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Record W4403510301 · doi:10.1093/neuonc/noae144.081

OS10.5.A MINIMALLY INVASIVE BRAIN METASTASES ARE CHARACTERIZED BY AN ACTIVE, ANTI-CANCER IMMUNE INFILTRATE

2024· article· en· W4403510301 on OpenAlexaffabout
Sarah M. Maritan, Elham Karimi, Matthew Dankner, Aldo Hernández-Corchado, Meng-Lin Yu, Morteza Rezanejad, B Liu, Benoit Fiset, Yuhong Wei, Ali Nehme, M Park, Y Riazalhosseini, Hamed S. Najafabadi, Kevin Petrecca, Marie‐Christine Guiot, Daniela F. Quail, Logan A. Walsh, Peter M. Siegel

Bibliographic record

VenueNeuro-Oncology · 2024
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsMontreal Neurological Institute and HospitalMcGill University Health CentreMcGill University
Fundersnot available
KeywordsImmune systemBrain cancerMedicineCancerPathologyImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract BACKGROUND Cancer metastasis to the brain is a common complication of advanced disease with limited therapeutic options. Although immune checkpoint inhibition has shown some efficacy in treating brain metastases (BrM), patient response is variable, in part due to the unique composition of the brain tumor immune microenvironment (TIME). Our group has shown that BrM can grow in two distinct patterns, either as minimally invasive (MI) masses with well-defined borders, or as tumors with highly invasive (HI) growth into surrounding brain tissue. Here, we investigate how the TIME differs between MI and HI BrM. MATERIAL AND METHODS We use Nanostring Digital Spatial Profiling coupled with the Cancer Transcriptome Atlas panel on BrM from 20 patients to quantify 1,825 cancer-specific RNA targets in cancer cells at the tumor-brain interface. Additionally, we perform imaging mass cytometry (IMC) on 119 BrM from 46 patients, encompassing over 350,000 cells. Samples represent BrM from various primary sites (lung, breast, melanoma, other), and include patient-matched samples from the brain-tumor interface and the centre of the metastatic lesion. Additional multiplexed immunohistofluorescence staining was performed on a subset of 18 patients with lung cancer BrM. RESULTS Gene set enrichment analyses revealed elevated interferon gamma signalling in MI versus HI BrM, which was confirmed by immunohistochemical staining for pSTAT1, consistent with an “immune hot” TIME in MI BrM. IMC revealed that MI BrM had significantly higher frequencies of lymphocytes, including B cells, CD4+, CD8+, CD4- CD8-, and regulatory T cells compared to HI BrM. Pairwise cellular association analyses revealed increased interactions between lymphoid cell populations in MI versus HI BrM, suggestive of specific regions enriched in B and T cells in MI BrM, rather than a generally diffuse infiltrate. Indeed, cellular neighbourhood analyses revealed a lymphoid-rich cellular neighbourhood that was more abundant in MI versus HI BrM and localized to the tumor-brain interface. Multiplexed immunohistofluorescence staining revealed that T cells were more likely to be cytotoxically active and exhausted in MI versus HI BrM. CONCLUSION These data suggest that HI BrM invade into an immunosuppressed microenvironment while MI BrM are characterized by an active anti-tumor immune infiltrate, predominantly localized to the tumor-brain interface. Together, this work suggests potential immune regulation of BrM invasion. Work is ongoing to identify factors present in the HI BrM environment that enforce an immunosuppressive environment. This research has been supported by grants from the Terry Fox Foundation and the Quebec Breast Cancer Foundation (Grant #: 251427-251690) and the Canadian Institutes of Health Research (CIHR PJT-175066) to P.M.S.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.040
GPT teacher head0.368
Teacher spread0.327 · 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 designObservational
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
Published2024
Admission routes2
Has abstractyes

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