Abstract PO-087: Single-cell transcriptome analysis reveals functional changes in tumor-infiltrating macrophages after nutraceutical momordicine-I treatment in head & neck cancer
Bibliographic record
Abstract
Abstract Head & neck cancer is the sixth most prevalent cancer in the world, and oral cancer is the most common subtype with limited effective treatment options. Therefore, there is a critical need to understand the pathogenesis and therapeutic modalities for successful management of head & neck cancer. Momordicine-I (M-I), an active component of bitter melon extract, displayed anti-tumor activity. However, M-I mediated immune modulation of head & neck tumor microenvironment (TME) remains unknown. To improve the efficacy of therapy, it is important to understand the heterogeneity of head & neck cancer in TME and its modulation following treatment at molecular level. In this study, we showed that M-I inhibits head & neck tumor growth in an immunocompetent mouse model. To understand the in-depth changes in immune system, we analyzed the transcriptome profiles of vehicle treated and M-I-treated tumors using single-cell RNA-sequencing. M-I treatment modulates several molecules in monocyte/macrophage clusters in CD45+ populations. Tumor-associated macrophages (TAMs) are crucial barriers to their antitumor effects. We observed that the expression of Sfln4, a myeloid cell differentiation factor, and Cxcl3, a neutrophil chemoattractant, in the monocyte/macrophage populations was significantly reduced following M-I treatment. We further showed that macrophages need to be in close contact with tumor cells to reduce Sfln4 or Cxcl3 expression, suggesting that TAMs are modulated by M-I treatment. Co-culture of macrophages and tumor cells enhances Nos2 expression following M-I treatment, implicating an alteration of the M2 to M1 phenotype of macrophages. In fact, differential expression of macrophage populations from M-I treated tumors showed downregulation of several M2 macrophage marker genes, and their further validations for functional consequence are underway. Together, our results highlight a potential mechanism of immune modulation by M-I, which may help in the development of a combination therapy for head & neck cancer. Citation Format: Subhayan Sur, Robert Steele, Ratna B. Ray. Single-cell transcriptome analysis reveals functional changes in tumor-infiltrating macrophages after nutraceutical momordicine-I treatment in head & neck cancer [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Innovating through Basic, Clinical, and Translational Research; 2023 Jul 7-8; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2023;29(18_Suppl):Abstract nr PO-087.
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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".