Abstract 1802: CD39 inhibition shapes the transcriptional landscape of myeloid cells and induces proinflammatory states in the CT26 syngeneic tumor model
Bibliographic record
Abstract
Abstract Extracellular adenosine triphosphate (ATP) generated by tissue damage or immunogenic cell death initiates proinflammatory responses that are potently restricted by adenosine produced through ATP hydrolysis. The ectonucleotidases CD39 and CD73 limit immune responses by sequentially converting ATP to adenosine monophosphate (AMP) and adenosine, respectively. Increased activity of CD39, the rate-limiting enzyme in ATP hydrolysis in tumor microenvironments (TME), results in significant reductions in extracellular ATP. The subsequent accumulation of adenosine contributes to tumor immune escape, induction of angiogenesis, and metastatic progression. Although pharmacological targeting of CD39 has antitumor effects in preclinical models, the immunological mechanisms of therapeutic CD39 blockade have not been finely parsed. In this study, the immunological effects of CD39 inhibition on tumor infiltrating lymphocytes (TILs) were examined by single-cell transcriptome analysis. Mice bearing syngeneic CT26 tumors were treated with an anti-murine CD39 antibody that blocks the conversion of ATP to AMP. After 3 doses, tumors were harvested on Day 15 post-implant, dissociated, and enriched for CD45+ cells. Isolated TILs were captured in droplets, and single-cell sequencing libraries were generated through standard 10× Genomics protocols. Raw reads were pre-processed through the CellRanger pipeline. Data quality, normalization, integration, and clustering were performed with Seurat. Cell type annotation and differential expression was performed with SingleR and EdgeR, respectively. Several defined immunocyte populations identified in the CT26 TME were conserved across all conditions tested. These cell clusters segregated along the lymphoid/myeloid axis, with many sub-clusters identified for each major lineage. Data for each cell were aggregated in a pseudo-bulk RNA-seq analysis to define broad effects of CD39 blockade, which revealed major changes to immunocyte transcriptional landscapes. Notably, CD39 blockade upregulated several proinflammatory genes, including Gzmf, Gzmg, Cxcl9, and Csf3. Mapping of these changes to single-cell clusters revealed that CD39 blockade predominantly altered the transcriptional profiles of myeloid cell subsets, generally inducing proinflammatory gene modules. This analysis also demonstrated a significant effect of CD39 inhibition on plasmacytoid dendritic cells, inducing Cxcl2, Il1b, Gadd45g, and Fabp4 expression. Interestingly, CD39 blockade repressed Klk1, a kallikrein previously implicated in tumorigenesis. In summary, single-cell RNA-seq of the CT26 syngeneic tumor model suggests that CD39 inhibition predominantly shaped the transcriptional landscape of myeloid cells and generally induced proinflammatory conditions. Citation Format: Devapregasan Moodley, Mayra Carneiro, Sonia D. Gas, Austin Dulak, Ricard Masia, Secil Koseoglu, Matthew Rausch, Marisella Panduro, Michael C. Warren, John Stagg, Benjamin Lee, Pamela M. Holland, Vito J. Palombella, Andrew C. Lake. CD39 inhibition shapes the transcriptional landscape of myeloid cells and induces proinflammatory states in the CT26 syngeneic tumor model [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1802.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".