Gemtuzumab Ozogamicin Reduces Relapse Risk in FLT3-ITD+ Acute Myeloid Leukemia: A Report from the Children's Oncology Group
Bibliographic record
Abstract
Abstract CD33 is variably expressed on most acute myeloid leukemia (AML) blasts and is the target of gemtuzumab ozogamicin (GO), a calicheamicin-conjugated anti-CD33 monoclonal antibody. COG studies AAML03P1 and AAML0531 evaluated the safety and efficacy of GO combined with conventional chemotherapy to determine the impact of GO on treatment outcomes. We have previously demonstrated that those with high CD33 expression are more susceptible to GO. As FLT3-ITD is associated with high levels of CD33 expression, this group of patients represents a subgroup of particular interest for this therapeutic approach. Patients with high-allelic ratio (HAR) FLT3-ITD have poor outcomes with conventional chemotherapy alone and experience improvement with allogeneic hematopoietic stem cell transplant (HCT). Thus, COG AAML0531 allocated HAR FLT3-ITD+ patients enrolled after April 14, 2008 to consolidation allogeneic HCT with the best available donor. In combined evaluation of COG AAML0531 and its preceding pilot study AAML03P1, 479 patients received conventional MRC based induction chemotherapy (0531 Arm A) and 735 patients received conventional chemotherapy + GO (03P1 and 0531 Arm B). A total of 183 FLT3-ITD+ patients were treated on 0531 Arm A (n=71) and on 03P1/0531 Arm B (n=112). Overall, patients with FLT3-ITD had significantly lower rates of complete remission (CR) compared to FLT3-ITD negative patients, 64% v. 77% respectively (p<0.001). Among FLT3-ITD+ patients, CR rates were identical in those with or without induction GO exposure of 64% vs. 64% respectively (p=0.98). Analysis of 5-year outcomes for FLT3-ITD+ patients treated with GO compared to no GO demonstrated no difference in overall survival (OS) (50% v 49% respectively, p=0.74). Importantly, cumulative incidence of relapse (CIR) at 5 years from CR for patients treated with GO was 37% vs. 59% in those who did not receive GO (p=0.018). This GO-associated improvement in relapse was offset by higher treatment related mortality (TRM) among GO compared to no GO recipients (16% v 0% respectively, p=0.008), leading to similar DFS of 47% vs. 41% respectively (p=0.45). The benefit of decreased relpase risk (RR) was most significant for patients receiving GO in addition to HCT. Among FLT3-ITD+ patients who underwent HCT, those who received GO (n=33) had a 5-yr RR of 22% compared to 56% for the no GO cohort (n=25, p=0.003). There was a trend towards increased TRM among patients receiving GO compared to no GO (22% v. 4% respectively, p=0.078), with a corresponding DFS in GO recipients of 56% vs. 40% for the no GO cohort (p=0.09). Evaluation of the 8 GO recipients who died at HCT revealed that 3 (38%) were the result of complications from transplant-associated sinusoidal obstructive syndrome. Patients with HAR FLT3-ITD, who experience poor outcomes with conventional chemotherapy alone, were analyzed separately to evaluate the impact of induction GO on outcomes. Among HAR FLT3-ITD+ patients who underwent HCT, those treated with GO (n=26) had a significantly lower RR of 15% compared to 53% among no GO recipients (n=15, p=0.007). Additionally, patients receiving GO had a trend towards higher DFS of 65% compared to 40% for no GO group, (p=0.079). In this cohort, TRM in GO vs. no GO recipients was 19% vs. 7% respectively (p=0.297). Among HAR FLT3-ITD+ patients who did not receive HCT, there were no significant differences in DFS, RR, and TRM among the GO versus no GO recipients. Data from the two consecutive COG studies AAML03P1 and AAML0531 suggest that FLT3-ITD+ patients may benefit from the addition of GO to intensive chemotherapy. There is further evidence that HCT may augment the therapeutic impact of induction GO by further reducing the risk of relapse. However, clinical impact of GO was tempered by higher incidence of TRM in GO recipients. CD33 targeting represents an attractive approach in FLT3-ITD+ patients as they often have elevated blast CD33 expression. Further understanding of the toxicity profile of GO, especially when used in conjunction with intensive chemotherapy and HCT, is needed to enhance its therapeutic benefit. Additionally, its impact may be most significant in certain biologic subsets of AML. Our findings demonstrate that CD33 targeting is an important treatment strategy in AML that warrants further investigation in FLT3-ITD+ patients. Disclosures No relevant conflicts of interest to declare.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".