47. UNCOVERING A NOVEL ROLE FOR HLA-G IN BRAIN METASTASES
Bibliographic record
Abstract
Abstract Brain metastases (BM) are the most common brain tumour in adults and are ten times more likely to develop than primary brain tumours. More than 20% of patients with cancer will develop BM with the three most common sources being primary cancers of the lung, breast, and melanoma. Unfortunately, current treatment options for BM do not effectively eradicate BM, with a mere median overall survival time of 12 months in treated patients. This indicates the need for better and more effective therapies against BM. Using patient-derived cell lines established from surgically removed brain metastatic tumours of lung-, breast- and melanoma-BM patients, we generated patient-derived orthotopic murine xenograft (PDX) models of lung-, breast-, and melanoma-BM. From these PDX models, we isolated a rare population of stem-like brain metastasis initiating cells (BMICs) we termed “pre-metastatic”, that had traveled from their primary/orthotopic tumours and lodged in the brain but had not yet developed into mature BM. Transcriptomic analyses performed on pre-metastatic and non-pre-metastatic BMICs from lung, breast and melanoma PDX models of BM, identified a set of deregulated genes exclusive only to pre-metastatic BMICs. Further analysis revealed HLA-G as being commonly up-regulated only during the pre-metastatic stage of the lung-, breast-, and melanoma-BM cascade. In vitro and in vivo analyses demonstrated that HLA-G knock-down reduced the proliferation and survival of BMICs from all BM cohorts, and attenuated the establishment of mature brain metastatic tumours, implying a crucial role for HLA-G in the formation of BM. Developing a therapeutic strategy that targets HLA-G in BM may prove effective at completely eliminating brain metastatic cells at an early stage of the BM cascade, thereby turning a fatal disease into an eminently more treatable one.
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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".