Abstract 759: Spatial transcriptomics identifies unique tumor and microenvironment pathomic programs that are associated with the lung premalignancy and adenocarcinoma continuum

作者：Yibo Dai, Fuduan Peng, Ansam Sinjab, Sujuan Yang, Minyue Chen, Warapen Treekitkarnmongkol, Lorena I. Gomez Bolanos, Tieling Zhou, Alejandra G. Serrano, Jianlong Liao, Guangsheng Pei, Yunhe Liu, Yang Liu, Jiahui Jiang, Kyung Serk Cho, Yanshuo Chu, Kai Yu, Ruiping Wang, Jiping Feng, Zahraa Rahal, Guangchun Han, Naoe Jimbo, Takuo Hayashi, Satsuki Kishikawa, Kazuya Takamochi, Akshay Basi, Avrum Spira, Steven M. Dubinett, Tomokazu Itoh, Takashi Yao, Kenji Suzuki, Luisa M. Solis, Stephen G. Swisher, Mingyao Li, Junya Fujimoto, Ignacio I. Wistuba, Jared Burks, Kadir C. Akdemir, Hind Refai, Katy Rezvani, Jeffrey N. Myers, Humam Kadara, Linghua Wang · 发表于：Cancer Research · 年份：2025 · DOI：10.1158/1538-7445.am2025-759 · 被引用次数：1 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Ferroptosis and cancer prognosis、Lung Cancer Diagnosis and Treatment

Abstract Background: Lung adenocarcinoma (LUAD) is one of the most prevalent and lethal cancer types worldwide. However, our understanding of the transition from normal-appearing tissue (NAT) to adenomatous lung premalignant lesions (aPMLs) and LUADs, particularly within a spatial context, remains limited. This study aims to address this gap by systemically analyzing the pathologic continuum of NAT > aPML > LUAD using spatial transcriptomics (ST). Methods: High-resolution spatial profiling was conducted on 56 samples from 25 patients with paired aPMLs and LUADs using the Visium ST platform. Non-negative matrix factorization was conducted to identify transcriptional programs for each sample, following clustering analysis to define consensus metaprograms across the cohort. Additionally, spatial molecular imaging was performed on an expanded cohort of 188 cores arranged into eight tissue microarrays using the Xenium in situ platform with a customized lung cancer gene panel to establish a single-cell spatial atlas of the disease continuum and systemically investigate spatial organization, cellular neighborhoods and interactions. Results: Eight distinct metaprograms (MP1∼8) were identified, distinguishing stromal (MP2), myeloid (MP4), lymphoid (MP6), and epithelial (MP3, MP5) compartments. Additionally, metaprograms were identified for the lung capillary bed (MP7), stressed cellular state (MP8), and mosaic cellular patterns (MP1). These metaprograms correlated strong...