EPCO-07. NON-GENOMIC DETERMINANTS OF TUMOR CELL PHENOTYPES IN ADULT GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Non-genomic determinates of oncogenic cellular phenotypes is an emerging concept that expanded our model of tumor hallmarks. We hypothesized that oncogenic programs in adult glioblastoma (GBM) such as angiogenesis, proliferation, DNA repair, epithelial to mesenchymal transition and quiescent states are achieved independently of mutational background or clonal linage. We therefore explored the assortations of large-scale chromosomal rearrangements and canonical driver mutations with oncogenic programs in single cell RNA sequencing (scRNA-seq) of over 3000 tumor cells in 4 adult GBM using open data. We find recurring patterns where tumor cells from diverse mutational background and clonal linage converge upon oncogenic tumor phenotypes. We validate this observation in 9 tumors comprising over 16,000 cells and in xenograft animal models of GBM pre- and post temozolamide treatment. We finally explore the epigenetic associations of oncogenic phenotypes via computational label transfer from scRNA-seq to chromatic accessibility data from single cell ATAC sequencing (scATAC-seq). We find open genomic regions associated with canonical regulators, such as the highly oncogenic mesenchymal phenotype driven by TWIST1. Our results suggest a paradigm shift towards non-genetic determinants of oncogenic phenotypes in GBM complementing the conventional concept of cancer clonal evolution.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 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".