Characterization of the minimal residual disease state reveals distinct evolutionary trajectories of human glioblastoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recurrence of solid tumors renders patients vulnerable to advanced, treatment-refractory disease state with mutational and oncogenic landscape distinctive from initial diagnosis. Improving outcomes for recurrent cancers requires a better understanding of cell populations that expand from the post-therapy, minimal residual disease (MRD) state. We profile barcoded tumor stem cell populations through therapy at tumor initiation, MRD, and recurrence in our therapy-adapted, patient-derived xenograft models of glioblastoma (GBM). Tumors show distinct patterns of recurrence in which clonal populations exhibit either a pre-existing fitness advantage or an equipotency fitness acquired through therapy. Characterization of the MRD state by single-cell and bulk RNA sequencing reveals a tumor-intrinsic immunomodulatory signature with prognostic significance at the transcriptomic level and in proteomic analysis of cerebrospinal fluid (CSF) collected from patients with GBM. Our results provide insight into the innate and therapy-driven dynamics of human GBM and the prognostic value of interrogating the MRD state in solid cancers.
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.
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 it