NCI Breast Cancer Steering Committee Working Group (WG) report on meaningful and appropriate endpoints for clinical trials (CT) in metastatic breast cancer (MBC).
Bibliographic record
Abstract
1073 Background: There is significant heterogeneity in the natural history of MBC. Several recent randomized CT have yielded statistically significant advantages for the experimental arm, but neither led to regulatory approval nor practice change. Formal guidance for industry on CT endpoints provided by the US FDA in 2007 was not disease-specific. Patient-focused drug development is mandated by Prescription Drug User Fee Act V. Our WG sought to create specific consensus on endpoints for MBC CT focusing on subtype and line of therapy, with sensitivity to various stakeholders. Methods: A WG composed of medical oncologists, statisticians, advocates, FDA and NCI liaisons performed a systematic literature review of MBC natural history, CT endpoints by subtype (HR+/HER2-, HR+/HER+, HR-/HER2-, HR-/HER2+), and line of therapy (n = 146 papers). External expertise was obtained on industry perspectives, big data and real world evidence (RWE), and patient reported outcomes (PROs). WG members voted anonymously on statements and positions generated from deliberation. Results: The WG reached consensus on definitions relevant to contemporary CT endpoints. WG recommendations on the appropriate choice of OS or PFS are sensitive to expected post progression survival (PPS), and proportional and absolute gains. Currently, for HR-/HER2- MBC, OS is preferred as the optimal primary endpoint regardless of line of therapy; PFS is preferred in settings where expected PPS is longer. Toxicity can outweigh modest gains in PFS; scant data exist to gauge how patients value PFS gain vs toxicity. Where new agents may prolong PFS without impacting OS, exploring/validating graphic approaches that capture grade and timing of toxicity and PROs is warranted. An overview of WG Consensus Statements will be presented in detail. Conclusions: CT design for MBC should be sensitive to natural history (PPS), availability of other effective agents, PROs and toxicity burden. As unique subtypes (e.g. AR+) and novel therapies (e.g. immunotherapy) emerge, reassessment of relevant benchmarks is indicated. Rigorous big data approaches providing insights from RWE may inform CT endpoints in the future.
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 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 | 0.499 | 0.451 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.019 | 0.016 |
| Research integrity | 0.022 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.012 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".