TMIC-39. Investigating the role of PDGF-AA in invasive melanoma brain metastases
Bibliographic record
Abstract
Abstract Approximately 20-40% of cancer patients develop brain metastases (BrM). Unfortunately, these patients suffer from poor outcomes, diminished quality of life and approximately 60% of patients that undergo BrM resection recur within 1 year. Our group described histological growth patterns associated with recurrence, where highly invasive (HI) BrM are more likely to recur locally, compared to minimally invasive (MI) BrM. Cancer cell invasiveness can be driven by crosstalk with the microenvironment, through secreted factors from brain cells or from cancer cells that colonize the brain. We have profiled the secretome of HI and MI patient-derived xenograft BrM using mouse- and human-specific multiplex ELISA. This screen revealed high levels of platelet-derived growth factor A (PDGF-AA) in HI melanoma BrM and these findings were confirmed in independent syngeneic melanoma brain tumour models. Importantly, overexpression of PDGF-AA in MI melanoma cells accelerated intracranial tumour growth and shortened survival in vivo. There are several studies reporting autocrine PDGFRα signaling downstream of PDGF-AA in aggressive glioblastomas. Therefore, we explored the initial hypothesis that PDGF-AA might activate cancer cell intrinsic PDGFRα signaling to promote invasion. However, we demonstrate that such an autocrine signaling mechanism is not operative in our models. Considering these results, we are now exploring the hypothesis that melanoma cell-derived PDGF-AA acts on parenchymal cells in the brain to promote BrM aggressiveness. Brain microenvironmental remodeling factors are important to investigate as potential druggable targets in BrM. Should PDGF-AA be involved, clinically available PDGFRα inhibitors could be a potential treatment option to disrupt such crosstalk in individuals with recurring BrM.
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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) | 0.002 | 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".