Abstract P08: INTERGRATING CELL SHAPE AND SPATIAL EXPRESSION IDENTIFIES MORPHOGENOMICS STATES IN LEUKEMIA

Abstract Spatial omics provides three types of information: location, gene expression, and morphology. While current algorithms typically integrate gene expression with location, they often overlook morphology. Chronic Myelomonocytic Leukemia (CMML) is a rare blood cancer characterized by sustained peripheral blood monocytosis, bone marrow dysplasia, and overlapping features of both myelodysplastic syndromes (MDS) and myeloproliferative neoplasms (MPN). A higher level of monoblasts, which are an immature monocytes with round nuclei, is a known risk factor in CMML, but the underlying biology and markers remain unclear. In this study, we developed a novel framework that integrates gene expression and morphology to investigate morphogenomic phenotypes in 12 CMML patients and 8 healthy controls. Recognizing that most existing spatial omics workflows are designed for adherent cells and are unsuitable for suspension cells like bone marrow mononuclear cells (BMMC), we first developed a protocol for immobilizing suspension cells. This was followed by MERFISH spatial RNA analysis of 492 genes across approximately 25, 000 cells in a 16 mm2 region of interest over 34 hours. We then introduced a Morphology and Expression Data Optimal Clustering (MEDOC) algorithm, which uses adaptive Fourier-domain alignment and graph clustering to analyze cell shapes. In healthy BMMC, MEDOC analysis identified genes correlated with cell shape, findings that were substantiated by single-cell RNA sequencing data, confirming their association with nuclear shape. Leveraging this shape marker, we successfully identified a promonocyte population, a monoblast subtype that was previously difficult to distinguish. Furthermore, MEDOC analysis in CMML patients revealed two distinct nuclear shapes in mature leukemic monocytes: one resembling a normal kidney bean shape and another characterized by dysmorphic features. Thus, our framework offers a powerful tool to uncover the underlying biology of clinically relevant malignant cell morphologies and to enhance CMML patient stratification. Citation Format: Jagadish Sankaran, Ignasius Joanito, Giovani Claresta Wijaya, Ziyue Chen, Joseph Lee, Vaidehi Krishnan, Than Hein, Shyam Prabhakar. INTERGRATING CELL SHAPE AND SPATIAL EXPRESSION IDENTIFIES MORPHOGENOMICS STATES IN LEUKEMIA abstract. In: Proceedings of Frontiers in Cancer Science 2024; 2024 Nov 13-15; Singapore. Philadelphia (PA): AACR; Cancer Res 2025;85 (15Suppl): Abstract nr P08.