PB1800: DEEPDECON: A DEEP-LEARNING METHOD FOR DETECTING MINIMAL RESIDUAL DISEASE OF ACUTE MYELOID LEUKEMIA
Jamie Zhong, A. Stucky, F. Sun, J. Huang · HemaSphere · 2022
Background: Acute myeloid leukemia (AML) is a heterogeneous disease that the myeloid lineages of hematopoietic progenitor cells (blasts) lose the ability of normal differentiation and build up in the bone marrow. The ability to identify minimal residual disease (MRD) below the morphology-based 5% blast threshold has significant impact in clinical outcome. Flow cytometry and real-time quantitative polymerase chain reaction [qPCR]) are the most common methods for MRD detection while next-generation sequencing (NGS) is emerging as a more sensitive method. Aims: Here, we report DeepDecon, a deep neural network model that utilizes NGS RNAseq data to detect MRD of AML. Methods: DeepDecon makes use of the non-linear transformation among the latent layers and learns to extract high-order representations of the input gene expression. Meanwhile, DeepDecon doesn’t need to select a subset of genes. The neural network will optimize and assign different weights to all genes to minimize the prediction error. A challenging issue with deep learning models is the need for large data sets of diverse examples for model training to obtain robust and good performance. With single-cell RNAseq data, we digitally create a large number of bulk RNAseq samples with known AML fraction for model training. Results: When applied to clinical data TARGET-AML from GDC Data Portal (https://portal.gdc. cancer.gov/), DeepDecon demonstrated better performance than other machine learning algorithms, including MuSic, NNLS, Bisque, RNA-Sieve, and Scaden, for detecting MRD of AML. Summary/Conclusion: Deepdecon can be applied to clinical NGS data for accurate and sensitive detection of MRD in AML treatment. It will significantly improve the clinical outcomes of current AML therapy.