The N⁶-methyladenosine (m⁶A) RNA modification is an important regulator of gene expression. m⁶A is deposited by a methyltransferase complex that includes methyltransferase-like 3 (METTL3) and methyltransferase-like 14 (METTL14). High levels of METTL3/METTL14 drive the growth of many types of adult cancer, and METTL3/METTL14 inhibitors are emerging as new anticancer agents. However, little is known about the m⁶A epitranscriptome or the role of the METTL3/METTL14 complex in neuroblastoma, a common pediatric cancer. Here, we show that METTL3 knockdown or pharmacologic inhibition with the small molecule STM2457 leads to reduced neuroblastoma cell proliferation and increased differentiation. These changes in neuroblastoma phenotype are associated with decreased m⁶A deposition on transcripts involved in nervous system development and neuronal differentiation, with increased stability of target mRNAs. In preclinical studies, STM2457 treatment suppresses the growth of neuroblastoma tumors in vivo. Together, these results support the potential of METTL3/METTL14 complex inhibition as a therapeutic strategy against neuroblastoma.

A deep learning model using attention-based multiple instance learning (aMIL) and self-supervised learning (SSL) was developed to perform pathologic classification of neuroblastic tumors and assess MYCN-amplification status using H&E-stained whole slide images from the largest reported cohort to date. The model showed promising performance in identifying diagnostic category, grade, mitosis-karyorrhexis index (MKI), and MYCN-amplification with validation on an external test dataset, suggesting potential for AI-assisted neuroblastoma classification.

Purpose: The importance of cellular context to the synergy of DNA damage response (DDR)-targeted agents is important for tumors with mutations in DDR pathways, but less well-established for tumors driven by oncogenic transcription factors. In this study, we exploit the widespread transcriptional dysregulation of the EWS-FLI1 transcription factor to identify an effective DDR-targeted combination therapy for Ewing sarcoma. Experimental Design: We used matrix drug screening to evaluate synergy between a DNA-PK inhibitor (M9831) or an ATR inhibitor (berzosertib) and chemotherapy. The combination of berzosertib and cisplatin was selected for broad synergy, mechanistically evaluated for Ewing sarcoma selectivity, and optimized for in vivo schedule. Results: Berzosertib combined with cisplatin demonstrates profound synergy in multiple Ewing sarcoma cell lines at clinically achievable concentrations. The synergy is due to loss of expression of the ATR downstream target CHEK1, loss of cell-cycle check-points, and mitotic catastrophe. Consistent with the goals of the project, EWS-FLI1 drives the expression of CHEK1 and five other ATR pathway members. The loss of CHEK1 expression is not due to transcriptional repression and instead caused by degradation coupled with suppression of protein translation. The profound synergy is realized in vivo with a novel optimized schedule of this combination in subsets of Ewing sarcoma models, leading to durable complete responses in 50% of animals bearing two different Ewing sarcoma xenografts. Conclusions: These data exploit EWS-FLI1 driven alterations in cell context to broaden the therapeutic window of berzosertib and cisplatin to establish a promising combination therapy and a novel in vivo schedule. See related commentary by Ohmura and Grünewald, p. 3358

Introduction: Tumor histology at diagnosis is used in conjunction with other prognostic features to risk stratify patients with neuroblastoma and to assign therapy regimens. In patients with high‐risk disease, adjustment of therapy is tailored to treatment response, largely based on disease imaging following induction chemotherapy and resection of the primary tumor. The goals of this study were to (i) quantify changes in histologic features in the primary tumor between diagnosis and resection, and (ii) assess the prognostic capability of such alterations. Methods: Tumor histology from paired samples at diagnosis and resection was evaluated from 94 patients with high‐risk neuroblastoma enrolled in Children's Oncology Group (COG) trials from 2001 to 2013. Presence of Schwannian stroma, neuropil, degree of differentiation, mitosis karyorrhexis index (MKI), necrosis, and percentage of neuroblastic cells were annotated. Changes in tumor histology between diagnosis and resection were analyzed for association with overall survival (OS) and progression‐free survival (PFS). Results: Significant changes between diagnosis and resection were observed in all histologic parameters ( p < 0.01), suggesting a more phenotypically differentiated tumor following induction therapy. A higher percentage of intermediate‐high MKI in tumor cells at diagnosis was associated with a lower PFS and OS ( p < 0.05). No other histologic factor was associated with survival at diagnosis or resection. The tumor percentage of intermediate‐high MKI decreased by a mean of 75% from diagnosis to resection ( p < 0.0001). The shift from intermediate/high MKI at diagnosis to low MKI at resection had a PFS hazard ratio (HR) of 2.1 (95% CI: 0.9, 4.9; p = 0.0908) and OS HR = 2.3 (95% CI: 0.9, 5.9; p = 0.0773). Conclusion: Our findings suggest that primary neuroblastoma tumors undergo a significant morphologic shift following induction chemotherapy to a differentiated, less mitotically active phenotype. However, the alterations are not prognostic of patient outcome either at resection alone, or when the change from diagnosis to resection is considered.

A deep learning model using attention-based multiple instance learning (aMIL) and self-supervised learning (SSL) was developed to perform pathologic classification of neuroblastic tumors and assess MYCN-amplification status using H&E-stained whole slide digital images. The model demonstrated strong performance in identifying diagnostic category, grade, mitosis-karyorrhexis index (MKI), and MYCN-amplification on an external test dataset. This AI-based approach establishes a valuable tool for automating diagnosis and precise classification of neuroblastoma tumors.