) and transcriptome (bulk and scRNA-seq). We ultimately aim to validate identified targets of interest by a wide range of molecular perturbation approaches such as knockdown, overexpression and targeting with small molecule inhibitors in panels of cell lines and different in vivo models.
LncRNAs as recruiters of histone modifying enzymes in MM
This project investigates the contribution of long non-coding RNAs to epigenetic control in MM, with a focus on how these molecules interact with chromatin-associated complexes. The aim is to clarify their biological significance and their role in tumour maintenance.
By identifying lncRNAs that physically interact with EZH2 with the help of RNA immunoprecipitation sequencing (RIP-seq) we recently reported that depending on the guide lncRNA, PRC2 targets different subsets of genes in a context dependent manner to support MM growth and survival. This provides a proof-of-concept for continuing to investigate lncRNAs as part of the complex epigenetic machinery and evaluating their biological relevance and function in MM.
However, we have not even scratched the surface of the numerous roles lncRNAs play in establishing the epigenomic landscape of MM. We thus aim to map all non-coding RNAs interacting with chromatin in an unbiased manner by applying RADICL-seq and to further study how their association affects and is affected by novel experimental treatment approaches.
Abundance of lipid droplets and their potential as therapeutic targets in MM
In this project we explore whether the accumulation of lipid droplets (LD) is a characteristic feature of MM cells and how lipid metabolism contributes to tumour cell survival. The ambition is to determine if modulation of lipid pathways might be beneficial in achieving enhanced therapeutic response, alone or in combination with current treatments.
Combining small-molecule inhibitors
We detect the abundance of LDs in MM cell lines and patient cells by a novel LD-specific fluorophore and advanced confocal microscopy. Furthermore, we investigate the effects of lipid metabolism inhibition on cell viability and apoptosis in MM cells by blocking either the exogenous uptake of lipids or de novo lipogenesis.
Our goal is to combine identified novel small-molecule inhibitors with currently applied in the clinic chemotherapies to potentiate their therapeutic effect. In vivo experiments will be performed to reinforce the clinical benefits of identified novel combinatorial strategies.
Blood plasma proteomics for biomarker discovery in MM
This project investigates protein expression in plasma samples from MM patient by using the Olink Explore platform. By comparing patient profiles to large reference cohorts and integrating multiple data types, we aim to identify biomarker signatures that could facilitate diagnosis, risk stratification and personalized therapy selection.
Integrating proteomics, large-scale RNA-seq data and patient clinical information
Within this project, we will also leverage proteomic data from the UK biobank to provide a robust reference for comparative analyses. To further support our findings from the proteomic analysis, we will utilize transcriptomic cohorts comprising of newly diagnosed MM patients and healthy controls. This integration of proteomics with large-scale RNA-seq data and patient clinical information will enhance our mechanistic understanding of MM to ultimately advance disease detection, improve prognosis and life quality of MM patients.
Epigenetic signatures pave the way to precision medicine in acute lymphoblastic leukaemia in infants
Several findings have led us to hypothesize that epigenomic reprograming is pivotal in the pathogenesis of acute lymphoblastic leukaemia in infants (iALL):
- the early onset of iALL during the first year of life, when developmental epigenetic regulation is a key;
- the critical involvement of the chromatin modifier of lysine 4 on histone H3 – KMT2A/MLL;
- the otherwise low mutational burden; and
- the fact that iALL treated with regimens directed toward the lymphoid lineage tends to undergo a phenotypic change to the myeloid cell lineage.
However, comprehensive epigenomic studies describing the chromatin landscape of iALL are lacking and little is known about what underlies development of the disease in patients with germline KMT2A. We thus aim to unravel the chromatin modification landscape and the interacting protein non-coding transcriptome of iALL by applying a wide range of sequencing methods, from CUT&Tag-seq to novel sequencing technologies such as RADICL-seq and CAGE, as part of a strong international collaboration.
By combining these technologies in our unique cohorts of iALL cell lines and patient samples, we aim to unveil the mechanisms underlying aberrant epigenomic reprograming in iALL and identify novel targets for treatment in one of the most difficult to treat and cure forms of childhood leukaemia world-wide.
Applied machine learning and transcriptomic studies in iALL
In this project, we apply an innovative approach to study this dismal prognosis disease by interpretable machine learning. This way relationship among features and outcomes can be identified, to ultimately uncover what postulates the aggressive nature of ALL in particularly infants when compared with paediatric ALL.
In a collaborative effort we apply rule-based machine learning to bulk and single-cell transcriptomic data from infant and paediatric ALL patients, and donor cord blood samples to identify relationships between transcriptional features and clinical outcomes, to improve understanding of disease aggressiveness in iALL.
Findings will be validated using alternative computational approaches and larger patient cohorts, followed by functional evaluation in experimental model systems, to be able to potentiate their clinical application.