Venetoclax Therapy in a Heavily Treated Cohort of Patients with Relapsed or Refractory Acute Myeloid Leukemia: Update of the Pethema Registry Experience
Bibliographic record
Abstract
Abstract Introduction The prognosis of patients with relapsed or refractory acute myeloid leukemia (RR-AML) is very poor, and treatment options are very limited. The exciting results of venetoclax (VEN) in untreated AML have led to its off-label use in RR-AML. However, evidence in RR-AML is still scarce and the available data are mostly from retrospective and single-center studies. The aim of our study was to analyze the effectiveness of VEN use in patients with RR-AML reported to the PETHEMA AML epidemiological registry. Initial results were presented previously (Labrador J, et al. ASH 2020). Here, we report an updated analysis. Methods We conducted a retrospective, multicenter, observational study of a cohort of patients with AML-RR who were treated with venetoclax in the hospitals of the PETHEMA group. We evaluated efficacy, CR/CRi rate and overall survival (OS). We performed a descriptive analysis. Overall survival (OS) was calculated using the Kaplan-Meier method. Results Fifty-one patients were included, 33 men and 18 women, with a median age of 68 years (25-82). The main characteristics of the included patients are shown in Table 1. With a median follow-up of 167 days, 10/51 patients (19%) continued to receive VEN at the time of analyses. Patients received a median of 2 cycles (0-8). VEN was administered with azacitidine (AZA) in 59%, with decitabine (DEC) in 29% and with low-dose cytarabine (LDAC) in 12% of patients, respectively. The CR/CRi and partial response (PR) rates were 12.4% and 10.4%, respectively. The CR/RCi and overall response (ORR, CR/CRi+PR) was higher in patients receiving VEN+AZA (17.9% and 32.1%) than in those receiving DEC + VEN (6.7% and 13.3%) or LDAC + VEN (0%). The presence of NPM1 or CEBPA variants were the only two variables associated with increased CR/CRi with VEN in RR-AML. Median OS was 104 days (95% CI: 56 - 151) (Figure 1A), 120 days in combination with AZA, 104 days with DEC, and 69 days with LDAC; p=0.875. Treatment response (Figure 1B) and ECOG 0 were the only variables that influenced OS in a multivariate model adjusted for age and sex (Table 2). VEN-resistant patients who received subsequent salvage therapy had superior median OS (98 vs. 5 days, p=0.004).Twenty-eight percent of patients required discontinuation of VEN due to toxicity. Sixty-one percent of patients required admission, mainly due to infections (45%), 10% due to bleeding and other causes in 12%. One case of tumor lysis syndrome was described. Conclusions Our real-life series depicts a marginal probability of CR/CRi and poor OS after venetoclax-based salvage. Patients treated with this regimen had very poor-risk features, and were heavily pre-treated, which could explain in part the observed poor outcomes. Although follow-up is still short, the small proportion of responders did not reach the median OS. Further studies will help to identify those patients potentially benefiting from venetoclax-based salvage regimens. Figure 1 Figure 1. Disclosures Belén Vidriales: Roche: Consultancy; Novartis: Speakers Bureau; Astellas: Consultancy, Speakers Bureau; Jazz: Consultancy, Speakers Bureau. Pérez-Encinas: Janssen: Consultancy. Tormo: Novartis: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Jazz Pharmaceuticals: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Pfizer: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Amgen: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Astellas: Consultancy, Membership on an entity's Board of Directors or advisory committees, Speakers Bureau. Montesinos: Pfizer: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Glycomimetics: Consultancy; Novartis: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Incyte: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Daiichi Sankyo: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Celgene: Consultancy, Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Forma Therapeutics: Consultancy; Sanofi: Membership on an entity's Board of Directors or advisory committees, Speakers Bureau; Teva: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Stemline/Menarini: Consultancy; Tolero Pharmaceutical: Consultancy; Agios: Consultancy; AbbVie: Membership on an entity's Board of Directors or advisory committees, Research Funding, Speakers Bureau; Astellas Pharma, Inc.: Consultancy, Honoraria, Other: Advisory board, Research Funding, Speakers Bureau. OffLabel Disclosure: Venetoclax for Patients with Relapsed or Refractory Acute Myeloid Leukemia
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".