Histone 3 lysine27-to-methionine (H3-K27M) mutations most frequently occur in diffuse midline gliomas (DMGs) of the childhood pons but are also increasingly recognized in adults. Their potential heterogeneity at different ages and midline locations is vastly understudied. Here, through dissecting the single-cell transcriptomic, epigenomic and spatial architectures of a comprehensive cohort of patient H3-K27M DMGs, we delineate how age and anatomical location shape glioma cell-intrinsic and -extrinsic features in light of the shared driver mutation. We show that stem-like oligodendroglial precursor-like cells, present across all clinico-anatomical groups, display varying levels of maturation dependent on location. We reveal a previously underappreciated relationship between mesenchymal cancer cell states and age, linked to age-dependent differences in the immune microenvironment. Further, we resolve the spatial organization of H3-K27M DMG cell populations and identify a mitotic oligodendroglial-lineage niche. Collectively, our study provides a powerful framework for rational modeling and therapeutic interventions.

Diffuse Midline Glioma (DMG), particularly the H3-K27-altered, is a highly aggressivetumor with limited therapeutic options. Through spatial transcriptomics, we identified a novel NSC-like malignant phenotype that is characterized by the upregulation of key stem andprogenitor cell markers, including SOX2, OLIG1/OLIG2, TCF12, EPN2, MFGE8,FAM181B, PHLDA1, and PDGFRA. The surrounding tumor microenvironment (TME) resembled neurogenic niches, suggesting interactions fostering tumor plasticity.

Triple-negative breast cancer (TNBC), an aggressive and highly heterogeneous subtype of breast cancer, poses significant challenges for treatment due to its molecular diversity and resistance to standard therapies. Accounting for 10-20% of all breast cancer cases, TNBC lacks specific biological markers, making it difficult to classify and treat effectively. Traditional approaches based on bulk RNA sequencing obscure intratumoral heterogeneity and fail to capture distinct cellular states within tumors. In this study, we constructed a comprehensive single-cell transcriptomic map of TNBC by analyzing a cohort of published TNBC patient datasets, identifying nine transcriptomic states, or metaprograms, which capture the core behaviors of TNBC cells, including cancer stem cell properties, epithelial-to-mesenchymal transition (EMT), immune modulation, metabolic adaptation, and vasculogenic mimicry. We observed that these metaprograms are variably expressed within and across patient tumors, underscoring the complexity of TNBC. By integrating TNBC-specific metaprograms with established breast cancer subtypes, we found significant prognostic associations, with specific metaprograms correlating with poor survival outcomes. This study highlights the need for single-cell approaches to uncover TNBC’s molecular heterogeneity and suggests that metaprogram-based classification could facilitate more precise therapeutic interventions. Our findings provide a foundation for developing multi-omic strategies aimed at improving TNBC patient outcomes through personalized medicine.

Glioblastoma, isocitrate dehydrogenase (IDH)-wildtype (hereafter, GB), is an aggressivebrain malignancy associated with a dismal prognosis and poor quality of life. Single-cellRNA sequencing has aided in grasping the complexity of the cell states and dynamic changesin GB. Large-scale data integration can help to uncover unexplored tumor pathobiology.Here, we resolved the composition of the tumor milieu and created a cellular map of GB(‘GBmap’), a curated resource that harmonizes 26 datasets, gathering 240 patients andspanning over 1.1 million cells. We showcase the applications of our resource for referencemapping, transfer learning, and biological discoveries. Reconstructing the tumor architectureusing spatially resolved transcriptomics unveiled consistent niches across patients and theirorganizational gradient. Our findings shed light on specific crosstalk within GB niches,including the intricate proangiogenic signaling. The GBmap represents a framework thatallows the streamlined integration and interpretation of new data and provides a platform forexploratory analysis, hypothesis generation, and testing.

Glioblastoma (GB), the most aggressive primary brain tumor, poses significant challenges due to its heterogeneity and resistance to current treatment modalities. This study investigates GB dynamics through a patient-derived organoid model to map cellular behaviors, including infiltration, migration, and transcriptional states. From a large cohort of patient-derived scRNAseq data, we identified 30 metaprograms encompassing key biological processes across GB samples using consensus Non-negative Matrix Factorization (cNMF). These metaprograms revealed distinct expression patterns and potential regulatory overlaps within malignant subpopulations, emphasizing their roles in GB development. By integrating temporal assessments, we traced metaprogram shifts in  our model, highlighting the plasticity of GB cells within the organoid environment. Comparisons with cortical and fetal brain studies and cancer hallmark signatures contextualized the findings, showing shared developmental and stress response features. This comprehensive approach underscores the potential of GB organoids for studying tumor behavior and improving the translation of therapeutic strategies.