MODL-05. DISCOVERY OF DYNAMIC MINIMAL RESIDUAL DISEASE STATES IN ADULT GLIOBLASTOMA USING SINGLE CELL TECHNOLOGY
Bibliographic record
Abstract
Abstract Persister states are proposed oncogenic substrates of disease recurrence in cancer. Recent concepts suggest persisters may occur in dynamic states of the cell cycle (such as cycling and non-cycling states). We therefore investigated biological programs at the post-treatment minimal residual disease (MRD) state following standard chemoradiotherapy in patient-derived xenograft models of GBM. Our analysis of single cell RNA sequencing (scRNA-seq) data from 2704 tumor cells (929 cells post treatment, 1775 matched controls) yielded a cellular profile for non-cycling and cycling persister states. We validated these programs in 3 independent scRNA-seq datasets of human glioblastoma specimens consisting of over 16,000 cells from various genetic backgrounds, including an internal two patient-matched primary and recurrent GBM pairs with over 13,000 cells. Finally, we determined that clones identified based on large scale chromosomal rearrangements converge on previously identified persister states including the dynamic states discovered in our study. Our results provide new evidence towards dynamic persister states in glioblastoma, further analysis of these dynamic states is critical to targeting this incurable disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".