CCRG-02. DEVELOPMENTALLY PARTITIONED PROLIFERATIVE COMPARTMENTS IN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract It is now well established from single-cell RNA-seq studies that glioblastoma (GBM) tumors are complex, maligned neuro-developmental ecosystems harboring diverse tumor cell types. Neoplastic cells can resemble astrocytes, neural progenitors, oligodendrocyte progenitor cells, mesenchymal cells, and radial glial cells that contribute to tumor growth and homeostasis in specific ways. However, GBM single-cell data sets have failed to produce general models for transitions in and out of specific developmental and proliferative states in tumors. One reason is that human GBM tumors do not neatly resolve into developmental hierarchies. Here we focused on modeling GBM tumor cellular heterogeneity by defining "proliferative compartments" in single-cell transcriptomic data derived from primary tumors and early passage tumorsphere cultures. Previously, we observed that each tumor cell entering S-phase has a unique developmental signature that can be leveraged to define broader partitioned proliferative compartments (PPCs) with distinct developmental gene expression and genomic alteration patterns. Thus, we extracted the S-phase cells from a tumor, defined the proliferative compartments in that tumor using de novo clustering, determined the marker genes for each compartment, and defined the broader PPCs across tumors by de novo clustering based on similarity in marker genes. From a cohort of six tumors we observed eight broader PPCs and found that tumors can contain as many as five PPCs or as few as two. Because tumor growth and recurrence both require cell proliferation, we propose that patient-specific PPCs represent the engines of GBM progression.
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 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".