Determination of human brain tumour therapy response using an <i>ex vivo</i> invasion assay provides a potential step toward individualized treatment
Bibliographic record
Abstract
11509 Background: Malignant brain tumours are the 6th leading cause of pre-mature death in Ontario with over 10,000 potential years of life lost each year. Improved treatment for malignant brain tumours is needed. Models assessing chemotherapy response employ clonal malignant human tumour cells while patient responses are heterogeneous. Tumour spreading is dependent on tissue invasion and in this study, a surgical sample of each patient’s tumour was used to assess invasion and growth while exposed to a panel of clinically relevant chemotherapies. Methods: Tissue specimens were placed into a nutrient-rich collagen gel that mimics the tumour environment in the body. Chemotherapy treatments were suspended in the matrix surrounding the tumour. Growth and invasion in the presence of chemotherapies was assessed for 5 days following surgical removal in this 3 dimensional matrix and compared to control conditions using student t- test. Results: 12 patient’s individual tumour response was assessed. 4 patients tumours did not respond to any chemotherapy tested. Table 1 shows the number of responders to each therapy tested. Conclusions: Individual response to chemotherapy is highly variable both clinically and in our ex vivo assessment of tissue fragments. Several patients (8/12 or 67%) tumour assessment displayed significant (p<.05) response to one or more therapies. Results from this data will continue to be compared to patient response, The overall predictive value of the data obtained using this ex vivo model will be determined by continuing to collect information time to recurrence and survival at 3, 6, 12 and 24 months) from 90 solid tumour patients per year. Pre-assessment each patient’s responsiveness to chemotherapies could lead to more individualized and therefore more effective treatment. [Table: see text] [Table: see text]
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".