Abstract 676: Neuro-oncology imaging mass cytometry panels enable spatial investigation of brain tumor microenvironment
Bibliographic record
Abstract
Abstract Brain neoplasms represent a complex form of cancer that is one of the most challenging to classify and treat. Over 120 different tumor subtypes originate from various parts of the central nervous system, which makes identifying the composition of the tumor microenvironment (TME) vital for early assessment of progression, treatment, and prevention. We developed high-plex proteomic analysis tools to thoroughly characterize the TME of both human and mouse brain tissues using Imaging Mass Cytometry™ (IMC™). IMC offers unprecedented insight into the TME by uncovering the spatial distribution of 40-plus distinct molecular markers without autofluorescence, facilitating the research of brain neoplasms. Here, we demonstrate the application of high-plex human and mouse neuro-oncology IMC panels on normal and tumor formalin-fixed paraffin-embedded brain tissues. A basic neurophenotyping panel was developed and used to customize the Maxpar® Human and Maxpar OnDemand™ Mouse Immuno-Oncology IMC Panel Kits. Human and mouse neuro-oncology panels provide deep phenotyping and characterization of brain TME composition. These neuro panels consist of cross-reactive clones and enable flexible panel design for brain-specific research goals, such as brain tumor classification, and assessment of neuronal inflammation, degeneration, and development. We applied the neuro-oncology panels on tissue microarrays (TMAs) containing a variety of human brain tumors and mouse glioblastoma and neuroblastoma tissues. Normal brain tissues were used for comparative analysis as controls. The Hyperion™ Imaging System was utilized to digitize images from the tissues followed by quantitative analysis to assess the cellular composition of normal and cancerous brain TME. We successfully identified major cell populations that make up human and mouse brain matter, such as neurons, astrocytes, microglia, and oligodendrocytes. Various tumor cell phenotypes, resident and infiltrating cells, and resting and activated microglia were detected in multiple tumor subtypes. Subsequent single-cell analysis provided a comprehensive and quantitative assessment of the brain TME in our samples. We classified the distinct states of neurons and quantified myeloid and lymphoid immune cell infiltration across normal, astrocytoma, and glioblastoma tissues. Empowered by high-plex neuro-oncology panels, IMC can accelerate brain tumor research and provide insights into the spatial complexity of neuronal neoplasms. Citation Format: Nick Zabinyakov, Qanber Raza, Christina Loh. Neuro-oncology imaging mass cytometry panels enable spatial investigation of brain tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 676.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".