Abstract P5-02-14: Identification of mechanisms of acquired resistance to ribociclib plus endocrine therapy using baseline and end-of-treatment circulating tumor DNA samples in the MONALEESA-2, -3, and -7 trials
Bibliographic record
Abstract
Abstract Background: Genetic alterations that contribute to resistance to therapy may be acquired during treatment (tx) for hormone receptor−positive/human epidermal growth factor receptor−negative (HR+/HER2−) advanced breast cancer (ABC). A previous pooled analysis of circulating tumor DNA (ctDNA) in MONALEESA (ML)-2, -3, and -7 identified potential predictive biomarkers for response and resistance to ribociclib (RIB) at baseline (BL). Here, we describe an analysis of paired BL and end of treatment (EOT) samples from ML-2, -3, and -7 to identify acquired mechanisms that may impact resistance to RIB + endocrine therapy (ET) vs placebo (PBO) + ET. Methods: ML-2 (NCT01958021), ML-3 (NCT02422615), and ML-7 (NCT02278120) evaluated efficacy and safety of RIB + ET vs PBO + ET in pre- and postmenopausal patients (pts) with HR+/HER2− ABC treated in first-line (1L) and second-line (2L) settings. Plasma samples were collected at cycle 1 day 1 (C1D1; prior to first therapy exposure) and at EOT (± 28 days of recorded progression). ctDNA was sequenced using a targeted next-generation sequencing panel of ≈550 genes. Genes with an alteration frequency of >5% at EOT, regardless of their frequency at BL, were included. Tumor mutational burden (TMB) was assessed by tx arm; a TMB cutoff of 10 mutations/MB was used to categorize pts as TMB high vs low. To assess differences in the presence of alterations, a McNemar test was performed on paired samples and adjusted (adj) for multiple testing using the false discovery rate (FDR). A Bayesian mixed effects model was used to account for ctDNA fraction and trial and to test for tx-specific resistance by including a tx × visit interaction term. Results: A total of 905 paired samples from ML-2, -3, and -7 were included in this analysis, 441 and 464 samples from pts treated with RIB + ET and PBO + ET, respectively. Overall, 17 genes had an alteration frequency of >5% at EOT. The ctDNA fraction was higher at EOT vs C1D1 in both the RIB (P=.037) and PBO (P=.033) arms. The frequency of alterations in RB1 (10.4% vs 2.0%), ATM (11.3% vs 8.4%), FAT1 (4.8% vs 3.0%), and FAT3 (5.0% vs 2.5%) was higher at EOT vs C1D1 in the RIB arm (FDR-adj P<.10). Alterations in ESR1 were also higher at EOT vs C1D1 in both the RIB (26.3% vs 9.1%) and PBO arms (28.9% vs 5.4%) (FDR-adj P<.0001). Conversely, alterations in GATA3 were higher at EOT in the PBO arm (FDR-adj P=.11). These results were consistent after adjusting for ctDNA fraction. The most common ESR1 mutations were D538G, Y537S/N/C/D, E380Q, and L536H/P/R. Tx × visit interaction effects were observed for RB1 in the RIB arm and GATA3 in the PBO arm, suggesting tx-specific resistance. A tx × visit interaction for ESR1 was also observed, suggesting a larger relative increase in ESR1 mutations with PBO vs RIB. The percentage of pts with high TMB (>10) at EOT increased from 1.1% to 5.7% in the RIB arm and from 1.7% to 3% in the PBO arm. After accounting for ctDNA fraction and trial, a larger numerical increase in TMB was observed for RIB (odds ratio [OR], 9.0; 95% CI, 2.9-32.7) vs PBO (OR, 2.1; 95% CI, 0.7-6.5); however, the model did not support a differential tx effect. Conclusions: This comprehensive analysis of pooled samples from ML-2, -3, and -7 identified acquired gene alterations in pts with HR+/HER2− ABC treated with 1L or 2L RIB + ET or PBO + ET. The frequency of several genes known to contribute to resistance (ESR1, RB1, ATM, FAT1, and FAT3) was higher at EOT vs C1D1 in pts treated with RIB + ET, while ESR1 and GATA3 alterations were higher at EOT vs C1D1 in pts treated with PBO + ET. This paired dataset of BL and EOT samples from pts with HR+/HER2− ABC treated with a CDK4/6 inhibitor and ET is the largest to date and could be used to validate and confirm acquired resistance mechanisms with low alteration frequency. Citation Format: Fabrice Andre, Nadia Solovieff, Faye Su, Aditya Bardia, Patrick Neven, Yoon-Sim Yap, Debu Tripathy, Yen-Shen Lu, Dennis Slamon, Stephen K. Chia, Mukta Joshi, Arunava Chakravartty, Agnes Lteif, Tetiana Taran, Carlos Arteaga. Identification of mechanisms of acquired resistance to ribociclib plus endocrine therapy using baseline and end-of-treatment circulating tumor DNA samples in the MONALEESA-2, -3, and -7 trials [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-02-14.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".