Abstract 5534: Building the organization framework for biopsy-driven translational research: The Quebec Clinical Research Organization in Cancer (Q-CROC) experience
Notice bibliographique
Résumé
Abstract Introduction: The success of personalized medicine in oncology relies on translational research efforts to identify biomarkers that will influence clinical management. The discovery and validation of biomarkers is a concerted effort requiring an organizational framework that is often underestimated. The Quebec Clinical Research Organization in Cancer (Q-CROC) consortium is a multi-disciplinary and multi-institutional group of scientists and clinicians devoted to integrating and enhancing translational and clinical research capacity in Quebec. We describe here the organizational framework driving a multicenter, prospective study to identify biomarkers of clinical resistance to first-line therapy in metastatic colorectal cancer (NCT00984048, Q-CROC-01). Results: The Q-CROC consortium has put in place an organizational infrastructure to support the activities and operations of its translational projects. We identified and addressed several critical issues during the course of the Q-CROC-01 translational project that were also common to our subsequent biomarker-driven trial in lymphoma (Q-CROC-02, NCT01238692) and breast cancer (Q-CROC-03, NCT01276899). Examples of these issues include: (i) feasibility and burden of tissue collection at participating sites, (ii) limiting pre-analytical variability in blood and tissue specimens for functional downstream applications, (iii) verification of tumor content on biopsy specimens, (iv) tracking sample flow, (v) integration of clinical data with discovery platforms, and (vi) engaging participation throughout all steps of the project. In part to address the above issues, we established five operational Cores: clinical, biobank, biospecimen processing, bioanalytical and bioinformatic. A further challenge was the integration between these Cores, who for the most part operated in silos. We observed that a critical element to unify all components of the consortium was a scientific project management team, consisting of dedicated individuals regularly interacting with each Core to ensure that objectives were aligned and deliverables were met. This academic framework for translational research may be comparable to that of multicenter clinical trials undertaken by industry, but some challenges, including financial and time constraints, data sharing and IP agreements, and engagement of its members, may be more palpable in the academic setting. Conclusion: Infrastructure science is underestimated and under-reported in translational cancer research and is crucial to the success of any large-scale biomarker discovery effort. Our experience with three multi-institutional biomarker-driven trials is that progress hinges upon the availability of an infrastructure that is not only the sum of its parts but that provides a concrete link between each component. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 5534. doi:1538-7445.AM2012-5534
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 | 0,213 | 0,100 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,014 | 0,015 |
| Communication savante | 0,023 | 0,008 |
| Science ouverte | 0,007 | 0,012 |
| Intégrité de la recherche | 0,007 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 ».