Sharpening the Tools to Get the Edge on Leukemia
Authors: Shai Shimony, Jacqueline S. Garcia
Journal: American Journal of Hematology (2025)

Risk stratification is essential in the management of acute myeloid leukemia (AML). It serves as a prognostic tool, that is, informs about the outcome in each subset of patients. In several instances, it may also serve as a predictive tool, which aids in therapeutic decision making (which is most relevant when there is more than one treatment option available). The conventional and widely-adopted European LeukemiaNet (ELN) 2022 AML risk criteria serve as both [1]. Patients in the "favorable" risk group associate with the best outcomes overall and are recommended for postremission chemotherapy as consolidation rather than allogeneic hematopoietic stem cell transplantation (HCT), in the absence of clear overall survival (OS) benefit with the latter treatment. Conversely, patients in the ELN 2022 intermediate and adverse risk groups are recommended to proceed with HCT as consolidation. The data driving the ELN 2022 guidelines were mainly derived from outcomes of patients who received intensive chemotherapy (IC), such as combination cytarabine and daunorubicin ("7 + 3"). Until recent years, receipt of an intensive regimen, such as 7 + 3 (and alike), was considered to be a necessary first step for remission induction prior to post-remission allo-HCT or consolidation chemotherapy [2]. The approval of 12 new drugs and/or combinations shifted the paradigm [3] including eight recommended for front-line use. Most relevant, based on the phase 3 VIALE-A trial, the combination of hypomethylating agents (HMA) plus venetoclax (ven) became a standard of care frontline therapy for patients aged ≥ 75 years or deemed ineligible for intensive regimens [4]. However, application of ELN 2022 provides minimal prognostic and predictive value in patients treated with HMA plus ven [5]. In 2024, Döhner et al. published a new risk prognostication model based on the VIALE-A and the azacitidine plus venetoclax phase Ib participants [5]. This model was proposed as the ELN risk criteria (ELN 2024) specifically for patients treated with lower intensity therapies, mainly HMA plus ven [6]. Since its approval in 2018, HMA plus ven use has expanded beyond the population studied in the VIALE-A phase 3, including in patients < 75 years eligible for intensive chemotherapy and otherwise transplant-eligible and fit. With increasing experience, the convenience of HMA plus ven (decreased time for inpatient hospitalization, decreased toxicity compared to intensive chemotherapy, ability to be administered at community cancer centers) has enabled treatment delivery in the community setting. While waiting for the final read-out from on-going prospective studies (NCT04801797 [7]; NCT03573024 [8]; MYELOMATCH MM1YA-CTG01 NCT05554393) that are required to determine the optimal frontline therapy for those that are otherwise fit for intensive chemotherapy, we can gain insight into potential broader applications of this approach by retrospective real-world analyses and observational studies. A recent prospective observational study among AML patients who received HMA plus ven demonstrated worse OS among those who would not have met VIALE-A eligibility compared with those who would have been considered eligible [9]. These findings suggest that the VIALE-A trial-based ELN 2024 risk stratification for AML treated with lower intensity therapies does not fully capture the patient populations actually receiving HMA plus ven and that additional guidance is needed to determine when to broaden its application. Real-world data are necessary to confirm the benefits of clinical trial findings across broader populations. However, defining the population that should be included in real-world analyses is the one of the main challenges in interpreting these datasets. Retrospective studies limited to single sites are challenging to interpret. To formulate a conclusion, real-world datasets should include patient-level data, matched cohorts when appropriate, minimum missing data, and long-term follow-up. Ideally, these datasets should be multi-site (international) and include socioeconomically and ethnically diverse populations. In the meantime, in a recent issue of American Journal of Hematology, Gangat et al. published a comprehensive real-world study of AML patients treated with HMA plus ven (N = 391) at the Mayo clinic that provides another step forward in refining risk classification [10]. A risk prediction model for OS was generated based on cytogenetics (ELN 2022 adverse karyotype) and molecular characteristics (IDH2 wild-type or presence of mutation in TP53 or KRAS, or rearrangement in KMT2A), which underwent validation with two external cohorts (n = 117). Compared to ELN 2024, the Mayo risk model demonstrated a superior performance of the former over the latter: area under the curve (AUC) of 0.77, corrected Akaike information criterion (AICc) 179 vs. AUC 0.69 and AICc 195, respectively. Importantly, this model was censored at transplant, and event that occurred in 65 patients (16%) in this study (compared to three patients who received treatment in VIALE-A) [4, 11]. The study also emphasized the prognostic value of achieving complete remission (CR) or CR with incomplete count recovery (CRi). This study emphasizes the need to sequence a panel of myeloid mutations at time of diagnosis beyond NPM1/IDH1-2/FLT3, which may not be readily performed in routine clinical practice outside of leukemia centers primarily due to cost, availability, and awareness. Of note, both the Mayo risk score and the ELN 2024 guidelines used OS censored at HCT as the outcome endpoint. Although this approach minimizes immortal time bias, the utility of these two prognostic models in estimating the long-term overall survival among an increasing number of patients who are now consolidated with HCT after HMA plus ven requires further investigation (or follow-up). Perhaps our greatest challenge is determining the clinical utility of newer prognostic tools for less-intensive strategies. As the AML treatment landscape evolves to include more remission-inducing options including intensive chemotherapy, monotherapies, doublets, and triplets, clinicians need more guidance on the best treatment to offer or when to escalate/de-escalate therapies. We need to refine our data-driven decision-making tools. We propose a focus on [1] validation of risk classification systems on larger or multicenter (international) AML populations, and [2] development and validation of predictive tools (that inform treatment selection). Therapeutic predictive tools should focus on both futility, which treatments are less likely to be useful, and on efficacy, which treatments are more likely to be remission inducing or importantly result in durable remissions. Finally, we are making meaningful progress in AML, making refined classification schemas relevant and necessary. Real-world data offer the unique opportunity to best reflect the population actually receiving treatment. External validation of new models as demonstrated by this study from Gangat et al. sets the standard for how to move the field forward with these practical classification systems. To continue making progress as an AML community with an expanding treatment landscape, we see these cumulative efforts as a call for generating large, robust datasets. This work is supported by grants from the National Institutes of Health (NIH) under award numbers CA066996 and K08CA245209. J. Garcia has received research grants from AbbVie, Newave, Pfizer, and Genentech, and was an advisory board member or on the steering committee for AbbVie, Genentech, and Servier. S. shimony has no conflicts to declare. Data sharing not applicable – no new data generated, or the article describes entirely theoretical research.