BSBM-07 HIGH-DIMENSIONAL HISTOPATHOLOGIC EVALUATION OF THE HYPOXIC MICROENVIRONMENT IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Rapidly growing solid tumors such as glioblastoma (GBM) are characteristically hypoxic, displaying large areas of necrosis surrounded by hyperproliferative pseudopalisading cells. Intra-tumoral hypoxia develops over time in the three-dimensional space and the degree of tissue oxygenation is a dynamic process that varies continuously. Combined with the extensive inter- and intra-tumoral heterogeneity associated with GBM at the bulk and single cell level, hypoxia contributes to a gradient of molecular alterations that are specific to the different cell populations that make up the bulk of the tumor and reside in specific niches. To date, high dimensional histopathologic analyses of the hypoxic regions within GBM tissue have not been performed. Here, we took a combined spatial and single-cell proteomic profiling approach to investigate the histopathologic features of hypoxia by leveraging a unique clinical study where the exogenous hypoxia marker pimonidazole (PIMO) is administered to patients with GBM prior to surgery. Tissue specimens were subjected to imaging mass cytometry and serial immunohistochemistry using a panel of 27 markers associated with cellular hallmarks of hypoxia, metabolism, proliferation, stemness, angiogenesis, and immune cell types. We took high-resolution imaging and statistical approaches to explore the interplay of the different markers within hypoxic regions of primary and recurrent GBMs, in addition to IDH-mutant gliomas. Our findings elucidate the expression pattern of key biological markers relative to one another, altered composition of different cell types, along with differential proliferative, transcriptional, and translational activation states associated with each cell type within the hypoxic regions of GBM.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".