Switching to Nilotinib Is Associated with Continued Deeper Molecular Responses in CML-CP Patients with Minimal Residual Disease After ≥ 2 Years On Imatinib: Enestcmr 2-Year Follow-up Results
Bibliographic record
Abstract
Abstract Abstract 694 Background: Superior rates of deeper molecular responses were achieved with nilotinib vs imatinib in patients newly diagnosed with Philadelphia chromosome–positive (Ph+) chronic myeloid leukemia in chronic phase (CML-CP) in the Evaluating Nilotinib Efficacy and Safety in Clinical Trials—newly diagnosed patients (ENESTnd) trial. In addition, the 12-month (mo) analysis of the ENEST—complete molecular response (ENESTcmr) study demonstrated that switching to nilotinib after a minimum of 2 years on imatinib led to increased rates of major molecular response (MMR) and deeper molecular responses vs remaining on imatinib. Results from ENESTcmr are presented here with minimum 24 mo of patient follow-up. Methods: Patients with Ph+ CML-CP who had achieved complete cytogenetic responses but still had persistent BCR-ABL positivity by real-time quantitative polymerase chain reaction (RQ-PCR) after ≥ 2 years on imatinib were eligible. Patients (n = 207) were randomized to switch to nilotinib 400 mg twice daily (BID; n = 104) or to continue on the same dose of imatinib (400 or 600 mg once daily [QD]; n = 103). Rates of MMR, MR4 (BCR-ABL ≤ 0.01% according to the International Scale [IS], corresponding to a 4-log reduction), MR4.5 (BCR-ABL ≤ 0.0032%IS, corresponding to 4.5-log reduction), and undetectable BCR-ABL via RQ-PCR with ≥ 4.5-log sensitivity were measured. Results: Among all randomized patients (intent-to-treat population), significantly more patients treated with nilotinib continued to achieve undetectable BCR-ABL by 24 mo (32.7% on nilotinib vs 16.5% on imatinib; P =.005; Table).The difference between the arms in achievement of this endpoint increased between 1 and 2 years (from 12.4% to 16.2%). The median time to MR4.5 and undetectable BCR-ABL was also significantly faster on nilotinib than on imatinib (P = .005 and .003, respectively). Cumulative rates of MR4.5 and undetectable BCR-ABL continued to be higher with nilotinib in patients without those responses at baseline, and the difference between arms appeared to increase over time. The safety profiles for nilotinib and imatinib were consistent with prior studies. By 24 mo, no patients in either arm progressed to accelerated phase/blast crisis. No patients on nilotinib died since the 12-mo analysis; 1 patient on imatinib died from metastatic prostate cancer in follow-up after discontinuation from the study. Conclusions: Switching to nilotinib led to significantly faster, deeper molecular responses in patients with minimal residual disease on long-term imatinib therapy. Since the 12-mo analysis, rates of deep molecular response (MR4.5 and undetectable BCR-ABL) have remained significantly higher in patients who did not have the response at baseline and were switched to nilotinib (vs those remaining on imatinib). In fact, the difference in favor of nilotinib increased between 1 and 2 years. These results suggest that switching to the more potent, selective tyrosine kinase inhibitor nilotinib is beneficial in patients with minimal residual disease after long-term imatinib therapy. Achievement of these deeper molecular responses (MR4.5 and undetectable BCR-ABL) after switching to nilotinib may enable a greater proportion of CML-CP patients to be eligible for future discontinuation studies. Cumulative rates of confirmed undetectable BCR-ABL by 24 mo will be presented as the confirmation assessments for several responders were not available at the time of this analysis. Disclosures: Hughes: Novartis Pharmaceuticals Corp: Consultancy, Honoraria, Research Funding; Bristol Myers Squibb: Consultancy, Honoraria, Research Funding; Ariad: Consultancy; CSL: Research Funding. Lipton:Novartis: Consultancy, Research Funding, Speakers Bureau. Spector:Novarits: Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Consultancy. Leber:Novartis: Advisory Board Other, Honoraria, Speakers Bureau. Schwarer:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees; BMS: Honoraria, Membership on an entity's Board of Directors or advisory committees; Pfizer: Honoraria, Membership on an entity's Board of Directors or advisory committees. Etienne:Novartis: Consultancy, Speakers Bureau; Pfizer: Consultancy; BMS: Consultancy, Speakers Bureau. Branford:Novartis: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Honoraria, Research Funding; Ariad: Research Funding. Purkayastha:Novartis Pharmaceuticals Corp: Employment. Collins:Novartis Pharmaceuticals Corp: Employment. Szczudlo:Novartis Pharmaceuticals Corp: Employment. Cervantes:Novartis: Membership on an entity's Board of Directors or advisory committees; Sanofi-Aventis: Membership on an entity's Board of Directors or advisory committees; BMS: Speakers Bureau; Celgene: Membership on an entity's Board of Directors or advisory committees; Pfizer: Membership on an entity's Board of Directors or advisory committees; Teva Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".