Abstract B003: Preliminary machine learning integration of DNA methylation-based tumor immune microenvironment deconvolution with histopathological slides for bladder cancer prognostication

Abstract

Abstract Background: Analyzing the cellular composition of the tumor microenvironment (TME) is crucial for bladder cancer (BCa) prognosis but is hampered by technical and financial limitations of immunofluorescence, flow cytometry and scRNASeq, primarily due to cell disaggregation challenges, antibody selection and cost/throughput constraints. A DNA methylation-based (DNAm) technique, HiTIMED, was introduced, enabling precise differentiation of 17 unique cell types within solid tumors– including those related to tumor growth, angiogenesis, and immune responses– enhancing our ability to investigate the complex interactions within TME tied to exposures, patient outcomes, and treatment efficacy. Automated morphological identification of cellular signatures on histopathological slides has been fraught with challenges. The potential to identify signatures at higher resolution, beyond immune cells, could improve disease management and application of Whole Slide Imaging (WSI) in clinical research. Our research explores direct estimation of HiTIMED-inferred cellular distributions from WSI and benefits of multimodal integration of WSI with HiTIMED proportions for BCa prognostication. Methods: BCa WSI and Illumina Methylation 450k DNAm data for BCa were obtained from TCGA (MIBC, n=456) and Dartmouth-Hitchcock (NMIBC, n=136), divided into training, validation, and testing sets. HiTIMED deconvolved DNAm into 17 cellular components. Survival, stage, and demographics were incorporated. Graph convolutional networks (GCNs) predicted HiTIMED-derived cellular proportions from WSI (WSI2HiTIMED). Logistic regression and Cox Proportional Hazards (CoxPH) were used to compare how standardized cellular proportions estimated by HiTIMED and WSI2HiTIMED each correlate with advanced T-stage (T>2) and survival with age and sex adjustment. We combined a GCN modeling WSI with a neural network using HiTIMED proportions to predict 10-year survival, adjusting for age and sex with CoxPH. This integrated approach was benchmarked against CoxPH models using only imaging or cellular data. Results: HiTIMED and WSI2HiTIMED demonstrated consistent associations with advanced T-stage, respectively: 1) endothelial cells (OR=2.70, p=0.0095; OR=2.24, p=0.033), 2) epithelial cells (OR=0.02, p=0.0005; OR=0.51, p=0.112), 3) lymphocytes (OR=2.04, p=0.0635; OR=2.13, p=0.041), 4) stromal cells (OR=4.30, p=0.001; OR=2.32, p=0.0235), and 5) tumor cells (OR=0.41, p=0.0165; OR=0.42, p=0.020). The prognostic value for survival based on endothelial cell estimates from HiTIMED and WSI2HiTIMED was comparable, with p-values of 0.006 and 0.009, respectively. The C-index for BCa survival prediction improved from 0.730 using WSI GCN-derived hazards to 0.766 with HiTIMED proportions. Conclusion: DNAm offers a cost-effective method for improving cell type estimation from WSI and provides complementary information for BCa analysis. Future efforts will focus on enhancing precision across additional cell types with larger cohorts and exploring synergy with pathologist annotations and spatial transcriptomics. Citation Format: Zarif L. Azher, Ze Zhang, Brock C. Christensen, Lucas A. Salas, Margaret R. Karagas, Louis J. Vaickus, Hideki Fuyura, Keluo Yao, Joshua J. Levy. Preliminary machine learning integration of DNA methylation-based tumor immune microenvironment deconvolution with histopathological slides for bladder cancer prognostication [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr B003.