Abstract 630: Directly engaging participants in rare cancer research is feasible: the osteosarcoma and leiomyosarcoma projects
Notice bibliographique
Résumé
Abstract Background and Purpose: Osteosarcoma (OS) and Leiomyosarcoma (LMS) are sarcomas with complex genomes for which there has been limited progress in identifying new treatments and improving outcomes. While slow progress is partially due to insufficient genomic characterization, generating large genomic datasets has been challenging because these are rare cancers. The OS and LMS Projects use various approaches to directly engage pediatric and adult participants with OS and LMS in genomics research. Methods: Working with patients and advocates at the design stage, we created websites (OSProject.org and LMSProject.org) where patients register and consent to participation. Any patient with OS or LMS living in the United States or Canada is eligible. Participant outreach approaches include partnership with advocacy organizations, webinars, social media posts, meeting presentations, stakeholder and physician engagement committees, and direct mailings. Blood and saliva are collected directly from consented participants by mail, archival FFPE tumor samples are obtained from pathology departments and medical records are requested from treating institutions. WES, WGS, DNA panel sequencing and RNASeq of tumor and germline (T/N) is performed. Results are shared with patient, advocacy, physician, and research communities in several ways. Individual participants receive a shared learning report describing the somatic variants identified in their tumor from T/N clinical WES and are offered clinical germline genetic testing and genetic counseling. Results: The study outreach team has participated in or led a total of 33 online and in person events not including social media posts or stakeholder meetings. So far, in 26 months 515 LMS patients (ages 16-83y; median 55) and 145 OS patients (ages 7-74y; median 20) have consented. Thus far, 274 and 76 tumor samples and 555 and 127 germline samples have been obtained from LMS and OS consented participants, respectively. Analysis of the first 20 LMS participants with T/N WGS showed widespread alteration of TP53 (11 pts), RB1 (8 pts), and PTEN (16 pts), concordant with findings in past studies. Likewise, the first 7 OS pts with T/N WGS demonstrated inactivation of TP53 (4 pts) and RB1 (2 pts), as expected from past OS cohorts. Conclusions: Using community partnerships and direct outreach to connect and engage with participants and a virtual consenting process for genomics research in rare cancers is feasible. It is possible to obtain germline samples directly from about half of participants and archival tumor samples from treating pathology departments for about one third of participants consented in direct-to-patient online genomics studies. We have been able to utilize archival tumor samples to identify, with T/N WES/WGS, expected genomic events in complex genome cancers. Recruitment and sequencing are ongoing. Citation Format: Julia M. Wong, David Merrell, Eirian Siegal-Botti, Noorshifa Arssath, Carrie Cibulskis, Evelina Ceca, Alanna Church, Alex Wilson, Lorena Lazo De La Vega, Jill Stopfer, Ellen Sukharevsky, Nia Daley, Anusha Sharma, Sidney Benich, Zachary Kahn, Lauren Fisher, Parker Chastain, Brendan Reardon, Taisha Hendrickson, Colleen Nguyen, Melissa Chiumiento, Melissa Mirick, Noriela Elia, Priscilla Merriam, Eliezer Van Allen, Judy Garber, Riaz Gillani, Chandrajit Raut, Stacey Gabriel, Timothy Rebbeck, Jason L. Hornick, Jennifer Mack, Suzanne George, Diane Diehl, Gad Getz, Katherine A. Janeway. Directly engaging participants in rare cancer research is feasible: the osteosarcoma and leiomyosarcoma projects [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 630.
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 | 0,056 | 0,043 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,003 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,022 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,042 | 0,010 |
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 ».