Abstract 3893: A multimodal spatial-omics atlas of lung precancer and progression to adenocarcinoma
Authors: Sinjab Ansam , Peng Fuduan , Liu Yunhe , Yang Sujuan , Zhou Tieling , Serrano Alejandra G. , Feng Jiping , Bolanos Lorena Gomez , Han Guangchun , Rosen Daniel Gustavo , Swisher Stephen G. , Spira Avrum , Dubinett Steven M. , Soto Luisa M. Solis , Li Mingyao , Fujimoto Junya , Burks Jared , Wistuba Ignacio I. , Wang Linghua , Kadara Humam 
Published: March 2024

Abstract
Understanding the earliest changes during lung adenocarcinoma (LUAD) development can set the stage for discovery of fertile targets for disease interception, thereby mitigating the dire public health burden of LUAD. We previously identified bulk-level molecular and immunological changes that are enriched in normal-appearing tissue (NAT) in the local niche of human LUAD, as well as those that commence in adenomatous premalignant lesions (aPMLs, LUAD precursors) and that are further enriched in LUADs. Yet, in-depth understanding of the identities, states, and properties of specific cell subsets in NAT and precancer that trigger LUAD remain largely elusive due to inherent roadblocks to sampling and characterizing aPMLs that are at the center of this trajectory. Here, we aimed to map molecular profiles, states, and interactions of cell subsets that underlie initiation in lesion-adjacent NAT, and to determine how features of these cells evolve along the aPML-LUAD spectrum. We analyzed an expanding cohort of archived matching NATs, aPMLs and LUADs, including challenging small samples, from up to 17 patients. Samples were analyzed using combined high-resolution, multi-modal Visium spatial transcriptomics (ST, n=17) and proteomics (n=12), as well as single-cell RNA-sequencing of fixed cells (scFFPE-seq) from consecutive sections of the same tissues from 15 patients. We also studied a subset of samples by high-plex spatial proteomics (COMET) or subcellular spatial gene expression analysis (Xenium). In total, we sequenced more than half a million single cells comprising diverse epithelial, immune, and stromal subsets, which were concordant with lineage clusters identified by ST analysis of the same samples. Loss of alveolar differentiation was a hallmark of tumor cell-enriched areas in aPMLs and LUADs, with a more pronounced effect in the LUADs. Alveolar intermediate cells (AICs), which we have previously shown to be involved in LUAD initiation, were found in lesion-adjacent NAT, and their signature was enriched in matching aPMLs and further in LUADs. In line with these findings, trajectory analysis showed that AICs are derived from normal alveolar type 2 cells and likely progress towards tumor cells. Spatial neighborhood analysis pointed towards strong crosstalk between AICs and macrophages. We also noted heterogeneity in immune infiltration including patterns indicative of progressive features along the NAT-aPML-LUAD continuum. For example, B cell lineages were not only increased in abundance in LUADs relative to aPMLs, but they also mobilized into relatively more mature tertiary lymphoid aggregates/structures. While T regulatory cells aggregated close to LUADs, they were rather scattered in matching aPMLs. Our multimodal and spatial atlas of NAT, nearby aPML and LUAD elucidates the earliest cellular events underlying transition of NAT to LUAD and that could inform of targets for interception of this trajectory.
Citation Format: Ansam Sinjab, Fuduan Peng, Yunhe Liu, Sujuan Yang, Tieling Zhou, Alejandra G. Serrano, Jiping Feng, Lorena Gomez Bolanos, Guangchun Han, Daniel Gustavo Rosen, Stephen G. Swisher, Avrum Spira, Steven M. Dubinett, Luisa M. Solis Soto, Mingyao Li, Junya Fujimoto, Jared Burks, Ignacio I. Wistuba, Linghua Wang, Humam Kadara. A multimodal spatial-omics atlas of lung precancer and progression to adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3893.