Search

Osteosarcoma (OS) is the most common primary malignant bone tumor in childhood. Patients who present with metastatic disease at diagnosis or relapse have a very poor prognosis, and this has not changed over the past four decades. The Wnt signaling pathway plays a role in regulating osteogenesis and is implicated in OS pathogenesis. DKK-1 inhibits the canonical Wnt signaling pathway, causing inhibition of osteoblast differentiation and disordered bone repair. Our lab previously demonstrated that a monoclonal antibody against DKK-1 prevented metastatic disease in a mouse model. This study expands upon those findings by demonstrating similar results with a small molecule inhibitor of DKK-1, WAY262611, both in vitro and in vivo . WAY262611 was evaluated in vitro on osteosarcoma cell lines, including proliferation, caspase activation, cell cycle analysis, and signaling pathway activation. We utilized our orthotopic implantation-amputation model of osteosarcoma metastasis in vivo to determine the impact of WAY262611 on primary tumor progression and metastatic outgrowth of disseminated tumor cells. Differentiation status was determined using single cell RNA sequencing. We show here that WAY262611 activates canonical Wnt signaling, enhances nuclear localization and transcriptional activity of beta-catenin, and slows proliferation of OS cell lines. We also show that WAY262611 induces osteoblastic differentiation of an OS patient-derived xenograft in vivo , as well as inhibiting metastasis. This work credentials DKK-1 as a therapeutic target in OS, allowing for manipulation of the Wnt signaling pathway and providing preclinical justification for the development of new biologics for prevention of osteosarcoma metastasis.

A critical area of recent cancer research is the emergence of transition states between normal and cancer that exhibit increased cell plasticity which underlies tumor cell heterogeneity. Pancreatic ductal adenocarcinoma (PDAC) can arise from the combination of a transition state termed acinar-to-ductal metaplasia (ADM) and a gain-of-function mutation in the proto-oncogene KRAS . During ADM, digestive enzyme-producing acinar cells acquire a transient ductal epithelium-like phenotype while maintaining their geographical acinar organization. One route of ADM initiation is the overexpression of the Krüppel-like factor 4 gene ( KLF4 ) in the absence of oncogenic driver mutations. Here, we asked to what extent cells acquire and retain an epigenetic memory of the ADM transition state in the absence of oncogene mutation. We identified differential DNA methylation at Kras-downstream PI3K and Rho / Rac / Cdc42 GTPase pathway genes during ADM, as well as a corresponding gene expression increase in these pathways. Importantly, differential methylation persisted after gene expression returned to normal. Caerulein exposure, which induces widespread digestive system changes in addition to ADM, showed similar changes in DNA methylation in ADM cells. Regions of differential methylation were enriched for motifs of KLF and AP-1 family transcription factors, as were those of human pancreatic intraepithelial neoplasia (PanIN) samples, demonstrating the relevance of this epigenetic transition state memory in human carcinogenesis. Finally, single-cell spatial transcriptomics revealed that these ADM transition cells were enriched for PI3K pathway and AP1 family members, linking epigenetic memory to cancer cell plasticity even in the absence of oncogene mutation.

Expression forecasting methods use machine learning models to predict how a cell will alter its transcriptome upon perturbation. Such methods are enticing because they promise to answer pressing questions in fields ranging from developmental genetics to cell fate engineering and because they are a fast, cheap, and accessible complement to the corresponding experiments. However, the absolute and relative accuracy of these methods is poorly characterized, limiting their informed use, their improvement, and the interpretation of their predictions. To address these issues, we created a benchmarking platform that combines a panel of 11 large-scale perturbation datasets with an expression forecasting software engine that encompasses or interfaces to a wide variety of methods. We used our platform to systematically assess methods, parameters, and sources of auxiliary data, finding that performance strongly depends on the choice of metric, and especially for simple metrics like mean squared error, it is uncommon for expression forecasting methods to out-perform simple baselines. Our platform will serve as a resource to improve methods and to identify contexts in which expression forecasting can succeed.

Methods that predict fate potential or degree of differentiation from transcriptomic data have identified rare progenitor populations and uncovered developmental regulatory mechanisms. However, some state-of-the-art methods are too computationally burdensome for emerging large-scale data and all methods make inaccurate predictions in certain biological systems. We developed a method in R (stemFinder) that predicts single cell differentiation time based on heterogeneity in cell cycle gene expression. Our method is computationally tractable and is as good as or superior to competitors. As part of our benchmarking, we implemented four different performance metrics to assist potential users in selecting the tool that is most apt for their application. Finally, we explore the relationship between differentiation time and cell fate potential by analyzing a lineage tracing dataset with clonally labelled hematopoietic cells, revealing that metrics of differentiation time are correlated with the number of downstream lineages., Competing Interests: CONFLICTS OF INTEREST The authors have no conflicts of interest to report.

Polyamine metabolism and signaling play important roles in multiple cancers but have not previously been studied in Ewing sarcoma. Here, we show that blocking polyamine synthesis with D, L-alpha-difluoromethylornithine (DFMO) causes a G1 cell cycle arrest, dose-dependent decreases in sarcosphere formation from Ewing sarcoma cell lines growing in non-adherent conditions and a decrease in clonogenic growth in soft agar. Further, we utilized our orthotopic implantation/amputation model of Ewing sarcoma metastasis to demonstrate that DFMO slowed primary tumor growth in addition to limiting metastasis. RNA sequencing demonstrated gene expression patterns consistent with induction of ferroptosis caused by polyamine depletion. Induction of ferroptosis was validated in vitro by demonstrating that ferrostatin-1, an inhibitor of ferroptosis, allows sphere formation even in the presence of DFMO. Collectively, these results reveal a novel mechanism by which DFMO prevents metastasis - induction of ferroptosis due to polyamine depletion. Our results provide preclinical justification to test the ability of DFMO to prevent metastatic recurrence in Ewing sarcoma patients at high risk for relapse.

The profound pain accompanying bone fracture is mediated by somatosensory neurons, which also appear to be required to initiate bone regeneration following fracture. Surprisingly, the precise neuroanatomical circuitry mediating skeletal nociception and regeneration remains incompletely understood. Here, we characterized somatosensory dorsal root ganglia (DRG) afferent neurons innervating murine long bones before and after experimental long bone fracture in mice. Retrograde labeling of DRG neurons by an adeno-associated virus with peripheral nerve tropism showed AAV-tdT signal. Single cell transcriptomic profiling of 6,648 DRG neurons showed highest labeling across CGRP+ neuron clusters (6.9-17.2%) belonging to unmyelinated C fibers, thinly myelinated Aδ fibers and Aβ-Field LTMR (9.2%). Gene expression profiles of retrograde labeled DRG neurons over multiple timepoints following experimental stress fracture revealed dynamic changes in gene expression corresponding to the acute inflammatory ( S100a8 , S100a9 ) and mechanical force ( Piezo2 ). Reparative phase after fracture included morphogens such as Tgfb1, Fgf9 and Fgf18 . Two methods to surgically or genetically denervate fractured bones were used in combination with scRNA-seq to implicate defective mesenchymal cell proliferation and osteodifferentiation as underlying the poor bone repair capacity in the presence of attenuated innervation. Finally, multi-tissue scRNA-seq and interactome analyses implicated neuron-derived FGF9 as a potent regulator of fracture repair, a finding compatible with in vitro assessments of neuron-to-skeletal mesenchyme interactions.

Computational modelling of cell state transitions has been a great interest of many in the field of developmental biology, cancer biology and cell fate engineering because it enables performing perturbation experiments in silico more rapidly and cheaply than could be achieved in a wet lab. Recent advancements in single-cell RNA sequencing (scRNA-seq) allow the capture of high-resolution snapshots of cell states as they transition along temporal trajectories. Using these high-throughput datasets, we can train computational models to generate in silico 'synthetic' cells that faithfully mimic the temporal trajectories. Here we present OneSC, a platform that can simulate synthetic cells across developmental trajectories using systems of stochastic differential equations govern by a core transcription factors (TFs) regulatory network. Different from the current network inference methods, OneSC prioritizes on generating Boolean network that produces faithful cell state transitions and steady cell states that mimic real biological systems. Applying OneSC to real data, we inferred a core TF network using a mouse myeloid progenitor scRNA-seq dataset and showed that the dynamical simulations of that network generate synthetic single-cell expression profiles that faithfully recapitulate the four myeloid differentiation trajectories going into differentiated cell states (erythrocytes, megakaryocytes, granulocytes and monocytes). Finally, through the in-silico perturbations of the mouse myeloid progenitor core network, we showed that OneSC can accurately predict cell fate decision biases of TF perturbations that closely match with previous experimental observations.