Helena Jernberg Wiklund's research projects

(Collaborators are shown in italics)

Novel and combinatorial experimental targeted therapy for multiple myeloma (MM) in vitro and in vivo

Antonia Kalushkova, Alba Atienza, Patrick Nylund, Klev Diamanti and Jan Komorowski

We have previously approached possible targets for therapeutic intervention in 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.

The hypothesis for the emerging resistance to EZH2/1 inhibition in a proportion of MM tumours is that gene silencing by collaborating partners would continue halting reactivation of crucial target genes for e.g., apoptosis. Owing to the fact that genes marked only by H3K27me3 overlap with DNA methylated genes in MM, and that PRC2 is known to interact with other epigenetic repressors, we are now embarking on the evaluation of a panel of epigenetic inhibitors towards DNA methyltransferases (DNMTs) and the Polycomb complex 2.   

Our initial results now show that co-occurrence of high DNA methylation and H3K27me3 in MM is enriched at possible regulatory regions corresponding to B cell distinct enhancer regions. Thus, the overlapping DNA-methylated and H3K27me3-marked genes may reflect cooperative regulatory networks. We are currently performing a comprehensive genome-wide comparison by aligning the Polycomb enriched regions to methylated CpG sites in MM and exploring the possibility that demethylating agents can sensitize MM cells to targeted inhibition of Polycomb proteins.

Alternate metabolite usage underlying resistance to selective epigenetic inhibitors

Patrick Nylund, Berta Garrido-Zabala, Alba Atienza, and Antonia Kalushkova

The field examining the complex interactions between cancer epigenetics and metabolism is fast expanding. The observed differential sensitivity to EZH2 inhibition (EZH2i) impelled us to undertake advanced in vitro metabolomic mass spectrometry analysis. Previous findings have demonstrated that metabolite profiles represent highly sensitive markers for phenotypic differences between cells and their responses to drug treatment.

We are performing small metabolite analysis by mass spectrometry in combination with gene expression analysis in a panel of sensitive and resistant to EZH2i MM cell lines to generate a comprehensive “omics” profile of the cellular response to EZH2i in MM. This metabolomic approach will allow us to investigate metabolic changes induced by drug treatment as well as basal differences in metabolic profiles between the MM cell lines displaying differential sensitivity to EZH2i.

Characterization of G9a function and investigation of dual inhibition of DNMT1 and G9a in multiple myeloma

Patrick Nylund, Berta Garrido-Zabala, Antonia Kalushkova, Elke De Bruyne and Karin Vanderkerken

Our research project will initially focus on studying the response of MM cells to G9a inhibition. G9A-mediated silencing is now considered a common feature in MM and targeting G9A in has been proven to reduce viability and induce transcription of pro-apoptotic genes. Therefore, we will investigate whether other proteins with a previously described G9A association are contributing to drug resistance.

DNMTs are highly overexpressed in MM and associated with genomic site-specific hypermethylation. However, an overall DNA methylation status analysis has revealed that the MM genome is largely hypomethylated. Interestingly though, local hypermethylation sites have previously been described at tumour suppressor genes, suggesting that the G9A-mediated silencing and DNA methylation may cooperate in the silencing of target genes. This will provide the basis for evaluating a potential combinatorial treatment with G9a and DNMT1 inhibition.

LncRNAs aids in the targeting of specific genomic regions with tumour suppression function by recruitment of histone modifying enzymes in multiple myeloma

Patrick Nylund and Berta Garrido-Zabala

Our initial investigation has been focused on identifying lncRNAs that are physically interacting with the PRC2 by RNA immunoprecipitation sequencing (RIP-seq) and by annotating the sequence attached to the complex will provide insight into what lncRNAs are associated with PRC2.

We have currently generated a list of PCR2 associated lncRNAs and are in the process to functionally validate their function by chromatin isolation by RNA purification (ChIRP-seq) in MM. This will be achieved by the use of biotin-labelled oligonucleotide probes for the lncRNAs of interest. Verification of histone mark and EZH2 binding enrichment to these identified regions will be accomplished by chromatin immunoprecipitation qPCR (ChIP-qPCR). 

Our initial experiments in MM cell lines have shown overexpression of a panel of lncRNAs associated with EZH2 and preventing PRC2 interaction with anti-tumour suppressive genes. Our preliminary data also support a novel physical interaction between the candidates and EZH2 in MM, and potential DNA binding sites. Many of these overlap with unique H3K27me3 and bivalent PRC2 targets identified by our group.

These data now provide a proof-of-concept for continuing to investigate lncRNAs as part of a complex epigenetic machinery and evaluating the biological relevance and function of disease associated lncRNAs. 

Epigenetic signatures pave the way to precision medicine in acute leukaemia of infants

Berta Garrido-Zabala, Patrick Nylund, Antonia Kalushkova, Arja Harila-Saari, Rita Niinimäki and Andreas Lennartsson

Infant acute lymphoblastic leukaemia (iALL) is a rare (<5% of all childhood ALL) haematological disease arising in children during their first year of life. Six-year event-free survival was 46% in the latest international treatment protocol. MLL chromosomal rearrangement is the strongest prognostic factor and occurred in 74% of the cases.

The observation that KMT2A rearrangements occur at a higher frequency in infants than in paediatric cases has prompted the hypothesis that the malignant clone arises at an early stage of multipotent hematopoietic progenitor cell, or stem cells, where tight developmental control is key. Epigenetic mechanisms are therefore likely essential regulators of both developmental and lineage-specification programmes, making them excellent candidates to understand the biology of iALL.

Extensive sequencing efforts have thus far provided little understanding to the aggressive nature of iALL and previous studies on the epigenome of iALL have merely focused on the over-activation of the KMT2A targets resulting from its rearrangements. In addition, KMT2A-r iALL treated with regimens directed toward the lymphoid lineage tend to undergo a phenotypic change to the myeloid 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 KMT2A-wt.

Applied machine learning to KMT2A-translocated iALL

Patrick Nylund, Berta Garrido-Zabala, Daniel Rivas Carillo Salvador, Arja Harila-Saari, Rita Niinimäki, Jan Komorowski and Andreas Lennartsson

In collaboration with Professor Jan Komorowski, we now present innovative approaches to study infant ALL (iALL) and its heterogeneity in unique model systems. With these we aim to increase our understanding of the underlying mechanisms that cause drug resistance in iAL. We intend to define groups of iALL patients that may benefit from drugs already in clinical use, to evaluate intra-tumoral subpopulations that contribute to leukemogenesis, and to identify new targets by mapping the transcriptome on a single-cell resolution.

To successfully fulfil the aims, methods for global epigenetic analysis in primary patient cells and strategies enabling functional analysis of candidate genes by physiological and pharmacological approaches have already been successfully implemented. Biobank approval for unique patient samples has also been obtained.

In the initiated projects, large-scale scRNA-seq of a cohort of 15 scRNA-seq iALL patients including 12 carrying KMT2A-r and 3 that are KMT2A-wt will be analysed. In addition, we will analyse bulk RNA-seq data from a total of 26 subtypes of childhood leukaemia (n = 1665), together with scRNA-seq on normal foetal bone marrow and CD34+ cord blood samples. This strategy will allow us to unravel mechanisms underlying aberrant transcriptomic regulation in iALL with or without genetic alterations.

To address these problems, we propose to use interpretable machine learning techniques, that is, methods where relationship among the features and the outcomes can be identified, to analyse subgroups of patient cohorts and classify them according to their outcomes. Thus, the results derived from this project may be useful for increasing the understanding of genomic structural variations on cancer and their contributing effect for treatment and prognosis. Moreover, this methodology could be potentially expandable to other types of oncological disorders.

Mapping super enhancer regions to the transcriptome of individual cells and cell clusters within and between iALL patients

Berta Garrido-Zabala, Patrick Nylund, Arja Harila-Saari, Rita Niinimäki, Xingq Chen and Andreas Lennartsson

As a first attempt to understand how the epigenome influences gene expression in iALL we aim to study super enhancers (SE) regions. SE are large clusters of enhancers with anomalously high levels of transcription factor binding and marked with histone modifications such as H3K27ac.

To define the SE clusters in iALL we will map H3K27ac by CUT&Tag in KMT2A-r cell line and patient samples in order to identify specific SE within the subtypes of KMT2A-r or between KMT2A-r vs KMT2A-wt. It is crucial to fully identify the differences between KMT2A-r and KMT2A-wt or among the KMT2A-r subtypes in order to comprehend mechanisms underlying patient relapse and treatment resistance.

Of pivotal importance is to point out that this analysis will be performed on the same patient samples as the scRNA-seq, which allows for in depth data integration and brings clinical significance in the era of personalized medicine.

Last modified: 2023-01-11