PDGFRα Enhanced Infection of Breast Cancer Cells with Human Cytomegalovirus but Infection of Fibroblasts Increased Prometastatic Inflammation Involving Lysophosphatidate Signaling
Bibliographic record
Abstract
Human cytomegalovirus (HCMV) infects 40–70% of adults in developed countries. HCMV proteins and DNA are detected in tumors and metastases, suggesting an association with increased invasion. We investigated HCMV infection in human breast cancer cell lines compared to fibroblasts, a component of tumors, and the role of platelet-derived growth factor receptor-α (PDGFRα). HCMV productively infected HEL299 fibroblasts and, to a lesser extent, Hs578T breast cancer cells. Infection of another triple-negative cell line, MDA-MB-231, and also MCF-7 cells, was extremely low. These disparate infection rates correlated with expression of PDGFRA, which facilitates HCMV uptake. Increasing PDGFRA expression in T-47D breast cancer and BCPAP thyroid cancer cells markedly increased HCMV infection. Conversely, HCMV infection decreased PDGFRA expression, potentially attenuating signaling through this receptor. HCMV infection of fibroblasts promoted the secretion of proinflammatory factors, whereas an overall decreased secretion of inflammatory factors was observed in infected Hs578T cells. We conclude that HCMV infection in tumors will preferentially target tumor-associated fibroblasts and breast cancer cells expressing PDGFRα. HCMV infection in the tumor microenvironment, rather than cancer cells, will increase the inflammatory milieu that could enhance metastasis involving lysophosphatidate.
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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".