TR-16 * PERSONALIZED TARGETED THERAPY IN REFRACTORY OR RELAPSED CANCER IN CHILDHOOD (TRICEPS STUDY)
Bibliographic record
Abstract
Despite all recent advances in cancer's treatment, 18% of children will die from their cancer. New tools that enable better individual tumor characterization and classification are required to improve patient care and outcomes on children. Personalized targeted therapy has proven to be a promising strategy for first line treatment in adult cancer. Given the high heterogeneity in childhood cancers, access to histopathology-based clinical trials for targeted therapy is challenging and facilitated by the capacity to sequence individual childhood tumors in real time. A feasibility study is currently underway at the CHU Sainte-Justine, Montreal, where 30 relapse or refractory cancer patients between 0 and 21 years, are being offered in-depth genomic and transcriptomic investigation to identify patient-specific alterations that may be targetable with alternative therapies. This pilot study will allow us to address the following questions: 1) determine the number of children with cancer who are suitable candidates for targeted therapy at our institution each year, 2) determine the number and type of driver mutation(s) found in our population of recurrent or refractory cancers, 3) determine the number of cancer patients who harbour actionable driver mutation(s) that can be targeted with a Health Canada approved targeted drug, 4) assess the feasibility of obtaining high quality/quantity biospecimen, performing genomic sequencing and data analysis, identifying a drug based on the genomic data and offering this information to the medical team, the patient and the family within a 10-week time frame. The following step toward translating our results into novel personalized therapeutic approaches will then be the development of phase II clinical trials to evaluate the efficacy of these targeted therapies in terms of therapeutic response, survival, and overall quality of life. Our overreaching goal is to be able to offer personalized targeted therapy options to all patients upon primary diagnosis of their cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".