Abstract 7361: Scientific rationale and successful implementation of biospecimen collection in the NCI Connect Cohort for Cancer Prevention
Notice bibliographique
Résumé
Abstract Introduction: The Connect for Cancer Prevention Study is a new prospective cohort with repeated exposure assessment and long-term follow-up to study the cancer continuum from initiation, multi-step carcinogenesis, diagnosis, to outcomes in a diverse US population. Over 50, 000 participants towards the goal of 200, 000 have been recruited so far at 10 integrated healthcare sites across the United States. Goals of Connect include studies of cancer etiology, risk prediction, and early detection. Biospecimens are a critical component for exposure assessment and biomarker measurement. We summarize the scientific rationale and successful implementation of biospecimen collections in Connect. Methods: A biospecimen collection protocol was informed by literature review, expert consultations, and pilot studies evaluating how different blood collection tubes and processing protocols influence DNA yield and quality, as well as nucleic acid and protein-based biomarkers. Baseline biospecimens include a 45ml blood draw, a urine collection, and a mouthwash sample. Biospecimens are collected in dedicated research labs or at clinical sites with home collection of mouthwash samples. A cell-free DNA collection for multi-cancer early detection is implemented at research collection sites. All biospecimens are shipped to an NCI laboratory for processing and long-term storage. Process metrics include sample completeness, sample deviations, temperature logging, and needle-to-freezer time, among others. Results: As of November 2024, 35, 790 participants of 53, 005 enrolled (68%, ranging from 57% to 75% across sites) donated blood and urine samples, with baseline collections still ongoing. Among collections, 58% were from clinical sites, and 42% from research labs. Over 80% of biospecimens collected at research labs were received at NCI within one day, while 70% of biospecimens collected at clinical sites were received within 2 days. Over 95% of all biospecimens were received within 4 days. The return of home-collected mouthwash samples was 77%. Among 276, 228 biospecimen tubes collected, 87% were complete with no deviations recorded. Over 90% of participants submitted a short survey relevant for sample collection after the biospecimen visit. Blood collections are planned every three years to study different exposure windows and biomarker changes within individuals. More frequent collections are considered among participants at high risk of cancer. Conclusions: Connect combines EHR data, state-of-the-art surveys, and biospecimen collections to address critical questions of cancer etiology and prevention. We successfully implemented a robust and efficient biospecimen collection approach at 10 recruitment sites across the U.S. The timeline and process for biospecimen access for the research community and initial biospecimen activities will be presented at AACR. Citation Format: Nicolas A. Wentzensen, Stephanie Weinstein, Amanda Black, Erin Schwartz, Hannah P. Yang, Michelle Brotzman, Paul Albert, Laura Beane-Freeman, Amy Berrington, Jonas De Almeida, Jonine D. Figueroa, Montserrat Garcia-Closas, Nicole Gerlanc, Gretchen Gierach, Rena Jones, Peter Kraft, Charles Matthews, Habib Ahsan, Brisa Aschebrook-Kilfoy, Chun-Hung Chan, Robert Greenlee, Stacey Honda, Ben Rybicki, Blythe Ryerson, Katherine Sanchez, Mark Schmidt, Kevin Sykes, Larissa White, Jeanette Ziegenfuss, Stephen Chanock, Christian Abnet, Mia Gaudet. Scientific rationale and successful implementation of biospecimen collection in the NCI Connect Cohort for Cancer Prevention [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7361.
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 | 0,573 | 0,592 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,002 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,007 | 0,006 |
| Intégrité de la recherche | 0,011 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,003 |
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; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».