AI-05 * IMPACT OF GBM MICROENVIRONMENT ON EXPRESSION PROFILE OF BONE MARROW DERIVED PROGENITOR CELLS
Bibliographic record
Abstract
We have recently shown that bone marrow derived cells (BMDC) provide a distinct tumor region dependent contribution to glioblastoma multiforme (GBM) neovascularization. The influence of GBM microenvironment on differentiation and modulation of expression factors by BMDC however remains unknown. In this study we establish the differential expression profile of BMDC as a consequence of recruitment and interaction with the GBM microenvironment and in response to radiation (RTx) and anti-angiogenic therapy (AATx). Chimeric mice with reconstituted green-fluorescent bone marrow were used to create intracranial GBM xenografts, by implanting red fluorescent glioma stem cells or U87 into the frontal lobes. Subsequently, BMDC recruited to the GBM were isolated from the GBM cells using FACS during GBM growth, and following treatment (RTx and AATx). RNA was extracted from both FACS-purified BMDC and GBM cells, and mRNA and miRNA array analyses were performed. We compared the expression profiles to systemic BMDC derived from control donor mice. BMDCs were found to exhibit significant plasticity and altered their expression profiles based on stage of GBM growth and in response to therapy. TGFb was significantly upregulated in BMDCs following recruitment to GBMs, with a compensatory increase in expression of IL6,4,8 by GBM cells. BMDC up-regulate cytokines, IFNG, CXCL and TNF pathways, and transcriptional regulators, SMAD2, all of which are able to influence the tumor microenvironment. BMDCs also prove to express angiogenic factors and angioMIRs, with a distinct differential expression pattern based on stage of GBM growth and in response to therapy. We demonstrate that a significant cross-talk exists between BMDC in the tumor microenvironment and GBM cells. There is a distinct angiogenic and invasive profile of BMDC once recruited to the GBM. Furthermore, recruited BMDC provide a source of angiogenic factors, that are differentially expressed based on the stage of GBM growth and in response to therapy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 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".