Abstract 4998: Deriving patient similarity networks in myeloid neoplasms using multi-modal representation learning

Abstract Myeloid neoplasms (MNs), such as Myelodysplastic Syndromes (MDS) and Acute Myeloid Leukemia (AML), are clinically overlapping hematologic malignancies. Their assessment includes the evaluation of bone marrow (BM) morphology, immunophenotyping by flow cytometry, cytogenetics, and gene mutation profiling. Established diagnostic criteria between the MN subtypes are primarily guided by empirical unimodal thresholds (i.e. 20% blasts differentiating MDS from AML) that may not always reflect the underlying disease biology. This study integrates different data modalities and produces patient-specific multi-modal views aiming to uncover associations between molecular subtypes and clinical phenotypes at the cellular and morphological level, show distinct biological and clinically relevant subgroups in MNs, and help inform future guidelines for disease classification. The study cohort consists of 523 MSKCC patients across the spectrum of MNs. The dataset includes mutation and cytogenetic profiles, raw hematoxylin and eosin (H&E) BM whole slide images (WSIs), raw single-cell flow cytometry measurements, and differential cell type counts. Data for all modalities are present for 269 patients. The flow cytometry and imaging data views were the products of in-house computational workflows. In flow cytometry, single cells were grouped in clusters by the Leiden algorithm. Then, the cell abundance distributions across these clusters were used to form patient-level representations. Our imaging framework first identified the cell-rich regions of the WSIs by training a tile-level vision transformer (ROCAUC: 0.95). After obtaining the encodings of the cell-rich regions from the QuiltNet foundation model (Ikezogwo et al. NeurIPS 2023), we trained a graph neural network (SlideGraph+, Lu et al. Medical Image Analysis 2022) to predict diagnostic labels (ROCAUC: 0.79), and we subsequently retrieved the WSI deep embeddings. Different combinations of the unimodal views (genomics, flow cytometry, WSIs, differential cell types) were integrated into multi-modal embeddings through the Multiple Similarity Network Embedding (Xu et al. Methods 2021) method. Low dimensional projections and transformation of these embeddings to patient similarity graphs show the formation of homogeneous subgroups driven by somatic mutation status (i.e. TP53 mutated MNs), clusters defined by contributions of features across modalities, and the presence of out-of-group patients indicative of disease heterogeneity. In summary, this work produces multi-modal patient embeddings in MNs and constructs patient networks indicative of both inter-patient similarity and heterogeneity. Characterizing the data-derived representations of disease profiles and the underlying inter-modality relationships will be critical for the future definition of diagnostic criteria that incorporate multi-modal measurements. Citation Format: Georgios Asimomitis, Jake Sauter, Darin Moore, Himanshu Bhurtel, Katherine Lopez, Armaan Kohli, Kevin Boehm, Christopher Fong, Arfath Pasha, Luke Geneslaw, Anika Begum, Tom Pollard, Jacob Glass, Gregory M. Goldgof, Nicholas Barasch, Ahmet Dogan, Mikhail Roshal, Elli Papaemmanuil. Deriving patient similarity networks in myeloid neoplasms using multi-modal representation learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4998.