Biology subjects

Mehtonen, J.

Publications and source records attributed to Mehtonen, J..

6 recordsLinked to original sources

bioRxiv2024

Linker histone H1-0 is a specific mediator of the repressive ETV6::RUNX1 transcriptional landscape

ETV6::RUNX1 is the most common oncogenic fusion in pediatric B cell precursor acute lymphoblastic leukemia (BCP-ALL). It induces a clinically silent preleukemic state that requires secondary mutations for progression to leukemia. However, the molecular mechanisms contributing to the characteristic quiescence of ETV6::RUNX1+ preleukemic cells remain elusive. Here, we detect factors involved in the preleukemic state by generating human induced pluripotent stem cell (hiPSC) models using CRISPR/Cas9 gene editing. We identified upregulation of linker histone H1-0 in our preleukemic models, which was preserved upon hematopoietic differentiation and transformation to BCP-ALL. ETV6::RUNX1 induces H1-0 promoter activity whereas depletion of H1-0 specifically inhibited ETV6::RUNX1 signature genes, indicating its role as a key mediator of the ETV6::RUNX1 transcriptome. Single-cell gene expression analysis revealed high H1-0 levels in quiescent cells during hematopoiesis and inverse correlation with transcriptional activity. Pharmacologically, H1-0 protein levels correspond to susceptibility of BCP-ALL towards histone deacetylase inhibitors (HDACi). Altogether, our study provides novel insights into ETV6::RUNX1-induced quiescence and suggests that further investigation into combinatorial treatment of BCP-ALL using the H1-0- inducing HDACi Quisinostat may be worthwhile.

cancer biology↗

bioRxiv2023

Sequential drug treatment targeting cell cycle and cell fate regulatory programs blocks non-genetic cancer evolution in acute lymphoblastic leukemia

Targeted therapies exploiting vulnerabilities of cancer cells hold promise for improving patient outcome and reducing side-effects of chemotherapy. However, efficacy of precision therapies is limited in part because of the cellular heterogeneity of tumors. A better mechanistic understanding of how drug effect is linked to cancer cell state diversity is crucial for identifying effective combination therapies that can overcome the heterogeneity to prevent disease recurrence. Here, we characterized at the level of gene regulatory networks and at single-cell resolution the effect of G2/M cell cycle checkpoint inhibition in acute lymphoblastic leukemia (ALL) and demonstrate that WEE1 targeted therapy impinges on cell fate decision regulatory circuits. We found highest inhibition of recovery of proliferation in ALL cells with KMT2A-rearrangment (KMT2A-r), compared to cells of other leukemia subgroups. Single-cell transcriptome and chromatin accessibility profiling of (KMT2A::AFF1) RS4;11 cells treated with the WEE1 inhibitor AZD1775 revealed diversification of cell states at the fate decision points, with a fraction of cells exhibiting strong activation of p53-driven processes linked to induction of apoptosis and senescence, and disruption of a core KMT2A-RUNX1-MYC regulatory network through CDK1-mediated RUNX1 degradation. In RS4;11 cells and in patient-derived xenograft (PDX) model, we uncovered that in this cell state diversification induced by WEE1 inhibition, a subpopulation transitioned to a cell state characterized by activation of transcription factors regulating pre-B cell fate, lipid metabolism and pre-BCR signaling which supported a drug tolerance. Sequential treatment targeting the drug tolerant subpopulation with BCR-signaling inhibitors dasatinib, ibrutinib, or perturbing metabolism by fatostatin or AZD2014 after AZD1775 administration, effectively counteracted drug tolerance that drove recovery of leukemic cells. Collectively, our findings provide new insights into the tight connectivity of gene regulatory programs associated with cell cycle and cell fate regulation, and a rationale for sequential administration of WEE1 inhibitors with low toxicity inhibitors of pre-BCR signaling or metabolism.

cancer biology↗

bioRxiv2020

Single cell characterization of B-lymphoid differentiation and leukemic cell states during chemotherapy in ETV6-RUNX1 positive pediatric leukemia identifies drug-targetable transcription factor activities

Tight regulatory loops orchestrate commitment to B-cell fate within bone marrow. Genetic lesions in this gene regulatory network underlie the emergence of the most common childhood cancer, acute lymphoblastic leukemia (ALL). The initial genetic hits, including the common translocation that fuses ETV6 and RUNX1 genes, lead to arrested cell differentiation. Here, we aimed to characterize transcription factor activities along the B-lineage differentiation trajectory as a reference to characterize the aberrant cell states present in leukemic bone marrow, and to identify those transcription factors that maintain cancer-specific cell states for more precise therapeutic intervention. We compared normal B-lineage differentiation and in vivo leukemic cell states using single cell RNA-sequencing (scRNA-seq) and several complementary genomics profiles. Based on statistical tools for scRNA-seq, we benchmarked a workflow to resolve transcription factor activities and gene expression distribution changes in healthy bone marrow lymphoid cell states. We compared these to ALL bone marrow at diagnosis and in vivo during chemotherapy, focusing on leukemias carrying the ETV6-RUNX1 fusion. We show that lymphoid cell transcription factor activities uncovered from bone marrow scRNA-seq have high correspondence with independent ATAC- and ChIP-seq data. Using this comprehensive reference for regulatory factors coordinating B-lineage differentiation, our analysis of ETV6-RUNX1-positive ALL cases revealed elevated activity of multiple ETS-transcription factors in leukemic cells states, including the leukemia genome-wide association study hit ELK3. The accompanying gene expression changes associated with natural killer cell inactivation and depletion in the leukemic immune microenvironment. Moreover, our results suggest that the abundance of G1 cell cycle state at diagnosis and lack of differentiation-associated regulatory network changes during induction chemotherapy represent features of chemoresistance. To target the leukemic regulatory program and thereby overcome treatment-resistance, we show that selective inhibitors of ETS-transcription factors could effectively reduce cell viability. Our data provide a detailed picture of the transcription factor activities that characterize both normal B-lineage differentiation and those acquired in leukemic bone marrow and provide a rational basis for new treatment strategies targeting the immune microenvironment and the active regulatory network in leukemia.

cancer biology↗

bioRxiv2019

SISUA: SemI-SUpervised generative Autoencoder for single cell data

Single-cell transcriptomics offers a tool to study the diversity of cell phenotypes through snapshots of the abundance of mRNA in individual cells. Often there is additional information available besides the single cell gene expression counts, such as bulk transcriptome data from the same tissue, or quantification of surface protein levels from the same cells. In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a semi-supervised model. The generative model is based on the deep variational autoencoder (VAE) neural network architecture.

bioinformatics↗

bioRxiv2019

Immunogenomic landscape of hematological malignancies

Understanding factors that shape the immune landscape across hematological malignancies is essential for immunotherapy development. Here, we integrated over 8,000 transcriptomes and over 1,000 samples with multilevel genomic data of hematological cancers to investigate how immunological features are linked to cancer subtypes, genetic and epigenetic alterations, and patient survival. Infiltration of cytotoxic immune cells was associated with distinct microenvironmental responses and driver alterations in different cancers, such as TP53 in acute myeloid leukemia and DTX1 in diffuse large B cell lymphoma. Epigenetic modification of CIITA regulating antigen presentation, cancer type-specific immune checkpoints such as VISTA in myeloid malignancies, and variation in cancer antigen expression further contributed to immune heterogeneity. Prognostic models highlighted the significance of immunological properties in predicting survival. Our study represents the most comprehensive effort to date to link immunology with cancer subtypes and genomics in hematological malignancies, providing a resource to guide future studies and immunotherapy development.

immunology↗

bioRxiv2018

Data-driven characterization of molecular phenotypes across heterogenous sample collections

The existing large gene expression data repositories hold enormous potential to elucidate disease mechanisms, characterize changes in cellular pathways, and to stratify patients based on their molecular profile. To achieve this goal, integrative resources and tools are needed that allow comparison of results across datasets and data types. We propose an intuitive approach for data-driven stratifications of molecular profiles and benchmark our methodology using the dimensional reduction algorithm t-SNE with multi-center and multi-platform data representing hematological malignancies. Our approach enables assessing the contribution of biological versus technical variation to sample clustering, direct incorporation of additional datasets to the same low dimensional representation of molecular disease subtypes, comparison of sample groups between separate t-SNE representations, or maps, and characterization of the obtained clusters based on pathway databases and additional multi-omics data. In the example application, our approach revealed differential activity of SAM-dependent DNA methylation pathway in the acute myeloid leukemia patient cluster characterized with CEBPA mutations that accordingly was validated to have globally elevated DNA methylation levels.

bioinformatics↗