Nathaniel D. Anderson

Studying monozygotic twins who present with identical cancers informs on the developmental origins of childhood tumours. Here, we perform whole genome sequencing on multiple tumour, normal, and placental samples to reconstruct the phylogeny of a soft tissue cancer that spread in utero between monozygotic twins. We demonstrate the origin of the tumour from one twin and time its diversification and transplacental spread to the other twin, showing that the spread occurred only once. This generalisable and scalable approach additionally allows us to dissect the earliest stages of twinning, revealing unexpected asymmetrical contributions of embryonic lineages to the placenta and each twin and challenging the classical model of twinning.

Fusion-positive and lethal fusion-negative cells converge on a high-risk transcriptome. A, UMAP representation of 117,791 cells across all patients in the discovery cohort. Dots are colored by malignant cell type, and normal cell type clusters are indicated by labels. Includes cells from four fusion-positive patients (brown), 12 fusion-negative patients (red and purple), and two patients with Ewing sarcoma (yellow). B, Heatmap showing the hierarchical clustering of aggregated patient profiles of cancer cells across the entire transcriptome. Hierarchical clustering is based on pairwise Spearman correlation. C, Heatmap showing the average scores per patient for various gene modules, including the “muscle lineage” module derived by Danielli and colleagues (top) and for each of the mesenchymal modules (middle). Below the heatmap we show the proportion of converged cells per patient (bottom). Converged cells are defined as having a convergence metric greater than zero (see Materials and Methods). Scores have been scaled and mesenchymal modules are detailed in Supplementary Table S2. Text annotations describe that the convergence metric is calculated as the log ratio of muscle score over nonmuscle mesenchymal score per cell. D, UMAP representation of 119,714 cells from the fetal connective tissue atlas, only cell types represented in the convergence metric (bone cortical, cartilage hypertropic, cartilage lining, tendon, and fibrous) are included in the UMAP. Dots are colored by convergence metric to contextualize the range of metric scores. E, UMAP from (A) but colored by convergence metric in separate facets for each malignant cell group: RMS cells from fusion-negative lethal cases, from fusion-positive cases, or from fusion-negative cases. F, Score landscape showing muscle lineage score against a composite nonmuscle lineage score (cartilage lining, fibrous, tendon, cortical bone, or cartilage hypertrophic), averaged per patient and colored by if the cancer cells come from a patient with fusion-positive RMS (brown), lethal fusion-negative RMS (red), fusion-negative RMS (purple), or Ewing sarcoma (yellow). Honeycomb background represents scores for either muscle (gray) or nonmuscle mesenchymal lineages (blue) from the fetal atlas used to derive the modules. All scores have been scaled. Text annotations describe that the convergence metric is calculated as the log ratio of muscle score over nonmuscle mesenchymal score per cell, and the high-risk transcriptome is derived from a comparison between the “high-risk” cluster (fusion-positive and lethal fusion-negative RMS cells, excluding the fusion-negative alveolar case) and fusion-negative RMS cells (see Materials and Methods).

Evidence of converged cells in fusion-positive and lethal fusion-negative patients across modalities and cohorts. A, UMAP representation of 172,370 nuclei across all patients in the extension cohort. Dots are colored by malignant cell type, and normal cell type clusters are indicated by labels. Includes nuclei from seven fusion-positive patients (brown), 17 fusion-negative (red and purple), two osteosarcomas (light blue), one desmoplastic small round cell tumor (dark blue), and one isolated myofibroma (no malignant nuclei). B, Overview of the “extension” cohort, with patients with RMS grouped according to whether they are fusion-negative but died of their disease (red), fusion-positive (brown), or fusion-negative (purple). The heatmap shows the average scores in RMS nuclei per patient for each of the mesenchymal modules. The row beneath the heatmap indicates whether spatial transcriptomics was performed, and patients that harbor MYOD1-mutant cells are indicated by an asterisk. Scores have been scaled and mesenchymal modules are detailed in Supplementary Table S2. C, Boxplots showing per-nuclei scores for the high-risk module across RMS cells from fusion-positive cases (brown), lethal fusion-negative cases (red), and nonlethal fusion-negative cases (purple). The dots represent the average score per patient, and the asterisks indicate that the patient’s cells are MYOD1-mutant. Patients with fewer than 50 RMS nuclei were removed. D, Bars indicate the proportion of cells per patient that are transcriptionally converged, in which a converged cell is defined as a cell with a convergence metric greater than zero. Bars are colored by whether the patient is fusion-positive (brown), fusion-negative (purple), or fusion-negative and died of their disease (red). Patients who harbor MYOD1-mutant cells are indicated by an asterisk. E and F, Boxplots showing the per-cell scores for either the convergence metric (E) or the high-risk module (F) across two external single-cell (10, 13) and two single-nuclei (10, 11) datasets. Boxplots are colored by whether the sample was from a fusion-negative (light gray) or fusion-positive (brown) patient. The dots represent the average score per patient, and the red asterisks indicate that the patient’s cells are MYOD1-mutant. G, Boxplots showing per-sample scores for the high-risk module across bulk RNA samples from three external datasets (6, 30, 31) and our cohort (“this study”). Boxplots are colored by whether the sample was taken from a fusion-negative patient (light gray), fusion-positive patient (brown), or a patient harboring a MYOD1 mutation (red). Each patient is represented by a dot and P values were calculated using a two-sided Wilcoxon test.

Characterization and functional validation of converged cells. A, Schematic illustrating the experimental workflow for RMS tumoroid perturbation models. Two RMS tumoroid lines (RMS000OYW and RMS444) derived from embryonal, fusion-negative tumors were experimentally induced with the PAX3–FOXO1 fusion gene and subjected to scRNA-seq. Text annotation describes that we define the FOXO1 fusion transcriptome (or module) based on what is upregulated in cells that were induced with the fusion (brown), compared with control fusion-negative RMS cells (light gray). BioRender was used to generate some components of this figure. B, Boxplots showing the convergence metric across fusion-positive (brown) and fusion-negative (light gray) cells from two tumoroid models (see Materials and Methods). C, A scatter plot showing scores for the high-risk module (Supplementary Table S3) against the fusion module (Supplementary Table S5), averaged across RMS cells per patient in the extension cohort, colored by whether the tumor of a patient is fusion-positive (brown), fusion-negative (purple), or fusion-negative and died of their disease (red). Dots are shaped as an asterisk if the patient harbors MYOD1-mutant cells. The high-risk module is as defined in Fig. 2, and the fusion module was derived from the fusion experiments in (A; See Materials and Methods). D, First, a schematic illustrating the analytic workflow for the snATAC-seq data in the extension cohort. We use the gene expression component of the data to obtain differentially expressed genes between converged and diverse cells. We then subset differentially expressed genes to TFs and calculate per-cell scores to assess chromatin availability of the motifs for these TFs. BioRender was used to generate some components of this schematic. The bottom half of the panel is a scatter plot summarizing both the differential gene expression (x-axis) and difference in TF motif availability (y-axis) between converged and diverse cells, where every dot represents a TF. Dots are colored if they are significantly differentially expressed in either converged cells (red) or diverse cells (purple) and left gray if nonsignificant. TFs upregulated in converged cells have a positive log FC. The y-axis shows the average chromVAR score in converged cells subtracted by the average chromVAR score in diverse cells (see Materials and Methods). TF names are colored red if they are annotated as being associated with a neural lineage. E, Boxplots showing the difference in the averaged TF motif accessibility of converged and diverse TFs (i.e., converged TF average − diverse TF average) across normal cells (gray), diverse cells from fusion-negative tumors (purple), and converged cells from fusion-positive (red) and lethal fusion-negative tumors (brown). F, Coverage tracks showing chromatin accessibility for converged nuclei from lethal fusion-negative cases (red), fusion-positive nuclei (brown), diverse fusion-negative nuclei (purple), and nonmalignant nuclei (gray) at the MYOD1 locus. G, A UMAP of 174,077 cells from the developmental atlas of the human fetal spine, colored by broad cell type provided by the authors (left), converged TF score (middle), diverse TF score (right). The TF scores are calculated in scanpy and represent an aggregated score of the TFs defined by the analysis in D. P values were calculated using a two-sided Wilcoxon rank-sum test. A, Created in BioRender. Anderson, N. (2026) https://BioRender.com/h758tn5; D, Created in BioRender. Anderson, N. (2026) https://BioRender.com/aw07wgo.

Prioritization of therapeutic targets in high-risk RMS. A, Flowchart outlines the analytic workflow used to identify potential therapeutic targets from scRNA-seq data. The pipeline integrates transcriptomic profiling, tissue specificity filters, and druggability annotations to systematically triage candidate genes into tiered categories based on predefined criteria. B, Heatmaps display the proportion of cells expressing candidate genes across samples, grouped into three tiers: tier 0, known RMS targets; tier 1, genes that are highly and selectively expressed in RMS tissue, also present in fetal muscle, and predicted to be located on the cell surface; and tier 2, genes satisfying any one of the tier 1 criteria. Within tiers 1 and 2, genes were further prioritized if they overlapped with entries in the Open Targets platform, indicating existing evidence of druggability. Genes are ranked by the proportion of expressing cells within the fusion-negative lethal group, highlighting potential targets enriched in aggressive disease. C, Heatmap illustrates the mean logCPM expression levels of tier 1 and tier 2 candidate genes across a range of normal human tissues from the GTEx database. Expression values were categorized into five discrete bins: extremely low (≤0), very low (>0 to ≤1), low (>1 to ≤3), moderate (>3 to ≤4), and high (>4). Both genes and tissues are hierarchically clustered. Genes exhibiting highly restricted expression across normal tissues form a distinct clade, emphasizing their potential therapeutic selectivity and potential to minimize expected off-target effects. D, IHC validation of candidate cell-surface targets in representative RMS subtypes. Representative IHC staining for CDH15 and FGFR4 in three primary RMS cases, shown alongside myogenin (MYOG) and H&E staining for histologic reference. Cases include one fusion-positive tumor, one lethal fusion-negative tumor, and one nonlethal fusion-negative tumor. These images illustrate the extent and pattern of tumor-cell staining for candidate cell-surface markers across clinically distinct RMS groups.

Cell type annotation of the ‘Discovery’ Cohort.

Detection of fAChR in RMS cell lines and RMS tumour tissue.

High-risk transcriptome scores by risk category.

Fusion-associated signatures do not capture the high-risk transcriptome.

Xenium spatial maps across RMS specimens.