Awarded in 2026
Home Department: Biomedical Data Sciences
Faculty Advisor: Sylvia Plevritis (Biomedical Data Science and Radiology)

Title: Integrative 3D Spatial Mapping of Lung Adenocarcinoma to Explore “Acinar Clefting” as an Immune Evasion Mechanism

Abstract:
Lung adenocarcinoma (LUAD) often shows an acinar-predominant pattern, and prognosis appears influenced by tumor-extrinsic factors in the microenvironment rather than mutations alone; we hypothesize a novel 3D morphogenetic process called “acinar clefting,” in which myofibroblasts reshape gland architecture and macrophages relocate to create macrophage-free progenitor ducts that invade surrounding tissue while evading immune attack. To test this, we will use a non-destructive 3D pathology workflow to preserve native tissue and map the true ductal branching, cleft points, and ECM directionality, identify intact 2D planes around cleavage sites, and perform serial spatial profiling at single-cell resolution with multimodal omics (spatial transcriptomics and spatial proteomics) anchored to histology. By integrating these data with AI to build a 3D atlas of acinar clefting, we aim to reveal the cellular conversations and signaling networks driving invasion and immune evasion, identify stromal and immune drivers as potential therapeutic targets, and establish biomarkers that could redefine gland morphogenesis in cancer and guide novel treatment strategies.