Faculty Bibliography

Linker histone H1 proteins bind to nucleosomes and facilitate chromatin compaction¹, although their biological functions are poorly understood. Mutations in the genes that encode H1 isoforms B-E (H1B, H1C, H1D and H1E; also known as H1-5, H1-2, H1-3 and H1-4, respectively) are highly recurrent in B cell lymphomas, but the pathogenic relevance of these mutations to cancer and the mechanisms that are involved are unknown. Here we show that lymphoma-associated H1 alleles are genetic driver mutations in lymphomas. Disruption of H1 function results in a profound architectural remodelling of the genome, which is characterized by large-scale yet focal shifts of chromatin from a compacted to a relaxed state. This decompaction drives distinct changes in epigenetic states, primarily owing to a gain of histone H3 dimethylation at lysine 36 (H3K36me2) and/or loss of repressive H3 trimethylation at lysine 27 (H3K27me3). These changes unlock the expression of stem cell genes that are normally silenced during early development. In mice, loss of H1c and H1e (also known as H1f2 and H1f4, respectively) conferred germinal centre B cells with enhanced fitness and self-renewal properties, ultimately leading to aggressive lymphomas with an increased repopulating potential. Collectively, our data indicate that H1 proteins are normally required to sequester early developmental genes into architecturally inaccessible genomic compartments. We also establish H1 as a bona fide tumour suppressor and show that mutations in H1 drive malignant transformation primarily through three-dimensional genome reorganization, which leads to epigenetic reprogramming and derepression of developmentally silenced genes.

Aims/UNASSIGNED:Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity. Methods/UNASSIGNED:We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score. Results/UNASSIGNED:In this study, we were able to show that such a model's generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05% and 18.59%. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51% and 32.79%. Conclusions/UNASSIGNED:This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s.

Introduction: T-cell Acute Lymphoblastic Leukemia (T-ALL) is an aggressive leukemia with a high incidence in children, adolescents and young adults. Although multiple therapeutic options are available, almost one fifth of patients affected by T-ALL eventually succumb to the disease, suggesting an unrecognized biological complexity that might contribute to drug resistance. To better understand the differences between T-ALL subtypes and their clinical course, we systematically analyzed cohorts of patients with different risk profiles at the genetic and epigenetic level. We recently demonstrated that differences in three-dimensional (3D) chromatin architecture can influence the integrity of topologically associating domains (TADs) and rewire specific enhancer-promoter interactions, impacting gene expression and leading to disease. As an example, we focused in particular on the Myc family of oncogenes, revealing disease-specific patterns of enhancer-promoter interactions.
Method(s): We initially profiled a large cohort of T-ALL patients falling under different risk categories in order to identify differences in their genetic and epigenetic features. We then systematically integrated matched in situ Hi-C, RNA-seq and CTCF ChIP-seq datasets to reveal widespread differences in intra-TAD chromatin interactions and TAD boundary insulation in patients affected by TALL.
Result(s): Using primary acute leukemia patient samples, we have, for the first time, identified recurrent TAD disruptions in leukemia involving key oncogenes (e.g. NOTCH1, c-Myc) and their targets. For example, we identified a recurrent disruption of 3D chromatin topology in the c-Myc locus at a previously uncharacterized non-coding CTCF-bound region that insulates MYC from a downstream super-enhancer. This disruption enables chromatin interactions between the c-Myc oncogene and the downstream super-enhancer leading to an increase in c-Myc expression. In parallel, while focusing patients falling into a higher risk category, namely the early T cell progenitor acute lymphoblastic leukemia (ETP-ALL), we discovered a previously uncharacterized region of high activity encompassing a novel lncRNA interacting with the proto-oncogene N-Myc.
Conclusion(s):With the current study we demonstrated an inherent difference between subtypes of T-ALL based on their epigenetic profile, which in turn influences the expression of key oncogenes. By focusing on a new methods of regulating a known family of transcription factors, we provide a new mechanism which could open interesting ways for targeted therapy of patients at different risk levels

Methylation of histone 3 at lysine 9 (H3K9) constitutes a roadblock for cellular reprogramming. Interference with methyltransferases or activation of demethylases by the cofactor ascorbic acid (AA) facilitates the derivation of induced pluripotent stem cells (iPSCs), but possible interactions between specific methyltransferases and AA treatment remain insufficiently explored. We show that chemical inhibition of the methyltransferases EHMT1 and EHMT2 counteracts iPSC formation in an enhanced reprogramming system in the presence of AA, an effect that is dependent on EHMT1. EHMT inhibition during enhanced reprogramming is associated with rapid loss of H3K9 dimethylation, inefficient downregulation of somatic genes, and failed mesenchymal-to-epithelial transition. Furthermore, transient EHMT inhibition during reprogramming yields iPSCs that fail to efficiently give rise to viable mice upon blastocyst injection. Our observations establish novel functions of H3K9 methyltransferases and suggest that a functional balance between AA-stimulated enzymes and EHMTs supports efficient and less error-prone iPSC reprogramming to pluripotency.

Using Hi-C, promoter-capture Hi-C (pCHi-C), and other genome-wide approaches in skeletal muscle progenitors that inducibly express a master transcription factor, Pax7, we systematically characterize at high-resolution the spatio-temporal re-organization of compartments and promoter-anchored interactions as a consequence of myogenic commitment and differentiation. We identify key promoter-enhancer interaction motifs, namely, cliques and networks, and interactions that are dependent on Pax7 binding. Remarkably, Pax7 binds to a majority of super-enhancers, and together with a cadre of interacting transcription factors, assembles feed-forward regulatory loops. During differentiation, epigenetic memory and persistent looping are maintained at a subset of Pax7 enhancers in the absence of Pax7. We also identify and functionally validate a previously uncharacterized Pax7-bound enhancer hub that regulates the essential myosin heavy chain cluster during skeletal muscle cell differentiation. Our studies lay the groundwork for understanding the role of Pax7 in orchestrating changes in the three-dimensional chromatin conformation in muscle progenitors.

Although B cell acute lymphoblastic leukemia (ALL) is the most common malignancy in children and while highly curable, it remains a leading cause of cancer-related mortality. The outgrowth of tumor subclones carrying mutations in genes responsible for resistance to therapy has led to a Darwinian model of clonal selection. Previous work has indicated that alterations in the epigenome might contribute to clonal selection yet the extent to which the chromatin state is altered under the selective pressures of therapy is unknown. To address this, we performed chromatin immunoprecipitation, gene expression analysis, and enhanced reduced representation bisulfite sequencing on a cohort of paired diagnosis and relapse samples from individual patients who all but one relapsed within 36 months of initial diagnosis. The chromatin state at diagnosis varied widely among patients: while the majority of peaks remained stable between diagnosis and relapse, yet a significant fraction were either lost or newly gained with some patients showing few differences and others showing massive changes of the epigenetic state. Evolution of the epigenome was associated with pathways previously linked to therapy resistance as well as novel candidate pathways through alterations in pyrimidine biosynthesis and downregulation of polycomb repressive complex 2 targets. Three novel, relapse-specific super-enhancers were shared by a majority of patients including one associated with S100A8, the top upregulated gene seen at relapse in childhood B-ALL. Overall, our results support a role of the epigenome in clonal evolution and uncover new candidate pathways associated with relapse.

Programmed cell death protein-1 (PD-1) checkpoint immunotherapy efficacy remains unpredictable in glioblastoma (GBM) patients due to the genetic heterogeneity and immunosuppressive tumor microenvironments. Here, we report a microfluidics-based, patient-specific 'GBM-on-a-Chip' microphysiological system to dissect the heterogeneity of immunosuppressive tumor microenvironments and optimize anti-PD-1 immunotherapy for different GBM subtypes. Our clinical and experimental analyses demonstrated that molecularly distinct GBM subtypes have distinct epigenetic and immune signatures that may lead to different immunosuppressive mechanisms. The real-time analysis in GBM-on-a-Chip showed that mesenchymal GBM niche attracted low number of allogeneic CD154+CD8+ T-cells but abundant CD163+ tumor-associated macrophages (TAMs), and expressed elevated PD-1/PD-L1 immune checkpoints and TGF-β1, IL-10, and CSF-1 cytokines compared to proneural GBM. To enhance PD-1 inhibitor nivolumab efficacy, we co-administered a CSF-1R inhibitor BLZ945 to ablate CD163+ M2-TAMs and strengthened CD154+CD8+ T-cell functionality and GBM apoptosis on-chip. Our ex vivo patient-specific GBM-on-a-Chip provides an avenue for a personalized screening of immunotherapies for GBM patients.