Spatial proteomics reveals sirtuin 1 to be a determinant of T-cell infiltration in human melanoma
Bibliographic record
Abstract
BACKGROUND: The tumour microenvironment significantly influences the clinical response of patients to therapeutic immune checkpoint inhibition (ICI), but a comprehensive understanding of the underlying immune-regulatory proteome is still lacking. OBJECTIVES: To decipher targetable biologic processes that determine tumour-infiltrating lymphocytes (TiLs) as a cellular equivalent of clinical response to ICI. METHODS: We mapped the spatial distribution of proteins in TiL-enriched vs. TiL-low compartments in melanoma by combining microscopy, matrix-assisted laser desorption mass spectrometry imaging and liquid chromatography-mass spectrometry, as well as computational data mining. Pharmacological modulation of sirtuin 1 (SIRT1) activity in syngeneic mouse models was used to evaluate the efficacy of pharmacological SIRT1 activation in two syngeneic melanoma mouse models, one known to be α-programmed cell death protein 1 (PD-1) sensitive and the other α-PD-1 resistant. RESULTS: Spatial proteomics and gene ontology-based enrichment analysis identified > 145 proteins enriched in CD8high tumour compartments, including negative regulators of mammalian target of rapamycin signalling such as SIRT1. Multiplexed immunohistochemistry confirmed that SIRT1 protein was expressed more in CD8high than in CD8low compartments. Further analysis of bulk and single-cell RNA sequencing data from melanoma tissue samples suggested the expression of SIRT1 by different lymphocyte subpopulations (CD8+ T cells, CD4+ T cells and B cells). Furthermore, we showed in vivo that pharmacological SIRT1 activation increased the immunological effect of α-PD-1 ICI against melanoma cells in mice, which was accompanied by an increase in T-cell infiltration and T-cell-related cytokines, including interferon (IFN)-γ, CCL4, CXCL9, CXCL10 and tumour necrosis factor-α. In silico analysis of large transcriptional data cohorts showed that SIRT1 was positively associated with the proinflammatory T-cell chemokines CXCL9, CXCL10 and IFN-γ, and prolonged overall survival of patients with melanoma. CONCLUSIONS: Our study deciphers the proteomics landscape in human melanoma, providing important information on the tumour microenvironment and identifying SIRT1 as having important prognostic and therapeutic implications.
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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".