Treatment-Free Remission in Patients with Chronic Myeloid Leukemia Following the Discontinuation of Tyrosine Kinase Inhibitors
Bibliographic record
Abstract
Abstract Background: Tyrosine kinase inhibitors (TKIs) discontinuation in patients with chronic myeloid leukemia (CML) is becoming an increasingly attempted practice. Evidence is not uniform regarding the optimal milestones to be reached before seeking treatment cessation. The objective of this analysis is to evaluate the outcomes of CML patients who stopped TKIs, and to determine the best targets that need to be achieved to allow for the longest treatment-free remission (TFR). Methods: We reviewed the medical records of all CML patients treated with TKIs at our institution between October 1999 and February 2017 and who subsequently discontinued therapy. We evaluated their time to response, the duration of TKI treatment, and the duration in best response before stopping therapy. Major molecular response (MMR) was defined as ≤0.1% BCR-ABL/ABL ratio in the international scale (IS) as determined by quantitative real-time polymerase chain reaction (qPCR), MR4 as a ratio of ≤0.01% IS, and MR4.5 as a ratio of ≤0.0032% IS. We performed a classification and regression tree (CART) analysis to determine the functional form of the durations of MR4.5 and TKI therapy. Cutoffs were then used to evaluate the association between these variables and the success of maintaining TFR after stopping treatment. Univariate and multivariate analyses were conducted to identify factors associated with loss of response following dug discontinuation. Results: We identified 297 patients with CML who attempted TKI discontinuation, with a median age at discontinuation of 63 years (range, 25-93). The characteristics of patients are summarized in Table 1. After a median follow-up of 38 months (95% confidence interval [CI], 33-44) after discontinuation, 28%, 26%, and 18% lost MR4.5, MR4, and MMR, respectively. The median time to MMR loss was 6 months (range, 1-75), with 26% of patients losing MMR 12 months or more after TKI discontinuation. The median TFR duration was not reached, with a 3-year TFR rate of 69% (Figure 1). The 3-year TFR rates were 91%, 76%, and 70% in patients with a sustained duration of MR4.5 of more than 72 months, 60-72 months, and less than 60 months, respectively (P <0.0001). Two hundred one of 297 patients (68%) remained on their frontline TKI at the time of discontinuation. No significant difference was found in the rates of 3-year TFR failure between patients who received frontline imatinib, dasatinib, or nilotinib (22%, 44%, 22%, respectively; P =0.066). The median overall duration of TKI treatment was 118 months (range, 16-242), while the median duration on treatment with the last TKI used before discontinuation was 96 months (range, 1-242). In univariate analysis, a consistent duration of 6 years or more in MR4.5 was associated with the lowest risk of loss of MMR following TKI discontinuation, with 3-year MMR loss rates of 9% versus 29% (P <0.0001). A duration of 6 years or more in MR4.5 was also associated with the lowest risk of loss of MR4.5 following TKI discontinuation, with 3-year MR4.5 loss rates of 15% versus 39% (P <0.0001). By multivariate analysis, a sustained MR4.5 duration of 6 years or more was the only significant predictor for maintained response (P<0.0001). Patients who relapsed were restarted on TKI after a median of 14 days (range, 0-930), and among 73 evaluable patients, the majority achieved deep molecular responses: MR4.5 in 92% of cases, MR4 in 3%, and MMR in 5%. Conclusion: Treatment-free remission can be successful in patients with a sustained MR4.5 duration of 6 years or more. Overall, around one third of patients attempting TKI discontinuation will relapse, with the vast majority achieving deep molecular responses after resuming TKIs. Figure 1 Figure 1. Disclosures Kantarjian: AbbVie: Honoraria, Research Funding; Amgen: Honoraria, Research Funding; Ascentage: Research Funding; BMS: Research Funding; Daiichi-Sankyo: Research Funding; Immunogen: Research Funding; Jazz: Research Funding; Novartis: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding; Aptitude Health: Honoraria; Astellas Health: Honoraria; Astra Zeneca: Honoraria; Ipsen Pharmaceuticals: Honoraria; KAHR Medical Ltd: Honoraria; NOVA Research: Honoraria; Precision Biosciences: Honoraria; Taiho Pharmaceutical Canada: Honoraria. Jabbour: Amgen, AbbVie, Spectrum, BMS, Takeda, Pfizer, Adaptive, Genentech: Research Funding. Issa: Syndax Pharmaceuticals: Research Funding; Kura Oncology: Consultancy, Research Funding; Novartis: Consultancy, Research Funding. Ravandi: Syros Pharmaceuticals: Consultancy, Honoraria, Research Funding; AstraZeneca: Honoraria; Jazz: Honoraria, Research Funding; Prelude: Research Funding; Celgene: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; Astex: Honoraria, Research Funding; Taiho: Honoraria, Research Funding; Bristol Myers Squibb: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; AbbVie: Honoraria, Research Funding; Novartis: Honoraria; Xencor: Honoraria, Research Funding; Agios: Honoraria, Research Funding; Amgen: Honoraria, Research Funding. Konopleva: Calithera: Other: grant support, Research Funding; Forty Seven: Other: grant support, Research Funding; AstraZeneca: Other: grant support, Research Funding; Reata Pharmaceuticals: Current holder of stock options in a privately-held company, Patents & Royalties: intellectual property rights; Sanofi: Other: grant support, Research Funding; Stemline Therapeutics: Research Funding; KisoJi: Research Funding; Eli Lilly: Patents & Royalties: intellectual property rights, Research Funding; Novartis: Other: research funding pending, Patents & Royalties: intellectual property rights; Cellectis: Other: grant support; Ablynx: Other: grant support, Research Funding; Rafael Pharmaceuticals: Other: grant support, Research Funding; Agios: Other: grant support, Research Funding; Ascentage: Other: grant support, Research Funding; AbbVie: Consultancy, Honoraria, Other: Grant Support, Research Funding; F. Hoffmann-La Roche: Consultancy, Honoraria, Other: grant support; Genentech: Consultancy, Honoraria, Other: grant support, Research Funding. Ferrajoli: AstraZeneca: Other: Advisory Board, Research Funding; BeiGene: Other: Advisory Board, Research Funding; Janssen: Other: Advisory Board . Kadia: Cure: Speakers Bureau; BMS: Other: Grant/research support; AbbVie: Consultancy, Other: Grant/research support; Amgen: Other: Grant/research support; Pulmotech: Other; Cellonkos: Other; Sanofi-Aventis: Consultancy; Ascentage: Other; Astellas: Other; AstraZeneca: Other; Pfizer: Consultancy, Other; Dalichi Sankyo: Consultancy; Novartis: Consultancy; Liberum: Consultancy; Genentech: Consultancy, Other: Grant/research support; Jazz: Consultancy; Aglos: Consultancy; Genfleet: Other. Pemmaraju: Incyte: Consultancy; Novartis Pharmaceuticals: Consultancy, Other: Research Support, Research Funding; Affymetrix: Consultancy, Research Funding; Protagonist Therapeutics, Inc.: Consultancy; ImmunoGen, Inc: Consultancy; Clearview Healthcare Partners: Consultancy; Celgene Corporation: Consultancy; Stemline Therapeutics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; LFB Biotechnologies: Consultancy; Abbvie Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Pacylex Pharmaceuticals: Consultancy; Bristol-Myers Squibb Co.: Consultancy; Blueprint Medicines: Consultancy; MustangBio: Consultancy, Other; Roche Diagnostics: Consultancy; DAVA Oncology: Consultancy; Springer Science + Business Media: Other; Aptitude Health: Consultancy; CareDx, Inc.: Consultancy; Cellectis S.A. ADR: Other, Research Funding; Daiichi Sankyo, Inc.: Other, Research Funding; Plexxicon: Other, Research Funding; ASH Communications Committee: Membership on an entity's Board of Directors or advisory committees; Samus: Other, Research Funding; ASCO Leukemia Advisory Panel: Membership on an entity's Board of Directors or advisory committees; HemOnc Times/Oncology Times: Membership on an entity's Board of Directors or advisory committees; Dan's House of Hope: Membership on an entity's Board of Directors or advisory committees; Sager Strong Foundation: Other. Takahashi: GSK: Consultancy; Celgene/BMS: Consultancy; Symbio Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy. Alvarado: Sun Pharma: Consultancy, Research Funding; Jazz Pharmaceuticals: Research Funding; MEI Pharma: Research Funding; CytomX Therapeutics: Consultancy; BerGenBio: Research Funding; FibroGen: Research Funding; Astex Pharmaceuticals: Research Funding; Daiichi-Sankyo: Research Funding. Yilmaz: Pfizer: Research Funding; Daiichi-Sankyo: Research Funding. Borthakur: Astex: Research Funding; Protagonist: Consultancy; ArgenX: Membership on an entity's Board of Directors or advisory committees; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees; University of Texas MD Anderson Cancer Center: Current Employment; Takeda: Membership on an entity's Board of Directors or advisory committees; Ryvu: Research Funding; GSK: Consultancy. DiNardo: Takeda: Honoraria; Celgene, a Bristol Myers Squibb company: Honoraria, Research Funding; Novartis: Honoraria; ImmuneOnc: Honoraria, Research Funding; Bristol Myers Squibb: Honoraria, Research Fundi
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".