Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using Deep Learning Neural Network

TL;DR

This study employs deep learning autoencoders to identify key proteins associated with FLT3-ITD mutation in acute myeloid leukemia, achieving high accuracy and reducing protein candidates from 231 to 20.

Contribution

It introduces a hierarchical deep learning model for feature extraction and dimensionality reduction in cancer proteomics data, highlighting novel protein associations with FLT3-ITD.

Findings

Reduced critical proteins from 231 to 20 with high correlation.

Achieved 97% accuracy, 90% sensitivity, and 100% specificity.

Provides a new method for analyzing big cancer proteomics data.

Abstract

Deep Learning can significantly benefit cancer proteomics and genomics. In this study, we attempt to determine a set of critical proteins that are associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia patients. A Deep Learning network consisting of autoencoders forming a hierarchical model from which high-level features are extracted without labeled training data. Dimensional reduction reduced the number of critical proteins from 231 to 20. Deep Learning found an excellent correlation between FLT3-ITD mutation with the levels of these 20 critical proteins (accuracy 97%, sensitivity 90%, specificity 100%). Our Deep Learning network could hone in on 20 proteins with the strongest association with FLT3-ITD. The results of this study allow a novel approach to determine critical protein pathways in the FLT3-ITD mutation, and provide proof-of-concept for an accurate approach to model big data in cancer proteomics and genomics.