TMIC-80. HIGH-DIMENSIONAL HISTOPATHOLOGIC EVALUATION OF THE HYPOXIC TUMOR MICROENVIRONMENT IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Gliobastoma (GBM) is a highly aggressive solid tumor of the brain, characterized by a hypoxic tumor microevironment (TME) leading to histopathological features of necrosis, microvascular proliferation and deregulated hypervascularization. The heterogeneous nature of GBM results in a gradient of intra-tumoral hypoxia that causes molecular changes to specific cell populations within the bulk of the tumor. Our study is the first clinical study to utilize the exogenous oxygen-independent marker pimonidazole (PIMO) to identify the hypoxic TME using high-dimensional spatial and single-cell proteomics of specific cell populations within GBM. A cohort of 35 patients with primary GBM, recurrent GBM or IDH-mutant glioma were administered PIMO prior to surgical resection of tumor tissue for downstream bulk proteomic analysis of bulk tissue by serial immunohistochemistry and imaging mass cytometry (IMC) and single-cell analysis of dissociated tissue by Cytometry by time-of-flight (CyTOF). We utilized high-resolution imaging analyses to validate PIMO as a sensitive and stable marker for hypoxia against a panel of transiently expressed HIF-target genes and examine the inter- and intra-tumoral heterogeneity of hypoxia within each tumor subtype. A custom CyTOF marker panel was developed for further single-cell analyses of T-cell subsets and their functional states, myeloid cell subpopulations, natural killer cells, glial cells, and tumor cells in the hypoxic TME. We demonstrate the utility of PIMO to effectively study the hypoxic TME through spatial and single cell methods and further elucidate mechanisms of hypoxia-driven treatment resistance.
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 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".