A Spatial Proteomic Atlas of Tertiary Lymphoid Structures in Non-Small Cell Lung Cancer Identifies a Novel Predictive Class of Lymphoid Aggregates

Tyler Risom(Acentech (United States)), Raj Jesudason(Acentech (United States)), Evan Liu, Andrew Hill(Rancho BioSciences (United States)), Niha Beig, Conrad Foo(Acentech (United States)), Ouida Liu(Acentech (United States)), Eloisa Fuentes(Acentech (United States)), Lisa Tai(Acentech (United States)), Kedar Prasad(Acentech (United States)), Jennifer Giltnane(Acentech (United States)), Robert J. Johnston(Gene Therapy Laboratory), Lisa McGinnis(Acentech (United States))
bioRxiv (Cold Spring Harbor Laboratory)
May 18, 2026
Cited by 0Open Access
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Abstract

Abstract Tertiary lymphoid structures (TLS) predict benefit from immune checkpoint inhibitors (CPIs), yet mature, germinal-center-rich TLS are infrequent in solid tumors by histological review. Here, using 38-plex MIBI spatial proteomics across 165 lymphoid structures from 14 NSCLC resections, we establish a continuum of TLS maturity using high dimensional compositional, spatial, and molecular features. We demonstrate that histologically-defined lymphoid aggregates (LA) comprise a heterogeneous class of structures, which span this continuum of maturity. We identify a subset of lymphoid aggregates that harbor follicular dendritic cell networks, T follicular helper cells, and activated B cell states characteristic of mature TLS, yet are not readily distinguished from other LA structures in our histological review. We developed a novel digital pathology classifier to identify mature LAs in CPI trials, and demonstrate in a retrospective analysis of Atezolizumab in advanced NSCLC that the inclusion of mature LAs greatly expands the biomarker-eligible population while maintaining strong predicted benefit. Together, these data redefine the biological spectrum of tumor-associated lymphoid aggregates and provide a framework for implementing maturity-informed TLS biomarker strategies.


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