Correlation Revealed between Gemtuzumab-Ozogamicin Efficacy and CD33+ Expression in AML Primary Samples through a Novel AML in Vitro Model
Authors: Gilardi Mara , Kaushik Garima , Walling Brandon , Sambandam Vaishnavi , Schiavini Paolo , Cairo Stefano , Zipeto Maria A. , Ritchie Michael 
Published: December 2023

Acute myeloid leukemia (AML) is one of the most difficult blood cancers to treat with increasing incidence in recent years. The disease is characterized by a clonal hematopoietic aggressive disorder of progenitor and stem cells. The prognosis for patients with AML is generally poor. After the first remission, most patients will develop recurrent disease, often within the first year 1.
Recent molecular deconvolution of the mechanisms underlying AML disease have impacted the way AML patients are managed in the clinic, going from chemotherapy to the implementation of monoclonal antibodies and antibody-drug conjugates (ADC) 2. One of the limitations to ADC development for AML patients is the lack of AML-specific antigens. CD33 is a recent target which, although being expressed by other normal hematopoietic cells, is also strongly expressed in 80-90% of all AML cells. For these reasons gemtuzumab-ozogamicin (Mylotarg), a monoclonal antibody targeting CD33 covalently linked to the toxin calicheamicin, is the only FDA approved ADC for AML treatment 2. Although these treatments have had an impact on patient care, new therapeutic strategies are required to succeed in effectively treating AML.
To date, several AML models have been exploited to predict patients' response 3. The model portfolio comprises in vivo and ex vivo models; however, the difficulties in culturing AML primary cells while maintaining clonal heterogeneity often limit the ability of the assay to recapitulate the disease. The development of a functional experimental model capable of mimicking the heterogeneity and complexity of AML with translational potential is still a major challenge.
To overcome the lack of clinically relevant translational models that can stratify sensitive and resistant tumors across different treatments, we developed our proprietary assay, Champions AML VitroScreen ®, to test therapeutic candidates in primary AML samples 4.
Our bank of primary, deeply characterized AML samples recapitulates the diversity of multiple AML subtypes from a phenotypic and molecular standpoint. This assay allows for evaluation of cell viability, proliferation, and clonal composition in response to therapeutic intervention using a variety of validated endpoint analyses.
To challenge the robustness of the assay, we used the VitroScreen platform to test Mylotarg, in primary AML samples used as models for the different AML subtypes including M1, M2, M3, M4 and M5. To select appropriate AML models, we evaluated the expression of CD33 in AML blasts by high complexity flow cytometry. CD33+ high, intermediate, and low expressing models were included in the study. We measured the IC50 for Mylotarg in more than 20 AML models and correlated the marker expression with drug efficacy. Dose-dependent responses to Mylotarg were observed across multiple models by evaluating cell growth and viability using the CellTiter-Glo ® assay. As additional endpoints cell proliferation, apoptosis, and phenotyping by flow cytometry were included to better interpret Mylotarg mechanism of action. Based on the assay results, primary AML models were classified into different categories, respectively responder, low responder and resistant, based on the IC50 value, and a strong positive correlation between sensitivity to Mylotarg and CD33+ expression was found.
The use of deeply characterized AML patient primary samples within the AML VitroScreen ® platform allowed for high throughput drug testing, which represents the ideal clinical translational setting in preclinical research. The use of this innovative system for patient selection and drug screening will support the development of new and tailored therapeutic strategies for patients with primary or recurrent AML, leading to improved patient outcomes.