81P Single-cell spatial landscape of NSCLC reveals subtype specific immune features
Bibliographic record
Abstract
Single-cell technologies have revealed novel insights about the complexity of the tumor immune microenvironment. Most clinical strategies rely on histopathological stratification of tumor subtypes, yet the spatial context of cellular interactions within these stratified subgroups remains poorly understood. We previously applied imaging mass cytometry, a novel technology, to describe the tumor and immunological landscape of lung adenocarcinoma (LUAD) patient tumors across five histological subtypes of lung adenocarcinoma. We resolved over 1 million cells, enabling an understanding of the cellular spatial relationships associated with distinct clinical correlates, such as survival. Here, we compare the immune landscape of LUAD with lung squamous cell carcinoma (LUSC), a different subtype of lung cancer. Imaging mass cytometry (IMC) is a novel technology which allows for the use of up to 50 antibodies labelled with metal isotopes to characterize the tumour and the tumour immune microenvironment. Our research group has optimized a panel of IMC markers delineating immune, tumour and structural cell types. Using IMC we have assessed the frequency of 16 different cell types, the interactions across these cells and their organization into cellular communities. We have compared these outputs across LUAD and LUSC patients. We show that LUAD and LUSC exhibit distinct immune profiles. Lung squamous cell carcinoma is characterized by a higher prevalence of neutrophils and a lower prevalence of macrophages compared to LUAD. In addition, our spatial analysis of immune lineages demonstrates contrasting cellular interactions across LUAD and LUSC as well as unique arrangements of immune cells into cellular neighborhoods. This highlights the spatial heterogeneity that exists across lung cancer subtypes beyond the prevalence of immune cell types alone. Our results describe the importance of spatial interactions by demonstrating how distinct spatial profiles can exist across subtypes of lung cancer. Overall, we find unique immune profiles across LUAD and LUSC as it relates to immune frequencies, pairwise cellular interactions and communities of cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".