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Easton, J.

Publications and source records attributed to Easton, J..

At least 19 recordsLinked to original sources

Reactivation of a TAL1 progenitor cell enhancer region by non-coding somatic variants in T-lineage acute lymphoblastic leukemia

Aberrant activation of TAL1, a key oncogenic driver, defines a major subgroup comprising [~]30% of childhood T-lineage acute lymphoblastic leukemias (T-ALLs). We and others have shown that somatic non-coding mutations within upstream and intronic cis-regulatory regions of TAL1 contribute to transformation by creating binding sites for MYB and other transcription factors. Here we investigated cis-regulatory mechanisms mediated by somatic mutations occurring in an intergenic region located 29 kilobase pairs downstream of the canonical TAL1 transcription initiation site, implicated in 6% of TAL1-expressing T-ALLs. These somatic variants include i) complex indels resulting in de novo MYB transcription factor binding sites (TFBSs) and ii) internal tandem duplications (ITDs) encompassing canonical MYB TFBSs. Chromatin immunoprecipitation sequencing (ChIP-seq) revealed binding of the TAL1 core regulatory circuit (CRC) transcription factors MYB, GATA3, and RUNX1, resulting in enhancer activity mediated by sequences with the mutant allele. Strikingly, ChIP-seq peaks for the repressive H3K27me3 mark and the active H3K27ac mark co-existed across TAL1 regulatory sequences but enriched for different haplotypes. TAL1 transcription from the mutant haplotype initiated from a promoter located within exon 4 of the canonical TAL1 transcript, resulting in a short isoform normally expressed by hematopoietic stem cells (HSC). Interestingly, neither the isoform expression nor the enhancer activity could be predicted by the sequence-to-function deep learning artificial intelligence (AI) model AlphaGenome, emphasizing the importance of experimental validation. Our findings indicate that selection for cis-regulatory, non-coding variants leads to reactivation of enhancers normally active in HSC but silenced in differentiated lineages during normal hematopoietic cell development.

cancer biology↗

Seq2Karyotype (S2K): A Method for in-silico Karyotyping Using Single-Sample Whole-Genome Sequencing Data

DNA abnormalities characterized by cytogenetic imaging at the single cell resolution, i.e. karyotyping, have long served as cancer diagnostic and prognostic biomarkers. To enable in-silico karyotyping using unpaired whole-genome sequencing data, we developed Seq2Karyotype (S2K), a tool that fits karyotype models with clonality estimation based on read-depth and allelic imbalance in a bulk sample and supports visualization-guided refinement. Analysis on 19 adult and pediatric cancer cell lines revealed unexpected intratumoral heterogeneity involving multiple copy number variation (CNV) states including whole-genome duplication, which were validated by imaging and single-cell omics profiling. Analyses on patient samples showed high concordance with clinical cytogenetic reports for acute myeloid leukemia, and revealed evolutionary trajectories from multi-region metastatic neuroblastomas implicating reversion. These findings highlight extensive and dynamic intratumoral heterogeneity contributed by CNV in both cell line models and patient samples, which may inform future research on tumor evolution under selective pressure such as drug exposure.

bioinformatics↗

Measuring Longitudinal Genome-wide Clonal Evolution of Pediatric Acute Lymphoblastic Leukemia at Single-Cell Resolution

Approximately 15-20% of children with acute lymphoblastic leukemia (pALL) are not cured despite years of treatment with more than ten chemotherapeutic agents, yet bulk sequencing reveals minimal somatic variation that could explain genetic treatment resistance. Using error-corrected bulk sequencing, and primary template-directed amplification (PTA)-enabled single-cell exome and whole-genome sequencing, we discovered much more genetic diversity in pALL. Individual leukemic cells harbor 3.7-fold more mutations (mean 3,553) than detected in bulk samples (mean 965). Many ETV6-RUNX1 patients harbor multiple independent ras-mutated clones, and we also identified convergent evolution of JAK2 mutations as well as copy number alterations. PTA-based phylogenetic analysis of over 150 single-cell genomes linked heritable phenotypes to specific genetic alterations and identified low-frequency clones harboring mutations in treatment resistance-associated genes (ITGA9, NTRK3, SULF2) that increased in frequency during treatment. These findings reveal extensive hidden genetic diversity in pALL while providing a framework for the early detection of high-risk leukemic populations.

cancer biology↗

Spotiphy enables single-cell spatial whole transcriptomics across the entire section

Spatial transcriptomics (ST) has advanced our understanding of tissue regionalization by enabling the visualization of gene expression within whole tissue sections1, but the approach remains dogged by the challenge of achieving single-cell resolution without sacrificing whole genome coverage2,3. Here we present Spotiphy (Spot imager with pseudo single-cell resolution histology), a novel computational toolkit that transforms sequencing-based ST data into single-cell-resolved whole-transcriptome images. In evaluations with Alzheimers disease (AD) and normal mouse brains, Spotiphy delivers the most precise cellular compositions. For the first time, Spotiphy reveals novel astrocyte regional specification in mouse brains. It distinguishes sub-populations of DAM (Disease-Associated Microglia) located in different AD mouse brain regions. Spotiphy also identifies multiple spatial domains as well as changes in the patterns of tumor-tumor microenvironment interactions using human breast ST data. Spotiphy enables visualization of cell localization and gene expression in tissue sections, offering key insights into the function of complex biological systems.

systems biology↗

Bone morphogenetic protein (BMP) signaling determines neuroblastoma cell fate and sensitivity to retinoic acid.

Retinoic acid (RA) is a standard-of-care neuroblastoma drug thought to be effective by inducing differentiation. Curiously, RA has little effect on primary human tumors during upfront treatment but can eliminate neuroblastoma cells from the bone marrow during post-chemo consolidation therapy--a discrepancy that has never been explained. To investigate this, we treated a large cohort of neuroblastoma cell lines with RA and observed that the most RA-sensitive cells predominantly undergo apoptosis or senescence, rather than differentiation. We conducted genome-wide CRISPR knockout screens under RA treatment, which identified BMP signaling as controlling the apoptosis/senescence vs differentiation cell fate decision and determining RAs overall potency. We then discovered that BMP signaling activity is markedly higher in neuroblastoma patient samples at bone marrow metastatic sites, providing a plausible explanation for RAs ability to clear neuroblastoma cells specifically from the bone marrow, seemingly mimicking interactions between BMP and RA during normal development.

cancer biology↗

Conditional c-MYC activation in catecholaminergic cells drives distinct neuroendocrine tumors: neuroblastoma vs somatostatinoma

The MYC proto-oncogenes (c-MYC, MYCN, MYCL) are among the most deregulated oncogenic drivers in human malignancies including high-risk neuroblastoma, 50% of which are MYCN-amplified. Genetically engineered mouse models (GEMMs) based on the MYCN transgene have greatly expanded the understanding of neuroblastoma biology and are powerful tools for testing new therapies. However, a lack of c-MYC-driven GEMMs has hampered the ability to better understand mechanisms of neuroblastoma oncogenesis and therapy development given that c-MYC is also an important driver of many high-risk neuroblastomas. In this study, we report two transgenic murine neuroendocrine models driven by conditional c-MYC induction in tyrosine hydroxylase (Th) and dopamine {beta}-hydroxylase (Dbh)-expressing cells. c-MYC induction in Th-expressing cells leads to a preponderance of Pdx1+ somatostatinomas, a type of pancreatic neuroendocrine tumor (PNET), resembling human somatostatinoma with highly expressed gene signatures of {delta} cells and potassium channels. In contrast, c-MYC induction in Dbh-expressing cells leads to onset of neuroblastomas, showing a better transforming capacity than MYCN in a comparable C57BL/6 genetic background. The c-MYC murine neuroblastoma tumors recapitulate the pathologic and genetic features of human neuroblastoma, express GD2, and respond to anti-GD2 immunotherapy. This model also responds to DFMO, an FDA-approved inhibitor targeting ODC1, which is a known MYC transcriptional target. Thus, establishing c-MYC-overexpressing GEMMs resulted in different but related tumor types depending on the targeted cell and provide useful tools for testing immunotherapies and targeted therapies for these diseases.

cancer biology↗

Topology-informed regulatory element collections coordinate cell identity gene expression programs

Transcription proteins are concentrated at nuclear transcriptional condensates. These condensates contain cis-regulatory elements (CREs), including enhancers and promoters, that are thought to regulate genes in the same condensate. The roles of condensates are of great current interest, but research into their function is limited by an inability to comprehensively identify their associated CREs. Here, we present a conceptual framework and algorithm, BOUQUET, for integrating genome topology, chromatin occupancy, and graph theory to associate CREs and transcription protein machinery with target genes and identify exceptionally protein-rich communities that interact with condensates. BOUQUET uncovers surprising quantitative correlations between community protein accumulation and gene expression phenotypes by combining accurate CRE-gene assignment with co-activator binding profiles. A small subset of communities, which we call "3D-super-enhancers," is exceptionally protein-rich. BOUQUET-predicted 3D-SEs are comparable in number to co-activator nuclear puncta, and all genes known to interact with co-activator condensates in embryonic stem cells are within 3D-SEs. 3D-SEs are enriched for association with cell identity genes across mammalian tissues. Microscopy analyses show frequent co-localization and co-expression of genes from the same 3D-SE within a single co-activator punctum, suggesting 3D-SE components interact with co-activator condensates. Thus 3D-SEs correspond to co-activator puncta, which nominates additional condensate-associated genes and CREs.

molecular biology↗

A CRISPR-drug perturbational map for identifying new compounds to combine with commonly used chemotherapeutics.

Combination chemotherapy is crucial for achieving durable cancer cures, however, developing safe and effective drug combinations has been a significant challenge. To improve this process, we conducted large-scale targeted CRISPR knockout screens in drug-treated cells, creating a genetic map of druggable genes that sensitize cells to commonly used chemotherapeutics. We prioritized neuroblastoma, the most common pediatric solid tumor, where 50% of high-risk patients do not survive. Our screen examined all druggable gene knockouts in 18 cell lines (10 neuroblastoma, 8 others) treated with 8 widely used drugs, resulting in 94,320 unique combination-cell line perturbations, which is comparable to the largest drug combination screens ever reported. Remarkably, using dense drug-drug rescreening, we found that the top CRISPR-nominated drug combinations were far more synergistic than standard-of-care combinations, suggesting existing combinations could be improved. As proof of principle, we discovered that inhibition of PRKDC, a component of the non-homologous end-joining pathway, sensitizes high-risk neuroblastoma cells to the standard-of-care drug doxorubicin in vitro and in vivo using PDX models. Our findings provide a valuable resource for the development of improved chemotherapeutic strategies and demonstrate the feasibility of using targeted CRISPR knockout to discover new combinations with common chemotherapeutics, a methodology with application across all cancers.

cancer biology↗

NSD2 E1099K drives relapse in pediatric acute lymphoblastic leukemia by disrupting 3D chromatin organization

The NSD2 p.E1099K (EK) mutation has been shown to be enriched in patients with relapsed ALL and found to play a role in clonal fitness dependent on the underlying genetic/epigenetic landscape of the cells. To uncover 3D chromatin architecture-related mechanisms underlying drug resistance, we systematically integrated Hi-C, ATAC-seq, RNA-seq and ChIP-seq data from three B-ALL cell lines heterozygous for NSD2 EK (RS4;11, RCH-ACV, SEM) and assessed changes upon knockdown. NSD2 knockdown revealed widespread remodeling of the 3D genome, specifically in terms of compartmentalization. Systematic integration of these datasets revealed significant switches in A/B compartments with a strong bias towards B compartments upon knockdown, suggesting that NSD2 EK plays a prominent role in maintaining A compartments through enrichment of H3K36me2 epigenetic marks. In contrast, we identified few changes in intra-TAD activity suggesting that the NSD2 EK impacts transcriptional changes through a remarkable dependence on compartmentalization. Furthermore, EK-mediated reorganization of compartments highlights the existence of a common core of compacting loci shared across the three cell lines that explain previously described phenotypes as well as serve as targets for therapeutic intervention. This study offers a novel mechanism by which NSD2 EK drives clonal evolution and drug resistance.

cancer biology↗

Jointly leveraging spatial transcriptomics and deep learning models for pathology image annotation improves cell type identification over either approach alone.

Spatial transcriptomics technologies have recently emerged as a powerful tool for measuring spatially resolved gene expression directly in tissues sections, revealing cell types and their dysfunction in unprecedented detail. However, spatial transcriptomics technologies are limited in their ability to separate transcriptionally similar cell types and can suffer further difficulties identifying cell types in slide regions where transcript capture is low. Here, we describe a conceptually novel methodology that can computationally integrate spatial transcriptomics data with cell-type-informative paired tissue images, obtained from, for example, the reverse side of the same tissue section, to improve inferences of tissue cell type composition in spatial transcriptomics data. The underlying statistical approach is generalizable to any spatial transcriptomics protocol where informative paired tissue images can be obtained. We demonstrate a use case leveraging cell-type-specific immunofluorescence markers obtained on mouse brain tissue sections and a use case for leveraging the output of AI annotated H&E tissue images, which we used to markedly improve the identification of clinically relevant immune cell infiltration in breast cancer tissue. Thus, combining spatial transcriptomics data with paired tissue images has the potential to improve the identification of cell types and hence to improve the applications of spatial transcriptomics that rely on accurate cell type identification.

bioinformatics↗

A Developmentally Prometastatic Niche to Hepatoblastoma in Neonatal Liver mediated by the Cxcl1/Cxcr2 Axis

Background and RationaleHepatoblastoma (HB) is the most common pediatric liver cancer. Its predominant occurrence in very young children led us to investigating whether the neonatal liver provides a protumorigenic niche to HB development. MethodsHB development was compared between orthotopic transplantation models established in postnatal day 5 and 60 mice (P5Tx and P60Tx models). Single-cell RNA-sequencing was performed using tumor and liver tissues from both models and the top candidate cell types and genes identified are investigated for their roles in HB cell growth, migration, and survival. ResultsWe found that various HB cell lines including HepG2 cells were consistently and considerably more tumorigenic and metastatic in the P5Tx model than in the P60Tx models. Sc-RNAseq of the P5Tx and P60Tx HepG2 models revealed that the P5Tx tumor was more hypoxic and had a larger number of activated hepatic stellate cells (aHSCs) in the tumor-surrounding liver which express significantly higher levels of Cxcl1 than those from the P60Tx model. We found these differences were developmentally present in normal P5 and P60 liver. We showed that the Cxcl1/Cxcr2 axis mediated HB cell migration and was critical to HB cell survival under hypoxia. Treating HepG2 P60Tx model with recombinant CXCL1 protein induced intrahepatic and pulmonary metastasis and CXCR2 knockout in HepG2 cells abolished their metastatic potential in the P5Tx model. Lastly, we showed that in metastatic HB patient tumors there was a similar larger population of aHSCs in the tumor-surrounding liver than in localized tumors, and tumor hypoxia was uniquely associated with HB patient prognosis among pediatric cancers. ConclusionWe demonstrated that the neonatal liver provides a prometastatic niche to HB development via the Cxcl1/Cxcr2 axis.

cancer biology↗

The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma.

Survival in high-risk pediatric neuroblastoma has remained around 50% for the last 20 years, with immunotherapies and targeted therapies having had minimal impact. Here, we identify the small molecule CX-5461 as selectively cytotoxic to high-risk neuroblastoma and synergistic with low picomolar concentrations of topoisomerase I inhibitors improving survival in vivo in orthotopic patient-derived xenograft neuroblastoma mouse models. CX-5461 recently progressed through phase I clinical trial as a first-in-human inhibitor of RNA-POL I. However, we also use a comprehensive panel of in vitro and in vivo assays to demonstrate that CX-5461 has been mischaracterized and that its primary target at pharmacologically relevant concentrations, is in fact topoisomerase II beta (TOP2B), not RNA-POL I. These findings are important because existing clinically approved chemotherapeutics have well-documented off-target interactions with TOP2B, which have previously been shown to cause both therapy-induced leukemia and cardiotoxicity--often-fatal adverse events, which can emerge several years after treatment. Thus, while we show that combination therapies involving CX-5461 have promising anti-tumor activity in vivo in neuroblastoma, our identification of TOP2B as the primary target of CX-5461 indicates unexpected safety concerns that should be examined in ongoing phase II clinical trials in adult patients before pursuing clinical studies in children.

cancer biology↗

Accurate Genomic Variant Detection in Single Cells with Primary Template-Directed Amplification

Improvements in whole genome amplification (WGA) would enable new types of basic and applied biomedical research, including studies of intratissue genetic diversity that require more accurate single-cell genotyping. Here we present primary template-directed amplification (PTA), a new isothermal WGA method that reproducibly captures >95% of the genomes of single cells in a more uniform and accurate manner than existing approaches, resulting in significantly improved variant calling sensitivity and precision. To illustrate the new types of studies that are enabled by PTA, we developed direct measurement of environmental mutagenicity (DMEM), a new tool for mapping genome-wide interactions of mutagens with single living human cells at base pair resolution. In addition, we utilized PTA for genome-wide off-target indel and structural variant detection in cells that had undergone CRISPR-mediated genome editing, establishing the feasibility for performing single-cell evaluations of biopsies from edited tissues. The improved precision and accuracy of variant detection with PTA overcomes the current limitations of accurate whole genome amplification, which is the major obstacle to studying genetic diversity and evolution at cellular resolution.

genomics↗

miR-424(322) is a molecular switch controlling pro-inflammatory vs anti-inflammatory skin DC subset differentiation by modulating TGF-β signaling

TGF-{beta} family ligands are key regulators of dendritic cell (DC) differentiation and activation. Epidermal Langerhans cells (LCs) require TGF- {beta} family signaling for their differentiation and canonical TGF-{beta}1 signaling secures a non-activated LC state. LCs reportedly control skin inflammation and are replenished from peripheral blood monocytes, which also give rise to pro-inflammatory monocyte-derived DCs (moDCs). Among all the miRNAs differentially expressed in LC vs moDCs, we observed miR-424 to be strongly induced during moDC differentiation from monocytes. We discovered that miR-424 is required for moDC differentiation from human and murine precursor cells in vitro and for inflammation-associated moDC in vivo. Mechanistically we found that low levels of miR-424 facilitate TGF-{beta}1-dependent LC differentiation at the expense of moDC differentiation. Loss of miR-424 in monocyte/DC precursors resulted in the induction of TGF-{beta} pathway. Therefore, miR-424 plays a decisive role in anti-inflammatory LC vs pro-inflammatory moDC differentiation from monocytes, and its repression allows TGF-{beta} ligands to promote LC differentiation. Short summaryMonocytes give rise to two distinct DC subsets in skin inflammation, exhibiting opposite roles in inflammation. This study identified miR-424(322) as a molecular switch controlling pro-inflammatory (moDC) vs anti-inflammatory LC subset differentiation by modulating TGF-{beta} signaling.

immunology↗

Single-cell analysis identifies TCF4 and ID3 as a molecular switch of mammary epithelial stem cell differentiation.

It is well known that the expansion of the mammary epithelium during the ovarian cycles in female mammals is supported by the transient increase in mammary epithelial stem cells (MaSCs). However, dissecting the molecular mechanisms that govern MaSC function and differentiation is poorly understood due to the lack of standardized methods for their identification and isolation. The development of robust single-cell mRNA sequencing () technologies and the computational methods to analyze them provides us with novel tools to approach the challenge of studying MaSCs in a completely unbiased way without. Here, we have performed the largest scRNA-seq analysis of individual mammary epithelial cells (~70,000 cells). Our study identified a distinct cell population presenting molecular features of MaSCs. Importantly, further purification and additional in-depth single-cell analysis of these cells revealed that they are not a fully homogenous entity. Instead, we identified three subpopulations representing early stages of lineage commitment. By tracking their molecular evolution through single-cell network analysis we found that one of these subpopulations represents bipotent MaSCs from which luminal and basal lineages diverge. Importantly, we also confirmed the presence of these cells in human mammary glands. Finally, through expression and network analysis studies, we have uncovered transcription factors that are activated early during lineage commitment. These data identified E2-2 (Tcf4) and ID3 as a potential molecular switch of mammary epithelial stem cell differentiation.

developmental biology↗

The Nucleome of Developing Murine Rod Photoreceptors

The nuclei of rod photoreceptors in mice and other nocturnal species have an unusual inverted chromatin structure: the heterochromatin is centrally located to help focus light and improve photosensitivity. To better understand this unique nuclear organization, we performed ultra-deep Hi-C analysis on murine retina at 3 stages of development and on purified rod photoreceptors. Predicted looping interactions from the Hi-C data were validated with fluorescence in situ hybridization (FISH). We discovered that a subset of retinal genes that are important for retinal development, cancer, and stress response are localized to the facultative heterochromatin domain. We also used machine learning to develop an algorithm based on our chromatin Hidden Markov Modeling (chromHMM) of retinal development to predict heterochromatin domains and study their dynamics during retinogenesis. FISH data for 264 genomic loci were used to train and validate the algorithm. The integrated data were then used to identify a developmental stage- and cell type-specific core regulatory circuit super-enhancer (CRC-SE) upstream of the Vsx2 gene, which is required for bipolar neuron expression. Deletion of the Vsx2 CRC-SE in mice led to the loss of bipolar neurons in the retina.

neuroscience↗

MYCN Amplification and ATRX Mutations are Incompatible in Neuroblastoma

Aggressive cancers often have activating mutations in growth-controlling oncogenes and inactivating mutations in tumor-suppressor genes. In neuroblastoma, amplification of the MYCN oncogene and inactivation of the ATRX tumor-suppressor gene correlate with high-risk disease and poor prognosis. Here we show that ATRX mutations and MYCN amplification are mutually exclusive across all ages and stages in neuroblastoma. Using human cell lines and mouse models, we found that elevated MYCN expression and ATRX mutations are incompatible. Elevated MYCN levels promote metabolic reprogramming, mitochondrial dysfunction, reactive-oxygen species generation, and DNA-replicative stress. The combination of replicative stress caused by defects in the ATRX-histone chaperone complex and that induced by MYCN-mediated metabolic reprogramming leads to synthetic lethality. Therefore, ATRX and MYCN represent an unusual example, where inactivation of a tumor-suppressor gene and activation of an oncogene are incompatible. This synthetic lethality may eventually be exploited to improve outcomes for patients with high-risk neuroblastoma.

cancer biology↗

LCA robustly reveals subtle diversity in large-scale single-cell RNA-seq data

Single-cell RNA sequencing has emerged as a powerful tool for characterizing the cell-to-cell variation and dynamics. We present Latent Cellular Analysis (LCA), a machine learning-based analytical pipeline that features a dual-space model search with inference of latent cellular states, control of technical variations, cosine similarity measurement, and spectral clustering. LCA has proved to be robust, accurate, scalable, and powerful in revealing subtle diversity in cell populations.

bioinformatics↗