BSCI-20. THERAPEUTIC TARGETING OF HLA-G IN BRAIN METASTASES
Bibliographic record
Abstract
Abstract Brain metastases (BM) are the most common brain tumor in adults, with an incidence ten times greater than that of primary brain tumors. The most common sources of BM in adult cancer patients include cancers of the lung, breast and melanoma, which together account for almost 80% of all BM. Current clinical modalities for BM include surgery, whole brain radiation therapy and stereotactic radiosurgery but these therapies still offer limited efficacy and reduced survival of only months in treated patients, emphasizing the need for novel BM research approaches and better therapeutic strategies. Our laboratory recently discovered that stem-like cells exist in patient-derived BM from lung, breast and melanoma cancers, which we termed “brain metastasis-initiating cells” or BMICs. Through clinically relevant human-mouse xenograft models established with these patient-derived BMICs, we captured lung, breast and melanoma BMICs at pre-metastasis – a key stage where circulating metastatic cells extravasate and initially seed the brain, prior to organization into micro-metastatic foci. Transcriptome analysis of pre-metastatic BMICs revealed a unique genetic profile and several genes commonly up-regulated among lung, breast and melanoma BM, including the non-classical human leukocyte class I antigen-G (HLA-G). Loss of HLA-G in lung, breast and melanoma BMICs using two HLA-G specific shRNAs attenuated sphere formation, migratory and tumor initiating abilities of lung, breast and melanoma BMICs compared to control BMICs. HLA-G knockdown also resulted in reduced phospho(p)-STAT3 expression in patient-derived BMICs suggesting a potential cooperative role between HLA-G and pSTAT3 in BM. Since HLA-G is highly expressed at the cell surface in control tumors, ongoing experiments are focused on developing HLA-G specific chimeric antigen receptor -T cells (CAR-Ts) and determining their efficacy in targeting lung-, breast- and melanoma-BM as blocking the brain metastatic process will markedly extend patient survival and ultimately transform a fatal systemic disease into a more treatable one.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".