Biology subjects

Dick, J. E.

Publications and source records attributed to Dick, J. E..

28 records · Page 2Linked to original sources

bioRxiv2022

A cellular hierarchy framework for understanding heterogeneity and predicting drug response in AML

The treatment landscape of AML is evolving with promising therapies entering clinical translation, yet patient responses remain heterogeneous and biomarkers for tailoring treatment are lacking. To understand how disease heterogeneity links with therapy response, we determined the leukemia cell hierarchy make-up from bulk transcriptomes of over 1000 patients through deconvolution using single-cell reference profiles of leukemia stem, progenitor, and mature cell types. Leukemia hierarchy composition was associated with functional, genomic, and clinical properties and converged into four overall classes, spanning Primitive, Mature, GMP, and Intermediate. Critically, variation in hierarchy composition along the Primitive vs GMP or Primitive vs Mature axes were associated with response to chemotherapy or drug sensitivity profiles of targeted therapies, respectively. A 7-gene biomarker derived from the Primitive vs Mature axis was predictive of patient response to 105 investigational drugs. Thus, hierarchy composition constitutes a novel framework for understanding disease biology and advancing precision medicine in AML.

cancer biology↗

bioRxiv2021

Identification of the Global miR-130a Targetome Reveals a Novel Role for TBL1XR1 in Hematopoietic Stem Cell Self-Renewal and t(8;21) AML

Gene expression profiling and proteome analysis of normal and malignant hematopoietic stem cells (HSC) point to shared core stemness properties. However, discordance between mRNA and protein signatures underscores an important role for post-transcriptional regulation by miRNAs in governing this critical nexus. Here, we identified miR-130a as a regulator of HSC self-renewal and differentiation. Enforced expression of miR-130a impaired B lymphoid differentiation and expanded long-term HSC. Integration of protein mass spectrometry and chimeric AGO2 eCLIP-seq identified TBL1XR1 as a primary miR-130a target, whose loss of function phenocopied miR-130a overexpression. Moreover, we found that miR-130a is highly expressed in t(8;21) AML where it is critical for maintaining the oncogenic molecular program mediated by AML1-ETO. Our study establishes that identification of the comprehensive miRNA targetome within primary cells enables discovery of novel genes and molecular networks underpinning stemness properties of normal and leukemic cells. HIGHLIGHTSO_LImiR-130a is a regulator of HSC self-renewal and lineage commitment C_LIO_LITBL1XR1 is a principal target of miR-130a C_LIO_LITBL1XR1 loss of function in HSPC phenocopies enforced expression of miR-130a C_LIO_LIElevated miR-130a levels maintain the AML1-ETO repressive program in t(8;21) AML C_LI

cell biology↗

bioRxiv2021

SmMIP-tools: a computational toolset for processing and analysis of single-molecule molecular inversion probes derived data

Single-molecule molecular inversion probes (smMIPs) provides a modular and cost-effective platform for high-multiplex targeted next-generation sequencing (NGS). Nevertheless, translating the raw smMIP-derived sequencing data into accurate and meaningful information currently requires proficient computational skills and a large amount of computational work, prohibiting wide-scale adoption of smMIP-based technologies. To enable easy, efficient, and accurate interrogation of smMIP-derived data, we developed SmMIP-tools, a computational toolset that combines the critical analytic steps for smMIP data interpretation into a single computational pipeline. Here, we describe in detail two of the softwares major components. The first is a read processing tool that performs quality control steps, generates read-smMIP linkages and retrieves molecular tags. The second is an error-aware variant caller capable of detecting single nucleotide variants (SNVs) and short insertions and deletions (indels). Using a cell-line DNA dilution series and a cohort of blood cancer patients, we benchmarked SmMIP-tools and evaluated its performance against clinical sequencing reports. We anticipate that SmMIP-tools will increase accessibility to smMIP-technology, enabling cost-effective genetic research to push personalized medicine forward.

genomics↗

bioRxiv2021

INPP4B promotes leukemia by restricting leukemic stem cell differentiation through regulation of lysosomal functions

Signaling pathways that control vital features of leukemic stem cells including multipotency, self-renewal, clonal expansion and quiescence remain unclear. Emerging studies illustrate critical roles for lysosomes in hematopoietic and leukemic stem cell fate. By investigating consequences of INPP4B alterations in AML, we have discovered its role in driving leukemic stemness. We observed that INPP4B is highly expressed leukemic stem cell populations and Inpp4b-deficeint leukemias demonstrate increased disease latency, reduced leukemia initiating potential which is associated with a differentiated leukemic phenotype. Molecular analyses show that Inpp4b-deficient leukemias have compromised lysosomal gene expression, lysosomal content, and lysosomal activity. Our discovery of a novel pathway linking INPP4B, lysosomal biogenesis and leukemic stemness, provides a mechanism to explain the association of high INPP4B expression with poor AML prognosis, and highlights novel patient stratification strategies and LSC-specific leukemic therapies. Key PointsOur findings highlight a novel pathway linking INPP4B, lysosomal function and leukemic stemness that explains the prognostic role of INPP4B in AML. Our data reveal the utility of INPP4B as a biomarker of aggressive AML and provide a rationale to explore INPP4B and its associated function in lysosome biology as novel strategies to target LSC and AML

cancer biology↗

bioRxiv2021

Dichotomous regulation of lysosomes by MYC and TFEB controls hematopoietic stem cell fate

It is critical to understand how quiescent long-term hematopoietic stem cells (LT-HSC) sense demand from daily and stress-mediated cues and transition into bioenergetically active progeny to differentiate and meet these cellular needs. Here, we show that lysosomes, which are sophisticated nutrient sensing and signaling centers, are dichotomously regulated by the Transcription Factor EB (TFEB) and MYC to balance catabolic and anabolic processes required for activating LT-HSC and guiding their lineage fate. TFEB-mediated induction of the endolysosomal pathway causes membrane receptor degradation, limiting LT-HSC metabolic and mitogenic activation, which promotes quiescence, self-renewal and governs erythroid-myeloid commitment. By contrast, MYC engages biosynthetic processes while repressing lysosomal catabolism to drive LT-HSC activation. Collectively, our study identifies lysosomes as a central regulatory hub for proper and coordinated stem cell fate determination.

cell biology↗

bioRxiv2020

Mapping the Cellular Origin and Early Evolution of Leukemia in Down Syndrome

Children with Down syndrome have a 150-fold increased risk of developing myeloid leukemia, but the mechanism of predisposition is unclear. As Down syndrome leukemogenesis initiates during fetal development, we characterized the cellular context of preleukemic initiation and leukemic progression using gene editing in human disomic and trisomic fetal liver hematopoietic cells and xenotransplantation. GATA1 mutations caused transient preleukemia only when introduced into trisomy 21 long-term hematopoietic stem cells, where a subset of chromosome 21 miRNAs triggers predisposition to preleukemia. By contrast, progression to leukemia was independent of trisomy 21 and originated in various stem and progenitor cells through additional mutations in cohesin genes. CD117+/KIT cells mediated the propagation of preleukemia and leukemia, and functional KIT inhibition targeted preleukemic stem cells, blocking progression to leukemia.

cancer biology↗

bioRxiv2020

Pairtree: fast reconstruction of cancer evolutionary history using pairwise mutation relationships

Cancers are composed of genetically distinct subpopulations of malignant cells. By sequencing DNA from cancer tissue samples, we can characterize the somatic mutations specific to each population and build clone trees describing the evolutionary relationships between these populations. These trees reveal critical points in disease development and inform treatment. Both liquid cancers and solid tumours can be profiled with this approach. Pairtree is a new method for constructing clone trees using DNA sequencing data from one or more bulk samples of an individual cancer. It uses Bayesian inference to compute posterior distributions over the evolutionary relationships between every pair of identified subpopulations, then uses these distributions in a Markov Chain Monte Carlo algorithm to perform efficient inference of the posterior distribution over clone trees. Pairtree also uses the pairwise relationships to detect mutations that violate the infinite sites assumption. Unlike previous methods, Pairtree can perform clone tree reconstructions using as many as 100 samples per cancer that reveal 30 or more cell subpopulations. On simulated data, Pairtree is the only method whose performance reliably improves when provided with additional bulk samples from a cancer. On 14 B-progenitor acute lymphoblastic leukemias with up to 90 samples from each cancer, Pairtree was the only method that could reproduce or improve upon expert-derived clone tree reconstructions. By scaling to more challenging problems, Pairtree supports new biomedical research applications that can improve our understanding of the natural history of cancer, as well as better illustrate the interplay between cancer, host, and therapeutic interventions. SignificanceClone trees describe the evolutionary history of a cancer and can provide insights into how the disease changed through time (e.g., between diagnosis and relapse). Pairtree uses DNA sequencing data from many samples of the same cancer to reconstruct a cancers evolutionary history with much greater detail and accuracy than previously possible.

bioinformatics↗

bioRxiv2019

Genetic predisposition to myeloproliferative neoplasms implicates hematopoietic stem cell biology

Myeloproliferative neoplasms (MPNs) are blood cancers characterized by excessive production of mature myeloid cells that result from the acquisition of somatic driver mutations in hematopoietic stem cells (HSCs)1. While substantial progress has been made to define the causal somatic mutation profile for MPNs2, epidemiologic studies indicate a significant heritable component for the disease that is among the highest known for all cancers3. However, only a limited set of genetic risk loci have been identified, and the underlying biological mechanisms leading to MPN acquisition remain unexplained. Here, to define the inherited risk profile, we conducted the largest genome-wide association study of MPNs to date (978,913 individuals with 3,224 cases) and identified 14 genome-wide significant loci, as well as a polygenic signature that increases the odds for disease acquisition by nearly 3-fold between the top and median deciles. Interestingly, we find a shared genetic architecture between MPN risk and several hematopoietic traits spanning distinct lineages, as well as an association between increased MPN risk and longer leukocyte telomere length, collectively implicating HSC function and self-renewal. Strikingly, we find a significant enrichment for risk variants mapping to accessible chromatin in HSCs compared with other hematopoietic populations. Finally, gene mapping identifies modulators of HSC biology and targeted variant-to-function analyses suggest likely roles for CHEK2 and GFI1B in altering HSC function to confer disease risk. Overall, we demonstrate the power of human genetic studies to illuminate a previously unappreciated mechanism for MPN risk through modulation of HSC function.

genetics↗

bioRxiv2019

A Quantitative Single-Cell Proteomics Approach to Characterize an Acute Myeloid Leukemia Hierarchy

Large-scale single-cell analyses are of fundamental importance in order to capture biological heterogeneity within complex cell systems, but have largely been limited to RNA-based technologies. Here we present a comprehensive benchmarked experimental and computational workflow, which establishes global single-cell mass spectrometry-based proteomics as a tool for large-scale single-cell analyses. By exploiting a primary leukemia model system, we demonstrate both through pre-enrichment of cell populations and through a non-enriched unbiased approach that our workflow enables the exploration of cellular heterogeneity within this aberrant developmental hierarchy. Our approach is capable of consistently quantifying approximately 1000 proteins per cell across thousands of individual cells using limited instrument time. Furthermore, we developed a computational workflow (SCeptre) that effectively normalizes the data, integrates available FACS data and facilitates downstream analysis. The approach presented here lays a solid foundation for implementing global single-cell proteomics studies across the world.

systems biology↗

bioRxiv2019

Functional Profiling of Single CRISPR/Cas9-Edited Human Long-Term Hematopoietic Stem Cells

In the human hematopoietic system, rare self-renewing multi-potent long-term hematopoietic stem cells (LT-HSCs) are responsible for the lifelong production of mature blood cells and are the rational target for clinical regenerative therapies. However, the heterogeneity in the hematopoietic stem cell compartment and variable outcomes of CRISPR/Cas9 editing make functional interrogation of rare LT-HSCs challenging. Here, we report high efficiency LT-HSC editing at single cell resolution using electroporation of modified synthetic gRNAs and Cas9 protein. Targeted short isoform expression of the GATA1 transcription factor elicited distinct differentiation and proliferation effects in single LT-HSC when analyzed with functional in vitro differentiation and long-term repopulation xenotransplantation assays. Our method represents a blueprint for systematic genetic analysis of complex tissue hierarchies at single cell level.

genetics↗