Reconstruction of Ewing Sarcoma Developmental Context from Mass-Scale Transcriptomics Reveals Characteristics of EWSR1-FLI1 Permissibility

Identification of common factors among fibrosarcoma, rhabdomyosarcoma, and osteosarcoma by network analysis

Sarcoma cancers are uncommon malignant tumors, and there are many subgroups, including fibrosarcoma (FS), which mainly affects middle-aged and older adults in deep soft tissues. Rhabdomyosarcoma (RMS), on the other hand, is the most common soft-tissue sarcoma in children and is located in the head and neck area. Osteosarcomas (OS) is the predominant form of primary bone cancer among young adults, primarily resulting from sporadically random mutations. This frequently results in the dissemination of cancer cells to the lungs, commonly known as metastasis. Mesodermal cells are the origin of sarcoma cancers. In this study, a rather radical approach has been applied. Instead of comparing homogenous cancer types, we focus on three main subtypes of sarcoma: fibrosarcoma, rhabdomyosarcoma, and osteosarcoma, and compare their gene expression with normal cell groups to identify the differentially expressed genes (DEGs). Next, by applying protein-protein interaction (PPI) network analysis, we determine the hub genes and crucial factors, such as transcription factors (TFs), affected by these types of cancer. Our findings indicate a modification in a range of pathways associated with cell cycle, extracellular matrix, and DNA repair in these three malignancies. Results showed that fibrosarcoma (FS), rhabdomyosarcoma (RMS), and osteosarcoma (OS) had 653, 1270, and 2823 differentially expressed genes (DEGs), respectively. Interestingly, there were 24 DEGs common to all three types. Network analysis showed that the fibrosarcoma network had two sub-networks identified in FS that contributed to the catabolic process of collagen via the G-protein coupled receptor signaling pathway. The rhabdomyosarcoma network included nine sub-networks associated with cell division, extracellular matrix organization, mRNA splicing via spliceosome, and others. The osteosarcoma network has 13 sub-networks, including mRNA splicing, sister chromatid cohesion, DNA repair, etc. In conclusion, the common DEGs identified in this study have been shown to play significant and multiple roles in various other cancers based on the literature review, indicating their significance.

RLSuite: An Integrative R-Loop Bioinformatics Framework

We recently described the development of a database of 810 R-loop mapping datasets and used this data to conduct a meta-analysis of R-loops. R-loops are three-stranded nucleic acid structures containing RNA:DNA hybrids and we were able to verify that 30% of expressed genes have an associated R-loop in a location conserved manner.. Moreover, intergenic R-loops map to enhancers, super enhancers and with TAD domain boundaries. This work demonstrated that R-loop mapping via high-throughput sequencing can reveal novel insight into R-loop biology, however the analysis and quality control of these data is a non-trivial task for which few bioinformatic tools exist. Herein we describe RLSuite, an integrative R-loop bioinformatics framework for pre-processing, quality control, and downstream analysis of R-loop mapping data. RLSuite enables users to compare their data to hundreds of public datasets and generate a user-friendly analysis report for sharing with non-bioinformatician colleagues. Taken together, RLSuite is a novel analysis framework that should greatly benefit the emerging R-loop bioinformatics community in a rapidly expanding aspect of epigenetic control that is still poorly understood.

Transmorph: a unifying computational framework for modular single-cell RNA-seq data integration

Data integration of single-cell RNA-seq (scRNA-seq) data describes the task of embedding datasets gathered from different sources or experiments into a common representation so that cells with similar types or states are embedded close to one another independently from their dataset of origin. Data integration is a crucial step in most scRNA-seq data analysis pipelines involving multiple batches. It improves data visualization, batch effect reduction, clustering, label transfer, and cell type inference. Many data integration tools have been proposed during the last decade, but a surge in the number of these methods has made it difficult to pick one for a given use case. Furthermore, these tools are provided as rigid pieces of software, making it hard to adapt them to various specific scenarios. In order to address both of these issues at once, we introduce the transmorph framework. It allows the user to engineer powerful data integration pipelines and is supported by a rich software ecosystem. We demonstrate transmorph usefulness by solving a variety of practical challenges on scRNA-seq datasets including joint datasets embedding, gene space integration, and transfer of cycle phase annotations. transmorph is provided as an open source python package.

Complex/cryptic EWSR1::FLI1/ERG Gene Fusions and 1q Jumping Translocation in Pediatric Ewing Sarcomas

Ewing sarcomas (ES) are rare small round cell sarcomas often affecting children and characterized by gene fusions involving one member of the FET family of genes (usually EWSR1) and a member of the ETS family of transcription factors (usually FLI1 or ERG). The detection of EWSR1 rearrangements has important diagnostic value. Here, we conducted a retrospective review of 218 consecutive pediatric ES at diagnosis and found eight patients having data from chromosome analysis, FISH/microarray, and gene-fusion assay. Three of these eight ES had novel complex/cryptic EWSR1 rearrangements/fusions by chromosome analysis. One case had a t(9;11;22)(q22;q24;q12) three-way translocation involving EWSR1::FLI1 fusion and 1q jumping translocation. Two cases had cryptic EWSR1 rearrangements/fusions, including one case with a cryptic t(4;11;22)(q35;q24;q12) three-way translocation involving EWSR1::FLI1 fusion, and the other had a cryptic EWSR1::ERG rearrangement/fusion on an abnormal chromosome 22. All patients in this study had various aneuploidies with a gain of chromosome 8 (75%), the most common, followed by a gain of chromosomes 20 (50%) and 4 (37.5%), respectively. Recognition of complex and/or cryptic EWSR1 gene rearrangements/fusions and other chromosome abnormalities (such as jumping translocation and aneuploidies) using a combination of various genetic methods is important for accurate diagnosis, prognosis, and treatment outcomes of pediatric ES.

Prognostic implication of cuproptosis related genes associates with immunity in Ewing's sarcoma

Growing evidence demonstrated that cuproptosis play critical roles in human cancers. We aimed to identify the roles of cuproptosis related genes (CRGs) in prognosis and immunity of Ewing's sarcoma. The data of GSE17674 and GSE63156 were obtained from GEO. The expression of 17 CRGs and immune cells were explored, then correlation was analyzed. Based on CRGs, two molecular clusters were identified by consensus clustering algorithm. KM survival and IME features including immune cells, immune response, checkpoint genes between clusters were evaluated. NFE2L2, LIAS, and CDKN2A were screened out as prognostic signatures by univariate, LASSO and step regression. A risk model was established, and validated by KM method with p = 0.0026, and perfect AUC values. The accuracy of risk model was also well validated in external dataset. A nomogram was constructed and evaluated by calibration curves and DCA. Low level of immune cells, immune response, and enriched checkpoint genes were found in high-risk group. GSEA of signatures and GSVA of ES-related pathways revealed the potential molecular mechanism involved in ES progression. Several drugs showed sensitivity to ES samples. DEGs between risk groups were screened out, and function enrichment was conducted. Finally, scRNA analysis of GSE146221 was done. NFE2L2, and LIAS played crucial role in the evolution of ES by pesudotime and trajectory methods. Our study provided new aspects for further research in ES.

Exploiting the Stemness and Chemoresistance Transcriptome of Ewing Sarcoma to Identify Candidate Therapeutic Targets and Drug-Repurposing Candidates

Outcomes for most patients with Ewing sarcoma (ES) have remained unchanged for the last 30 years, emphasising the need for more effective and tolerable treatments. We have hypothesised that using small-molecule inhibitors to kill the self-renewing chemotherapy-resistant cells (Ewing sarcoma cancer stem-like cells; ES-CSCs) responsible for progression and relapse could improve outcomes and minimise treatment-induced morbidities. For the first time, we demonstrate that ABCG1, a potential oncogene in some cancers, is highly expressed in ES-CSCs independently of CD133. Using functional models, transcriptomics and a bespoke in silico drug-repurposing pipeline, we have prioritised a group of tractable small-molecule inhibitors for further preclinical studies. Consistent with the cellular origin of ES, 21 candidate molecular targets of pluripotency, stemness and chemoresistance were identified. Small-molecule inhibitors to 13 of the 21 molecular targets (62%) were identified. POU5F1/OCT4 was the most promising new therapeutic target in Ewing sarcoma, interacting with 10 of the 21 prioritised molecular targets and meriting further study. The majority of small-molecule inhibitors (72%) target one of two drug efflux proteins, p-glycoprotein (n = 168) or MRP1 (n = 13). In summary, we have identified a novel cell surface marker of ES-CSCs and cancer/non-cancer drugs to targets expressed by these cells that are worthy of further preclinical evaluation. If effective in preclinical models, these drugs and drug combinations might be repurposed for clinical evaluation in patients with ES.

Molecularly Defined Subsets of Ewing Sarcoma Tumors Differ in Their Responses to IGF1R and WEE1 Inhibition

PURPOSE

Targeted cancer therapeutics have not significantly benefited patients with Ewing sarcoma with metastatic or relapsed disease. Understanding the molecular underpinnings of drug resistance can lead to biomarker-driven treatment selection.

EXPERIMENTAL DESIGN

Receptor tyrosine kinase (RTK) pathway activation was analyzed in tumor cells derived from a panel of Ewing sarcoma tumors, including primary and metastatic tumors from the same patient. Phospho-RTK arrays, Western blots, and IHC were used. Protein localization and the levels of key markers were determined using immunofluorescence. DNA damage tolerance was measured through PCNA ubiquitination levels and the DNA fiber assay. Effects of pharmacologic inhibition were assessed in vitro and key results validated in vivo using patient-derived xenografts.

RESULTS

Ewing sarcoma tumors fell into two groups. In one, IGF1R was predominantly nuclear (nIGF1R), DNA damage tolerance pathway was upregulated, and cells had low replication stress and RRM2B levels and high levels of WEE1 and RAD21. These tumors were relatively insensitive to IGF1R inhibition. The second group had high replication stress and RRM2B, low levels of WEE1 and RAD21, membrane-associated IGF1R (mIGF1R) signaling, and sensitivity to IGF1R or WEE1-targeted inhibitors. Moreover, the matched primary and metastatic tumors differed in IGF1R localization, levels of replication stress, and inhibitor sensitivity. In all instances, combined IGF1R and WEE1 inhibition led to tumor regression.

CONCLUSIONS

IGF1R signaling mechanisms and replication stress levels can vary among Ewing sarcoma tumors (including in the same patient), influencing the effects of IGF1R and WEE1 treatment. These findings make the case for using biopsy-derived predictive biomarkers at multiple stages of Ewing sarcoma disease management.

Exploration and analysis of R-loop mapping data with RLBase

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA. In 2012, Ginno et al. introduced the first R-loop mapping method. Since that time, dozens of R-loop mapping studies have been conducted, yielding hundreds of publicly available datasets. Current R-loop databases provide only limited access to these data. Moreover, no web tools for analyzing user-supplied R-loop datasets have yet been described. In our recent work, we reprocessed 810 R-loop mapping samples, building the largest R-loop data resource to date. We also defined R-loop consensus regions and developed a framework for R-loop data analysis. Now, we introduce RLBase, a user-friendly database that provides the capability to (i) explore hundreds of public R-loop mapping datasets, (ii) explore R-loop consensus regions, (iii) analyze user-supplied data and (iv) download standardized and reprocessed datasets. RLBase is directly accessible via the following URL: https://gccri.bishop-lab.uthscsa.edu/shiny/rlbase/.

The importance of fusion protein activity in Ewing sarcoma and the cell intrinsic and extrinsic factors that regulate it: A review

Accumulating evidence shows that despite clonal origins tumors eventually become complex communities comprised of phenotypically distinct cell subpopulations. This heterogeneity arises from both tumor cell intrinsic programs and signals from spatially and temporally dynamic microenvironments. While pediatric cancers usually lack the mutational burden of adult cancers, they still exhibit high levels of cellular heterogeneity that are largely mediated by epigenetic mechanisms. Ewing sarcomas are aggressive bone and soft tissue malignancies with peak incidence in adolescence and the prognosis for patients with relapsed and metastatic disease is dismal. Ewing sarcomas are driven by a single pathognomonic fusion between a FET protein and an ETS family transcription factor, the most common of which is EWS::FLI1. Despite sharing a single driver mutation, Ewing sarcoma cells demonstrate a high degree of transcriptional heterogeneity both between and within tumors. Recent studies have identified differential fusion protein activity as a key source of this heterogeneity which leads to profoundly different cellular phenotypes. Paradoxically, increased invasive and metastatic potential is associated with lower EWS::FLI1 activity. Here, we review what is currently understood about EWS::FLI1 activity, the cell autonomous and tumor microenvironmental factors that regulate it, and the downstream consequences of these activity states on tumor progression. We specifically highlight how transcription factor regulation, signaling pathway modulation, and the extracellular matrix intersect to create a complex network of tumor cell phenotypes. We propose that elucidation of the mechanisms by which these essential elements interact will enable the development of novel therapeutic approaches that are designed to target this complexity and ultimately improve patient outcomes.

Repression of enhancer RNA PHLDA1 promotes tumorigenesis and progression of Ewing sarcoma via decreasing infiltrating T‐lymphocytes: A bioinformatic analysis

Background: The molecular mechanisms of EWS-FLI-mediating target genes and downstream pathways may provide a new way in the targeted therapy of Ewing sarcoma. Meanwhile, enhancers transcript non-coding RNAs, known as enhancer RNAs (eRNAs), which may serve as potential diagnosis markers and therapeutic targets in Ewing sarcoma.

Materials and methods: Differentially expressed genes (DEGs) were identified between 85 Ewing sarcoma samples downloaded from the Treehouse database and 3 normal bone samples downloaded from the Sequence Read Archive database. Included in DEGs, differentially expressed eRNAs (DEeRNAs) and target genes corresponding to DEeRNAs (DETGs), as well as the differentially expressed TFs, were annotated. Then, cell type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) was used to infer portions of infiltrating immune cells in Ewing sarcoma and normal bone samples. To evaluate the prognostic value of DEeRNAs and immune function, cross validation, independent prognosis analysis, and Kaplan–Meier survival analysis were implemented using sarcoma samples from the Cancer Genome Atlas database. Next, hallmarks of cancer by gene set variation analysis (GSVA) and immune gene sets by single-sample gene set enrichment analysis (ssGSEA) were identified to be significantly associated with Ewing sarcoma. After screening by co-expression analysis, most significant DEeRNAs, DETGs and DETFs, immune cells, immune gene sets, and hallmarks of cancer were merged to construct a co-expression regulatory network to eventually identify the key DEeRNAs in tumorigenesis of Ewing sarcoma. Moreover, Connectivity Map Analysis was utilized to identify small molecules targeting Ewing sarcoma. External validation based on multidimensional online databases and scRNA-seq analysis were used to verify our key findings.

Results: A six-different-dimension regulatory network was constructed based on 17 DEeRNAs, 29 DETFs, 9 DETGs, 5 immune cells, 24 immune gene sets, and 8 hallmarks of cancer. Four key DEeRNAs (CCR1, CD3D, PHLDA1, and RASD1) showed significant co-expression relationships in the network. Connectivity Map Analysis screened two candidate compounds, MS-275 and pyrvinium, that might target Ewing sarcoma. PHLDA1 (key DEeRNA) was extensively expressed in cancer stem cells of Ewing sarcoma, which might play a critical role in the tumorigenesis of Ewing sarcoma.

Conclusion: PHLDA1 is a key regulator in the tumorigenesis and progression of Ewing sarcoma. PHLDA1 is directly repressed by EWS/FLI1 protein and low expression of FOSL2, resulting in the deregulation of FOX proteins and CC chemokine receptors. The decrease of infiltrating T‐lymphocytes and TNFA signaling may promote tumorigenesis and progression of Ewing sarcoma.

Quality-controlled R-loop meta-analysis reveals the characteristics of R-loop consensus regions

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA. While the pathological consequences of R-loops have been well-studied to date, the locations, classes, and dynamics of physiological R-loops remain poorly understood. R-loop mapping studies provide insight into R-loop dynamics, but their findings are challenging to generalize. This is due to the narrow biological scope of individual studies, the limitations of each mapping modality, and, in some cases, poor data quality. In this study, we reprocessed 810 R-loop mapping datasets from a wide array of biological conditions and mapping modalities. From this data resource, we developed an accurate R-loop data quality control method, and we reveal the extent of poor-quality data within previously published studies. We then identified a set of high-confidence R-loop mapping samples and used them to define consensus R-loop sites called 'R-loop regions' (RL regions). In the process, we identified a stark divergence between RL regions detected by S9.6 and dRNH-based mapping methods, particularly with respect to R-loop size, location, and colocalization with RNA binding factors. Taken together, this work provides a much-needed method to assess R-loop data quality and offers novel context regarding the differences between dRNH- and S9.6-based R-loop mapping approaches.

Pediatric Sarcomas: The Next Generation of Molecular Studies

Pediatric sarcomas constitute one of the largest groups of childhood cancers, following hematopoietic, neural, and renal lesions. Partly because of their diversity, they continue to offer challenges in diagnosis and treatment. In spite of the diagnostic, nosologic, and therapeutic gains made with genetic technology, newer means for investigation are needed. This article reviews emerging technology being used to study human neoplasia and how these methods might be applicable to pediatric sarcomas. Methods reviewed include single cell RNA sequencing (scRNAseq), spatial multi-omics, high-throughput functional genomics, and clustered regularly interspersed short palindromic sequence-Cas9 (CRISPR-Cas9) technology. In spite of these advances, the field continues to be challenged by a dearth of properly annotated materials, particularly from recurrences and metastases and pre- and post-treatment samples.

Modeling Progression of Single Cell Populations Through the Cell Cycle as a Sequence of Switches

Cell cycle is a biological process underlying the existence and propagation of life in time and space. It has been an object for mathematical modeling for long, with several alternative mechanistic modeling principles suggested, describing in more or less details the known molecular mechanisms. Recently, cell cycle has been investigated at single cell level in snapshots of unsynchronized cell populations, exploiting the new methods for transcriptomic and proteomic molecular profiling. This raises a need for simplified semi-phenomenological cell cycle models, in order to formalize the processes underlying the cell cycle, at a higher abstracted level. Here we suggest a modeling framework, recapitulating the most important properties of the cell cycle as a limit trajectory of a dynamical process characterized by several internal states with switches between them. In the simplest form, this leads to a limit cycle trajectory, composed by linear segments in logarithmic coordinates describing some extensive (depending on system size) cell properties. We prove a theorem connecting the effective embedding dimensionality of the cell cycle trajectory with the number of its linear segments. We also develop a simplified kinetic model with piecewise-constant kinetic rates describing the dynamics of lumps of genes involved in S-phase and G2/M phases. We show how the developed cell cycle models can be applied to analyze the available single cell datasets and simulate certain properties of the observed cell cycle trajectories. Based on our model, we can predict with good accuracy the cell line doubling time from the length of cell cycle trajectory.

SPARC-mediated long-term retention of nab-paclitaxel in pediatric sarcomas

Secreted protein acidic and rich in cysteine (SPARC) is a matricellular glycoprotein overexpressed by several cancers. Because SPARC shows high binding affinity to albumin, we reasoned that pediatric sarcoma xenografts expressing SPARC would show enhanced uptake and accumulation of nanoparticle albumin-bound (nab)-paclitaxel, a potent anticancer drug formulation. We first evaluated the expression of SPARC in patient-derived xenografts (PDXs) of Ewing sarcoma, rhabdomyosarcoma and osteosarcoma, finding variable SPARC gene expression that correlated well with SPARC protein measured by immunoblotting. We revealed that the activity of the fusion gene chimera EWSR1-FLI1, the genetic driver of Ewing sarcoma, leads to lower expression of the gene SPARC in these tumors, likely due to enriched acetylation marks of the histone H3 lysine 27 at regions including the SPARC promoter and potential enhancers. Then, we used SPARC-edited Ewing sarcoma cells (A673 line) to demonstrate that SPARC knocked down (KD) cells accumulated significantly less amount of nab-paclitaxel in vitro than SPARC wild type (WT) cells. In vivo, SPARC KD and SPARC WT subcutaneous xenografts in mice achieved similar maximum intratumoral concentrations of nab-paclitaxel, though drug clearance from SPARC WT tumors was significantly slower. We confirmed such SPARC-mediated long-term intratumoral accumulation of nab-paclitaxel in Ewing sarcoma PDX with high expression of SPARC, which accumulated significantly more nab-paclitaxel than SPARC-low PDX. SPARC-high PDX responded better to nab-paclitaxel than SPARC-low tumors, although these results should be taken cautiously, given that the PDXs were established from different patients that could have specific determinants predisposing response to paclitaxel. In addition, SPARC KD Ewing sarcoma xenografts responded better to soluble docetaxel and paclitaxel than to nab-paclitaxel, while SPARC WT ones showed similar response to soluble and albumin-carried drugs. Overall, our results show that pediatric sarcomas expressing SPARC accumulate nab-paclitaxel for longer periods of time, which could have clinical implications for chemotherapy efficacy.

One oncogene, several vulnerabilities: EWS/FLI targeted therapies for Ewing sarcoma

EWS/FLI is the defining mutation of Ewing sarcoma. This oncogene drives malignant transformation and progression and occurs in a genetic background characterized by few other recurrent cooperating mutations. In addition, the tumor is absolutely dependent on the continued expression of EWS/FLI to maintain the malignant phenotype. However, EWS/FLI is a transcription factor and therefore a challenging drug target. The difficulty of directly targeting EWS/FLI stems from unique features of this fusion protein as well as the network of interacting proteins required to execute the transcriptional program. This network includes interacting proteins as well as upstream and downstream effectors that together reprogram the epigenome and transcriptome. While the vast number of proteins involved in this process challenge the development of a highly specific inhibitors, they also yield numerous therapeutic opportunities. In this report, we will review how this vast EWS-FLI transcriptional network has been exploited over the last two decades to identify compounds that directly target EWS/FLI and/or associated vulnerabilities.

Correlation AnalyzeR: functional predictions from gene co-expression correlations

BACKGROUND

Co-expression correlations provide the ability to predict gene functionality within specific biological contexts, such as different tissue and disease conditions. However, current gene co-expression databases generally do not consider biological context. In addition, these tools often implement a limited range of unsophisticated analysis approaches, diminishing their utility for exploring gene functionality and gene relationships. Furthermore, they typically do not provide the summary visualizations necessary to communicate these results, posing a significant barrier to their utilization by biologists without computational skills.

RESULTS

We present Correlation AnalyzeR, a user-friendly web interface for exploring co-expression correlations and predicting gene functions, gene-gene relationships, and gene set topology. Correlation AnalyzeR provides flexible access to its database of tissue and disease-specific (cancer vs normal) genome-wide co-expression correlations, and it also implements a suite of sophisticated computational tools for generating functional predictions with user-friendly visualizations. In the usage example provided here, we explore the role of BRCA1-NRF2 interplay in the context of bone cancer, demonstrating how Correlation AnalyzeR can be effectively implemented to generate and support novel hypotheses.

CONCLUSIONS

Correlation AnalyzeR facilitates the exploration of poorly characterized genes and gene relationships to reveal novel biological insights. The database and all analysis methods can be accessed as a web application at https://gccri.bishop-lab.uthscsa.edu/correlation-analyzer/ and as a standalone R package at https://github.com/Bishop-Laboratory/correlationAnalyzeR .

Systems Biology Analysis for Ewing Sarcoma

Ewing sarcoma (EwS) is a highly aggressive pediatric bone cancer that is defined by a somatic fusion between the EWSR1 gene and an ETS family member, most frequently the FLI1 gene, leading to expression of a chimeric transcription factor EWSR1-FLI1. Otherwise, EwS is one of the most genetically stable cancers. The situation when the major cancer driver is well known looks like a unique opportunity for applying the systems biology approach in order to understand the EwS mechanisms as well as to uncover some general mechanistic principles of carcinogenesis. A number of studies have been performed revealing the direct and indirect effects of EWSR1-FLI1 on multiple aspects of cellular life. Nevertheless, the emerging picture of the oncogene action appears to be highly complex and systemic, with multiple reciprocal influences between the immediate consequences of the driver mutation and intracellular and intercellular molecular mechanisms, including regulation of transcription, epigenome, and tumoral microenvironment. In this chapter, we present an overview of existing molecular profiling resources available for EwS tumors and cell lines and provide an online comprehensive catalogue of publicly available omics and other datasets. We further highlight the systems biology studies of EwS, involving mathematical modeling of networks and integration of molecular data. We conclude that despite the seeming simplicity, a lot has yet to be understood on the systems-wide mechanisms connecting the driver mutation and the major cellular phenotypes of this pediatric cancer. Overall, this chapter can serve as a guide for a systems biology researcher to start working on EwS.

Exploiting Replication Stress as a Novel Therapeutic Intervention

Ewing sarcoma is an aggressive pediatric tumor of the bone and soft tissue. The current standard of care is radiation and chemotherapy, and patients generally lack targeted therapies. One of the defining molecular features of this tumor type is the presence of significantly elevated levels of replication stress as compared with both normal cells and many other types of cancers, but the source of this stress is poorly understood. Tumors that harbor elevated levels of replication stress rely on the replication stress and DNA damage response pathways to retain viability. Understanding the source of the replication stress in Ewing sarcoma may reveal novel therapeutic targets. Ewing sarcomagenesis is complex, and in this review, we discuss the current state of our knowledge regarding elevated replication stress and the DNA damage response in Ewing sarcoma, one contributor to the disease process. We will also describe how these pathways are being successfully targeted therapeutically in other tumor types, and discuss possible novel, evidence-based therapeutic interventions in Ewing sarcoma. We hope that this consolidation will spark investigations that uncover new therapeutic targets and lead to the development of better treatment options for patients with Ewing sarcoma. IMPLICATIONS: This review uncovers new therapeutic targets in Ewing sarcoma and highlights replication stress as an exploitable vulnerability across multiple cancers.