Abstract IA001: An overview of applying discovery science to care for patients in need: working together to advance our understanding and treatment of sarcomas
Notice bibliographique
Résumé
Abstract There are myriad subtypes and clinical scenarios for “sarcomas.” These heterogeneous malignancies, linked by a mesenchymal phenotype and/or cell of origin, serve as both microcosm and model to study the complexities of all cancers. Sarcomas prove the concept that advanced molecular diagnostics are key enabling factors to allow expert pathologists to make more specific, potentially life-preserving diagnoses. It is clear that molecular diagnostics alone cannot be relied upon to define diagnosis. Translocations of EWSR1 can be found in a multiplicity of sarcoma subtypes as well as carcinomas; thus, an EWSR1 fusion is not diagnostic per se. Expert pathologists remains critical to the first step of care: making the most accurate and clinically meaningful diagnosis. Even within a given diagnostic term (e.g., GIST), there are obviously different oncogenic drivers (KIT vs. PDGFRA vs. SDH(x) vs. NTRK) that require uniquely different management decisions. Splitting, rather than lumping together, molecularly-different sarcomas may be a key success factor to understanding inter-patient differences in outcomes, as well as the clinical impact of the evolution of tumor heterogeneity over time, with progressively more challenging resistance to any therapeutic intervention, in any single individual. The fact that stromal and mesenchymal factors may contribute dramatically to resistance to immunotherapy techniques is also very relevant for sarcomas, as we may learn key lessons to extrapolate into improvement of effective immunotherapy for other more common forms of malignancy. The key elements of merging the best discovery science with focused clinical translation in well-defined clinical investigations are key to making advances in patients who rely upon us; this is also where sarcoma investigators have proven our ability to join forces worldwide in collaborative trials that can provide reliable data to achieve regulatory approvals for new targeted therapies in diseases as rare as PEComa or TGCT, or as common as KIT-driven GIST. New approaches to modifying the epigenetic landscape of fusion-associated sarcomas in patients will add to our knowledge about chromatin remodeling and aberrant transcriptional regulation. Finally, new research initiatives in liposarcomas are bringing together teams of investigators to apply the most sophisticated tools to understand this family of diseases characterized by pathognomonic gene amplifications, aberrantly driven ubiquitin-pathway function with loss of p53, oncogenic fusions, and blocked adipocytic differentiation. New discovery collaborations are needed to bring in the best innovative techniques and investigators to work with expert clinical investigators to drive advances. It is very timely for AACR to host this focused research meeting again as a focal point for new discovery and innovation in sarcomas, and this introduction will serve to set the stage for a week of interactive discussions regarding many lines of relevant research to advance our field. Citation Format: George D. Demetri. An overview of applying discovery science to care for patients in need: working together to advance our understanding and treatment of sarcomas [abstract]. In: Proceedings of the AACR Special Conference: Sarcomas; 2022 May 9-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2022;28(18_Suppl):Abstract nr IA001.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,002 | 0,006 |
| Intégrité de la recherche | 0,005 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».