Venetoclax for the treatment of elderly or chemotherapy-ineligible patients with acute myeloid leukemia: a step in the right direction or a game changer?

MDS and AML show elevated fractions of CD34-positive blast cell populations with a high anti-apoptotic versus proliferation ratio

This study investigates the intertwined processes of (anti-)apoptosis and cell proliferation in myelodysplastic syndromes (MDS) and acute myeloid leukemia (AML). Utilizing antibodies to Bcl-2 and Ki-67, the CD34-positive blast cell compartments in bone marrow aspirates from 50 non-malignant cases, 25 MDS patients, and 25 AML patients were analyzed for their anti-apoptotic and proliferative cell fractions through ten-color flow cytometry. MDS patients exhibited a significantly increased anti-apoptotic (p=0.0014) and reduced proliferative cell fraction (p=0.0030) in their blast cell population as compared to non-malignant cases. AML patients showed an even more exacerbated trend than MDS patients. The resulting Bcl-2:Ki-67 cell fraction ratios in MDS and AML were significantly increased as compared to the non-malignant cases (p=0.0004 and p<0.0001, respectively). AML patients displayed, however, a high degree of variability in their anti-apoptotic and proliferation index, attributed to heterogeneity in maturation stage and severity of the disease at diagnosis. Using double-labeling for Bcl-2 and Ki-67 it could be shown that besides blast cells with a mutually exclusive Ki-67 and Bcl-2 expression, also blast cells concurrently exhibiting anti-apoptotic and proliferative marker expression were found. Integrating these two dynamic markers into MDS and AML diagnostic workups may enable informed conclusions about their biological behavior, facilitating individualized therapy decisions for patients.

Therapy-related core binding factor acute myeloid leukemia

Therapy-related acute myeloid leukemia (t-AML) usually stems from exposure of the bone marrow to cytotoxic chemotherapy and/or radiation therapy. t-AML is usually associated with poor overall survival, but occasionally t-AML can involve favorable-risk cytogenetics, including core binding factor AML (CBF-AML), which shows a recurrent chromosomal rearrangement with t(8;21) (q22;22) and 'inv(16) (p13.1;q22)/t(16;16)(p13.1;q22)', leading to 'RUNX1::RUNX1T1 and CBFB::MYH11' fusion genes, respectively. Therapy-related CBF-AML (t-CBF-AML) accounts for 5-15% of CBF-AML cases and tends to have better outcomes than t-AML with unfavorable cytogenetics. Although CBF-AML is sensitive to high-dose cytarabine, t-CBF-AML has worse overall survival than de novo CBF- AML. The objective of this review is to discuss the available data on the pathogenesis, mutations, and therapeutic options in patients with t-CBF-AML.

TP53 signal pathway confers potential therapy target in acute myeloid leukemia

TP53 mutation is a frequent tumor suppressor mutation and a critical prognostic indicator across studies in many malignant tumors including hematologic malignancies. However, the role of TP53 and its correlative pathway in acute myeloid leukemia (AML) is enigmatic, which may provide possible emerging strategies with the potential to improve outcomes in AML. Accordingly, we focus not only on the TP53 mutation but also on the underlying mechanisms of the mutated TP53 signal pathway. While it is now generally accepted that TP53 mutations are widely associated with a dismal prognosis, resistance to chemotherapy, and high incidence of relapse and refractory AML. Hereby, the current therapeutics targeting TP53 mutant AML are summarized in this review. This will address emerging TP53-based therapeutic approaches, facilizing the TP53-targeted treatment options.

Machine Learning–Based Exploratory Clinical Decision Support for Newly Diagnosed Patients With Acute Myeloid Leukemia Treated With 7 + 3 Type Chemotherapy or Venetoclax/Azacitidine

PURPOSE

There are currently limited objective criteria to help assist physicians in determining whether an individual patient with acute myeloid leukemia (AML) is likely to do better with induction with either standard 7 + 3 chemotherapy or targeted therapy with venetoclax plus azacitidine. The study goal was to address this need by developing exploratory clinical decision support methods.

PATIENTS AND METHODS

Univariable and multivariable analysis as well as comparison of a range of machine learning (ML) predictors were performed using cohorts of 120 newly diagnosed 7 + 3-treated AML patients compared with 101 venetoclax plus azacitidine–treated patients.

RESULTS

A variety of features in the two patient cohorts were identified that may potentially correlate with short- and long-term outcomes, toxicities, and other considerations. A subset of these diagnostic features was then used to develop ML-based predictors with relatively high areas under the curve of short- and long-term outcomes, hospital stays, transfusion requirements, and toxicities for individual patients treated with either venetoclax/azacitidine or 7 + 3.

CONCLUSION

Potential ML-based approaches to clinical decision support to help guide individual patients with newly diagnosed AML to either 7 + 3 or venetoclax plus azacitidine induction therapy were identified. Larger cohorts with separate test and validation studies are necessary to confirm these initial findings.