STEM-03. MOLECULAR PROFILING OF PRIMARY VERSUS RECURRENT GLIOBLASTOMA BRAIN TUMOR STEM CELLS UNCOVERS SIGNALING MECHANISMS THAT PROMOTE THE AGGRESSIVENESS OF RECURRENT GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is a devastating brain cancer with a median overall survival of a mere 12-15 months. Patients receive the standard of care treatment, comprised of maximum surgical resection, ionizing radiation, and temozolomide chemotherapy, however the tumor inevitably recurs. Recurrent GBM is more invasive, difficult to resect, and treatment resistant. GBM brain tumor stem cells (BTSCs), a sub-population of stem-like tumor cells with the ability to self-renew and differentiate into a heterogeneous tumor, are thought to be at the root of recurrent disease. BTSCs isolated from primary and recurrent tumors from the same patient are rare due to fewer resections of recurrent tumors and reduced quality of recurrent tumor samples used to initiate cell lines. As a result, the process of tumor recurrence is vastly understudied in BTSCs. To further understand the process of recurrence and elucidate potential mechanisms of invasion and treatment resistance, we explored global transcriptomics in 40 primary versus 17 recurrent BTSC cultures. Additionally, we profiled changes at the chromatin level in a subset of primary versus recurrent BTSCs using ATAC sequencing. Several pathways were found to be upregulated in recurrent GBM BTSCs, including innate immune signaling at the mRNA and chromatin level, which may mediate the aggressive nature of recurrent BTSCs. Moreover, these signaling changes may be unique to the stem cell population within the tumor, as they are not observed when profiling primary versus recurrent bulk tumors. We are currently targeting these pathways genetically and pharmacologically in primary and recurrent GBM BTSCs established from the same patient and have uncovered mechanisms for treatment resistance, invasion, and recurrence. Our work investigating global signaling changes in primary versus recurrent BTSCs holds promise for uncovering new therapeutic targets or biomarkers for treatment of recurrent 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".