Abstract B120: A call for a modernized TNBC/mTNBC clinical trial: Ensuring equity, precision medicine, and survivorship for young Black women
Bibliographic record
Abstract
Abstract Background: While the median age of breast cancer diagnosis in the U.S. is 61, over 14,000 women under 40 are diagnosed annually and early onset cancers are on the rise globally. The magnitude and speed at which early onset cancer incidence has increased is unlike most cancer trends ever observed. This phenomenon has been observed on all continents, in men and women, and in many cancer sites. Younger Black women face a disproportionate burden of aggressive triple-negative breast cancer (TNBC) and metastatic TNBC (mTNBC), with a 40% higher mortality rate than White women—a gap unchanged for 25 years. Black women are three times more likely to develop TNBC, often with larger and more aggressive tumors, making breast cancer the leading cause of cancer death for women ≤40. Despite precision medicine advances and guideline driven integrative care approaches, young Black women remain underrepresented in research and face significant systemic barriers to equitable care. Evidence-based integrative oncology services- such as psychosocial support, stress reduction, nutrition counseling, and yoga- are recommended by national guidelines to complement treatment yet remain underutilized in clinical trials. What is the definitive, proven therapy for newly diagnosed Black Women with TNBC/mTNBC? Methods: A comprehensive review of FDA-approved TNBC/mTNBC PH3/pivotal trials (2020–2025) assessed representation and outcomes for young Black women. Inclusion criteria required trials to be completed or have reported results in peer-reviewed literature or FDA databases. Five trials were analyzed for racial/ethnic inclusion, age-specific data, and integrative care accessibility. Additional review of recent TNBC precision medicine publications evaluated whether therapeutic recommendations considered race/ethnicity or age-specific factors. Results: Severe underrepresentation of Black participants was identified across reviewed trials. Only 6% (216/3,545) of total trial participants identified as Black; well below U.S. census-based targets for proportional representation (14–15%). Age-specific inclusion was inconsistent: though eligibility typically included adults ≥18 years, no trials reported disaggregated outcomes for patients under 40 or by race-age intersection. No studies incorporated or measured the impact of guideline-supported integrative care approaches, despite recommendations by national guidelines supporting such services for symptom management and quality of life. Additionally, recent systematic reviews and therapeutic consensus papers on TNBC lacked discussion of race or ethnicity as a determinant of outcomes or therapeutic efficacy. Conclusions: Despite progress in treatment, young Black women with TNBC remain underrepresented in trials and face barriers to equitable care. Emerging data underscore the need for inclusive research and integrative, patient-centered approaches. A modernized trial design should test whether a culturally responsive, biomarker-driven care model with supportive integrative care services improves outcomes in this population. Citation Format: Jeanne M. Regnante, Nicole Simone, Sara Horton, Maimah Karmo, Adrienne G. Tilbor. A call for a modernized TNBC/mTNBC clinical trial: Ensuring equity, precision medicine, and survivorship for young Black women [abstract]. In: Proceedings of the 18th AACR Conference on the Science of Cancer Health Disparities; 2025 Sep 18-21; Baltimore, MD. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2025;34(9 Suppl):Abstract nr B120.
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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 | 0.039 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".