(P 53) CHARACTERIZING THE SPATIAL TRANSCRIPTOMIC LANDSCAPE OF OSTEOSARCOMA FROM DIAGNOSIS TO RELAPSE

Objective: Therapies aimed at targeting the immune microenvironment in osteosarcoma (OSA), similar to other sarcomas, have had limited success, with only a small number of patients showing favorable responses. This highlights gaps in our understanding of the cellular dynamics that influence pro-tumoral infiltration and immune exclusion. We aim at elucidating some of the underlying mechanisms through spatial transcriptomics analysis of an OSA cohort.

Methods: Here, joint analysis of spatial transcriptomics (ST, Visium) data from 26 formalin-fixed paraffin embedded OSA samples at diagnosis (n=9) and relapse (n=17, MAPPYACTS NCT02613962 & OS2006 NCT00470223) from 19 different donors (including 5 diagnosis/relapse pairs) was performed using the CellsFromSpace unbiased reference-free signal deconvolution and analysis workflow. This methodology enabled the definition of the topology and the molecular signatures of the cancerous, stromal and immune components across all samples, and allowed for the phenotyping of individual tumors based on cancer subset composition. Spatial colocalization analysis was performed using Lee’s bivariate spatial association measure of different cell populations, enabling the characterization of tumor-infiltrating immune cells found across samples. Similar ST analysis of Patient-derived xenograft (PDX) samples was performed on fresh-frozen samples. Subsequent separation of human and mouse reads using the Xenome algorithm allowed for a clear dissection of the cancer vs. microenvironment compartments. Preliminary signature validation on larger cohorts was performed on bulk RNAseq samples from the OS2006 and MAPPYACTS cohorts.

Results: Our joint spatial transcriptomics analysis of 26 primary and relapse tumors revealed 64 transcriptomic signatures associated with cancer cells, some of which were shared across multiple samples. These signatures could be consolidated into 18 broad phenotypic categories frequently detectable in various samples, showcasing significant intratumoral heterogeneity within the cancerous compartment. By averaging the composition per sample, we found that samples clustered based on the relative intratumoral abundance of cancer phenotypes, indicating the presence of at least two main differentiation archetypes in OSA: “High intratumoral heterogeneity” and “Undifferentiated.”
Additionally, spatial niche analysis of immune cells within tumors identified immune populations consistently associated with tumors across samples. This analysis revealed a specific myeloid lineage capable of infiltrating tumors in all samples, likely mediated by chemotactic signals linked to cancer differentiation, as suggested by ligand-receptor analysis. To validate these findings, PDX models of OSA were analyzed, confirming the presence of a similar phenotype of tumor-infiltrating myeloid cells and underscoring the relevance of PDX models in OSA research. Furthermore, preliminary analyses querying the transcriptomic signature of infiltrating myeloid cells in larger bulk RNAseq cohorts revealed promising associations with patient outcomes, detectable even at diagnosis.

Conclusion: Our research elucidates patterns of intratumoral phenotypic heterogeneity in OSA and identifies the primary immune cell populations specifically infiltrating OSA tumors. This paves the way for developing novel therapies by leveraging these cellular tropism mechanisms.