Integrative Multi-Omics and Machine Learning Reveals Spatial Niches in NSCLC Immunotherapy

A 22.4 KB research document from figshare, authored by Qingsheng Liu and last updated on March 18, 2026. It describes a study integrating single-cell and spatial transcriptomic data to analyze the tumor immune microenvironment in non-small cell lung cancer patients with varying responses to immune checkpoint blockade. The work identifies specific spatial niches and the role of CYP27A1+ tumor-associated macrophages.

Use Cases

• Training machine learning models to predict immunotherapy response based on spatial niche composition.
• Analyzing the role of CYP27A1+ tumor-associated macrophages in CD8+ T cell recruitment and patient prognosis.
• Investigating the LXR pathway activation in macrophages following immune checkpoint blockade treatment.
• Validating spatial transcriptomic findings against external patient cohorts and in vivo experimental data.

Strengths

• Data is integrated from single-cell and spatial transcriptomic sources from the same patients.
• Findings are validated through external cohorts, immunohistochemistry, immunofluorescence, and in vivo experiments.
• Released under a permissive CC-BY-4.0 license.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The primary file is a 22.4 KB DOCX document, suggesting the dataset itself may be small or summarized.

Provenance

Source

figshare

Collection Method

Integrated single-cell and spatial transcriptomic data analyzed using ISCHIA to construct spatial niches.

Time Range

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Freshness

Last updated 2026-03 07:41:22; freshness should be verified.

Geography

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