Background/Objectives: Pediatric cancers are disorders of dysregulated development driven largely by genomic and epigenetic alterations. Precisely modeling these developmental differences is essential for understanding the unique biology of childhood cancers. Patient-derived organoids (PDOs) offer a powerful in vitro platform that recapitulates tumor heterogeneity, plasticity, microenvironment (including immune cells) and disease-relevant features. Methods: Here, we describe a step-by-step protocol for the establishment of PDOs from cells derived from pediatric brain tumors and extracranial solid tumor biopsies and bone marrow aspirates, including tumor processing, organoid culture/subculture, and cryopreservation. Results: Furthermore, we present the use of PDOs for further experimental analysis such as fluorescence imaging, Western blotting, flow cytometry, and immunohistochemistry (IHC) to investigate the underlying pathophysiology of tumorigenesis. Conclusions: Expanding the application of organoids to childhood malignancies holds exceptional promise for elucidating pediatric tumor biology and advancing therapeutic strategies, representing the long-overdue convergence of technology and clinical need.

Single-cell multiome assays enable direct measurement of chromatin accessibility and gene expression within the same cell. Still, most experimental designs remain constrained to two (and, less commonly, three) modalities per cell. This limitation motivates computational models that can predict unmeasured layers and, simultaneously, help dissect how cis-regulatory accessibility relates to transcription at gene resolution. Existing cross-modal methods often prioritize latent alignment or modality reconstruction, making it difficult to isolate the impact of model inductive bias under a shared cis-regulatory feature definition. We present SPEAR, a configuration-driven framework for gene-centric egression of single-cell gene expression from chromatin accessibility sing a fixed transcription-start site-centered representation shared cross model families. Here we show that, under identical features, splits, and valuation, model performance stratifies reproducibly across two multiome systems (mouse embryonic development and human hemogenic endothelium), with transformer encoders achieving the strongest mean test correlations (0.546 and 0.470, respectively). Per-gene performance distributions reveal substantial heterogeneity in predictability, indicating that accessibility driven signal is concentrated in subset of genes across contexts. Shapley value-based feature attribution further localizes predictive signal to promoter-proximal bins, with feature importance decaying with distance from the transcription start site, supporting a promoter-centered regime of cis-regulatory control within the modeled window. Together, these results provide a controlled comparison of inductive biases for chromatin-to-expression prediction and deliver analysis-ready outputs for gene-level interpretation. SPEAR is open source and publicly available for use at https://github.com/UzunLab/SPEAR.