Real-world impact of TAILORx on chemotherapy use and SOFT/TEXT on ovarian function suppression uptake in a population based-cohort of women 40 years and under with HR-positive, HER2-negative, axillary lymph node negative breast cancer.
Bibliographic record
Abstract
534 Background: Gene expression profiling (GEP) testing is prognostic for distant recurrence risk in women with hormone receptor positive (HR+), human epidermal growth factor receptor-2 negative (HER2-), axillary lymph node negative (LN-) breast cancer (BC). The TAILORx trial generated prospective evidence demonstrating the 21-gene recurrence score (RS) is also predictive of chemotherapy (CT) benefit. The literature suggests that GEP testing reduces adjuvant CT prescription and is cost-effective. In the same era, the SOFT/TEXT trials demonstrated disease-free survival benefit for the addition of ovarian function suppression (OFS) to endocrine therapy in premenopausal women. Real-world uptake and impact of these advances in the management of HR+, HER2-, LN- BC in young women may be limited due to complex reasons. This retrospective study evaluated GEP and CT use post TAILORx and OFS use post SOFT/TEXT in women 40 years old or younger diagnosed with HR+, HER2-, LN BC from 2011 to 2020 in Alberta, Canada. Methods: Clinical variables were retrieved from the Alberta Health Services Cancer Care Breast Data Mart and through review of the electronic medical record. GEP testing and CT use were compared between 3 cohorts of women defined by diagnosis: pre RS funding/pre TAILORx (before April 2014), post RS funding/pre TAILORx (April 2014-May 2018), post TAILORx (June 2018 and beyond). OFS use was compared between 2 cohorts of women defined by diagnosis pre- and post SOFT/TEXT (May 2015). In subgroup analyses, we compared CT use by GEP status and RS category and OFS use by CT status. Results: Among the 291 women identified, GEP testing increased by 37% post GEP funding (pre 2% vs. post 39%; P = < .0001) and by additional 15% post TAILORx (pre 39% vs. post 54%; P < .0001) whereas overall CT use declined by 15% post GEP funding (pre 85% vs. post 70%; P = .01) and by additional 16% post TAILORx (pre 70% vs. post 54%; P = .01). Although not significant, OFS use increased by 8% post SOFT/TEXT (pre 13% vs. post 21%; P = .08). In subgroup analyses, post TAILORx was associated with a significant reduction in CT use of 19.5% in non-GEP tested women (pre 85% vs. post 65% P = .01) and non-significant increase in CT use of 6% in RS tested women (pre 40% vs. post 46%; P = .61) mainly driven by high mid range RS 21-25 and lower high range RS 26-30. There was no significant change in OFS use in non-CT treated women post SOFT/TEXT (Pre 5.3% vs. Post 8%, P = .68) whereas OFS use increased by 15% in women treated with CT post SOFT/TEXT (pre 15% vs. post 30%; P = .01). Conclusions: Public GEP funding and TAILORx led to widespread adoption of GEP and real-world decline in CT use in women 40 years of age or younger diagnosed with HR+, HER2-, LN- BC in Alberta. SOFT/TEXT led to real-world incline in OFS use in those treated with CT but adoption remained suboptimal.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".