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Abstract A039: Detection of immune cell glycosylation as an indicator of metabolic activity in the tumor tissue microenvironment using multimodal mass spectrometry imaging

2023· article· en· W4389227565 on OpenAlexaboutno aff
Richard R. Drake, Kameisha Radford, Caroline Kittrell, Kristin Wallace, Peggi M. Angel

Bibliographic record

VenueCancer Immunology Research · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGlycosylation and Glycoproteins Research
Canadian institutionsnot available
Fundersnot available
KeywordsImmune systemGlycanTumor microenvironmentBiologyGlycosylationCancer researchImmunologyMolecular biologyBiochemistryGlycoprotein

Abstract

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Abstract Glycosylation on the cell surface is a major target and mediator of the immune response to cancers. It is expected that any immune cells present in these tissues will be actively engaged in responding to the presence of the tumor, however, multiple escape mechanisms used by the targets are known to suppress these immune responses. It follows that in this tumor microenvironment, any response, or non-response, to a cancer immunotherapeutic will also be mediated by glycans present in the target tissues and immune cells interacting with them. From a data archive of over 500 FFPE human tumor tissues assessed for N-glycan imaging MS analysis, a subset of tissues (prostate, colon, pancreas, lung) with notable intra- and peri-tumor immune cell clusters were re-evaluated for detection of N-glycans that co-localize to these regions. The metabolic premise for this is that the Warburg metabolites glucose and glutamine are both required for N-glycan biosynthesis and therefore active immune cells would have detectable glycan signatures. In most tumor types evaluated, there was minimal to no detection of N-glycans. This would be consistent with a tumor microenvironment deficient in metabolites due to the presence of the tumor, or other immunosuppressive mechanisms. A subset of the tissues did have glycan signatures associated with immune cell clusters, and the glycan structures present in each cluster were recorded. The same tissues were assessed by multiplexed MALDI-immunohistochemistry (IHC) to identify the immune cell types present in each cluster. Established method workflows were used for the N-glycan MALDI imaging and MALDI-IHC analyses. Previously characterized N-glycans and immune cell clusters (CD4, CD8, CD11b, CD163) in SARS-CoV2 infected autopsy lung tissues were used as positive controls for an active immune microenvironment. Distinct N-glycan species are associated with each immune cell type in these tissues. Detected N-glycans included high mannose structures, and a series of tri- and tetra-antennary structures with one fucose and a bisecting N-acetylglucosamine (GlcNAc). While the high mannose glycans were detected in other areas of the tissues, the bisecting branched N-glycans were distinctly enriched in the immune cell clusters. In these selected tissue subsets, immune cell clusters distal to tumor regions had readily detected high mannose N-glycans and tri-and tetra-antennary bisecting GlcNAc structure. In general, the immune cell clusters adjacent to the tumor region had minimal to no glycan expression relative to the more distal regions. We hypothesize that when N-glycans are detected by imaging mass spectrometry in tissue immune cell clusters that this represents tumors with more active immune functional states, and the lack of detection represents immune-suppressed tumors. Citation Format: Richard R Drake, Kameisha Radford, Caroline Kittrell, Kristin Wallace, Peggi M Angel. Detection of immune cell glycosylation as an indicator of metabolic activity in the tumor tissue microenvironment using multimodal mass spectrometry imaging [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A039.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.028
GPT teacher head0.359
Teacher spread0.331 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

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