Association of electronic-health record (EHR)-derived race with BRCA testing in patients (pts) with breast cancer (BC) with similar genetic ancestry (GA) in a clinicogenomic database (CGDB).
Bibliographic record
Abstract
6524 Background: Disparities in health outcomes can be affected by biological factors associated with GA and social determinants of health. These factors can be teased apart using GA data from comprehensive genomic profiling (CGP) in pts with cancer. CGDBs that link EHR data with CGP enable the selection of pts with similar GA. Holding GA constant provides an opportunity to directly study the effects of reported race in health disparities. This study evaluated a published racial disparity (BRCA testing rates in African American [AA] vs White pts with BC) in a population with fixed, similar GA. Methods: The nationwide (US-based) deidentified Flatiron Health and Foundation Medicine (FMI) BC CGDB (Q3 2020) was used. For each pt, GA fractions from 5 geographic ancestry groups (African [AFR]; Admixed American; East Asian; European [EUR]; South Asian) were derived by FMI using an admixture analysis workflow using genes captured in the CGP assay. To focus on BRCA testing in AA vs White pts and find a sufficient population with similar GA but AA or White race, pts with admixture of both EUR and AFR ancestry were selected. The chosen fractions were: Cohort 1=35%-65% AFR and EUR each; Cohort 2=25%-60% AFR and EUR each; Cohort 3=30%-60% AFR. Cohorts overlap but were chosen to increase sample size. In each cohort, documented BRCA testing prevalence, time from diagnosis to BRCA test date, age at BRCA test and overall survival (OS) were compared between races. Other race (OR) and missing race (MR) were also reported. Results: Most pts (4130/6903) in the BC CGDB had ≥75% EUR ancestry; 129 pts had AFR ancestry fractions ≥25% with EUR ancestry >0%. AA pts had the lowest BRCA testing rates (39%, 43%, 44% for Cohorts 1-3, respectively), which were 18%, 10% and 17% lower compared with White pts, respectively (Table). In Cohorts 1-3, AA pts experienced a longer median time between diagnosis and testing (399, 668, 900 days) compared with White pts (93, 667, 106 days). The median age at BRCA test was 16, 9 and 8 years younger in AA pts (49, 47 and 50 years) compared with White pts. Although pts with MR data had the lowest OS compared with the other races within each cohort, the sample size of each arm for all cohorts was too small to make conclusions. Conclusions: This study demonstrated that when holding GA constant, racial disparities persist in BRCA testing patterns and outcome in pts with BC from a CGDB. With increasing availability of linked clinical and genomic data, further exploration of disparities in genetically similar cohorts can provide deeper insight for cancer outcomes and health disparities research.[Table: see text]
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".