Abstract 1489: STniche: An approach to identify functional niches in the tumor microenvironment from spatial transcriptomics

Abstract Background: Spatial transcriptomics preserves spatial context in the tumor microenvironment (TME), enabling precise mapping of cellular interactions and immune architecture. However, translating these data into functional niche maps remains difficult. We developed STniche, an integrated framework that identifies spatially coherent and biologically interpretable functional niches that link local transcriptomic activity to tissue organization. Methods: STniche assigns functional phenotypes to spots or cells using pathway signatures, incorporates spatial dependency between neighboring cells with local Moran’s I, and detects spatial niches via Gaussian model-based clustering. The optimal number of niches is selected by BIC. We applied STniche to the Meylan et al. (2022) clear cell renal cell carcinoma (ccRCC) dataset containing pathology-annotated tertiary lymphoid structures (TLS). Across ten tumor sections, we used a curated TLS meta-pathway integrating five coordinated programs—chemokine signaling, lymphotoxin axis, antigen presentation, germinal-center B-cell activation, and dendritic-cell maturation—to capture core molecular processes of TLS biology. STniche-identified TLS niches were compared with pathology annotations. Results: STniche was able to identify spatially coherent functional immune niches corresponding to TLS. TLS meta-pathway-defined clusters showed strong agreement with pathologist annotations, with mean concordance of 0.39 (range 0.28-0.56) and mean relative symmetry of 0.64 (range 0.36-0.96). On average, ∼42% of STniche-defined spots overlapped pathology-confirmed TLS, and the clusters captured ∼42% of all TLS-positive spots. These findings demonstrate that STniche reliably recovers functional TLS microenvironments directly from spatial transcriptomic data, providing a quantitative and unsupervised framework for immune niche discovery Conclusion: STniche provides a statistically rigorous and biologically interpretable framework for discovering functional spatial niches in tumor tissues. Its ability to detect TLS-enriched immune architectures highlights potential applications in understanding prognosis, immunobiology, and therapeutic response. Further benchmarking is ongoing. Citation Format: Sasi Arunachalam, Oscar Ospina, Alex C. Soupir, Xiaoqing Yu, Brooke L. Fridley. STniche: An approach to identify functional niches in the tumor microenvironment from spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 1489.