Biomarker Analyses of Response to Cyclin-Dependent Kinase 4/6 Inhibition and Endocrine Therapy in Women with Treatment-Naïve Metastatic Breast Cancer
Bibliographic record
Abstract
Abstract Purpose: Preclinical data identified the cyclin-dependent kinase 4/6 (CDK4/6) inhibitor palbociclib as synergistic with antiestrogens in inhibiting growth of hormone receptor-positive/human epidermal growth factor receptor 2-negative (HR+/HER2−) human breast cancer models. This observation was validated clinically in the randomized, placebo-controlled, phase III PALOMA-2 study. Experimental Design: To determine markers of sensitivity and resistance to palbociclib plus letrozole, we performed comprehensive biomarker analyses, investigating the correlation with progression-free survival (PFS), on baseline tumor tissues from PALOMA-2. Results: Despite a broad biomarker search, palbociclib plus letrozole demonstrated consistent PFS gains versus placebo plus letrozole, with no single biomarker or cassette of markers associated with lack of benefit from combination treatment. Palbociclib plus letrozole confers efficacy on both luminal A and B patients. Higher CDK4 levels were associated with endocrine resistance which was mitigated by the addition of palbociclib, whereas lower PD-1 levels were associated with greater palbociclib plus letrozole benefit. Tumors with more active growth factor signaling, as exemplified by increased expression of FGFR2 and ERBB3 mRNA, appeared to be associated with greater PFS gain from the addition of palbociclib. Conclusions: These data underscore the importance of CDK4/6 signaling in HR+/HER2− breast cancer and suggest that the interplay between steroid hormone and peptide growth factor signaling could drive dependence on CDK4/6 signaling. See related commentary by Anurag et al., p. 3
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".