Influence of Tumor Cell Culture Supernatants on Macrophage Functional Polarization: In Vitro Models of Macrophage-tumor Environment Interaction
Bibliographic record
Abstract
AIMS AND BACKGROUND: Macrophages are heterogeneous cells with extensive functional plasticity; they can change their functional profiles repeatedly in response to environmental changes anywhere between their extreme phenotypical programs (labeled as M1 and M2 polarization, respectively). In terms of antitumoral immune response, M1 macrophages are considered to be beneficial, while M2 macrophages supposedly promote tumor progression. Tumor-associated macrophages (TAMs) represent a major leukocyte population present in many tumors. Although many studies indicate that TAMs elicit several M2-associated protumoral functions, including promotion of angiogenesis, matrix remodeling and suppression of adaptive immunity, their role regarding tumor progression is still controversial. The aim of the present study was to develop an appropriate in vitro model to study the effect of tumor-secreted soluble factors on the functional phenotype of macrophages. METHODS AND STUDY DESIGN: THP-1 human monocytic line cells and peripheral blood mononuclear cells from healthy volunteers were used for macrophage differentiation; primary tumor cell culture supernatants or tumor cell line supernatants were employed along with various cytokines, growth factors and other stimuli to design different model variants and to better mimic the in vivo tumor microenvironment. RESULTS: The cytokine secretion patterns of these macrophages suggest that primary tumor cell culture supernatants are able to switch the macrophage phenotype or to induce functional polarization of macrophages toward a mixed M1/M2 phenotype. Conclusions. These data support the hypothesis that TAM behavior is modulated by the tumor microenvironment itself.
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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) | 0.001 | 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) | 0.001 | 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".