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Record W4282982979 · doi:10.1158/1538-7445.am2022-3999

Abstract 3999: HLA-G, SPAG9 and STAT3 signalling: An alliance that promotes early-stage brain metastases

2022· article· en· W4282982979 on OpenAlexaff
Blessing Bassey‐Archibong, Chirayu Chokshi, Nikoo Aghaei, Agata Kieliszek, Nazanin Tatari, Dillon McKenna, Mohini Singh, Minomi Subapanditha, Arun Parmar, Neil Savage, Yu Lu, Chitra Venugopal, Sheila K. Singh

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldImmunology and Microbiology
TopicReproductive System and Pregnancy
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineBrain metastasisMetastatic breast cancerBreast cancerMelanomaOncologyMetastasisCancerInternal medicineCancer research

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.147
GPT teacher head0.396
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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