Serial multiomics uncovers anti-glioblastoma responses not evident by routine clinical analyses
Bibliographic record
Abstract
Recurrent glioblastoma (rGBM) remains incurable. One barrier to the development of effective rGBM therapies is the difficulty in collecting posttreatment tumor tissue. Serial multiomic assays from longitudinal rGBM biopsies may uncover tumor responses to a treatment. Here, we obtained 97 serial rGBM biopsy cores over 4 months from the first two patients participating in a clinical trial of repeated intratumoral dosing of the immunotherapeutic agent CAN-3110. Multiomic analysis of the biopsy cores revealed therapeutic effects, including longitudinal and spatial reshaping of the rGBM's microenvironment, expansion of new T cell tissue-resident effector memory clonotypes against CAN-3110 epitopes and other undetermined antigens, and expression of human leukocyte antigen (HLA)-presented immunopeptides, including cancer testis antigens. Moreover, serial integrated multimodal analyses provided evidence of therapeutic responses to CAN-3110 despite traditional magnetic resonance imaging indicating progression. Clinically, the two treated patients achieved a pathologic response or stable clinical disease, respectively. These results show the value of longitudinal tissue sampling to understand rGBM's evolution during administration of an investigational therapy.
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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 | 0.000 | 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) | 0.001 | 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 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".