Abstract A012: Investigating the heterogeneity and clinical significance of tumor-associated macrophages in renal cell carcinoma milieu
Bibliographic record
Abstract
Abstract Background: Tumor-associated macrophages (TAMs) exert a range of immunomodulatory functions in the tumor microenvironment (TME) and have been considered key regulators of tumor development. The interactions of TAMs with cancer and stromal cells in the TME have been shown to promote and sustain tumor progression; however, TAMs are largely phenotypically and functionally heterogeneous. Studies at a single-cell level are therefore necessary to understand biological and clinical relevance of heterogeneity of TAMs. Objective: To investigate the heterogeneity of TAM populations in renal cell carcinoma (RCC), the most common form of Kidney cancer, and to determine their connections to patient outcome. Methods and Results: We leverage single-cell RNA sequencing analysis of six primary RCC tumors and three Lung metastatic clear cell RCC (ccRCC) tumors, from which we identified eight TAM and two monocyte clusters shared across all nine patient tissue samples. Differential gene expression and pathway analyses revealed distinct subsets of upregulated genes and cellular pathways activated in each subpopulation. Survival analysis using gene signatures characteristic of each TAM population in datasets of ccRCC tumors (n=533) from TCGA revealed two subpopulations associated with poor patient outcome in ccRCC. Of these, one subpopulation, which we labelled as TAM-8, was characterized by upregulation of pro-inflammatory cytokines, chemokines, and growth factors such as CCL20, CXCL8, and IL1A/B. To our knowledge, this TAM population has not been described in RCC previously. We performed additional in silico functional analyses to further characterize the function of TAM-8 subpopulation, which indicated an active role in promoting immune cell infiltration and angiogenesis via various pro-inflammatory signaling pathways. Conclusion: This study provides new insight into the heterogeneity of TAM populations in RCC and defines CCL20/CXCL8/IL1High TAM-8 population as a potential driver of poor outcome and a candidate therapeutic target. Citation Format: Evelyn M. Zavacky, Minjun Kim, Ariel Madrigal, Zohreh Mehrjoo, Jonathon Spicer, Simon Tanguay, Fadi Brimo, Hamed Najafabadi, Yasser Riazalhosseini. Investigating the heterogeneity and clinical significance of tumor-associated macrophages in renal cell carcinoma milieu [abstract]. In: Proceedings of the AACR Special Conference: Advances in Kidney Cancer Research; 2023 Jun 24-27; Austin, Texas. Philadelphia (PA): AACR; Cancer Res 2023;83(16 Suppl):Abstract nr A012.
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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 | 0.001 | 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".