Abstract OT1-19-01: The metastatic breast cancer project: Generating the clinical and genomic landscape of metastatic breast cancer through patient-partnered research
Bibliographic record
Abstract
Abstract The Metastatic Breast Cancer Project (MBCproject) is an ongoing 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. 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 US or Canada to register. From 10/20/15-6/1/21, 6100 patients with MBC registered for the MBCproject. Registered pts are sent an online consent form that asks for permission to obtain and analyze their medical records and samples. Consented pts are sent a saliva and/or 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 to obtain medical records and a portion of their stored tumor biopsies. 3456 pts receiving care at over 1700 different institutions have consented to share medical records and tumor/saliva/blood samples and to have genomic analysis performed. 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. Table 1 highlights clinical data collection, biospecimen acquisition, and genomic data generation to date. Examples of clinicogenomic analyses are shown in Table 2. De-identified linked genomic, clinical, and pt-reported data is shared regularly via public databases (mbcproject.org, cBioPortal, dbGaP, NCI Genomic Data Commons). To date, this data has been cited in over 40 publications. Study updates are shared with participants regularly. The MBCproject continues to enroll new patients, generate additional data, and perform integrated clinical and genomic analyses with the goal of building a dataset that is representative of patients with MBC. We have partnered with over 30 non-profit breast cancer advocacy groups. We also have several community engagement efforts underway to more directly reach patients in underrepresented communities, including partnerships with faith-based organizations and colleges/universities, as well as targeted engagement with the African American community. In addition, in partnership with Latinx patients, advocates, and researchers, a Spanish-language version of the MBCproject was launched in June 2021. Partnering directly with pts rapidly enables thousands of pts to remotely share tumors, blood, saliva, and medical records to accelerate research. The resulting publicly shared clinically annotated dataset is a resource that allows researchers to identify patients with specific phenotypes, who have often been challenging to identify with traditional approaches. Clinical data collection, biospecimen acquisition, and genomic data generation:NumberConsent signed (US & CA)3456 ptsPatient-reported data collected (demographics, diagnosis details, receptor status, clinical experiences, pathology details, sites of metastasis, treatments with start and stop dates3456 ptsMedical record received from clinical institution1365 ptsSaliva sample received from pt2124 ptsBlood sample received from pt1114 ptsTumor samples received from clinical institution631 tumor samples from 398 ptsWES from germline complete505 germline samplesWES from tumor (primary and metastatic) samples complete429 tumor samplesRNA-seq from tumor (primary and metastatic) samples complete351 tumor samplesULP-WGS from cfDNA (taken in metastatic setting) complete953 blood samplesWES from circulating tumor DNA (taken in metastatic setting) complete144 blood samples CohortConsented (US & CA)Tumor WES completeTumor RNA-seq completePts diagnosed < 40 yrs of age114615292De novo MBC1207158109Late recurrence (>5 years after dx)9099141Long term survivors (MBC > 10yrs)163138Resistance to CDK4/6 inhibitors70914839NED at time of f/u survey4305445Triple Negative Breast Cancer3304632Patients with 2 or more tumor biopsies/cfDNA samples collected by the MBCproject29810882 Citation Format: Nikhil Wagle, Corrie Painter, Elana Anastasio, Mary McGillicuddy, Esha Jain, Tania G. Hernandez, Brett N. Tomson, Beena Thomas, Daniel Abravanel, Dewey Kim, Sara Balch, Alyssa L. Damon, Shahrayz Shah, Rafael Ramos, Delia Sosa, Ilan Small, Colleen Nguyen, Sarah Winnicki, Taylor Cusher, Parker Chastain, Michael Dunphy, Jorge Gomez Tejeda Zanudo, Netsanet Tsegai, Lauren Sterlin, Ulcha F. Ulysse, Imani Boykin, Oyin Alao, 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 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr OT1-19-01.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".