Abstract PD8-01: The metastatic breast cancer project: Generating the clinical and genomic landscape of metastatic breast cancer through patient-partnered research
Notice bibliographique
Résumé
Abstract Background: The Metastatic Breast Cancer Project (MBCproject) is a research study that directly engages patients (pts) through social media and advocacy groups, and empowers them to share their samples, clinical information, and experiences. The goal is to create a publicly available dataset of linked genomic, clinical, and pt-reported data to enable research. Methods: In collaboration with pts, advocates, and advocacy groups, a website (MBCproject.org) was developed that allows pts with metastatic breast cancer (MBC) anywhere in the U.S. or Canada to register. Registered pts are sent an online consent form that asks for permission to obtain and analyze their medical records and samples. Once enrolled, pts are sent a saliva kit and a blood kit and asked to mail back a saliva sample, which is used to extract germline DNA, and/or a blood sample, which is used to extract germline DNA and cell free DNA (cfDNA). We contact participants’ medical providers and obtain medical records and a portion of their stored tumor biopsies. Whole exome sequencing (WES) is performed on tumor DNA, germline DNA, and cfDNA; transcriptome sequencing (RNA-seq) is performed on tumor RNA. Medical records and pt-reported data are abstracted to create a detailed clinical record for each pt. All de-identified data are shared regularly via public databases (cbioportal.org, mbcproject.org, dbGaP, NCI Genomic Data Commons) without restrictions. Study updates are shared with participants regularly. Results: From 10/20/15-7/8/19, 5357 women and men with MBC registered. 3290 pts receiving care at over 1700 different institutions consented to share medical records and tumor/saliva/blood samples, and to have genomic analysis performed. Details of clinical data collection, biospecimen acquisition, and genomic data generation to date are outlined in the Table. WES from 463 tumors obtained from 326 pts have been generated (with matched germline WES), including 61 pts with 2 timepoints, 19 pts w 3 timepoints, and 11 pts w 4+ timepoints. 278 tumor exomes were from the breast/regional lymph nodes, 63 from distant metastatic sites and 122 from cfDNA. 110 tumor exomes were from samples obtained before the diagnosis of MBC, 258 from after the diagnosis of MBC, and 95 to be determined (TBD). 161 tumor exomes were obtained prior to any therapy, 204 following some therapy, and 98 TBD. Clinically annotated genomic data are used to study specific pt cohorts (including rare subsets and outliers) and to identify mechanisms of response and resistance to therapies. Examples of the clinical and genomic analyses that will be presented include: - Pts diagnosed <40 yrs of age (1108 pts enrolled; 120 with tumor WES) - de novo MBC (1122 pts enrolled; 121 with tumor WES) - Late recurrence, >5 yrs after diagnosis (830 pts enrolled; 77 with tumor WES) - Long-term survivors, >10 yrs with MBC (159 pts enrolled; 11 with tumor WES) - Resistance to CDK4/6 inhibitors (709 pts enrolled; 148 with tumor WES) Conclusions: Partnering directly with pts enables rapid identification of thousands of pts willing to share tumors, blood, saliva, and medical records to accelerate research. This approach allows for identification of patients with specific phenotypes, who have been challenging to identify with traditional approaches. Remote acquisition of medical records and saliva/blood/tumor samples is feasible. This clinically annotated dataset is a shared resource for the research community. Table 1Clinical data collection, biospecimen acquisition, and genomic data generation:NumberConsent signed3290 ptsSurvey #1 submitted3290 pts(demographics, diagnosis details, receptor status, clinical experiences)Survey #2 submitted1435 pts(pathology details, sites of metastasis, treatments with start and stop dates)Medical record received1307 ptsSaliva sample received1976 ptsBlood sample received1121 ptsTumor samples received482 tumor samples from 346 ptsDigital image of tumor slide H&E generated482 tumor samplesWES from germline complete310 germline samplesWES from tumor sample complete341 tumor samplesRNA-seq from tumor sample complete229 tumor samplesULP-WGS from cfDNA complete947 blood samplesWES from circulating tumor DNA complete122 blood samples Citation Format: Nikhil Wagle, Corrie Painter, Elana Anastasio, Michael Dunphy, Mary McGillicuddy, Esha Jain, Tania G Hernandez, Sara Balch, Beena Thomas, Dewey Kim, Alyssa L. Damon, Shahrayz Shah, Brett N. Tomson, Rachel Stoddard, Colleen Nguyen, Jorge Buendia-Buendia, Ofir Cohen, Jorge Gomez Tejeda Zanudo, Netsanet Tsegai, Lauren Sterlin, Ulcha Fergie Ulysse, Kathryn Sine, Oyin Alao, Jacqueline Lucia, Eric S. Lander, Todd R. Golub. The metastatic breast cancer project: Generating the clinical and genomic landscape of metastatic breast cancer through patient-partnered research [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD8-01.
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 | 0,017 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,003 | 0,011 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,025 | 0,008 |
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 ».