MétaCan
Menu
Back to cohort
Record W4387402628 · doi:10.1016/j.esmoop.2023.101891

81P Single-cell spatial landscape of NSCLC reveals subtype specific immune features

2023· article· en· W4387402628 on OpenAlexaff
Mark Sorin, Lysanne Desharnais, Elham Karimi, B. Liu, Morteza Rezanejad, Anikka Swaby, Aline Atallah, Jonathan Spicer, Daniela F. Quail, Logan A. Walsh

Bibliographic record

VenueESMO Open · 2023
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of TorontoMcGill University
Fundersnot available
KeywordsMass cytometryImmune systemAdenocarcinomaBiologyTumor microenvironmentLung cancerCellCancer researchContext (archaeology)Flow cytometryCell typePathologyImmunologyCancerMedicinePhenotypeGeneGenetics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.032
GPT teacher head0.285
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueESMO OpenSame topicCancer Immunotherapy and BiomarkersFrench-language works237,207