Cancer Ribbon Colors, Meanings, and Months
Studies Examine Real-World Pediatric AML Treatment And Outcomes
Two posters presented at the 2024 American Society of Hematology meeting reported real-world outcomes data for patients treated for pediatric acute myeloid leukemia (AML).
At the 2024 American Society of Hematology (ASH) Annual Meeting & Exposition, research on real-world treatment and outcomes for patients with pediatric acute myeloid leukemia (AML) added to the understanding of clinical trial outcomes. Highlights included posters exploring the role of allogeneic hematopoietic cell transplantation (allo-HSCT) in treating pediatric AML, along with updates from APAL2020SC (NCT04726241), an ongoing Leukemia and Lymphoma Society-sponsored screening trial and registry for patients with relapsed or refractory acute leukemias.
A retrospective analysis using data from the AML-BFM trials conducted in 2004 and 2012 and the AML-BFM 2012 registry, spanning 2004 to 2019, provides a detailed analysis of the effects of allo-HSCT. The study includes 381 pediatric patients diagnosed with various AML subtypes, excluding those with Down syndrome, acute promyeloblastic leukemia, and treatment-related AML.
The median age at the time of transplantation was 8.9 years, with donor sources including matched sibling donors (27%), matched unrelated donors (60%), haploidentical donors (12%), and other mismatched donors (1%). The remission status at the time of transplantation was categorized as first complete remission (CR1, 44%), second or greater complete remission (≥ CR2, 36%), or refractory diseases (NR) with or without leukemic blasts (14% and 6%, respectively) after the second course of induction. The study reported a median follow-up of 7.7 years.
Studies presented at ASH 2024 added to the understanding of real-world treatment and outcomes for patients with pediatric AML.Image credit: LASZLO - stock.Adobe.Com
The study's results demonstrate significant improvements in overall survival (OS) and disease-free survival (DFS) estimates over time, with 4-year OS and DFS probabilities of 71.5% and 62.9%, respectively. The study highlights those patients transplanted in CR1 showed markedly better outcomes, with OS and DFS rates of 84.2% and 73.4%, compared to those in ≥ CR2 (73.4% and 65.4%) or NR without (54.2% and 50.0%) or with (35.9% and 30.7%) leukemic blasts.
Additionally, the study found comparable survival rates for matched siblings and unrelated donors, demonstrating superior outcomes compared with haploidentical donors. Specifically, OS and DFS rates were 79.4% and 73.9% for matched sibling donors, 69.8% and 62.4% for matched unrelated donors, and 60.7% and 41.2% for haploidentical donors.
The study concludes that improved risk stratification, pre-transplant strategies, and advances in transplantation have steadily increased survival rates, particularly for patients transplanted in CR1, where survival now approaches 85%. The study authors note, "This data emphasizes the inclusion of children and adolescents with AML into clinical trials to further enhance treatment responses through novel promising drugs and expand indications for allo-HSCT in CR1."
An additional poster highlighted data from the APAL2020SC Pediatric Acute Leukemia (PedAL) Screening Trial, which collects "valuable real-world treatment and response data, as well as comprehensive molecular characterization," for children with relapsed and refractory AML who are not enrolled in clinical trials. This study aims to address knowledge gaps surrounding relapsed (rAML) and primary refractory (prAML) AML in children, with the goal of guiding future therapeutic trials.
Data from 124 patients, comprising 79 with rAML and 45 with prAML, were used in the study to examine the molecular features, re-induction treatments, and response data of patients with rAML and prAML. Central next-generation sequencing (NGS) revealed that 87% of rAML and 74% of prAML patients had identifiable fusions or copy number alterations. A notable finding was the higher prevalence of KMT2A fusions in patients with rAML, observed in 36.5% compared to 17% in prAML patients. It was also found that RAS pathway mutations were prevalent, with some mutations disappearing at relapse while others persisted or emerged anew.
Treatment outcomes were also evaluated, as one of the primary objectives of APAL2020SC is to gather treatment and outcome data for patients not participating in clinical trials. The study reports that, among 40 patients with rAML, 21 (52.5%) achieved complete remission after the first re-induction cycle, with fludarabine/cytarabine-based combinations being the most commonly used regimen. In patients with prAML, 10 of 23 (43.5%) patients achieved remission after cycle 1, using FLA (n = 2), venetoclax (n = 5), or CPX-351 (n = 3) regimens.
The authors noted, "These data will fill gaps in knowledge regarding this patient population and will inform the design of future therapeutic trials."
References
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Functional Precision Medicine Tool Predicts Treatment Response In AML
A new study evaluating the quadratic phenotypic optimization platform shows it can accurately predict personalized drug combination sensitivities, paving the way for improved treatment strategies and outcomes in acute myeloid leukemia (AML).
Despite progress in targeted therapies, the treatment of acute myeloid leukemia (AML) remains challenging due to persistent issues with relapse and treatment resistance. A recent study published in Cancer Medicine evaluates a functional precision medicine tool aimed at overcoming these challenges through personalized drug response prediction.1
In a small subset of cases, myeloproliferative neoplasms (MPNs) can progress to AML, and outcomes are often poor in these cases.2 There are no standardized treatment strategies for AML secondary to MPNs.
Among the 55 evaluable samples analyzed, QPOP achieved an 87.3% success rate in generating individualized drug sensitivity reports.Image credit: Eleni - stock.Adobe.Com
The current research focuses on the quadratic phenotypic optimization platform (QPOP), which demonstrated the potential to streamline patient drug selection based on combinatorial drug sensitivity testing in AML. The platform operates through ex vivo drug testing, analyzing patient-specific cellular responses to combinations of drugs. The trial achieved a rapid turnaround time, with a median of 5 days (range 4-10 days) from sample collection to report generation.
In this study, 51 patients with AML were enrolled, including both newly diagnosed (ND) and refractory/relapsed (R/R) cases. Among the 55 evaluable samples analyzed, QPOP achieved an 87.3% success rate in generating individualized drug sensitivity reports. To assess clinical feasibility, the researchers compared QPOP-predicted responses to actual clinical treatment outcomes. The analysis revealed high concordance, with 83.3% sensitivity, 90.9% specificity, and an overall accuracy of 86.2%, suggesting that QPOP could be a valuable tool for predicting AML treatment responses.
Researchers highlight QPOP's ability to assess combinatorial drug effects, an area where traditional methods often fall short. The study extensively analyzed the azacytidine-venetoclax (AZA-VEN) regimen, a standard treatment for newly diagnosed (ND) patients and patients with refractory/relapsed (R/R) AML. Among 11 patients treated with AZA-VEN—7 with ND, and 4 with R/R—QPOP correctly predicted clinical response in 80% of cases, with a specificity of 100%.
Beyond AZA-VEN, the platform identified alternative drug combinations, such as fludarabine-cytarabine and fludarabine-venetoclax, as promising alternatives to standard therapies like AZA-VEN, particularly in ND patients and those with intermediate or favorable European LeukemiaNet risk classifications. For R/R cases and those with adverse risk profiles, where standard treatments often fail, these alternatives could lead to reduced exposure to less effective therapies and optimized patient outcomes.
In one case, serial QPOP analyses of a patient with an FLT3 mutation revealed a gradual decline in sensitivity to FLT3 inhibitors (FLT3i) over time. This decline correlated with an increasing FLT3 allelic burden, reflecting the development of drug resistance. Interestingly, QPOP also demonstrated the ability to detect FLT3i sensitivity in patients who did not have FLT3 mutations. "This further reiterates QPOP's ability to predict effective drug response without reliance on genomic biomarkers," the researchers noted. In addition, the study uncovered molecular mechanisms of drug resistance, such as the AKT-FOXM1 axis, which emerged as a critical pathway contributing to FLT3 inhibitor resistance.
"Overall, this study demonstrates the feasibility of applying QPOP as a functional combinatorial precision medicine platform to predict therapeutic sensitivities in AML and provides the basis for prospective clinical trials evaluating ex vivo-guided combination therapy," the study authors noted, adding that the platform's utility extends beyond AML treatments. Previous studies have demonstrated QPOP's efficacy in predicting drug responses for other malignancies, including non-Hodgkin lymphoma and multiple myeloma.3,4
Studies have shown that functional precision medicine has successfully incorporated ex vivo functional test results into complex tumor board decision processes.5,6 However, they face limitations such as reliance on single-drug sensitivity, which may not reflect combinatorial effects or dependence on time-intensive molecular profiling. Researchers point out a "need for improved combination treatment selection guidance for patients in a clinically relevant time frame." QPOP's ability to rapidly assess combination therapies could potentially fill this gap in clinical practice.
References
1. Sahib NRBM, Mohamed JS, Rashid MBMA, et al. A combinatorial functional precision medicine platform for rapid therapeutic response prediction in AML. Cancer Med. 2024;13(22):e70401. Doi:10.1002/cam4.70401
2. McKinnell Z, Karel D, Tuerff D, Sh Abrahim M, Nassereddine S. Acute Myeloid Leukemia Following Myeloproliferative Neoplasms: A Review of What We Know, What We Do Not Know, and Emerging Treatment Strategies. J Hematol. 2022;11(6):197-209. Doi:10.14740/jh1042
3. Snijder B, Vladimer GI, Krall N, et al. Image-based ex-vivo drug screening for patients with aggressive haematological malignancies: interim results from a single-arm, open-label, pilot study. Lancet Haematol. 2017;4(12):e595-e606. Doi:10.1016/S2352-3026(17)30208-9
4. Rashid MBMA, Toh TB, Hooi L, et al. Optimizing drug combinations against multiple myeloma using a quadratic phenotypic optimization platform (QPOP). Sci Transl Med. 2018;10(453):eaan0941. Doi:10.1126/scitranslmed.Aan0941
5. Liebers N, Bruch PM, Terzer T, et al. Ex vivo drug response profiling for response and outcome prediction in hematologic malignancies: the prospective non-interventional SMARTrial. Nat Cancer. 2023;4:1648-1659. Doi:10.1038/s43018-023-00645-5
6. Malani D, Kumar A, Brück O, et al. Implementing a functional precision medicine tumor board for acute myeloid leukemia. Cancer Discov. 2022;12(2):388-401. Doi:10.1158/2159-8290.CD-21-0410
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