Neuroblastoma is the most common extracranial solid tumor of childhood, and high-risk disease remains difficult to treat. Increasing evidence suggests that interactions among tumor cells, immune populations, and stromal elements influence progression and therapeutic response. However, the spatial organization and subtype diversity of these populations within intact tumors remain poorly defined.
\n\n\n\nWe applied seqFISH, a spatial transcriptomic platform, to eight regions of interest (ROIs) from fresh-frozen tumors of seven patients using a custom 2,514-gene panel, and to three ROIs from two tumors using a commercial 516-gene immuno-oncology panel. Tumor specimens represented a range of clinical risk groups and included primary, metastatic, MYCN-amplified, and post-therapy states. All samples were analyzed and integrated using the scVI Python package, a deep generative model that extracts latent embeddings from high-dimensional data while mitigating batch effects and preserving biologically relevant structure. To assign cell identities, we developed a novel joint-analysis algorithm that integrates seqFISH data with the NBAtlas single-cell RNA-seq reference (362,991 cells across 61 patients), enabling initial mapping of major neuroblastoma, immune, and stromal lineages, followed by refinement through spatial context, proximity relationships, and canonical marker gene expression. CAFs, TAMs, and T-cell populations were re-integrated separately to resolve subtype structure, and spatial statistics methods were used to identify cell-type associations occurring more frequently than expected by chance.
\n\n\n\nWe profiled over 450,000 spatially resolved cells and identified major cell types with high-confidence NBAtlas-guided assignments. Neuroblastoma tumor cells displayed proliferative signatures associated with clinical risk, mirroring patterns observed in NBAtlas. We resolved diverse stromal states - including vascular, inflammatory, interferon-stimulated, myofibroblastic, and tumor-like CAFs - and distinguished M1- and M2-like TAM subsets in situ. Spatial analyses revealed conserved neighborhoods, including enrichment of CD4+ naïve/central-memory T cells adjacent to inflammatory CAFs and strong co-localization between vascular CAFs and endothelial cells.
\n\n\n\nBy integrating seqFISH with a large neuroblastoma single-cell reference, we generate a detailed spatial map of the neuroblastoma tumor microenvironment. This combined approach enables refined identification of cellular subtypes and reveals reproducible microenvironmental structures that may influence tumor behavior and therapeutic vulnerability. Ongoing efforts include expanding sample size, incorporating spatial copy-number analysis, and applying this framework to additional pediatric solid tumors.
", "doi": "10.1158/1538-7445.am2026-3964", "issn": "0008-5472", "publisher": "American Association for Cancer Research (AACR)", "publication": "Cancer Research", "publication_date": "2026-04-03", "series_number": "7_Supplement", "volume": "86", "issue": "7_Supplement", "pages": "3964-3964" }, { "id": "authors:ykx5n-cc918", "collection": "authors", "collection_id": "ykx5n-cc918", "cite_using_url": "https://authors.library.caltech.edu/records/ykx5n-cc918", "type": "conference_item", "title": "Abstract 3959: Novel methodology to explore glioma malignant transformation with spatial multi-omics", "author": [ { "family_name": "Polonsky", "given_name": "Michal", "orcid": "0000-0003-3871-460X", "clpid": "Polonsky-Michal" }, { "family_name": "Fox", "given_name": "Jonathan", "orcid": "0000-0001-8262-2126", "clpid": "Fox-Jonathan-J" }, { "family_name": "Shah", "given_name": "Sheel" }, { "family_name": "Hadas", "given_name": "Noa", "clpid": "Hadas-Noa" }, { "family_name": "Everson", "given_name": "Richard" }, { "family_name": "Cai", "given_name": "Long", "orcid": "0000-0002-7154-5361", "clpid": "Cai-Long" } ], "abstract": "Low-Grade Gliomas (LGG) generally have an indolent course and good prognosis after maximal safe resection; however, these tumors can progress into high grade gliomas through a poorly understood process of Malignant Transformation (MT). We sought to identify molecular drivers and cellular interactions predictive of MT, with the aim of informing early detection and providing new treatment targets. We developed a novel spatial multi-omics approach termed seqFISH+ which allows us to quantify the transcriptional states and DNA profiles of single cells within patient biopsies. With this approach, clones of cancer cells can be identified by shared DNA profiles, and matched with their transcriptional profiles and cellular interactions within the tumor microenvironments. We applied our experimental methodology to biopsies of 19 LGG patients as well as three Glioblastoma patients and compared the transcriptomic and genomic landscape of LGG patients which underwent MT to those that did not. We used a tailored gene panel to measure expression of 1150 genes and identified 12 cell types within the tumor biopsies encompassing malignant cell subtypes, immune cells and normal stromal cells. In addition to transcriptomic data, our novel pipeline allowed us to quantify hundreds of DNA loci within the same cells encompassing the entire genome at 3.3Mb resolution. Our final data set contained transcriptomic data of >600k single cells with matched DNA data for >200k cells. With our transcriptomic data we were able to identify known as well as novel gene markers associated with malignancy of LGG tumors. Our DNA data identified known chromosomal alterations such at 1p/19q deletion within malignant cells. As the location of the cells is left intact, we can now probe the spatial organization of the tumor-microenvironment and identify spatial signatures correlating with MT. DNA information will be used to identify individual cancer clones which contribute to progression. This information will enable us to identify clinically relevant molecular events and cellular interactions, which can be used as biomarkers for progression. With this highly multiplexed data we are constructing a comprehensive dictionary of cell intrinsic changes coupled with changes in the microenvironment to elucidate drivers of the MT process.
", "doi": "10.1158/1538-7445.am2026-3959", "issn": "0008-5472", "publisher": "American Association for Cancer Research (AACR)", "publication": "Cancer Research", "publication_date": "2026-04-03", "series_number": "7_Supplement", "volume": "86", "issue": "7_Supplement", "pages": "3959-3959" } ]