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1219 High-plex spatial profiling of tumor metabolic reprogramming and cell signaling dynamics in breast cancer using imaging mass cytometry

2025· article· W4415899250 on OpenAlexaff
Thomas D. Pfister, Nick Zabinyakov, Qanber Raza, David Howell, Liang Lim

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2025
Typearticle
Language
FieldBiochemistry, Genetics and Molecular Biology
TopicSingle-cell and spatial transcriptomics
Canadian institutionsCanadian Standards Association
Fundersnot available
KeywordsBreast cancerReprogrammingMass cytometryFlow cytometryCellDynamics (music)Human breast

Abstract

fetched live from OpenAlex

Background Understanding intricate cellular interactions within the tumor microenvironment (TME) is essential for understanding disease progression and advancing cancer treatments, such as immunotherapy. The cancer ecosystem is complex, composed of cells with dysregulated metabolism and signaling pathways, contributing to tumor heterogeneity, growth and differential treatment response. Targeting metabolic and signaling pathways represents a growing strategy to enhance immunotherapy treatments, often in combination with standard of care treatments. Imaging Mass Cytometry™ (IMC™) is a spatial biology imaging technique utilizing CyTOF™ technology, which enables deep characterization of 40-plus markers in TME simultaneously. IMC offers scalable and high-throughput acquisition while generating high-quality data with true dynamic range of signal without amplification or fluorescence-based limitations such as spectral overlap and autofluorescence.Methods IMC was used to explore the TME and interrogate key pathways in metabolic reprogramming and signaling by utilizing antibody panels that integrate markers from the Human Immuno-Oncology IMC Panel (201509) combined with the Human Metabolism IMC Panel (201521) or Human Cell Signaling IMC Panels (201522). This enabled investigation of energy production, cellular homeostasis and mitogenic signaling pathways in human breast cancer samples. To phenotype immune and tumor cells and assess the activation status of immune cells, we used Preview Mode to acquire the whole tissue, followed by higher-resolution imaging of regions of interest using Cell Mode or whole tissue sections using Tissue Mode ( figure 1).Results IMC analysis elucidated the spatial organization and metabolic profile of cells in breast cancer ( figure 2A). Heterogeneity within tumor is highlighted by differential utilization of energy sources across the tumor. Immune cells primarily infiltrated tumor areas utilizing fatty acid oxidation, while tumor cells using anaerobic metabolism or aerobic respiration were classified as immune deserts. Differences in signaling pathways were also observed in these tumor cell populations (figure 2B). Elevated glycolysis and mTOR pathway activation suggested adaptations to hypoxia and anabolic growth. Wnt signaling and PTEN expression were mainly localized in tumor cells, whereas MAP kinase signaling was localized in stroma. Unsupervised pixel clustering and hierarchical clustering using MCD™ SmartViewer highlighted metabolic activity and activation of signaling pathways within tumor regions.Conclusions Comprehensive spatial profiling using IMC technology illuminates the heterogeneity of metabolism and signaling pathways in tumors. IMC allows us to detect many clinically relevant targets simultaneously with intact spatial resolution. This is crucial for developing future prognostic assessments and guiding more effective, personalized cancer therapies.For Research Use Only. Not for use in diagnostic procedures.Abstract 1219 Figure 1Whole tissue evaluation of metabolic activity in human breast cancer. Whole slide Tissue Mode image, human breast cancer stained with IMC panels. A) Multi-color IMC image, metabolic activity in breast cancer. B) Shows differential activation of signaling cascades in breast cancer cellsAbstract 1219 Figure 2High-resolution evaluation of metabolic and cell signaling activity in human breast cancer. Cell Mode IMC images from 3 separate regions of interest from human breast cancer stained with A) Human Metabolism IMC Panel or B) Human Cell Signaling IMC Panels

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2025
Admission routes1
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