Abstract A039: Detection of immune cell glycosylation as an indicator of metabolic activity in the tumor tissue microenvironment using multimodal mass spectrometry imaging
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".