What does this research mean for the field?

A deep learning framework integrating single-cell and spatial transcriptomics identifies an OSM-primed neutrophil-associated mesenchymal niche in lung adenocarcinoma and reveals that the prognostic signal of SEC61G is independent of 7p11.2 copy number. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To dissect neutrophil contributions within the mesenchymal tumor microenvironment of lung adenocarcinoma and identify tumor-intrinsic dependencies using integrated deep learning and spatial transcriptomics.

September 14, 2026Cancer Immunology ImmunotherapyOpen Access

Deep learning integration of single-cell and spatial transcriptomics reveals a neutrophil-associated mesenchymal niche and a candidate translocon dependency in lung adenocarcinoma

View Full Paper

Ask AI

Bookmark

Authors

RHRuizhe HuangXinHua Hospital ZLZhiyi LiuWestlake University YZYawei ZhaoNanjing Drum Tower Hospital

Discussion

Member takes

Implication

Deep learning integration uncovers a neutrophil-driven mesenchymal niche and SEC61G dependency in lung adenocarcinoma, highlighting targetable cellular circuits.

Key Points

To dissect neutrophil contributions within the mesenchymal tumor microenvironment of lung adenocarcinoma and identify tumor-intrinsic dependencies using integrated deep learning and spatial transcriptomics.
Integrated seven single-cell RNA-sequencing cohorts, two spatial transcriptomic cohorts, bulk RNA-sequencing datasets with survival, CRISPR essentiality screens, and three immunotherapy cohorts.
Combined consensus non-negative matrix factorization, deep generative models (scVI/scANVI), spatial deconvolution (cell2location), and transformer-based foundation model perturbation (Geneformer).
Mesenchymal program MP3 predicted poor prognosis in context (HR 1.43 per 1 SD, 95% CI 1.12–1.82, p = 0.0045), and the composite score z(MP3) − z(MP4) was independently prognostic (HR 1.40, p = 3.4 × 10⁻⁶), balanced by protective MP4 (HR 0.72, p = 0.010).
Identified an OSM-primed neutrophil axis communicating indirectly with SPP1+ macrophages within a reproducible two-compartment niche across both spatial cohorts.
Foundation model perturbation nominated SEC61G across four orthogonal endpoints, linking it to worse overall survival (HR 1.64, p = 1.3 × 10⁻⁶) independently of 7p11.2 copy number.

Cite This Study

Huang et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b3aa0926e14a848b2d4d https://doi.org/10.1007/s00262-026-04560-3

View Full Paper

Ask AI

Bookmark

Implication

Deep learning integration uncovers a neutrophil-driven mesenchymal niche and SEC61G dependency in lung adenocarcinoma, highlighting targetable cellular circuits.

Deep learning integration of single-cell and spatial transcriptomics reveals a neutrophil-associated mesenchymal niche and a candidate translocon dependency in lung adenocarcinoma

Discussion

Member takes

Implication

Key Points

Cite This Study

Deep learning integration of single-cell and spatial transcriptomics reveals a neutrophil-associated mesenchymal niche and a candidate translocon dependency in lung adenocarcinoma

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider