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Original citation: J Clin Invest. 2022;132(22):e157101. https://doi.org/10.1172/JCn57101. During the preparation of Supplemental Figure 12A, an image for day 35 was inadvertently used instead of an image for day 28. The [...]

Accurate assessment of therapy response in myelodysplastic neoplasm (MDS) has been challenging. Directly monitoring mutational disease burden may be useful, but is not currently included in MDS response criteria, and the correlation of mutational burden and traditional response endpoints is not completely understood. Here, we used genome-wide and targeted next-generation sequencing (NGS) to monitor clonal and subclonal molecular disease burden in 452 samples from 32 patients prospectively treated in a clinical trial. Molecular responses were compared with International Working Group (IWG) 2006-defined response assessments. We found that myeloblast percentage consistently underestimates MDS molecular disease burden and that mutational clearance patterns for marrow complete remission (mCR), which depends on myeloblast assessment, was not different than stable disease or bone marrow aplasia, underscoring a major limitation of using mCR. In contrast, achieving a complete remission (CR) was associated with the highest level of mutation clearance and lowest residual mutational burden in higher-risk MDS patients. A targeted gene panel approach was inferior to genome-wide sequencing in defining subclones and their molecular responses but may be adequate for monitoring molecular disease burden when a targeted gene is present in the founding clone. Our work supports incorporating serial NGS-based monitoring into prospective MDS clinical trials., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Acute myeloid leukemia (AML) is a rapidly progressive malignancy without effective therapies for refractory disease. So far, chimeric antigen receptor (CAR) T cell therapy in AML has not recapitulated the efficacy seen in B cell malignancies. Here we report a pilot study of autologous anti-CD123 CAR T cells in 12 adults with relapsed or refractory AML. CAR T cells targeting CD123 + cells were successfully manufactured in 90.4% of runs. Cytokine release syndrome was observed in 10 of 12 infused individuals (83.3%, 90% confidence interval 0.5-0.97). Three individuals achieved clinical response (25%, 90% confidence interval 0.07-0.53). We found that myeloid-supporting cytokines are secreted during cell therapy and support AML blast survival via kinase signaling, leading to CAR T cell exhaustion. The prosurvival effect of therapy-induced cytokines presents a unique resistance mechanism in AML that is distinct from any observed in B cell malignancies. Our findings suggest that autologous CART manufacturing is feasible in AML, but treatment is associated with high rates of cytokine release syndrome and relatively poor clinical efficacy. Combining CAR T cell therapies with cytokine signaling inhibitors could enhance immunotherapy efficacy in AML and achieve improved outcomes (ClinicalTrials.gov identifier: NCT03766126 )., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Multiple technologies have been used to monitor response to therapy in acute myeloid leukemia (AML) to improve detection of leukemia over the standard of practice, morphologic counting of blasts. The two techniques most frequently used in a routine clinical setting, flow cytometry and RQ-PCR, differ in their targets, sensitivity, and ability to detect residual disease. Both flow cytometry and RQ-PCR detect the expression of abnormal gene products, at the protein level or RNA level, respectively. Flow cytometry can be applied to a broad range of AML cases while RQ-PCR is limited to specific genetic abnormalities identified in subsets of AML. This article compares the results when both techniques were used in a reference laboratory to monitor AML over the course of treatment, comparing quantitative and qualitative results., (Copyright © 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)