IMMU-40. SINGLE-CELL LEVEL COMPARISON OF HISTOPATHOLOGY AND SINGLE-CELL RNA-SEQ DATABASES BETWEEN IDH-MUT AND –WT GLIOBLASTOMAS REVEALS DISTINCT INNATE IMMUNE MICROENVIRONMENTS THAT CAN BE EXPLOITED FOR THERAPEUTIC GAIN
Bibliographic record
Abstract
Most immune cells in the glioblastoma (GBM) microenvironment are microglia and macrophages (MMs), but they are poorly understood. We sought to characterize these innate immune cells in human untreated IDH-WT and rare IDH-MUT GBM tissue to elucidate differences underlying their disparate prognoses relevant to immunotherapy design. An in-house automated segmentation protocol that quantifies at the single-cell level was used to analyze newly diagnosed human GBM (9 IDH-WT, 4 IDH-MUT). Three large sections (3-8mm in diameter) were quantitated to capture potential spatial heterogeneity. Expression of CD68, HLA-A/B/C, TNFa, CD163, IL10, TGFB2, Iba1 intensity, and surface area were enumerated and combined into an activation profile in Iba1+ cells (MMs). Results were validated with flow cytometry. Human IDH-MUT (GSE89567) and –WT (GSE84465) single-cell RNA-seq databases were then compared using novel bioinformatics techniques to affirm results. MM content is drastically reduced in IDH-MUT compared to –WT GBMs (4.9 ± 1.4% vs. 37.2 ± 7.3% of all GBM cells, respectively; p=0.0154). Surprisingly, a large range of MM content was found in IDH-WT GBMs, from 1.6 ± 0.6% to 71.9 ± 13.4%. Positive correlation with flow cytometry corroborated these results (Pearson r=0.7296; p=0.026). Inflammatory phenotypic variability was again seen in MMs in both IDH-MUT and –WT GBMs, but IDH-MUT GBM-associated MMs were more activated/pro-inflammatory (124.5 ± 21.6 pro-inflammatory units vs. 54.0 ± 15.6 pro-inflammatory units; p=0.0265). Comparison of single-cell RNA-seq databases after normalization and dynamic pruning of hierarchical clustering dendrograms verified MMs in IDH-MUT GBMs were more pro-inflammatory, but that this was driven by anti-inflammatory macrophages in IDH-WT GBMs as opposed to microglia which were pro-inflammatory in all tumors (p < 0.01). This is one of the first studies to characterize MMs in untreated human IDH-MUT GBMs and identify dissimilarities to the IDH-WT innate immune microenvironment that can be targeted by immunotherapies. Also, considerable MM phenotypic heterogeneity suggests precision immunotherapy approaches are crucial.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".