Real world experience with exemestane and everolimus for advanced HR-positive breast cancer.
Bibliographic record
Abstract
e12538 Background: BOLERO-2 demonstrated a significant progression free survival (PFS) advantage for EvEx compared to Ex alone in the treatment of metastatic hormone receptor positive breast cancer (HP-MBC) following failure with aromatase inhibitor (AI) in menopausal patients. Benefits and harms reported in clinical trials do not always mirror real life, thus we examined the frequency of dose modifications for toxicity and PFS in a real-world population. Methods: Women with HP-MBC started on EvEx between Dec. 2, 2013 and Nov. 1, 2015 in any one of five cancer centres in British Columbia, Canada were identified via the provincial pharmacy database. Clinical and prior treatment details, adverse events, and outcomes were compared to those of the BOLERO-2 study participants. Results: We identified151 patients treated with EvEx: median age was 53 (range 25 – 85), and 60 (40%) were ≤49. ECOG performance status was 0-1 in 122 (81%) and 2-3 in 29 (19%). 135 (89%) received prior treatment for MBC, which included non-steroidal AI in 117 (87%) and chemotherapy in 74 (55%). 94 (62%) received EvEx as 3rd line treatment and beyond. Median follow-up was 714 days (range 10 – 1127); 66 (44%) were alive at time of analysis. Dose reductions, interruptions and discontinuations of everolimus due to toxicity occurred in 32%, 31%, and 30%, respectively, higher than reported in BOLERO-2. Median PFS was 6.1 months (95% CI: 4.9 – 7.7) in our cohort, similar to that of the investigator assessed EvEx arm of BOLERO-2. Majority, 52%, of dose reductions/interruptions occurred within the first month of treatment initiation. Conclusions: We observed similar PFS forEvEx in a real world population compared with BOLERO-2. Dose interruptions and reductions were frequent, but may have enabled patients to tolerate therapy for longer. Discontinuation rates for toxicity were high and may limit the efficacy of the combination in the real world. Recent findings of primary prophylaxis against mucositis may help enhance continuation and dose maintenance (Beck et al. JCO 2016). Alternately, starting at a lower dose of Ev and increasing to tolerance may be worth exploring, as despite higher rates of dose modifications, our PFS rate was similar to that in BOLERO-2.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".