Phase II Study of Venetoclax Added to Cladribine (CLAD) and Low Dose AraC (LDAC) Alternating with 5-Azacytidine (AZA) in Older and Unfit Patients with Newly Diagnosed Acute Myeloid Leukemia (AML)
Bibliographic record
Abstract
Abstract Background The combination of venetoclax and 5-azacytidine (5-AZA) for older and unfit patients with newly diagnosed AML has led to significant improvements in remission rates and survival compared to 5-AZA alone. We previously reported encouraging results with a low-intensity backbone of CLAD/LDAC alternating with a hypomethylating agent (HMA) for older patients with AML observing better outcomes than historical experience with HMA alone. We hypothesized that the addition of venetoclax to the CLAD/LDAC alternating with HMA backbone may further improve outcomes for an expanded cohort of older patients with newly diagnosed AML. Methods This is a phase II study investigating the combination of venetoclax with CLAD/LDAC alternating with AZA in older (age ≥ 60y) or unfit patients with newly diagnosed AML (excluding APL, CBF). The primary objective was composite complete response rate (CRc; CR+CRi); secondary endpoints were overall survival (OS), disease-free survival (DFS), overall response rate (ORR), and toxicity. Induction was cladribine 5 mg/m 2 IV over 30 minutes on D1-5 and araC 20mg SQ BID on D1-10. Consolidation/maintenance consisted of 2 cycles of cladribine 5 mg/m 2 IV on D1-3 and araC 20 mg SQ BID on D1-10 alternating with 2 cycles of AZA 75 mg/m 2 on D1-7, for up to 18 cycles. Venetoclax 400 mg was added on days 1-21 of each cycle with dose adjustments for concomitant CYP3A inhibitors. One cycle was 4 weeks and up to 2 cycles of induction were allowed. Results A total of 60 patients were treated on study with a median age was 68 years (IQR 64 - 73, range: 57 - 84); 22 (37%) patients were ≥ 70 yrs and 1 pt < 60 yrs who was unfit for intensive chemotherapy was enrolled. 14 (23%) patients had secondary AML (sAML). 36 (60%) had diploid cytogenetics with 12 (20%) patients having adverse cytogenetics at enrollment. By European Leukemia Network (ELN) risk, 23%, 33%, and 43% were favorable, intermediate, and adverse risk, respectively. The most commonly mutated genes were NPM1 in 21 patients (33%), DNMT3A in 20 (32%), TET2 in 18 (30%), SRSF2 in 15 (25%), NRAS in 12 (20%), IDH2 in 11 (18%), RUNX1 in 11 (18%), and ASXL1 in 9 (15%). TP53 was mutated in 4 (7%) patients. Baseline characteristics are summarized in table 1. Among 60 evaluable patients the CRc rate was 93%. Best response was CR in 48 (80%), CRi in 8 (13%), no response in 3 (5%), and death in 1 (2%) patient. Responses are summarized in Figure A. In responding patients with a bone marrow sample evaluable for assessment of measurable residual disease (MRD), 43/51 (84%) were negative for MRD at response assessment. Among patients with sAML, with adverse karyotype, or ELN adverse risk the CR/CRi rate was 86% (64%/21%), 83% (58%/25%), and 96% (81%/15%) respectively. 19 (34%) responders received a subsequent allogeneic stem cell transplantation. Early mortality was low with one patient (2%) dying within 4 weeks and four patients (7%) dying with in 8 weeks. Responses are summarized in table 2. The most frequent grade 3/4 non-heme adverse events were febrile neutropenia (n=10), pneumonia (n=5), atrial fibrillation (n=2), and allergic reaction (n=2). One patient developed grade 4 tumor lysis syndrome. With a median follow up of 20.4 months, the median duration of response (DOR) is not reached (95% CI: 18 - NE months). Estimated 12- and 24-month DOR are 69.2% (95% CI: 57.5 - 83.1%) and 60.5% (95% CI: 47.7 - 76.8%), respectively. Median OS is not yet reached (95% CI: 21 - NE months). Estimated 12- and 24-month OS are 71.5% (95% CI: 60.5 - 84.5%) and 60.4% (95% CI: 47.7 - 76.6%), respectively (figure B). The estimated 12-month OS for patients aged <70 years and ≥70 years was 75% and 73%, respectively. Median DFS is not yet reached (95% CI: 18.0 - NE months). Estimated 12- and 24-month DFS are 69.2% (95% CI: 57.5 - 83.1%) and 60.5% (95% CI: 47.7 - 76.6%), respectively (figure C). Conclusion CLAD/LDAC plus venetoclax alternating with AZA plus venetoclax is an effective, lower-intensity regimen that is well tolerated among older patients (≥ 60 years) with newly diagnosed AML, producing high response rates with durable MRD negative remissions. The rates of overall and disease-free survival are encouraging in this cohort of older AML patients with comparable efficacy in patients ≥70 as in patients <70 years old. Further study of this non-anthracycline containing backbone in younger patients unfit for intensive chemotherapy, as well as comparisons to standard frontline therapies are warranted. Figure 1 Figure 1. Disclosures Kantarjian: AbbVie: Honoraria, Research Funding; Taiho Pharmaceutical Canada: Honoraria; Ascentage: Research Funding; BMS: Research Funding; Aptitude Health: Honoraria; Daiichi-Sankyo: Research Funding; Astellas Health: Honoraria; Pfizer: Honoraria, Research Funding; Immunogen: Research Funding; Novartis: Honoraria, Research Funding; Jazz: Research Funding; Amgen: Honoraria, Research Funding; Ipsen Pharmaceuticals: Honoraria; KAHR Medical Ltd: Honoraria; Astra Zeneca: Honoraria; Precision Biosciences: Honoraria; NOVA Research: Honoraria. Borthakur: Ryvu: Research Funding; ArgenX: Membership on an entity's Board of Directors or advisory committees; University of Texas MD Anderson Cancer Center: Current Employment; Astex: Research Funding; Protagonist: Consultancy; Takeda: Membership on an entity's Board of Directors or advisory committees; GSK: Consultancy; Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees. Pemmaraju: Affymetrix: Consultancy, Research Funding; Dan's House of Hope: Membership on an entity's Board of Directors or advisory committees; Blueprint Medicines: Consultancy; ASH Communications Committee: Membership on an entity's Board of Directors or advisory committees; DAVA Oncology: Consultancy; Stemline Therapeutics, Inc.: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Sager Strong Foundation: Other; LFB Biotechnologies: Consultancy; Daiichi Sankyo, Inc.: Other, Research Funding; Springer Science + Business Media: Other; Aptitude Health: Consultancy; Protagonist Therapeutics, Inc.: Consultancy; Incyte: Consultancy; Novartis Pharmaceuticals: Consultancy, Other: Research Support, Research Funding; CareDx, Inc.: Consultancy; Clearview Healthcare Partners: Consultancy; HemOnc Times/Oncology Times: Membership on an entity's Board of Directors or advisory committees; MustangBio: Consultancy, Other; Plexxicon: Other, Research Funding; ASCO Leukemia Advisory Panel: Membership on an entity's Board of Directors or advisory committees; Samus: Other, Research Funding; Bristol-Myers Squibb Co.: Consultancy; Cellectis S.A. ADR: Other, Research Funding; Roche Diagnostics: Consultancy; Abbvie Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Other, Research Funding; Celgene Corporation: Consultancy; ImmunoGen, Inc: Consultancy; Pacylex Pharmaceuticals: Consultancy. DiNardo: Forma: Honoraria, Research Funding; Foghorn: Honoraria, Research Funding; AbbVie: Consultancy, Research Funding; GlaxoSmithKline: Membership on an entity's Board of Directors or advisory committees; Novartis: Honoraria; Takeda: Honoraria; Notable Labs: Current holder of stock options in a privately-held company, Membership on an entity's Board of Directors or advisory committees; Bristol Myers Squibb: Honoraria, Research Funding; Agios/Servier: Consultancy, Honoraria, Research Funding; ImmuneOnc: Honoraria, Research Funding; Celgene, a Bristol Myers Squibb company: Honoraria, Research Funding. Sasaki: Novartis: Consultancy, Research Funding; Pfizer: Membership on an entity's Board of Directors or advisory committees; Daiichi-Sankyo: Membership on an entity's Board of Directors or advisory committees. Daver: Bristol Myers Squibb: Consultancy, Research Funding; Novimmune: Research Funding; Glycomimetics: Research Funding; ImmunoGen: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; Trovagene: Consultancy, Research Funding; Gilead Sciences, Inc.: Consultancy, Research Funding; Hanmi: Research Funding; Abbvie: Consultancy, Research Funding; Daiichi Sankyo: Consultancy, Research Funding; Trillium: Consultancy, Research Funding; Genentech: Consultancy, Research Funding; Astellas: Consultancy, Research Funding; Sevier: Consultancy, Research Funding; Pfizer: Consultancy, Research Funding; FATE Therapeutics: Research Funding; Novartis: Consultancy; Jazz Pharmaceuticals: Consultancy, Other: Data Monitoring Committee member; Dava Oncology (Arog): Consultancy; Celgene: Consultancy; Syndax: Consultancy; Shattuck Labs: Consultancy; Agios: Consultancy; Kite Pharmaceuticals: Consultancy; SOBI: Consultancy; STAR Therapeutics: Consultancy; Karyopharm: Research Funding; Newave: Research Funding. Issa: Syndax Pharmaceuticals: Research Funding; Novartis: Consultancy, Research Funding; Kura Oncology: Consultancy, Research Funding. Short: Novartis: Honoraria; NGMBio: Consultancy; Takeda Oncology: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy; AstraZeneca: Consultancy; Astellas: Research Funding; Amgen: Consultancy, Honoraria. Jain: Incyte: Research Funding; Genentech: Honoraria, Research Funding; Adaptive Biotechnologies: Honoraria, Research Funding; Cellectis: Honoraria, Research Funding; ADC Therapeutics: Honoraria, Research Funding; Pfizer
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".