Abstract 2428: Artificial intelligence for predicting spatial proteomics using high-plex digital spatial profiling in carcinomas

Abstract Background: High-plex digital spatial profiling (DSP) enables mapping protein expression onto tissue context to study heterogeneity of tumor microenvironment (TME). However, current techniques rely on manual annotations to select representative regions for profiling, introducing subjectivity and uncertainty. We present a deep-learning approach to predict spatial proteomics associated with immune response and proliferation in carcinomas from tissue space to high-throughput protein expression analysis across the full sections while preserving spatial context. Methodology: Our study utilized a diverse dataset of 62 carcinomas and adenocarcinomas cases, with major types including Papillary Urothelial Carcinoma (25) and Colorectal Adenocarcinoma (5) among others, totaling 721 regions of interest (ROIs). Using morphology markers (panCK, CD45) for mIF images and 12 proteins for profiling, we conducted a two-phase study. First, YOLOv8 identified epithelial (panCK+) and immune cells (CD45+) from mIF images and correlated their abundance with protein expression. Second, we developed a multi-instance learning framework using Swin Transformer to predict protein expression from mIF images, processing 256×256 pixel adjacent patches (average 1, 947 per slide, total 120, 729) with attention mechanisms. The dataset was split into training (47), validation (9), and test (6) sets. Results: We observed strong correlations between cell abundance from mIF images and protein expression, (panCK: rho=0.624, p<0.001; CD45: rho=0.676, p<0.001), validating the effectiveness of the YOLOv8 model in cell recognition. The multi-instance model demonstrated promising prediction performance, marginally improving the correlations for panCK (rho=0.701, p<0.001) and CD45 (rho=0.699, p<0.001) when compared with YOLOv8. For markers included in the protein profiling DSP panel but not for the morphology, our model captured the signals for immune checkpoint and proliferation markers: PD-L1 (rho=0.741, p<0.001), Ki-67 (rho=0.730, p<0.001). Moderate correlations were observed for stromal and immune cell markers (FAP-alpha: rho=0.569, p<0.001; CD163: rho=0.465, p=1.45e-03; CD3: rho=0.409, p=2.02e-02; CD4: rho=0.381, p=7.46e-03; FOXP3: rho=0.344, p=3.24e-02) while a weak correlation for CD8 (rho=0.210, p=4.83e-02). These findings suggest that cellular morphology and spatially encoded information in the TME can be learned by our model to predict immune checkpoint expression and proliferation. Conclusion: Our integration of mIF images with spatial proteomics enables high-throughput protein expression prediction from limited markers, potentially extending to H&E images. This could reduce sequential biopsies, enabling real-time treatment monitoring. The model's PD-L1 prediction capabilities provide insights into tumor-immune interactions to guide immunotherapy decisions. Citation Format: Yasin Shokrollahi, Tanishq Gautam, Alejandra Serrano, Simon P. Castillo, Pingjun Chen, Karina Pinao, Maria Esther Salvatierra, B. Leticia Rodriguez, Patient Mosaic Team, Luisa M. Solis Soto, Yinyin Yuan, Xiaoxi Pan. Artificial intelligence for predicting spatial proteomics using high-plex digital spatial profiling in carcinomas [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 2428.