Abstract 3999: HLA-G, SPAG9 and STAT3 signalling: An alliance that promotes early-stage brain metastases
Bibliographic record
Abstract
Abstract Brain metastases (BM) are the most common brain tumours in adults and a prominent cause of cancer-related mortality globally. Leading sources of BM are cancers of the lung, breast and melanoma, which together account for approximately 80% of all BM. Unfortunately, current clinical modalities for BM including surgery, radiation therapy and chemotherapy still offer limited efficacy and median survival times of 4 - 12 months in treated patients, emphasizing the need for more effective therapeutic strategies and generally a better understanding of the disease. We recently identified the presence of stem-like cells termed “brain metastasis-initiating cells” or BMICs in patient-derived BM from lung, breast and melanoma cancers that are able to recapitulate the complete brain metastatic cascade in pre-clinical models of BM. Through these models, we serendipitously captured lung, breast and melanoma BMICs at the “pre-metastatic” stage of BM - a stage where circulating metastatic cells have seeded the brain, but not yet formed full-blown (macro-metastatic) brain lesions. Transcriptomic analysis of pre-metastatic and macro-metastatic lung, breast and melanoma BMICs revealed a unique genetic profile in pre-metastatic BMICs that was distinct from their macro-metastatic counterparts. Further analysis identified several genes commonly up-regulated in all pre-metastatic BMIC cohorts irrespective of their primary tumour of origin. Intriguingly, we found that inhibition of the non-classical human leukocyte class I antigen-G or HLA-G gene (one of the top up-regulated genes in the pre-metastatic cohorts), reduced the ability of BMICs to form mature brain lesions. Correspondingly, HLA-G over-expression increased the capacity of BMICs to establish secondary brain tumours. Mechanistically, we discovered that over-expressing HLA-G levels in BMICs (to simulate the high levels that occurs in pre-metastatic BMICs), increased the activation of STAT3 signalling and this was mediated in part via a novel HLA-G binding partner - SPAG9. Our work thus uncovered a potential cooperative role between HLA-G, SPAG9 and STAT3 signalling during the early stages of BM. Indeed, attenuation of SPAG9 protein levels or STAT3 signalling in HLA-G over-expressing BMICs using CRISPR knockout and a STAT3 inhibitor respectively obstructed the ability of high HLA-G levels to promote mature brain lesions. This is the first study to reveal a role for an HLA-G-SPAG9-STAT3 axis in BM and highlights the potential of targeting this axis to inhibit BM, which will markedly extend patient survival. Citation Format: Blessing I. Bassey-Archibong, Chirayu R. Chokshi, Nikoo Aghaei, Agata Kieliszek, Nazanin Tatari, Dillon McKenna, Mohini Singh, Minomi Subapanditha, Arun Parmar, Neil Savage, Yu Lu, Chitra Venugopal, Sheila Singh. HLA-G, SPAG9 and STAT3 signalling: An alliance that promotes early-stage brain metastases [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3999.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".