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Abstract OT-18-01: The metastatic breast cancer project: Generating the clinical and genomic landscape of metastatic breast cancer through patient-partnered research

2021· article· en· W4205712587 on OpenAlexaboutno aff
Nikhil Wagle, Corrie Painter, Elana Anastasio, Michael Dunphy, Mary McGillicuddy, Esha Jain, Brett N. Tomson, Tania G. Hernandez, Beena Thomas, Dewey Kim, Alyssa L. Damon, Shahrayz Shah, Rafael Ramos, Colleen Nguyen, Oneil Lee, S Winnicki, Sara Balch, Rachel Stoddard, Taylor Cusher, Parker Chastain, Jorge Gómez Tejeda Zañudo, Jorge E. Buendia-Buendia, Ofir Cohen, Netsanet Tsegai, Lauren Sterlin, Ulcha F. Ulysse, Imani Boykin, Kate Sine, Oyin Alao, Jacqueline Lucia, Eric S. Lander, Todd R. Golub

Bibliographic record

VenueCancer Research · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerMetastatic breast cancerMedicineBiobankCancerInternal medicineOncologyMedical recordExome sequencingFamily medicineBioinformaticsBiologyGeneticsGene

Abstract

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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 10/20/15-3/31/20, 5708 women and men 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. 3245 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 and semi-public databases (mbcproject.org, cBioPortal, dbGaP, NCI Genomic Data Commons). To date, this data has been cited in over 20 published journal articles. 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, the project has been translated into Spanish and is expected to launch in late 2020. 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 database 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)3245 ptsSurvey #1 submitted(demographics, diagnosis details, receptor status, clinical experiences)3245 ptsSurvey #2 submitted(pathology details, sites of metastasis, treatments with start and stop dates)1638 ptsMedical record received1352 ptsSaliva sample received2004 ptsBlood sample received1121 ptsTumor samples received585 tumor samples from 424 ptsDigital image of tumor slide H&E generated585 tumor samplesWES from germline complete458 germline samplesWES from tumor (primary and metastatic) samples complete343 tumor samplesRNA-seq from tumor (primary and metastatic) samples complete228 tumor samplesULP-WGS from cfDNA (taken in metastatic setting) complete993 blood samplesWES from circulating tumor DNA (taken in metastatic setting) complete143 blood samples CohortConsented (US & CA)Tumor WES completeTumor RNA-seq completePts diagnosed < 40 yrs of age107312071De novo MBC112712183Late recurrence (>5 years after dx)8307752Long term survivors (MBC > 10yrs)158115Resistance to CDK4/6 inhibitors70914839NED at time of f/u survey4238939Triple Negative Breast Cancer3107531Patients with 2 or more tumor biopsies / cfDNA samples collected by the MBCproject2876138 Citation Format: Nikhil Wagle, Corrie Painter, Elana Anastasio, Michael Dunphy, Mary McGillicuddy, Esha Jain, Brett Tomson, Tania G. Hernandez, Beena Thomas, Dewey Kim, Alyssa L. Damon, Shahrayz Shah, Rafael Ramos, Colleen Nguyen, Lee O'Neil, Sarah Winnicki, Sara Balch, Rachel Stoddard, Taylor Cusher, Parker Chastain, Jorge Gomez Tejeda Zanudo, Jorge Buendia-Buendia, Ofir Cohen, Netsanet Tsegai, Lauren Sterlin, Ulcha F. Ulysse, Imani Boykin, Kate 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 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr OT-18-01.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0020.009
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0230.006

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.

Opus teacher head0.166
GPT teacher head0.466
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2021
Admission routes1
Has abstractyes

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