Pooled ctDNA analysis of the MONALEESA (ML) phase III advanced breast cancer (ABC) trials.
Bibliographic record
Abstract
1009 Background: Biomarker analyses have been presented separately for each Phase III ML trial, which tested efficacy and safety of ribociclib (RIB) with different endocrine therapy (ET) combination partners as first- or second-line treatment for hormone receptor–positive, HER2-negative (HR+/HER2−) ABC. Here, using the largest pooled biomarker dataset of a CDK4/6 inhibitor in ABC to date, we identify potential biomarkers of response or resistance to RIB across ML trials. Methods: Baseline ctDNA from 1503 patients (pts) enrolled in ML-2, 3, and 7 was assessed using next-generation sequencing with a targeted panel of 557 genes. Genes with an alteration frequency ≥2% and in ≥15 pts per treatment arm were included (83 genes). Genetic alteration was defined as presence of a mutation, short insertion/deletion, or copy number alteration. Cox proportional hazard model of progression-free survival (PFS) was fit with gene-by-treatment interaction. Genes with interaction P< 0.10 and genes of interest were investigated. Results: Pts with alterations in FRS2 and PRKCA (treatment interaction P< 0.05) as well as MDM2, ERBB2, AKT1, and BRCA1/2 ( P> 0.05 but considered actionable) had a trend for increased PFS benefit of RIB vs PBO (Table). Pts with alterations in CHD4, BCL11B, ATM, or CDKN2A/2B/2C derived little to no added PFS benefit with RIB vs PBO ( P interaction < 0.10; hazard ratio [HR] > 0.80). Data on genes implicated in the literature as potential mechanisms of resistance to ET and/or CDK4/6 inhibition ( ESR1, PTEN, FAT1, RB1, and NF1) will be presented. Conclusions: Results of this pooled analysis of the ML-2, 3, and 7 trials, the largest biomarker analysis of any CDK4/6 inhibitor in ABC, revealed several potential biomarkers of response ( FRS2, MDM2, PRKCA, ERBB2, AKT1, and BRCA1/2) or resistance ( CHD4, BCL11B, ATM, or CDKN2A/2B/2C) to RIB. Clinical trial information: NCT01958021; NCT02422615; NCT02278120 . [Table: see text]
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".