Abstract 4885: Identification of actionable targets for refractory/relapsed childhood cancer leading to personalized targeted therapy (TRICEPS Study)
Bibliographic record
Abstract
Abstract Childhood cancer is a group of heterogeneous complex diseases. Although 80% of these children are cured with conventional therapies, it remains the first cause of death among children in Western countries. A significant number of refractory/relapse patients will eventually succumb to their disease and the lack of therapeutic advances for these patients is even more worrisome. Indeed, no significant progress has been noted over the last decade for these patients, urging the need for new and more effective therapeutic approaches. Precision medicine and more effective personalized targeted therapies (PTT) are a major breakthrough leading to increased cure rates and decreased treatment-related morbidity and mortality for the patients with refractory or relapsed tumors. To address this challenge, the TRICEPS study was initiated on April 2014 at the Sainte-Justine UHC (Montreal, Canada) with an overreaching goal to explore the feasibility of performing genomic-driven targeted therapy in pediatric and adolescent (aged 0-21 years) patients with relapsed or refractory childhood cancer. This study offers in-depth genomic and transcriptomic investigation of patient’s tumoral material to identify patient-specific alterations and actionable driver mutation(s) that can be targeted with approved targeted drug and within a reasonable clinically relevant timeframe to assess the feasibility of going from biopsy to a detailed tumor analysis report. Over a period of 30 months, 44 relapsed/refractory cancer patients were recruited. Twenty-two of them underwent extensive genomic investigation (exomic and transcriptomic sequencing) within a median timeframe of 9.7 weeks from patient enrolment to return of results. Patient screen failures occurred due to benign/necrotic tumor biopsies or low tumor purity resulting in suboptimal DNA/RNA quantity or quality for genomic analysis. In all 22 patients, we have identified clinically relevant genomic alterations (SNVs, indels, fusions, CNAs) and relapse-specific mutations influencing patient management and providing options for personalized interventions. We assessed the functional impact of some of these cancer-specific alterations. This was the case of a novel relapse-specific rearrangement, identified on relapsed childhood ETP-ALL, and leading to asparagine synthetase (ASNS) up-regulation through a promoter exchange. The expression of this fusion was associated with reduced apoptosis following l-asparaginase treatment. This study shows that PPT based on next generation sequencing technology is a powerful approach that could be implemented in the clinic within a foreseeable future to guide treatment of hard-to-treat childhood cancers and to further improve patient care and outcomes. Citation Format: Fida Khater, Stephanie Vairy, Sylvie Langlois, Jasmine Healy, Sophie Dumoucel, Mathieu Lajoie, Thomas Sontag, Pascal St-Onge, Henrique Bittencourt, Dorothée Dal Soglio, Anne-Sophie Carret, Sonia Cellot, Josette Champagne, Michel Duval, Maja Krajinovic, Jean-Marie Leclerc, Valerie Larouche, Natalie Patey, Sébastien Perreault, Nelson Piché, Yvan Samson, Pierre Teira, Monia Marzouki, Daniel Sinnett. Identification of actionable targets for refractory/relapsed childhood cancer leading to personalized targeted therapy (TRICEPS Study) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4885. doi:10.1158/1538-7445.AM2017-4885
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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 | 0.002 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".