Spatial profiling of solid tumor microenvironment using imaging mass cytometry with high-plex panels.
Bibliographic record
Abstract
e14574 Background: Understanding cellular interactions within the tumor microenvironment (TME) is essential for elucidating disease progression and advancing immunotherapy. The TME is a complex ecosystem composed of cells with dysregulated metabolism immune response and signaling pathways, which in turn influence tumor development and treatment response. Multiplexed assessment serves as an important tool for clinical oncology. The simultaneous readout of multiple processes can provide biological insights and elucidate disease mechanism. Imaging Mass Cytometry (IMC) is a spatial biology imaging technique that utilizes CyTOF technology and enables deep characterization of the diversity and complexity of the TME. IMC technology offers scalable, 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: To study cellular processes and their roles in tumor progression, we utilized the Human Cell Metabolism and Human Cell Signaling Panels to investigate energy production, cellular homeostasis and mitogenic signaling pathways. We then mapped these processes to the types of cells in the TME by using the Human Immuno-Oncology IMC Panel and the Human T Cell Exhaustion IMC Panel or the Maxpar Neuro Phenotyping IMC Panel Kit to characterize immune cell and neurological phenotypes in detail. We first acquired data using Preview Mode to assess the whole tissue, followed by higher-resolution imaging of selected regions of interest using Cell Mode or of the whole tissue section using Tissue Mode. Results: Our data analysis revealed significant insights into the spatial organization and metabolic profile of cells across cancer tissues. Elevated glycolysis and mTOR pathway activation suggested adaptations to hypoxia and anabolic growth in tumor areas, while interactions between fibroblasts and immune cells highlighted crosstalk within the TME. Our Neuro Phenotyping Panel was used to reveal potential for immune response in glioblastoma. Unsupervised pixel clustering and hierarchical clustering using MCD SmartViewer highlighted metabolic activity and activation of signaling pathways within tumor regions. Conclusions: Comprehensive spatial biology profiling using the IMC approach highlights the interconnected roles these pathways play in promoting tumor survival and resistance to therapies. These findings, which illuminate the metabolic and signaling heterogeneity of the TME, are crucial for developing future prognostic assessments and have the potential to guide more effective, personalized cancer therapies. For Research Use Only. Not for use in diagnostic procedures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".