Integrated spatial transcriptomics and pan-cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response

Sep 22, 2026

Immunotherapy has transformed cancer treatment, but mapping the spatial complexity of the tumor immune microenvironment remains difficult. This study builds an integrated computational framework combining multi-omics profiling across 27 cancer types to reconstruct immune-related non-coding RNA regulatory networks. Spatial transcriptomics clarifies where regulators act, identifying the SNHG6–BIRC5 axis as a driver of the immune-cold phenotype in lung adenocarcinoma. The axis localizes to tumor nests, negatively associates with T-cell infiltration, and supports spatial immune exclusion. CRISPR-Cas9 screening validates functional essentiality, while pharmacogenomic analysis links high axis expression to chemotherapy sensitivity, and a pan-cancer XGBoost model predicts immunotherapy response (AUC 0.771), outperforming PD-L1.

LanguageEnglish

CategoryResearch & Report

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Cancer Immunology, Immunotherapy (2026) 75:131 [https://doi.org/10.1007/s00262-026-04374-3](https://doi.org/10.1007/s00262-026-04374-3) Integrated spatial transcriptomics and pan‑cancer XGBoost modeling uncover spatial drivers of immune exclusion and predict immunotherapy response Hongying Zhao1 · Wangyang Liu1 · Haotian Xu2 · Lu Wang1 · Zushun Chen1 · Yanwu Sun1 · Li Wang1 Received: 26 December 2025 / Accepted: 19 March 2026 © The Author(s) 2026 Abstract Immunotherapy has revolutionized cancer treatment, yet characterizing the spatial complexity of the tumor immune microenvironment remains a challenge. In this study, we established a comprehensive computational framework integrating multiomics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks. Moving beyond traditional bulk analysis, we utilized spatial transcriptomics to dissect the spatial localization of these regulators. We identified the SNHG6–BIRC5 axis as a critical driver of the “immune-cold” phenotype in lung adenocarcinoma. We provide visual evidence that this axis localizes to tumor nests and negatively correlates with T-cell infiltration, elucidating a mechanism of spatial immune exclusion. Validating the clinical relevance of these findings, genome-scale CRISPR-Cas9 screening data confirmed the functional essentiality of these targets for cancer cell survival. Furthermore, pharmacogenomic analysis revealed that high expression of this axis correlates with sensitivity to chemotherapy agents like Vinblastine, suggesting a potential stratification strategy for patients with immune-excluded tumors. To expand the clinical utility to immunotherapy prediction, we developed a pan-cancer XGBoost machine learning model incorporating 14 high-performance regulatory features. This model achieved robust performance in distinguishing immunotherapy responders from non-responders with an AUC of 0.771, outperforming traditional markers such as PD-L1. Collectively, this study highlights spatial determinants of immune exclusion and chemotherapy sensitivity-and presents a generalized machine-learning tool for precision immunotherapy stratification. The developed online resource is freely available to facilitate community-wide biomarker discovery. Keywords Immunotherapy response · Spatial transcriptomics · XGBoost model · Biomarker Introduction Cancer has a high mortality rate and threatens human life and health [1] . Dysregulation of the immune system is a major cause of cancer development. While immunotherapy has emerged as a promising treatment strategy by harnessing Hongying Zhao, Wangyang Liu and Haotian Xu have contributed equally to this work. * Li Wang [wangli@hrbmu.edu.cn](wangli@hrbmu.edu.cn) 1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China 2 NHC Key Laboratory of Cell Transplantation, Department of Cardiology and Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, China the immune system [2], only a small fraction of patients (~ 5%) with advanced cancers derive durable benefits, largely due to immune evasion and tumor heterogeneity [3] . Therefore, deciphering the dysregulated gene expression underlying immune resistance is essential for improving therapeutic outcomes [4] . Many mechanisms contribute to gene expression regulation and ensure transcriptional responses to external signals[5] . The competing endogenous RNAs (ceRNA) hypothesis proposes a complex post-transcriptional regulatory network where lncRNAs and mRNAs regulate each other by competing for miRNAs binding via miRNA response elements (MREs) . This competition affects the expression levels of various RNAs through MREs, which play a crucial role in cancer development, progression, recurrence, prognosis, and immune regulation[6 , 7] . For instance, specific ceRNA axes have been identified as prognostic biomarkers in lung cancer, such as, MLETA1-miR-

FAQs

What computational framework does the study use to model immune regulation across cancers?

The study integrates multi-omics profiling across 27 cancer types to decode immune-related non-coding RNA regulatory networks, and then uses spatial transcriptomics to resolve the regulators’ spatial localization.

Which molecular axis is identified as a spatial driver of immune exclusion?

The SNHG6–BIRC5 axis is identified as a critical driver of the “immune-cold” phenotype in lung adenocarcinoma, localizing to tumor nests and showing a negative correlation with T-cell infiltration.

How does the pan-cancer XGBoost model evaluate immunotherapy response?

A pan-cancer XGBoost model incorporating 14 regulatory features distinguishes immunotherapy responders from non-responders with an AUC of 0.771, outperforming traditional markers such as PD-L1.

Sep 22, 2026

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