First versus second-generation molecular profiling tests: How both can guide decision-making in early-stage hormone-receptor positive breast cancers?
Bibliographic record
Abstract
Hormone receptor-positive (HR+) and human epidermal growth factor receptor 2-negative (HER2-) tumors represent the most common types of early-stage breast cancer. However, their response to adjuvant systemic treatments varies widely due to tumor heterogeneity. Current decisions for adjuvant treatment rely heavily on clinical and pathological characteristics, which can sometimes lead to overtreatment. Accurately identifying patients who will benefit from adjuvant chemotherapy at an individual level remains a challenge. Multigene profiling assays are now widely used in clinics to better assess recurrence risk and chemotherapy response for HR+ disease. In this report, we examine the advantages and limitations of two widely used molecular profiling tests-Oncotype DX and Prosigna. Both Oncotype DX and Prosigna have been demonstrated to be effective prognostic tools in early breast cancer, with Oncotype DX also being validated as a predictive tool to guide chemotherapy decisions. We focus on studies that directly compare these molecular tests and discuss how their strengths can be leveraged to improve clinical decision-making for early-stage HR+ breast cancers. Finally, we highlight remaining knowledge gaps and propose directions for future research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".