Personalized Circulating Tumor DNA (ctDNA) Profiling Enables Superior and Universal Measurable Residual Disease (MRD) Detection in Acute Myeloid Leukemia (AML)
Gunaratne, R.; Zhou, C.; Rajaram, S.; Tai, J. W.; Tanaka, K.; Tiwari, C.; Yang, E.; Kim, S.; Gao, G.; Yin, R.; Carleton, M.; Alkaitis, M. S.; Schwede, M.; Sworder, B. J.; Mannis, G. N.; Khodadoust, M. S.; Majeti, R.; Kurtz, D. M.; Zhang, T. Y.
Show abstract
Relapsed and/or refractory disease remains the leading cause of death in AML, highlighting the need for broadly applicable, high-sensitivity approaches to MRD detection. We developed AML-CAPP-Seq (Cancer Personalized Profiling by Deep Sequencing), a personalized hybrid-capture assay that tracks both canonical AML drivers and patient-specific variants identified by whole-exome sequencing. In 56 patients with longitudinal plasma and matched peripheral blood and bone marrow samples, AML-CAPP-Seq enabled universal MRD assessment and resolution of clonal dynamics using a median of 30.5 variants per patient. Plasma ctDNA outperformed cellular compartments for MRD detection and more strongly predicted relapse-free (HR 17.8, p<0.0001) and overall survival (HR 17.0, p<0.0001) than standard-of-care MRD methods. Among 29 allogeneic transplant recipients, peri-transplant ctDNA-MRD dynamics markedly improved relapse risk stratification (HR 36.0, p=0.0009). Together, these results establish personalized ctDNA profiling as a minimally invasive, highly sensitive, and generalizable platform for enhanced clinical MRD detection and clonal surveillance in AML. Significance StatementWe present a personalized blood test for acute myeloid leukemia that tracks patient-specific circulating tumor DNA, enabling sensitive, universal, noninvasive detection of residual disease. It outperforms standard-of-care marrow and cell-based methods for predicting relapse and survival, including after transplant, reveals clonal dynamics, and supports individualized disease monitoring and risk-adapted treatment.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- CEBPA repression by MECOM blocks differentiation to drive aggressive leukemias 96%
- Leukemia escapes immunity by imposing a Type-1 regulatory program on neoantigen-specific CD4+ T cells. 96%
- Enhancer heterogeneity in acute lymphoblastic leukemia drives differential gene expression between patients 96%
Similar papers in this journal
- Acute myeloid leukemia stratifies as two clinically relevant sphingolipidomic subtypes 97%
- Modeling IKZF1 lesions in B-ALL reveals distinct chemosensitivity patterns and potential therapeutic vulnerabilities 96%
- Single cell long-read genotyping of transcriptomes reveals discrete mechanisms of clonal evolution in post-myeloproliferative neoplasm acute myeloid leukemia. 95%
Similar papers in this journal
- Single cell dissection of developmental origins and transcriptional heterogeneity in B-cell acute lymphoblastic leukemia 96%
- DUSP6 mediates resistance to JAK2 inhibition and drives leukemic progression 96%
- A single-cell atlas characterizes dysregulation of the bone marrow immune microenvironment associated with outcomes in multiple myeloma 95%
Similar papers in this journal
- AML/T cell interactomics uncover correlates of patient outcomes and the key role of ICAM1 in T cell killing of AML 97%
- Identification of leukemia stem cell subsets with distinct transcriptional, epigenetic and functional properties 97%
- Resistance to decitabine and 5-azacytidine emerges from adaptive responses of the pyrimidine metabolism network 97%
Similar papers in this journal
- Signatures of immune senescence predict outcomes and define checkpoint blockade-unresponsive microenvironments in acute myeloid leukemia 95%
- Germline RUNX1 Variation and Predisposition to Childhood Acute Lymphoblastic Leukemia 95%
- CXCL8 secreted by immature granulocytes inhibits wildtype hematopoiesis in chronic myelomonocytic leukemia 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.